esa-sceva/satcom-chunk-collection huggingface dataset:
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.2 Ring-Opening Polymerization  
According to the article published by [PERSON] et al., a mesoporous, silica-coated Fe core-shell nanoparticle was synthesized via a surfactant template sol-gel method utilizing magnetic nanoparticles (MNPs) stabilized with nucleation seeds exhibiting superparamagnetic behavior [119]. The magnetic behavior and adsorptive properties of these nanoparticles show that these MNPs are appropriate candidates for supporting catalyst preparation via titanium immobilization achieved through post-synthetic procedures. The synthetic parameters are controlled in order to obtain high-quality, core-shell, stable, and biocompatible magnetic mesoporous nanoparticles (MMNPs), which were used to help to prepare heterogeneous catalysts for the ring-opening polymerization of \(\varepsilon\)-caprolactone (\(\varepsilon\)-CL) in an aqueous and organic medium with excellent activity.  
Since this titanium catalyst showed excellent activity for the ring-opening polymerization of \(\varepsilon\)-CL, it could also be used under similar conditions. Electrochemical measurements verified Fe (III)' s electron reduction process in magnetite with diverse coordination environments, which could be useful for fast and simple electrochemical measurements of the growth of a silica layer and the complete coating of MNPs [119].
## Discussion
### Limitations and Future Research  
With the TRI-GPP reconstruction in two phases, we confirmed that the more plots that are involved, the higher accuracy the model had (models in Phase 2 were always better than those in Phase 1). In further studies, we would collect more tree cores and increase the plot density in our study areas. From the simulated GPP values, the reconstruction of extreme values needs to be improved where some low GPP values in extreme drought could not be simulated very well. We speculated that less extreme values in training samples might lead to this issue. We tested the Convolutional Neural Network (CNN) in two small areas within our study site whose performances were worse than our chosen model due to the small sample size. We assume that if a deep-learning approach can adjust the size of our sample, the model may have a substantial improvement in future regression.  
Once our model (TRI-GPP) is constructed successfully, there are two fields where we can make full use of our models. Firstly, we will enlarge our research areas into a larger area or introduce certain advanced methods. Trees are around the whole world, so in theory,we could reconstruct a long-term simulated GPP for every vegetation area and witness some long-term ecological processes, including forest succession, vegetation dynamics, and others. Secondly, we might challenge some obsolete conclusions only based on 40-year satellite images (1986-present). We also found that there were some contradictions between the short-term dataset and the long-term dataset. It is worthwhile to list the scope of both datasets where researchers should know which dataset is more appropriate to their research. But this is not the end. With the simulated GPP, we expect more updates on outmoded findings established in the old era.
## 1 Introduction
A few studies have been conducted on web-based systems to integrate, process, and disseminate LiDAR data in a 3D webGIS framework [29; 30]. Further studies concentrated on cultural resource protection by importing more data types (such as 360\({}^{\circ}\) panoramas, remote sensing, and the Internet of things) into the 3D webGIS application [16] functionalities. The development of 3D data collecting methods and 3D webGIS applications in various fields has made great progress, all of which inspired us to construct a 3D webGIS-based MIDA application. To our best knowledge, few researchers try to develop a unified framework that couples data collecting, storage, visualization, dynamic prediction of mine surface deformation, and spatial analysis into a 3D webGIS-based application for a MIDA application. Similar research was carried out by [PERSON] et al. [8]; they established a 2D and 3D webGIS application in the service of the open-pit mining industry by integrating photogrammetric models, laser-scanned point clouds, and geological strata data, in which the basic GIS functionalities were provided, such as measuring geographical data, drawing 2D profiles, and querying spatial databases. Their webGIS system only used small-scale open-pit mines and lacked a MIDA application. Hence, this study provides early warning information caused by mining surface deformation. There is no custom network geographic information system, only a few different, difficult to learn, and separate software can achieve such requirements. It is because of this shortcoming that we have formulated the proposed system. The remaining contents of this study include the following: Section 2 describes the system design and components of the proposed methodology and the related technologies of this web-based framework; Section 3 evaluates the proposed system on case-based application tests to demonstrate its capabilities, feasibility, and reliability; Section 4 summarizes and discusses our findings; and Section 5 summarizes our work.
# On the Impact of Wavelength Dependency on Supercontinuum Generation in Photonic Crystal Fibers
## 2 Materials and Methods
### PCF Parameters' Wavelength Dependency  
As we have already pointed out, in our work, we will try to prove the relevance of the dependence on the wavelength of the dispersion parameters used for the propagation of the pulse within the PCF.  
In principle, the process to simulate the supercontinuum generation starts by choosing a PCF structure; this part has been explained in a previous section. At this point, we will calculate its parameters with a numerical algorithm for the Helmholtz equations. These parameters will be then employed to numerically solve the generalized NLSE and model the propagation of an input pulse through the given PCF.  
A specific numerical method based on centered finite differences [21] was employed to solve the Helmholtz Equations (2) and (3). Once performed, the fiber is characterized over the whole range of the studied wavelengths, obtaining the effective index and effective area relations.  
While it is a common practice to assume a working wavelength and refer all the parameters to this value [16], the results that will be presented in the next section show that the parameters' dependency must be calculated individually for each wavelength, for all the wavelengths in the simulation range.  
As can be seen in Figure 2, the variability of the dispersion parameters with the wavelength is significant from one end to the another of the simulated range. The impact on the lowest and highest wavelengths is usually neglected with the assumption of constant averaged values. In the Section 3, the influence of each individual value over its associated wavelength in the pulse propagation will be shown.
## 4 Description of the Structure: Optimization Algorithm  
In this paper, the genetic algorithm PSO (Particle Swarm Optimization) was used for optimization. Particle Swarm Optimization belongs to the category of Swarm intelligence methods [5], a dynamic optimization tool which can solve very complex optimization problems. The method operates on a population (swarm) of the particles, where each particle is placed at the position in the solution space. The algorithm operates using iterative selection of the best solution with all quality measures taken into account. The method is precisely described in the literature concerning biology inspired algorithms [6, 7, 16, 17].  
This research is being undertaken to perform learning of the chosen frequency response and to select a varying function of the parameters. The expected results of this study should reduce the transient states as much as possible. In this paper, a structure consisting of six elements was used. In the first case, we examined a three-layer network, where each layer had two elements. The second two-layer structure had three elements in each layer.  
The shaping of the frequency response was developed by choosing the values of the input weights (matrix A) and the output weights (matrix B). It was assumed that weights could be set between values 0 and 2.  
The learning process occurred using the time-invariant network. The parameters were varied in time during the transient state which was omitted in the filtration process. After the transition processes were completed, the network became stationary.  
In the course of the learning process, the expected amplitude characteristic was assumed. Selection of the weights was conducted by minimization of the error function [18]. The chosen function was the sum of squared error for subsequent values of angular frequency \(\omega\). The main advantage of such function was the maximization of large deviations without taking minor errors into account.  
\[min\;e=\sum\left(|G_{A}(j\omega)|-|G_{E}(j\omega)|\right)^{2}, \tag{6}\]  
where:  
\(G_{A}(j\omega)\)--the assumed value of the module at the point \(\omega\);  
\(G_{E}(j\omega)\)--value of the learned network module at the point \(\omega\).  
The examples of configurations and their performance of the analog networks are described precisely in preceding papers [15, 19]. To compare the possibilities of the network composed of the same number of elements yet in different configurations, tests were carried out analyzing their dissimilarity.
# Mdpj
## 1 Introduction  
Today, Von Neumann architecture, which consists of a CPU (Control Processor Unit), memory, and I/O (Input and Output), is considered a standard in computer architecture. Among other components, memory is a significant device as it stores command sets and calculated data. Many memory devices have been introduced today, such as SRAM, DRAM, SDRAM, etc.; however, DRAM (Dynamic Random Access Memory) has become the standard for reasons such as high reliability and ease of integration due to its 1T1C structure. Over time, DRAM has become required to process more data, and these requirements were particular accelerated by the Fourth Industry Revolution. To meet these demands, DRAM has been developed via scaling down like other semiconductors, leading to increased performance and decreased power consumption. However, scaling down has introduced several negative effects, such as GIDL (Gate Induced Drain Leakage), GIJL (Gate Induced Junction Leakage), 1-Row Hammer, and the PGE (Pass Gate Effect), which have emerged due to increased interferences [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. These negative effects can lead to data loss and therefore adversely impact the reliability of DRAM. Figure 1a illustrates the layout of DRAM in the unit array area. Although data can be recovered by performing a refresh operation, this results in higher power consumption and decreased performance [11]. The PGE occurs when the FPWL (Field Pass Word Line) is in the on state. Through the supplied voltage of the FPWL, the active word line is affected by an electric field. This mechanism decreases the threshold voltage of the active word line and leads to unwanted data access. Due to the structural characteristic where the word line crosses multiplecells, DRAM can lose data simultaneously because of this effect [12; 13]. Refreshing, which offers data recovery, can restore lost data; however, it consumes more power and decreases overall performance.  
In this study, we propose a 6F\({}^{2}\) structure with buried oxide under the active word line that protects the energy field from the FPWL to mitigate the PGE. To find the optimal value, we studied various modifications to the buried oxide, such as increasing oxide thickness, increasing oxide length, and decreasing the distance from the AWL to the buried oxide. Additionally, by adding interface traps to the buried oxide, we were able to conduct a study about the effect of traps. Figure 1a illustrates a diagram of the 6F\({}^{2}\) DRAM array at the top view, and Figure 1b shows a cross-section of the cell indicated by a red dotted line.
# Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
## 3 Proposed Calculation Process  
Figure 2 shows the whole calculation process of the proposed technique. A random phase distribution is used as an initial phase distribution of the CGH. The phase-only CGH is Fourier transformed to obtain the reconstruct the image, which is multiplied by the difference of the intensity distributions of the reconstructed image and the target image. Next, the inverse Fourier transform is performed and the phase distribution of the CGH of the previous iteration is multiplied. Then, the imaginary part is extracted and -4 is multiplied to obtain the gradient. The step length coefficient is calculated to obtain the increment of the phase distribution. Finally, the phase distribution of the CGH is revised. This process is performed iteratively.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
###### Abstract  
The Sentinel-3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean, land and atmospheric parameters with its four on-board sensors. Two Sentinel-3 satellites are currently on orbit, providing near-daily global coverage. Sentinel-3A was launched on 16 February 2016 and Sentinel-3B on 25 April 2018. For the early part of its operation, Sentinel-3B flew in tandem with Sentinel-3A, flying 30 s ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work, we consider the tandem-phase data from the infrared channels of one of the on-board instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1, the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of \(\sim\)0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level.  
Sentinel-3; sea and land surface temperature radiometer (SLSTR); tandem phase; earth observation metrology; calibration; validation 2020 IEEE/RSJ Web Conference 2020, 12, 2893; doi:10.3390/rs12182893
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.6 Lack of Experience  
Lack of experience was a challenge related to AR application that was identified by four papers. As mentioned earlier, AR highly depends on virtual manipulation. Researchers have reported that this manipulation in addition to the interface constitutes challenges, thus practice and experience is needed. Independent of any contextual factor, this challenge is related to the practitioners using AR, their use cases, and how much they are exposed to the technology. Recommendations were provided in some studies suggesting training sessions and practice to overcome this challenge.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 5 Experimental Comparison and Discussion
### Effectiveness Analysis  
In order to test the detection effectiveness of the proposed method in practice, the detection results of the original YOLOX algorithm, the improved algorithm in this paper and the improved algorithm trained by the data domain transfer strategy are visualized using a real nighttime vehicle/pedestrian dataset, as shown in Figure 7.  
From the above figure, it can be seen that the improved YOLOX algorithm has fewer false and false positive rates compared to the original algorithm, and it is more effective for nighttime vehicle/pedestrian detection when combined with the data domain transfer training strategy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**AP**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{**Inference**} & \multirow{2}{*}{**Model**} \\  & **Person** & & & & & & **Time (ms)** & **Size (MB)** \\ \hline Faster R-CNN & 0.762 & 0.757 & 0.760 & 0.753 & 0.705 & 0.810 & 84.7 & 166.0 \\ Cascade R-CNN & 0.765 & 0.765 & 0.765 & 0.749 & 0.710 & 0.792 & 97.1 & 277.0 \\ YOLOv3 & 0.700 & 0.743 & 0.722 & 0.676 & 0.564 & 0.836 & 34.9 & 235.0 \\ YOLOv4 & 0.747 & 0.745 & 0.746 & 0.687 & 0.570 & **0.863** & 44.2 & 244.0 \\ YOLOv5 & 0.740 & 0.737 & 0.738 & 0.721 & 0.636 & 0.831 & **10.6** & **13.7** \\ YOLOX & 0.768 & 0.762 & 0.765 & 0.736 & 0.685 & 0.796 & 11.2 & 17.2 \\ Improved YOLOX(ours) & 0.794 & 0.798 & 0.796 & 0.758 & 0.723 & 0.797 & 17.8 & 15.6 \\ Improved YOLOX + & 0.820 & 0.815 & 0.817 & 0.767 & **0.742** & 0.793 & 17.8 & 15.6 \\ Dataset Expansion & & & & & & & & \\ Improved YOLOX + & **0.831** & **0.816** & **0.824** & **0.779** & 0.734 & 0.829 & 19.7 & 15.9 \\ Domain Transfer (ours) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different algorithms.
## 4 Discussion
However, the zonal histogram analysis is limited by the difficulties in determining the extent of archaeological sites both from remote sensing and on the ground (Section 2.2), and it can reduce the samples significantly. Both analyses are also influenced by the fact that the images used are monthly or annual composites, and many variations might be observed with shorter temporal intervals, as shown by the 2018 monthly analysis. Of course, any accuracy in measurements is tied to the precision at which it is possible to georeference archaeological sites in GIS. While extreme care was taken during this process (see Section 2.2) by refining the dataset and including polygons only with specific criteria, the impact of any inaccuracy should be acknowledged. This will manifest mainly in the pixel analysis, because if the actual position of a site is different, the result will change as well. Of course, accurate field data coupled with a thorough analysis of remote sensing or cartographic data, will improve any inherent inaccuracy in the data. Another element to consider is the possible atmospheric impact on the results. All the images used to generate the NDWI were acquired at the TOA reflectance, meaning that they have not been atmospherically corrected. It must be said that the use of ratios (e.g., NDWI) can already lower the atmospheric impact [83; 84; 85; 86] (see also [PERSON] et al., 2015 [79], pp. 518-522). Applying a low cloud filter to generate cloudless images and a cloud masking function also minimises these effects [74; 87]. Nonetheless, its impact should not be underestimated, because the two products will have a slight difference in the pixel values (atmospheric correction usually lowers reflectance [88; 89; 90]. It should be  
Figure 33: The site of ‘Ana: (**a**) view before the Haditha Dam construction on a CORONA image (December 1967); (**b**) view during 2018 on a Planetscope Image (November 2018) ©TPMO (2020), a meander bar is visible along the river, covering the original island. mentioned that, if applied to the identification of archaeological remains, BOA reflectance data are, however, highly suggested [91; 92; 93]. Future studies focused on single areas might use atmospherically corrected data and compare TOA data results to see which one works best, especially when reference ground data are not available. Lastly, although it could be argued that the medium resolution of the images used could have a negative impact over the classification, the results highlighted above show that Sentinel-2- and Landsat-generated NDWI can still be used with a high degree of reliability. Of course, accuracy will always be tied to the resolution of reclassified and reference images, and higher-resolution data could always improve the results. However, as it has been already shown, when these are not available, testing with different thresholds can strengthen the results from medium-resolution images and eliminate noise from other ground sources caused by the surrounding context of each area [51; 67].