esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain
## 1 Introduction
### Impact Elastic Wave Method (IEW)
recommend that the \(V_{P}\) used in the calculation is corrected according to the shape of the target. In the case of a plate, the correction factor \(\beta\) (\(\beta=V_{P}/V_{P}=0.96\)) is used. This correction factor \(\beta\) varies depending on the cross-sectional shape of the measurement target [4]. This principle is also used in the techniques to evaluate the existence of internal defects. This utilizes the frequency change caused by the reflected wave from the boundary surface by defect and the apparent change in propagation distance bypassing the defect. Figure 4 shows a schematic of the propagation path of elastic waves around the internal void. Since elastic waves are reflected at the boundary of different acoustic impedance, the voids produce reflected waves as well. In addition, there are reflected waves that bypass the void and return from the bottom surface. The observed dominant frequency changes, due to the reflected waveform, the void and propagation path because of the influence of internal voids, and the evaluation of the internal defects is conducted by using the change of the dominant frequency [4; 5; 6; 7; 8; 9; 10]. However, there are difficulties to apply the technique to the evaluation if the size of the defect is small or the location is deep in the thickness direction [8; 21]. Figure 3: Schematic illustration of the IEW method: (**a**) Schematic wave path; (**b**) Example of waveform. Furthermore, in the case of a column member having a rectangular cross-section or beam member having a complicated cross-section as shown in Figure 5a,b, the propagation path is complicated on the cross-section that is orthogonal to the measuring surface and the reflecting surface. As a result, the multiple peak frequencies are observed and it makes it difficult to detect the frequencies that reveal the thickness [22; 23; 24; 25]. For this reason, \"ASTM-C1383-04\" defined the configuration of the structure as a structure or portion of a structure in which the lateral dimensions are at least six times the thickness [26]. Therefore, the principle of thickness measurement cannot be applied to structures having these shapes. In other words, it is impossible to evaluate the defects by frequency as well. For overcoming difficulty, a new index that indicates the existence of the defects properly regardless of the shape of the structures has been demanded.
## 2 Materials and Methods
### Data Analysis  
Data were analyzed using the IBM SPSS software package (version 28.0.1.0). In the first step, outliers of the test scores in the two groups were identified as any observation outside the range (Tukey's fences): [Q1 \(-\) 1.5(Q3 \(-\) Q1), Q3 \(+\) 1.5(Q3 \(-\) Q1)] [26], where Q1 and Q3 are the lower and upper quartiles. After excluding cases with one or more test scores being outliers, the data for Group 1 (n = 85) and Group 2 (n = 95) were not significantly different from the normal distribution when tested with the Shapiro-Wilk and Kolmogorov-Smirnov tests.  
The set of 15 observed variables underwent the principal component analysis (PCA) to reveal significant latent factors. Regarding the fact that we observed strong internal Pearson correlations between variables, we used Oblimin rotation with Kaiser normalization to improve information on factor loadings [27]. The number of significant principal components in the matrix of factor patterns extracted by the PCA was determined by the Kaiser-Guttman criterion [28], which retains principal components with eigenvalues >1. The PCA with the Oblimin rotation and Kaiser normalization procedure was performed on data from both groups.  
The data obtained from the PCA for each group was then used in the linear multi regression analysis (MRA). The MRA was used to determine the relationship between IJS scores (dependent variables) and individual PCA factors as representatives of individual items from the lower-limb explosive strength tests. To evaluate the sampling adequacy for each variable in the multi regression model, we used the Kaiser-Meyer-Olkin (KMO) and Bartlett test. The results of the Republic Championship figure skating competitions (RCH) and the best three results during the season (SB3) were used as dependent variables for the MRA. The MRA linear models were constructed separately for each of Group 1 and Group 2 and the IJS score includes seven partial scores: the total score, free skating results (FS_score, FS_PCs, FS_Tes), and short program results (SP_score, SP_Tes, SP_Tes). The coefficient of determination (R\({}^{2}\)) was used as a measure of the size of the effect, with thresholds following [PERSON] (1988) for small (0.01), medium (0.09), and large (0.25) effects.
# Spectral Broadening in a Continuously Pumped Singly Resonant Second-Harmonic Cavity
## 2 Experimental Setup  
The experimental scheme we used is sketched in Figure 1a. A 15 mm long periodically poled MgO:LiNbO\({}_{3}\) crystal, with a poling period of \(\Lambda=6.95\) um, was placed in a traveling wave bow-tie cavity that was resonant for the pump and with a free spectral range (FSR) of 501 MHz. The cavity had a mirror mounted on a piezoelectric actuator for cavity length control and the crystal temperature was actively stabilized by a Peltier element for phase-matching control. The pump source is a narrow linewidth (40 kHz over 1 ms) cw Yb-doped fiber laser, emitting at 1064 nm. A laser power of a few mW seeded a Yb-doped fiber amplifier that amplifies it up to 6 W and pumped the second-harmonic generation (SHG) cavity. We obtained high-power buildup by implementing the cavity using a 99%-reflectivity plane coupling mirror, a curved mirror with reflectivity \(R>99.98\%\), while the remaining mirrors were high-reflection coated (R \(>99.9\%\)). The measured cavity resonance full width at half maximum is 1.2 MHz (finesse, 400; Q-factor, \(\sim 2\cdot 10^{8}\)) when the pump power is 10 mW (cold cavity). In these conditions, we observed internally pumped OPO as in [11], but we measured a lower threshold of 20 mW input pump power. We thermally stabilized the dedicated cavity breadboard by active control to enable robust long-term operation.  
We performed active control of the cavity length through frequency offset locking by applying a modified Pound-Drever-Hall (PDH) technique to a few-mW laser beam that was coupled into the SHG cavity in the opposite direction of the pump beam, similarly as reported in [33]. The main advantage of this system is that the control beam does not interfere with the comb generation process because we use a weak probe laser, counter-propagating and orthogonally polarized to the pump beam. The carried-out locking scheme provided control of the cavity length and allowed us to change the resonator-pump frequency detuning \(\Delta\) on-demand.
## 3 Results and Discussion
### Test Result Analysis
#### 3.3.4 Comparison and Analysis with Other Instance Segmentation Models  
To further analyze the superiority of the MSEU R-CNN model, this paper compared it with three typical instance segmentation models: BlendMask [54], BoxInst [55], and CondInst [56]. To achieve a fair comparison, the training, verification, and testing sets of Dataset 1 were used for training and testing for all models. Figure 12 shows the performance of each model in terms of accuracy and speed. It can be seen from Figure 11 that the bbox AP50 of the MSEU R-CNN was 7.7 and 25% higher than those of BlendMask, BoxInst, and CondInst. The bbox AR was 9.2, 19.4, and 18.6%, the seg AP50 was 6.2%, 23.4%, and it was 26.3% higher. The F1-scores were 20%, 21.7%, and 24.1% higher. The results show that the proposed model had stronger feature learning ability and higher segmentation accuracy than the other three models. By analyzing the structure of MSEU R-CNN, it can be seen that the Unet++ network effectively combined the multiscale characteristics of each convolution layer and improved the segmentation accuracy to a certain extent. Compared with ResNet, the SE-ResNet paid more attention to the effective features in the image, and the feature extractability was improved. The real-time performance of the model was evaluated using the segmentation time of a single image, as shown in Figure 11. The MSEU  
Figure 11: Precision-recall curves graphs during testing stage before and after Mask R-CNN optimization: (**a**) Model 1; (**b**) Model 2; (**c**) Model 3; (**d**) Model 4; (**e**) Model 5; (**f**) Model 6.  
Figure 10: Change curve of evaluation index during training stage before and after Mask R-CNN optimization: (**a**) change curve of bbox AP50; (**b**) change curve of seg AP50.  
R-CNN only took \(\sim\)0.19 s to complete an instance segmentation, and the processing time was reduced by 38.7 and 41.9%, respectively, compared with BoxInst and CondInst. Because MSEU R-CNN's residual network (SE-ResNet-18) had a shallower network layer than theirs (ResNet-50), fewer training parameters and hardware configuration were needed. Notably, the MSEU R-CNN model ran relatively fast. Moreover, Figure 13 illustrates the test results of BlendMask, BoxInst, CondInst, MSEU R-CNN, DeepLab version three (DeepLabv3) [57], and Unet on the self-defined dataset for a comparison. Because DeepLabv3 and Unet are semantic segmentation networks, they can achieve pixel-level segmentation but cannot locate the border box by instance segmentation. Further, when compared with BlendMask, BoxInst, and CondInst, MSEU R-CNN can achieve a more refined segmentation and accurate positioning. Thus, in summary, the MSEU R-CNN model proposed in this paper achieved the best model accuracy and operation efficiency compared with other models.  
Figure 12: Performance evaluation results of three different models: (**a**) histogram of accuracy, (**b**) histogram of real-time.  
Figure 13: Comparison of test results among different algorithms: (**a**) BlendMask; (**b**) BoxInst; (**c**) CondInst; (**d**) MSEU R-CNN; (**e**) DeepLabv3; (**f**) UNet.
# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 5 Experiment and Analysis
### Efficiency and Precision Comparison  
We conducted this experiment to compare the efficiency and precision of these four methods, and based on the above analysis, we set \(\omega_{1}=0.3\), \(\omega_{2}=0.7\), \(SSim_{0}=USim_{0}=0.5\), \(e_{0}=3\), \(TDgr_{0}=0.2\), \(RSim_{0}=0.4\) and \(k=5\).  
Figure 6 shows the average efficiency. It is obvious that the completion time is getting longer as the number of services increases. Among these four methods, CSDRM costs the shortest completion time from beginning to end. Specifically, compared with the flooding algorithm REM, the completion time of CSDRM is improved by 50.53% on average; compared with the Ant-Algorithm-Based algorithm ABSDA, the completion time is increased by 33.58% on average; and compared with the trust and recommendation based algorithm TRTSD, and the completion time is increased by 29.91% on average. This is because CSDRM can rapidly narrow the scope of matching based on \(SL\), discard irrelevant services. Comparing with the others, it has the minimum number of services required to be matched. The results indicate that CSDRM can improve the efficiency of Web service discovery effectively.  
Figure 7 describes the result of comparing the average precision of the four approaches. In the best case, CSDRM can achieve the precision of 72.23%. Meanwhile, the best Success Rates of TRTSD and ABSDA are 70.32% and 56.52% respectively, and the highest precision in REM is only 46.52%. Furthermore, when the service quantity is over 500, CSDRM is always the best one in the precision performing. On average, CSDRM improves the percentage of Success Rate by nearly 22% compared to REM. The results reveal that \(SL\) can also benefit the precision in Web service discovery.  
Figure 6: Comparison of efficiency.
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
###### Abstract  
More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price-demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective.  
m +
Footnote †: journal: applied sciences  
Article
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 2 Data and Methods
### Trends in Regional Vegetation Indices  
#### 2.1.1 Multiple Vegetation Parameters  
Satellite observed vegetation indices, included normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from Moderate-Resolution Imaging Spectroradiometer (MODIS) (MOD13C1 v6) [38] and NDVI from Advanced Very-High-Resolution Radiometer (AVHRR) (GIMMS 3g) [39] were used for greening trend analysis. Leaf area index (LAI), indicating vegetation canopy, from MODIS (MCD15A3H v6) [40] and AVHRR (NOAA CDR v4) [41], were also used for estimating vegetation growth. In addition, GPP (MOD17A2H v6) [42] from MODIS was also used for identifying the trend of photosynthetic productivity.  
MOD13C1 v6 was downloaded from National Aeronautics and Space Administration (NASA: [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/))). GIMMS 3g was from National Oceanic and Atmospheric Administration (NOAA: [[https://data.nodc.noaa.gov/](https://data.nodc.noaa.gov/)]([https://data.nodc.noaa.gov/](https://data.nodc.noaa.gov/))). MCD15A3H v6, MOD17A2H v6, and NOAA CDR v4 were retrieved from Google Earth Engine [54]. NDVI/EVI from MOD13C1 v6, LAI, and GPP were resampled to temporal and spatial resolution of 16 days and \(1\times 1\) degree using bilinear interpolation. NDVI from GIMMS 3g was also integrated to \(1\times 1\) degree.
## 6 Discussion on Clinical Relationship Extraction
### Rule-Based Methods  
Though rule-based methods are not the most popular method nowadays to extract relations from clinical texts, they are still being used and have been used in the past in good numbers. These methods require defining some rules in the beginning based on the nature of the input dataset. These methods of extracting information by using well-defined rules and patterns are often not very computationally efficient such as the machine learning models with respect to their performance, and hence, these methods are not very popular these days. [PERSON] et al. (2011) [84] developed a linguistic hybrid rule-based method to extract drug-disease interactions via the combined use of shallow parsing, syntactic simplification, and pattern matching. A pharmacist defined the domain-specific lexical patterns of the drug-disease interactions that were matched with the generated sentences. This method did not perform well with an average precision and a very low recall. [PERSON] et al. (2011) [85] combined rule-based methods with ML to engineer features for structured RE from clinical discharge summaries as provided by the i2b2 2010 challenge. The RE task received a micro-averaged F1-score of 0.7326. [PERSON] et al. (2015) [86] matched the drug names to their attributes in a prescription list, and then the matching was confirmed by means of the co-location information and RxNorm dictionary. It helped in identifying the medication discrepancies with very high performances. [PERSON] et al. (2021) [87] used NLP-based regular expressions to extract the words from the text document of different medical data using scraping and POS tagging. Then, the relations between different medical terms were extracted using a path similarity analysis. [PERSON] et al. (2021) [70] used the co-location information between the drug and the non-drug entity types by using a breadth-first-search (BFS) algorithm to find the adverse drug effects. The left-only rule-based approach (macro-average F1-score: 0.83) eclipsed the performance of other rule-based models. Overall, the rule-based approaches for clinical RE can perform well, depending on how the rules are defined. Some clinical RE tasks using rule-based methods are tabulated in Table A3.
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 3 Materials and Methods
### Energy and Priority Model  
The total energy consumption of task-processing is obtained using three phases: energy consumption due to task transmission between IoT and FN, task-offloading at FN, and task transmission from FN to IoT after processing. The consumption of energy while transmitting the task from IoT device to the Fog access node is denoted as \(\mathrm{E_{up}}\), and the consumption of energy during task execution is denoted as \(\mathrm{E_{exe}}\). This can be represented mathematically as follows:  
\[\mathrm{E_{consumed}}=\mathrm{E_{up}}+\mathrm{E_{exe}} \tag{7}\]  
where  
\[\mathrm{E_{up}}=\mathrm{T_{up}}\times\mathrm{P_{k}} \tag{8}\]  
\[\mathrm{E_{exe}}=\mathrm{T_{exe}}\times\mathrm{P_{na}} \tag{9}\]  
where \(\mathrm{p_{k}}\) and \(\mathrm{p_{na}}\) denote the power consumption of the IoT device and \(\mathrm{F_{na}}\), respectively, during task execution.  
When the IoT devices generate a task for processing, the corresponding Fog server identifies the priority level of the task and monitors the residual energy level for each FN. The faulty FNs are determined based on the residual energy level and removed from the Fog networks before task-offloading activities. The residual energy (Er) is obtained by subtracting the energy consumed by the Fog access nodes during task-processing from the initial energy, and the energy level rate can be obtained by dividing the obtained result by the initial energy.  
\[\mathrm{E_{r}}=\frac{\mathrm{E_{initial}}-\mathrm{E_{consumed}}}{\mathrm{E_{ initial}}} \tag{10}\]  
The proposed model considered the characteristics of residual energy for fault identification. The residual energy mainly depends on the battery life of the Fog nodes, which is represented by Equation (11):  
\[\mathrm{BL}=\frac{\mathrm{E_{r}}}{\mathrm{I_{avg}}} \tag{11}\]  
where BL defines the battery life of the Fog nodes, and this can be obtained by dividing the residual energy by the average current. The residual energy is also used to estimate the active time of the Fog nodes during task-processing, and these relations are represented below:  
\[\mathrm{FAt_{n}}=\frac{\mathrm{E_{r}}}{\mathrm{X_{i}}\times\mathrm{duty\ cycle}} \tag{12}\]  
The energy level of each node [28] will be categorized into three levels, as follows:  
1. \(\mathrm{Er}\geq 0.3\rightarrow\) used for executing high-priority (time-sensitive) tasks from the user/IoT devices.
2. (0.1 \(\leq\) Er < 0.3) \(\rightarrow\) used for executing low-priority (time-tolerance) tasks, and may also act as a reserve node for some forms of task-processing.
3. \(\mathrm{Er}<0.1\rightarrow\) identified as faulty FN because the battery life is too short, and this indicates that the FNs will not complete the task efficiently with in the battery's lifespan.
## Results
### The Effects of Extreme Drought on CO\({}_{2}\) Fluxes  
In 2016, the seasonal dynamic change of the net ecosystem CO\({}_{2}\) exchange (NEE), ecosystem respiration (R\({}_{\rm e}\)), and gross primary productivity (GPP) showed an asymmetric single-peak curve in the ecosystem of the Zoige peatland (Figure 5). NEE and GPP reached their maximum values in July, which were 510.4 mg C/m\({}^{2}\)h and 791.2 mg C/m\({}^{2}\)h, respectively, while the total primary productivity was the highest and the dynamic trend of GPP was consistent with that of NEE during the growing season. The peak of R\({}_{\rm e}\) was different from those of NEE and GPP, as it appeared in June at a value of 370.3 mg C/m\({}^{2}\)h. The carbon exchange was the strongest in the Zoige peatland ecosystem from June to August, the lowest values of NEE, R\({}_{\rm e}\), and GPP were \(-\)153.4 mg C/m\({}^{2}\)h, 145.3 mg C/m\({}^{2}\)h, and 298.7 mg C/m\({}^{2}\)h, respectively, occurred in September at the end of the growing season. Throughout the whole growing season, the peatland ecosystem of the Zoige plateau performed the function of a carbon sink under the conditions of the control treatment (CK) and extreme drought (D) treatment.  
The whole growing season was divided into three stages in this study: before the control experiment (May to mid-July), the control experiment stage (mid-July to late August), and after the control experiment (late August to September) (Figure 5). Before the control experiment, there was no significant difference in NEE, R\({}_{\rm e}\), or GPP between CK and D (\(p>0.05\)), and the carbon flux of the ecosystem was basically the same. During the control experiment, extreme drought significantly reduced R\({}_{\rm e}\) and GPP (\(p<0.05\)), with GPP and R\({}_{\rm e}\) decreasing 14.5% and 12.6%, respectively, which resulted in a 11.3% decrease in carbon storage capacity of the ecosystem. GPP was first affected by the extreme drought and secondly by R\({}_{\rm e}\) and NEE. After the control experiment, the enduring effects of extreme drought were still apparent, as the NEE, R\({}_{\rm e}\), and GPP still showed significant decreases. NEE, R\({}_{\rm e}\), and GPP were significantly different between the CK and D treatments on 24 August (\(p<0.05\)), but there was no significant difference between NEE, R\({}_{\rm e}\), and GPP under the CK and D treatments on 20 September (\(p>0.05\)). According to local meteorological data, there were 11 ecologically effective precipitation events from 22 August to 20 September, adding up to 134.3 mm. As a result, the extreme drought treatment gradually diminished, which alleviated the drought stress in the extreme drought treatment area to a certain extent.  
Figure 5: Effects of extreme drought on CO\({}_{2}\) fluxes (NEE, R\({}_{\rm e}\) and GPP) in 2016.
## 3 Results and Discussion
### Sample Characterization
#### 3.1.4 SEM Analysis  
Scanning electron microscopy (SEM) is a valuable tool for identifying active adsorption sites on biosorbent surfaces. In this study, SEM analysis was conducted to examine the shape and structural properties of the biosorbents LA and L, with images shown in Figure 4. The biosorbents displayed a porous, heterogeneous structure with a variety of well-defined pores of different sizes, which is advantageous for metal ion adsorption [28]. Macropores predominated, acting as transport channels that facilitate rapid adsorption kinetics by allowing adsorbate molecules to reach active centers efficiently. However, macropores are not the primary contributors to adsorption capacity.