esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Effectiveness of Spectral Indices in Predicting Crop Type Detection  
The main purpose of this study was to evaluate each vegetation index on the accuracy of crop type mapping. For the classification results, accuracy measures of _OA_ and Kappa have been calculated and are presented in Figures 5 and 6.  
The highest values of _OA_ and Kappa were obtained based on the MCARI (_OA_ = 86%, Kappa = 0.81), NDI45 (_OA_ = 85%, Kappa = 0.80), and WDVI (_OA_ = 85%, Kappa = 0.80). On the other hand, both the GNDVI and IRECI exhibited the lowest accuracy (_OA_ = 72% and Kappa = 0.65). Taking this into account, for the indices which demonstrated the best performance (MCARI, NDVI45, and WDVI) classification based on these three indices has been carried out. Unfortunately, despite the high accuracy of each of these indices used separately, their combination does not give better results (_OA_ = 78%, Kappa = 0.72).  
Furthermore, the results of the PCA showed that the highest accuracy could be obtained by using the initial two and three principal components. The accuracies of these variants were very close to each other, which may indicate that the three initial components contained most of the relevant information. However, these results do not have particularly high accuracy and may not be sufficient for the accurate detection of crop types. The use of only the first component provided an \(OA\) of 74% and Kappa of 0.66. The combination of the first four components provided an \(OA\) of 72% and Kappa of 0.65. Taking this into account, higher values have been delivered by MCARI or NDI45 or WDVI separately.  
The classification performed by combining all the indices also did not generate more accurate results than the separate indexes alone (\(OA\) = 73% and Kappa = 0.65). This may be because each of the indices is slightly different and may respond differently to a given plant species. Although each of the indices used performed very well in detecting individual crop types, combining them could still lead to inconclusive results. Figure 7 shows a graphical representation of these results.
# Intelligent Neural Network Schemes for Multi-Class Classification
## 3 Dimensionality Reduction
### Feature Extraction  
Linear discriminant analysis (LDA) [42] is adapted here for multi-class classification. Let \(\mathbf{G}\) be a linear transformation, \(\mathbf{G}\in R^{n\times\ell}\), \(\ell<n\), that maps \(\mathbf{x}_{i}\) in the \(n\)-dimensional space to \(\mathbf{x}_{i}^{L}\) in the \(\ell\)-dimensional space as  
\[\mathbf{x}_{i}\ \in R^{n}\ \rightarrow\ \mathbf{x}_{i}^{L}\in R^{\ell}\ = \mathbf{G}^{T}\mathbf{x}_{i}\in R^{\ell}. \tag{20}\]  
Firstly, all the training input vectors \(\mathbf{x}_{1}\),...,\(\mathbf{x}_{N}\) are divided into \(m\) sets, \(\{\mathbf{X}_{1}\),...,\(\mathbf{X}_{m}\}\), where \(\mathbf{X}_{j}\in R^{n\times N_{j}}\) with \(N_{j}\) being the number of instances belonging to category \(j\). BR and LP can be adopted to deal with the target vectors for multi-label classification. Three scatter matrices, called within-class (\(\mathbf{S}_{w}\)), between-class (\(\mathbf{S}_{b}\)), and total scatter (\(\mathbf{S}_{t}\)) matrices in the \(n\)-dimensional space are defined as follows:  
\[\mathbf{S}_{w}\ =\ \frac{1}{N}\sum_{j=1}^{m}\sum_{\mathbf{x}\in\mathbf{X}_{j}} \Bigl{(}\mathbf{x}-\mathbf{c}_{j}\Bigr{)}\Bigl{(}\mathbf{x}-\mathbf{c}_{j} \Bigr{)}^{T}, \tag{21}\]  
\[\mathbf{S}_{b}\ =\ \frac{1}{N}\sum_{j=1}^{m}N_{j}\Bigl{(}\mathbf{c}_{j}-\mathbf{ c}\Bigr{)}\Bigl{(}\mathbf{c}_{j}-\mathbf{c}\Bigr{)}^{T}, \tag{22}\]  
\[\mathbf{S}_{t}\ =\ \mathbf{S}_{w}+\mathbf{S}_{b} \tag{23}\]  
where \(\mathbf{c}_{j}\) is the centroid of \(\mathbf{X}_{j}\) and \(\mathbf{c}\) is the global centroid.  
After transformation, the corresponding matrices in \(\ell\)-dimensional space are:  
\[\mathbf{S}_{w}^{L}\ =\ \mathbf{G}^{T}\mathbf{S}_{w}\mathbf{G},\ \mathbf{S}_{b}^{L}\ = \mathbf{G}^{T}\mathbf{S}_{b}\mathbf{G},\ \mathbf{S}_{t}^{L}=\mathbf{S}_{w}^{L}+\ \mathbf{S}_{b}^{L}. \tag{24}\]  
LDA computes the optimal transformation \(\mathbf{G}^{\text{LDA}}\) by solving the following optimization problem:  
\[\mathbf{G}^{\text{LDA}}\ =\underset{\mathbf{G}}{\operatorname{argmax}}\{ \operatorname{trace}\bigl{(}\mathbf{S}_{b}^{L}\bigl{(}\mathbf{S}_{t}^{L}\bigl{)} ^{-1}\bigr{)}\}. \tag{25}\]The obtained \(\mathbf{G}^{\mathrm{LDA}}\) is used for mapping from \(\mathbf{x}_{i}\) to \(\mathbf{x}_{i}^{L},\ 1\leq i\leq N\). Since \(\mathbf{x}_{i}\) is \(n\)-dimensional, \(\mathbf{x}_{i}^{L}\) is \(\ell\)-dimensional, and \(\ell<n\), dimensionality reduction is achieved.
# Surface Characteristics and Cell Adhesion Behaviors of the Anodized Biomedical Stainless Steel
###### Abstract  
In this study, an electrochemical anodizing method was applied as surface modification of the 316L biomedical stainless steel (BSS). The surface properties, microstructural characteristics, and biocompatibility responses of the anodized 316L BSS specimens were elucidated through scanning electron microscopy, X-ray photoelectron spectroscopy, X-ray diffractometry, transmission electron microscopy, and in vitro cell culture assay. Analytical results revealed that the oxide layer of dichromium trioxide (Cr\({}_{2}\)O\({}_{3}\)) was formed on the modified 316L BSS specimens after the different anodization modifications. Moreover, a dual porous (micro/nanoporous) topography can also be discovered on the surface of the modified 316L BSS specimens. The microstructure of the anodized oxide layer was composed of amorphous austenite phase and nano-Cr\({}_{2}\)O\({}_{3}\). Furthermore, in vitro cell culture assay also demonstrated that the osteoblast-like cells (MG-63) on the anodized 316L BSS specimens were completely adhered and covered as compared with the unmodified 316L BSS specimen. As a result, the anodized 316L BSS with a dual porous (micro/nanoporous) oxide layer has great potential to induce cell adhesion and promote bone formation.  
Keywords:316L biomedical stainless steel; anodization; microstructure; dichromium trioxide; biocompatibility +
Footnote †: journal: Applied Sciences
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## Materials and Methods
### MalB-Nanoliposome Preparation
#### 2.3.3 Encapsulated Efficiency  
The encapsulated efficiency (EE) of the loaded malB was calculated by UV-spectrophotometry method. The pure malB compound was dissolved in DMSO (100%) as a standard curve with a two time diluted concentration range which was started from0.25 mg/mL. The malB-nanoliposomes were re-diluted in DMSO (100%) to release all packed malB. Then, 100 \(\upmu\)L of both the standard curve and the nanoliposomal samples were placed into a 96-well plate, repeated three times to ensure accuracy. The optical density values (ODs) were measured at 280 nm on a ThermoScientific(tm) Varioskan(tm) Flash Multimode Reader. The standard curve drawn using Microsoft Office Excel 2016 software was used to calculate the amount of active ingredient that was packed into the nanoliposomes. The encapsulated efficiency (EE) of the process was determined using the following equation:  
\[\text{EE(\%)}=100\times\text{(weight of conjugated malB)/(weight of initial malB)}\]
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 2 Categories of Precipitation Polymerization
### Controlled/\"Living\" Radical Precipitation Polymerization
#### 2.4.3 RAFT Precipitation Polymerization (RAFTPP)  
RAFTPP was developed by introducing the RAFT polymerization mechanism into the PP system by simply adding an appropriate chain-transfer agent (or RAFT agent), which is  
Figure 6: The synthetic scheme of the uniformly crosslinked “living” polymer microspheres. Reprinted with permission from Ref. [98]. Copyright 2011 American Chemical Society.  
highly promising for the development of various well-defined advanced functional polymer materials [103; 104] used in drug delivery and molecular recognition (MIPs) [105; 106]. [PERSON] et al. has developed a series of highly efficient approaches for the preparation of MIP particles with surface-grafted well-defined hydrophilic polymer layers, involving the \"one-step approach\" and \"two-step approach\" [107; 108; 109; 110]. They have obtained \"living\" PMAA particles via RAFTTPP of MAA or a mixture of MAA and a functional comonomer and expected that the growth of such \"living\" PMAA particles could take place not only by capturing oligomeric radicals with particle surface vinyl groups (Figure 7a) and adsorbing oligomeric polymer chains from the continuous phase through hydrogen-bonding interaction (Figure 7b) but also by the surface-initiated RAFT polymerization through the surface dithioster groups (Figure 7c) [48]. Subsequent surface-grafting polymerizations have also been obtained to prepare uniform hairy hollow polymer particles with different hydrophilic polymer brushes and hydrodynamic diameters [111].  
Taken together, all the prepared advanced functional polymers possess great potential for applications such as controlled drug release vehicles, carriers for reagents, enzymes, catalysts, and so on. Other methods, such as free-radical precipitation polymerization [112], have also been developed rapidly and as a powerful support for biomedical applications.
## 3 Results and Discussion
### Physical Adsorption of CH\({}_{3}\)Cl by Activated Carbon  
There are two adsorption configurations of CH\({}_{3}\)Cl on the activated carbon plane, as depicted in Figure 5a-f. The green isosurfaces in Figure 5a-c reveal that the interaction between CH\({}_{3}\)Cl and activated carbon primarily involves dispersion and hydrogen bonding, predominantly caused by the C- and H-atoms of CH\({}_{3}\)Cl. The physical adsorption energy of CH\({}_{3}\)Cl on the activated carbon plane depends on the ESP of the activated carbon plane, resulting in a physical adsorption energy order of PR plane > NF plane > PD plane, as shown in Table 1. Notably, no significant variation is observed in the physical adsorption energy among these three configurations, indicating the minimal influence of functional groups on the physical adsorption behavior of CH\({}_{3}\)Cl on the activated carbon plane.  
Another adsorption configuration of CH\({}_{3}\)Cl on the activated carbon plane is depicted in Figure 5d-f. The interaction between CH\({}_{3}\)Cl and activated carbon involves dispersion forces, halogen bonds, and hydrogen bonds, primarily contributed by the C, Cl, and H atoms of CH\({}_{3}\)Cl. Table 1 shows that the promotion effect of the pyrrole functional group on CH\({}_{3}\)Cl adsorption by activated carbon is more pronounced compared to that of the pyridine functional group. Figure 5d,f reveal similar adsorption configurations and interactions of CH\({}_{3}\)Cl on the planes of NF and PR. However, due to the higher absolute value of ESP on the PR plane than that on NF plane, the interaction strength and physical adsorption energy for CH\({}_{3}\)Cl adsorbed by PR are greater than those by NF. This is supported by the electron density values at the bond critical points shown in Figure S2. Despite having a lower ESP value than NF, PD exhibits slightly higher adsorption energy for CH\({}_{3}\)Cl because its Cl atom's adsorption site lies in the middle of the PD plane and has a larger action area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **NF** & **PD** & **PR** \\ \hline Plane & \(-0.216\) (\(-0.221\)) & \(-0.201\) (\(-0.223\)) & \(-0.229\) (\(-0.265\)) \\ Edge & — & \(-0.115\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical adsorption energy (eV) of CH\({}_{3}\)Cl on the plane and edge of activated carbon.  
Figure 4: The surface ESP of VOCs on the vdW surface: (**a**) CH\({}_{3}\)Cl, (**b**) CS\({}_{2}\), (**c**) C\({}_{2}\)H\({}_{6}\)S, (**d**) C\({}_{6}\)H\({}_{6}\), and (**e**) a color scale bar.  
CH3 Cl selectively adsorbs onto the PD edge, primarily through vdW forces and hydrogen bonding, resulting in a relatively weak physical adsorption energy of \(-0.115\) eV. In comparison to edge adsorption, CH3 Cl demonstrates a higher affinity for the activated carbon plane.
## 2 Study Area and Data
### Data and Processing  
#### 2.2.1 Satellite Data and Products  
In this study, 2576 eight-day MODIS composites (MOD09A1, from the Terra platform) from 2000 to 2013 were obtained from the Earth Observing System (EOS) data gateway ([[http://redhook.gsfc.nasa.gov](http://redhook.gsfc.nasa.gov)]([http://redhook.gsfc.nasa.gov](http://redhook.gsfc.nasa.gov)))  
Figure 1: Location of the study area.  
of the National Aeronautics and Space Administration (NASA). MOD09A1 has 500-m resolution and seven spectral bands, with each pixel containing the best observation during the eight-day period. Data were reprojected to the Universal Transverse Mercator (UTM) projection. For each date, four MODIS tiles (h26v05, h27v05, h27v06, h28v06) were mosaicked, and then clipped to the extent of the study area.  
The absence of a flux tower around the study area made validations of GPP difficult. In this study, MOD17A2, an eight-day composite GPP product at 1-km resolution, was resampled to 30-m spatial resolution using the nearest-neighbor interpolation method. MOD17 is the GPP product computed with the MODIS land-cover (MOD12), MODIS Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI) product, and Global Modeling and Assimilation Office (GMAO) surface meteorology for the vegetated land surface [13; 29]. MOD17 has been validated in different ecosystems, showing a high correspondence with field measurements [29; 30; 31; 32]. The resampled MOD17 GPP was used to validate the estimated GPP based on the pixels of the same land cover types in land cover maps derived from both Landsat images and MOD12 land cover data.  
From 2000 to 2013, 99 Landsat surface reflectance images (path 123 row 039) with a cloud cover of less than 20% were downloaded from the United States Geological Survey (USGS) Earth Resources Observation Systems (EROS) data center, including 45 TM images, 48 ETM+ images, and six images acquired by Landsat-8 Operational Land Imager (OLI) (Figure 2). The scan line corrector (SLC)-off effect (which results in missing data lines in the image) was evident within some of the ETM+ scenes, and the missing data were filled with a gap-filling method [33]. For each year, one to three images were used to evaluate the accuracy of the STARFM-generated synthetic NDVI (referred to as the synthetic NDVI in the following text), and the rest of the available images were used as the inputs to the STARFM simulations. For the year 2009, since the available images were scarce, in order to maintain the quality of the synthetic NDVI, the selected image was used for both prediction and validation. In addition, the clearest image (cloud cover less than 10%) acquired between April and August was selected for each year to create the land cover map.
## 2 Methodology
### Data Pre-Processing Module
#### 2.1.3 Selection of Input Features  
For the input selection and better data understanding, the [PERSON]'s correlation coefficient (\(\gamma\)) was determined to catch nonlinear monotonic correlation between two variables. Figure 6 represents the correlation among all input variables, by showing a value between \(-1\) and \(+1\). A negative \(\gamma\) refers to negative correlation, while a positive one is positively correlated. There is no correlation between the two variables if the correlation coefficient is zero. Concerning the correlation illustration, target peak load (\(L(t,d)\)) positively correlates with \(L(t,d-7)\), \(L(t,d-1)\), \(L(t-2,d-1)\), \(L(t-1,d-1)\), \(T(t,d-1)\), \(SI\) and vice versa. It is slightly negatively correlated with \(MoY\), \(DoW\), and \(H\), whereas there is a close to zero correlation with \(BH\). According to the correlation coefficient, we consider historical peak load and temperature data as main inputs, while other seasonal variables are used as dummy variables. The formula of [PERSON]'s rank correlation coefficient (\(\gamma\)) is expressed as below:  
\[\gamma=1-(6\sum D_{i}^{2})/N(N^{2}-1) \tag{2}\]  
where \(D_{i}\) is the difference between the two ranks of each observation and \(N\) is the number of observations.  
For five input features,  
\[F_{(t,d)}=\left[L_{(t,d-7)},L_{(t-2,d-1)},L_{(t-1,d-1)},L_{(t,d-1)},\ S I\right] \tag{3}\]  
For six input features,  
\[F_{(t,d)}=\left[L_{(t,d-7)},L_{(t-2,d-1)},L_{(t-1,d-1)},L_{(t,d-1)},T_{(t,d-1) },\ S I\right] \tag{4}\]  
Figure 6: Correlation for all input variables.  
For nine input features,  
\[F_{(t,d)}=\left[L_{(t,d-7)},L_{(t-2,d-1)},L_{(t-1,d-1)},L_{(t,d-1)},\ S1,\ DoW,\ MoY,\ H,\ BH\right] \tag{5}\]  
For ten input features,  
\[F_{(t,d)}=\left[L_{(t,d-7)},L_{(t-2,d-1)},L_{(t-1,d-1)},L_{(t,d-1)},T_{(t,d-1)}, \ S1,\ DoW,\ MoY,\ H,\ BH\right] \tag{6}\]  
where,  
\(F_{(t,d)}=\) the forecasting vector for input features at time \((t)\) on the day \((d)\),  
\(L_{(t,d-7)}=\) load demand at time \(t\) from the previous week \((d-7)\),  
\(L_{(t-2,d-1)}=\) load demand at two periods before \((t-2)\) on yesterday \((d-1)\),  
\(L_{(t-1,d-1)}=\) load demand at one period before \((t-1)\) on yesterday \((d-1)\),  
\(L_{(t,d-1)}=\) load demand at a time \((t)\) on yesterday \((d-1)\),  
\(T_{(t,d-1)}=\) temperature at time \((t)\) on yesterday \((d-1)\),  
\(SI=\) monthly seasonal index \(=\) monthly load/yearly load,  
\(DoW=\) day of week (Mon = 1, Tue = 2,..., Sun = 7),  
\(MoY=\) month of year (Jan = 1, Feb = 2,..., Dec =12),  
\(H=\) holiday (0 or 1) (only holiday is 1, others are 0),  
\(BH=\) bridging holiday (0 or 1) (only bridging holiday is 1, others are 0).  
The example of data arrangement with five inputs for all models is shown in Table 1. According to Table 1, there are five input features to train and test the forecasting models. For training, the dataset from 2019 to 2020 is used and for testing, the dataset from January 2021 to December 2021 is applied.
# Sand and Dust Storms: Impact Mitigation
###### Abstract  
Sand and dust storms (SDS) play an integral role in the Earth system but they also present a range of hazards to the environmental and economic sustainability of human society. These hazards are of considerable importance for residents of dryland environments and also affect people beyond drylands because wind erosion can occur in most environments and desert dust events often involve long-range transport over great distances (>1000 km). This paper makes an assessment of the scale of SDS impacts by totalling the countries affected using an appraisal of peer-reviewed published sources, arriving at a conservative estimate that 77% of all parties to the United Nations Convention to Combat Desertification (UNCCD) are affected directly by SDS issues. We then present a synthesis of the environmental management techniques designed to mitigate SDS hazards for disaster risk reduction and review policy measures, both historical and contemporary, for SDS impact mitigation. Although many SDS hazards are well-known, the processes involved and their impacts are not all equally well-understood. Policies designed to mitigate the impacts of wind erosion in agricultural areas have been developed in certain parts of the world but policies designed to mitigate the wider impacts of SDS, including many that are transboundary, are geographically patchy and have a much shorter history. Further development and wider implementation of such policies is advocated because of the recent marked increase in wind erosion and associated dust storms in several parts of the world.  
dust storm; sand storm; aerosol; wind erosion; air quality; drylands; climate hazards Article  
Sand and Dust Storms: Impact Mitigation  
[PERSON] & [PERSON]  
1 St Anne's College, University of Oxford, Oxford OX2 6 HS, UK 1  
2 The Secretariat of the United Nations Convention to Combat Desertification (UNCCD), Platz der Vereinten Nationen 1, Bonn 53113, Germany; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]  
13 June 2017
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Experiments on Trajectory Tracking of CMRRs
#### 4.2.2 Trajectory Tracking Test on a Bumpy Road Surface in the Simulated Laneway  
The bumpy road surface deep in the simulated laneway is shown in Figure 37. Due to the narrow roadway, the robot can only walk in a straight line and cannot track the sinusoidal curve type trajectory, so only the straight line trajectory tracking test is conducted here.  
At 1 m directly in front of the robot, a linear trajectory with a length of 5 m is issued in the positive direction towards the X-axis. The MPC and PID trajectory tracking algorithms  
Figure 37: Simulated roadway bumpy road test environment.  
Figure 36: Comparison diagram of MPC and PID tracking errors.  
are run separately to compare the tracking effect of the two algorithms. The RVIZ visualization results of the MPC algorithm and the PID algorithm for tracking a straight-line trial in this scenario are shown in Figure 38.  
Shown in Figure 39 is a comparison of the motion trajectories of the robot executing the two algorithms MPC and PID from the initial point to the target point in this scenario. The maximum tracking error of the MPC algorithm is 0.028 m, and the standard deviation of the error is 0.0045 m. The maximum tracking error of the PID algorithm is 0.031 m, and the standard deviation of the error is 0.0065 m.  
From the above data and Figure 39, it can be seen that under the tracking control of the two algorithms, the robot is able to track the desired trajectory well, with the maximum error being around 0.03 m. The difference in the standard deviation of the two algorithms is also very small, still within 0.002 m. The standard deviation of the two algorithms is also very small.  
In summary, both the MPC trajectory tracking algorithm and the PID algorithm in this paper are capable of tracking a given trajectory. However, when tracking sinusoidal continuous curves, the tracking results of the MPC algorithm are more in line with the position of the desired trajectory, responding to the heading angle of the target trajectory from the first point of the target trajectory, and the turning scenarios in the tunnel are safer, with better overall results. After the many tests shown above, the MPC in this paper is successfully applied to tracked CMRR, which has a certain practical application value.