esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 3 Solution Methodology
### Summary of the Optimization Methodology  
The application of the GNDO approach to solve the MINLP model defined from Equations (1)-(11) is summarized in Algorithm 1.  
Note that the application of Algorithm 1 is general for any optimization problem. Nevertheless, two modifications are necessary to adapt the GNDO approach to multiple engineering optimization problems: (i) selecting the adequate codification of the studied problem and (ii) modifying the slave stage for the adequate fitness function evaluator.  
Figure 2 illustrates the general implementation of the proposed GNDO approach to locate and size PV sources in monopolar DC networks. Note that the codification is carried out using matrix representation, and power flow evaluation is performed via a power flow function also implemented in the MATLAB coding.  
```
Data: Select the monopolar DC network
1 Obtain the per-unit network equivalent;
2 Chose the values of \(N_{i}\) and \(t_{\text{max}}\);
3 Assign the \(\mu\) parameter as \(\frac{1}{2}\) and make \(t=0\);
4 Create the initial population with the structure in (15);
5 Solve the power flow problem for each \(x_{i}^{t}\) to obtain \(A_{f}\big{(}x_{i}^{t}\big{)}\);
6 Assign the best individual as the solution in the current population with the minimum objective function value, i.e., \(x_{\text{best}}^{t}\);
7 for\(t\leq t_{\text{max}}\)do
8 for\(i=1:N_{i}\)do
9 Obtain a random value for \(\zeta\) between 0 and 1;
10 if\(\zeta>\frac{1}{2}\)then
11 /*Local exploration search*/;
12 Calculate the value of the vector \(M\) through Equation (19);
13 Compute the value of \(\mu_{i}\) through Equation (16);
14 Compute \(\delta_{i}\) through Equation (17);
15 Find the penalty factor \(\eta\) using Equation (18);
16 Perform the local exploration using Equation (14);
17
18 else
19 /*Local exploration search*/;
20 Obtain numbers for \(j\), \(k\), and \(m\) which are different from each other and from \(i\);
21 Calculate the value of \(v_{1}\) with Formula (21);
22 Calculate the value of \(v_{2}\) with Formula (22);
23 Apply the global exploration rule (20) to reach \(v_{i}^{t}\);
24 Check the minimum and maximum values of \(v_{i}^{t}\) using the rule (23);
25 Evaluate the power flow solution to obtain \(A_{f}\big{(}v_{i}^{t}\big{)}\);
26 Chose the next individual \(x_{i}^{t+1}\) with Formula (24); Result: Present the best solution found, i.e., \(x_{\text{best}}^{t+1}\)
```  
**Algorithm 1**Application of the GNDO method to locate and size PV sources in monopolar DC networks.  
Figure 2: General implementation of the GNDO approach in MATLAB software for the studied problem.
###### Abstract  
Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7,TMPA-3B42 RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijing and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE', and NSSE values) than the uncorrected ones, particularly in the Beijing River, which has a larger drainage area; (2) after recalibration under Scenario II, the two daily merged precipitation products (NSSE values: 0.73-0.87 and 0.69-0.82 over the Beijing and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.  
satellite precipitation; reanalysis precipitation; CREST model; error analysis; Pearl River Basin 2023  
18 October 2023  
10 November 2023  
1 Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 5010070, China 1  
Guoqiang Tang  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Results of the Test under S1 and S2 Conditions
#### 3.1.3 Rainfall Test of S2  
Before rainfall, the pressure measuring pipes were removed in order to avoid affecting the failure process of the fly ash dam slope. After the rainfall lasted for 1 h 19 min, the gullies were first formed at the dam-slope toe by runoff erosion, as shown in Figure 8a. With time passing by, the gullies at the dam-slope toe continued to develop in the upstream direction; at the same time, the number of gullies increased, and the depth increased as well.  
After 1 h 50 min, the gullies reached the middle position of the dam-slope surface. The gullies were narrow and shallow in the upper part and wide and deep in the lower part. The adjacent gullies gradually merged into larger gullies, which aggravated the erosion on the dam-slope surface, as shown in Figure 8b.  
After 2 h 32 min, the gullies continued to develop, becoming deeper and wider. The gullies at the lower part of the dam slope eroded the dam-slope toe, and the flow-slide failure began to appear on the right side of the dam-slope toe. The upper part of the flow-slide body generated small multi-level cracks, and the first local flow-slide failure occurred, as shown in Figure 8c.  
After 2 h 35 min, runoff erosion damage and flow-slide damage occurred simultaneously and developed upstream of the dam slope. Small tensile cracks were generated in the upper soil mass, and the dam-slope erosion was more serious, as shown in Figure 8d.  
After 2 h 42 min, the third flow slide occurred on the left side of the dam slope. With the loss of soil in the lower part of the dam slope, the slope ratio of the dam slope increased significantly, and the upper dam body became unstable. At this time, transverse tensile crack 1 appeared on the dam-slope crest, indicating possible local sliding, as shown in Figure 8e.  
After 2 h 46 min, the soil at the edge of transverse crack 3 on the dam-slope crest was liquefied, and the sliding body divided by crack 3 began to slide slowly, as shown in Figure 8f. After 2 h 55 min, the sliding body accelerated, and local-sliding damage was  
Figure 8: Progressive erosion failure mode by runoff erosion and flow-slide damage. (**a**) Gullies’ development after 1 h 19 min. (**b**) Gullies’ development after 1 h 50 min. (**c**) First flow-slide failure after 2 h 32 min. (**d**) Second flow-slide failure after 2 h 35 min. (**e**) Third flow-slide failure after 2 h 42 min. (**f**) Transverse crack 3 on the top after 2 h 46 min. (**g**) Local sliding after 2 h 55 min. (**h**) Final failure mode (test termination after 2 h 58 min).  
formed. Under the action of rainfall, the sliding body formed mud flow downstream, as shown in Figure 8g. After 2 h 58 min, the dam-slope damage extended to the dam-slope crest, and the rainfall was stopped. The final failure form of the dam slope is shown in Figure 8h. The failure process of S2 was flow slide at the dam-slope toe caused by runoff erosion, followed by several flow-slide failures. The failure position gradually developed upstream, and finally led to a local-sliding failure.
## 2 Background and Related Work
### Task Division in Parallel Fuzzing  
The problem of duplicate work also exists in parallel fuzzing using a single fuzzer and impacting the overall efficiency of the parallel fuzzing seriously. So, there is already some research aiming to overcome this limitation for parallel fuzzing by using task division methods. The methods that have been proposed are as follows:  
_Global seeds scheduling_: Both P-Fuzz [32] and UltraFuzz [33] are designed based on this type of approach. They implement a manager called the main node on top of the fuzzer and centralize the management and selection of seeds in the main node. Whenever a fuzzing instance needs to select the next seed for mutation, it requests a seed from the main node. Similarly, when any fuzzing instance discovers a new seed, it sends the new seed to the main node. However, this is not a method of task division in actuality. In this type ofapproach, the fuzzer cannot perform the entire fuzzing process independently and requires integration with the main node for management and seed selection. In other words, in the framework using this type of method, the fuzzer is no longer an individual part. So, they actually treat the tested program as a complete task. Indeed, it is precisely because the fuzzer used in this type of approach is inherently incomplete that it is theoretically unsuitable for generalization to the ensemble fuzzing.  
_Bitmap-based task division_: Diverging from the aforementioned global seeds scheduling method, a framework named PAFL [16] introduces a novel approach for addressing the issue of significant redundancy among paralleled fuzzing instances. Specifically, PAFL evenly divides the bitmap, which is widely used in CGF to indicate the covered branched of the tested program, into multiple blocks, with the number of blocks matching the number of paralleled fuzzing instances, and assigns each block to a fuzzing instance in sequential order. The fuzzer it provides will only mutate seeds whose rarest covered branch falls within the bitmap region allocated to it. However, as adjacent branches on the bitmap have no logical relationship with each other, the corresponding subtasks of the bitmap blocks are composed of scattered branches in various functions of the program under test. So, it is difficult for each fuzzing instance in PAFL to conduct concentrated and in-depth testing on their allocated subtasks.  
_Callgraph-based task division_: To overcome the issue of PAFL, a parallel fuzzing framework called AFLTeam [17] proposes a task division approach based on the function call graph of the target program. In AFLTeam, the callgraph of the tested program, represented as a minimum spanning tree, is partitioned into subgraphs based on workload balancing principles. Each subgraph is sequentially assigned to a fuzzing instance as its allocated subtask. The fuzzer will only mutate those seeds that can reach at least one function in its assigned subtask.
# Itarare Group Sandstone as a Sustainable Alternative Material for Photon Radiation Shielding
## 1 Introduction
Because the composition and structural features of sandstones can vary significantly based on their geological origin, it is essential to evaluate their radiation shielding effectiveness in specific regions. Although other studies have examined sandstones [17; 18; 19], the rocks analyzed in those studies often come from different origins and have different chemical compositions and structures. Our study focuses on analyzing sandstones that are relatively common in our study region and throughout Brazil. The abundance of this rock type makes it an interesting potential material for radiation shielding [20]. Therefore, the findings from our research will not only advance the understanding of alternative materials for radiation shielding but also aim to identify options that are both cost-effective and widely available. The objectives of the study were as follows: (1) to determine the oxide content of sandstones from the same geological profile, (2) to examine how variations in the oxide composition and density of the samples impact the shielding parameters, (3) to identify the parameters that exhibit the most significant differences between the samples, and (4) to create reference data for selecting alternative materials for radiation shielding. It is important to mention that all analyses were conducted using computer simulations of the shielding parameters, which were based on the samples' chemical composition and density data. This method was chosen because of the photon energies being studied and the unavailability of radioactive sources with sufficient activity for experimental measurements.
## 4 Methodology
### Sliding Window Optimization Based on Image Semantic Information  
For a single point \(p\) in keyframe \(I_{i}\), its projection to another keyframe \(I_{j}\) in the sliding window is \(p^{\prime}\), and the photometric error between \(p\) and \(p^{\prime}\) is defined by Equation (2).  
Similar to Section 4.2, we define a label for each point to eliminate the residual formed by the point projected in the dynamic region:  
\[C_{pj}=I_{j_{com}}(p^{\prime}) \tag{9}\]  
where \(I_{j_{com}}\) is the binary semantic image of keyframe \(I_{j}\).  
The full photometric error over all keyframes in the sliding window and points is given by:  
\[E_{photo}=\sum_{i\in F}\sum_{p\in P_{i}}\sum_{jcols(p)}C_{pj}E_{pj} \tag{10}\]  
where \(F\) represents the set of all keyframes in the sliding window, \(P_{i}\) is the set of all the points in keyframe \(I_{i}\), and \(obs(p)\) is the set of the keyframes where the point \(p\) is visible.  
The pose of each keyframe and depth of each selected point can be obtained by applying Gauss-Newton method to \(E_{photo}\).
## 5 Analysis and Findings
To further understand the significance of the interaction terms, in Figures 2 and 3, the data are divided into a group with high psychological resources and a group with low psychological resources. The horizontal axis shows the group with high anxiety and the group with low anxiety, and the vertical axis shows compliance in Figure 2 and fatigue in Figure 3. In Figure 4, the data are divided into a group with high social resources and a group with low social resources; the horizontal axis shows the group with high compliance and the group with low compliance, and the vertical axis shows turnover intention. The criterion for high and low is whether the score is 1 SD higher or lower than the average, following the recommendation of [PERSON] et al. (2018). \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Path**} & \multicolumn{1}{c}{**Estimate**} \\ \hline Anxiety & \(\rightarrow\)\(>\) & Compliance & 0.125 \\ Social resources & \(\rightarrow\)\(>\) & Compliance & 0.293 \\ Psychological resources & \(\rightarrow\)\(>\) & Compliance & 0.379 \\ Anxiety \(\times\) Psychological resources & \(\rightarrow\)\(>\) & Compliance & \(-\)0.446 \\ Anxiety & \(\rightarrow\)\(>\) & Fatigue & 0.404 \\ Social resources & \(\rightarrow\)\(>\) & Fatigue & \(-\)0.377 \\ Psychological resources & \(\rightarrow\)\(>\) & Fatigue & \(-\)0.083 \\ Anxiety \(\times\) Psychological resources & \(\rightarrow\)\(>\) & Fatigue & \(-\)0.141 \\ Compliance & \(\rightarrow\)\(>\) & Turnover intention & \(-\)0.185 \\ Fatigue & \(\rightarrow\)\(>\) & Turnover intention & 0.330 \\ Tenure & \(\rightarrow\)\(>\) & Turnover intention & \(-\)0.181 \\ Age & \(\rightarrow\)\(>\) & Turnover intention & \(-\)0.116 \\ Social resources & \(\rightarrow\)\(>\) & Turnover intention & \(-\)0.203 \\ Social resources \(\times\) Compliance & \(\rightarrow\)\(>\) & Turnover intention & \(-\)0.272 \\ \hline \hline \end{tabular} Note: The numbers in the table are standardized path coefficients. All paths are significant at the 0.1% level. Correlation between variables is omitted (available upon request).  
\end{table} 
Table 4: Results of path analysis. Figure 1: Results of path analysis. Linear paths are significant at the 0.1% level. Dashed line paths were not significant even at the 5% level and were therefore excluded from the final model. Goodness-of-fit indices: \(\chi^{2}\) = 40.57, df = 28, root mean square error of approximation (RMSEA) = 0.024, probability of close fit (PCLOSE) = 0.999, goodness of fit index (GFI) = 0.992, adjusted goodness of fit index (AGFI) = 0.977, normed fit index (NFI) = 0.987, comparative fit index (CFI) = 0.996. \(n\) = 813. Figure 4: The moderating effect of social resources between compliance and turnover intention. Figure 3: The moderating effect of psychological resources between anxiety and fatigue. Figure 2: The moderating effect of psychological resources between anxiety and compliance.
## 4 Case Study
### Results and Discussion
Figure 9: Results of incremental dynamic analysis from 28 accelerograms: (**a**) sloshing wave height, (**b**) base shear, (**c**) overturning moment, and (**d**) elephants foot buckling. The median (\(\mu\)) represents the intensity level at which there is a 50% probability of failure, while the standard deviation (\(\beta\)) provides insight into the spread or uncertainty in the tank's performance under varying levels of seismic demand. In terms of median values, \(DS_{OM}\) exhibits the highest \(\mu\) value (1.141), indicating that the tank is highly resistant to overturning moments, requiring significant \(a_{g}\) to reach this damage state. \(DS_{LO}\) has a median capacity of 0.461, suggesting that liquid overspill occurs at moderate seismic intensities. \(DS_{EFB}\) demonstrates a much lower median capacity (0.249), indicating its susceptibility to elephant's foot buckling under relatively low PGA levels. \(DS_{BS}\) shows the lowest \(\mu\) value (0.118), confirming that base shear exceedance is the most vulnerable damage state, occurring even under mild seismic excitations. In terms of logarithmic standard deviation, \(DS_{BS}\) (0.056) and \(DS_{EFB}\) (0.058) exhibit the smallest \(\beta\) values, indicating consistent behavior across simulations with minimal variability. \(DS_{LO}\) has a higher \(\beta\) value (0.149), suggesting greater variability in the occurrence of liquid overspill, likely influenced by geometric and operational factors. \(DS_{OM}\) shows a moderate \(\beta\) value (0.126), reflecting some variability in the overturning moment capacity.
## 5 Results and Discussion
### [45\({}^{\circ}\)]\({}_{10}\) Model Calculation Results and Validation
#### 5.2.3 Fibre Damage  
When the torque load reached 9554.38 Nm, all layers of the matrix had failed to varying degrees since they could not match the original material stiffness, and the structure became laterally destabilized. At this time, the fibres were nearly providing all the load-bearing capacity; then, the fibres began to fail, and the calculated MDI of the fibres is shown in Figure 24. A few fibres broke in the 8 th, 9 th, and 10 th layers; the MDI decreased gradually from the 10 th layer to the 8 th layer. The load continued to increase, the matrix damage area gradually became larger, and the fibres quickly lost all support. The fibre strength was very high, but the fibres lost the role of the matrix transverse support and dispersion of the force, leading to rapid transverse fracture in the fibres. When the load reached 9601.85 Nm, all layers of the fibres failed, and the reinforced section between the layers of the matrix was rapidly sheared off by the matrix in large quantities. Point d in Figure 15b denotes the stiffness reduction caused by the fibre damage, and the fibre failure zone is at an angle of 90\({}^{\circ}\) to the axial axis of the axial structure model, as shown in Figure 26. This result is consistent with the macroscopic fracture electron microscopy test results.  
When the SiC\({}_{\ell}\)/TC\({}_{4}\) composite shaft was subjected to forward/reverse torque, damage occurred first at the outermost layer of the reinforced section composite layer, and the damage extended from the outside to the inside. All of this damage started at the interface because of the particularity of the interface strength. SiC fibres and Ti form a thermodynamically non-equilibrium system. During the process of composite formation and high-temperature operation, chemical reactions and element diffusion will inevitably occur between the matrix and fibre, forming a certain thickness of the interfacial reaction layer. The residual stress is mainly concentrated near the interface between the fibre and matrix and along the fibre axial and circumferential direction. The fibre is compressed, and the matrix is under tensile stress. Along the radial direction of the fibre, both the fibre and the matrix are compressively stressed. This is an important factor in determining the strength of the interface. The two damage processes have the same point at which they start from the interface; however, the difference is that, due to the interface being subjected to different loads, slow damage occurs under reverse loading, while fast damage occurs in forward loading.  
Figure 25: M = 9554.38 Nm matrix MDI: **(a–j)**:1 st layer–10 th layer.
## 4 Data Processing Scheme
### Ambiguity-Fixing  
As is well known, integer ambiguity resolution is critical in GPS data processing for obtaining the most accurate result. It improves orbit accuracy for GPS satellites significantly [18]. However, due to the very small movement of GEOs and IGSOs with respect to the tracking network, ambiguities could be biased differently, so that the integer property cannot be recovered by forming double-differenced ambiguity. Even if the ambiguities can be fixed to integer, its improvement on orbits is not definitely comparable to that of GPS. Because of the very small change of the tracking geometry, GEOs and IGSOs are usually tracked continuously or over a long time. Theoretically, there should be one ambiguity for each satellite-station pair in each solution. Then ambiguity estimates must be rather stable thanks to the long continuous data and the ambiguity-fixing may bring nearly no improvement.  
Figure 5: Tracking networks defined for the impact study of tracking geometry on satellite orbits. The Chinese regional network is indicated by a violet cycle, Asian-Pacific network in red, Asian network in green, and global network in yellow.  
Anyway, we employ the fixing approach developed by [19] and adapted by [20] based on an ionosphere-free solution and the Melbourne-Wibbena combination. In this test, we try to fix ambiguities of different satellite types sequentially in order to confirm their fixing efficiencies and impact on satellite orbits. The details are in subsequent discussions in sub-Section 5.5.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
###### Abstract  
In Multimedia Internet of Things (IoT), in order to reduce the bandwidth consumption of wireless channels, Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is often used to support the low-bitrate video communication. In this paper, we propose a spatial predictive algorithm which is used to improve the performance of MC-FRUC. The core of the proposed algorithm is a predictive model to split a frame into two kinds of blocks: basic blocks and absent blocks. Then an improved bilateral motion estimation is proposed to compute the Motion Vectors (MVs) of basic blocks. Finally, with the spatial correlation of Motion Vector Field (MVF), the MV of an absent block is predicted based on the MVs of its neighboring basic blocks. Experimental results show that the proposed spatial prediction algorithm can improve both the objective and the subjective quality of the interpolated frame, with a low computational complexity.  
m _future internet_