esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The global atmospheric concentration of carbon dioxide and its contribution to global warming requires technical measures to limit the emission of this greenhouse gas. One possibility is by separating the carbon dioxide from the waste gas stream of fossil power plants by applying absorption technologies. The absorption process of CO\({}_{2}\) can be performed using solvents with different additives. Amine solvents such as monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA) are common for CO\({}_{2}\) capture ([PERSON] et al. 2017) [1]. The reversible reactions and the moderate reactivity between CO\({}_{2}\) and amine solutions enable efficient CO\({}_{2}\) capture ([PERSON] 2016) [2]. [PERSON] et al. (2014) [3] studied nonaqueous amine solvents such as 2-(isopropylamino)ethanol (IPMEA), 2-(tert-butylamino)ethanol (TBMEA) and N-methyl-2,2'-iminodiethanol(MDEA) for CO\({}_{2}\) capture and found that CO\({}_{2}\) removal efficiency was in the range of 87-95% at equilibrium, depending on the operational conditions. Although amine absorption processes are widely used for CO\({}_{2}\) capture, they have some disadvantages related to equipment corrosion, amine degradation ([PERSON] 2014) [4], and high energy consumption ([PERSON] et al. 2017) [1]. The absorption technology for CO\({}_{2}\) capture consists mainly of the absorber column and the regeneration unit. The absorber column can be a tray or packed column. The absorbent enters the absorber from the top, and the waste gas, which contains CO\({}_{2}\), enters the absorber from the bottom. The gas and the liquid phases contact each other on the trays or the packing materials. The trays or the packed material enhance the gas/liquid interfacial area, which increases the mass and heat transfer between the contact phases. The CO\({}_{2}\) component transforms from the gas phase to a liquid phase and then is absorbed.
## 1 Introduction
With the goal to obtain more information than each sensor can independently provide [26], a number of blending algorithms have been developed that focus on spatial and temporal dynamics [27]. One such method is the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). It was designed to predict surface reflectance with the spatial resolution of Landsat and the temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) [28]. STARFM is, as of 2013, widely used [27]. Originally shown to capture phenology changes for forested or cropland areas, as well as a mixture of cropland, evergreen, and deciduous forest [28], it has since been applied to observe, for example, community level phenology [29], crop growth stages [30], urban phenomena such as the land surface temperature (LST) of the urban heat island [31], and the Normalized Difference Vegetation Index (NDVI) values of a semi-arid rangeland of northern Utah [32]. This study aims to build on the existing research and use blended, also known as fused or synthetic, satellite imagery to examine how the built up environment affects LSP of developed land in a metropolitan area of Northern Utah. We derived 12 years of fused Landsat and MODIS reflectance data using the STARFM method with three different selection criteria for input data. Then, from the resulting fused datasets, we derived three time-series of a standard vegetation index for the study area to estimate phenology metrics and make comparisons of the urban and exurban area. Our study addressed three important research questions:  
1. In our study area, is there an observable difference in key phenological parameters (Start of Season (SOS), End of Season (EOS), and Length of Season (LOS)) between the urban and exurban areas when using fused imagery over several years and how sensitive those differences to base pair selection? 2. If there is an observable difference in SOS, EOS, or LOS between the urban and exurban areas when using fused imagery, how often do these differences appear? 3. Does the amount of impervious area at a location affect any observable differences in SOS, EOS, or LOS between the urban and exurban areas when using fused imagery?
# Feature-Weighted Sampling for Proper Evaluation of Classification Models
## 2 Materials and Methods
### Evaluation of FWS Method
#### 2.3.2 Benchmark Datasets and Classifiers  
To compare the proposed FWS and RBS, we used 20 benchmark datasets with various numbers of features (attributes), classes, and instances. The datasets were collected from the UCI Machine Learning Repository ([[http://archive.ics.uci.edu/mL/](http://archive.ics.uci.edu/mL/)]([http://archive.ics.uci.edu/mL/](http://archive.ics.uci.edu/mL/)), accessed on 25 February 2021) and Kaggle site ([[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)), accessed on 25 February 2021), and are listed in Table 1. Four classification algorithms, KNN, SVM, RF, and C50 were tested. They are supported by R packages. The packages and parameters used are listed in Table 2. We divided the entire dataset into training and test sets at a 75:25 ratio to build and evaluate the classification models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Name** & **\# of Features** & **\# of Instances** & **\# of Class** \\ \hline
1 & audit & 25 & 772 & 2 \\
2 & avila & 10 & 10,430 & 12 \\
3 & breastcancer & 30 & 569 & 2 \\
4 & breastTissue & 91 & 106 & 6 \\
5 & ceoil & 7 & 336 & 8 \\
6 & Frogs\_MFCCs & 22 & 7127 & 3 \\
7 & gender\_classification & 7 & 5001 & 2 \\
8 & glass & 9 & 214 & 6 \\
9 & hill\_Valley & 100 & 1212 & 2 \\
10 & ionosphere & 33 & 351 & 2 \\
11 & iris & 4 & 150 & 3 \\
12 & liver & 6 & 345 & 2 \\
13 & music\_genre & 26 & 1000 & 10 \\
14 & pima\_diabetes & 8 & 768 & 2 \\
15 & satimage & 36 & 4435 & 6 \\
16 & seed & 7 & 210 & 3 \\
17 & statlog\_segment & 16 & 2310 & 7 \\
18 & wdbc & 30 & 569 & 2 \\
19 & winequality & 11 & 4893 & 6 \\
20 & Wireless\_Indoor & 7 & 2000 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of benchmark datasets.  
Table 3 shows the final experimental results when k = 3 and the bin width is hybrid. The 61 cases (76%) of _MAI_ of FWS were better than those of RBS, and RBS was better than FWS in 19 cases (24%). This result indicates that FWS is a more improved method than original RBS and previous methods. The details are discussed in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Classifier** & **MA** & **SD** &
\begin{tabular}{c} **RBS** \\ **Accuracy** \\ \end{tabular} &
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 2 System Model
### Transmitter  
Each transmission frame consists of a pilot block with \(N_{p}\) symbols and \(D\) data blocks. The \(d\)-th data block contains \(N_{d}\) symbols, where \(d\in\{1,2,\ldots,D\}\) as shown in Figure 1. The set of symbol indices in the \(d\) data block is defined as \(\mathcal{N}_{d}=\{(d-1)N_{d}+1,\ldots,dN_{d}\}\). The information bits are encoded using a channel encoder with code rate \(R_{c}\), and then mapped to \(M\)-ary quadrature amplitude modulation (QAM) symbols by \(\bar{\mathbf{s}}[n]\in\mathbb{C}^{N_{t}\times 1}\). The QAM symbol set is \(\tilde{\mathcal{X}}=\{\mathbf{s}_{1},\ldots,\mathbf{s}_{K}\}\) where \(\mathcal{K}=\{1,2,\ldots,K\}\) is the index set of possible QAM symbol. The size \(|\mathcal{K}|=M^{N_{t}}\) is determined by the modulation order \(M\) and the number of transmit antennas \(N_{t}\).
## 4 Performance evaluation
### Data Analysis
In general, we observed that the average CCA values for nodes in the close group were higher than the average CCA values of the nodes in the medium and far groups. This could be explained because the nodes were closer to the building entrance, where other wireless technologies were known to operate in the same sub-GHz band. In addition, we observed that there were certain days where nodes experienced sporadic interference peaks of up to 5-10 dB for nodes in the medium and far groups and 20 dB for nodes in the close group. Interestingly enough, such peaks did not seem to be correlated among nodes and seemed to affect the SUN-FSK and SUN-OQPSK modulations more, which could be attributed to concurrent transmissions from other networks operating in the same sub-GHz band. Figure 12 shows the CCA histogram grouped by clusters (i.e, close, medium, and far in each row). In general, it is interesting to observe that for nodes in the close group the CCA values for SUN-OFDM are spread, whereas for nodes in the medium and far groups, they are more concentrated. As mentioned earlier, this confirms the fact that nodes in the close group operate near the warehouse office, where other wireless technologies transmitting in the 868 MHz band are known to be operating. Finally, Figure 13 shows the RSSI vs. PDR scatter plot for each modulation and node, where each point is the eight hour average of both parameters. Contrary to common wisdom, where nodes experience connected, transition, and disconnected zones according to their communication distance, our measurements showed that the PDR is not only related to distance, but largely impacted by  
Figure 11: Per-modulation CCA value for each node. multi-path propagation and interference. In particular, for nodes in the close and medium groups, where the average RSSI is well above the modulation sensitivity, we observe that the PDR ranges from 0% to 100% in a random pattern. In contrast, for nodes in the far group, we observe that the connected, transition and disconnected zones start to appear in the plots. This confirms the idea that nodes in the close and medium groups are limited by multi-path propagation and external interference, whereas nodes in the far group are limited by propagation effects. Figure 12: CCA histogram for each node (grouped by cluster). Figure 13: Per-modulation RSSI vs. PDR values for each node.
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 2 Integrated Photonic Structures
### Optical 2D and 3D Waveguides  
Resonance can be geometric, as occurs in integrated optical waveguides where constructive interferences result in guiding light due to total internal reflection. Thus, optical waveguides can be classified depending on geometry. Planar (2D) dielectric waveguides are built of layers of high- and low-refractive-index materials, providing confinement only in vertical direction. Nonplanar (3D) waveguides, providing confinement in two directions, can have different cross-sections, such as ridge, rib, stripe-loaded, or buried; slot structures, moreover, can have different forms of guiding core surrounded by cladding material (see Figure 1 for some examples) [63]. Correspondingly, the most suitable fabrication methods must be selected (e.g., high- or low-energy diffusion, thin-film deposition, direct laser writing). The need of efficient coupling to other integrated components makes the channel waveguide the most commonly used structure. Numerical and analytical methods are widely available to optimize the design of waveguides and circuits [64].  
The next step, from cm to micrometer scale, was motivated by the wish to investigate light behavior on the microscale and its interaction with micro-objects. The key challenges were a reduction in size of optical devices and improvement in their performances. The main goal was to go beyond the limit of integrated optics, offering a reliable platform for dense integration [65].
## 4 Result and Analysis  
### The Calibration and Validation of Parameters  
Upon completion of the SWAT model, the SWAT-CUP (version10.6) software is employed to conduct a calibration study of the outcomes. Given the attributes of the study region, 500 iterations are executed utilizing the SU-F2 sampling algorithm integrated into the software to derive the ideal values for each parameter. The model's appropriateness is assessed using the coefficient of determination (R\({}^{2}\)) and the Nash-Sutcliffe efficiency coefficient (NS). In hydrology, R\({}^{2}\) (coefficient of determination) and NS (Nash-Sutcliffe efficiency coefficient) are critical metrics for assessing the performance of hydrological models, with R\({}^{2}\) values typically ranging from 0 to 1. When R\({}^{2}\) = 1, it indicates that the measured value is entirely congruent with the simulated value, representing an idealized condition of perfect fit. Typically, an R\({}^{2}\) value of 0.6 indicates that the model's fit to the data is regarded as satisfactory. The fitting impact is moderate between 0.3 and 0.6; below 0.3, the fitting effect is inadequate. This norm is not absolute and will be marginally modified based on varying research domains and data attributes. In certain intricate extended series with significant fluctuation in hydrological data settings, an R\({}^{2}\) value exceeding 0.5 is considered favorable; Basic, short-duration laboratory-scale hydrological models may necessitate exceeding 0.8; Theoretically, the value of the Nash-Sutcliffe efficiency coefficient (NS) ranges from \(-\infty\) to 1. When NS = 1, the model simulation aligns precisely with the actual conditions; conversely, when NS \(\leq\) 0, the model simulation findings are highly untrustworthy. In real hydrological applications, a Nash-Sutcliffe efficiency (NS) greater than 0.5 typically indicates a superior performance of the hydrological model simulation. Evaluation criteria for these indicators will be adjusted according to varying hydrological conditions, including humid regions with ample rainfall, desert regions with limited
###### Abstract  
The demand for functional foods is increasing, particularly concerning plant-based probiotici products. These products are becoming a popular alternative to dairy-based options. However, fermented vegetables have a high salt content, which can have negative effects on health if consumed excessively. There has been difficulty in finding the right balance in terms of the salt concentration for the high-quality fermentation of vegetables. To preserve its valuable nutrients and ensure maximum quality, as a vegetable, yellow beetroot was used. The aim of this study was to increase the lactic acid bacteria and preserve the active compounds in fermented yellow beetroot. The research used varying salt levels in brine (0-6%) to examine their impact on the physical, chemical, and microbiological properties, as well as the degradation temperatures and thermal analysis. Moreover, to create a new product, fermented beetroot was freeze-dried. This study found that stable products were obtained after 4 and 8 days due to the development of lactic acid bacteria. The addition of salt did not affect the stability of the products, but it did affect the active ingredients. Higher salt additions resulted in more salt penetrating into the beetroot tissue, impacting the amount of salt consumed. Our study found that a 1% addition of salt led to higher levels of active ingredients and satisfactory amounts of bacteria. Yellow beetroot, with its high content of polyphenols, pigments and lactic acid bacteria, can be used as a healthy ingredient in a diet after undergoing a freeze-drying process. These research findings provide valuable insights into optimizing the salt content to preserve the lactic acid bacteria, nutrients, and desirable color characteristics of fermented yellow beetroot.  
lactic acid fermentation; beetroot; _Beta vulgaris_ L.; betalains; salt addition
## 4 Discussion
### Output Variables Validity  
Strong and very strong correlations were observed between GPS and radar for the three main force-velocity profile variables (P\({}_{\mathrm{max}}\), F\({}_{0}\) and V\({}_{0}\)). These correlations are higher than those obtained in other studies [6; 8; 20] (Figure 3). In our study, the GPS units tested had a sampling rate of 50 Hz, which is higher than in other studies (5 Hz, 10 Hz and 20 Hz) and closer to the radar sampling rate. [PERSON] et al. compared two GPS systems with different sampling rates and showed that results were better at higher sampling rates [6].  
Regarding typical errors of estimate, the values found in the present study are overall smaller or similar to those reported by [PERSON] et al. [8] (Figure 4) and [PERSON] et al. [21]. This can also be explained by the higher sampling rate of the models tested here (50 Hz vs. 10 Hz).  
However, the mean biases we obtained were greater than the mean biases obtained by [PERSON] et al. [8] (Figure 5), who performed measurements on an open field with no surrounding metallic structures. Contrastingly, our measurements were performed outdoors  
Figure 4: Typical errors of estimate (%) for P\({}_{\mathrm{max}}\), F\({}_{0}\) and V\({}_{0}\) found for the two GPS units tested here and for the GPS used in [PERSON] et al.’s study [8].  
Figure 3: Plots showing Pearson correlation coefficients for P\({}_{\mathrm{max}}\), F\({}_{0}\) and V\({}_{0}\), found by our two GPS units and by GPS compared to two other studies [6; 7; 8].  
as well, but on a field surrounded by metallic barriers, which may partly explain why the force-velocity profile variables show greater mean biases in our study. In addition, the greater bias in our results could also be explained by the fact that the K-Sport(r) manufacturer does not apply any filter to the raw data, whereas others apply a proprietary filter to the raw data. This filter, designed to improve data quality, may also help reduce bias when fitting smoothed raw data with the exponential model used. The environment can also influence measurements. In fact, we have observed a deterioration in measurements when we are close to metal structures or grandstands.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 4 Conclusions
that it selects the operating frequency on either the 2.4 or 5.8 GHz band and achieves diversity of the operating frequency, unlike the conventional RF WPT system that uses only a single band. Thus, the proposed RF WPT system can be flexibly operated according to the given frequency environment, the WPT scenario, and the operating frequency of the power-receiving device. For these reasons, it is expected that the dual-band WPT system proposed in this study can be applied to advanced RF WPT technology and can provide convenient wireless charging for various IoT devices, such as sensors, home appliances, medical devices, and robots. Furthermore, the optimizing method of the dual-band array based on the analysis of the received power (or power transmission efficiency) as presented here will be widely applicable in the radar and 5G and 6G communication fields based on array antenna technology. Writing, C.-M.S. ; array optimization, C.-M.S. ; antenna design and simulation, C.-M.S. and S.T.-V.; Implementation system, K.-Y.L., Y.Y. and K.C.H. ; Experiment, C.-M.S. and H.-J.L. ; Methodology, K.C.H. ; Writing Review and Editing, H.-J.L. and K.C.H. ; Conceptualization, C.-M.S., S.T.-V., K.-Y.L., Y.Y., and K.C.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2018R1C1B6005024). Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
## 5 Shortcomings and Future Works  
In this paper, we evaluated the consistency of vegetation phenological parameters under different extraction methods of the same fitting function applied to Northern Hemisphere in 1982-2015 based on the GIMMS3g dataset, but there are still some areas that need to be further explored in-depth. When using different methods to extract vegetation phenological parameters, the effects of water stress on vegetation photosynthesis are not considered, especially in arid ecosystem, which may also lead to the uncertainty of phenological parameters. The spatial resolution of the GIMMS3g NDVI dataset is lower than that of the land cover dataset, which will lead to inaccurate phenological results for different vegetation types after resampling. Furthermore, the period of the GIMMS3g NDVI dataset is only from 1982 to 2015, and we will integrate a series of products over a longer time and at a higher resolution to further discuss the consistency of vegetation phenological parameters under different extraction methods in future studies.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
###### Abstract
To better focus on the most discriminative region for judging the above middle-sized nodules, we propose a Local Focus Scheme (LFS) to exploit fine-grained local information. Inspired by channel attention [7] that models channel-wise topology of each sample, different channels of features can be regarded as multiple neurons corresponding to objects, thus understanding the relations of various channels is a vital way to focus on the local discriminative features. We formulate a simple yet effective scheme to implement the above intention, i.e., calculating a regularized channel-wise grouped attention and using the proposed keyMask to force networks to explore the most discriminative local information. Our key contributions are summarized as three-fold:  
* We propose a novel Multi-Granularity Dilated Transformer (MGDFormer) to learn pixel-wise global attention of objective region, with an aim to construct a more robust long-range global representation;
* To better focus on local discriminative features for classifying hard samples (i.e., middle-sized nodules), we innovatively design a Local Focus Scheme (LFS) to force networks learning fine-grained local information by modeling channel-wise topology;
* The experimental results on mainstream datasets demonstrate the superiority of our model has a competitive performance compared with the state-of-the-art approaches. Figure 1: Visualizations of different samples in LIDC-IDRI dataset. _be_ and _mal_ represents benign and malignant, respectively.
# On the Finite Element Modeling of the Lumbar Spine: A Schematic Review
## 3 Results
### Modelling of the Different Components
#### 3.3.2 Intervertebral Disc  
The nucleus pulposus can be modelled in different ways. It can be represented as an empty cavity filled with fluid, or by modelling its elastic behaviour as an incompressible material. Even if the nucleus is modelled with hyperelastic behaviour, as a rule the Neo-Hookean, Mooney-Rivlin, or Ogden model is used. Therefore, the annulus ground substance is mostly subdivided into eight or more concentric layers of fibers, starting from the outermost layer and moving to the innermost layer by varying orientation and thickness. The annular ground substance is modelled with hyperelastic behaviour using with a Neo-Hookean, Mooney-Rivlin, or Ogden model. The annular collagen fibers are modelled in terms of their nonlinear behavior or by using elastic isotropic material properties that vary with each layer.