esa-sceva/satcom-chunk-collection huggingface dataset:
References [22; 23] propose that each REG station allocates the section margin based on an equal ratio of ultra-short-term power prediction, and considers the specific resource distribution of each REG station, which reflects the refined scheduling of REG clusters. All of the above methods have the primary objective of making full use of the section margin and increasing the system's capacity to consume renewable energy. However, when the load within the REG cluster rises, and when the actual output of the REG station is less than the output command after the margin allocation due to the forecast errors, it will result in the REG station being unable to complete the output command issued by the dispatch center. And the transmission power of the section cannot reach the upper limit of the transmittable power of the section, resulting in the waste of resources and reducing the overall transmission capacity of the section. At present, little research has been carried out on the impact of different operating modes of REG clusters on the section margin allocation, the probability of each REG station realizing the command in section margin allocation, and the failure of REG stations to meet the output command after margin allocation due to the forecast errors. In response to the above problems, this paper proposes a method for allocating section margins that takes into account the randomness of REG stations' output and load fluctuations under different operating modes on the REG cluster side. This method is based on the probability density function of the prediction error of each REG station, combined with the prediction of the active power output of the REG station, and the introduction of the output realization probability of the REG station, considering the load rise within the REG cluster under different typical operation modes. This method aims at ensuring the highest probability of output after each new energy station is able to meet the margin allocation and allocates the section margin to improve the grid's ability to consume the renewable energy. The rest of the paper is organized as follows: Section 2 introduces the proposed section margin allocation model. The calculation methods of the output realization probability of REG stations and load rise space are presented in Sections 3 and 4, respectively. The simulation results of the proposed section margin allocation method are given in Section 5, and Section 6 concludes the paper.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Structure of MAS for Train Rescheduling Problem
The advantage of the hierarchical MAS lies in its ability to manage complexity through decomposition. By breaking down the rescheduling problem into manageable local areas handled by individual junction agents and allowing the collaborative agent to coordinate these local solutions, the overall computational complexity is significantly reduced. Additionally, the parallel processing of local solutions by multiple junction agents can further decrease the total computational time, making the system more efficient and scalable.
## 7 Conclusions and Future Work  
The main objective of this paper is to improve the performance of software defect prediction using a combination of GAN-based methods and boosting ensembles. We proposed combining GAN-based methods with boosting ensembles, where GAN-based methods are used to balance the datasets, and AdaBoost ensembles are employed to classify the modules into defective and non-defective modules. Furthermore, we used Optuna, the Bayesian model-based optimization algorithm, to find the optimal GAN hyperparameter values and explore the impact of the optimization process on the defect prediction performance. Paper outcomes can be summarized as follows: (1) GAN-based methods need hyperparameters optimization when used for imbalanced defect prediction. (2) GAN-based methods, namely CWGANGP and CTGAN, outperformed all traditional techniques: ROS, RUS, and SMOTE when used for imbalanced defect prediction. (3) GAN-based methods should be used to handle imbalance problems without combining it with undersampling techniques.  
GAN-based methods proved to be a reliable sampling method to handle imbalanced software defect prediction due to their ability to generate new artificial and realistic samples from the minority class. However, GAN-based methods were found sensitive to their hyperparameters optimization. This work can be extended by exploring the performance of GAN-based methods with other ensemble learning algorithms (e.g., bagging). Moreover, other traditional and GAN-based methods could be included in future work to increase the generalizability of our study findings. Lastly, further studies might be conducted to evaluate the quality of the generated synthetic data using GAN-based methods and traditional methods.  
A.A.: Conceptualization, methodology, software, data curation, writing the manuscript. H.A.: Conceptualization, methodology, software, data curation, writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All utilized datasets are publicly available in [39; 40; 41; 42; 43].  
The authors would like to acknowledge the support of the King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia in the development of this work.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Combined Hash Storage Strategy  
To provide a more accurate cost assessment and demonstrate the system's flexibility, we extended our analysis over one month and developed a hash writing strategy that combines both time-based and threshold-based approaches. This strategy defines a minimum and maximum threshold, which allows for an adaptive storage mechanism. At fixed 10-min intervals, the system evaluates the volume of accumulated critical data. If the volume falls below the minimum threshold, hash computation and writing are skipped, reducing unnecessary transactions and optimizing cost efficiency. Conversely, if the maximum threshold is reached before the next scheduled interval, the system immediately performs a hash computation and writes the critical data to the blockchain. This ensures timely data aggregation while maintaining an efficient balance between cost and performance.  
We conducted a detailed cost analysis of the combined strategy using various threshold pairs. The analysis was based on data from July 2023, the month with the highest recorded volume of critical data. In July 2023, 128,296 unique incidents were recorded, corresponding to a total of 799,008 critical timestamps. Figure 8 illustrates the estimated daily costs for four threshold configurations (125-175, 115-190, 135-195, and 145-205), compared to the time-based strategy.  
Table 7 summarizes the total number of hash-writing operations and the estimated costs associated with each threshold pair. These estimates are based on an average transaction fee of USD 0.29 (see Table 4). For comparison, the corresponding values for the time-based strategy are also provided.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threshold** & **Total Writings** & **Mean Cost (USD)** & **Mean Writings** & **Max Writings** \\ \hline
100 & 281 & 81.49 & 11.7 & 16 \\
150 & 187 & 54.23 & 7.8 & 11 \\
175 & 160 & 46.4 & 6.67 & 9 \\
200 & 140 & 40.60 & 5.83 & 8 \\ \hline Time strategy & 144 & 41.76 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Blockchain hash writing operations and their associated costs for different thresholds, on 3 October 2023, the day with the highest recorded volume of critical incident data.  
Figure 8: Cost analysis of combined blockchain data writing strategies over July 2023, the month with the highest critical data volume.
# Base Isolation Compared to Capacity Design for Long Corner Periods and Pulse-Type Seismic Records
## 3 Results
### Structural Performance during Design Earthquake
#### 3.1.1 Building Design According to the Capacity Method  
The force-displacement relation (total earthquake force at the bottom of the upper structure and horizontal displacement in the centre of mass at the upper level) is shown in Figure 7 and results are detailed in Table 3.  
The displacement demand of 40 cm was determined according to Anne B of EN 1998-1 [32]. The model was pushed in the horizontal direction up to a displacement equal to 1.5 the displacement demand.  
The overall strength of the building designed according to the capacity method is equal to 3. The ratio between horizontal failure force and building weight equals 5861/18,972 = 0.3. This is three times the global seismic coefficient considered for design.  
Figure 7: Indicative slider dimensions: L = 1,53 m; W = 1,08 m; B = 0,3 m; H = 0,17 m; D\({}_{\max}\) = 0.6 m [37].  
The behaviour factor considered for the design was based on an \(\alpha_{\text{u}}\)/ \(\alpha_{1}\) ratio equal to 1.35 [18]. According to the force-displacement relation determined by non-linear static analysis, this ratio equals \(0.163/0.041\approx 4.0\). This is 2.9 times more the value considered in the design.  
Results of the non-linear time history analysis are shown in Table 4 for the building designed according to the capacity method. For the results obtained using the three spectrum-compatible accelerometers, the mean values are presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Result**} & **INCERC 1977** & **INCERC 1986** & **INCERC 1991** & **Spectrum Compatible** \\  & **Original** & **Original** & **Original** & **Tc = 1.6 s** \\ \hline Maximum base shear force [kN] & 6108 & 3256 & 1260 & 7840 \\ Maximum interstorey drift [\%] & 7.40 & 2.80 & 0.86 & 12.10 \\ Maximum accelerator with the underground level [g] & 0.20 & 0.10 & 0.08 & 0.36 \\ Maximum acceleration at the building top [g] & 0.38 & 0.23 & 0.11 & 0.50 \\ \hline \hline \end{tabular}  
* OG represents over ground level.  
\end{table}
Table 4: Results from non-linear time-history analysis for the building designed according to the capacity method.  
Figure 8: Force–displacement curve for the capacity design of the building, X-direction.
## 2 Related Work
### Evaluation Method  
In almost all of the studies, the evaluation method involved questionnaires in which participants answered questions regarding their experience with the game, its ease of use, and whether they prefer serious games to be integrated into education. Specifically, five studies [11; 12; 15; 18; 19] assessed the game using Likert scale questionnaires with questions about player satisfaction, game difficulty, and whether they believed the content helped broaden their knowledge. Additionally, two studies [16; 17] evaluated the educational effectiveness of the game using questionnaires containing knowledge-based questions. Finally, another evaluation method was discussion with the participants [5; 14] to record their experiences, which was used in conjunction with a questionnaire.
## 5 Discussion
### Effect of the Number of Images  
The number of images required for 3D reconstruction is important for subsequent processing. The larger the number of images needed, the greater the computational overhead and running time costs. Table 3 lists the UAV-NeRF reconstruction effects when 5-50 training images were used. We found that the NeRF series methods could achieve optimal performance with fewer training images. The results of NeRF, Sat-NeRF, and our proposed UAV-NeRF methods tended to be stable at 30-50 images, while the traditional SfM methods often required 40-50 images to achieve performance convergence. In addition, in the case of there being incredibly few images (for example, only 5-15 images), the 3D reconstruction performance of NeRF was far better than that of the traditional SfM methods. This also shows that if there is not enough time to photograph a survey area in an emergency situation, the NeRF series method can produce preliminary 3D reconstruction results in a limited time so as to serve emergency mapping.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Area** & \multicolumn{4}{c}{**DengFeng Area\#1**} & \multicolumn{4}{c}{**XinMi Area\#1**} \\ \hline
**Method** & **N Images** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **Alt MAE[m] \(\downarrow\)** & **N Images** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **Alt MAE[m] \(\downarrow\)** \\ \hline \multirow{6}{*}{NeRF} & 5 & 14.620 & 0.711 & 4.37 & 5 & 13.670 & 0.672 & 5.34 \\  & 10 & 15.354 & 0.753 & 3.27 & 10 & 16.848 & 0.762 & 3.48 \\  & 15 & 16.457 & 0.780 & 2.16 & 15 & 18.625 & 0.801 & 2.22 \\  & 30 & 18.382 & 0.801 & 2.03 & 30 & 18.638 & 0.803 & 2.17 \\  & 40 & 18.383 & 0.800 & 2.03 & 40 & 18.640 & 0.803 & 2.16 \\  & 50 & **18.381** & **0.798** & **2.02** & 50 & **18.639** & **0.802** & **2.15** \\ \hline \multirow{6}{*}{Sat-NeRF [10]} & 5 & 18.547 & 0.725 & 2.74 & 5 & 19.625 & 0.775 & 3.92 \\  & 10 & 20.635 & 0.779 & 1.58 & 10 & 20.982 & 0.806 & 2.95 \\ \cline{1-1}  & 15 & 23.971 & 0.875 & 0.91 & 15 & 22.348 & 0.853 & 1.69 \\ \cline{1-1}  & 30 & 24.936 & 0.898 & 0.85 & 30 & 24.934 & 0.875 & 1.32 \\ \cline{1-1}  & 40 & 24.938 & 0.888 & 0.84 & 40 & 24.933 & 0.877 & 1.33 \\ \cline{1-1}  & 50 & **24.936** & **0.887** & **0.86** & 50 & **24.935** & **0.875** & **1.32** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison using different numbers of images.
## 4 Result and Discussion
### Distribution and Evolution of Fan Deltas of S3 Sand Group  
#### 4.4.1 Evolution of Multistage Fan-Delta Complex  
The frequent changes in the water level of the lake in the study area correspond to the base-level cycle control of the multistage evolution of the fan body. The single-stage fan body is the result of eventful sedimentation, and the developed lake pan mudstone represents the termination of a sedimentary period [71; 72; 73].  
The Integrated Prediction Error Filter Analysis (INPEFA) is a prediction error filtering analysis method based on the maximum entropy spectrum analysis method. The INPEFA of the GR curve can extract and enhance the trend in lithology to reveal the vertical lithology combination mode. It is an effective means of understanding the base-level cycle and its sedimentary record through the lithology curve [74; 75].  
The INPEFA processing results of GR curves show two clear depositional cycles (Figure 9). Positive turning points are found at the top and bottom of S3, while a negative turning point is found at the boundary between S31 and S32 (Figure 9). The analysis results indicate that the point of sedimentary environment change corresponds to the top and bottom of the strata. Furthermore, the level of the sublayer in S31 is equivalent to the scale of the single-stage fan body. The study area shows two stages of fan deposition during the S3 period, corresponding to the S31 and the S32 sublayers that have undergone the sedimentary transgression to regression process.  
Figure 9: Fan stage identification by single-well division of S3 sand group. Red circle with a “+” means a positive turning point while the blue circle with a “\(-\)” means a negative turning point.
## 4 Discussion of Grout Diffusion and Reinforcement
### Grout Diffusion and Pile-Soil Failure Mechanism  
Figure 18 displays the top and front views of the pile side grout body of pile TS26. The grout body is composed of solidified cement grout and soil mass. The cement grout appears as a dense block near the pile, while, on the outside of the grout body, several traces of broken grout veins are embedded in the soil. This indicates that, during the grouting process, a grout bubble is formed near the outlet, and splitting occurs on the outer side of the bubble due to higher grout pressure than the splitting pressure of soil. With the continuous injection of grout, a certain size compaction zone is formed inside the bubble. The final size of the grout body is related to the grouting volume. Furthermore, the cement grout spreads along the axial splitting of the pile body to form a particular reinforcement area. The grout diffusion diagram for pile tip, pile side, and combined grouting is presented in Figure 19.  
Figure 19: Grout diffusion diagram of pile tip, pile side, and combined grouting.  
There are two primary modes of pile-soil failure. The first occurs either in or outside the splitting diffusion zone of the grout body (as exemplified by the side grout body of pile TS26), while the second occurs between the pile and the grout (as exemplified by the side grout body of pile TS16). The ultimate lateral friction resistance of piles TS26 and TS16 is 30.2-31.7 kPa and 19.8-21.9 kPa, respectively, at a depth of 0.75 m to 1.00 m. This indicates that the ultimate friction resistance of the pile lateral under the first failure mode is significantly higher than that under the second failure mode. For instance, the grout body of pile T27 is relatively intact, while that of pile T26 is partially destroyed, explaining why the ultimate pile lateral friction resistance of T27 at a depth of 1.50 m to 1.75 m is 39.5 kPa, which is higher than that of T26 at 22.2 kPa. The second failure mode typically occurs in areas away from the grouting outlet, and the primary reason for this is that the combination of grout and pile is less dense than that at the outlet.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 5 Performance Evaluation
### Attacks Resilience  
In this sub-section, security comparative evaluation is accomplished to show the robustness of the proposed protocol against conventional smart home attacks. As shown in Table 13, the scheme in [47] offers only three security features and is robust against two attacks only. On the other hand, the protocol in [39] provides only four security features and resilience to only three attack vectors.  
This is followed by the schemes in [40; 56], which offer four security features and resilience against four attacks. On the other hand, the proposed protocol provides five security features and is robust against six attack vectors. It is, therefore, the most secure among other related schemes.
## 4 Discussion  
### DART-Based Simulation of f\({}^{\text{esc}}\) in Discontinuous Forest Canopies  
Although several 3-D SIF models can simulate canopy-leaving SIF [16; 17; 18], SIF escape probability can be simulated directly only by 3-D models that simulate the SIF emitted per unit area (i.e., per leaf). This is especially important for sparse 3-D vegetation scenes [12]. Since it simulates the total and SIF 3-D radiative budgets, we used the DART model (Figure S2) to simulate the total SIF emitted by the canopy simultaneously with canopy-leaving SIF radiance in a sparse 3-D vegetation scenes (see methods in Section 2.2 and results in Section 3.1). Furthermore, we also simulated canopy interceptance (\(i_{\text{o}}\)) and fAPAR for the same 3-D scenes (Figure 3), which enabled the calculation of escape probabilities using SIT.  
Recently, researchers from [34] developed a relatively simple SIF model based on escape and recollision probability (FluorRTER), which is capable to simulate canopy-leaving and total emitted SIF in 3-D discontinuous canopies. Compared to the DART model, however, the scenes simulated by FluorRTER are much simpler. Tree crowns are cylinders, ellipsoids, or conics, and the heterogeneity within tree crowns is not considered, just like its parent model [35]. This missing complexity is important, as woody elements influence forest reflectance and SIF [25; 29; 36; 37]. With the simulation of fAPAR of leaves and woody elements using the DART model, respectively, we evaluated the impact of woody elements on the simulation and estimation of SIF escape probabilities (see Section 3.4). Although the fine structure of the simulation scene is well described in DART, the computational cost of simulating the 3-D radiative budget of very large regions is too large for practical applications. Then, spatial sampling of 3-D radiative budget should be considered in order to keep reasonable computer time and memory according to the complexity of canopy. Here, we did not meet such limitations because we conducted a theoretical analysis of canopy-leaving and total SIF for assessing the SIF escape probability of a complex but small canopy. It is interesting to note that the very recent bi-directional Monte Carlo mode of DART, called DART-Lux, decreases computer time and memory of simulations of bottom and top of the atmosphere SIF radiance images by a hundredfold [38]. This mode opens new perspectives but must be combined with the standard DART mode because it does not simulate 3-D radiative budgets. Moreover, DART-based simulation can also be used to evaluate and validate other models with simplified canopy structure that are more suitable for global application, such as the FluorRTER model.
# Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
###### Abstract  
Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak's net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.  
gamma spectroscopy; peak location; approximate Bayesian computation; radioisotope identification +
Footnote †: journal:  
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