esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Rheological Testing
The elastic and viscous values were in good harmony with those previously reported for other gelatin-based gels incorporated with antioxidant extracts from guava leaves [24]. It should be highlighted that in both cases (i.e., starch-based and gelatin-based systems), a consistent trend between softening of the gelling features and phenolic behavior was exhibited, as analyzed in a parallel work [17]. The latter authors found increasing total phenol content with temperature for above aqueous extracts in the range of 1-4.8% for petioles, 2.2-4.2% for bark, and 5.2-7.1% for leaves. Another factor closely associated with the gel strength is the protein content [27], although in this particular case a random tendency between both properties was found except for bark hydrogels. Namely, bark protein content remained practically constant until 180 \({}^{\circ}\)C with average values around \(195.6\pm 11.2\)\(\mu\)g ABS eq/L, decreasing significantly (\(p<0.05\)) above this temperature following the trend: \(131.2\pm 16.0\)\(\mu\)g ABS eq/L at 200 \({}^{\circ}\)C, 67.2 \(\pm\) 3.4 \(\mu\)g ABS eq/L at 220 \({}^{\circ}\)C, and 34.6 \(\mu\)g ABS eq/L \(\pm\) 3.1 at 240 \({}^{\circ}\)C. In contrast, an increase in this parameter with rising temperature (up to 200 \({}^{\circ}\)C) was previously reported for the corresponding leaves and petioles [17]. In this parallel work, the authors found that the leaves exhibited protein values varying between 65 and 637 \(\mu\)g ABS eq/L, whereas petioles ranged from 45 to 367 \(\mu\)g ABS eq/L. The differences observed could be related to other factors such as the different protein types or sizes, due to a denaturalization of these biopolymers during high autohydrolysis temperatures [28; 29; 30]. Figure 2: Mechanical spectra of starch-based hydrogels enriched with representative Paulownia extracts treated at the lowest autohydrolysis temperature (140 \({}^{\circ}\)C). Symbols: G’ (closed), G” (open), bark (squares), leaves (circles), petioles (triangles). Figure 4 provides an overview of the impact of autohydrolysis temperature during the Paulownia extracts recovery on the mechanical features of the final hydrogels developed. A relevant influence of extracts processing temperature on the viscoelastic features of the developed hydrogels was clearly observed. For bark and petiole extracts, the strength of starch-based and gelatin-based hydrogels dropped with increasing autohydrolysis temperature. For leaves extracts, the softening of the gels was noticed below 180 \({}^{\circ}\)C, whereas the reverse trend was identified at the highest temperatures. Although similar trends were detected for both tested matrices (i.e., starch and gelatin), the decay in the gel strength was promoted in starch hydrogels, which is suggested to be related to the highest water holding capacity of the potato starch [20]. This delivers an extensive variety of viscoelastic characteristics for the recovered extracts which enlarge the possible uses in food and nonfood sectors.
## 4 Analysis of Approaches to Modelling Agricultural Systems
### Deterministic Approach  
The deterministic approach consists of mathematical modelling using measured and known input parameters, with no degree of randomness. Deterministic models are based on the hypothesis that models, once calibrated, are able to project output events and magnitude of consequences (Figure 2). They are mostly based on phenomenological, mechanistic, or functional relationships between physical or biological elements.  
Deterministic approaches are widely used for crop models to simulate plant growth under climate/soil/water conditions. For example, the multidisciplinary simulator for standard crops model (Simulateur mulTIdisciplinaire pour les Cultures Standard--STICS) [59; 60] has been developed to simulate crop water and nitrogen balance under environmental (climate, soil) and agricultural conditions (cropping system), and to determine best souring dates or predict yields. Deterministic models are also used in agronomy, for example, to predict sugar concentration of grapes from field observation and temperature-based models [61] that could help farmers to anticipate grape harvesting. Other agricultural-based modelling efforts have been developed using deterministic approaches for land use and crop yields' mapping [62], crop rotation modelling [63], or environment-ecosystem process simulation [64; 65].  
**Strengths**:  
* At a regional to national scale, the deterministic approach is well designed for prediction that is required for decision-making and policy solution design, for example, climate change predictions and government actions [66; 67].
* Many models are freely available, while some are free of charge, free of copyright, and free of restrictions for modification (see Section 5). Many of these models are already parameterised for an easier application.
* Models based on deterministic approach can be coupled with all other approaches to add stochasticity or stakeholders' involvement and be part of different scenario modelling.  
**Weaknesses**:
* Deterministic models can be highly complex and difficult to readily adapt to specific study areas.
* It requires very specific data to develop precise and complex modelling [59; 63; 68].
* Upscaling or downscaling these models is a challenge and requires coupling with multiple source of data [69; 70; 71].
## 3 Results
### Analysis of Prediction Models  
Regarding the linear regression models, the NGRDI showed a better fit for FB (RMSE = 4002.04, MAE = 3551.13, CCC = 0.83, R\({}^{2}\) = 0.75) and DB (RMSE = 1743.56, MAE = 1514.18, CCC = 0.71, R\({}^{2}\) = 0.69), and the VARI for LAI (RMSE = 0.31, MAE = 0.26, CCC = 0.77, R\({}^{2}\) = 0.71), in which it showed a less obvious difference between the indexes than for FB and DB (Table 5).  
For the machine learning methods, the best models were found for the partitioned regression tree method (Table 6), even compared to linear regression (Table 5), presenting a lower RMSE, that is, with values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher precision and accuracy for the predicted values (CCC), presenting values of 0.94 for FB, DB, and LAI.  
Analyzing the influences of the variables in the CART prominence model, one notices that NGRDI and VARI are prominent, as well as in the linear correlation indexes (Figure 5), followed by the * HUE index.  
Figure 4: [PERSON]’s correlation coefficients of the relationship between the considered features and the vegetation indexes for each approach on orthomosaics. The asterisk (*) represents a statistically significant difference (\(p\) < 0.05). **(a)** Original file containing all components of the study area (soil, crops, trees). **(b)** File with soil and tree components removed. **(c)** File with tree component removed. **(d)** File with solo component removed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Index** & **Response** & **RMSE** & **MAE** & **CCC** & **R\({}^{2}\)** & **Model** \\ \hline NGRDI & Fresh biomass & 4002.04 & 3551.13 & 0.83 & 0.75 & y = 22,022 + 152,776x \\ VARI & (kg ha\({}^{-1}\)) & 4164.82 & 3656.81 & 0.82 & 0.73 & y = 22,610 + 98,632x \\ \hline NGRDI & Dry biomass & 1743.56 & 1514.18 & 0.71 & 0.69 & y = 7258 + 52,400x \\ VARI & (kg ha\({}^{-1}\)) & 1805.94 & 1623.18 & 0.70 & 0.66 & y = 7446 + 33,669x \\ \hline NGRDI & Leaf area index & 0.31 & 0.26 & 0.76 & 0.68 & y = 1.678 + 9.232x \\ VARI & (m\({}^{2}\) m\({}^{-2}\)) & 0.31 & 0.26 & 0.77 & 0.71 & y = 1.727 + 6.133x \\ \hline \end{tabular}
\end{table}
Table 5: Results of the cross-validation process for the simple linear regression models.
## 5 Security Analysis
### Collusion Attack and Independent Vote  
The threshold based on collusion attack has been discussed in detail in Section 4.4, and it is not covered here.  
At the same time, each voter independently receives information from the revocation information initiator through the publish-subscribe mode and votes independently. Except for the deliberate collusion attack, voters will not be affected by other voters and judge the credibility of the revocation information independently, which enables each voting user to make a fair judgment on this voting event.
## 1 Introduction
Furthermore, to mitigate the unpredictability of scheduling risk, the PERT analysis approach is employed to estimate the time of essential tasks via three-point estimation. This study uses the Monte Carlo Simulation probabilistic method to assess the influence of risk, estimate variations in activity length, and predict project completion time with greater precision. Figure 1: Progress of Jakarta Central Railway Station Construction Project Phase II at the end of the contract on 30 September 2021. Source: [7] and author’s research. This research is grounded in a case study of the Phase II Development of the Jakarta Central Railway Station, with a focus on three main problem formulations:  
* What are the indicators that cause the risk of delay in completing the construction project (RQ1)? * How can the risk-based simulation of the project implementation scheduling be established to optimize the project's completion time (RQ2)? * What are the appropriate risk response strategies and recommendations to respond to the risks that have a major impact on the construction schedule to optimize the project completion time (RQ3)? This study's primary benefit is quantifying the risk of project delays, which can be used as lessons to prevent future occurrences, particularly in railway station construction projects.
# New Inorganic Scintillators' Application in the Electromagnetic Calorimetry in High-Energy Physics
## 3 Conclusions  
We performed a simulation of an electromagnetic calorimeter based on scintillation crystals PWO, GAGG, and the composite version GAGG + PWO. It was shown that the composite calorimeter has approximately the same energy resolution as GAGG based at low energies and a somewhat better energy resolution than PWO based at high energies. The GAGG + PWO calorimeter has nearly the same spatial resolution as the PWO-calorimeter. A combined calorimeter of this type will enable the measurement of photon energy in an unprecedentedly broad range of energies. A combined calorimeter will also allow for particle identification based on the longitudinal and transverse shape of the shower. The results regarding the efficiency and purity of \(\lambda\) cuts in the case of the GAGG + PWO calorimeter with a 10 cm GAGG section are summarized in Table 2. For lower lengths of the GAGG section, \(\gamma\) efficiency stays almost the same, but hadron contamination becomes worse, by \(\approx\) 20% for \(\pi^{+}\) and \(\approx\) 40% for \(\bar{n}\) for the 3 cm GAGG section.  
The optimization of the length of the GAGG section of the GAGG+PWO calorimeter cell is performed on the basis of the energy resolution improvements in different energy ranges for lengths from 0.5 to 10 cm. It was shown that, for 10 MeV photons, the dependence on the GAGG-section length is most prominent, and the optimal value is about 3 cm.  
Conceptualization, D.B.; Software, D.A.; Formal analysis, D.A.; Writing--original draft, D.A.; Writing--review & editing, D.B.; Visualization, D.A.; Supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
The research was carried out with the financial support of the Russian Science Foundation, grant No. 22-42-04405. The electromagnetic calorimeter modeling program was developed with the support of the National Research Center Kurchatov Institute.  
The International Review Board Statement: Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cut** & **Energy** & **\(\gamma\) Efficiency, \%** & **Contamination \(\pi^{+}\), \%** & **Contamination \#, \%** \\ \hline (\(\lambda_{short}\leq 1.5\) cm\({}^{2}\), \(\lambda_{long}\leq 2\) cm\({}^{2}\)) & 5 GeV & 98.2 & 10.1 & 1.3 \\ (\(\lambda_{short}\leq 2.2\) cm\({}^{2}\), \(\lambda_{long}\leq 5\) cm\({}^{2}\)) & 5 GeV & 99.8 & 22.9 & 10.4 \\ (\(\lambda_{short}\leq 1.5\) cm\({}^{2}\), \(\lambda_{long}\leq 2\) cm\({}^{2}\)) & 500 MeV & 39.5 & 17.1 & 0.9 \\ (\(\lambda_{short}\leq 2.2\) cm\({}^{2}\), \(\lambda_{long}\leq 5\) cm\({}^{2}\)) & 500 MeV & 92.8 & 44.1 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiency and purity after applying dispersion cuts.  
Figure 15: The dependence of the energy resolution on the GAGG-section length of the GAGG + PWO-calorimeter for photons of different energies.
# \(K\)-Means Cloning: Adaptive Spherical \(K\)-Means Clustering
## 2 K-Means Cluster Cloning
### Diversification  
In the proposed KMC method, we use a diversification generation method to generate a diverse solution. This diverse solution can help the search process to scape from local minima. The search space of the parameter \(K\), which is the interval \([K_{\min},K_{\max}]\), is divided into \(n_{K}\) equal sub-intervals. When a predefined consecutive number of iterations is reached without hitting any of these sub-intervals, a new diverse solution should be generated from the unvisited sub-intervals. In order to generate a new diverse solution, a new value of \(K^{\textit{div}}\) is randomly chosen from the unvisited sub-intervals and the \(K\)-means clustering process is implemented to compute new clusters \(C_{\kappa}^{\textit{div}}\), \(\kappa=1,\dots,K^{\textit{div}}\). Procedure 4 formally explains the steps of the diversification process.  
**Procedure 4**.: _Diversification_  
1. _Input: the lower and upper bounds_ \(K_{\min}\) _and_ \(K_{\max}\) _of the number of clusters, respectively, the number_ \(n_{K}\) _of cluster number sub-intervals, and numbers_ \(v_{1},\dots,v_{n_{K}}\) _of visiting each sub-interval._
2. _Select an_ \(\eta\) _from_ \(\{1,\dots,n_{K}\}\) _with a probability inversely proportional to its corresponding visiting number_ \(v_{\eta}\)_._
3. _Choose a_ \(K^{\text{div}}\) _randomly from the selected sub-interval_ \(\eta\)_._
4. _Generate new_ \(K^{\text{div}}\) _centers._
5. _Adjust the generated centers based on_ \(K\)_-means, and return with the adjusted clusters._  
Figure 5: An example of cluster splitting in two dimensions.
## 4 Experimental Evaluation of the New Estimator  
Figure 9 shows the interface driving unit of the new estimator generation unit with the DC/DC converters enclosing an inductor, a capacitor, a diode, a switch element, and a resistor. When the internal resistance \(R_{M}\) equals the load impedance \(Z\), the condition is called impedance matching [30], where the power for each rotation speed is at maximum. The condition of impedance matching is defined as \(R_{M}I_{T}=V_{T}\). The estimator control is used to control the command value \(V_{Ref}\).  
\[V_{Ref}=\ R_{M}\widehat{I_{T}}=R_{M}\widehat{I_{L}} \tag{29}\]  
The system consists of a double-stator generator, a DC/DC boost converter, an MCU (microcontroller unit), and a load resistance \(R_{0}\), as shown in Figure 10. Primarily the MCU measures the input voltage \(V_{T}\) from the voltage sensor. Secondly, it estimates the inductor current \(I_{L}\) and the inductive voltage \(V_{M}\), using \(V_{T}\). Finally, the MCU calculates a suitable duty cycle \(D\) for the maximum power with each rotational speed and load resistance. This series of events is called the maximum power point estimator control strategy. During the experimental testing of the estimator, the generator is started, and the value of the load resistance is decreased from 100 to 5 \(\Omega\) to perform the estimator control. Then, the rotation speed is varied from 700 to 1500 rpm to repeat the experimentation further  
Figure 10: Experimental system with the new estimator control method. MCU: microcontroller unit; DSPMG: double-stator permanent magnet generator.  
Figure 9: Configuration of the system using a DC/DC boost converter. \(R_{\text{M}}\): machine resistance; PI: proportional-integral; PWM: pulse width modulation.  
Figure 11 shows the experimental result of the comparison with and without the estimator. The black line indicates the maximum power points for each rotation speed of the generator by analytical computations. The red line indicates the result of using the experimental estimator control method. The experimental results are in close agreement to that of the ideal maximum power point result with the estimator curve developed for maximum power output. Therefore, the results confirm that the new control strategy is suitable for a generator with dynamic load changes.
# Evaluation of Land Subsidence during Groundwater Recovery
## 1 Introduction
After the groundwater level increased due to the law established for groundwater pumping, land subsidence still occurred after 1997 [1]. Evaluations of soil surface displacement have been conducted since groundwater pumping was prohibited. [PERSON] et al. [17] evaluated the settlement behavior with the centrifuge test. The centrifuge test was separated into three stages of groundwater level: before groundwater level drawdown, groundwater level drawdown, and groundwater level recovery back to the ground surface. Settlement still continuously occurred even when groundwater reached the ground surface, but the rate of settlement significantly decreased in the last stage. The settlement rate in the last stage was almost constant. Soil surface displacement was calculated by using a numerical model. [PERSON] et al. [18] used a series of two-dimensional finite element analyses with fully coupled flow-deformation analysis in PLAXIS2D to predict soil surface displacement. The model referred to a previous centrifuge test. All soil layers were considered to simulate the groundwater rise. The results showed that the soil surface will rebound about 14 cm (about 35% of the total surface settlement) from 1997 to 2037. [PERSON] and [PERSON] [19] predicted the subsurface displacement of both the groundwater level drawdown (for years 1960 to 1997) and recovery (for years 1997 to 2016) in three locations in the Bangkok area by using FEM transient coupled pore pressure/effective stress analysis with ABAQUS software. During groundwater drawdown, land subsidence of about 51, 62, and 76 cm had occurred in each location, respectively. During groundwater level recovery, the rebound of soil of about 5, 6, and 4 cm was calculated at the three locations, respectively. The increase in groundwater after the prohibition of groundwater pumping is very interesting. Pore-water pressure increases can lead to foundation problems and differential settlement issues. As such, in this study, we focused on calculating and predicting the soil surface displacement during groundwater level recovery by hand calculation. Finally, the soil displacement result from theoretical calculation was compared with the findings of previous research from [PERSON] et al. [18] and [PERSON] and [PERSON] [19].
scale of micro-volts means that EEG recordings require a level of neural synchronicity in order to have measurable activity [3]. Even with synchronous activity, the electrodes and EEG recordings are often subject to artifacts from differing sources. The most common artifacts seen result from physiological, environmental, and experimental sources [4].  
Artifacts are undesired signals that can cause recordings to change and impact the signal of interest [4]. The most common physiological artifacts found are ocular artifacts, due to eye blinks and eye movement, muscle artifacts due to inherent flexion and relaxation of muscles present on the forehead and scalp, and cardiac artifacts due to electrode placement on or near a blood vessel [5]. Because noise sources are so varied and have so many different properties, most authors concentrate on removing certain types of artifacts. The removal of artifacts plays a key role in EEG signal processing for both clinical applications and as well as brain-machine interfaces.  
Several denoising techniques have been developed for the purpose of artifact correction in EEG signals [6; 7; 8]. Rejecting artifactual portions of the EEGs is the simplest way, which deletes epochs containing artifacts. However, removal of artifactual portions can be a time-consuming process that can lead to significant information loss, which is in turn detrimental for data analysis [6]. Traditionally, regression and linear filtering-based analyses are employed to reject artifactual noises from corrupted signals [8]. Due to the overlap of brain activities and artifactual noises in the spectrum of an EEG signal, filtering them in either the frequency-domain or the time-domain may result in loss or distortion of physiological activity [9]. Regression-based methods rely on an extra one or more regressive channels, which gives rise to a fundamental weakness in that the spectral range of some artifacts overlaps with the spectral range of an EEG signal [10]. In both the temporal and frequency domains, wavelet transform-based analysis has been proven to be more efficient in repairing EEG artifacts while keeping the original EEG signal [11; 12].  
EEG signals are inherently non-stationary. One of the most widely used approaches for studying non-stationary signals is the wavelet transform. Its efficiency in transforming a time-domain signal into time-frequency-domain offers major advantages in the extraction of multiple components of a signal. A wavelet-based method eliminates the artifacts while retaining the integrity of the EEG signal. [PERSON] et al. [2] comprehensively addresses techniques found frequently in EEG artifact rejection and how these techniques work as individual tools as well as hybrid techniques such as EMD-BSS, wavelet-BSS, and BSS-Support vector machine. This paper will attempt to serve as a survey focused specifically on using the wavelet transform as an artifact rejection tool in EEG signals and will attempt to hone in on some specific wavelet transforms hybrids that have shown promising performance.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 3 Datasets and Processing Strategy  
### Datasets  
The experimental data in this paper were collected from several sites of a provincial CORS network in central China. The site distribution is shown in Figure 2, among which WH02, WH04, XN01, HG02, and HG05 are five actual CORS reference stations. According to Delaunay triangle rules, five reference stations can form seven baselines, and the average distance between stations is 92 km. SC17 is a rover station set up in the network to verify the network RTK service effect. In practical application, WH04, which is closest to SC17, is selected as the main reference station with a distance of 47 km. It should be pointed out that all site coordinates are uniformly calibrated by unified high-precision solution software before data processing, including actual reference stations and rover stations. All sites can receive BDS-3 satellites (up to PRN46) and frequency signals (B1I, B1C, B2a, B2b, B3I). The data collection time was 2021/09/21/00:00:00 to 24:00:00 (GPS), and the sampling interval was 1 s. Figure 3 shows the skyplot map of BDS-2 and BDS-3 satellites of the WH04 station during the data acquisition period. In Section 4.1, the WH04-HG02 baseline is selected as the representative to make a detailed comparative analysis of the double-difference atmospheric correction accuracy under the combination of B1C+B2a and B1I+B3I. In Section 4.2, the WH04-SC17 baseline was selected to compare and analyze the interpolation precision of regional atmospheric modeling under the two signal combinations. Section 4.3 takes the GPS system as a reference to compare and analyze the network RTK terminal positioning performance of the SC17 station under different system and frequency signal combinations. It should be pointed out that the B1I+B3I frequency signal combination is used in both BDS-2 and BDS-2/3 schemes.  
Figure 3: Skyplot map of BDS-2 (green) and BDS-3 (red) satellites at station WH04.  
Figure 2: Distribution of the reference stations (red triangles) and rover stations (blue triangles).