esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Study of the Structural Performance
#### 2.4.3 Durability Analysis  
* Moisture Susceptibility Analysis (Rolling Bottle Test)  
A Rolling Bottle test was carried out to study the moisture sensitivity of the asphalt. The test was performed in accordance with BS EN 12697-11:2005. The 6.3 mm to 10 mm of aggregates' fraction sieved was used as per the standard EN 12697-2. The weight of the aggregates was 170 g. The weight of the binder was 8 g. The aggregates were heated and uniformly coated with the binder. 150 g of aggregate coated with binder was placed in the bottle. 400 mL of distilled water was added to the bottle. Two samples were prepared against each binder. All of the bottles were placed in the rolling bottle machine. The speed of the machine was 60 revolutions per minute. The samples were taken out and studied for the percentage of bitumen coverage after 6, 24, 48, and 72 h.
* Bitumen-Aggregate Adhesion Analysis  
The Bitumen Bond Strength was studied using a Pneumatic Adhesion Tensile Test Instrument (PATTI). The standard followed for this test was ASTM D 4541. Plates of limestone were prepared by cutting out slabs. The dimensions of the slabs were 381 mm \(\times\) 152.4 mm \(\times\) 50.8 mm. The slabs were cleaned for 60 min in an ultrasonic cleaner at 60 \({}^{\circ}\)C and then heated in the oven at 150 \({}^{\circ}\)C for an hour. Prior to the start of the test, the binder, stubs, and slabs were heated in the oven for 30 min at 75 \({}^{\circ}\)C. The type of stubs used for the test was F-4, with a diameter of 12.7 mm. 0.04g of bitumen was placed on the stub and then the stub was placed on the slab. Three samples were prepared against each binder. The samples were tested after 12 h of conditioning at room temperature. The PATTI gives the value of burst pressure at which the stub gets detached from the substrate, which was converted to the Pull-Off Tensile Strength (POTS) by using the following Formula (2).  
\[POTS=[(BP*Ag)-C]/Aps \tag{2}\]  
where:  
\(Ag\) = Contact area of the gasket with the reaction plate = 2619.35 mm\({}^{2}\)  
\(BP\) = Burst pressure (MPa)  
\(Aps\) = Area of the pull stub = 126.64 mm\({}^{2}\)  
\(C\) = Piston Constant = 129.73 g.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
### Reinforcement Learning: An Overview  
Q-learning, which is a popular RL approach, enables an agent (or decision maker) to gain knowledge independently in order to take the right action at the right time in its operating environment for individual performance enhancement. At any time instant \(t\), an agent \(i\) observes its operating environment in the form of state \(s_{t}^{i}\), and then selects and takes action \(a_{t}^{i}\) in the operating environment. At the next time instant \(t+1\), the agent \(i\) receives a reward \(r_{t+1}^{i}(s_{t+1}^{i})\) and the state changes to \(s_{t+1}^{i}\gets s_{t}^{i}\). The Q-value \(Q_{t}^{i}(s_{t}^{i},a_{t}^{i})\), which represents the long-term reward of a state-action pair \((s_{t}^{i},a_{t}^{i})\) of an agent \(i\), is updated using the Q-function, as follows:  
\[\begin{split} Q_{t+1}^{i}(s_{t}^{i},a_{t}^{i})=Q_{t}^{i}(s_{t}^{ i},a_{t}^{i})+\alpha[r_{t+1}^{i}(s_{t+1}^{i})\\ +\gamma(Q_{t+1}^{i}(s_{t}^{i},a_{t}^{i})-Q_{t}^{i}(s_{t}^{i},a_{t} ^{i}))]\end{split} \tag{1}\]  
where \(0\leqslant\alpha\leqslant 1\) represents the learning rate and \(0\leqslant\gamma\leqslant 1\) represents the discount factor, which is the next state-action pair emphasizing the future reward. The discount reward always has a lesser weight than the immediate reward.
## 4 Onsite Blasting Experiments
### Engineering Background  
The Shuangjiangkou Hydropower Station is located in Jinchuan County, Aba Tibetan, and Qiang Autonomous Prefecture, Sichuan Province. It serves as an upstream-controlled reservoir project for the hydropower cascade development in the Dadu River basin. The riverbed's covering layer consists of three layers, from bottom to top: the drift-gravel layer, the (sand) gravel layer, and another drift-gravel layer. These layers generally range in thickness from 48 m to 57 m, with a maximum thickness of 67.8 m.  
The rockfill dam at Shuangjiangkou Hydropower Station is a gravel core wall rockfill dam with a height of 312 m. The primary distinction between rockfill dam blasting and other types of blasting, such as mining or drilling and blasting, lies in the requirement that the graded materials produced through blasting must meet strict design specifications. Specifically, the rock grading curve from screening tests must fall within the predetermined design grading envelope. To meet this requirement, several onsite experiments on rock blasting fragmentation were conducted at Shuangjiangkou Hydropower Station. These onsite blasting experiments allow for the determination of blasting parameters that meet engineering requirements with minimal testing, ensuring that the graded raw materials produced align with the required design standards.
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Local Gradient Sparse Descriptor Extraction
#### 3.1.1 Dictionary Learning in the Gradient Domain  
During the process of sparse representation, it is meaningful to obtain a suitable over-complete dictionary [28]. Therefore, such a dictionary in the gradient domain should be obtained.  
The dictionary learns from a large number of example image patches, using a certain training algorithm, such as K-means or K-SVD [29], and has better representative ability. Firstly, the luminance gradient map of the training image, \(I\), is calculated:  
\[S_{L\
abla}=\
abla(L(I)), \tag{4}\]  
where \(L\) is the operation for obtaining image luminance, and \(\
abla\) is the operation for obtaining the image gradient.  
Figure 3: Schematic diagram of the proposed multi-exposure.  
Then, the K-SVD algorithm is used to obtain the dictionary of gradient domain from the clear images of indoors and outdoors.  
\[\underset{D,A}{\text{min}}\|S_{L\
abla}-DA\|_{F}^{2}\text{ subject to }\forall i\|\alpha_{i}\|_{0}<t\text{ and }\forall j\|d_{j}\|_{2}=1. \tag{5}\]  
The dictionary learning framework in the gradient domain is shown in Figure 4. First, lots of luminance gradient maps are obtained from a large number of clear images, including indoors and outdoors. To construct the training set for dictionary learning from those luminance gradient maps, numerous image patches of size \(n\times n\) are randomly sampled in the same manner as Reference [30], but the patch size is odd, \(n=2r+1\). Then, all of image patches are transformed into vectors via column by column extraction. Finally, the over-completed dictionary of the gradient domain can be obtained by the K-SVD algorithm.
###### Abstract  
Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.  
tuberculosis; chest x-rays; particle swarm optimization; hand-crafted features; deep convolutional neural network; support vector machine; Bayesian algorithm +
Footnote †: journal: Applied Sciences
# Multi-Objective Optimization of Drilling Trajectory Considering Buckling Risk
## 3 Formulation of Directional Drilling Trajectory Problem
### Definitions of Constraints for Optimization  
The constraints for the design variables in this study are listed in Table 7. For the constraints, a pre-survey can be conducted to establish points to be avoided in the design of the drilling trajectory owing to environmental and geological factors. In this study, the constraints used in [13] were used.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Value** \\ \hline Friction factor \((\mu)\) & 0.15 \\ Drill pipe weight \((\varpi)\) (lbft/ft) & 6.65 \\ Drill pipe ID (in) & 1.815 \\ Drill pipe OD (in) & 2 3/8 \\ Buoyancy factor \((\beta)\) & 0.8 \\ Tool joint OD (in) & 3 3/8 \\ Modulus of elasticity \((E)\) (psi) & \(3\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Setting of the fixed value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Application A (Actual Well)** & **Application B (Theoretical Well)** \\ \hline True vertical depth (ft) & 10,850 \(\leq\)\(TVD\)\(\leq\) 10,900 & 9385 \(\leq\)\(TVD\)\(\leq\) 9435 \\  & 10 \(\leq\)\(\phi_{1}\)\(\leq\) 20 & 10 \(\leq\)\(\phi_{1}\)\(\leq\) 30 \\ Inclination angle \((^{\circ})\) & 40 \(\leq\)\(\phi_{2}\)\(\leq\) 70 & 30 \(\leq\)\(\phi_{2}\)\(\leq\) 50 \\  & 90 \(\leq\)\(\phi_{3}\)\(\leq\) 95 & 90 \(\leq\)\(\phi_{3}\)\(\leq\) 95 \\  & 270 \(\leq\)\(\phi_{1}\)\(\leq\) 280 & 300 \(\leq\)\(\phi_{1}\)\(\leq\) 360 \\  & 270 \(\leq\)\(\theta_{2}\)\(\leq\) 280 & 300 \(\leq\)\(\theta_{2}\)\(\leq\) 360 \\ Azimuth angle \((^{\circ})\) & 270 \(\leq\)\(\theta_{3}\)\(\leq\) 280 & 300 \(\leq\)\(\theta_{3}\)\(\leq\) 360 \\  & 330 \(\leq\)\(\theta_{4}\)\(\leq\) 340 & 360 \(\leq\)\(\theta_{4}\)\(\leq\) 460 \\  & 330 \(\leq\)\(\theta_{5}\)\(\leq\) 340 & 360 \(\leq\)\(\theta_{5}\)\(\leq\) 460 \\  & 355 \(\leq\)\(\theta_{6}\)\(\leq\) 360 & 360 \(\leq\)\(\theta_{6}\)\(\leq\) 460 \\  & \(T_{1}\)\(\leq\) 5 & \(T_{1}\)\(\leq\) 5 \\ Dogleg severity & \(T_{2}\)\(\leq\) 5 & \(T_{2}\)\(\leq\) 1 \\ \((^{\circ}\)/100 ft) & \(T_{3}\)\(\leq\) 5 & \(T_{3}\)\(\leq\) 5 \\  & \(T_{4}\)\(\leq\) 5 & \(T_{4}\)\(\leq\) 1 \\ Depth of kickoff point (ft) & 600 \(\leq\)\(D_{K00}\)\(\leq\) 1000 & 500 \(\leq\)\(D_{K00}\)\(\leq\) 2000 \\ Depth of 2 nd build point (ft) & 6000 \(\leq\)\(D_{1,2}\)\(\leq\) 7000 & 2000 \(\leq\)\(D_{1,2}\)\(\leq\) 7000 \\ Depth of 3 rd build point (ft) & 10,000 \(\leq\)\(D_{1,3}\)\(\leq\) 10,200 & 8000 \(\leq\)\(D_{1,0}\)\(\leq\) 10,000 \\ Casing setting depth after 1 st build (ft) & 1800 \(\leq\)\(\phi_{1}\)\(\leq\) 2200 & 1500 \(\leq\)\(\phi_{1}\)\(\leq\) 4000 \\ Casing setting depth after 2 nd build (ft) & 7200 \(\leq\)\(\phi_{2}\)\(\leq\) 8700 & 7000 \(\leq\)\(\phi_{2}\)\(\leq\) 9000 \\ Casing setting depth after 3 rd build (ft) & 10,300 \(\leq\)\(\phi_{1}\)\(\leq\) 11,000 & 9000 \(\leq\)\(\phi_{1}\)\(\leq\) 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Constraints used in this research. Adapted with permission from Ref. [13]. 2004 [PERSON], [PERSON] et al.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure  
### Analysis of the Influence of the Horizontal Damping Ratio of the Seismic Isolation Layer  
Inputting an X-direction El Centro seismic wave with the peak value of ground motion acceleration of 0.15 g, with the change of the damping ratio \(\xi\) of the isolation layer (0.05-0.5), the variation law of the acceleration of the second isolation sub-frame and the displacement response of the isolation layer is shown in Figure 2.  
It can be seen from Figure 2 that, for acceleration, when the damping ratio \(\xi\) of the isolation layer is less than 0.2, the acceleration response of the isolation sub-frame decreases sharply with the increase of the damping ratio; when the damping ratio \(\zeta\) is (0.2-0.3) [36; 37; 38; 39], the peak response of acceleration decreases slowly, at which time the damping rate of the sub-frame reaches more than 50% and the damping effect is relatively better. When the damping ratio continues to increase, it causes the shear deformation of the seismic isolation  
\begin{table}
\begin{tabular}{c} X + Z \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Study content.  
layer to become relatively smaller, the energy dissipation decreases, and the acceleration response increases instead. As the damping ratio increases, the displacement becomes smaller and nearly linear.  
Through the analysis and study of the damping ratio of the isolation layer in this section, we know that: for the seismic isolation subframe, the best damping effect is achieved when the damping ratio is around 0.3; in the seismic isolation design of the mega-sub structure, considering the comfort and the economy of the design of the seismic isolation bearing, it is recommended to design the damping ratio of the seismic isolation bearing at around 0.3, which is a better damping effect and more economical and reasonable.
## 4 Results and Discussion
The cause of the increase of surface roughness was cutting force. For surface roughness, while maximum deviation between experimental results and ANN was obtained as 12.5% in the 23 rd experiment, it was obtained as 5.7% at the first experiment between experimental results and RSM. However, total average deviations obtained from all experimental studies were found 1.98% and 2.13% for ANN and RSM, respectively. As could be understood from Figure 9, tool wear increased with increasing of cutting speed, feed rate, and depth of cut. The reason of tool wear was the increase in the cutting forces formed with increasing of the feed rate and the depth of cut. The heat was a result of friction between the tool and the workpiece with increasing cutting speed. The excessive heat led to tool wear. The maximum deviation obtained from both ANN and RSM according to experimental results was found as 5% from third and seventh experiments respectively for tool wear. However, total average deviations obtained from all experimental studies were found to be 0.15% and 0.76% for ANN and RSM, respectively. Figure 3:2 Comparison of tool wears of experimental data, ANN and RSM. **(a)** Cutting Speed: 20 m/min; Depth of Cut: 2 mm; **(b)** Feed Rate 0.08 m/min; Depth of Cut: 1.5 mm; **(c)** Cutting Speed: 10 m/min; Feed Rate: 0.12 mm/rev. From Tables 5-7, it can be observed that \(F\)-values producing by the models imply the significance of the model. There is only a 0.01% chance that a model \(F\)-value this large could occur due to noise. The values of \"Prob \(>F\)\" for models are less than 0.0500, they indicate that the model terms are significant. R\({}^{2}\) is very important for models as the accuracy increases as this value increases.
## 5 Data Preparation and Filtering
### Types of Errors
#### 5.1.2 Inconsistent Data  
Duplicates of records may arise due to error in the handling of data while moving data between systems. In such cases, the duplicate data are easy to spot and appear as clones or duplicates as shown in Figure 8a. The two units of duplicated data by the sensor are denoted by the open and close square symbols in the red circle. The inclusion of duplicate data in the data analytic may indicate inaccurate or stale data. Fortunately, the duplicate data could be removed by simply deleting these clones, but one has to be careful to not deduplicate the original data.  
Contradictive data occurs when multiple data are being recorded at a certain time. An example of contradictive data is shown in Figure 8b where there are two RPM values recorded at unit time 5. There are several methodologies proposed to remove contradictory data [53; 54], but the removal of contradictory data contributes to the incompleteness in the dataset, thereby reducing the soundness of any information from such set of data [55]. [PERSON] et al. [55] proposed a novel approach for visually identifying contradictory data in a large and noisy dataset by applying a mutual exclusion rule in identifying contradictory data.  
Outliers  
Outliers are values in a random population sample that deviates abnormally from other values. An example of outliers is shown in Figure 9 presented by a spike in the temperature between 50 and 51 days. Outliers could be due to anomalies resulting from a faulty instrument. These outliers should be removed from the dataset but if a gathered value is extremely rare, it might cause the mean or standard deviation to drift drastically. As a result, removing such values is a crucial component of the data filtering process [56]. In some ways, this definition of outliers is delegated to the analyst in deciding the data point that constitutes abnormality. It is important to describe normal observations before aberrant observations may be identified. One way to do that is to examine the graphed data's overall form for significant aspects such as symmetry and deviations from assumptions. Another way is to examine the data for outlier findings that are not found in the rest of the data. Scatter plots and box plots are two graphical approaches for finding outliers, as well as an analytical procedure for detecting outliers when the distribution is normal.  
Figure 9: Example of an outlier recorded by RPM sensor.
## 5 Conclusions  
We presented an operational downscaling method of Solar-Induced Chlorophyll Fluorescence for regional drought monitoring. The method uses only regional MODIS data and TROPOSIF data for training and prediction of the local model, making it easier to update and reproduce machine learning models. The relatively high observation frequency and spatially continuous coverage of TROPOMI make it possible to obtain high-resolution downscaled _SIF_ and train machine learning models over small regions due to sufficient sample sizes. The spatiotemporal patterns of the downscaled TSIF show a very strong consistency with satellite derived TROPOSIF but at a finer resolution. TSIF can also reproduce TROPOSIF with the same magnitude and variation. A comparison between CASIF and tower GPP showed that TSIF is consistent in spatial and temporal patterns with CASIF and displayed a strong temporal consistency with tower GPP over six eddy covariance sites. Moreover, TSIF can also capture the double peak in the seasonality of California's photosynthesis found in [PERSON]'s work [24]. TSIF shows a strong correlation with GPP with R\({}^{2}\) > 0.81, except for one site (R\({}^{2}\) = 0.58). Finally, we used the relative difference TSIF-Diff for drought monitoring which captured well the spatial and temporal patterns of drought and was more sensitive to drought and precipitation compared to the universal SIF-Diff and EVI-Diff. In general, our _SIF_ is a promising indicator for regional drought monitoring.  
Conceptualization, Z.H. and L.M.; methodology, Z.H., Y.H. and L.M.; software, Z.H., W.L. and C.C.; validation, Y.H., C.C., W.Z. and L.L.; formal analysis, W.Z. and C.T.; investigation, L.M.; resources, C.C.; data curation, Z.H.; writing--original draft preparation, Z.H.; writing--review and editing, X.Y.; visualization, C.C. and C.T.; supervision, L.M.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number No.2021 YFB3900603.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** We thank the scientists and researchers of the TROPOMI mission, MODIS products, SPEI data products, Chiprs2 data products, and AmeriFlux data products. Funding for AmeriFlux data resources was provided by the US Department of Energy's Office of Science. The TROPOSIF products were generated by the TROPOSIF team conducted by NOVELTIS under the European Space Agency (ESA) Sentinel-5p+ Innovation activity Contract N\({}^{\circ}\) 400012/461/19/I-NS.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 3 Results and Discussion
### Quantitative Summary and Visualization of Gene Set Overlaps  
The outcome of the GO-based gene selection pipeline, illustrated in Figure 3, is quantitatively and visually summarized in Figure 5.  
The left panel of Figure 5 presents a horizontal bar chart showing the number of significant genes identified in each GO category individually, as well as the total number of unique genes (_union_) and the number of genes shared across all categories (_intersection_). Specifically, 11,758 genes were identified within the Biological Process (BP) category, 15,288 genes in the Molecular Function (MF) category, and 17,162 genes in the Cellular Component (CC) category. Applying a union operation across all three categories yielded 18,564 unique genes, while the intersection contained 9924 genes that were significant in all three categories simultaneously.  
These results demonstrate that each GO category contributes a functionally specific subset of significant genes. Applying set-based operations such as union() and intersect() enables the selection of either a broad gene set with maximal functional coverage or a high-confidence core set of biologically consistent markers.  
The right panel of Figure 5 displays a Venn diagram visualizing the overlap between the three GO categories. The central region, labeled 9924, corresponds to genes identified as significant in all three categories simultaneously. In addition, 4190 genes are shared between the MF and CC categories but are not present in BP; 1866 genes are unique to CC; 750 genes are unique to MF; and 228 genes are specific to BP.  
This visualization emphasizes both the redundancy and the uniqueness of GO-annotated gene subsets, highlighting the potential for targeted selection depending on the analytical goal--whether it prioritizes broad generalization or high biological specificity.  
Figure 5: **(Left) Gene set sizes across GO categories: BP, MF, CC, their union (genes significant in at least one category), and their intersection (genes significant in all categories). (Right) Venn diagram illustrating the overlaps between significant gene subsets across the three GO categories.**