esa-sceva/satcom-chunk-collection huggingface dataset:
# Terahertz Single-Pixel Imaging Improved by Using Silicon Wafer with SiO\({}_{2}\) Passivation
## 5 Conclusions  
In conclusion, we have demonstrated that THz photomodulation and single-pixel imaging is improved by using SiO\({}_{2}\) passivated silicon. By exploring different SiO\({}_{2}\) thicknesses, we have found that for laser illumination power of 330 mW, the largest MF of 0.9 is achieved at a thickness of 148 nm, for which the optical reflectivity reaches the minimum. This MF is three times the MF of bare silicon wafer. We have shown that this great improvement originates from chemical passivation and optical anti-reflection that are both introduced by the SiO\({}_{2}\) passivation. THz single-pixel imaging experiments based on the CS approach have shown that by using the conventional THz photomodulator based on the bare silicon wafer, the reconstructed image had very poor quality; however, by using a greatly improved photomodulator based on the passivated silicon wafer, the reconstructed image is very clean and clear. We have further shown that by using the new photomodulator, sampling ratios above 25% can result in single-pixel THz imaging with acceptable quality. This can greatly reduce the imaging time. Since surface passivation of silicon with SiO\({}_{2}\) is efficient, we expect our work will advance and promote the development of applications of THz photomodulation and single-pixel imaging.  
Conceptualization, R.S., W.L. and Y.L.; methodology, R.S., W.L. and G.W.; software, R.S.; validation, W.L., G.L. and Y.L.; formal analysis, R.S.; investigation, R.S.; resources, Y.L.; data curation, R.S.; writing--original draft preparation, R.S.; writing--review and editing, R.S. and G.L.; visualization, R.S.; supervision, G.L. and Y.L.; project administration, Y.L.; funding acquisition, G.L. and Y.L. All authors have read and approved the final version of the manuscript.  
This research was funded by the Shenzhen Research Foundation (Grant Nos. JCYJ20160608153308846, JCYJ20170413152328742, JCYJ20180507182444250), National Key Research and Development Program of China (No. 2017 YFC0803506), and Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. 2016320).  
The authors declare no conflict of interest.
## 5 Experimental Verification
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Measuring Point 1\#**} & \multicolumn{3}{c}{**Measuring Point 2\#**} \\ \hline (Hz) & Passive & M-FxLMS & WPTDDM-FxLMS & Passive & M-FxLMS & WPTDDM-FxLMS \\ \hline
37 & \(-1.82\) & \(-3.38\) & \(-17.2\) & \(-2.18\) & \(-5.94\) & \(-19.59\) \\ \hline
60 & \(-11.12\) & \(-11.80\) & \(-24.25\) & \(-11.35\) & \(-14.21\) & \(-27.59\) \\
110 & \(-12.05\) & \(-3.36\) & \(-24.13\) & \(-14.26\) & \(-29.32\) & \(-28.92\) \\ \hline \multicolumn{6}{c}{**Measuring Point 3\#**} & \multicolumn{3}{c}{**Measuring Point 4\#**} \\ \hline (Hz) & Passive & M-FxLMS & WPTDDM-FxLMS & Passive & M-FxLMS & WPTDDM-FxLMS \\ \hline
37 & \(-1.82\) & \(-12.18\) & \(-22.92\) & \(-1.99\) & \(-10.39\) & \(-19.83\) \\
60 & \(-11.34\) & \(-16.83\) & \(-24.99\) & \(-10.6\) & \(-11.02\) & \(-28.37\) \\
110 & \(-17.23\) & \(-21.14\) & \(-28.53\) & \(-16.76\) & \(-12.40\) & \(-27.88\) \\ \hline \end{tabular}
\end{table} 
Table 2: Test control effect for two algorithms. Figure 19: (**a**) The time domain acceleration signal in measuring point 4#; (**b**) the acceleration signal spectrum in measuring point 4#. Figure 18: (**a**) The time domain acceleration signal in measuring point 3#; (**b**) the acceleration signal spectrum in measuring point 3#. the improved algorithm can make the acceleration signal significantly reduced, and all of the three line spectra are obviously attenuated in points 1#-4#. Their average drops reach 17.93 dB, 15.19 dB, and 12.29 dB, respectively. On the basis above, in order to further analyze the effects, the theoretical simulation and experimental verification will be comprehensively compared in Figure 20.  
In general, it can be seen from Figure 20 that the simulation and experiment are basically consistent with each other, and the multi-line spectrum control effect of the novel algorithm is much better in terms of both simulation and experiment. In particular, the control effect is consistent with the simulation in low frequency, while the others are not better than the simulation in a slightly higher frequency, which indicates that the off-line identification does not contain sufficient frequency bands or the output capacity of the actuator is limited and the excitation amplitude is relatively small. In addition, the traditional algorithm has a certain effect on the excitation. However, the downside is that it excites other line spectrum signals in measuring point 1#. Overall, the updated algorithm has a better effect in each line spectrum and does not induce other line spectra. Therefore, the WPTDDM-FxLMS algorithm is more suitable in multi-frequency excitation and multi-path vibration isolation systems. In summary, the application value of the algorithm proposed in this paper is in good agreement with the theoretical research and experimental verification; it can be used as one of the effective methods to deal with the problem of active vibration control in underwater vehicles.
## 3 Development of Hybrid RF Models for Predicting the Rock EM
### Hybrid RF Models  
In this paper, the BSA, MVO, GEO and PRO are used to optimize the RF model for predicting the rock EM. The definition of the RF model is described in the literature [57; 58; 59; 60]. The hyperparameter combination selection (i.e., number of trees (Nt) and random features (Maxdepth)) is a key step to tap into the RF model prediction potential. Therefore, four hybrid RF models were generated to find the optimal hyperparameter combination according to the following process:  
1. Data preprocessing  A total of 120 rock samples with four input variables were used to predict the EM in this paper. All variables need to be extracted and normalized to [\(-\)1, 1]. The purpose of this step is to prevent a failure for establishing the accurate prediction relationships due to the parameter variability. After that, the train and test sets are separated from the initial database. The ratio of the train set to the test set is set equal to 4 to 1. It should be noted that the same train or test set is used to generate each hybrid RF model for predicting the rock EM and comparing their performance.
2. Parameter settings Although the Nt increase will not cause an overfitting of the RF model, a large parameter selection range can greatly increase the computation time. Therefore, the ranges of Nt and Maxdepth are set equal to [1, 100] and [1, 10], respectively. For the four MOA algorithms, the number of initial solutions (i.e., individuals of BSA, candidates of MVO, population of GEO and human of PRO) and the iteration time are the core factors that affect the optimization performance of these algorithms. To better activate the optimization performance, the solutions are set equal to 30, 60, 90, 120 and 150 during the 200 iterations.  
Figure 4: Position distribution for the rich and poor populations.  
3. Optimization evaluation The fitness function is utilized to evaluate the performance of each hybrid RF model with different solutions during the 200 iterations. The RMSE is adopted to represent the fitness values of all models in this paper. They do not need an absolute value to evaluate the model performance [51]. In other words, the best-optimized RF model has the lowest RMSE value among all hybrid models based on the same MOA. The flowchart for developing four hybrid RF models for predicting the rock EM is shown in Figure 5.
# Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
## 3 Methodology
### Feature Selection  
The meteorological indices with the greatest influence on the _PAR_ estimation models were selected to improve the precision and reduce the complexity of obtaining the models in Burgos, regardless of whether all sky types that can appear in Burgos were considered or whether the data were classified into clear, partial, and overcast skies. The first step determines the relationship of the different MIs to the _PAR_. The Pearson criterion, based on the Pearson correlation coefficient (\(r\)), was used to determine the influence or weight that each of index has on the _PAR_ component. The criteria are as follows: if \(r\) is close to 0, the corresponding MI has a very weak relationship with _PAR_, whereas if it is close to 1, or \(-1\), the relationship is very strong. The Thumb rule [35] established five \(r\) intervals for the correlation: direct (\(1\geq\)\(r\) (_PAR_, \(Ml_{j}\))\(\mid\)\(\geq\) 0.9), strong (\(0.9>\)\(\mid\)\(r\) (_PAR_, \(Ml_{j}\))\(\mid\)\(\geq\) 0.7), moderate (0.7 \(>\)\(\mid\)\(r\) (_PAR_, \(Ml_{j}\))\(\mid\)\(\geq\) 0.5), weak (0.5 \(>\)\(\mid\)\(r\) (_PAR_, \(Ml_{j}\))\(\mid\)\(\geq\) 0.3), and negligible ( \(\mid\)\(r\)(_PAR_, \(Ml_{j}\)) \(\mid\) < 0.3). Table 2 shows the different intervals of Pearson's coefficients for the different MIs according to the classification of skies over Burgos with the \(k_{l}\) sky classification (clear, partial, and overcast).  
From the results shown in Table 2, it can be observed that _RaGH_ is the MI that has a very strong and direct influence on _PAR_ for all types of skies, coinciding with the results obtained by [PERSON] et al. [15]. Likewise, for all-sky types, _cosZ_ and \(k_{l}\) also have a strong relationship, and \(k_{d}\) and \(\varepsilon\) have a moderate relationship with _PAR_. In the case of analyzing clear skies, there is a direct and strong relation with _RaGH_ and a moderate relation with, \(k_{t}\), _cosZ_, and, \(\varepsilon\). For partial skies, there is also a direct and strong relation, in addition to _RaGH_, with _cosZ_. For overcast skies, there is also a direct and strong relation with _cosZ_ and a moderate relation with \(k_{t}\), and \(\Delta\). The rest of the indices (\(T\), \(k_{d}\), \(P\), \(T_{d}\)) presented a weak or negligible relation with _PAR_, so these MIs were discarded as inputs for modelling _PAR_. These results agree with the literature, insofar as _RaGH_ and _cosZ_ are two of the variables with the strongest influence on _PAR_ measurements [17; 36].
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 5 Numerical Simulations  
Three sets of numerical simulations based on the 3-DOF planar model of the ship (vessel) were carried out to demonstrate the effectiveness of the proposed control design. The first set of simulations demonstrates the improved design, the second shows the role of PSO tuning method, and the third shows the robustness of the closed-loop against external disturbances. All the simulations have been implemented in the Matlab/Simulink environment.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 3 Results
### Application for Large-Scale Area  
The processing chain is applied to identify flooded areas across the entire island of Madagascar, covering an extensive area of 587,041 square kilometres for the 12 days following the passage of Cyclone Batsirai on 5 and 6 February 2022 (Figure 5). We utilised both VV and VH polarisations with a spatial resolution of 20 metres and applied a refined Lee filter to the images. We obtained 30 scenes covering Madagascar in its entirety.  
For reference Sentinel-1 images, we used a series of images acquired annually during the same 12 days period in previous years. Six or seven reference images were accessible for each scene. By combining the series of flood detection by majority vote, each pixel was considered flooded if it emerged in more than three of the reference images. Frequent submersions are thus excluded from the flood detection results.  
To efficiently process the 30 scenes, we simultaneously submitted requests to Google Earth Engine (GEE) from the Google Colab session executing our Python code. GEE, equipped with automated resource allocation, managed concurrent processing seamlessly. Remarkably, all requests were completed in under an hour. The entire processing chain, from data processing to result visualisation, was implemented within a single Colab session [53].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter** & **Mono 1 (\%)** & **Multi 2 (\%)** \\ \hline No filter & 64.95 & - \\ BOXCAR & 71.36 & 70.67 \\ Gamma MAP & 68.98 & 68.48 \\ LEE & 70.81 & 70.08 \\ LEE SIGMA & 70.48 & 69.77 \\ REFINED LEE & 72.42 & 71.74 \\ \hline \hline \end{tabular}  
* Processing with post-flood image and a single reference image. 2 Processing with post-flood image and all available pre-flood images and applying Queen multidate filters [49].  
\end{table}
Table 1: Kappa of processing chain results according to the type of filter used by comparing with the result obtained from Sentinel-2 optical images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predicted** & \multicolumn{3}{c}{**Reference**} \\ \hline  & **Non-Flood** & **Flood** & **Total** \\ \hline
**Non-flood** & 92 & 8 & 100 \\
**Flood** & 22 & 78 & 100 \\
**Total** & 114 & 86 & 200 \\
**Overall accuracy:** & 85\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of results of the flood detection in Antananarivo.
## 5 Model Results
### Comparison between MARS Model with Previously Developed Models  
In order to thoroughly assess the reliability of our newly proposed models, we extended the statistical evaluation to compare them with one of the best predictive correlations, namely Multi Expression Programming (MEP) [61]. The comparison is based on various statistical criteria and prediction performance, as presented in Table 5 and visually represented in the bar plots of Figure 8. Analyzing Table 5 and Figure 8 reveals that, despite the satisfactory prediction performance of MEP, our suggested models outperform it in accurately estimating the values of \(w_{opt}\) and \(\rho_{\text{dmax}}\) for compacted soil. Furthermore, MEP frequently creates intricate nonlinear empirical models that can be difficult to handle [62]. In turn, the MARS algorithm offers some distinct benefits. Notably, it effectively captures the intricate interactions between independent and dependent variables. Moreover, it eliminates the need to exert extra effort to confirm any preliminary assumptions regarding their relationship. This advantage becomes increasingly vital as the complexity of the problem grows [63].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Training** & **Testing** & **Overall** \\ \hline \(w_{opt}\) & MAE (\%) & 1.12 & 1.276 & 1.15 \\  & RMSE (\%) & 1.392 & 1.577 & 1.428 \\  & R\({}^{2}\) & 0.942 & 0.948 & 0.943 \\ \(\rho_{\text{dmax}}\) & MAE (Mg/m\({}^{3}\)) & 0.04 & 0.047 & 0.041 \\  & RMSE (Mg/m\({}^{3}\)) & 0.05 & 0.062 & 0.052 \\  & R\({}^{2}\) & 0.936 & 0.919 & 0.931 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of the MARS models for training, testing, and overall datasets.  
Figure 7: Comparison of relative errors vs. real value: (**a**) MARS-based model for \(w_{opt}\); (**b**) MARS-based model for \(\rho_{\text{dmax}}\).
## 4 GNSS-IR SM Retrieval  
Based on the methods and principles introduced above, a GNSS-IR SM retrieval model was developed in this paper. Figure 5 shows the overall strategy for the parameter and model determination for SM retrieval using GNSS data. Among the steps, to verify the feasibility and effectiveness of the method, the statistical indicators for the model test results and the SM reference values, including the correlation coefficient (\(r\)), the root-mean-square error (RMSE, unit cm\({}^{3}\) cm\({}^{-3}\)), the mean absolute error (MAE, unit cm\({}^{3}\) cm\({}^{-3}\)), and the maximum error (Max, unit cm\({}^{3}\) cm\({}^{-3}\)), were used to analyze and assess the model test results from different combination schemes.
## 3 Materials and Methods
### Gas Chromatographic-Mass Spectral Analysis  
Compound identification of _P. pabularia_ essential oil was carried out by gas chromatography-mass spectrometry using Agilent 6890 GC, Agilent 5973 (Agilent Technologies, Palo Alto, CA, USA) mass selective detector with electron ionization mass spectrometry (EIMS), (electron energy = 70 eV, scan range = 45-400 amu, and scan rate = 3.99 scans/s), with HP-5 ms capillary column (30 m length \(\times\) 0.25 mm inner diameter and 0.25 \(\upmu\)m phase thickness). Helium was the carrier gas with a flow rate of 1 mL/min. Over temperature program: Hold at 40 \({}^{\circ}\)C for 10 min, increase at 3 \({}^{\circ}\)C/min up to 200 \({}^{\circ}\)C, and then increase at 2 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C. The injector and the interface temperatures were 200 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. A 1% _w/v_ solution of the essential oil in CH\({}_{2}\)Cl\({}_{2}\) was prepared, and 1 \(\upmu\)L was injected with a splitless injection mode. Identification of the oil components was based on their Kovats indices determined by reference to a homologous series of _n_-alkanes and by comparison of their mass spectral fragmentation patterns with those reported in the literature ([PERSON] 2007) and stored in the MS databases (NIST 17, WILEY 10, FFNSC versions 1.2, 2, and 3).
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## Abbreviations  
The following abbreviations are used in this manuscript:  
OAM Orbital angular momentum EM Electromagnetic AM Angular momentum SAM Spin angular momentum LHCP Left-handed circular polarization RHCP Right-handed circular polarization LG Laguerre-Gaussian CGH Computer generated hologram SPP Spiral phase plates FSS Frequency selective surface RCP Right circularly polarized LCP Left circularly polarized SRR Split-ring resonators PEC Perfect electric conductor PMC Perfect magnetic conductor PCB Printed circuit board CSRR Complementary split-ring resonators LC Inductor-capacitor
## 2 Geological Background of the Study Area  
The Mt. Gongga region (29\({}^{\circ}\)30\({}^{\prime}\)-30\({}^{\circ}\)20\({}^{\prime}\)N, 101\({}^{\circ}\)30\({}^{\prime}\)-102\({}^{\circ}\)15\({}^{\prime}\)E) is located at the southeastern edge of the Tibetan Plateau, in the middle of the transition from the Sichuan Basin to the Tibetan Plateau [12; 13]. It is one of the areas with the highest concentration of modern glaciers in the Hengduan Mountains. The main peak of Mt. Gongga is 7556 m high [14; 15]. The high-elevation area has abundant rainfall, which is conducive to the development of monsoon marine valley glacier topography. There are 74 modern glaciers located in the region of Mt. Gongga, which are radially distributed around the main peak [16]. The total area of the glaciers is 255.1 km\({}^{2}\), and most glaciers are distributed in the southern and eastern parts of the basin [17]. Ridge lines, narrow upper edges, and ridges of hills that can separate large areas of glaciers to form separate specific glaciers flow in different directions. The main ridges of Mt. Gongga extend roughly from north to south. In terms of glacier distribution, the southern part of Mt. Gongga has significantly more glacier cover than the northern part. The direction of water vapor transport makes the eastern slope of Mt. Gongga more humid than the western slope with larger mountain glaciers, such as Hailuogou Glacier, Mozigou Glacier, Yanzigou Glacier, and Nannenguanogou Glacier [18]. The glaciers on the western slope are more numerous but smaller in scale, with representative glaciers being Dagongba Glacier and Xiaogongba Glacier. The epicenter of the Luding earthquake and the regional distribution of the major fault zones are shown in Figure 1.  
Ice avalanches are a typical glacial hazard, and their frequency and scale are related to climate change and associated with glacier surges, glacial fracture disintegration, and regional earthquakes. Under certain conditions, catastrophic ice avalanches may induce ice avalanche-induced debris flow chains, forming an isolated catastrophic effect. Mt. Gongga is the largest active marine glacier area in the southeastern margin of the Hengduan Mountain system and the Tibetan Plateau and the largest modern glacier concentration center in Sichuan. The glacier and its surrounding mora located at a high elevation. Glacier debris flows, glacier outburst floods, landslides, and other disasters caused by moving glaciers are extremely destructive, posing a serious threat to the safety and property of residents in the surrounding and downstream basins and the stable operation of infrastructure. Therefore, the dynamic monitoring of glacier movements after earthquakes can effectively reflect their dynamic evolution and is of great significance for mountain disaster early warnings and forecasting.
## 3 Results and Discussion  
### Preparation and Characterization of CoPc@s-BC  
CoPc@s-BC nanocomposites were prepared by covalent immobilization of CoPc onto the nanofibers after BC had been oxidized with NaIO\({}_{4}\); this was spacer-attached with a series of flexible spacers and activated with glutaraldehyde. All the functionalization reactions were monitored by  
Figure 2: Synthesis route of spacer-incorporated, tetra-amino cobalt (II) phthalocyanine immobilized bacterial cellulose nanocomposites (CoPc@s-BC).  
X-ray photoelectron spectrum (XPS), with the preparation of CoPc@TP-BC as a typical representative (Figure 3), and the chemical compositions of the samples during the series of reactions were calculated and are shown in Table 1. For the as-prepared pure BC, the two characteristic peaks at 284.6 eV and 530.6 eV were ascribed to the binding energies of C 1s and O 1s, respectively (Figure 3a). No significant changes were found after NaIO\({}_{4}\) oxidation (Figure 3b). Upon the TP attachment process, an additional peak at ca. 400 eV was detected, which corresponds to the characteristic peak of N 1s, implying the successful attachment of the TP spacer onto the oxidized BC (Figure 3c). The glutaraldehyde activation step did not obviously change the relative fractions of C, O and N elements (Figure 3d and Table 1). After the CoPc functionalization process, a marked increase (from 6.91% to 19%) of N 1s peak was found (Figure 3e and Table 1); Furthermore, two new characteristic peaks at 779.6 eV and 795.3 eV were also detected (Figure 3e, inset). These two peaks were assigned to the binding energies of Co 2p\({}_{3/2}\) and Co 2p\({}_{1/2}\), respectively. These results verify the success of all of the functionalization processes and the immobilization of CoPc on BC.
## 3 The Proposed CTHE Method
### Relation Check and Confidence Discrimination Module  
In this module, the main goal is to automatically check the relation. First, it will check the unknown relation. Unknown relation means that the current model cannot predict its relation and proceed directly to the open relation prediction module. Second, if the relation is known, then the model will give the relation R with the largest probability value. At this time, the probability value \(p\) (calculated by the SoftMax function) will be mapped to the confidence level CL. Next, it is determined if the confidence level is lower than a certain threshold value C. If CL \(\geq\) C, the relation R will be output, and the prediction ends; otherwise, it means that the confidence of the predicted relation is low, and it will directly go to the open relation prediction module.