esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Spectral Variables  
From the ARD and HLS surface reflectance, we generated three sets of annual spectral variables, including the 20 th, 50 th, and 80 th percentiles of blue (B), green (G), red (R), near infrared (NIR, band 8A in HLS), shortwave infrared 1 (SWIR 1 - S1), and SWIR 2 (S2). For each set of percentiles, twelve normalized band ratios were computed, including: \((\text{G}-\text{R})/(\text{G}+\text{R})\); \((\text{NIR}-\text{R})\)/\((\text{NIR}+\text{R})\); \((\text{NIR}-\text{B})\)/\((\text{NIR}+\text{B})\); \((\text{NIR}-\text{G})\)/\((\text{NIR}+\text{G})\); \((\text{S}1-\text{R})\)/\((\text{S}1+\text{R})\); \((\text{S}1-\text{B})\)/\((\text{S}1+\text{B})\); \((\text{S}1-\text{G})\)/\((\text{S}1+\text{R})\); \((\text{S}1-\text{NIR})\)/\((\text{S}1+\text{R})\); \((\text{S}2-\text{R})\)/\((\text{S}2+\text{R})\); \((\text{S}2-\text{B})\)/\((\text{S}2+\text{B})\); \((\text{S}2-\text{G})\)/\((\text{S}2+\text{G})\); and \((\text{S}2-\text{NIR})\)/\((\text{S}2+\text{NIR})\). The 20 th and 80 th percentiles were used to reduce sensitivity to shadows and residual cloud and atmospheric contamination effects. Similar variables were used previously to produce the NLCD-like land cover map for North America using WELD data (a monthly composited Landsat surface reflectance product) [35]. In total, 42 spectral variables were generated for each 30 m pixel location. Those variables were named using the following convention: \"percentile_band (normalized ratio)\" (e.g., \"P50_S2R\" is a normalized ratio between the 50 th percentile of SWIR-2 and Red bands).
## 4 Literature Review: Other Research on CWSN
### Sensing Techniques
#### 4.1.1 Signal Processing Technique  
[PERSON] _et al._[69] proposed an algorithm that estimates the interference temperature using a generalization of the direction of arrival (DOA) algorithm by considering cooperative frequency spectrum sensing based on a spatial spectral estimation. Their simulation results show that this algorithm can acquire a 30% gain in the ratio of frequency spectrum utilization than the conventional method. [PERSON] _et al._[70] presented a hybrid sensing method that finds the optimal sensing period according to the characteristics of both PU and SUs. The proposed method varies its parameters adaptively to avoid unnecessary sensing tasks based on a continuous time Markov chain model. [PERSON] _et al._[71] introduced the concept of a joint source and channel sensing (JSCS) for CR-WSNs. A specific slotted sensing and transmission scheme delivers the application source information to the access point energy efficiently. [PERSON] _et al._[72] proposed a new spectrum sensing scheme for CR-WSNs based on a spatially-decaying, time-incremental updating algorithm. This algorithm automatically assigns weights to channel information based on the distance between the source node and observing node. This algorithm is an extension of gossiping updates for an efficient spectrum sensing scheme that adopts the Flajolet-Martin aggregation to reduce the volume of data. [PERSON] _et al._[73] proposed spectrum sensing in OFDM based on energy detection in MIMO CR-WSNs. The OFDM-based MIMO CR-WSNs detect the primary user OFDM signal, where the CR receiver is equipped with a multiple antenna-based energy detector. They examined the soft combination of the observed energy values from different cognitive radio users and proved that square-law-combining is almost optimal in the low SNR region.
## 3 Results and Discussion
### Validation of MW-FLR Method
#### 3.3.2 Precision and Accuracy  
The precision of the proposed MW-FLR method was assessed for intra- (\(n=6\)) and inter-assay (\(n=9\)) precisions by analyzing nominated concentrations of DUV taken from copiktra capsules and spiked in human plasma samples (Table 3) and the relative standard deviation (RSD) percentages were calculated. It was found that the RSD values for intra-assay precision were in the ranges of 1.54-2.41 and 2.52-4.26% for analysis of DUV concentrations taken from copiktra capsules and spiked in plasma samples, respectively, where the RSD values for inter-assay precision were in the ranges of 1.53-2.82 and 2.45-4.68% for capsules and plasma, respectively. These findings supported the MW-FLR method's high precisions for DUV quantitation in both capsule and plasma samples. This high accuracy was attributed to the precision of the volumes' dispensing into the microwells by 8-channel pipettes and the minimum experimental manipulations of the samples prior to measuring the fluorescence by the reader.  
The results of both intra- and inter-day accuracy (expressed as recovery, %) are given in Table 3. The intra-day recovery values were in the range of 98.5-103.2% (\(\pm\)1.1-2.4) and 97.5-104.2% (\(\pm\)3.5-5.4) for copiktra capsules and plasma samples, respectively. The inter-day recovery values were in the range of 98.8-101.2% (\(\pm\)1.4-2.8) and 98.4-104.1% (\(\pm\)3.1-5.6) for copiktra capsules and plasma samples, respectively. These good recovery values confirmed the accuracy of the proposed MW-FLR method for the determination of DUV in both copiktra capsules and plasma samples.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery
### Magnetic-Responsive Drug Delivery  
A magnetic-responsive controlled released system based on magnetic nanoparticles brings drug-loaded nanoparticles to the tumor site under the influence of an external magnetic field. In 2011, [PERSON] et al. constructed MSN@Fe\({}_{3}\)O\({}_{4}\) by capping MSN with monodispersed Fe\({}_{3}\)O\({}_{4}\) nanoparticles through chemical amidation [207]. The results of this study showed that in the presence of a magnetic field, the amount of the drug released from the MSN@Fe\({}_{3}\)O\({}_{4}\) increased. Magnetic-responsive drug delivery is usually dependent on the temperature and nanoparticles, obeying two mechanisms. The first mechanism is the Brownian motion provoked by the fast rotation of the magnetic nuclei, and the second mechanism concerns the Nell functions describing the rotation of the magnetic moments [213; 214]. In an interesting report, [PERSON] et al. used an alternating magnetic field (AMF) to heat Fe\({}_{3}\)O\({}_{4}\) NPs loaded inside the pores [210]. In this work, MSNs were functionalized with the hexadecylmethylenediamine (HDMD) ligand and formulated as a complex. At the time of reinforcement, the drug was also inserted into the mesoporous cavities. Finally, the silica mesopores were exposed to the magnetic field, and, following [PERSON]'s law, the Fe\({}_{3}\)O\({}_{4}\) NP vibrated and released the drug (Figure 21).  
Figure 20: Cyclodextrin functionalized with silica mesopores for light-responsive drug delivery.
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
###### Abstract  
This paper empirically evaluates two kinds of features, which are extracted, respectively, with traditional statistical methods and convolutional neural networks (CNNs), in order to improve the performance of seismic patch image classification. In the latter case, feature vectors, named \"CNN-features\", were extracted from one trained CNN model, and were then used to learn existing classifiers, such as support vector machines. In this case, to learn the CNN model, a technique of transfer learning using synthetic seismic patch data in the source domain, and real-world patch data in the target domain, was applied. The experimental results show that CNN-features lead to some improvements in the classification performance. By analyzing the data complexity measures, the CNN-features are found to have the strongest discriminant capabilities. Furthermore, the transfer learning technique alleviates the problems of long processing times and the lack of learning data.  
seismic patch classification; CNN-features; transfer learning; data complexity Article
# Automatic Defect Detection for Web Offset Printing Based on Machine Vision
## 2 Methodology
### Image Registration Based on Shape Context with Concave-Convex Feature  
#### 2.2.1 Introduction of Shape Context  
Image registration plays an important role for defect detection based on image subtraction, as it enables the detected image to be in the same position as the reference image for comparison. In the process of image registration, common features shared by the detected image and reference image, such as feature point, region or shape, should be extracted first. Among these features, shape is the most popular feature for image registration, which has invariant characteristics for light changing. Thus, shape descriptor is explored in this paper for image registration.  
Since shapes are represented by a series of contour points, shape description can be expressed by the relative position of contour points. In 2002, [PERSON] et al. [21] proposed a shape context descriptor to describe this relation. The main idea of shape context is to create a feature descriptor for each contour point relative to other contour points. As shown in Figure 8, for any point, a series of circles centered on that point were set up, and these circles were divided into different sectors, then the feature descriptor of this point was obtained by counting the number of contour points in each sub-region with a different circle radius and sector. For two shape matching, any point in a shape should be matched with all points in another shape by computing a cost function, which is computationally expensive.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 5 Results
### Results with Phase Measurements from a Single Satellite  
Now consider the results obtained if phase measurements (12) from a single GLONASS satellite are used in the filtering problem. The pseudorange (10) and radial velocity (11) measurements are used from minimum five satellites.  
Figure 13 presents the heading errors, and Figure 14, phase residuals for Kalman filtering using phase measurements (12) of a single GLONASS satellite from the elevation range 30\({}^{\circ}\)-75\({}^{\circ}\). As seen from the figures, the heading error in steady mode does not exceed 1.5\({}^{\circ}\) (without account for errors in synchronization with the reference INS).  
Figure 14 presents the phase residuals used in the algorithm for rejection of unreliable phase measurements. The values of residual \(L_{kfi}\) are given for one GLONASS satellite under vibrations during the car motion. Figure 14 clearly shows distinctive periodic perturbations (with periods of units of minutes, indicated by marker \"1\"), whose character suggests their connection with GNSS signal rereflections from the surrounding objects.  
Figure 15 below shows FOG estimated biases and rhumb drifts in these conditions.  
Figure 11: Estimated FOG rhumb drifts: (**a**) During the stop; (**b**) During the car motion.  
Figure 12: Estimated FOG bias: (**a**) During the stop; (**b**) During the car motion.  
Figure 14: Phase residuals during car motion using phase measurements from a single GLONASS satellite.  
Figure 13: Heading errors during car motion using phase measurements from a single GLONASS satellite.  
Figure 15: Estimated parameters of FOG error model using phase measurements from a single GLONASS satellite: (**a**) Rhumb drift; (**b**) Bias.  
Comparison of Figures 11, 12 and 15 reveals that even using effective phase measurements from a single GLONASS satellite FOG bias and rhumb drift can be effectively estimated.
## 3 Results
### Magnetic Susceptibility of Some Construction Materials for Pavilions
#### 3.1.2 Concrete Magnetization Due to Its Solidification
For this, we used the GSM-19 proton Overhauser magnetometer as a rover and another GSM-19 as a base station for diurnal correction of total field measurements. The step of the survey was 0.5 \(\times\) 0.5 m. The survey was performed at two sensor height levels: the 1 st level was close to the floor, and the 2 nd level was 1.5 m high from the floor (which is an approximate level for pillar tops). The magnetic anomaly maps for the absolute pavilion can be seen in Figure 5a (1 st level) and Figure 5b (2 nd level). As seen, the 1 st level survey (Figure 5a) revealed significant positive magnetic anomalies close to the floor, produced by concrete feet tops. A slight effect from them can also be seen in Figure 5b on the map of the 2 nd level magnetic survey. Thus, to avoid the magnetic effect from concrete, the installation of 1.5 m glass brick columns was obviously not enough. The reason is that magnetized particles in the concrete body are arranged along the external magnetic field direction during solidification, which results in larger magnetization of the solidified concrete body than that of the loose concrete. Then, 0.5 m of the concrete was cut off, and the place left was filled with glass blocks. The pillar height is about 1.5 m, so the overall distance from the magnetized concrete feet to the tops of the pillars is 2 m. The new maps of magnetic anomalies in the absolute pavilion (Figure 5c,d respectively) show that the effect is negligible. We have not found the source of the anomaly in the middle of the pavilion. However, as the magnetometers will be installed high on pillars, this magnetic effect should be negligible. Moreover, we fulfill the IAGA requirements [3], as the gradients between the pillars are small enough at the 2 nd level. The results of a magnetic survey carried out in the variation pavilion are not displayed here, but they show similar positive anomalies above the tops of concrete feet and the absence of them after the corresponding rework. Figure 4: Absolute pavilion of the Gyulagarak observatory: (**a**) Construction process (the pavilion frame is seen); (**b**) The built pavilion; (**c**) Pavilion interior. Figure 5: Magnetic field survey results in the absolute position of the Mikhnevo magnetic observatory: (**a**) Magnetic anomaly map at the floor level before the concrete bases for the pillars were shortened; (**b**) magnetic anomaly map at the level of 1.5 m from the floor before the concrete bases for the pillars were shortened; (**c**) magnetic anomaly map at the floor level after the concrete bases for the pillars were shortened; (**d**) magnetic anomaly map at the level of 1.5 m from the floor after the concrete bases for the pillars were shortened.
## 2 Methods
### Numerical Simulation Scheme  
In this study, the flow is regarded as three-dimensional steady and compressible flow, and the governing equations in the Cartesian coordinate system can be written as follows [31]:  
\[\frac{\partial\big{(}\rho u_{j}\big{)}}{\partial x_{j}}=0 \tag{1}\]  
\[\frac{\partial\big{(}\rho u_{j}u_{i}\big{)}}{\partial x_{j}}=-\frac{\partial p }{\partial x_{i}}+\frac{\partial\tau_{ij}}{\partial x_{j}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cells Number** & **Mass Flow Rate kg/s** & **Difference** & **Max Vorticity \(\times\)10\({}^{7}\) s\({}^{-1}\)** & **Difference** \\ \hline
10 million & 2.682 & \(\backslash\) & 1.0502 & \(\backslash\) \\
15 million & 2.713 & 1.2\% & 1.0613 & 1.1\% \\
20 million & 2.725 & 0.4\% & 1.0678 & 0.6\% \\
25 million & 2.743 & 0.6\% & 1.0704 & 0.2\% \\
30 million & 2.763 & 0.7\% & 1.0776 & 0.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass flow rate under different numbers of cells.  
Figure 3: Mesh and boundary conditions: (**a**) computational domain; (**b**) aircraft fuselage; and (**c**) exit of the inlet.  
\[\frac{\partial(\rho E+pu_{i})}{\partial x_{i}}=-\frac{\partial q_{i}}{\partial x_{i }}+\frac{\partial\left(u_{i}\tau_{ij}\right)}{\partial x_{i}} \tag{3}\]  
where \(\rho\) is the fluid density; \(x_{i}\) is the Cartesian coordinate component; \(\mu_{i}\) is the velocity component; \(q_{i}\) is the heat flux; \(p\) is the pressure; \(E\) is the total energy; and \(\tau_{ij}\) is the viscous stress. For ideal gas, \(p=(\gamma-1)(E-0.5\rho u_{i}^{2})\). \(\tau_{ij}\) and \(q_{i}\) can be described by the following Equations (4) and (5):  
\[\tau_{ij}=\mu(2S_{ij}-\frac{2}{3}\delta_{ij}S_{kk}) \tag{4}\]  
\[q_{i}=-\frac{\mu}{(\gamma-1)\Pr}\frac{\partial T}{\partial x_{i}} \tag{5}\]  
where \(\gamma\) is the specific heat of air; \(\mu\) is the viscous coefficient; \(\Pr\) is the Prandtl number; \(S_{ij}\) is the strain tensor; and \(T\) is the temperature.  
The commercial CFD software Fluent 18.0 is used to solve the above three governing equations. A second-order upwind scheme is used. The \(k\)-\(\varepsilon\) turbulence model [26] with standard wall functions is used to describe the flow field. For all equations, convergence is considered when the residual is less than \(10^{-6}\). The reliability of the numerical simulation scheme will be further verified by wind tunnel test.
## Materials and Methods
### Condition Optimization  
#### 2.5.1 Use of Catalysts  
In order to optimize the operation conditions, the implementation of homogeneous catalysts was suggested. Homogeneous acid and basic catalysts play a significant role in the hydrothermal vaporization of biomass. In the hydrothermal liquefaction process, inorganic acid catalysts such as H\({}_{2}\)SO\({}_{4}\) are extensively applied to facilitate the conversion of biomass into valuable products, including aviation fuel precursors [24]. Additionally, homogeneous basic catalysts, such as carbonates and hydroxides of alkali metals, have been studied for their role in the valorization of biomass feedstocks, including red macroalgae and livestock manure, to enhance biocrude oil and hydrochar production [25; 26]. These catalysts promote the depolymerization and upgrading of biomass components under hydrothermal conditions, offering a sustainable approach to converting biomass into biofuels and chemicals [27]. In this research, solutions of H\({}_{2}\)SO\({}_{4}\) 0.02 M, CH\({}_{3}\)COOH 0.2 M, KOH 0.02 M, and NaHCO\({}_{3}\) 0.2 M were prepared and used instead of water in the biomass/water ratio of 1:9 (3 g of biomass and 7 g of catalyst solution) to improve the yield and selectivity of the biomass conversion.  
Figure 1: Chromatographic design for platform chemicals (PC).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standard** & **Retention Time (min)** \\ \hline Glucose & 16.7971 \\ Xylose & 17.1698 \\ Formic acid & 20.6158 \\ Levulinic acid & 22.3054 \\ HMF & 35.2803 \\ Furfural & 51.3499 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Retention time for separation of standards of PC.
## 4 Conclusions  
In this paper, pH-responsive brush-modified particles have been obtained by growing PDEAEMA brushes on ca. 250 nm MSNs via surface-initiated ARGET ATRP technique. Halogens at the end of polymer chains were successfully derivatized to primary amines, which has elaborated strategies that allow modification of these brushes with molecules. POEGMEA have reacted successfully with thiol via thiol-Michael addition. XPS confirmed the successful attachment of POEGMEA molecules when the peak intensity of C-O increased, resulting from the presence of additional C\(-\)O bonds in the POEGMEA. The encapsulation efficiency of Rh B was found to be ca. 74% for all selected samples. The dye release profiles illustrated that the high release rate (ca. 60%) occurred when brushes are protonated at low pH, compared to the release rate in basic media (ca. 20%). MSNs-PDEAEMA, MSNs-PDEAEMA-SH, and MSNs-PDEAEMA-POEGMEA were found to be non-toxic to hMSC. Nevertheless, the results showed an increase in cell proliferation in some instances, without being statistically significant, except in the case of MSNs-PDEAEMA-POEGMEA at 50 \(\mathrm{\SIUnitSymbolMicro m}\)/mL after both 24 and 48 h of incubation. This finding may indicate a potential role where cell proliferation is desirable, as in wound healing cases.  
Data curation, A.A. ([PERSON]), A.B., and R.L.; formal analysis, [PERSON] ([PERSON]), A.N., H.A., L.A., and A.A. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 7: Cell viability of human bone marrow-derived mesenchymal stem cells (hMSCs) after exposure to the selected nanosystems. Cells were exposed for 24 h and 48 h to 10, 50, and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of. (**A**) MSNs–PDEAEMA, (**B**) MSNs–PDEAEMA–POEGMEA, and (**C**) MSNs–PDEAEMA–SH. Astride indicate the level of significance as follows: * \(P>0.05\), **\(P>0.01\).  
**Data Availability Statement:** The data presented in this study are included in this article.  
**Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RG-1439-026.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 3 Results
### Validation of the Model  
WaterUNet was implemented under a Google Tensorflow framework and deployed on a Debian Linux workstation, with two Intel(R) Xeon(R) Gold 6134 CPUs and NVIDIA Quadro P5000 GPU. Due to the compact structure of WaterUNet (small size and fewer parameters), the training process achieved an almost optimal performance after 10 epochs with a high accuracy and small loss. The accuracy-loss curves of the training process are shown in Figure 10, where the accuracy curve was used to measure the performance of the model, while the loss curve was used to further optimize the model [43]. During the training process, the loss and accuracy values obtained for training samples were 0.98 and 0.03, while they were 0.98 and 0.03 for validation samples, which proved the validity of the WaterUNet's network structure.  
Then, the performance of WaterUNet was evaluated using the testing samples and the samples manually and randomly selected from the two scenes of SAR data acquired on 11 July 2015, and 21 April 2019. The confusion matrix and corresponding classification accuracy parameters are as shown in Table 2.  
From Table 2, it can be seen that the Kappa coefficient, accuracy, precision, and F1-scores for WaterUNet are 0.97, 0.98, 0.99, and 0.98, respectively, indicating the superior classification accuracy of the proposed method. Based on the validation of the effectiveness of WaterUNet, we applied the trained model for all the data in the 10 m Sentinel-1 SAR dataset.