esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Relationship between WUE and Drought in Two Periods  
In this study, we found that the response of WUE to drought was the opposite in drought periods (2000-2010) and post-drought periods (2011-2014). In the different vegetation types, negative correlations were mainly found in grasslands and closed shrublands during drought periods. Previous studies have shown that WUE has a negative correlation with drought in arid ecosystems, but the main reason for these results was that ET is more sensitive to hydrological climate change than GPP, and the decline in ET to values lower than GPP led to a negative correlation between WUE and drought in arid areas [60]. Other studies suggested that the response of the ecosystem WUE to drought varies with drought intensity, duration, and vegetation type [14]. In this study, GPP, ET, and WUE all exhibited a negative correlation with drought (Figure 10). GPP was more sensitive to drought than ET, which was the main reason for the negative correlation between the WUE and drought. Grassland was the main vegetation type with a negative correlation. Grassland is usually highly sensitive to water utilization, although grassland has the ability to survive after biomass losses, because of long-term adaptation. However, this ability is based on the intensity and duration of the drought. Therefore, the decline in GPP due to persistent drought is the main reason for the negative correlation between WUE and drought in this study. The positive correlation between ecosystem WUE and drought in the post-drought periods suggests that vegetation gained resilience after obtaining moisture. Therefore, when studying the relationship between WUE and drought, it is important to determine whether the study area experienced drought first, because WUE was found to be negatively correlated with drought in the drought period, while WUE and drought showed a positive correlation after the drought event in this study.
## 6 Conclusions  
This paper presents a numerical study of a GRS berm extension project in the MSW landfill with a height of 35 m and a steep slope with a gradient of 42.3\({}^{\circ}\) (1V:1.1H). A series of FE analyses were performed to study the stability and effects of influencing factors including geotextile strength, leachate level, and anti-slide pile arrangement. It was found that:  
1. The leachate level has significant impact on the stability of a berm. The operational leachate level should be controlled at a height of 20 m; in such a case, an FOS value of 1.42 can be obtained and thus meet the specification (CJJ/176-2012) requirement of 1.35.
2. The stability of the berm is higher when it is reinforced with a higher strength geotextile and the predicted tensile force of the geotextile is lower.
3. An anti-slide pile arrangement has a certain influence on the slip surface. The failure surface goes through the toe of the berm, develops along the interface between the new and old berms, and finally extends into the MSW mass on the upper left of the GRS berm.
4. When the total length of piles is the same, the reinforcement effect of single row of piles is better than that of a double row of piles.  
The construction of this project described above was completed in 2017 and has been put into use for more than three years without any safety problems. Therefore, the design and analysis of this project can be used as reference for landfill expansion--especially for a site condition with limited space, a GRS berm is a safe, reliable, and promising alternative.  
Data curation, H.J.; formal analysis, H.J. and X.Z.; funding acquisition, X.Z.; methodology, H.J. and X.Z.; software, H.J. and Z.X.; writing--original draft, H.J., X.Z., and Z.X. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Science and technology program of Guangzhou (201707020047). Grateful appreciation is expressed for the support.  
We sincerely acknowledge the former researchers for their excellent work, which greatly assisted our academic study.  
The authors declare no conflict of interest.  
Figure 13: FOS and \(T_{max}\) versus total pile length (m).
## 1 Introduction
The infrared images of compressed air leak pipes contain information about temperature changes caused by compressed air leaks. However, the infrared image cannot directly display the characteristics of leaked compressed air due to presence of the atmospheric window [12]. Therefore, how to use appropriate mean to introduce the intelligent algorithms into the compressed air leak detection is a problem that needs to be studied deeply. In order to ensure the leak recognition effect, the infrared image technology is integrated with some related methods, such as wavelet threshold denoising (WTD), firefly algorithm (FA), [PERSON]'s method, Grabcut, histogram of oriented gradient (HOG), gray-level co-occurrence matrix (GLCM) and support vector machine (SVM). These methods have their own advantages and disadvantages in actual applications. For example, wavelet threshold denoising can decompose the signal into high and low frequencies, and remove part of the high frequencies to denoise the signal, but it is not suitable to decompose the high frequency, and hard to find the optimal threshold. The FA is simple and easy to implement and has fast solving speed, but it is easy to fall into local extreme point. The [PERSON]'s method is easy to separate the pixels of one picture into two classes, foreground and background, but it returns a binary image. The Grabcut is an easy image segmentation method based on graph cuts, but it has to point out the misclasses regions by user. The HOG can describe the local shape information of the image, but it is quite sensitive to noise. The GLCM can describe texture features, but small changes in the image can cause large changes in the energy distribution. The SVM can solve the small-sample, nonlinear and high-dimensional problems, but when it is used to solve nonlinear problems, the selection of kernel function directly affects the final classification result. Therefore, in order to effectively recognize the pipeline leak, the WTD, IFA, Otsu-Grabcut, HOG-GLCM and SVM are fully integrated to propose a novel intelligent diagnosis method for compressed air leaks. Furthermore, the IFA is used to optimize the parameters of the WTD (WTD-IFA) for better denoising efforts. Otsu-Grabcut is used to segment the picture to remove the irrelevant information. The HOG-GLCM is used to effectively extract the leak fault features, and the IFA is used to optimize the parameters of SVM to establish an optimal SVM classifier with high classification accuracy.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 4 Experimental Results and Analysis
### Performance Evaluation of Active Learning  
To assess the effectiveness of the active learning method utilizing an uncertain selection strategy for conserving numerical simulation resources, the amount of training data needed for the BDAL model developed with MC dropout was analyzed. This was compared to the prediction accuracy of the BDL model trained on the full training set. Furthermore, a comparison was also made with the BDAL model based on VI.  
The variation curve of prediction error for the developed model in relation to the amount of training data is illustrated in Figure 7. The \(x\)-axis represents the percentage of actual training data relative to the total training data, while the \(y\)-axis indicates the prediction error of the model based on the test set for different amounts of training data. The horizontal red dashed lines denote the prediction accuracy of the BDL model using the complete training data, while the blue and yellow solid lines represent the changes in prediction accuracy for the BDAL model based on MC dropout and the BDAL model based on VI, respectively. Figure 7a,b utilize MAPE and R\({}^{2}\) as evaluation metrics for model prediction accuracy, respectively. As shown in the figures, the prediction error for both the BDAL model based on MC dropout and the BDAL model based on VI decreases gradually as the training data increase, eventually stabilizing. The final prediction accuracy of the BDAL model based on MC dropout surpasses that of the BDAL model based on VI, as indicated by a lower MAPE value and a higher R\({}^{2}\) value for the former. This suggests that the proposed method offers greater accuracy in predicting blast loading. Furthermore, the BDAL model based on MC dropout achieved MAPE values comparable to those of the benchmark BDL model using approximately 60% of the actual training data and R\({}^{2}\) values comparable to the benchmark BDL model when utilizing about 40% of the actual training data. This demonstrates that the active learning method developed in this study can significantly reduce the data required for training deep learning models without compromising the accuracy of blast loading predictions.  
Figure 6: Confidence intervals for prediction results obtained for the four random test subsets. (**a**) Random test subset 1; (**b**) random test subset 2; (**c**) random test subset 3; (**d**) random test subset 4.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions  
Supervised ML involves creating a system model from a labeled training dataset, allowing the learner to generalize representations based on input, output, and system parameters. A training approach is employed to develop ML models, and this process is reiterated until the model achieves the desired accuracy [44,45]. Examples of prevalent supervised ML algorithms include the naive Bayes model, decision trees, Support Vector Machines (SVMs), artificial neural networks (ANNs), hidden Markov models (HMMs), instance-based learning (such as k-nearest neighbor learning), ensemble methods (bagging, boosting, random forests), logistic regression, and genetic algorithms [46,47]. Supervised learning techniques are commonly employed in smart buildings to resolve diverse challenges. Examples of supervised ML in IEMS include the following:  
1. Supervised ML for occupancy detection: In smart buildings, supervised ML methods have been used to identify occupancy. These algorithms can precisely anticipate the occupancy state of various spaces within a building by training models on past occupancy data collected from sensors. For instance, ref. [48] classified the occupancy state of office rooms using sensor data and an SVM model. The model's high precision in determining room occupancy made it possible to operate the lighting, heating, and cooling systems more effectively. 2. Supervised ML in energy consumption forecasting: Supervised ML algorithms are commonly employed in smart buildings to forecast energy usage. To accurately predict future energy use, these models analyze patterns from previous energy data, weather information, and other relevant factors. A Long Short-Term Memory (LSTM) neural network was employed to predict a building's electricity consumption, demonstrating high accuracy in forecasting energy demand, thereby optimizing energy efficiency and reducing costs. 3. Supervised ML in FDD: Supervised ML approaches have effectively facilitated fault identification and diagnosis in HVAC systems within smart buildings. These algorithms can recognize and categorize various kinds of equipment defects by training models on labeled data that indicate the existence of faults. For instance, the random forest classifier is used to find problems with air-handling equipment in a building. The model accurately identified different problem kinds, allowing for quick maintenance and effective system operation.
## 6 Results
### Classification Performance
#### 6.1.2 Using Alternative Optimization Techniques  
Beside DE, there are other optimization techniques that are commonly used for HPO tasks, with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) being two of the most known ones. To compare the results of our proposed CNN-TL-DE method, we performed two additional experiments:  
* CNN-TL-GA, where GA was used instead of DE for the optimization of hyper-parameters for fine tuning of the transfer learning; and
* CNN-TL-PSO, where PSO was used instead of DE for the same purpose.  
In the two cases of using GA and PSO for performing HPO, the whole training process was completely the same as explained in the case of using DE, only the optimization technique was different. The parameters were set in the same manner as for the DE. For GA, the dimension of the problem \(D=5\), population size \(Np=10\), mutation rate \(MR=0.25\), and crossover rate \(CR=0.25\). For PSO, the dimension of the problem \(D=5\), population size \(Np=10\), cognitive component C1 = 2.0, social component C2 = 2.0, inertial weight \(w=0.7\), minimal velocity \(vMin=-1.5\), and maximal velocity \(vMax=1.5\). For all optimization techniques, the number of function evaluations was limited to 50.  
The obtained classification performance results are summarized in Table 6. The CNN-TL-GA and CNN-TL-PSO performed very similarly in all the presented metrics, and generally not as good as the CNN-TL-DE. The only two exceptions are a bit shorter training time and slightly lower number of consumed epochs in the case of CNN-TL-GA. On the other hand, both alternative optimization techniques performed better than the two baseline methods CNN and CNN-TL, which speaks in favor of using a proper HPO method in general.
# Noise Attenuation Based on Wave Vector Characteristics
## 3 Numerical Data Test
### Two-dimensional Two-Component Synthetic Data
From Figure 13c,d, it is clear that our noise attenuation method does not damage the valid waves. As seen in the frequency spectra shown in Figure 14a,b, before applying our filtering method, ground roll dominates the signal energy while the background random noise is spread out over the high-frequency area of the spectrum. However, as shown in Figure 14c-f, our filtering method effectively recovers the frequency spectra of the _PP_- and _PS_-reflections without damaging their low- and high-frequency energy parts. Figure 12: Wave vector moduli calculated from the synthetic shot records after ground roll attenuation. Calculation using (**a**) a 3-trace and 5-ms moving window; (**b**) a 3-trace and 7-ms moving window; (**c**) a 5-trace and 7-ms moving window, and (**d**) a 9-trace and 9-ms moving window. Figure 13: Final filtered shot records. (**a**) The valid \(Z\)-component signal; (**b**) the valid \(R\)-component signal; (**c**) noise removed from the \(Z\)-component and (**d**) noise removed from the \(R\)-component.
###### Abstract  
Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.  
**Keywords: grey correlation; topsis; multi-criteria decision making**
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 6 Experimental Results
### The Number of Patients Is 30  
The initial location points of the 30 patients are shown in Figure 8. The maximum number of iterations is specified at 200. The BSO algorithm, BSO+LNS algorithm, and HBSO algorithm are used to handle the model, respectively. The optimal relief solutions from the three algorithms are acquired when the number of patients is 30, as shown in Figures 9-11, respectively. The final results are shown in Table 4.  
When the number of patients is 30, the solution from the HBSO algorithm functions better than the other two methods. Comparing the results of the BSO algorithm with that of the BSO+LNS algorithm, it can be found that the LNS can significantly enhance the BSO algorithm's performance. In particular, the BSO+LNS algorithm has greatly improved the variance of the objective function and the variance of the total travel distance. In the variance of the objective function, the BSO+LNS algorithm is two orders of magnitude  
Figure 6: The optimal relief route from the BSO+LNS algorithm (20).  
Figure 7: The optimal relief route from the HBSO algorithm (20).  
smaller than the BSO algorithm. At the same time, in the variance of the total travel distance, the BSO+LNS algorithm is 70% smaller than the BSO algorithm. The outcomes indicate that the solutions of the algorithm could be more stable after adding the LNS. For the objective function and the total travel distance, the BSO+LNS algorithm also has a narrowing of more than 10%. Meanwhile, the BSO+LNS algorithm and the HBSO algorithm also show some advantages in terms of the deprivation cost. Similar to the previous experiment, all the findings of the HBSO algorithm outperform those of the BSO+LNS algorithm. Compared with BSO, the stability of BSO+LNS has been substantially improved. Furthermore, both variance metrics of HBSO have been reduced by more than 50% on top of BSO+LNS. This also proves that the SA algorithm could aid the algorithm in finding better solutions and having a more stable capability.  
Figure 8: The location of patients (30).  
Figure 9: The optimal relief route from the BSO algorithm (30).  
Figure 11: The optimal relief route from the HBSO algorithm (30).  
Figure 10: The optimal relief route from the BSO+LNS algorithm (30).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BSO** & **BSO+LNS** & **HBSO** \\ \hline Mean value of the objective function & 75,703.4705 & 66,433.5287 & **66,114.7729** \\ Variance of the objective function & 48,289,558.19 & 446,829.1021 & **204,987.2278** \\ Mean value of total travel distance & 16,826.8956 & 11,857.3197 & **11,548.8393** \\ Variance of the total travel distance & 1,574,368.618 & 483,280.751 & **185,060.18** \\ Final value of the deprivation cost & 5850.0669 & 5420.0303 & **5419.0027** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The final results from three algorithms.
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
## 2 Materials and Methods
### Outcome Measures  
The outcome measures used to assess neuromotor development were selected because they are frequently used in clinical practice, adaptable to online assessment, and easy to administer. These outcomes included motor acquisitions related to gross motor development, assessed using the AIMS and the NEM assessment. The assessors have specific training and wide experience in their use in clinical practice. The AIMS is an observational assessment scale that evaluates children's motor development and identifies gross motor delays in infants from birth to 18 months or until they achieve independent walking [21]. The assessment is based on the progressive development and integration of antigravity muscular control [25; 26]. The AIMS includes 58 observational items organized into four subscales: 21 positions are evaluated for the prone scale; for the supine scale, 9 positions are evaluated; for the sitting scale, 12 positions are evaluated; and, for the standing scale, 16 positions are evaluated. Each item is scored as observed or not observed, assessing three qualitative components of spontaneous motor performance: weight-bearing, posture, and antigravity movement. A motor development window is established by identifying the lowest and highest observed items. The total score is determined by summing the observed items in the four positions and all items below this motor development window. This total score can be converted into a percentile rank (PR) when analyzed based on the chronological or corrected age [21; 26]. This scale has been translated into Spanish, showing excellent validity and reliability [26]. In addition, it has also shown high interrater reliability when assessments are conducted remotely in prone (r = 0.976), supine (r = 0.965), sitting (r = 0.987), standing (r = 0.945), and total score (r = 0.980) [27]. The assessment of the NEM based on [PERSON]'s concept [24; 28] is used to identify motor delays in high-risk children and to prevent functional and motor consequences. An NEM is defined as motor sequences acquired through the conjunction of central nervous system maturation, innate perceptive-motor skills, and repeated experiences that allow for the adjustment of intentional movement achieved by the child throughout their development. The main abilities to be acquired by the children involve postural skills, the antigravity function, and the locomotion function, including motor skills such as crawling or turning over. These motor sequences evolve from axis organization in the supine position or eye-hand-mouth coordination to gross motor skills such as rolling, sitting, or standing under different circumstances. The children progress through each level, moving from one to the next, until they develop standing and walking capabilities [29].