esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Works Combining GNSS and InSAR in Europe
## 2 Data Collection  
Data collection was conducted through Google Scholar's freely accessible web search engine to gather a high number of scientific contributions published that considered both InSAR and GNSS/GPS data.  
To collect the scientific articles, book chapters and conference abstracts published on the Google Scholar's search engine were based on the following criteria. The first keyword adopted was the name of each European country to which the following two lists of keywords related to GNSS and SAR were connected. For the SAR group, the keywords used were \"InSAR\" (and related acronyms as \"MT-InSAR\", Multi Temporal InSAR, \"DInSAR\", Differential InSAR, or \"A-DInSAR\", Advanced DInSAR), \"PS\" (Persistent Scatterers), and \"PSI\" (Persistent Scatterers Interferometry). For the global absolute measurements, the terms \"GNSS\" and \"GPS\" (with related derived names as cGPS) were used.  
The use of Google Scholar for this type of research review is considered an appropriate choice, as this search engine can be considered \"_essentially a superset of WoS and Scopus, with substantial extra coverage_\" [36]. The same author states that, in Google Scholar, approximately 95% of the available citations correspond with those contained in the Web of Science (WOS) database and approximately 92% correspond with those of Scopus in addition to a relevant number of other citations. For this reason, Google Scholar is considered an adapted search engine and a powerful tool for researching the existing literature considering the topic of the combination of GNSS and InSAR data. However, the source of the contributions highlighted by research on Google Scholar has to be validated since this system is less automatized than others, and its database can be completed with information added by the users [37].  
The collected scientific contributions, including articles, reviews, book chapters and congress proceedings, were arranged in different folders based on the various searched countries. Then, the contributions were examined iteratively and catalogued in a table considering the following derivable information:  
- Title of the scientific contribution;  
- List of authors;  
- Type of submission (journal, book chapter, conference paper);  
- Year of the publication;  
- Country;  
- Area of interest (province, municipality, local toponym);  
- Scale of the presented work (national, regional local);  
- Field of application (i.e., Atmosphere, Glacial; Infrastructure, Landslide, Subsidence; Subsidence/Uplift; Tectonic; Uplift; Vulcanic, Other, and Not Specified);* Aim of the research;
* Investigated period;
* Type of processing (DInSAR or MTInSAR);
* InSAR approach and algorithm;
* Sensor band;
* Satellite name;
* Data used for the investigation;
* How the two data were used (combination, comparison, validation).
## 2 Data and Methods
### Inversion and Modeling Methods
#### 2.3.2 LGF Modeling Method  
According to the continuous absolute value data of the geomagnetic observatories (as shown in Figure 1), the diurnal variation of the observed data is corrected to eliminate the external field component of the geomagnetic field using the data processing method described by [PERSON] et al. [25; 43]. The secular variation of the geomagnetic field is eliminated by using the natural orthogonal component model in mainland China [4]. The 8 th-order spherical cap harmonic model of the Chinese geomagnetic reference field is used to eliminate the main magnetic field component to obtain the lithospheric magnetic field change every year, and then the lithospheric magnetic field of the adjacent two years is used to obtain the annual change [39]. Finally, the surface spline fitting method is used to construct a high-precision model of the annual change in the lithospheric magnetic field in the study area.
## 4 Experiments and Comparisons
### Experimental Analysis
(**A**) HR (**B**) Bicubic (**C**) SRCNN (**D**) FSRCNN (**E**) VDSR (**F**) DRCN (**G**) LapSRN (**H**) RCAN (**I**) Ours. Figure 13: Comparison of restored results on bay by different super-resolution methods (\(\times\)4). (**A**) HR (**B**) Bicubic (**C**) SRCNN (**D**) FSRCNN (**E**) VDSR (**F**) DRCN (**G**) LapSRN (**H**) RCAN (**I**) Ours. Figure 13 shows the results of the experiment obtained from the \"bay\" picture using test4 data that are at 4\(\times\) magnification. The edges, corners and cylinders recovered by our method are closer to the real images, the recovered infrared image can clearly see the gap and number of fences, and the ship behind the dock can also be clearly displayed. The results provide strong evidence for our real-time marine monitoring and identification of targets. while other algorithms (such as DRCN and FSRCNN) cannot reconstruct fence lines and gaps.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 1: Computation of Non-Dominated QoS Paths
#### 5.1.1 Initialization  
* The algorithm initializes paths for each node in \(P\), taking \(O(|V|)\) time, where \(|V|\) is the number of nodes.
* Creating a fictive path and assigning it to the border router (\(BR\)) takes constant time, \(O(1)\).
## 5 Experimental Results and Simulations
### Validation Results  
In this sub-section, we aim to access the performance of the proposed model on the unseen dataset. Once the classification model has been trained using 10-folds cross-validation, the validation process starts. Validation is an essential phase in building predictive models; it determines how realistic the predictive models when applied to real-world applications. In this research, the data obtained from the Physionet's CinC challenge-2000 database consist of 70 records, divided into a learning set of 35 records and a test set of 35 records. The model is trained using the learning set, and then we applied the unseen test set to investigate the true performance of the trained model. After performing preprocessing steps, the generated test data contains 4935 samples such that 3197 samples are labeled as normal while 1738 samples are labeled as OSA affected.  
Table 10 presents the evaluation results using the top classification model (ensemble DT*) through testing and validation process. Based on the findings, the ensemble DT* has retained the testing accuracy, AUC, precision, fscore, and G-mean of 74.47%, 71.29%, 82.16%, 81.06%, and 70.76%, respectively.  
From the empirical analysis, the optimized classifiers have retained better classification results than those predefined parameter methods. Our findings prove that a better tuning of hyper-parameters significantly increased the classifiers' performance, which can substantially help the learning model explain the target concepts. The results obtained in Table 6 support the arguments. Among the optimized classifiers, the KNN* and ensemble DT* was the best due to their high-performance measurement in the sleep apnea classification. Besides, we found that the implementation of ADASYN has a positive effect on the imbalance dataset, which offered a higher value of AUC and G-mean in the classification process. Moreover, the feature selection method was applied to select the optimal feature subset. The learning model's accuracy can be further enhanced (See Table 9). All in all, it can be concluded that the utilization of both synthetic sampling and feature selection can be excellent ways to improve the performance of the learning process.
## 5 SMT-Based BMC of \(\mathsf{ELTLKD_{q}}\) Properties
### Computation of \(f_{k}\) for the Formula in \(\text{ELTLKD}_{\text{q}}\)  
Now, let us compute the value of the function \(f_{k}\) for the following formula:  
\[\text{G}(\
eg q_{[0,\Delta)}\vee\overline{\text{O}}_{C}\Big{(}\overline{ \text{K}}_{T1}\big{(}\
eg approach_{T2}\vee\text{G}(\
eg q_{[0,\delta)}\vee \
eg i\text{\emph{n}}_{T2})\big{)}\wedge\overline{\text{D}}_{\{T1,T2\}}out_{ T1}\Big{)}).\]  
1. Innermost formula: \[\
eg approach_{T2}\vee\text{G}(\
eg q_{[0,\delta)}\vee\
eg i\text{\emph{n}}_{T2})\] * \(f_{k}(\
eg approach_{T2})=f_{k}(\
eg q_{[0,\delta)})=f_{k}(\
eg i\text{\emph{ n}}_{T2})=0;\) * \(f_{k}(\
eg q_{[0,\delta)}\vee\
eg i\text{\emph{n}}_{T2})=\max(0,0)=0;\) * \(f_{k}(\text{G}(\
eg q_{[0,\delta)}\vee\
eg i\text{\emph{n}}_{T2}))=(k+1)\cdot 0 =0.\) Therefore, \[f_{k}(\
eg approach_{T2}\vee\text{G}(\
eg q_{[0,\delta)}\vee\
eg i\text{ \emph{n}}_{T2}))=\max(0,0)=0.\]
2. For \(\overline{\text{K}}_{T1}\): \[\overline{\text{K}}_{T1}\big{(}\
eg approach_{T2}\vee\text{G}(\
eg q_{[0, \delta)}\vee\
eg i\text{\emph{n}}_{T2})\big{)}\] Using the rule for \(\overline{\text{K}}_{\text{c}}\): \[f_{k}(\overline{\text{K}}_{T1}(\
eg approach_{T2}\vee\text{G}(\
eg q_{[0, \delta)}\vee\
eg i\text{\emph{n}}_{T2})))=0+1=1.\]
3. For \(\overline{\text{D}}_{\{T1,T2\}}\): \[\overline{\text{D}}_{\{T1,T2\}}out_{T1}\] \[f_{k}(\overline{\text{D}}_{\{T1,T2\}})=0+1=1.\]4. Conjunction: \[\overline{\mathbb{K}}_{T1}\wedge\overline{\mathbb{D}}_{\{T1,T2\}}out_{T1}\] \[f_{k}(\overline{\mathbb{K}}_{T1}\wedge\overline{\mathbb{D}}_{\{T1,T2\}})=1+1=2.\]
5. For \(\overline{\mathcal{O}}_{C}\): \[\overline{\mathcal{O}}_{C}\left(\overline{\mathbb{K}}_{T1}\wedge\overline{ \mathbb{D}}_{\{T1,T2\}}\right)\] \[f_{k}(\overline{\mathcal{O}}_{C})=2+1=3.\]
6. For the outermost G: \[\mathrm{G}(\
eg q_{[0,\Delta)}\vee\overline{\mathcal{O}}_{C})\] \[\bullet f_{k}(\
eg q_{[0,\Delta)})=0.\] \[\bullet f_{k}(\overline{\mathcal{O}}_{C})=3.\] \[\bullet f_{k}(\
eg q_{[0,\Delta)}\vee\overline{\mathcal{O}}_{C})=\max(0,3 )=3.\]
7. Finally, for the G operator: \[f_{k}(\mathrm{G}(\
eg q_{[0,\Delta)}\vee\overline{\mathcal{O}}_{C}))=(k+1) \cdot 3=3k+3.\]  
The value of \(f_{k}\) for the given formula is \(3k+3\). This means that \(3k+3\) paths are required to test this formula, where \(k\) is the bound on the length of the temporal model used in bounded model checking (BMC). Some examples are as follows:  
* If \(k=1\), the number of paths required is \(3*1+3=6\);
* If \(k=2\), the number of paths required is \(3*2+3=9\), and so on.
# The Effect of Chemical Activating Agent on the Properties of Activated Carbon from Sago Waste
## 3 Results
### Structural Properties  
The structural properties of the activated carbon were examined through its crystalline structure and morphology images. Figure 2 shows the X-ray diffraction profiles of the activated carbons from sago waste prepared with various activating agents. The carbon samples demonstrated broad diffraction peaks at around 20 of 23\({}^{\circ}\), while those prepared with KOH activating agent showed slight peaks at around 20 of 43\({}^{\circ}\), with no sharp peaks. Furthermore, the two broad bands centered around 26 of 23\({}^{\circ}\) and 43\({}^{\circ}\) were associated with the diffraction of the 002 and 100/101 planes, respectively [48]. In addition, the washing after the activation is an important process, in order to avoid residue from the activating agent solution, which prompts additional peaks [41].  
Figure 3 shows morphology images taken from scanning electron microscopy with 10,000\(\times\) magnification. All samples show a combination of micropores and mesopores, verified the porosity properties that were previously analyzed. Figure 3b shows that activated carbon from the ZnCl\({}_{2}\) treatment has more dominant micropores, as confirmed by the lowest V\({}_{\text{Mes.}}\)/V\({}_{\text{Mic.}}\) of 33.78%. The mesopore is clearly seen in Figure 3a,c as the V\({}_{\text{Mes.}}\)/V\({}_{\text{Mic.}}\) value increase for H\({}_{3}\)PO\({}_{4}\) (50.01%) and KMnO\({}_{4}\) (60.53%). Furthermore, the activated carbon from the KOH treatment from Figure 3d has more heterogeneous pores, yielding the highest V\({}_{\text{Mes.}}\)/V\({}_{\text{Mic.}}\) value of 84.16%. The acquired energy dispersive spectroscopy (EDS) provides elemental composition data for each sample, which are summarized in Table 2. It can be seen that the elemental composition for each activator  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Activating** & **V\({}_{\text{Mic.}}\)** & **V\({}_{\text{Mes.}}\)** & **V\({}_{\text{Total}}\)** & **V\({}_{\text{Mes.}}\)/V\({}_{\text{Mic.}}\)** & **Surface Area** & **Average Pore** & **Yield** \\
**Agent** & **(cm3/g) (\%)** & **(cm3/g) (\%)** & **(cm3/g)** & **(\%)** & **(m2/g)** & **(nm)** & **(\%)** \\ \hline H\({}_{3}\)PO\({}_{4}\) & 0.194 (66.7) & 0.097 (33.3) & 0.291 & 50.01 & 480.23 & 4.21 & 22.39 \\ ZnCl\({}_{2}\) & 0.225 (74.8) & 0.076 (25.2) & 0.301 & 33.78 & 546.61 & 3.32 & 23.34 \\ KMnO\({}_{4}\) & 0.152 (62.3) & 0.092 (37.7) & 0.244 & 60.53 & 274.92 & 4.30 & 29.98 \\ KOH & 0.101 (54.3) & 0.085 (45.7) & 0.186 & 84.16 & 374.03 & 5.12 & 30.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Porous properties of activated carbons from sago waste prepared using various chemical activating agents.  
Figure 2: X-ray diffraction patterns of activated carbons from sago waste prepared using various chemical activating agents.  
agent was only less than three percent, even almost undetectable for KMnO4 and KOH. This confirmed that the sample washing process has been successful.
## 2 Methodology
### Geometry
In triangle \(\triangle\)OHE, the length of \(\overline{OH}\) can be found using the law of cosines:  
\[\overline{OH}^{2}=\overline{HE}^{2}+\overline{OE}^{2}-2*\overline{HE}* \overline{OE}*cos(\alpha) \tag{3}\]  
Therefore, the angle between \(\overline{OH}\) and \(\overline{OE}\) can be calculated applying the law of cosines a second time:  
\[\overline{HE}^{2}=\overline{OE}^{2}+\overline{OH}^{2}-2*\overline{OE}* \overline{OH}*cos(\beta) \tag{4}\]  
The area of the lamp heating the specimen outside the wanted region can be calculated by:  
\[A_{\text{outside}}=\beta*r^{2} \tag{5}\]  
This area is the combination of the circular sectors in the triangles \(\triangle\)OHE and OH\({}_{2}\)E\({}_{2}\). One should note that \(\beta\) is in radians to calculate the area of the circular sector. The percentage of the lamp heating the specimen directly can finally be found:  
\[\%_{\text{outside}}=\frac{A_{\text{outside}}}{\Pi*r^{2}}*100\%=\frac{\beta }{\Pi}*100\% \tag{6}\]  
Ending the elliptical reflector shape at 10 cm from the top leads to an illuminated region of 2.7 cm and the wanted maximum width of the region is 1 cm. Conforming to the previous calculations, the difference between the heated and desired region of 1.7 cm equals an area of 2.68% of our excitation source. The purpose of the research is to focus the light in a heated region of maximum 1 cm. Because of the opening in the elliptical reflector shape, a circular sector of the radiating tungsten halogen tube lamp will directly heat the specimen. To assure the wanted 1-cm-wide concentrated radiation area is attained, extra reflectors are added in the reflector shape. The length and the placement of the additional reflectors can be found using simple mathematics. Considering a rectangle with height \(\overline{OF}\) and width \(\overline{FE}\), \(\overline{OE}\) is a diagonal in this rectangle. The other diagonal starting in point F has the same equation as the additional reflector. The reflector starts at the intersection of the diagonal and \(\overline{OH}\) and ends at the intersection of the diagonal and \(\overline{OE}\). This way, the unwanted heated region is nullified. Figure 3 shows the optimised reflector geometry as result of the ray-tracing simulations. The halogen tube lamp has a diameter bigger than the focal point of the ellipse; therefore, the radiation will not originate precisely in the focal point of the ellipse. Assuming that the radiation leaves the halogen tube lamp perpendicular to the length of it, the extended rays intersect the focus of the ellipse, authorising the use of an elliptical reflector.
## 2 Materials and Methods
### Mann-Kendall Test  
The Mann-Kendall trend test (M-K) is a widely used statistical test that can be used to predict the long-term trends of meteorological elements such as temperature, precipitation, and air pressure. The non-parametric test method is also known as the no distribution test. The change elements do not necessarily have normal distribution characteristics and will not be affected by a few outliers. It has a high degree of quantification, wide detection range, small interference degree, and simple calculation, so it is applicable to the change trend analysis with non-normal distribution characteristics.
## 3 Results
### Design and Analysis of Orthogonal Test
#### 3.2.1 Establishment of the Regression Equation and Significance Analysis of the Model  
As shown in Equation (5), using the Design-Expert software to perform multiple regression fitting analysis on the test data in Table 4 [35], a regression equation with the evaluation index as the response function and 3 test factors as independent variables is obtained. Then, this paper performs an analysis of variance on the model, and the results are shown in Table 5.  
Figure 14: Velocity vector diagram of cross section 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{3}{c}{**Test Factors**} \\ \cline{2-4}  & **Rotating Speed of the Vane Type** & **Inclination Angle** & **Diameter of the** \\  & **Film Removal Roller \(X_{1}\)/(rad\(\cdot\)min\({}^{-1}\))** & **of the Blade \(X_{2}\)/(\({}^{\circ}\))** & **Roller \(X_{3}\)/(mm)** \\ \hline \(-1\) & 100 & 5 & 140 \\
0 & 200 & 15 & 179.5 \\
1 & 300 & 25 & 219 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test factors and levels.  
The results show that in the aspect of the significant influence of test factors on the evaluation index, the rotating speed of the vane type film removal roller is larger than the inclination angle of blade, which is larger than the diameter of the roller. The \(p\)-value of the model is less than 0.0001, which indicates that the regression model is extremely significant. The lack of fit item \(p=0.2526>0.05\) shows that the model has a high degree of fit and has good practical research significance.  
\[\begin{array}{c}Y=12.57-6.48X_{1}-3.42X_{2}-0.62X_{3}+0.85X_{1}X_{2}-0.94X_{1}X _{3}+\\ 0.57X_{2}X_{3}+3.94X_{1}^{2}+0.30X_{2}^{2}+1.31X_{3}^{2}\end{array} \tag{5}\]
## 3 Results  
### Segmentation Performance  
The segmentation performance of all three architectures was comprehensively evaluated using Dice similarity coefficient (DSC), precision, sensitivity (recall), specificity, and 95 th percentile Hausdorff distance (HD95). Table 1 summarizes these metrics for both right and left adrenal glands in the testing dataset and Figure 1 presents the visual representation.  
U-Net consistently demonstrated superior performance across all metrics compared to both NablaNet and SegNet.  
Segmentation results for the left adrenal gland were generally better than those for the right gland across most metrics, though some specific parameters showed slight variations from this pattern.  
SegNet exhibited the poorest boundary delineation accuracy, as indicated by substantially higher HD95 values.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Image Mosaicking and Calibration  
Images acquired from UAVs can be mosaicked using a Pix4D mapper (Pix4D, Prilly, Switzerland), Agisoft Photoscan Pro (Agisoft LLC, 52 St. Petersburg, Russia), and any available commercial software to generate qualitative, high-resolution orthomosaic images. After mosaicking, the process will continue with radiometric calibration and rescale the intensity of the electromagnetic radiation or digital number (DN) into the percentage of reflectance values [53]. Researchers have implemented numerous methods, such as the traditional empirical line correction approach and modern automatic radiometric calibration using available commercial software.  
The empirical line correction approach is an atmospheric correction technique that provides a straightforward surface reflectance calibration method, if a set of invariants in the time calibration target measurement is provided. [PERSON] and [PERSON] [54] implemented this approach to improve six multispectral UAV data quality bands for quantitative data analysis. Similarly, [PERSON] et al. [55] applied the same method and obtained a reflectance value of r = 0.997 (\(p\leq 0.01\)) with an overall root mean square of 0.63. Nevertheless, when dealing with high-quality data, the performance and accuracy must be re-evaluated [56].  
In order to improve radiometric calibration accuracy, [PERSON] et al. [57] introduced a spectral angle correction approach, where their method uses all information in each spectral band. Compared to the empirical line correction approach, they successfully improved the mean relative percent error (MRPE) range up to 3% in the visible band and 1% in the near-infrared (NIR) band. This finding will highly benefit the agriculture remote sensing field.  
However, the user can also run the radiometric calibration automatically using available commercial software such as Agisoft Photoscan Pro (Agisoft LLC, 52 St. Petersburg, Russia) and Pix4D mapper (Pix4D, Lausanne, Switzerland). The'reflectance map' tool in Pix4D mapper software is also similar to calibrate 'calibrate reflectance' in Agisoft Photoscan Pro that employs multiple image attributes to determine surface reflectance [58]. In addition, these software packages provide 'color correction/balancing' functions to develop the image information based on a radiometric block correction algorithm. However, the algorithms used in these packages only calculate the homogeneity of the neighbouring image's histogram homogeneity, not the bidirectional reflectance distribution function (BRDF) effect in a single image [59].
## 4 Discussion
### Discussion on the Results of KAA-LSTM  
It can be seen from Table 1 that DNN and LSTM have lower accuracy, while KAA-LSTM can achieve almost 100% accuracy in the verification set. This is due to the information mining of important actions by the attention mechanism, which enhances the discrimination ability of the model. Furthermore, the recall and precision levels of KAA-LSTM are much higher than those of the other two models. This indicates that our model is capable of a high level of discernment.
## 3 Numerical Simulation Results
### Comparison between Numerical Simulation Results and Experimental Results  
A comparison of the experimental results and simulation results under different flow conditions is shown in Figure 7. It can be seen that the results of the internal flow model are very close to the experimental values, with the maximum absolute error of the head at 0.3 m and the maximum absolute error of the efficiency at 1.7%. Since some mechanical  
Figure 5: Velocity vector diagram of the meridional surface of the vaneless diffuser. (**a**) Tip leakage model; (**b**) tip–hub leakage model; (**c**) internal flow model.  
Figure 6: Stream line distribution of the internal flow passage of the motor and the outlet under different flow rates. (**a**) Stream line distribution at \(Q=1\) m\({}^{3}\cdot\)h\({}^{-1}\); (**b**) stream line distribution at \(Q=3\) m\({}^{3}\cdot\)h\({}^{-1}\); (**c**) stream line distribution at \(Q=5\) m\({}^{3}\cdot\)h\({}^{-1}\).  
losses were ignored in the simulation calculation, the overall predicted value for efficiency is larger than the experimental value.  
Generally, based on the CFX-MATLAB coupling calculation method, the prediction performance of the internal flow model is sufficiently accurate and reliable for high-speed centrifugal pumps with a fully sealed structure. Hence, the proposed method is of qualitative and even quantitative significance to the numerical simulation of centrifugal pumps with a fully sealed structure.