esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 4 Results and Discussion  
### Performance of DBT  
Tables 2-7 report the performances of six different classifiers, namely DT, kNN, LR, NB, RF, and SVM, for different data-balancing techniques, namely None, US, OS, HS, ROSE, and CBUS. The performance of the classifier is measured using the area under receiver operating characteristics curve, i.e., the AUC value. The first column in the table indicates the name of the imbalanced dataset. The second column indicates the performance of \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sr. No.** & **Dataset Name** & **No. of Samples** & **No. of Features** & **Imbalance Ratio** \\ \hline \multicolumn{5}{c}{Imbalance ratio from 1–10} \\ \hline
1 & Pima & 768 & 8 & 1.87 \\ \hline
2 & ecoli1 & 336 & 7 & 3.36 \\ \hline
3 & segment0 & 2308 & 19 & 6.02 \\ \hline
4 & yeast3 & 1484 & 8 & 8.1 \\ \hline
5 & vowel0 & 988 & 13 & 9.98 \\ \hline \multicolumn{5}{c}{Imbalance ratio from 10–20} \\ \hline
6 & glass2 & 214 & 9 & 11.59 \\ \hline
7 & yeast-1\_vs\_7 & 459 & 7 & 14.3 \\ \hline
8 & ecoli4 & 336 & 7 & 15.8 \\ \hline
9 & page-blocks-1-3\_vs\_4 & 472 & 10 & 15.86 \\ \hline
10 & glass-0-1-6\_vs\_5 & 184 & 9 & 19.44 \\ \hline \multicolumn{5}{c}{Imbalance ratio from 20–30} \\ \hline
11 & yeast-1-4-5-8\_vs\_7 & 693 & 8 & 22.1 \\ \hline
12 & yeast-2\_vs\_8 & 482 & 8 & 23.1 \\ \hline
13 & yeast4 & 1484 & 8 & 28.1 \\ \hline
14 & poker-9\_vs\_7 & 244 & 10 & 29.5 \\ \hline
15 & winequality-red-4 & 1599 & 11 & 29.17 \\ \hline \multicolumn{5}{c}{Imbalance ratio from 30–40} \\ \hline
16 & yeast-1-2-8-9\_vs\_7 & 947 & 8 & 30.57 \\ \hline
17 & winequality-white-9\_vs\_4 & 168 & 11 & 32.6 \\ \hline
18 & yeast5 & 1484 & 8 & 32.73 \\ \hline
19 & winequality-red-8\_vs\_6 & 656 & 11 & 35.44 \\ \hline
20 & ecoli-0-1-3-7\_vs\_2-6 & 281 & 7 & 39.14 \\ \hline \multicolumn{5}{c}{Imbalance ratio from 40–50} \\ \hline
21 & abalone-21\_vs\_8 & 581 & 8 & 40.5 \\ \hline
22 & yeast6 & 1484 & 8 & 41.4 \\ \hline
23 & winequality-white-3\_vs\_7 & 900 & 11 & 44 \\ \hline
24 & winequality-red-8\_vs\_6-7 & 855 & 11 & 46.5 \\ \hline
25 & abalone-19\_vs\_10-11-12-13 & 1622 & 8 & 49.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset Information. the classifier without balancing the imbalanced dataset (None strategy). Column 3 to Column 8 indicate the performance of the classifier for US, OS, HS, ROSE, SMOTE, and CBUS strategies. The mean rank of the classifiers based on their performance over 25 different datasets, along with Friedman test statistics, is also reported in each table in the last two rows. Tables 8-13 report the post hoc analysis using the Nemenyi multiple comparison test. The Nemenyi statistics are used to understand which DBT performance is significantly different.
## 4 Conclusions  
Metasurface-inspired antennas have been proposed to enlarge the bandwidth of small antennas embedded in miniature circular cavities. The design is inspired from the distribution of the E and H fields of cavity mode TE\({}_{11}\), and the principle is based on imposing a capacitive-only impedance at the aperture of the cavity to enlarge the antenna bandwidth. We demonstrated numerically and experimentally that these structures exhibit a larger bandwidth and efficiency compared to conventional cavity patch antennas, without degrading their radiation characteristics. Moreover, we also showed that the proposed concepts can also be applied to very small cavity diameters (0.15 \(\lambda_{0}\) and 0.12 \(\lambda_{0}\)), in which standard patch antennas cannot be integrated due to their too-large size. These innovative small cavity antennas bring new opportunities for the instrumentation of smaller carriers and have been successfully tested for telemetry applications in harsh environments.  
\begin{table}
\begin{tabular}{c c c c} \hline Substrate & RO4350+ RO3010 +PP & Sim. BW (MHz) & 15 \\ \(a\) (mm) & 16.0 & Meas. BW (MHz) & 20 \\ Dielectric layers (mm) & \(1\times 0.5+7\times 0.63\) & Sim. gain (dB) & \(-3.0\) \\ \(h_{c}\) (mm) & 12.6 & Meas. gain (dB) & \(-5.5\) \\ \(f_{x}\) (mm) & 1.4 & Meas. efficiency (\%) & 14.5 \\ \(g\) (mm) & 0.3 & Meas. XPD (dB) & 33.5 \\ \(L_{t}\) (mm) & 4.2 & Meas. HPBW E-plane (\({}^{\circ}\)) & 126 \\ \(W_{t}\) (mm) & 4.0 & Meas. HPBW H-plane (\({}^{\circ}\)) & 97 \\ \hline \end{tabular}
\end{table}
Table 4: Prototype dimensions and performances for a cavity of 16 mm and a thickness of 12.6 mm.  
Figure 18: Measured radiation patterns in the E- and H-planes of the metasurface antenna for the projectile SOCBT, at the frequency of \(f\) = 2290 MHz.  
**Author Contributions:** All authors contributed to all tasks, but below are mentioned the main contributors for each tasks. Concept and design: M.M. and K.M.; Simulations and experiments: M.M., S.C. and L.B.; Draft Preparation: L.B., M.M., S.C., K.M. and R.S.; Review: L.B., S.C. and R.S.; Supervision: [PERSON] and R.S.  
**Funding:** This research was partially funded by the Region Bretagne ARED program, and the French-German Research Institute of Saint-Louis.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 2 Study Area
According to the Copernicus EEA European urban atlas (UA), the land use within the London area is dominated by dense to medium density urban fabric with multiple industrial units and stretched port areas along the river Thames [37]. Additionally, according to the 2011 population census of the UK, the total population of London is around 9.5 million and has been continuously increasing since the 1980s. The ever-increasing population is exerting a pressure on groundwater to meet the increasing demand and thus can cause the problem of land subsidence.
## 3 AQM Framework for Smart City Applications
### Analysis and Improvement  
Following the verification and assessment part, the final process in the AQM framework is the analysis and improvement phase as presented in Figure 5. This analysis process is performed as one of the phases in the quality management cycle. After checking whether critical states have been reached and assessing the agent quality statuses, in this phase, the statuses are classified and presented in a time series to show the level of agent run-time quality statuses. The analysis of the verification and assessment results can be used by the agent owner to minimize the quality violation levels by changing the preference settings and increasing or reducing the interaction activities. For example, by changing the privacy setting from public to private or friend only for a certain parameter, the exposure level can be reduced. The improvement procedures are also performed here to increase the effectiveness of the solution by identifying new quality metrics for the next verification cycle. From the analysis process, improvements can be suggested to add, remove, refine, or redefine agent quality requirements, metrics, and rules for checking and measuring the quality of agent systems from time to time.
###### Abstract  
Concrete cover has an important role in reinforced concrete (RC) structures because it protects reinforcement bars from the bad effects of weather, fire, and bad environmental conditions that cause the corrosion of the reinforcements. Although it is an essential parameter to be considered for structural health monitoring (SHM), its detection by infrared thermography, especially in the heating phase, has not been accessed yet. The detailed analysis and discussions of physical phenomena, known as diffraction and interference, affecting the thermograms during the detection of the steel bars by microwave thermography have given an essential key for resolving this issue. The present paper proposes an innovative methodology with microwave thermography for determining the concrete cover thickness of one-layer reinforcements (12 mm in diameter and regularly placed at 10 cm) in an RC wall (1 m \(\times\) 1 m \(\times\) 6.5 cm). By using the transmission approach with five angles of microwave antenna direction (\(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), and \(60^{\circ}\)) and the Snell-Descartes law and linear law, the proposed methodology leads us to deduce the approximate value of the concrete cover thickness (37.74 mm), which is close to the real value (38 mm), as well as the spacing of the steel bars and dielectric constant of the concrete. The detection of the concrete cover thickness is another new remarkable achievement of infrared thermography methods.  
s +
Footnote †: journal: Applied  
article  
tutorials (SHM); electromagnetic nondestructive evaluation (NDE); microwave thermography; concrete cover thickness; steel reinforcements; reinforced concrete (RC) wall
## 3 Experimental Validation and Analysis
### Similarity Clustering
#### 3.3.2 Reconstruction by Superposition  
The clustered results of displacement and acceleration subsequences were reconstructed by superposition. As shown in Figures 11 and 12, In the displacement sequence, IMF1 is separately considered as a sequence, forming the reconstructed sequence GS-1; IMF2 and IMF3 are superimposed to form the reconstructed sequence GS-2; IMF4 to IMF7 are superimposed to form the reconstructed sequence GS-3; while the remaining components such as IMF8 are superimposed to form the reconstructed sequence GS-4.  
Figure 11: Reconstructed GNSS displacement sequences.  
Figure 10: Power Spectral Density of Decomposed Acceleration Components.  
Similarly, in the acceleration sequence, IMF1 and IMF2 are superimposed to form the reconstructed sequence AC-1; IMF3 and IMF4 are superimposed to form the reconstructed sequence AC-2; IMF5 to IMF12 are superimposed to form the reconstructed sequence AC-3; IMF13 and the remaining components are superimposed to form the reconstructed sequence AC-4. These reconstructed sequence signals not only retain the basic information of the original sequence but also possess different time scales and amplitudes, which are not present in the original sequence. Based on these reconstructed signals, they serve as inputs for subsequent deep learning of time series data, to further explore and analyze the potential information and patterns in these sequences.
## 3 Results
### Photocatalytic Performance Evaluation
After UV irradiation of 12 h, the normalized value of 0.874 was shown and the photodegradation rate was 12.6%. In methylene blue solution with TiO\({}_{2}\) nanofibers, the normalized value decreased to 0.581 and the photodegradation rate was 41.9%. In methylene blue solution with TiO\({}_{2}\)/ZnO nanofibers, the normalized value dramatically decreased. This reveals that UV irradiation and photocatalytic reaction of TiO\({}_{2}\)/ZnO nanofibers affect the decomposition of methylene blue. After UV irradiation of 12 h, the normalized values in methylene blue with TiO\({}_{2}\)/ZnO-1 nanofibers and TiO\({}_{2}\)-ZnO-2 nanofibers were 0.054 and 0.037 and a photodegradation rate of 94.6% and 96.3% were observed, respectively. The photodegradation rate in methylene blue with TiO\({}_{2}\)/ZnO nanofibers was higher than with TiO\({}_{2}\) nanofibers. This shows that TiO\({}_{2}\)/ZnO nanofibers decompose methylene blue more efficiently than TiO\({}_{2}\) nanofibers, and the photodegradability of TiO\({}_{2}\)/ZnO nanofibers is better than that of TiO\({}_{2}\) nanofibers. The best photocatalytic decomposition performance for methylene blue was observed in methylene blue with TiO\({}_{2}\)/ZnO-2 nanofibers. It was found that TiO\({}_{2}\)/ZnO-2 nanofibers with a small diameter have better photolytic properties than TiO\({}_{2}\)/ZnO-1 nanofibers [46; 47; 48; 49; 50; 51]. Figure 11: Ultraviolet–visible spectroscopy (UV–Vis) spectral change of photocatalytic decomposition of methylene blue over time using diameter-controlled nanofibers: (**A**) UV without nanofibers, (**B**) UV with 0.5 g TiO\({}_{2}\) nanofiber added, (**C**) UV with 0.5 g TiO\({}_{2}\)/ZnO-1 nanofiber added, (**D**) UV with 0.5 g TiO\({}_{2}\)/ZnO-2 nanofiber added.
## 3 Results and Discussion
### Twin Skeg LNG Carrier  
For the twin skeg LNG carrier, two strain gauge systems were installed on propeller shafts in the engine room at the PORT and starboard side. The speed trial was performed at ballast draught with three sets of engine load, 65%, 75%, 90% of MCR. The environmental condition was moderate below Beaufort scale 4. Table 4 shows the conditions of speed trials in detail.  
The measurement results during speed trials are shown in Table 5 and Figure 9. The thrust, torque and revolution of the propeller at PORT and starboard side were measured simultaneously.  
Figure 9 presents the thrust and torque measurement results during speed trials compared with the prediction result from the model test. In the graphs, a good comparison is shown between the full-scale measurement via the torque and thrust sensor at PORT and starboard side and prediction results from model test. The results of torque measurement showed a discrepancy with model test results of approximately 2.5%. The results of thrust measurement showed the discrepancy with the model test results of 0.8% at 65% of MCR, 3.6% at 75% of MCR, and 5.7% at 100% of MCR. The comparison results of torque-thrust measurements from both sides with the prediction results show the same tendency.  
The difference between the prediction results from model test and measurement during speed trial increases gradually as the engine load is increasing. This comes from the difference in the thrust measurement. The torque from the speed trial and model test agreed well with respect to the revolution of propeller. However, the measured thrust shows the differences from the prediction values from the model test, as shown in Figure 9.
## 4 Experimental Results
### Number of Symbol Comparisons  
To put the practical running times presented above in context, we also investigated the number of comparisons required by the algorithms and the number of pattern shifts performed during the match. These two parameters are the basic determinants of the running time of the algorithms. Below, we report on the performance of the two best algorithms, QS and FQS.  
Table 2 shows the number of comparisons, their corresponding standard deviation (STD) and statistical significance (\(p\)-value) from algorithms QS and FQS, for pattern lengths \(m\) = 10, 100, 500 and 1,000, respectively. From the table, we can observe that, in all cases, the number of comparisons used by FQS is less than that of QS. The Student's \(t\)-test compares whether there is a statistical difference between these two algorithms by using \(p\)-value = 0.05 as the threshold. The \(p\)-value is shown in bold where there is a significant difference. For Bible and _E. coli_, there are significant differences in all cases. For World192, there is a statistically significant difference when pattern length \(m\) = 1,000; the other three cases (\(m\) = 10, 100, 500) do not show any statistically significant difference. Table 3 shows the corresponding results for the number of pattern shifts, the corresponding standard deviation (STD) and statistical difference (\(p\)-value) from algorithm QS and FQS, for the pattern lengths \(m\) = 10, 100, 500, 1,000. Again, the results show that in all cases, the number of pattern shifts performed by FQS is less than the number for QS. From a statistical point of view, in seven out of 12 cases, there are statistically significant difference in the performance of FQS over QS. Taken together, these two tables provide an explanation for the superior performance of FQS on the practical files when compared with  
Figure 12: Execution time _versus_ pattern length, \(m\), for large patterns (\(100\leq m\leq 1000\)) using practical text files.  
the other QS variants. More importantly, the results show the effectiveness of the innovative use of an intelligent pre-testing stage before embarking on the more time-consuming pattern matching. In our FQS algorithm, this pre-testing is performed using \(pos\), the location with the maximal expected shift in our FQS algorithm.
## 5 Conclusions  
In summary, we have analyzed in detail the effects of various factors that affect the quantum entanglement for a NOPA system. Based on this, we designed a NOPA experimental system and prepared high-quality two-color CV quantum-entangled optical beams at 0.8 \(\upmu\)m and 1.5 \(\upmu\)m with 6.0 dB of entanglement. Since a high degree of entanglement is crucial for quantum information applications, the current system should be optimized further. For instance, we can enhance the transmission of the output coupler to 12%, the propagation efficiency to 99%, the interference efficiency to 99.8%, and the quantum efficiency of the photodiode to 99%. Then, the total loss will increase to 89.2%; in this situation, we can reach \(-8.9\) dB of entanglement. If the intracavity loss is further reduced to 0.5%, a higher entanglement of \(-11\) dB can be generated.  
This work was supported by the Key Project of the National Key R&D Program of China (2016 YFA0301403), the Natural National Science Foundation of China (NSFC) (11774209, 61378010), the Shanxi 1331 KSC, and the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi.  
**Author Contributions:** [PERSON] and [PERSON] performed the experiments, [PERSON] conceived the original research idea, [PERSON] and [PERSON] developed the theoretical formalism, performed the data analysis, and wrote the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Works** & \(T\) & \(L\) & \(\eta_{\text{t}}\) & \(\eta_{\text{vis}}\) & \(\eta_{\text{pd}}\) & \(\eta_{\text{en}}\) & \(\theta\)**(Mrad)** &
\begin{tabular}{c} **Observed Entangled** \\ **Level (dB)** \\ \end{tabular} \\ \hline [PERSON] et al. [6] & 6.7\% & 1.6\% & 96\% & 94\% & 75\% & 100\% & 40 & 3.3 \\ This work & 10.2\% & 1.1\% & 96\% & 96\% & 92\% & 99.8\% & 8.7 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental parameters between the two systems.
## 2 Materials and Methods
### Data Sources and Processing  
#### 2.2.1 NPP Data  
NPP data from 2001 to 2019 were derived from the annual NPP dataset (MOD17A3 HGF V6) ([[https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/]([https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/), accessed on 2 September 2021) provided by NASA, with a spatial resolution of 500 m and a conversion factor of 0.0001. MOD17A3 HGF is synthesized from all 8-day net photosynthesis (PSN) products (MOD17A2H) from the given year, where the PSN value is the difference between gross primary productivity (GPP) and maintenance respiration (MR). MOD17A3 HGF V6 improves the accuracy of NPP estimation by using updated Biome Property look-up tables (BPLUT) and an updated version of the Global Modeling and Assimilation Office (GMAO) meteorological data compared with MOD17A3 V5.5 [54]. The function to obtain the dataset in GEE is ee.ImageCollection(\"MODIS/006/MOD17A3 HGF\").
###### Abstract  
Three-dimensional (3D) printing, or additive manufacturing (AM), is a production can be utilised to fabricate 3D shapes from a simulated file. This technology has gained global popularity in the construction industry since 2014 due to its wide range of applications. AM promotes a more automated, innovative, flexible, and sustainable construction method, making it an integral part of the Construction Industry 4.0. However, there need to be more detailed studies regarding the effectiveness of AM as the future direction in the construction industry. This paper investigates the application of the finite element method (FEM) in assessing 3D-printed structures to get insight into the performance of these structures. Three leading 3D-printed structures were selected, including Dubai Future Foundation in the United Arab Emirates, Apis Cor house in Russia and PERI house in Germany. Structural and thermal analyses, including linear static, natural frequency, spectral response, and steady state heat, were performed using Strand7 to assess the effectiveness of AM in construction and the reliability of FERI in analysing 3D-printed structures. Although there are limited standards and regulations for 3D-printed structures in most countries, it was concluded that 3D-printed structures presented a similar strength to traditional ones. Moreover, FEM can be used to provide a reasonable analysis of the performance of these structures, while complying with the relevant standards. This paper presents a novel numerical procedure to assess the performance of small-scale 3D-printed structures under various mechanical and thermal loadings by checking against the relevant standards.  
finite element modelling; 3D printing; Design and Analysis; Construction Industry 202320  
to end poverty, protect the planet and ensure the global well-being of human beings by 2030 [3]. Hence, the adaption of AM in construction can contribute to the SDGs.  
In addition, 3D printing provides constructability and sustainability benefits, leading to a faster, safer, cheaper, and more sustainable construction method.  
Awareness of 3D printing in construction is proliferating. According to Google Trend [4], the search query \"3D printing in construction\" has reached multiple breakouts since 2014, showing the popularity of AM over the past decade. However, there are limited construction regulations set in most countries regarding the use of 3D printers. This is mainly due to the immaturity of AM in construction and limited research around the performance of 3D-printed structures.  
The finite element method (FEM) has a wide range of applications in structural analysis and is used to assess the performance of 3D-printed structures. One of the benefits of FEM is its high accuracy due to the fine discretisation of the elements [5]. As a result, FEM has been deemed a reliable numerical method in structural analysis [6] and was used in this paper to analyse three 3D-printed structures.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.7 Saint Petersburg  
The city of St. Petersburg is located along the banks of the Neva River, on the Gulf of Finland in the Baltic Sea. The city's topography is nearly flat, with an elevation range of 1-5 m. Regular floods have historically impacted the city's coastal area [450]. From 1703 to 2010, 339 flood events have been recorded [261; 451]. An average of one flood per year was recorded from 1703 to 1975, and this rate has approximately doubled in the last forty years [261]. The sea level of Neva bay was calculated in [450] for three floods in Saint Petersburg on 21 November-2 December 2011, on 20 December 2011-1 January 2012, and on 19-30 October 2013, using the BALT-P hydrodynamic flooding model. This revealed a maximum water column height of about 183 cm in December 2010. To prevent significant economic losses due to frequent floods and efficiently manage land subsidence and its joint risk with SLR, a flood prevention facility complex was designed and built by the municipal government [452]. The recent land subsidence of the Saint Petersburg Bay has recently been investigated in [261] through the multiple-satellite Minimum Acceleration (MinA) combination technique [215], which allows the discrimination of east-west and up-down deformation time series, relying on two sets of Sentinel-1 SAR images collected between 2016 and 2018. An independent investigation of this phenomenon has also been reported elsewhere [453], showing that the ground deformation of the city is nearly steady, except for the western sector of the Neva river delta, where ground deformation associated with considerable land reclamation projects has a maximum rate of about 10 cm/year (see Figure 2).