esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 2 Automatic Reading Algorithm of Dial Gauge Based on Coordinate Positioning
### Image Enhancement  
Many factors influence the level of brightness, such as weather, time, indoor or outdoor, etc. For example, the brightness level will be particularly strong when the weather changes. It is an effective method to use the MSRCR to enhance color images that lack brightness [18]. The theoretical underpinning of the MSRCR algorithm is that the color of the object is not affected by the non-uniformity of illumination. The main idea of the MSRCR algorithm is to calculate the brightness image from the original image effectively. Therefore, the MSRCR algorithm has the characteristics of color constancy. Since the MSRCR algorithm is based on the single-scale Retinex (SSR) and the multi-scale Retinex (MSR), SSR and MSR will be introduced first. The single-scale Retinex (SSR) algorithm is as follows [19; 20]:  
\[R_{i}(x,y)=\log I_{i}(x,y)-\log[F(x,y)*I_{i}(x,y)] \tag{1}\]  
where \(R_{i}(x,y)\) is the Retinex output, \(I_{i}(x,y)\) is the distribution in the \(i\)th spectral band, \"\" indicates the convolution operation and where \(F(x,y)\) is the following surround function:  
\[F(x,y)=Ke^{-\gamma^{2}/c^{2}} \tag{2}\]  
where \(c\) is the Gaussian surround space constant, \(r^{2}=x^{2}+y^{2}\), and \(K\) satisfies Equation (3), as follows:  
\[\iint F(x,y)dxdy=1 \tag{3}\]  
Figure 2: The flow chart of the algorithm.  
Multi-scale Retinex (MSR) is adopted for Gaussian filtering on an image at different scales, and then a weighted average of the filtering results is performed to obtain the output image. The MSR is defined as follows:  
\[\begin{array}{ll}R_{MSR_{i}}&=\sum\limits_{n=1}^{N}\omega_{n}R_{n_{i}}\\ &=\sum\limits_{n=1}^{N}\omega_{n}[\log I_{i}(x,y)-\log(F_{n}(x,y)*I_{i}(x,y))] \end{array} \tag{4}\]  
where \(n\) is the number of scale parameters; if \(n=1\), it is SSR, which is the weight of the \(n\)th scale.  
The standardized scaling function of MSR is defined as follows:  
\[F_{n}(x,y)=Ke^{-r^{2}/c_{n}^{2}} \tag{5}\]  
The SSR and MSR algorithms may result in excessive noise in the image, distorting the color of the local details of the image which may fail to show the true color of the object. Therefore, on the basis of MSR, MSRCR introduces a color restoration factor, \(C\), to compensate for the defect of color distortion caused by the contrast enhancement of the local area of the image, which improve the robustness of the algorithm [21], as follows:  
\[C_{i}(x,y)=\beta\Bigg{[}\log(\alpha I_{i}(x,y))-\log\Bigg{(}\sum\limits_{i=1} ^{N}I_{i}(x,y)\Bigg{)}\Bigg{]} \tag{6}\]  
where \(C\) is the gain constant and \(\alpha\) is an adjustment factor.  
Finally, by using the Equations (1), (4) and (6), the equation of MSRCE can be written as follows:  
\[R_{MSRCR_{i}}(x,y)=G\{C_{i}(x,y)[\log(I_{i}(x,y))-\log(I_{i}(x,y)*F_{n}(x,y))] +b\} \tag{7}\]  
where \(G\) is \(a\) gain and \(b\) is an offset coefficient, which are used to display the final image well on the screen. Some basic parameters of MSRCR are shown in Table 1.
# Shear Resistance Assessment of the Y-Type Perfobond Rib Shear Connector under Repeated Loadings
## 3 Monotonic and Repeated Loading Tests
### Monotonic Loading Test
#### 3.1.3 Load-Strain Curve  
Based on steel's yield strength, the yield strains for Y-rib and rebar were calculated using Equation (1) [24] and listed in Table 6.  
\[\varepsilon_{s,y}=f_{yt}/E_{s} \tag{1}\]  
where \(\varepsilon_{s,y}\) is the yield strain, \(f_{yt}\) is the yield strength of either Y-rib or rebar (Table 3), and \(E_{s}\) is the Young's modulus (210,000 MPa for both of Y-rib and rebar) [24].  
The load-strain curves for SD400-D16-3M, SD500-D16-3M, and SD500-D19-3M under the monotonic loadings are plotted in Figure 7. The yield points are plotted in dotted lines in Figure 7 and the yield loads are summarized in Table 6. It is found that the Y-rib yields in advance to the yield of rebar. The intensities of the repeated loads were selected based on the yield load intensities for the Y-ribs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Y-Rib**} & \multicolumn{2}{c}{**Transverse Rebar**} \\ \cline{2-5}  & **Yield Strain** & **Yield Load** & **Yield Strain** & **Yield Load** \\ \hline SD400-D16-3M & 0.0019 & 1440.3 kN & 0.0022 & 1556.4 kN \\ SD500-D16-3M & 0.0019 & 1214.9 kN & 0.0023 & 1567.3 kN \\ SD500-D19-3M & 0.0019 & 1381.4 kN & 0.0026 & 1757.3 kN \\ Average & 0.0019 & 1345.5 kN & 0.0024 & 1627.0 kN \\ \hline \hline \end{tabular}
\end{table}
Table 6: Yield strength corresponding to the yield strains for each steel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **No.** & \(P_{u}\) **(kN)** & \(\delta_{u}\) **(mm)** & \(\delta_{uk}\) **(mm)** & \(\delta_{00}\) **(mm)** & \(\delta_{y}\)/\(\delta_{00}\) \\ \hline SD400-D16 & 1 M & 2240.3 & 28.7 & 25.8 & 3.2 & 9.0 \\  & 2 M & 2234.3 & 37.5 & 33.2 & 4.1 & 9.2 \\  & 3 M & 2210.9 & 35.4 & 32.0 & 2.7 & 13.2 \\  & Avg. & 2228.5 & 33.9 & 30.4 & 3.3 & 10.5 \\ \hline SD400-D19 & 1 M & 2458.0 & 45.6 & 41.0 & 3.9 & 11.6 \\  & 2 M & 2456.1 & 34.2 & 30.8 & 4.6 & 7.5 \\  & 3 M & 2496.3 & 45.7 & 39.0 & 5.0 & 9.1 \\  & Avg. & 2470.1 & 41.8 & 36.9 & 4.5 & 9.4 \\ \hline SD500-D16 & 1 M & 2321.1 & 24.9 & 22.3 & 4.0 & 6.3 \\  & 2 M & 2240.3 & 23.8 & 21.4 & 3.0 & 7.9 \\  & 3 M & 2239.7 & 41.0 & 35.1 & 3.0 & 13.5 \\  & Avg. & 2267.0 & 29.9 & 26.3 & 3.3 & 9.2 \\ \hline SD500-D19 & 1 M & 2493.2 & 50.5 & 45.1 & 5.1 & 9.8 \\  & 2 M & 2547.0 & 56.1 & 40.9 & 4.6 & 12.3 \\  & 3 M & 2375.7 & 45.4 & 49.0 & 6.0 & 7.5 \\  & 4 M & 2493.7 & 55.9 & 46.9 & 4.4 & 12.8 \\  & Avg. & 2477.4 & 52.0 & 45.5 & 5.0 & 10.6 \\ \hline SD500-D22 & 1 M & 2614.7 & 46.7 & 42.0 & 5.9 & 7.9 \\  & 2 M & 2656.5 & 47.9 & 43.2 & 7.0 & 6.9 \\  & Avg. & 2635.6 & 47.3 & 42.6 & 6.4 & 7.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of shear resistance and ductility.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### Methods  
In this study, MODIS LST was first reconstructed using four common interpolation methods, namely, linear temporal interpolation, smoothing filter, fitting, and fusion, in order to compare the performance of these methods. Among them, the Savitzky-Golay method and HANTS were used for filtering in order to compare the performance of these two filtering methods. Fitting was performed on the basis of the relationship between LST and NDVI over a series of times. The fusion used AMSR2 data and ERA5 data. Then, a new interpolation method was applied with the advantages of both time and space. This method takes the central pixel from missing values and uses the image of another clear-sky day as the fill LST to reconstruct the invalid values. The reconstruction is based on the assumption that a ground feature's surface temperature will change linearly, and that equivalent changes will occur for nearby similar features. As a result, the missing surface temperature value can be recovered using data obtained for a different time, and a linear relationship can be established using the closest matching pixels. To make the reconstruction results more accurate, we first carried out different land cover classifications in the Heihe River Basin. For the selection of images on another clear-sky day, we assumed that the correlation between the two images was highest within 10 days. Therefore, the image with the most effective pixels acquired within 10 days was selected as the fill LST. For each missing value, the nearest similar pixels were searched on the basis of the classification results. Local robust regression was then performed on similar pixels to reconstruct the missing value. Figure 2 shows the overall method flow chart of this research.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
###### Abstract
The approach is commonly known as the \"candidate list\" and results in a considerable computational performance improvement for huge graphs. An empirical comparison of the effect of utilising this technique to reduce the TSP complexity with respect to the quality of the solution is provided. A comprehensive experimental evaluation of some of the most well-known algorithms and heuristics used to solve TSP problems is provided, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Our proposedalgorithm utilises an edge swap technique mixed with some carefully studied steps to generate near-optimal results. In the following, we list the steps of the 2-Opt++ algorithm; every step is further described in detail. Figure 1 shows the flow diagram of the algorithm. * Compute a basic solution to the input problem. * Compute an active edge matrix using the graph compression technique (optional and recommended for larger problem instances). * Use the 2-Opt technique for edge swapping. * Accept the new solution using acceptance criteria. * Reshuffle the cycle and repeat until convergence or pre-defined end of iterations.
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 2 Materials and Methods  
### Participants  
The sample size was determined using G*Power ver.3.1.9.4, following assessment based on data from an internal pilot study. As hemodynamic changes due to VM are associated with increased blood pressure, the effect size for the appropriate number of participants was calculated from the change in systolic blood pressure (SBP) after VM. As a result, the average SBP values before and after the VM were found to be 111.645 and 113.161 mmHg, respectively, the partial eta squared was 0.084, and the effect size was calculated to be 0.303. By applying a power of 0.9 and an \(\alpha\) error probability of 0.05, the requisite sample size was calculated as 26 participants. Considering a potential dropout rate of 10%, 29 participants were recruited. A total of 29 participants were prospectively recruited for this study after providing written informed consent. All participants were free from disease, were not taking any medication, and did not experience any problems performing the 30 and 40 mmHg VM. Participants who had abnormal vital signs were excluded. The vital signs (SBP, diastolic blood pressure (DBP), and heart rate (HR)) were obtained using an electric blood pressure monitor (TMB-1112, Guangdong Transtek Medical Electronics, Guangdong, China), and participants who were not within the normal vitals range were excluded. The normal vitals range is as follows: SBP < 120 mmHg, DBP < 80 mmHg [16] (However, when the pulse pressure (PP) was normal, an SBP of up to 130 mmHg was allowed [17]), HR = 60-90 ppm [18].  
Of the total cohort, two participants who could not maintain an expiratory pressure greater than 40 mmHg were excluded. All participants abstained from alcohol, smoking, and caffeine intake for at least 24 hours before the experimental trial due to their modulating effects on the cardiovascular system.
## 3 Results
### Main Impact Factors of the Field GPP Footprint
#### 3.5.2 Influence of Surface Roughness on Footprint of Field GPP  
In general, field GPP footprint distance decreased with the increase in surface roughness [38]. When the surface roughness increased from 0.01 m to 0.10 m, footprint distance of field GPP decreased about 33.3%-49.2% (footprint distances decreased more than 40% at Huazhaizi, Huyanglin, Bajitan, Sidaoqiao and Daman, and decreased about 33.0%-38% at Hunhelin, Arou, Shidi and Dashalong) (Figure 9).  
Figure 8: Changes of field GPP footprint with the changes of measurement height.  
Figure 9: Changes in field GPP footprint with the changes in surface roughness.
## 6 Analysis of Experimental Results
### Analysis of Experimental Results  
To evaluate the detection accuracy, we collected the dataset with 216 IVR videos for biology teaching. The dataset has 2 splits and contains 216 RGB video data taken of 54 individuals, performing 2 experiments, twice, learnt in a Biology class. The dataset has 18 object classes with 24,123 frames of annotated data. Among them, 2/3 are used for the training set and the other third is used for testing. The dataset is an expanding version on the basis of the preliminary study on the evaluation of the cognitive engagement model with Capital Normal University.  
This article selects overall accuracy to evaluate the performance of the model, which represents how many correctly predicted samples are identified as corresponding categories and is commonly used to evaluate the performance of the model. NTP is enabled to represent the number of objects that the model correctly recognizes as the target object; NFP represent the number of non-objects recognized by the model as target objects; the definition formula of the model evaluation indicator is as follows (5):  
\[Precision=\frac{\sum N_{TP}}{\sum N_{TP}+\sum N_{FP}} \tag{5}\]  
Figure 26: Experimental flow chart.  
For comparison, we test the detection accuracy of visual learning objects. The overall accuracy of our approach is about 98% on the dataset, while the method performed by [PERSON] et al. [34] is 97%, which indicates that our approach can effectively distinguish the learning objects and the controller interaction objects.  
Furthermore, we also evaluate the performance of the proposed approach on the dataset in terms of learning effect. Due to the small sample size and the data not obeying the normal distribution, the Spearman correlation analysis method is used in this study. We first conduct statistics on visual coverage (the object is detected in the students' view), visual attention duration (the object is detected in a period of time from the students' view) of learning object, then make the correlation analysis between visual coverage, attention duration, knowledge retention, and knowledge transfer. The results are shown in Table 1.  
As shown in Table 1, it can be observed that the visual coverage of learning object (the students noticing the learning object) is significantly correlated with students' knowledge retention scores, as well as with their knowledge transfer scores at the same level of significance. However, the visual attention duration of the learning object does not exhibit a significant correlation with the students' knowledge retention scores or knowledge transfer scores. The result suggests that in an IVR environment, whether students notice important learning object or not will impact their final knowledge retention and knowledge transfer scores, but the visual attention duration of the learning object does not significantly affect their performance in knowledge retention and transfer.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 4 Results
### Application Test  
We conducted experiments on two visual tasks--low-light image enhancement and local feature-point matching--to demonstrate the positive role of our method in vision.  
The visibility and contrast of the image are often reduced in low-light environments, making it difficult to extract valuable information or make further use of the image [40]. Here, we apply our method to images captured in low-light environments, as shown in Figure 7. Our approach significantly improves visibility and contrast in low-light images.  
To demonstrate that the images processed in this article can be applied to visual matching tasks, we used the local feature-point matching algorithm (SIFT) to establish the corresponding relationship between two similar scenes. We apply this algorithm to a pair of real underwater images and their improved matching results. Figure 8 shows the local feature-point matching results. In Figure 8a,b, the correctly matched feature points in the original image pair are 4 and 87, respectively. Figure 8c,d show the number of feature points matched in the image pairs processed by our proposed algorithm, which are 26 and 140, respectively. This indicates that our method has played a positive role in image preprocessing.  
Figure 6: Enhancement results of different methods and the red box represents the corresponding locally enlarged image. From left to right are raw underwater images, and the result of ARC, Fusion, GDCP, IBLA, TS, UDCP, MLLE, ACCDO, and the proposed method.  
Figure 7: The upper column is the original image, and the lower column is the image processed by our method.
# Phenolic and Volatile Composition of a Dry Spearmint (_Mentha spicata_ L.) Extract
###### Abstract  
The present paper reports a complete mass spectrometric characterization of both the phenolic and volatile fractions of a dried spearmint extract. Phenolic compounds were analysed by ultra-high performance liquid chromatography-electrospray ionization-mass spectrometry (UHPLC-ESI-MS\({}^{\text{n}}\)) and a total of 66 compounds were tentatively identified, being the widest phenolic characterisation of spearmint to date. The analysis suggests that the extract is composed of rosmarinic acid and its derivatives (230.5 \(\pm\) 13.5 mg/g) with smaller amounts of salvianolic acids, caffeoylquinic acids, hydroxybenzoic acids, hydroxcinamic acids, flavones, and flavanones. Head space solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) technique, that was applied to characterize the volatile fraction of spearmint, identified molecules belonging to different chemical classes, such as \(p\)-cymene, isopiperitone, and piperitone, dihydroethanol II, menthone, \(p\)-cymen-8-ol, and \(\beta\)-linalool. This comprehensive phytochemical analysis can be useful to test the authenticity of this product rich in rosmarinic acid and other phenolics, and when assessing its biological properties. It may also be applied to other plant-derived food extracts and beverages containing a broad range of phytochemical compounds.  
spearmint; phenolic composition; volatile fraction; phytochemical characterization; UHPLC-ESI-MS\({}^{\text{n}}\); HS-SPME/GC-MS Article
## 6 Statistical Analysis
### Statistics in Olfactoryometry and Behavior
#### 6.1.2 Statistical Tools for Olfactory Data Treatment  
Corresponding to statistical tests, which can be used on a data matrix with a normal distribution (Gaussian) and whose data groups exhibit equal variances, certain parameters must be determined before using the test. Parametric tests can be applied directly on original matrix data or in those that achieved normal distribution after transformation, using precaution in the latter case for considering the changes in relationships among variables derived from conversion when conclusions are given [203]. The parametric tests are widely but sometimes incorrectly used in olfactory assays, and their main advantage is that they have better statistical power for conducting a null hypothesis rejection compared to nonparametric tests, but the data requirements also restrict their utilization [206].  
Among the most cited parametric tests, the \(t\)-test or Student's test can be found, as well as the analysis of variance or ANOVA (and their derivative tests). The Student's test has been applied for key VOC identification in samples with particular conditions, such as terpenes of _Pinus sylvestris_ L. under induced defense [24], volatiles emitted by the roots of a plant colonized by an endophyte [43], in the search of compounds associated with a defective-status in seeds of California nuts [70] and coffee [108], for characterization of volatiles present in sexual glands from populations of _Spodoptera frispierda_ specialized in different crops [28], and in the validation of pheromone collection by using a GC-columnfragment [45]. It has also been used for evaluating the responses of VOCs in EAG [37; 67], in attraction and repellency assays [32; 68], and in the comparison of an insect response to a natural VOC source compared to a synthetic mixture, among others.  
ANOVA has been employed mainly in the determination of significantly different responses among groups of data matrices associated with biological activity results, which implies the creation of several replicates. ANOVA is usually complemented with [PERSON]'s HSD tests. Several examples using ANOVA are found in literature such as the evaluation of insecticidal activity of different essential oils on well-known pests, i.e., _Stiophilus zemais_[106] and _Drosophila suzukii_[110], the attractive-repellent nature of selected plants on the coffee berry borer (_Hypothenenus hampei_) [163], the attractive-repellent effect of _Aphitobius diaperinus_ abdominal gland extracts on individuals of the same species [29], the assessment of the aphid preference for emissions coming from _Chrysanthemum morifolium_ and _Artemisia annua_[72]; and the characterization in EAG of the dose-response of floral VOCs on a parasitoid as a complement to the research on the effect of emissions on its fecundity [1].
Quantitative Interpretation of TOC in Complicated Lithology Based on Well Log Data: A Case of Majiagou Formation in the Eastern Ordos Basin, China  
[PERSON]  
1 Research Institute of Petroleum Exploration & Development, Beijing 100083, China;  
[EMAIL_ADDRESS]  
[PERSON]  
2 College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China;  
[EMAIL_ADDRESS] (H.Z.): [EMAIL_ADDRESS] (L.J.) 2019210112@student.cup.  
Esso proposed the overlapping method of interval transit time and resistivity curve in 1979, that is, \(\Delta\)logR analytical method. The porosity curve and resistivity curve were overlapped to identify source rocks through the amplitude differences between two curves. [PERSON] et al. [5] proposed the calculation formula of TOC based on data statistics:  
\[\text{TOC}=(\Delta\text{logR})\times 10^{\alpha} \tag{1}\]  
\[\alpha=2.297-0.1688\text{ LOM} \tag{2}\]  
\[\Delta\text{logR}=\text{lg}(R/R_{base})+\text{K}\cdot(\Delta t-\Delta t_{base}) \tag{3}\]  
where \(R_{base}\) and \(\Delta t_{base}\) are baseline values of resistivity and interval transit time, respectively. K is the proportionality coefficient and LOM is the maturity of organic matters. This is the relatively common \(\Delta\)log R method present being used. Although the \(\Delta\)log R method has strong applicability, it needs artificial determination of logging baselines. Baselines vary significantly in different wells as well as different strata and sedimentary environments [6].  
More and more scholars believe that the linear relationship between TOC of source carbon and logging parameters is not simple and it is difficult to be reflected by an explicit function [7; 8; 9]. The neural network model possesses remarkable advantages in addressing nonlinear problems, which cannot be expressed in explicit functions. Hence, neural network modelling becomes a new way for TOC logging interpretation of source rock [10; 11]. Neural networks have obvious advantages in TOC logging interpretation of source rock. Its interpretation result not only conforms well to laboratory analysis data, but also can retain detailed changes of TOC. However, TOC logging interpretation of source rock based on neural networks has complicated principles and requires professional software programming, which determines the high threshold of applications. The operation is completed automatically by software, without any disclosure of details. As a result, TOC logging interpretation of source rock based on neural networks is difficult to be promoted.  
Recently, many new TOC log interpretation methods have been proposed. [PERSON] [12], [PERSON] [13], [PERSON] [14] and [PERSON] [15] carried out TOC log interpretation using SVM. [PERSON] [16] and [PERSON] et al. [17] introduced the optimal estimation and Bayesian discriminant into TOC log interpretation. [PERSON] [6] and [PERSON] [18] interpreted TOC of source rock through Gaussian regression. Although there are many TOC log interpretation methods of source rock, no method is universally applicable. The optimal TOC log interpretation method shall be chosen according to practical conditions of the study area.