esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 4 Results and Discussions  
### Spatial Textural Feature Analysis  
#### 4.1.1 Spatial Textural Feature Extraction  
Seven texture images were derived from seven spectral bands of Landsat 8 data using the box-counting method. To examine the effectiveness of different texture images for lithological identification, the \(FD\) values of the rock samples were extracted from the seven texture images, and the mean \(FD\) value of each object is detailed in Figure 4.  
Figure 4 provides the following information:  
1. On the whole, \(FD\) shows a remarkable contrast between serpentinite and other rocks. The serpentinite has the highest \(FD\), which means that its surface morphology is rougher than that of other rocks. In \(TF1\) and \(TF2\), the \(FD\) values for gabbro and serpentinite are close, while from \(TF3\) to \(TF7\), there is an obvious difference between the \(FD\) values for gabbro and serpentinite, which may result from the fact that texture images derived from different source data have a variable effect on rock identification.
2. Regardless of the different texture images, different \(FD\) values for the four rock types exist because of their different surface roughness, which would assist rock identification. From \(TF2\) to \(TF6\), the \(FD\) values for gabbro and granite are almost the same, while for other pairwise rocks, the \(FD\) contrast is not immutable.  
In conclusion, textural features measured by \(FD\) may aid in separating certain different types of rocks. Texture images derived from different spectral bands have different sensitivities for lithological identification. Different texture images have varying abilities to identify the same pairs of rocks. For example, \(TF2\) is not sufficient to distinguish gabbro from serpentinite, but it can be used to distinguish granite from quartzose rock in this example. [PERSON] et al. (2016) demonstrated that using only a single textural measured by fractal dimension is not effective to characterize objects in an image. Hence, it is necessary to examine the ability of different texture images and combinations of them before image classification.  
Figure 4: Mean fractal dimension \((FD)\) value for the four rock types in seven texture images (\(TFi\) represents textural feature image derived from spectral _band i_).
## 2 Materials and Methods
### Nanotoxicological Bioassays
#### 2.3.2 Cytotoxicity Assay by MTT Reduction  
The effect of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles on cell viability was evaluated using the MTT reduction, following the recommendations provided by the OECD testing programme for the safety of manufactured nanomaterials. The cytotoxicity effect of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles was determined using the 3-(4,5-dimethyl-2-thiazol)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, a widely used method to assess cell viability [38; 39]. HFF-1 and MCF-7 cells were seeded at a density of 10,000 cells per well in a 96-well plate and incubated in 100 \(\mu\)L of DMEM-supplemented media at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere for 24 h. After this, Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated NPs were suspended in MiliQ-water at 1 mg/mL concentrations. Before each assay, each nanoparticle suspension was prepared freshly and underwent 10 min of sonication. Then, solutions of nanoparticles at different concentrations were made in DMEM media and promptly added to the cell cultures as described below. HFF-1 and MCF-7 cells were exposed to Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated NPs at 0.1; 1, 5, 10, 25, 50, and 100 \(\mu\)g/mL in a final volume of 100 \(\mu\)L of DMEM-supplemented media in a 96-well plate. Cells were incubated for 24 h at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere using DMEM media. Afterward, the media was discarded, and cells were rinsed thrice with 200 \(\mu\)L of PBS 1x. Then, 10 \(\mu\)L of MTT and 90 \(\mu\)L of DMEM media were added to each well and incubated for 4 h at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere for 24 h. After this, 100 \(\mu\)L of isopropanol was added to each well to dissolve formazan crystals, and the 96-well plate was incubated for 30 min at 25 \({}^{\circ}\)C in darkness. Absorbance readings were taken at 570 and 690 nm wavelengths using a microplate reader (ThermoFisher Scientific, Waltham, MA, USA). Each cytotoxicity experiment included a control group with a culture medium lacking NPs to establish a baseline. The positive control for inducing cell death consisted of cells treated with 1% (_v/v_) Triton X-100, while the negative control contained only cell culture medium without nanoparticles. These controls facilitated the comparison of cell viability between the negative and positive controls and the damage caused by various concentrations of NPs. Experiments were conducted in triplicate. Cell viability was calculated relative to the absorbance values of the positive control.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 4 Performance Evaluation
### Key Frame Coding Evaluation  
Firstly, we compare the performance of the key frame coding scheme in the proposed JEM-DVC with that in the H.265-DVC and DISCOVER DVC, because the performance of the key frame coding has a strong effect on the performance of these DVC codecs, as explained above.  
In the proposed JEM-DVC, the key frames are encoded while using the JEM Intra coding in the H.265-DVC and DISCOVER DVC; the key frames are encoded using the H.265/HEVC and H.264/AVC Intra coding, respectively.  
For comparison, Figure 7 shows the PSNR performance of the key frames that were obtained for the test video sequences with four values of QPs. As shown in Figure 7, the performance of the key frames that were encoded by the JEM Intra coding significantly outperforms that encoded by the H.265/HEVC and H.264/AVC for all of the test video sequences. For example, as shown in Figure 7, the JEM Intra coding provides up to 2.6 dB and 0.9 dB gains as compared with the H.264/AVC and H.265/HEVC Intra coding, respectively, for the _Foreman_ sequence when QP = 27.
## 3 Materials and Methods
### Feature Transformation  
#### 3.2.1 Textual Data  
To obtain numerical feature vectors from textual data, the widely used 'bag-of-words' model is applied after preprocessing the concatenated title and abstract by removing stop-words not providing any additional information. Final feature vectors for text categorization are created utilizing a TF-IDF value computation for each input term per text document [6].
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 3 Fusion of Lane Location Information and SLAM Positioning Algorithm
### Initialization of Starting Point Position  
The result of SLAM output is the position in the body coordinate system, and the road information stored on the map is the global latitude and longitude. Therefore, first obtain the latitude and longitude coordinates and heading angle at the moment through manual positioning and location recognition at the starting point. The SLAM output is converted to latitude and longitude coordinates.  
First, take the current position as the center point, and take the nearest \(K\) points in the map stored in the directed graph along the current yaw angle \(\theta\) a candidate points for the vehicle's possible poses. Each time a new candidate point is obtained, the probability of the vehicle at K candidate points on the map will be calculated, and the candidate point with the highest probability will be output as the vehicle position. At the initial moment, it is assumed that the probability of the vehicle at each candidate point of the map obeys the Gaussian distribution and satisfies the following formula.  
\[\begin{array}{c}P_{Xn}=\lambda*\exp\Bigl{(}-\frac{1}{2}*X^{T}*Q^{*}X\Bigr{)} \\ X=\{x-x_{0},y-y_{0}\}\\ \lambda=\frac{1}{2\pi}|Q|^{-\frac{1}{2}}\\ \sum\limits_{n=1}^{K}P_{X_{n}}=1\end{array} \tag{10}\]  
\(P_{X_{n}}\) represents the probability that the vehicle position is at the current candidate point \(n\), \(Q\) represents the covariance matrix, which is set to the identity matrix at the initial moment; and \(X\) is the state variable at this time. At the initial moment, it only contains the position \((x,y)\) in the world coordinate system; \((x_{0},y_{0})\) is the map position obtained by initial positioning, that is, the center point of the Gaussian distribution; and the dimension of \(X\) is 2. At the same time, in order to avoid the influence of extreme conditions, after each probability calculation is completed, the probability of \(K\) points is normalized.
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 3 Results
### Multilayer Yagi-Uda: Simulated and Measured  
Concerning the multilayer antenna from Figure 7, it was simulated and analysed according to its main properties. Figure 10 shows the comparison between the simulated and measured results of the reflection coefficient of the antenna.  
A good impedance matching can be observed, showing that the antenna was perfectly tuned in terms of simulation despite the frequency shift of 710 MHz in relation to the measured curve. However, at the operation frequency, 24 GHz, a measured S\({}_{11}\) of \(-\)15.51 dB makes these results satisfactory. Moreover, the designed multilayer antenna presents roughly 6.9 GHz of bandwidth, equivalent to 27%, from 22.6 GHz up to 29.5 GHz--a frankly encouraging result.  
Looking at the radiation pattern, the simulated results are shown in Figure 11, where it is possible to verify the alignment of the main lobe with the directors of the antenna, in accordance with the vertical plane.  
Figure 10: Simulated and measured reflection coefficient of the multilayer Yagi antenna.  
Figure 9: Simulated radiation pattern of the 24 GHz planar antenna: (**a**) 3D view; (**b**) polar diagram of plane \(\phi=0^{\circ}\).  
Figures 12 and 13 show the variation of the maximum gain of the antenna and its total efficiency, respectively, over the frequency. It can be seen that the structure has a maximum gain of 10.9 dBi at 24 GHz, the frequency for which it was designed. Furthermore, there is a range of about 2 GHz around the operating frequency where the gain is always greater than 10 dBi.  
Figure 11: Simulated radiation pattern of the 24 GHz multilayer antenna: (**a**) 3D view; (**b**) polar diagram.  
Figure 12: Gain over frequency.  
Figure 13: Total efficiency over frequency.  
In terms of the antenna's efficiency, according to Figure 13, it is possible to see that it is high, about 86% at the 24 GHz frequency. This result is very important, given the complexity of the structure, its compactness, consisting of several stacked substrate dielectric layers. It should also be noted that between 23.5 and 24.9 GHz, the total efficiency of the antenna is always greater than 80%.
## 2 Materials and Methods
### Statistical Data Analysis  
On raw data, the univariate descriptive statistics (mean and standard deviation) were calculated. To assess the effects of breed, age and lactation period, a Bayesian approach to ANOVA was adopted. [PERSON] ANOVA analysis is essentially based on the evaluation of one or more effects given a prior probability of an outcome. ANOVA Bayesian analysis was performed with the software jamovi for Windows. The significance (strength) of posterior probabilities was derived from the [PERSON] classification of Bayes Factor (BF10) or, otherwise, the inverse (BF01) [24]. A notation similar to the frequentist statistics results was adopted, following the [PERSON] suggestions, resumed in Table 2.  
The notation with asterisks was chosen arbitrarily by authors considering the strength of evidence.  
Data were processed for univariate descriptive analysis: mean value and standard deviation were considered for data summarization. The effects of breed, age class and days in milk class on response variables were analyzed by the Bayesian ANOVA analysis procedure (Jamovi ver. 1.1.7.0. for Windows). The model analyzed was resumed in the following relation:  
\[Output\ variable_{ijkl}=\mu+ breed_{i}+age_{j}+DIM_{k}+\varepsilon_{ijkl}\]  
where:  
\(\mu=\) grand mean  
\(breed=\) fixed factor for breed  
\(age=\) fixed factor for age class  
\(DIM=\) fixed factor for days in milk class  
\(\varepsilon=\) error  
The breeds differences on age class and DIM classes were based on the interaction between factors.  
\begin{table}
\begin{tabular}{c c c} \hline
**Breed** & **Age Class (Years)** & \(n\) \\ \hline \multirow{4}{*}{Alpine} & 1–2.9 & 45 \\ \cline{2-3}  & 3–4.9 & 21 \\ \cline{2-3}  & 5+ & 55 \\ \hline \multirow{4}{*}{Verzasca} & 1–2.9 & 35 \\ \cline{2-3}  & 3–4.9 & 78 \\ \cline{2-3}  & 5+ & 57 \\ \hline \multicolumn{3}{c}{**DIM class (days)**} \\ \hline \multirow{4}{*}{Alpine} & 0–30 & 9 \\ \cline{2-3}  & 31–50 & 20 \\ \cline{2-3}  & 51–100 & 24 \\ \cline{2-3}  & 101–150 & 37 \\ \cline{2-3}  & 150+ & 31 \\ \hline \multirow{4}{*}{Verzasca} & 0–30 & 7 \\ \cline{2-3}  & 31–50 & 27 \\ \cline{2-3}  & 51–100 & 33 \\ \cline{2-3}  & 101–150 & 46 \\ \cline{2-3}  & 150+ & 57 \\ \hline \end{tabular}
\end{table}
Table 1: Sample distributions for breed, age class and days in milk class (DIM class) for the enrolled goats.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Evaluation** & **BF10** & **Inverse BF10** & **Significance** \\ \hline Anecdotal & 1–3 & 1–0.33 & ns \\ Substantial & 3–10 & 0.33–0.1 & ns \\ Strong & 10–30 & 0.1–0.03 & * \\ Very strong & 30–100 & 0.03–0.01 & ** \\ Decisive & \textgreater{}100 & \textless{}0.01 & *** \\ \hline \end{tabular} The different asterisks indicate different levels of statistical significance: strong (*), very strong (**), decisive (***),  
\end{table}
Table 2: BF10, inverse BF10 [24] and arbitrary significance asterisks, inserted by authors.  
Moreover, the Bayesian correlation coefficients between variables in each breed were calculated.
###### Abstract  
Nowadays, the interest in preparing new, cheap and simple adsorbents that are used in sample preparation is on the rise. Graphene oxide (GO) nanomaterials and nanocomposites have become increasingly popular due to the novel methods of syntheses that have been published. Owing to their vast specific surface area and their \(\pi\)-delocalized electron system they possess, they are appropriate for the adsorption of a variety of aromatic organic compounds, being utilized either as adsorbents in analytical methods or as filter materials for the removal of pollutants in water. Pharmaceutical compounds, such as benzodiazepinepines, end up in surface waters caused by consumption or their disposal through sewage, thus becoming pollutants. In the present study, an analytical method has been developed and validated for the determination of two model-analytes of benzodiazepinepines by HPLC-DAD and their sample preparation protocol which consists of the Stir bar magnetic solid phase extraction (SB-MSPE) method, evaluating therefore the nanocomposite material as a decent adsorbent. The separation took place with the usage of an analytical column C\({}_{18}\) RP-HPLC in 10 min. For the alprazolam (ALP) and the flunitrazepam (FLT), the LODs and LOQs were 3 ng/mL and 10 ng/mL, respectively, while the relative recoveries ranged between 93.6-112.9% and the RSDs were 1.11-9.50%. Finally, the material was examined for its reusability and was found that it can be used for over eight cycles of extraction/elution.  
Keywords:environmental analysis; surface water samples; benzodiazepinepines; stir bar magnetic solid phase extraction; nanocomposite; graphene oxide; magnetic chitosan; high pressure liquid chromatography +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Formulation of Liposomes with Mulberry Extract and Their Characterization  
#### 2.8.1 Formulation of Liposomes Using the Lipid Film Hydration Method  
Liposomes were formulated using the lipid film hydration method. The lipid part of the liposomes is composed of cholesterol (C) and phosphatidylcholine (PC) in a ratio of 1:3 (\(w/w\)). The hydrophilic phase is represented by the phosphate buffer pH = 7.6 which was used to hydrate the lipid film [34; 35]. Briefly, C and PC were dissolved and mixed in an organic solvent (20 mL) consisting of dichloromethane and methanol (3:2 \(v/v\) ratio) to obtain a homogenous mixture of lipids. Then, the solvent was removed using the Heidolph Rotary Evaporator (Laborota 4000 rotavirusp, Schwabach, Germany) maintaining the parameters of 37 \({}^{\circ}\)C and 100 rpm to yield a lipid film. Hydratation of the dry lipid film was achieved with phosphate buffer pH = 7.6 (10 mL) followed by sonication for 30 min, centrifugation for 3 min at 4050 rpm. The supernatant is collected and then centrifuged again for 30 min at 10,000 rpm [24].  
Two types of liposomes were formulated, one without included mulberry extract (L) and another including 25 ug/mL of mulberry extract (MnL).
## 2 Overview of the Proposed Concept
As seen from Figure 6, the home energy management module, which is an embedded device with internet connection, is capable of controlling electrical appliances such as the air conditioner, washing machine, dishwasher, etc. via smart plugs. The proposed module also allows end-users to review the status and consumption of the appliances through internet connection or mobile devices. The node block shown in Figure 7 is mainly used for secure and real-time two way connection between user mobile phones and the server. At the same time, the system can send an update at any time without user requests. In the last block presented in Figure 8, the user interface, which is a mobile application, allows users to receive updates about demand response events, configure HEM module behavior and view current or past household data. Moreover, the developed application receives updates automatically with the background service and real-time connection. Figure 4: Detailed flowchart of the block hypertext preprocessor (PHP). Figure 5: Detailed flowchart of the block system operator. Figure 8: Detailed flowchart of the block user interface. Figure 2.0 Detailed flowchart of the block home energy management (HEM) device. Figure 2.1 Detailed flowchart of the block Node.JS.
## 1 Introduction
Given the huge spatial extent of these environments and the large number of functions they perform, understanding trends and potential climate-induced shifts in grassland phenology is impelling [35]. Studies of different environments in Europe have already shown changes in phenological phases over the period 1951-2018 in a limited number of countries for which long series of observed data were available [36]. In this regard, long time series of vegetation indices, such as those elaborated from MODIS satellite images, allow the study of phenology over a relatively long period of time, providing information at 250 m spatial resolution and 16-day temporal resolution. This involves assessing changes in phenological dates over the past decades and quantifying the extent of the impacts of climate change on the plant life cycle that are already visible in grassland systems [37]. Analyses of satellite-derived phenology over the period 1982-2001 also showed a general advance driven by climate change [38]. However, a comprehensive phenological analysis specific to European grasslands in recent decades is still lacking, as is the evaluation of the impact of rising temperatures on these changes. Building on these premises, the objectives of this research were, therefore, twofold:  
(_i_) Identification of a reliable approach to determine the start (SOS), peak (POS) and end of the growing season (EOS) through the use of specific vegetation indices (i.e., kernel normalized difference vegetation index, kNDVI) processed from MODIS satellite imagery, relying on observed GPP data from grassland sites as comparison;  
(_ii_) Analysis of phenological trends of different European grasslands in the period 2001-2021 using the methodology identified in point (_i_) in order to highlight possible changes in the dates of SOS, POS and EOS and the relevant climatic drivers.
## 2 Materials and Methods
### Acquisition of Remote Sensing Data  
In this study, we used GPP (MOD17A2), ET (MOD16A2) [38], and land surface temperature (LST, MOD11A2) [39] data at a spatial resolution of 1 km \(\times\) 1 km, obtained from the website ([[https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)]([https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)), accessed on 10 May 2022).  
Elevation, roughness, and slope data referring to the MATOPIBA region were obtained from the EarthEnv website ([[https://www.earthenv.org/topography/](https://www.earthenv.org/topography/)]([https://www.earthenv.org/topography/](https://www.earthenv.org/topography/)), accessed on 11 May 2022), resulting from the products Global Multi-resolution Terrain Elevation Data 2010 (GMTED 2010) and 90 m Shuttle Radar Topographic Mission (SRTM) void-filled SRTM4.1 dev [40], which has a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\).  
Land Use and Land Cover data were obtained from the MAPBIOMAS website ([[https://mapbiomas.org/](https://mapbiomas.org/)]([https://mapbiomas.org/](https://mapbiomas.org/)), accessed on 15 May 2022), which has a spatial resolution of 30 m \(\times\) 30 m and annual time resolution. The data are separated by biomes; thus, it was necessary to produce a mosaic referring to the study area.  
The Rainfall product was taken from Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) data [3; 38; 41], with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) ([[https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)]([https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)), accessed on 11 May 2022). Annual rainfall accumulated data between 2001 and 2019 were used in this study. CHIRPS data are consistent and validated when compared to regions with a high density of stations and meteorological data, as noted by [PERSON] et al. [41]., [PERSON] et al. [42], and [PERSON] et al. [38], which are helpful in studies of remote regions or regions with poor or absent rain gauge coverage [43]. Extraction, manipulation, and calculation were performed using R version 3.6.3 [44] and Quantum GIS (QGIS) version 3.4.6 [45].