esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cytocompatibility Experiment  
Cytocompatibility of the citrate-coated AuNPs in terms of cell viability was examined by the WST-1 assay. Concisely, HEK-293 cells were exposed to standard culture in DMEM (containing 10%of FBS, 1% _v/v_ penicillin and streptomycin), which was pre-warmed in water baths maintained at 37 \({}^{\circ}\)C. After 24 h, cells were harvested using trypsin and afterwards re-suspended in fresh DMEM for seeding in 96-well plates (10\({}^{3}\) cells/well). Following cell attachment, different concentrations of AuNPs (0.1-0.5 mg/mL) in DMEM were added to cells then cells were incubated at 37 \({}^{\circ}\)C in a humidified environment of air (95%) and CO\({}_{2}\) (5%) for a different time; 24 h and 48 h. After incubation time, the cell proliferation reagent WST-1 was newly prepared at 10% in DMEM in the dark. The medium of each well was carefully removed and 10 \(\mu\)L of WST-1 solution plus 100 \(\mu\)L DMEM were added to each well. Following that, plates were returned into the incubator at 37 \({}^{\circ}\)C for 45 min to 2 h. Then, each well was read using a microplate reader (ELx800, BioTek Instruments, USA) at a primary wavelength of 480 nm. The percentage of cell viability was determined by the underlying equation (Equation (2)):  
\[\text{cell viability (\%) = (OD Sample/OD control) \times 100} \tag{2}\]
## 4 An Investigation for a Remaining Issue
### System Model  
The system model is given in Figure 3, in which the direct link is assumed to be unavailable, which may be a result of severe fading and shadowing between the source and destination. In this network, we assume that all channels are subject to block Rayleigh fading, which is when the fading keeps constant during a transmission block and changes independently from one to another. For each relay \(R_{k}\), its channels, \(h_{SR_{k}}\) and \(h_{R_{k}D}\), are modelled as correlated zero-mean complex Gaussian RVs with variance \(\delta_{k}^{2}\). In addition, for any pair of relays \(R_{l}\) and \(R_{k}\), their channels are independent. It is noteworthy again that the assumption of the correlation between the two channels of each relay is justifiable when (1) there are common scatterers that affect both \(h_{SR_{k}}\) and \(h_{R_{k}D}\)[9; 14; 15]; (2) the relays are moving between the source and the destination [9; 22]; and (3) when the considered system is deployed in an indoor environment [8]. The end-to-end SNR at the destination of the selected relay is given by  
\[\gamma_{SR_{k}D}=\frac{\gamma_{SR_{k}}\gamma_{R_{k}D}}{\gamma_{SR_{k}}+\gamma_{ R_{k}D}+1}, \tag{12}\]  
which is bounded as follows [23]  
\[\max_{k=1,\cdots,K}\left[\frac{1}{2}\min\left(\gamma_{SR_{k}},\gamma_{R_{k}D} \right)\right]\leq\gamma_{SR_{k}D}\leq\max_{k=1,\cdots,K}\left[\min\left(\gamma _{SR_{k}},\gamma_{R_{k}D}\right)\right]. \tag{13}\]
## 3 Methods
### Phenological Analysis
#### 3.2.2 Calculation of Phenological Metrics  
To derive the timing of SOS and EOS phenological metrics in this study, we chose to follow the widely adopted dynamic threshold approach [37]. The basic principle of this method involves first normalising the data of the time series \(NDVI\) (which ranges from \(-1\) to \(1\)) into an \(NDVI_{\text{ratio}}\) of the phenological cycle amplitude of \(NDVI\) (with values between 0 and 1). Subsequently, this \(NDVI_{\text{ratio}}\) is used to establish dynamic thresholds for SOS and EOS retrieval. Specifically, when \(NDVI_{\text{ratio}}\) exceeds or is below a certain threshold, the corresponding day of the year is determined as the SOS/EOS (see an example of different thresholds in Figure 3a). The \(NDVI_{\text{ratio}}\) was calculated using Equation (4).  
\[NDVI_{\text{ratio}}=\frac{NDVI_{t}-NDVI_{\text{min}}}{NDVI_{\text{max}}-NDVI_{ \text{min}}} \tag{4}\]  
where \(NDVI_{\text{t}}\) refers to the \(NDVI\) value on day t, \(NDVI_{\text{min}}\) and \(NDVI_{\text{max}}\) are the maximum and minimum \(NDVI\) values in the phenological cycle, respectively.
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 3 System Framework
### Spatial-Temporal Graph Attention Neural Network  
This article proposed a Spatial-Temporal Graph Attention Neural Network (STGAT) to extract features from decomposed temporal data and graph features preprocessed, respectively, in Sections 3.1 and 3.2. Through fusing spatial dependency information and temporal dynamic price fluctuation extracted using a graph attention module (GAT module) and temporal convolution module (TCN module), the framework can simultaneously capture the spatial and temporal features in the stock price prediction task.  
Due to the long time span of the dataset used in this experiment, 20 consecutive trading days were treated as a time step to facilitate the construction of graph data. STGAT takes decomposed temporal components from one time step as temporal data, including the trend feature \(T_{i}=(t_{1}^{i},t_{2}^{i},\ldots,t_{20}^{i}),i=1,2,\ldots,m\), seasonal feature \(S_{i}=(s_{1}^{i},s_{2}^{i},\ldots,s_{20}^{i})\), \(i=1,2,\ldots,m\), and residual feature \(R_{i}=(r_{1}^{i},r_{2}^{i},\ldots,r_{20}^{i}),i=1,2,\ldots,m\). The temporal data above serve as input featured for the \(i_{th}\) stock, which functions as a node within a single graph. Subsequently, the trend and seasonal features of stocks, embedded within the graph structure, are separately fed into a graph attention module and a temporal convolution module. This dual-pathway approach enables the model to further capture the intrinsic spatiotemporal features inherent in the stock data.  
Notably, due to the inherent unpredictability of the residual feature, which often contains noise or random fluctuations that cannot be explained by trend or seasonal patterns, it is filtered out and processed separately by a multi-layer perceptron (MLP). This approach aims to mitigate the potential negative impact of noise while preserving the useful features in residuals.
## Author Contributions  
[PERSON], [PERSON], [PERSON] and [PERSON] contributed in the design of Generalized sidelobe canceller for the suppression of sidelobes in NCOFDM based cognitive radio systems, simulations of the results and preparation of manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Techniques used**} & \multicolumn{6}{c}{**Sidelobe Power in Regions**} \\ \cline{2-6}  & **I** & **III** & **V** & **VII** & **IX** \\ \hline Original & \(-\)30 dB & \(-\)29 dB & \(-\)20 dB & \(-\)27 dB & \(-\)32 dB \\ CCs (Brandes) & \(-\)45 dB & \(-\)42 dB & \(-\)35 dB & \(-\)41 dB & \(-\)46 dB \\ CCs (GA) & \(-\)56 dB & \(-\)62 dB & \(-\)42 dB & \(-\)52 dB & \(-\)62 dB \\ CCs (DE) & \(-\)70 dB & \(-\)68 dB & \(-\)42 dB & \(-\)66 dB & \(-\)68 dB \\ ACCs & \(-\)35 dB & \(-\)34 dB & \(-\)26 dB & \(-\)32 dB & \(-\)38 dB \\ ASCW & \(-\)40 dB & \(-\)40 dB & \(-\)28 dB & \(-\)36 dB & \(-\)42 dB \\ Proposed technique & \(-\)135 dB & \(-\)133 dB & \(-\)95 dB & \(-\)125 dB & \(-\)150 dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of sidelobe suppression between existing and proposed techniques.  
Figure 13: The PSD comparison between the proposed technique and existing techniques including CC (Brandes), CC (GA), and CC (DE).
## 2 Methods
### RQ Decomposition
#### 2.1.1 Principal Component (PC)  
PC is one of the methods to determine weight matrix for SSI algorithm. PC algorithm incorporates a right weight matrix to calculate singular values. The block Toeplitz matrix is obtained using the output covariance matrix of the past and future data \(\left(\Phi_{[Y_{p},Y_{f}]}\right)\). The principle components of the covariance Toeplitz matrix \((C_{i})\) are used for weighting of the estimated results.  
\[C_{i}=\Phi_{[Y_{f},Y_{p}]}=\Gamma_{i}\Delta_{i}^{c}, \tag{6}\]  
The SVD of the estimated \(C_{i}\) matrix reveals system order, column range \((\Gamma_{i})\) and the row range \(\left(\Delta_{i}^{c}\right)\). A deterministic balanced realization of the state-space equation is achieved through using weighting matrices of \(W_{1}\) and \(W_{2}\).  
\[W_{1}=I_{li}, \tag{7}\]  
\[W_{2}=Y_{P}^{T}\Phi_{[Y_{p},Y_{f}]}^{-1/2},Y_{p}, \tag{8}\]  
By using the left singular vectors and the singular values of the weighted projection \(W_{1}O_{2}W_{2}\), the extended observability matrix \((\Gamma_{i}\) and \(\Gamma_{i-1})\) could be determined as shown in Equations (13) and (14). Equation (14) presents how the extended observability matrix is used to calculate state parameters.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### CFAR Detector Based on K-Distribution  
When considering the large scale of the original image, recognition speed using only the deep learning model will be relatively slow. Therefore, this paper proposed using CFAR to perform the initial ship detection, the results of which are then used as the input for the YOLO method to obtain the final, more accurate, detection results. The initial ship detection results are stored as sub-images that are suitable for neural network recognition, and then input into the YOLO model to obtain accurate detection results.  
The false alarm rate is the probability that the detector will misjudge noise or other interference signals as valid target signals in a given unit of time. By setting a constant false alarm rate, stable target detection results can be obtained during signal detection. It is necessary to carry out statistical analyses of the characteristics of sea clutter to accurately model background sea clutter in order to obtain a constant false alarm effect under different sea and environmental conditions. For this reason, different clutter distribution models have been proposed, including the Rayleigh, Lognor-mal, Weibull, \(\alpha\)-steady distribution, \(G^{0}\) distribution [26], and K-distribution [27] models, among which the K-distribution is the most commonly used statistical model. It not only accurately matches the clutter amplitude distribution over a wide range, but also accurately simulates the correlations between clutter echo pulses. As such, it has become a classic model for describing sea clutter.  
Figure 2: Representative examples of sea-land segmentation. (**a,c**) Original images. (**b,d**) Images after sea-land segmentation.
In addition, if this safety distance can be reduced, a work environment in which other production processes such as welding and painting work, etc. can be performed together during radiation inspection is possible, thereby improving productivity. These constraints and challenges associated with using radiation require the development of an optimal automatic inspection device that can satisfy radiation exposure restrictions and reduce the testing speed. As part of our study on automatic inspection devices using radiation, we developed an automatic device for inspecting pipe circumference welds by using industrial digital radiographic testing (DRT) technology. This technology can electrically convert radiation signals into digital images by using a digital detector array to replace conventional RT techniques using films. This method allows the real-time inspection results to be viewed on a monitor [3; 4; 5]. The structure and overall research content of this paper are as follows. \begin{table}
\begin{tabular}{l l} \hline \hline
**Items** & **Main Content** \\ \hline \multirow{7}{*}{Restrictions on the use of radiation sources} & (1) Ir-192, Se-75 \\  & - In radiological facilities, less than 150 Ci \\  & - In other areas, less than 20 Ci (Se-75: 47 Ci) \\  & (2) Co-60, X-ray generator \\  & - For use only in radiological facilities \\  & (1) Restricted access area accessible to the general public \\ \multirow{2}{*}{Control of public access during radiation work} & - Access is restricted when more than 1 μVv/h occurs. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contents of radiation safety management standards of the Republic of Korea. Firstly, as described in Section 2, we utilized a digital detector array, a key equipment in digital radiography testing, to verify the quality differences among radiation sources and determine the radiation sources to be applied in the inspection device. Furthermore, we established inspection techniques and sequences for automated testing and assembled the equipment. As described in Section 3, conservative theoretical calculations regarding radiation leakage were implemented, and these were modeled to predict radiation leakage values and radiation values according to distance using FLUKA simulation. Subsequently, based on these predictions, we compared and verified experimental values against simulation values by using radiation on the actual equipment at the location of leakage. Through verification, additional shielding designs were incorporated, and through two times of simulation and experiment comparisons, radiation leakage was mitigated, satisfying the target radiation distribution values in theory.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 2 Preparation of IIPs
### Preparation Methods of IIPs
#### 2.2.3 Suspension Polymerization  
In suspension polymerization, monomers are dispersed into monomer droplets in the dispersed phase containing water, monomer, initiator and porogen, and suspended in water under the action of agitation and dispersant [19]. The monomer droplet is equivalent to a small reactor for bulk polymerization, and its polymerization mechanism is the same as that of bulk polymerization [42]. Compared with the bulk polymerization method, the adsorption capacity, affinity constant and homogeneity of the associated binding sites of the IIPs prepared using this method were significantly improved [43]. Suspension polymerization usually results in large spherical polymer beads in the range of micron to millimeter, which is very conducive to the large-scale synthesis of imprinted polymers. It is very important to choose the appropriate dispersion medium because it may adversely affect the identification of targets [37]. [PERSON] et al. prepared copper(II) IIPs by suspension polymerization using copper(II) sulfate pentahydrate as the template ion, 4-VP and MAA as  
Figure 1: Schematic diagram of synthesis principle of IIP: (**A**) Complexation, (**B**) Polymerization, (**C**) Template Removal, (**D**) Selective Binding. Reprinted with permission from Ref. [24]. Copyright 2022, Elsevier.  
the functional monomers, EGDMA as the crosslinker, 1,1\({}^{\prime}\)-azobis(cyclohexanecarbonitrile) as the initiator, toluene as the porogen, and hydroxypropyl methyl cellulose as the stabilizer in the polymerization reaction [44]. [PERSON] and [PERSON] synthesized ion imprinted and carbon nanofibers-grafted polymeric (CNF-IIPs) beads by suspension polymerization using Pb\({}^{2+}\) as the template, allyl thiourea as the ligand, CNF as the carrier material, EGDMA as the crosslinking agent, toluene as the porogen, and AIBN as the initiator [45].
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
###### Abstract  
Solar flares (SFs) and intense radiation can generate additional ionization in the Earth's atmosphere and affect its structure. These types of solar radiation and activity create sudden ionospheric disturbances (SIDs), affect electronic equipment on the ground along with signals from space, and potentially induce various natural disasters. Focus of this work is on the study of SIDs induced by X-ray SFs using very low frequency (VLF) radio signals in order to predict the impact of SFs on Earth and analyze ionosphere plasmas and its parameters. All data are recorded by VLF BEL stations and the model computation is used to obtain the daytime atmosphere parameters induced by this extreme radiation. The obtained ionospheric parameters are compared with results of other authors. For the first time we analyzed physics of the D-region--during consecutive huge SFs which continuously perturbed this layer for a few hours--in detail. We have developed an empirical model of the D-region plasma density and gave a simple approximative formula for electron density.  
oservations; solar radiation; sun activity; atmosphere; disturbances; modeling 2021  
1 1 2021
# Study of the Properties of Human Milk Fat Substitutes Using DSC and GC Methods
###### Abstract  
The development of infant milk fat similar in its structure and composition to that present in human milk receives a considerable amount of attention and has become a challenge for the world's food industry. The aim of this work was an attempt to obtain structured lipids resembling human milk fat (HMF) by modifying mixtures of lard (L) and rapeseed oil (RO) catalyzed by Lipozyme RM IM. The enzymatic reactions were carried out at 70 \({}^{\circ}\)C for 4, 8, and 24 h. The second objective was to study the properties of obtained substitutes of human milk fat (HMFS) using gas chromatography, thin layer chromatography, and differential scanning calorimetry (DSC) methods. Chromatographic methods were used to determine the composition of fatty acids (FAs) and the rapesiogenic structure of triacylglycerboards (TAG) of obtained HMFS. DSC methods were used to establish the oxidative stability of HMFS and to calculate their kinetic parameters of oxidation. Structured lipids obtained from a mixture of L and RO after 4 h interesteficration in 70 \({}^{\circ}\)C were characterized by the closest FA content and their distribution in TAG to HMF. Interesteficration influenced the decrease of the induction time of obtained structured lipids.  
Keywords:human milk fat substitutes; differential scanning calorimetry; gas chromatography +
Footnote †: journal: Applied Sciences
# Analytical Model for Angular Distortion in Multilayer Welding under Constraints
## 2 Methods
### Bead-on-Plate Welding Experiment
In addition, the reverse distorted shape generated during heating might be maintained until being completely cooled down. [PERSON] et al. [21] has reported that the distortion direction can be affected by the asymmetry of the cross-sectional profile along the thickness direction and even pores generated by insufficient flow of the molten pool. Consequently, it is almost impossible to quantitatively predict the amount and direction of deformation above the threshold. Therefore, we only address cases in which the distance between the load position and neutral axis is sufficiently long, as assumed in Equation (6). Thus, we used data that were below the threshold (dashed line in Figure 6). When all of the the welding passes are completed at the same heat input, the third layer or beyond results in welding within this range. Figure 4: Angular distortion according to number of welding passes. Figure 5: Bead cross-sectional area and square of height according to heat input. Without external constraint, the calculated value of \(C\) in the linear section below the threshold was 4.75 from Figure 6. Hence, we verified the relationship between bead area and angular distortion in Equation (8). The relationship between heat input, bead area, and angular distortion, as shown in Figures 5 and 6, implies that heat input is proportional to plastic area as in Equation (7), which is proportional to angular distortion shown in Equation (6).
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 6 Evaluation
### Bandwidth
#### 6.1.4 Simulation Evaluation Metrics  
In order to observe the optimization effect of BLOCK-ICN compared to the bitcoin network, the variables of the experiment included the blockchain message broadcast coverage, blockchain network scale, proportion of ICN nodes, ratio of unicast neighbors of ICN nodes, bandwidth between ICN nodes, and positions of miner nodes. By controlling the above variables, we selected the following metrics to evaluate the optimization of the blockchain network:  
* Broadcast delay: The broadcast delay of the blockchain network refers to the time required for transactions and blocks to be broadcast in the network. In the experiment, we selected the broadcast delay of blocks in the network as the main reference, and we fixed the number of blockchain network nodes to 16,000, which is the current scale of the actual Bitcoin network. We compared the changes in block broadcast time under different broadcast coverage rates between BLOCK-ICN and the Bitcoin network.
* Network scalability: Network scalability refers to the changes in network broadcast delay as the number of network nodes increases. According to data from bitnodes [41], the number of Bitcoin nodes was about 6000 in 2015, about 9000 in 2019, and was estimated to be about 16,000 in 2023. Therefore, in our experiment, network scalability refers to the changes in the time required to broadcast blocks to the entire network as the number of nodes increased from 2000 to 16,000.
* Network load: Since simulating ICN multicast in the P2P network is difficult, we based our analysis on the existing research on NDN multicast and IP unicast bandwidth utilization in blockchain networks [14], and we applied the bandwidth optimization parameters set in the simulation experiment. We compared the bandwidth consumption under different network scales.
## 5 Results
### First Test Case
Rows designated as S = 2 indicate both intermediate and final model accuracy in Scenario 2. It is noticeable that the initial federated averaging significantly diminishes model accuracy as expected. However, additional training on dataset D2 leads to marked accuracy \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**C**} & & & & & & & & & & & \\ \hline \multicolumn{2}{c}{**S**} & \multicolumn{2}{c}{**T**} & & & & & & & & & & \\ \hline
**0** & **D1** & 90.37 & 88.70 & 87.07 & 78.90 & 92.53 & 91.03 & 87.06 & 79.36 & 91.34 & 87.70 \\ \hline
**1** & **D2** & 92.07 & **97.43** & 87 & 85.10 & 91.34 & 23.33 & 89.63 & 93.13 & 95.8 & 91.23 \\ \hline \multirow{3}{*}{**2**} & **Avr1** & 78.20 & 74.77 & 74.43 & 73.20 & 76.33 & 76.63 & 76.13 & 76.20 & 75.80 & 75.07 \\  & **D2** & **94.33** & 93.56 & 91.77 & **95.23** & **95.4** & **94.64** & 90.63 & **94.07** & **94.03** & 92.33 \\ \cline{1-1}  & **Avr2** & 93.5 & 93.30 & **92.87** & 93.90 & 93.93 & 93.90 & **94.20** & 93.67 & 93.00 & **93.14** \\ \hline \hline \end{tabular} The bold values represent maximal values per column.  
\end{table} 
Table 2: Results of the evaluation for both scenarios. improvement in comparison to models trained without FL. Finally, the results suggest that the second application of FL yields, on average, superior performance compared to models trained without this methodology, as shown in Table 3.  
Interestingly, the results indicate that Scenario 2 produces comparable outcomes before and after the second federated averaging procedure. The ratio between the models labeled S = 2, T = Avr2 and S = 2, T = D2 is 0.999, suggesting that \"knowledge\" is effectively shared among the client devices without a significant loss of accuracy on average.