esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 4 Validation of the Predicted Clocks
### PPP Validation  
Now, precise point positioning is carried out to further evaluate the capability of the improved model for BDS-2 satellites. Eight IGS MGEX stations, CEDU, COCO, DARW, HKSL, IISC, KRGG, LHAZ, and MRO1, in the Asian-Pacific region well covered by BDS-2 are selected and shown in Figure 9. The main processing strategies and parameters for both static and kinematic PPP are listed in Table 4, and the Position and Navigation Data Analysis (PANDA) software is applied for calculations [31; 32].  
\begin{table}
\begin{tabular}{c c} \hline
**Solutions** & **Setting** \\ \hline Software used & PANDA \\ Tracking stations & Eight stations in Asia-Pacific Area \\ Observations & Ionospheric-free linear combination \\ Sample rate & 30 s \\ Elevation cutoff angle & 7\({}^{\circ}\) \\ Weight & Elevation dependent \\ Orbit & GFZ ultra-rapid orbit \\ Trop zenith delay & Initial model (Global Mapping Function) and random walk \\ Phase windup & Phase windup correction \\ \hline \end{tabular}
\end{table}
Table 4: Strategies and parameters for PPP validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Prediction Time (h)** & **GBU-P (ns)** & **SA (ns)1** & **GRNN (ns)** & **IM (ns) 1** \\ \hline
0.5 & 1.541 & 1.486 & 0.429 & 0.412 \\ \hline
1.0 & 1.631 & 1.523 & 0.513 & 0.437 \\ \hline
2.0 & 2.226 & 1.946 & 0.915 & 0.505 \\ \hline
3.0 & 3.051 & 2.512 & 1.578 & 0.841 \\ \hline \end{tabular}  
* 1: SA means the spectrum analysis model, and IM means the improved model.  
\end{table}
Table 3: Prediction errors at different prediction times.  
Figure 9: Tracking network for precise point positioning (PPP) validation.
## 3 Detailed Description of the Existing Protocols  
In this subsection, system models of the existing protocols, i.e., Hydrocast, Co-Improved Hydrocast, LETR, GEDAR and LMPC are discussed. The brief explanation and pictorial description of these routing protocols are presented in the following subsections.  
Figure 2: Forwarder node selection in EP-VIR-Three and BF-SPR-Three.
## 2 Materials and Methods
### Statistical Analyses  
The extensive data collection procedure resulted in 160 variables obtained at the start of the study and after three months for a total of 21 subjects. Out of these, three variables (both HAM-D scales and BDI-II) that represent mood were evaluated separately to test the hypothesis that statin use improves mood in individuals with MDD. Using GraphPadP Prism Software (Version 8.0, La Jolla, California, US), the normality of the data was confirmed using the Kolmogorov-Smirnov test (placebo group \(p>0.1\), statin group \(p>0.1\)). A Sidak's multiple comparison's test (\(\alpha=0.05\)) was performed as reported in the Results section.  
To consider the variance in the population at the start of the study, the statistical differences between the two groups were evaluated for delta (\(\Delta\)) variables calculated by subtracting each variable's initial values from their values at three months. The percentage of missing data was calculated and variables with more than 30% missing data were rejected from the analysis since data imputation for larger amounts of missingness may become very imprecise [52]. Using MATLAB version 2019b (The MathWorks, Inc., Natick, MA, USA), missing data for the remaining variables were imputed by the k-nearest neighbors technique using \(k=3\)[53]. Each \(\Delta\) variable was normalized with respect to its absolute maximum value. The high number of \(\Delta\) variables for a small number of subjects presented  
Figure 1: Experimental procedure for data collection.  
a challenge in detecting, with sufficient power, the variables that might be participating in a perfusion-related mechanism for mood improvement due to statin use. To handle this, machine learning techniques were applied as described in the following sections and depicted in Figure 2.
## 2 Literature Review
### Fundamental Concepts
#### 2.1.2 Least-Squares Policy Iteration
Assuming \(\hat{X}^{0}=0\) and \(\hat{y}^{0}=0\) initially, the current learned approximates of \(X\) and \(y\) for a fixed policy \(\pi\), will be \(\hat{X}^{i}\) and \(\hat{y}^{i}\), respectively. Therefore, Equations (17) and (18) will give the approximated values of \(\hat{X}^{i+1}\) and \(\hat{y}^{i+1}\), respectively for a new sample \((s_{i},a_{i},r_{i},\xi_{i})\). \[\hat{X}^{i+1}=\hat{X}^{i}+\varphi(s_{i},a_{i})\bigg{[}\varphi(s_{i},a_{i})- \gamma\varphi(\xi_{i},\pi(\xi_{i}))\bigg{]}^{T} \tag{17}\]  
\[\hat{y}^{i+1}=\hat{y}^{i}+\varphi(s_{i},a_{i})r_{i} \tag{18}\]  
The weight is updated as the iteration procedure repeats with the improved policy until the optimal policy is reached. That is, the weights of policies between successive iterations do not differ significantly. Therefore, the learning agent in a given state chooses its action in each learning round using the learned policy given in Equation (19). \[\pi(s|w_{\pi})=\underset{a}{\text{argmax}}(\varphi(s,a)^{T}w_{\pi}) \tag{19}\]
## 4 Performance Analysis of Proposed Algorithm
### Convergence Speed  
The convergence speed of the algorithm is related to how quickly it reaches a stable state wherein the performance metrics (e.g., average waiting times) do not change significantly with additional iterations. For a number of users \(N\), an increase in the number of users leads to better estimates of average waiting times but increases the computation time. The larger the buffer capacity \(K\), the more users can be queued, affecting the total waiting times and their stabilization. For practical precision, the number of iterations required for convergence depends on the variability in arrival and service times. Typically, the system needs enough iterations to capture the stochastic behavior accurately. Empirically, convergence is often achieved after a sufficient number of arrivals and services have been simulated, and this number is typically \(O(N)\). The algorithm processes each user in \(O(K)\) time, so the convergence speed can be approximated by the time it takes to process allusers sufficiently. If each user needs to be processed multiple times for stabilization, the convergence iterations can be estimated as a factor of the number of users \(N\).  
Combining these factors, the convergence speed can be approximated by the product of the number of users \(N\) and the buffer capacity \(K\), i.e., \(O(N.K)\). Hence, the time complexity of the algorithm is \(O(N.K)\), and the convergence speed is typically reached after \(O(N)\) iterations, with each iteration taking \(O(K)\) time, resulting in an overall complexity of \(O(N.K)\).
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 1 Introduction
The scallion price series is a nonlinear and complex mixture, its data features are not obvious, and it is often difficult to directly utilize the raw data for forecasting to achieve the desired accuracy [34]. Some studies have shown that by extracting the effective features in the original data and reconstructing them into new series through decomposition denoising techniques, and then using them as inputs for a single model, the forecasting accuracy and the ability to capture trend changes can be significantly improved [35; 36; 37]. For example, some scholars combined empirical modal decomposition (EMD) with ARIMA for electricity price prediction, which enabled the model to better capture the nonlinear fluctuations of prices, and found that the prediction effect was significantly better than that of a single ARIMA [38]. Additionally, some scholars have combined singular spectrum analysis (SSA) with a wavelet neural network (WNN) for training on denoised data; this combination can better capture the complex patterns and trends in price series and has been used for predicting crude oil and gold, which are characterized by high price uncertainty [39]. Other scholars combine variational modal decomposition (VMD) with LSTM, which not only improves the robustness of the prediction model, but also enhances the model's ability to recognize long-term price trends, forming a more advantageous crude oil price prediction model [40]. Therefore, combining advanced decomposition and denoising techniques with an accurate single prediction model is an effective way to analyze and predict the price dynamics of agricultural products. Compared with large agricultural products, the prices of small agricultural products tend to change drastically, with obvious cyclical and seasonal characteristics [41], and there are almost no previous case studies on the price of scallions in Shandong Province optimizing the weights of the combination model. Therefore, in order to accurately predict the price of agricultural products and further improve the forecasting performance, this study proposes another novel optimized forecasting combination framework based on the improved PSO-GA method for predicting the price of spring scallions in Shandong Province (SSA). Province. This study proposes adopting the idea of decomposition-denoising-aggregation, combining the three decomposition and denoising techniques of SSA, EMD, and VMD with the three single prediction models of ARIMA, LSTM, and GRUs, respectively, classifying all the combined models into three major categories based on isomorphic, heteromorphic, and semi-heteromorphic strategies, and applying the averaging method, the median method, and the improved PSO-GA. The average method, median method, and improved PSO-GA algorithm are applied to optimize the weights of each combined model to find the optimal combination of forecasting models and to promote research and development in the field of price forecasting in small agricultural products and the optimization of the weights of combined models.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 3 Results
### Questions Related to Software Used for Hazard Detection  
Regarding the question in Table 5, on the method of hazard detection and the type of software used, respondents stated that none of them performed detection automatically. Methods developed in the solutions mentioned in the introduction or similar automation processes of hazard detection or HS equipment modeling in 3D or 4D BIM were therefore not yet implemented. The CDE systems were among the most widely used software for manual detection because their hardware requirements are low, are user-friendly and are more-or-less implemented in the companies where the respondents worked. The use of 2D drawing viewers in portable document format (PDF) or drawing (DWG) format in CAD software is also common.  
The popularity of CDE systems among users can also be observed in the answers to the question in Table 6: \"What type of software would be the most appropriate/practical/effective for automatic hazard detection?\"  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Software Type** & **BIM Coordinators** & **Project Managers** & **Safety Inspectors** & **Total** \\ \hline BIM software for modeling & 0 & 1 & 1 & 2 \\ BIM software for coordination & 5 & 2 & 1 & 8 \\
3D/4D BIM software & 4 & 1 & 1 & 6 \\ CDE software & 6 & 4 & 1 & 11 \\ \hline I do not know & 0 & 1 & 1 & 2 \\ \hline \end{tabular} \({}^{1}\) More than one answer could be selected.  
\end{table}
Table 6: Responses to the question: “What kind of BIM software would be the most appropriate/practical/efficient for automatic hazard detection?” 1.  
Figure 4: Responses to the question: “What stage do you think is the most appropriate for applying detection so that it directly increases the level of health and safety on the construction site?”  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Software Type** & **BIM Coordinators** & **Project Managers** & **Safety Inspectors** & **Total** \\ \hline No manual detection & 0 & 0 & 1 & 1 \\ Visualizations (image, video) & 1 & 1 & 0 & 2 \\
2D plans viewer & 2 & 2 & 0 & 4 \\ \hline BIM software for modeling & 2 & 0 & 0 & 2 \\ IFC viewer & 2 & 0 & 0 & 2 \\ BIM software for coordination & 3 & 0 & 0 & 3 \\
3D/4D BIM software & 2 & 1 & 0 & 3 \\ CDE software & 4 & 3 & 0 & 7 \\ \hline \end{tabular} \({}^{1}\) More than one answer could be selected.  
\end{table}
Table 5: Responses to the question: “What kind of software do you currently use for manual hazard detection?” 1.  
The respondents, especially BIM coordinators, said the second-most suitable was BIM software for BIM coordination (e.g., Solibri). In this type of software, it is possible to check the geometry and non-graphical information semi-automatically based on rules set by the user. Therefore, because of these built-in functions or extensions, which are programmable via an API environment, this kind of BIM software is suitable for implementing automated hazard detection.
# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 2 Proposed Depth from Focus
### Iterative Depth Refinement  
Initial depth is estimated by investigating the appearance changes in both spatial and frequency domains in the previous section. To obtain more accurate result, we build a _uniformly focused image set_ (UFIS) by taking pixels from the focus stack using resulting depth values. In other words, an all-in-focus image and following uniformly out-focused images were created. Then, using UFIS, we improve our depth estimation iterative manner. Let \(n\) be a number of frames of UFIS. \(n\) is an odd number, and the central frame \(c\) should be an all-in-focus frame generated by the initial depth result (if n = 7 then c = 4, if n = 5 then c = 3). We fill all other frames of UFIS using the following equation:  
\[T^{i}_{x,y}=F^{f}_{x,y}, \tag{4}\]  
where \(x\) and \(y\) are pixel indexes, \(T\) is UFIS (synthetic focal stack), \(i\) is an index of the reconstructed frame number of \(T\), \(F\) is initial focus stack, and \(f\) is the corresponding frame number calculated by Equation (5) using initial depth estimation result:  
\[f=round(D_{x,y})+(i-c),\qquad f=\begin{cases}d&0\leq d\leq n\\ n&d>n\\ 0&\text{otherwise}\end{cases}, \tag{5}\]  
where \(i\) is an index of the reconstructed frame (same with Equation (4)), \(D_{x,y}\) is estimated depth at \([x,y]\) position (same position with Equation (4)), and \(c\) is the central frame index of \(T\) reconstructed frame.  
After UFIS is constructed, we repeat the initial depth estimation process with decreasing window size. Based on the result of _Iterative Depth Refinement_, we update the depth result. We continue this iteration process with a smaller window size until the changes in the depth value at each iteration falls below a stopping criteria. We update depth result at each iteration using Equation (6):  
\[D_{x,y}=D^{p}_{x,y}+\left(d_{x,y}-c\right), \tag{6}\]  
where \(D^{p}_{x,y}\) is previous depth at (\(x\),\(y\)) location, \(d_{x,y}\) is depth
## 5 Numerical Simulation on the Basis of NDT Results
### Stability Evaluation Using the Numerical Results  
The numerical results indicate that the local damage causes stress concentration of the tunnel lining, which can easily induce crack propagation and reduce the service life of the structure. In order to ensure the stability of the structure, it is critical to evaluate structural stability and prevent disasters.  
Figure 12: The influence of local damage under various water pressures, where \(C\) represents the direction of circumference and \(L\) represent the longitudinal direction: (**a**) Von [PERSON] stress distribution; (**b**) Von [PERSON] stress curves.  
The numerical results, shown in Figures 12 and 13, indicate that the tensile stress value is 1.74 MPa when water pressure is 487 KPa. The maximum stress response value longitudinally is 45 KPa in the case of water level changes per meter. Based on this, the maximum stress under the largest water pressure--of which the value is 500 KPa, as determined through field investigation results--can be calculated. Thus, the maximum value of stress distributed around damaged positions is about 1.79 MPa. Subsequently, the maximum tensile stress theory (MTST) was adopted as a criterion to evaluate the state of the concrete structure [27]. Based on MTST, a crack would develop if the tensile stress reached the ultimate strength. The criterion results can be expressed as  
\[K\leq\frac{\sigma_{u}}{\sigma_{t}} \tag{5}\]  
where \(\sigma_{u}\) is ultimate strength and the value of concrete (C60) is 2.85 MPa. \(\sigma_{t}\) is the tensile stress of the material. \(K\) is the safety factor. Industry standards have indicated that the critical value of the safety factor is 1.5 [28]. This means the structure is safe if its safety factor is over 1.5; otherwise, the structure is unsafe.  
Thus, the safety factor around damaged positions in the study site is 1.57, which is greater than the critical value. The structure is therefore considered safe. In order to prevent disasters, it is critical to take measures to support and reinforce the influenced region. Considering that steel is a material with high strength and a low price, it was selected to reinforce the damaged positions, as shown in Figure 14. A steel plate was installed for the purpose of anchoring, and the distance from the center of the hole to the side of the steel plate was 25 cm, which is larger than the radius of the influenced area.  
Figure 14: Reinforcement measures in the study site.  
Figure 13: The influence of local damage in different positions: (**a**) [PERSON] stress distribution; and (**b**) Von [PERSON] stress curves.
## 3 Results
### Estimation of T/ET for Typical Crops  
The T/ET of eight typical crops (including maize, paddy rice, potato, rapeseed, soybean, sugar beet, winter barley, and winter wheat) are calculated based on the crop rotation information (Supplementary Table S6) and the four ET partitioning methods, and the results are shown in Figure 7 and Supplementary Table S7. Obvious differences in the T/ET values are found among the eight crops, which is mainly due to the intrinsic difference in water usage of crops and partly attributed to the ET partitioning methods. Generally, maize possesses the highest T/ET, ranging from 0.458 (Y22) to 0.752 (N18) with a multi-year mean value of 0.619 \(\pm\) 0.119, followed by soybean (0.618 \(\pm\) 0.085). Winter wheat (0.614 \(\pm\) 0.08) and sugar beet (0.611 \(\pm\) 0.065) have similar T/ET values, and both of them are slightly lower than maize and soybean. The lowest T/ET is observed in paddy rice, reaching 0.505 \(\pm\) 0.055. In comparison, the T/ET of winter barley (0.590 \(\pm\) 0.058), potato (0.540 \(\pm\) 0.088), and rapeseed (0.522 \(\pm\) 0.107) are at the moderate level, lower than 0.6, but apparently higher than that of paddy rice.  
For the four ET partitioning methods, it is worth noting that both N18 and X24 generated higher T/ET values of crops than Y22 and Z16, which is consistent with the  
Figure 6: Comparison of the estimated daily T using the Z16, N18, Y22, and X24 methods at (**a**) DE-Kli, maize was planted from 23 April to 2 October 2007; (**b**) DE-Rus, sugar beet was planted from 27 March to 1 October 2014; (**c**) US-Twt, paddy rice was planted from 2 April to 2 September 2013; and (**d**) FR-Gri, winter wheat was planted before 15 July 2006, and winter barley was planted after 4 October 2006.  
evaluation results from Figure 5. N18, X24, and Z16 all indicate that maize (C4 crop) gets the highest T/ET value in comparison with other C3 crops. In comparison, Y22 yields a relatively lower T/ET for maize (0.458) than for some C3 crops (range: 0.477-0.582), which is mainly due to the low g\({}_{1}\) value of maize [31]. Among the investigated C3 crops, soybean, winter wheat, and sugar beet generally possess relatively higher T/ET values, and all of them exceed 0.6. These results are consistent with [PERSON] et al. [5] and [PERSON] et al. [60], but different from a study [29] in North China Plain that showed the T/ET of winter wheat (0.77 \(\pm\) 0.04) was higher than that of summer maize (0.72 \(\pm\) 0.03) based on N18 and a newly developed ET partitioning method. This difference may result from the divergences in crop planting mode and background climate conditions. In addition, it should be noted that there is no significant difference in the T/ET among maize, winter wheat, and soybean based on the four ET partitioning methods. Overall, all the four methods indicate obvious differences among those crops, which suggests that precise agricultural water management for each crop is crucial for the sustainability of water resources and improvements in cropland water use efficiency.
## 7 Analysis of the Results
Thus, it is particularly useful to apply a reactive approach in a dynamic environment subject to unexpected disruptions in the delay times. Specifically, the reactive approach allows for the AGV to react to unexpected disruptions when traversing the environment, and alter the assignment of the tasks to minimize the total time. This is achieved because the reactive approach considers the actual delays happening as the AGV completes the assigned tasks, not only estimating the delays that could happen to the AGV while completing the assigned tasks. Next, an analysis of variance (ANOVA) is used to analyze the differences among means. The gap is the dependent variable, and the approach, the dynamism level, and the instance are the independent variables. Table 5 displays the results obtained. The conclusion of this analysis is that there are statistically significant differences among the means of the different approaches, and dynamism levels, but not among the means of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FIFO Approach** & \multicolumn{2}{c}{**Non-Reactive Approach**} & \multicolumn{2}{c}{**Reactive Approach**} \\ \cline{2-6}
**Instance** & **OBF** & **OBN** & **GAP(\%)** & **OBR** & **GAP(\%)** \\  & **[1]** & **[2]** & **[1–2]** & **[3]** & **[1–3]** \\ \hline Small & 90,084.8 & 84,662.7 & \(-\)6.02 & 83,959.6 & \(-\)6.80 \\ Medium & 179,177.4 & 171,094.5 & \(-\)4.51 & 166,810.5 & \(-\)6.90 \\ Large & 360,197.0 & 344,949.7 & \(-\)4.23 & 334,807.1 & \(-\)7.05 \\ Very large & 716,942.5 & 690,075.2 & \(-\)3.75 & 665,469.4 & \(-\)7.18 \\ \hline Average: & 336,600.4 & 322,695.5 & \(-\)4.6 & 312,761.6 & \(-\)7.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational results for the test instances with a high level of dynamism. Figure 5: Barplot of the mean gaps (%) of non-reactive and reactive approaches with respect to the FIFO approach considering different levels of dynamism (from low to high). different instances. Finally, the Tukey multiple comparisons of means among approaches (95% family-wise confidence level) are applied to find means that are significantly different from each other. Table 6 shows the results obtained. Notice that there are significant differences between the non-reactive and the reactive approaches, as the \(p\)-value is lower than 0.05.