esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[37] proposed a method for analyzing local relative deformation of bridge body based on the characteristics of ground-based interferometric radar monitoring data and used IBIS-S to monitor the dynamic deformation of a Yangtze River bridge, verifying the feasibility of GB-RAR technology in dynamic health monitoring of actual Bridges. [PERSON] et al. [38] used GB-RAR technology to obtain the information of dynamic influencing factors of railway bridges and provide data for bridge evaluation. Most previous studies assume that only white noise exists in the deformation time series. When extracting deformation information from high-frequency data of ground-based interferometric radar, white (time-independent) and colored (time-dependent) noises exist in the radar signal. When ground-based interferometric radar used a high-frequency mode to monitor deformation, the radar signal will be affected by white and colored noises at the same time. A GB-RAR deformation information estimation method considering the influence of colored noise was proposed in this work. The GB-RAR technology was used in this work to monitor the settlement deformation change of the East Lake High-tech (ELH) Bridge during the crossing of the Wuhan Metro Line 11 shield tunnel underneath this bridge. The time series deformation of the main piers during the monitoring period were extracted. Moreover, the settlement time series results derived by GB-RAR data were verified by leveling monitoring data. The noise characteristics in the settlement time series derived based on radar data were analyzed by using power spectral density (PSD) analysis and maximum likelihood estimation (MLE) methods to accurately extract the deformation information of the ELH Bridge during the underpass of Metro Line 11 and provide a reliable basis for the safety assessment of the bridge. Furthermore, we can estimate the white noise and colored noise components in the deformed time series. According to each noise component, a GB-RAR deformation information estimation method that can consider the influence of white and colored noises was proposed to estimate the settlement rate, uncertainty of settlement rate, and cumulative settlement of the ELH Bridge in the monitoring process and evaluate the deformation state of the bridge.
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 4 Results and Analysis
### Dependence on the Polarization and the Link Distance  
Having established from the results in Sections 4.2 and 4.3 that the wind-induced attenuation can be added on top of the rain attenuation (and vice versa), we may apply the models to show dependency of the polarization and the link distance. Figure 15a,b show the attenuations at different rain rates and misalignment angles (caused by the wind effect) for the vertical and horizontal polarizations, respectively. An angle misalignment of 1 degree, due to wind effect coupling with the initial misalignment, could add as much as 20 dB of attenuation. The horizontal polarization attenuations were inferred from the available measurement data by changing the parameters \(k_{H}=1.0996\) and \(\alpha_{H}=0.7230\) in Equation (10), provided using the ITU-R P.838 standard. At the R0.01 rain rate and the distance \(r_{0}=150\) m, the attenuation of the horizontal polarization attenuation is estimated to be 0.7244 dB higher than that of the vertical polarization. The difference is larger at heavier rain rates due to the oblate shape of the larger rain drop sizes.  
The attenuations at longer path distances can also be estimated under various weather conditions, as shown in Figure 16a,b. In these two figures, the initial misalignment effect has been included, but the gaseous and free-space path losses are excluded. The attenuations  
Figure 15: 3D plot of the attenuation as the function of rain rate and misalignment angle for (**a**) the vertical polarization and (**b**) the horizontal polarization.  
caused by different wind speeds are significant at a low rain rate (the rain rate exceeded for 1% of the time, R = R\({}_{1}\) = 4.3 mm/h), as seen in Figure 16a. On the other hand, at a high rain rate (R = R\({}_{0.01}\) = 83.20 mm/h) in Figure 16b, the rain attenuations dominate the total attenuations. The values of low, medium and high wind speeds are the values at 0.5, 0.999 and 0.9999 of cumulative probability in Figure 8, respectively.
## Materials and Methods
### Characterization and Testing  
#### 2.4.1 Characterization  
The chemical constitutions of nano-PDA@GO-TiO\({}_{2}\) composites, graphene oxide (GO), and nano-TiO\({}_{2}\) were characterized by FT-IR. The spectrums were recorded on Fourier transform infrared spectrometer (FT-IR, Nicolet IS5, Thermo Fisher, Madison, WI, USA) over the wave number range of 4000-400 cm\({}^{-1}\). The testing samples were prepared by Potassium bromide tableting. The nano-PDA@GO-TiO\({}_{2}\) composites, graphene oxide (GO) and nano-TiO\({}_{2}\) were also analyzed by X-ray photoelectron spectroscopy (XPS, Thermo ESCALAB 250 XI, Thermo Fisher Scientific, Madison, WI, USA) to determine the composition. The crystal structure of the nano-PDA@GO-TiO\({}_{2}\) composites, graphene oxide (GO), and nano-TiO\({}_{2}\) were examined by X-ray diffraction (XRD, D/MAX2500, Rigaku, Tokyo, Japan) with \"Cu K\(\alpha\)\" radiation (1.540A) source at \(\theta\) between 5\({}^{\circ}\) and 80\({}^{\circ}\), the diffraction angle(28) was scanned at a rate of 5\({}^{\circ}\)/min. The surface structure and morphology of the coating were observed by scanning electron microscopy (SEM, SUPRA 55, Carl Zeiss AG, Jena, Germany). Raman characterization was operated on an in Via Reflex confocal Laser Micro Raman spectrometer (Raman, Lab RAM HR Evolution, HORIBA, Market (China) Trading Co., Ltd. Shanghai, China), excitation source: 532 nm Ar+ laser and power of 25%. The microstructure and morphology of GO and nano-PDA@GO-TiO\({}_{2}\) composites were estimated by transmission electron microscopy (TEM, JEOL 2100, Japanese electronics, Tokyo, Japan).
## 5 Evaluation Approach  
### Project Metrics  
The main metrics were the precision (P), recall (R), and F-measure (F) of the match results between key-value pairs in each step of the life cycle, as compared to the truth set. The calculations for each metric used are defined and shown in Table 3 and discussed below [19].  
1. True positive matches (TP): The number of times the system matched a key and its correct, corresponding value together.
2. False positive matches (FP): The number of times a key was matched with a value other than its actual value as found in the truth set. Reasons for this can include: 1. The field name and value are present in the tax image file and the system detects the field name through the system. However, the system fails to detect the correct value for the field due to some spelling errors in the value. 2. The field name and value are present in the tax image file and the system detects the field name. However, the incorrect value is paired with the field name. 3. The field name has no corresponding value in the original tax document, but the system matches this field name with some value in the tax document.  
Figure 4: (**a**) An extract from the truth set and (**b**) an extract from corresponding OCR output. Note how the fields are read out of order in certain scenarios.  
Figure 5: (**a**) Example extract from the truth set and (**b**) corresponding fields in the raw OCR output. This illustrates how the field names are misread by OCR in certain scenarios.  
3. False negative matches (FN): The amount of times a key or value was not identified. This could be due to a few different reasons: 1. There is a key-value pair in the original tax document, and the system identifies the key successfully. However, it fails to identify the value from the document and so concludes that the key has no value. 2. There is a key-value pair in the original tax document, but the system fails to identify the key or the value.  
The metrics are measured at three points in the system (Figure 6), to show the effect as new improvements are introduced to the workflow. The results and comparisons are discussed below.
## 6 Biotechnological Approaches to Increase Steviol Glycos Production
### Transformation Technique to Enhance SvGIs Production  
Altering the gene activity via gene manipulation is the most powerful technique to elucidate the function of genes involved in the biosynthesis of SvGIs. However, the expression of a manipulated gene requires efficient stevia tissue culture and transformation system. Earlier discussed studies report the successful tissue culture methodologies using various explants. Recently, a few studies were conducted to establish a stable transformation system in stevia using _Agrobacterium_-mediated gene transfer. The studies targeted the SvGIs synthesis enzymes by using RNAi constructs and transient expression system. Following this, _Agrobacterium_-mediated transformation system of stevia leaf disc using the GUS reporter gene was introduced. The callus was successfully generated, although there was no report for whole plant regeneration [63]. Several studies overexpressed key regulatory genes such as _SrDXS1_, _SrKAH_ % [64] and isoforms of _SrUGIT76G1_[22] and were found to be effective in enhancing the SvGIs production without compromising the development of stevia plants, thus suggesting the potential use of a transformation system and mass production of stevia plants with enhanced desirable SvGIs.  
Recently, the application of hairy root culture has also been employed in stevia plants by transforming it by _A. rhizogens_ for induction of hairy root [21]. More studies and clarification need to be made to utilize the technique of transformed hairy root culture. The study suggests the potential of hairy root culture for mass production and elucidation of the synthetic pathway by genetic manipulation of the genes. Table 2 depicts the various biotechnological strategies applied by several researchers to enhance SvGIs production in _S. rebaudiana_.
## 4 Simulations and Analysis
### Comparison of Spectrograms  
Figure 2 shows a comparison of the spectrograms, where the SNR was 10 dB, the number of snapshots was 100, and the real DOAs of the target source and interference source were \(70^{\circ}\) and \(20^{\circ}\), respectively. The initial DOA of the target source was \(80^{\circ}\), which had an error of \(10^{\circ}\). In Figure 2, the values of the color bar represent the logarithmic amplitude of the spectrum. Figure 2 shows that the IMB effectively recovered the target speech  
source and suppressed the interference source and noise. This confirmed that the MVDR beamformer can effectively enhance the target source with an accurate steering vector and an effective CMIN. Moreover, Figure 2 also shows that the target speech was seriously distorted by the CMB and BPR methods, a little distorted by the HKB and LNR methods and slightly distorted by the rest of the methods. Meanwhile, the residual components of the interference source severely remained in the SMF, GLC, NRP-TMMSE, PFLI and IDL-SMI methods. This indicates that the proposed method has a better performance than the reference methods except for the IMB.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 3 HSI Classification Based on SPCM  
Based on the proposed SPCM, a spectral-spatial HSI classification method is proposed for HSI classification in this paper. Figure 4 describes the general process of SPCM-based classification method in detail. Algorithm 1 details the pseudocode of SPCM. Firstly, the dimension reduction method MNF is used to reduce noise and computational complexity. This paper reduces the dimension to 20. Next, the window closest to the target pixel is selected according to spatial perception and similar pixels are obtained. The size of the window and the number of selected pixels are two important hyperparameters. Then, the correntropy matrix is constructed according to each pixel and its similar pixels. So, each pixel can be represented by a matrix. Finally, a set of matrices are fed into SVM for classification. It is easy to find that the matrix constructed in this paper is a symmetric positive definite matrix, but SVM cannot accept it as input. Therefore, a logm function mentioned in Refs. [40; 41] can be used to transform points into Euclidean space for the input of SVM. The logarithm-Euclidean kernel can be defined by:  
\[k_{logm}(M_{1},M_{2})=trace[logm(M_{1})\cdot logm(M_{2})], \tag{6}\]  
where \(M_{1}\) and \(M_{2}\) are two different correntropy matrices. Given a symmetric positive definite matrix \(C=U\sum U^{T}\), its logm can be defined as:  
\[logm(C)=Ulog(\sum)U^{T}. \tag{7}\]  
This way, the proposed HSI classification method, making use of spectral and spatial correlation simultaneously, can achieve a high classification accuracy when the training sample set is small, which solves the problem of poor classification performance in the case of small training samples.  
Figure 4: Image display is an HSI classification method based on SPCM. First, we perform dimensionality reduction of a hyperspectral image based on MNF. Then we convert each pixel to a correntropy matrix in turn by SPCM. Finally, all the matrices are input to SVM for classification.  
```
Input: Hyperspectral image cube \(Q\in\mathbb{R}^{m\times n\times b}\) Output: Classified results \(R\in\mathbb{R}^{m\times n\times 1}\)
1 Normalization and dimensionality reduction \(X\in\mathbb{R}^{m\times n\times d}\) ;
2 for\(i=1:m\times n\)do
3 Constructing local structure of pixel \(p_{i}\) ;
4 fort = 1:9 do
5 Calculate the optimal \(\mathbf{W}_{t}\) ;
6
7 end for
8
9 Constructing correntropy matrix \(C_{i}\) ;
10 end for
11 Using SVM for classification
```  
**Algorithm 1**SPCM.
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
###### Abstract  
This study aims to investigate the impact of the presence of buildings on the radiation characteristics of MF broadcast antennas. Two different antennas are considered: a monopole operating at 1494 kHz and a two-element linear array radiating at 1008 kHz. The buildings were modeled as wire-grids and the total electric field intensity was calculated as the sum of the scattered field by the wire-grid and the field radiated from the antenna in free space. The radiation pattern of the antennas, when one or two buildings were situated in their vicinity, were the end result of the analysis, and they were compared to the corresponding patterns in free space. The results demonstrate that the radiation characteristics of antennas are mostly affected by the heights of buildings. If these heights are less than a critical value, the buildings do not significantly obstruct the operation of the antenna, despite the value of other parameters, such as the length and the width of the buildings, as well as their distance from the antenna.  
AM broadcasting; antenna radiation; building modeling; MF antenna; wave propagation; wave scattering; wire-grid model +
Footnote †: journal: _Sci._  
_Applied_
# Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
## 1 Introduction
More specifically, carbon dioxide column densities have been observed at 1600 nm [15; 16], while water vapor shows characteristic absorbances around 1130 nm and 1400 nm [14], and methane features a spectral line at 1670 nm [17]. Cameras and telescopes operating within the visible wavelength range (400-900 nm) are already widely available. When supplementing these visible imaging designs with the proposed telescope, a more complete view of the Earth's total reflected radiation can also be obtained. In addition, and given the ongoing tendency towards more compact designs enabling the use of low-cost satellites, we aim for our telescope to fit within 1 CubeSat Unit. This way, in the longer term, the use of several CubeSats would allow a constellation of satellites for the same cost as a large-scale space mission, and with the benefit of a much higher scientific yield. This paper presents our novel two-mirror freeform telescope design, starting from the optical design and proceeding to tolerancing, manufacturing, and proof-of-concept demonstration. Section 2 presents the optical design and performance evaluation, as well as a tolerance analysis. Section 3 discusses the manufacturing and proof-of-concept demonstration. Next, the results and future perspectives are discussed in Section 4. Finally, we close this paper with conclusions given in Section 5.
## 3 Results and Discussions
Moreover, higher values of the R\({}^{2}\) coefficients were obtained from the experiments regarding the removal of lead as compared to those obtained from experiments regarding the removal of copper (Table 3), indicating a better agreement between the models and the real phenomenon. This fact can also be observed from the Figures 3 and 4. The Cu removal efficiency increased with the increase of the chitosan concentration in the washing solution. Thus, in the case of using 2% chitosan solution, 31% to 46% of Cu was removed from the soil, while 39% to 60% Cu was removed when 5% chitosan solution was used. Similar results were obtained by [PERSON] et al. (2011) [38]. By comparing the Cu extraction yields using chitosan obtained in the present research (21.08-74.57%) with the ones obtained in the case of using other washing agents, similar results were reported in the case of using dissolved organic matter (57%) [26] and 0.1 N HCl (57.9%) [28]. Better removal efficiencies of Cu were reported in the case of using soluble humic substances (51-95%) [49; 50; 51], Ethylenediaminetetraacetic acid (82.5%), and FeCl\({}_{3}\) (95.8%) [28]. By using a chitosan-based washing agent, the Pb removal efficiency reached values between 25.59% and 79.65%. Similar results were reported when soluble humic substances were used (44.8-47.6%) [49; 50; 51], while higher removal efficiencies were obtained when soluble humic-like substances (86.5-98%) [52], FeCl\({}_{3}\) (98.7%) [4], and Ethylenediaminetetraacetic acid (97.4-99.4%) [28] were used. Also, chitosan-based washing agents showed better extraction efficiencies as compared to those obtained when using 0.1 N HCl (28.5%) [28]. The mathematical model of the soil washing is valid for all of the studied parameters influencing the washing process (S/L ratio and stirring time). The results of the comparison between the values obtained through the proposed mathematical model and the ones experimentally determined present a good concordance, indicating the integrity of the obtained mathematical relations and the degree of accuracy with which they describe the real experimental conditions. Figure 3: Copper removal efficiency over the stirring time using 2% chitosan washing solution at a S/L ratio of (**a**) 0.2 dec.fr. and (**b**) 0.0625 dec.fr; copper removal efficiency over the stirring time using 5% chitosan washing solution at a S/L ratio of (**c**) 0.125 dec.fr. Figure 4: Lead removal efficiency over the stirring time using 2% chitosan washing solution at a S/L ratio of (**a**) 0.2 dec.fr. and (**b**) a S/L ratio of 0.1 dec.fr. ; lead removal efficiency over the stirring time using 5% chitosan washing solution at a S/L ratio of (**c**) 0.1 dec.fr. and (**d**) 0.2 dec.fr.
## 2 Materials and Methods
### Density Functional Theory (DFT) Computations  
DFT calculations were performed using the VASP5.2 [41, 42] code with projector augmented wave (PAW) [43, 44] potentials to account for the core-valence interaction and a kinetic energy cut-off of 400 eV. Cu was substitutionally doped onto a Ti site in an anatase (4 \(\times\) 4 \(\times\) 3) supercell. This gives a dopant percentage of \(\sim\)0.5%, which is consistent with the experimental concentrations of Cu in TiO\({}_{2}\)[25, 32, 45]. \(\Gamma\)-point sampling was used and structures were relaxed until forces were less than 0.01 eV A\({}^{-1}\).  
Calculations were performed taking into account spin polarization and no symmetry constraints were imposed. For DFT calculations corrected for onsite Coulomb interactions (DFT + U), U = 4.5 eV was applied to the Ti 3d states and U = 7 eV was applied to the Cu 3d states, arising from the inability of the local density and generalized gradient approximations to consistently describe both Ti\({}^{3+}\) and Cu\({}^{2+}\) oxidation states [46, 47]. These choices for U are based on previous studies [48, 49, 50, 51, 52]. In addition, the DFT + U correction was applied to the O 2p states, as a result of the hole states formed when substituting the lower valance Cu cation on a Ti site [53]. Comparisons of the DFT + U results were made for these systems with no +U correction. However, this set-up is unable to consistently describe the oxygen hole states and the Ti\({}^{3+}\) and Cu\({}^{2+}\) oxidation states of the cations and thus results are not shown for this computational set-up. The starting geometries were manually distorted from the symmetric bulk geometry in order to examine relaxation to distorted geometries, which can be compared with undistorted geometries. To study the charge compensation that arises as a result of the difference in the Cu\({}^{2+}\) and Ti\({}^{4+}\) oxidation states, one oxygen ion was removed from different sites of the most stable Cu-doped structures and the energy of formation was calculated by the following equation:  
\[\mathrm{Evac}=\mathrm{E}\big{(}\mathrm{Cu_{x}Ti_{1-x}O_{2-y}}\big{)}+\frac{1} {2}\mathrm{E}(\mathrm{O_{2}})-\mathrm{E}(\mathrm{Cu_{x}Ti_{1-x}O_{2}}) \tag{1}\]  
where \(\mathrm{E}\big{(}\mathrm{Cu_{x}Ti_{1-x}O_{2-y}}\big{)}\) is the total energy of Cu-doped TiO\({}_{2}\) with a charge compensating oxygen vacancy and \(\mathrm{E}(\mathrm{Cu_{x}Ti_{1-x}O_{2}})\) is the total energy of Cu-doped TiO\({}_{2}\). The formation energy is referenced to half the total energy of O\({}_{2}\).  
Copper ions were initially in the 2+ oxidation state and calculations were therefore performed with 1 and 3 unpaired spins taking into account the electron deficit due to substitutional doping of Cu\({}^{2+}\) for Ti\({}^{4+}\) and the d9 configuration of Cu\({}^{2+}\). The oxidation states of Cu dopant, lattice Ti and O ions were determined from Bader charge analysis [54].
# Carbon Nano-Onion Peroxidase Composite Biosensor for Electrochemical Detection of 2,4-D and 2,4,5-T
###### Abstract  
Carbon nano-onions are emerging electrode materials in biosensing due to their high conductivity and biocompatibility. Phenoxy-based herbicides are a source of environmental contamination that can be detected using their property to inhibit the activity of some enzymes. Here we report a biosensor based on peroxidase immobilized on carbon nano-onions in a cyclodextrin polymer matrix for the amperometric detection of 2,4-D and 2,4,5-T. The inhibition mechanism of 2,4-D and 2,4,5-T on peroxidase activity was first elucidated by activity measurements and molecular docking. The biosensor was characterized by electrochemical and microscopy methods and applied to the amperometric detection of these herbicides. The incorporation of carbon nano-onions enhanced the sensitivity of the biosensor and improved its stability and repeatability. The application of the developed biosensor to the detection of 2,4-D in soil and 2,4,5-T in river water samples is also reported.  
carbon nano-onion; peroxidase; phenoxy herbicide; 2,4-D; 2,4,5-T; electrochemical biosensor; cyclodextrin Article