esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We conclude from our study that the research questions raised in Section 1 can now be answered as follows.  
1. How sensitive is the merged sea-ice concentration towards the choice of the MODIS ice tie-point? Choosing an arbitrary starting position for the ice temperature background field used as ice tie-point introduces a SIC uncertainty of 1.9%, which is mitigated by varying the starting position and taking the average of 48 iterations as ice tie-point. The standard deviation within these 48 iterations serves as uncertainty estimate for the MODIS ice tie-point.
2. How do the single-sensor and merged sea-ice concentration datasets compare with each other and the independently derived Sentinel-2 sea-ice concentration dataset? The mean merged and MODIS SIC are 93.1% and 92.8%, respectively, i.e., they agree within their uncertainties. The RMSD between the merged and the MODIS SIC amounts to 5%. They are closer to the reference SIC if thin ice is treated as ice (mean thin-ice SIC is 94.4%) than if it is treated as water (mean thick-ice SIC is 87.3%). The slight negative bias for the merged and MODIS SIC compared to the thin-ice SIC but large positive bias compared to the thick-ice SIC means that most thin ice is correctly identified as ice and that thin ice is only causing a small, about \(-1\%\) negative bias for the 66 cases which we evaluated. This, however, only applies as average for our cases, which on the other hand give a quite good spatial coverage of the winter time ice conditions along the Arctic marginal seas. For cases with extensive thin ice coverage the SIC underestimation can be larger.
3. What are the uncertainties of the single datasets? The MODIS and merged SIC uncertainties are close to each other and between 5% and 15% until the beginning of April, when the MODIS SIC uncertainty starts to increase more strongly. In May, the MODIS uncertainty is on average 25% and increases to 30%, while the mean merged SIC uncertainty is 18%. Thus, the merging significantly reduces the uncertainty later in the season. The AMSR2 SIC is mostly between 6% and 8%, but also uses a simpler uncertainty estimation. Comparison with the differences to the Sentinel-2 reference SIC shows that these differences are mostly smaller than the uncertainties, so that the uncertainties can be treated as a conservative estimate. In the majority of cases the accuracy of the merged SIC will be lower than the provided uncertainty estimate.  
In summary, the benefit of combining MODIS and AMSR2 SIC becomes manifest in (a) a closer agreement with the reference SIC and lower uncertainty compared to the MODIS SIC, (b) a finer spatial resolution and enhanced potential to resolve leads compared to the AMSR2 SIC and (c) assuring spatial continuity in the presence of clouds, which is not possible with MODIS data alone.
## 2 Description of Test Campaigns and Properties of the Materials
### Description of Fire Tests  
Fire tests were conducted at TU Kaiserslautern and CSTB according to the specifications of EOTA TR 020 (2019). Bonded anchors were installed according to manufacturers' instructions and loaded with varying degrees of constant tensile load at ambient temperatures. The fire scenario applied in the furnace on test specimens was ISO 834-1 (2019) until anchor failure. For each test, a fire resistance in terms of load and failure time were reported for the failed anchor.  
Testing conditions in both campaigns were similar, with slight differences in the load transfer system and concrete bearing elements. The TU Kaiserslautern approach consisted of loading the anchors (installed in slabs) by dead load or hydraulic jacks at the bottom (exterior) of the furnace, connected to the anchor with the help of a steel arm and a metallic fixture (Figure 1). The CSTB approach consisted of loading the anchors (installed in beams) using a metallic frame connected to the fixture of the anchor inside the furnace and to a hydraulic jack outside the furnace (Figure 2).  
In a previous experimental work (2019), [PERSON] et al. investigated the influence of the loading system on temperature profiles and resulting load-bearing capacity of bonded anchors and found it negligible. Details of load-transfer fixtures were adopted from EOTA TR 020 (2019) depending on the applied load of the anchor.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 2 Related Work  
### CNN-Based Methods  
Since the convolutional neural networks can effectively represent the nonlinear mapping between LR images and HR images, they have been widely used to solve the SISR problem. [PERSON] et al. [13] pioneered the application of a CNN to SISR, and the proposed SRCNN achieved impressive performance with a three-layer convolutional layer as the network architecture. [PERSON] et al. [25] proposed an ESPCN that can reconstruct images in real time, which extracts features from the input LR images and further applies subpixel convolutional layers embedded to upsample the reconstructed images. Later, this method of upsampling with subpixel convolutional layers at the end of the network architecture became the primary choice for subsequent SISR network architectures. Some methods improve the model's ability to represent image features by increasing the network size. [PERSON] et al. alleviated the problems of model training difficulty and slow convergence, by introducing residual strategies, and proposed VDSR [14] and DRCN [26] with deep network architectures. [PERSON] et al. [27] proposed an efficient network, namely, SRDenseNet, by introducing dense blocks to maintain connections between different network layers. The EDSR proposed by [PERSON] et al. [28] achieves better performance metrics than previous network models by improving the residual structure. Perceptual quality is an important evaluation metric for reconstructed images, [PERSON] et al. [29] proposed the SRGAN, which significantly improves the texture realism of SR images by using generative adversarial networks and utilizing a multitask loss. When compared to conventional classic algorithms, the majority of these methods have shown significant improvement, but, since they ignore the feature correlations of intermediate network layers, it is difficult to reconstruct SR results with natural texture details.
## 2 Preliminaries
### The Medical System Model  
The system model includes three roles: patient \(Pat\), doctor \(Doc\) and medical-center server \(MCS\). The medical-center server \(MCS\) provides \(Pat\) with registration and issues smartcards and is responsible for providing medical services. Doctors or medical staff \(Doc\) must first register with the \(MCS\) to obtain medical staff credentials. Patients \(Pat\) need to sign a privacy contract with medical-center server \(MCS\), obtain a smartcard \(SC\) during the registration phase, and then receive medical services using the smartcard \(SC\). \(Pat\) and \(Doc\) store and exchange protected medical records and health information in the cloud via the help of \(MCS\). Figure 1 illustrates the relations among \(Pat\), \(Doc\) and \(MCS\) in the medical system model.  
Figure 1: The relations between \(Pat\), \(Doc\) and \(MCS\) in the medical system model. The \(MCS\) provides \(Pat\) and \(Doc\) with registration. \(Pat\) needs to sign a privacy contract with \(MCS\). \(Pat\) and \(Doc\) store and exchange protected PHI via the help of \(MCS\).
## 5 Conclusions and Discussion  
This study focuses on a horizontal bidirectional seismic response calculation method for eccentrically compressed steel piers. An improved fiber model for centrally compressed steel piers considering the local instability effect was introduced. The improved fiber and hybrid-element calculation models were established and the in-plane and out-of-plane pseudo-static analysis and horizontal bidirectional seismic response analysis of the eccentrically compressed steel piers were conducted. The following conclusions were obtained.  
1. The hysteretic curves in the in-plane direction of the eccentrically compressed steel piers can be obtained by translating those of the centrally compressed piers. The improved fiber model can accurately simulate the hysteretic performance of the structures in the in-plane direction;
2. The bearing capacity and ductility of the eccentrically compressed steel piers in the out-of-plane direction are lower than those of the centrally compressed piers. The improved fiber model can also accurately calculate the structural hysteretic performance in the out-of-plane direction;
3. By taking five piers as examples, the improved fiber model was used to analyze the horizontal bidirectional seismic response of the eccentrically compressed steel piers. The results show that this kind of beam-based model can obtain accurate seismic response analysis results.  
The research in this paper provides guidance for the application of the improved fiber model to the seismic demand calculation of eccentrically compressed steel piers. It was verified that the seismic response of the eccentrically compressed steel piers can be directly calculated by using the improved hysteresis model after actual modeling without making more modifications and avoiding some troublesome operations, such as using specific bending moment-curvature model or other fiber sections.  
It should be noted that the bridge seismic performances and the seismic evaluation methods are another important issue. The existing seismic-performance-checking methods are all based on the results of shell-element models, resulting in the incompatibility between the seismic capacity and demand values. Therefore, the seismic evaluation method based on the improved fiber model should also be proposed for eccentrically compressed steel piers, which needs further study.  
Not applicable.  
The authors would like to express their gratitude to Dassault Systemes Simulia Corporation for allowing the use of their powerful commercial FE package-ABAQUS6.14 during this research. The authors declare no conflict of interest.
# Active Biodegradable Packaging Films Based on the
## 3 Results and Discussion
### The Characterization of the Phenolic Fraction from the Olive Defatted Flow
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Peak No.**} & \multicolumn{2}{c}{**Class/Phenolic Compounds**} & \multirow{2}{*}{\(\mathbf{R_{t}}\)} & \multicolumn{2}{c}{\(\mathbf{[M-H]^{-}}\)} \\  & & & **(_m/2_)** & **mg/kg DF** \\ \hline
1 & Gallic acid & 2.78 \(\pm\) 0.00 & 169 & 1.69 \(\pm\) 0.33 \\ \hline
2 & Hydroxyytrosol (HT) & 3.69 \(\pm\) 0.02 & 153 & 1524.74 \(\pm\) 178.16 \\ \hline
3 & Tyrosol (TYR) & 4.92 \(\pm\) 0.57 & 137 & 156.86 \(\pm\) 41.95 \\ \hline
4 & Caffe acid & 4.63 \(\pm\) 0.00 & 179 & 0.76 \(\pm\) 0.12 \\ \hline
5 & Syringic acid & 4.66 \(\pm\) 0.01 & 197 & 0.25 \(\pm\) 0.03 \\ \hline
6 & Vanillic acid & 4.72 \(\pm\) 0.00 & 167 & 1.40 \(\pm\) 0.17 \\ \hline
7 & Luteolin-7-O-glucose & 4.92 \(\pm\) 0.00 & 447 & 210.56 \(\pm\) 10.10 \\ \hline
8 & Isoquercitine & 4.93 \(\pm\) 0.00 & 463 & 2.27 \(\pm\) 0.39 \\ \hline
9 & Ferulic acid & 5.50 \(\pm\) 0.00 & 193 & 1.02 \(\pm\) 0.00 \\ \hline
10 & Oleuropein & 5.53 \(\pm\) 0.01 & 539 & 4324.74 \(\pm\) 408.87 \\ \hline
11 & p-Coumaric acid & 5.66 \(\pm\) 0.02 & 163 & 0.05 \(\pm\) 0.00 \\ \hline
12 & Elenolic acid (EA) & 6.08 \(\pm\) 0.02 & 241 & 3602.75 \(\pm\) 1555.62 \\ \hline
13 & Luteolin & 6.47 \(\pm\) 0.00 & 285 & 13.31 \(\pm\) 0.68 \\ \hline
14 & Pinoresinol & 6.66 \(\pm\) 0.00 & 357 & 9.22 \(\pm\) 0.63 \\ \hline
15 & Erythrodiol & 6.81 \(\pm\) 0.02 & 441 & n.q. \\ \hline
16 & Apigenin & 7.08 \(\pm\) 0.00 & 269 & 0.45 \(\pm\) 0.02 \\ \hline \multicolumn{6}{c}{\(\Sigma\) Polyphenols} & \multicolumn{2}{c}{8891.11 \(\pm\) 763.72} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assignment and content of phenolic compounds of defatted flour (DF), mean \(\pm\) standard deviation expressed in mg/kg of DF, retention time (Rt), and deprotonated molecule (\(\left[\mathrm{M-H}\right]^{-}\)). n.q. : not quantified. rated in food packaging, providing food with a bioactive effect and an environmentally sustainable option.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower matching Network  
In this Section, we provide a thorough explanation of the matching network's structure. The network is divided into training and inference stages for application. Key technologies in the training phase include image augmentation, the triplet generation strategy, and construction of the loss function for the network.
## 3 Finite Element Analysis
### Confinement Analysis and Effectiveness Evaluation  
The influence of steel tubes on infilled DS concrete is assessed by analyzing radial stress (\(\sigma_{r,c}\)). A stronger external steel tube enhances mechanical performance, particularly reflected in higher core radial stress; however, relying solely on radial stress becomes insufficient as the DS-CFST stub column nears its ultimate bearing capacity. Therefore, to evaluate confinement efficacy, we use the radial confinement coefficient \([\bar{\varsigma}_{r,c}=\sigma_{r,c}/\left(\rho f_{y}\right)]\) for infilled DS concrete, where a higher coefficient signifies better confinement by the steel tube. The interaction between the steel tube and DS concrete is examined by looking at the intersection between longitudinal stress (\(\sigma_{L,s}\))--strain (\(\epsilon_{L}\)) and transverse stress (\(\sigma_{\theta,s}\))--strain (\(\epsilon_{L}\)) curves of the steel tube. Additionally, the lateral deformation coefficient (\(v_{sc}=\varepsilon_{p}/\varepsilon_{a}\)) at ultimate bearing capacity, where \(\varepsilon_{p}\) and \(\varepsilon_{a}\) represent transverse and longitudinal strains of the steel tube, respectively. An earlier intersection and a larger lateral deformation coefficient indicate better confinement efficiency. These four indicators comprehensively assess confinement effects as design parameters vary within this study.
## 1 Introduction
Herein, we empirically tested four different methods of measuring the Gc of GFRP ship components: a widely used theoretical calculation proposed in the classification rules; direct measurement of the volume and weight or relative density of a specimen followed by calculation of Gc; and the burn-off method, wherein the resin matrix is combusted from the laminate. The methods were used to assess two types of composite hull plates for a 16-m ship. A comparative analysis of the results methods was then conducted to determine:  
1. The reliability of each method for analyzing the laminate structure and establishing the manufacturing precision according to the material design
2. The accuracy of each method for testing the fabrication quality of the laminate structure
3. The advantages and disadvantages of each method  
Overall, we found that the calculation method is effective at measuring Gc if the quality of the manufacturing process is known, but that it cannot detect fabrication errors. The direct measurement methods are also unsuitable for determining the volume of inner defects. In contrast, the burn-off method can accurately measure Gc, the defect volume, and the non-uniformity of the external shape; hence, this method is recommended for ship design when the exact Gc and fabrication quality need to be known. There has been considerable research in other industries on fiber-reinforced composites. However, limited research has been conducted on the use of GFRPs in shipbuilding. Therefore, we expect that this comparative study on methods for assessing laminate structures of actual GFRP ship components will provide a good reference for GFRP ship design.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Key Parameters Selection
#### 5.2.1 DOA Estimation Window Size  
The DOA estimation window size is the number of virtual antenna array elements used for each DOA estimation. According to the principle of DOA estimation, the beamwidth of the spatial spectrum decreases with the increasing number of antenna array elements and the resolution improves. However, when the number of array elements is very large, the effectiveness of increasing the array elements number is relatively small and the amount of operations required to perform DOA estimation increases greatly. Therefore, it is necessary to choose a reasonable estimation window under the condition of ensuring the estimation performance of the algorithm. As shown in Figure 7, we show the trend of localization accuracy of DNCL when the DOA estimation window size is from 3 to 9. The results show that the algorithm localization accuracy does not receive too much influence, and the estimation window size of 6 can be chosen.  
Figure 6: The layout of the RFID localization system.  
Figure 7: DOA estimation window size selection.
## 4 Analysis of Different Water Temperature Models
### Characteristics of Each Water Temperature Method  
The empirical equation method was summarized by researchers on the basis of analyzing and studying a large amount of measured data. It is not only easy to apply, but the parameters required are also easy to obtain. As long as the temperature of the water at the bottom and at the surface of the reservoir are known, the empirical method can calculate the water temperature distribution each month. It is mainly used for preliminary prediction of the water temperature of the reservoir when the information of the reservoir is limited. As the parameters are empirical, they need to be determined according to the similar projects that have been built. When there is a lack of engineering projects with high similarity, it will be difficult to determine the parameters, and the accuracy is low, which will naturally affect the accuracy of the water temperature simulation.  
Numerical algorithms are divided into 1D, 2D, and 3D numerical models, which can assess the influence of many factors on the water temperature of the reservoir (such as topographic conditions, climate, reservoir regulation, water discharge, human factors, etc.). Numerical algorithms are rigorous in theory, focusing on the simulation of the water temperature of a reservoir when operating, mainly for ecosystem and water quality monitoring. Taking the 3D mathematical model of the MIKE3 software as an example, the model needs to collect a large amount of data during the operating stage of the reservoir and perform a lot of parameter calibration work. The boundary conditions and parameters that need to be provided include more than 20 parameters such as topographic data, heat exchange boundary, turbulent viscosity coefficient, riverbed roughness, and the Smagorinsky parameter. This shows that the application of numerical algorithms is complex and requires a lot of preliminary work.  
Figure 7: Water temperature of bottom slag of different thicknesses in Xulong Reservoir.
## 5 Parameter Estimation
Step 1: Parameter estimation of the marginal distribution  
According to the edge distribution of the performance degradation data of the TWTA, the likelihood function of the edge distribution as shown in Equations (21) and (22) can be obtained, and the estimated value can be obtained by the MCMC method [25], which is used to estimate unknown parameters in the likelihood function of marginal distributions referred to in the Wiener process and the Gamma process, respectively. In addition, since the parameter estimation of the marginal distribution of the degradation model belongs to the parameter estimation in the case of small samples, the results obtained by the statistical method in which the mean, variance and shape parameters, and size parameters are unknown are generally low in reliability. For the overall characteristics that characterize the sample, such as the diffusion parameter and size parameter, the Bayes-Bootstrap method is used for resampling and re-estimation. The parameter estimation results are \(\hat{\theta_{1}}=\hat{\mu},\hat{\sigma_{\lambda}},\hat{\sigma}\) and \(\hat{\theta_{2}}=\hat{\kappa},\hat{\kappa},\hat{\tau}\). Step 2: Parameter estimation of the Copula function model  
For the binary degradation case, substitute the obtained parameter estimates \(\hat{\theta_{1}}\) and \(\hat{\theta_{2}}\) of the marginal distribution function into the Copula density function part, as shown in Equation (24), and then use the MLE method to obtain the parameter estimates, \(\hat{\theta}\), of the Copula density function. Then, all unknown parameter estimates \(\Theta=\left\{\hat{\mu},\hat{\sigma_{\lambda}},\hat{\sigma},\hat{\kappa},\hat {\tau},\hat{\theta}\right\}\) have been received.
## 2 Materials and Methods
### Spectral Processing and Data Analysis  
Preprocessing of each spectrum was accomplished by 4 sequential steps. First of all, the background signal was subtracted from measured spectra. Then, a signal simulating fluorescence and stray light was subtracted using the adaptive algorithm of Spectragryph software (version 1.2.16) [18], with a coarseness value of 30. This algorithm is able to remove the broad, featureless spectral features underlying the Raman peaks while maintaining the effective peaks. Next, the spectra were area-normalized. Finally, each spectrum was smoothed with a Savitzky-Golay filter with interval size 5 and polynomial order 3 using the Spectragryph software [18].  
After the preprocessing, the two spectra datasets were independently split to obtain a calibration set (50% of the spectra of both healthy and cancerous cells) and a test set (the remaining cells). This separation was performed to estimate the classification performance of the investigated methods by considering only the test set. The spectra in the calibration group were randomly selected by a random number generator.  
Principal component analysis (PCA) of the calibration set spectra was carried out to investigate (i) the possibility of discriminating the spectra of healthy cells from those of cancerous cells and (ii) the spectral variables mainly involved in such a discrimination. A full cross-validation was used to validate the PCA results.  
Next, the calibration set was used to develop and optimize classification models discriminating the spectra of the healthy and cancerous classes using PCA-LDA and PLS-DA techniques. The model optimization step, performed by a full cross-validation procedure, allows us to properly choose the number of components (as for PCA-LDA) and factors (as for PLS-DA) to be used in the classification of unknown spectra. In the end,spectra from the test set were used to estimate the classification accuracy of each of the two models. The PCA, PCA-LDA and PLS-DA techniques were performed with Unscrambler X CAMO software (version 10.4).