esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Flow State Factors  
To answer RQ1, the frequency and percentage for EEG-F by predictor variable (i.e., difficulty, LT-tp, ST-tp, self-efficacy, and cognitive load) were listed in the contingency table (Table 1). Among 766 test items, 423 (55.2%) were identified as low-EEG-F items, and 343 (44.8%) were high-EEG-F items. While answering the high-EEG-F items, the EEG detected the participant's high attention and engagement levels. Although the participant's ST-tp did not have a significant correlation with the flow state (\(\chi^{2}\) [1, N = 766] = 1.654, \(p\) = 0.198 > 0.05), as revealed by the chi-square test of independence for association, there were significant correlations between the participant's flow state and item difficulty (\(\chi^{2}\) [1, N = 766] = 10.965, \(p\) = 0.001 < 0.05), LT-tp (\(\chi^{2}\) [1, N = 766] = 13.566, \(p\) < 0.001), self-efficacy (\(\chi^{2}\) [1, N = 766] = 70.334, \(p\) < 0.001), and cognitive load (\(\chi^{2}\) [1, N = 766] = 102.999, \(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Predictor** \\ **Variables** \\ \end{tabular} } & \multirow{2}{*}{**Groups**} & \multicolumn{2}{c}{**EEG-F**} & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-5}  & & & **N (\%)** & \\ \cline{3-3} \cline{5-5}  & & & **Low EEG-F 1** & & **High EEG-F 2** \\ \hline \hline \multirow{2}{*}{Cognitive load} & Low load & 209 (27.3\%) & 290 (37.9\%) *** & 499 (65.1\%) \\  & High load & 214 (27.9\%) *** & 53 (6.9\%) & 267 (34.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contingency table for EEG-detected, real-time flow states (EEG-Fs) by predictor.  
Figure 1: Collection of the participant’s electroencephalogram (EEG) wave bands.  
As seen in the chi-square test of independence, item difficulty, LT-tp, self-efficacy, and cognitive load had a significant correlation with EEG-F. Therefore, such significant factors can be included in establishing the construct of the student's flow states.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Comparison with Other Combination Strategies  
In this subsection, we list the experimental results of the proposed pixel-level combination strategy and other combination strategies on 10 commonly used test images and 50 images randomly chosen from the BSD, and highlight the highest PSNR values for each noise level in bold. In addition to CsNet, which we mentioned in our related works, we added three state-of-the-art image combination methods that can be applied to image denoising, to validate the effectiveness and generalization ability of our method. The first is SPD, which we reviewed in the Related Works section. The second is U2 Fusion, proposed by [PERSON] et al. Similar to our method, U2 Fusion is also an unsupervised end-to-end image combination method, which has great effectiveness and extensibility. U2 Fusion performs well in solving multi-focus, multi-exposure and multi-modal image combination problems. Another is MEFNet, proposed by [PERSON] et al., which generates low-resolution weight maps of the initial image using deep learning methods and then combines the initial image with the weight maps to obtain the final result. The average PSNR results with different noise levels ranging from 10 to 60 with a step of 10 are listed in Table 4. Based on the table, our method outperformed the other four methods and achieved the highest average PSNR scores for all cases. On the 10 commonly used test images, our method outperformed even the second-best method by 0.23 to 1.42 dB, which is a significant improvement. On the more complex BSD dataset, our method is still superior to that of the other four methods, and it increased the PSNR result by 0.03 to 5.75 dB, confirming that the proposed pixel-level combination strategy surpassed the image-level and patch-level combination strategies in exploiting the complementarity of different denoised images to preserve the optimal denoised part, as well as other state-of-the-art image combination methods.
## 3 Results and Discussion
### Flammability Tests  
The Limit of Oxygen Index (LOI) and UL-94V  
The LOI and UL 94V tests were performed to evaluate the flame retardant properties of the formulated samples. The results of the LOI and UL 94 tests for the samples are presented in Table 2 and Figure 8. The LOI value of the F0 composite was 21.3%; the pristine sample (F0) was highly flammable compared with ZrO\({}_{2}\) NPs loaded samples (F1, F2, and F3). We observed that the LOI value increased when ZrO\({}_{2}\) NPs were added into the composition. When loading 1% (_w/w_) ZrO\({}_{2}\) NP, the LOI value of the F1 composite was 31.8%. When increasing ZrO\({}_{2}\) NPs to 2% (_w/w_), the LOI value of the F2 composite was 35.4%. At 3% (_w/w_) ZrO\({}_{2}\) NP for loading sample F3, the LOI value was 38.7%.  
Figure 6: FT-IR spectra of (**a**) Pure Zr NPs, (**b**) F0, and (**c**) F3 samples.  
Figure 7: XRD pattern of F0 and F3 samples.  
Thus, ZrO\({}_{2}\) NPs were more effective at improving the LOI values of formulations. According to the UL-94 V test, the F0 composite showed a V-1 rating. However, the content of ZrO\({}_{2}\) NPs at 1%, 2%, and 3% of composites (F1, F2, and F3) could still pass the UL-94 V-0 rating. Based on these results, the flame-retardant efficiency of F1, F2, and F3 composites was much higher than that of the F0 composite. From the LOI and UL-94V results, we observed that the addition of ZrO\({}_{2}\) NPs can play a synergistic action up to an optimum loading concentration.
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 2 Materials and Methods
### Study Area and Sampling Design
Our transects crossed the entire disturbance gradient, ranging from areas with little or no disturbance (i.e., unimpacted old-growth forest) to severely impacted forest with few or no surviving trees. In the field subplots, we recorded, identified, and measured the diameter at breast height (DBH) of all living trees with DBH \(\geq\) 10 cm. Due to logistic and safety issues, we did not count dead trees in severely impacted areas. With this we also aimed to reduce random errors due to possible missing trees hidden below the large amount of coarse wood debris that is typical of windthrows (Figures S1-S4) [1; 14]. Instead, we estimated the number of dead trees by subtracting the number of living/surviving trees recorded in each subplot from 15, which corresponds to mean tree density in adjacent old-growth forests (i.e., \(\sim\)590 trees ha\({}^{-1}\), or 15 trees in 250 m\({}^{2}\), Table S1, [1; 54; 55; 56; 57; 58; 59; 68; 70]). The subplots with more than 15 live trees were considered as undisturbed (i.e., no associated windthrow tree-mortality).
# Assessing the Effect of Drivers' Gender on Their Intention to Use Fully Automated Vehicles
## 1 Introduction  
### Automated Vehicles: What Could Drive People to \"Make the Shift\"?  
Nowadays, it is widely known that vehicle-related technologies constitute a core focus to increase safety, efficiency and sustainability of mobility. Accordingly, several technological improvements aimed at supporting a safer and easier driving experience have been developed during the last few decades (e.g., ADAS and other active/passive safety improvements), bringing the automotive market closer and closer to full automation [1, 2], thus progressively increasing the SAE level of the vehicles available on the market, looking ahead to the next decade in which, for the case of European countries, about 30% of them are expected to be fully automated (SAE level 5) vehicles [3].  
Further, most of the prospective sources on the matter agree on the fact that (just like in any other market) users' perceptions and attitudes play a crucial role for the future of automated vehicles (AVs) and their related transportation dynamics, even though the available empirical information in this regard remains considerably limited [4, 5].  
However, and as a consequence of different technological, safety and mass communication-related constraints, some studies have argued that the intention to shift to fully automated vehicles could decrease if potential users do not attribute enough value to their different features, especially those related to safety, efficiency and stability [6]. An example of it is the considerable number of non-specialized sources often under or overstressing the actual capabilities of automated vehicles that, far from improving their market-transforming possibilities, can negatively influence both potential consumers' perceptions, same as the willingness of policymakers and transport planners to further invest on adapting road infrastructures as a way to enhance a safer, more sustainable and cost-effective mobility [1, 4].  
Among all the potential improvements that automated vehicles may represent for their potential users (yet drivers of \"conventional\" vehicles), there are some features that might be critical for influencing the intention to get involved in this new trend of transport dynamics. For instance, literature emphasizes key aspects such as information flows, ease of driving, energy efficiency, travel swiftness, and perceived safety as essential user-related perceptions to consider with the aim of fostering a more holistic and participatory transition towards automated driving [1, 5, 6]. Concretely, five of them were addressed in this study.
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 2 Elastoplastic Representation of Mechanical Properties
### Potential Function  
Figure 1d shows that rock is not a fully elastic material, as its volumetric strain exhibits significant dilatancy characteristics. These characteristics are commonly described using the dilatancy angle \(\psi\) in continuum mechanics. Therefore, Figure 1e assumes that after yielding, the plastic strain increment direction of the rock is always perpendicular to the Mohr-Coulomb potential surface. Based on the non-associated flow rule, this potential surface can be expressed by the potential function as follows [9,18]:  
\[g=\sigma_{1}-\sigma_{3}N_{\psi} \tag{9}\]  
where \(N_{\psi}=\left[1+\sin\psi\big{(}\gamma_{p}\big{)}\right]/\left[1-\sin\psi \big{(}\gamma_{p}\big{)}\right]\), and \(\psi\big{(}\gamma_{p}\big{)}\) is a function of \(\gamma_{p}\) representing the dilatancy angle. The principal plastic strain increments can be calculated as follows:  
\[\Delta\epsilon_{i}^{p}=\lambda\frac{\partial g}{\partial\sigma_{i}},i=1,2,3 \tag{10}\]  
where \(\lambda\) is the plastic multiplier, and \(\Delta\epsilon_{i}^{p}\) can be expressed as follows:  
\[\left\{\begin{array}{c}\Delta\epsilon_{1}^{p}=\lambda\\ \Delta\epsilon_{2}^{p}=0\\ \Delta\epsilon_{3}^{p}=-\lambda N_{\psi}\end{array}\right. \tag{11}\]  
Given that under conventional triaxial compression, \(\sigma_{2}=\sigma_{3}\), the stress state on the potential function in Equation (9) can be expressed as follows:  
\[\left\{\begin{array}{c}g_{1}=\sigma_{1}-\sigma_{2}N_{\psi}\\ g_{2}=\sigma_{1}-\sigma_{3}N_{\psi}\end{array}\right. \tag{12}\]According to the non-associated flow rule:  
\[\Delta\varepsilon_{i}^{p}=\lambda_{1}\frac{\partial g_{1}}{\partial\sigma_{i}}+ \lambda_{2}\frac{\partial g_{2}}{\partial\sigma_{i}},i=1,2,3 \tag{13}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are the plastic multipliers. Substituting Equation (11) into Equation (13) yields the following:  
\[\begin{cases}\Delta\varepsilon_{1}^{p}=\lambda_{1}+\lambda_{2}\\ \Delta\varepsilon_{2}^{p}=-\lambda_{1}N_{\psi}\\ \Delta\varepsilon_{3}^{p}=-\lambda_{2}N_{\psi}\end{cases} \tag{14}\]  
The plastic volumetric strain increment is the sum of the three components in Equation (14):  
\[\Delta\varepsilon_{\rm v}^{p}=\Delta\varepsilon_{1}^{p}+\Delta\varepsilon_{2 }^{p}+\Delta\varepsilon_{3}^{p}=(\lambda_{1}+\lambda_{2})\big{(}1-N_{\psi} \big{)} \tag{15}\]  
From the first part of Equations (14) and (15), the dilation angle can be determined as follows:  
\[\psi\big{(}\gamma_{p}\big{)}=\arcsin\Big{[}\Delta\varepsilon_{\rm v}^{p}/ \Big{(}-2\Delta\varepsilon_{1}^{p}+\Delta\varepsilon_{\rm v}^{p}\Big{)} \Big{]} \tag{16}\]  
Equation (16) can be further simplified using Equation (7):  
\[\psi\big{(}\gamma_{p}\big{)}=\arcsin\Big{[}\Big{(}2\Delta\varepsilon_{3}^{p}/ \Delta\varepsilon_{1}^{p}+1\Big{)}/\Big{(}2\Delta\varepsilon_{3}^{p}/\Delta \varepsilon_{1}^{p}-1\Big{)}\Big{]} \tag{17}\]
## 3 Landing Trajectory Data Correction
### Landing Trajectory Correction
#### 3.3.1 Trajectory Correction Based on Projection  
Based on the prior knowledge that an aircraft must run along the runway, we first project the aircraft's landing trajectory onto the centerline of the runway. Specifically, given the latitude and longitude coordinates of the two ends of the runway centerline, the runway direction is calculated first based on these coordinates. Then, the latitude and longitude coordinates of the aircraft's starting point and end point on the runway are denoted as (\(x_{1}\)  
Figure 1: Landing interval extraction.  
Figure 2: Landing trajectory visualization.  
\(y_{1}\)) and (\(x_{2}\), \(y_{2}\)), respectively, where \(x\) represents the latitude, \(y\) represents the longitude, and both are converted to radians. The calculation for the runway direction is as follows:  
\[\alpha=\arctan\frac{\sin\Delta y\times\cos x_{2}}{\cos x_{1}\sin x_{2}-\sin x_{1 }\cos x_{2}\cos\Delta y} \tag{4}\]  
where \(\alpha\) represents the angle between the runway direction and the north direction taken clockwise and \(\Delta y=y_{2}-y_{1}\) is the difference in longitude. Additionally, the distance between any two points with latitude and longitude coordinates (\(x_{1},y_{1}\)) and (\(x_{2},y_{2}\)) can be calculated as follows:  
\[d=2R\arcsin\sqrt{\sin^{2}\frac{\Delta x}{2}+\cos x_{1}\cos x_{2}\sin^{2}\frac {\Delta y}{2}} \tag{5}\]  
where \(R\) is the radius of the Earth (approximately 6371.393 km), and \(\Delta x=x_{2}-x_{1}\) is the difference in latitude. Based on the above formulas and the projection formula in vector space, a point on the aircraft's landing trajectory can be projected onto the runway centerline. The effect of trajectory correction based on projection is shown in Figure 3, where the original trajectory is represented in blue and the projected trajectory is represented in red. It can be observed that the red trajectory aligns perfectly with the centerline of the runway.  
Although the trajectory correction method based on projection addresses the lateral offset error of the landing trajectory on the runway, it can be seen that the distance between adjacent projected points along the runway direction fluctuates significantly, and it cannot reflect the actual landing situation of an aircraft, as the speed of the aircraft during the landing process will not fluctuate as dramatically.
## 3 Results and Discussion
### Application of Ensemble Prediction  
#### 3.3.1 Ensemble Prediction Method  
Previous evaluation results of each model's performance show that the predictive accuracy of the models varied for different months. That means ensemble prediction is needed to integrate the strengths of different candidate models. This also allows for the quantification of prediction uncertainty. This section shows whether ensemble forecasting results with 3-months lead time are applicable to the dam operation. To illustrate this point, the prediction accuracy of 3-months lead time forecasting was analyzed using the models trained first by historical climate indices and reservoir inflow data for the preceding years before a target year. For example, the forecast results for 2013 are predicted using the models trained by monthly climate indices and reservoir inflow of 1998-2012. Four years (2013-2016) were selected as the target years, which is considered to reflect recent climate change impacts.  
Model averaging is frequently used to quantify or reduce the uncertainty of various models and to take the good characteristics of each model. The multiple model ensemble technique are known to provide more reliable prediction results than the individual models by combining the results of various outflow models [60]. This study compared the SMA with BMA methods used widely in the various field. The SMA method means averaging that each model's result has an equal value of the weight as described in Equation (9):  
\[\alpha_{sma}=\left(\frac{1}{k},\ldots,\ldots,\frac{1}{k}\right) \tag{9}\]  
where \(k\) is the total number of models.  
Forecast density of the BMA model, \(g_{j}\) is computed as follows in Equation (10):  
\[g_{j}=\sum_{k=1}^{k}\beta_{k}f_{k}\left(\overline{y}_{j}\right) \tag{10}\]  
where \(\beta_{k}\) denotes the weight vector,\(f_{k}\) is unknown forecast distribution and \(\overline{y}_{j}\) is a measurement vector. If a normal distribution is used as a forecast distribution, \(f_{k}\) can be described as Equation (11):  
Figure 6: Comparison of the observed (dot shape) and predicted (dashed line) reservoir inflow from three models for January (**a**), February (**b**), March (**c**), April (**d**), May (**e**), November (**f**) and December (**g**) of years 1998–2016.  
\[f_{k}\Big{(}\overline{y}_{j}\Big{|}D_{jk},\sigma_{k}^{2}\Big{)}=\frac{1}{\sqrt{2\pi \sigma_{k}^{2}}}\exp\!\left(-\frac{1}{2}\sigma_{k}^{-2}\Big{(}\overline{y}_{j}- D_{jk}\Big{)}\right) \tag{11}\]  
where \(D_{jk}\) is the point forecasts, and \(\sigma_{k}^{2}\) is variance. The MODELAVG tool box was used for BMA analysis and the details are explained in the manual [61].
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Radionuclide \({}^{125}\)I  
Pure \(\gamma\) emitter. AuNRs were conjugated to the tumor necrosis factor (TNF-\(\alpha\)) antibody and radiolabeled with \({}^{125}\)I by a simpler, green, and unsophisticated method by [PERSON] et al. [97]. Since TNF-\(\alpha\) is highly concentrated in inflamed tissues, AuNRs conjugated with anti-TNF-\(\alpha\) were aimed at quantitatively characterizing the degree of inflammation within joints affected by rheumatoid arthritis as well as monitoring drug delivery. Indeed, [PERSON] et al. obtained a hybrid imaging (SPECT/CT) to demonstrate the distribution of these AuNRs in the articular tissues of the rat tail joints. \({}^{125}\)I was also used to radiolabel AuNRs conjugated to anti-intercellular adhesion molecule 1 (ICAM-1) antibody. Imaging was obtained through a \(\gamma\) camera, evaluating an induced arthritic rat model, which presented higher levels of ICAM-1 in the affected ankle joints [92]. Another approach was to radiolabel AuNPs with \({}^{125}\)I and \({}^{111}\)In (another \(\gamma\) emitter with some Auger electrons) and to functionalize it with a peptide matrix metalloproteinase (pMMP9), resulting in a multispectral SPECT imaging agent. Studies on phantoms and on mice bearing two types of tumors with differing pMMP9 levels were performed to differentiate tumors with different MMP9 expressions [137].
## 2 Data and Methods  
### CryoSat-2 Altimetry Data  
CS2 level 1-B baseline-D data from ESA are used ([[https://earth.esa.int/eogateway/missions/cryosat](https://earth.esa.int/eogateway/missions/cryosat)]([https://earth.esa.int/eogateway/missions/cryosat](https://earth.esa.int/eogateway/missions/cryosat)), accessed on 20 August 2021). Individual files are available for each orbital path and instrument mode. All data over 60\({}^{\circ}\)N are selected and identified as open ocean for pulse peakiness \(<\)4.0 (LRM), \(<\)6.0 (SAR) and for the multi-look echo power standard deviation \(<\)0.07 (LRM), \(<\)0.10 (SAR). These waveforms are downsampled from 20 Hz to 1 Hz taking the mean echo power after alignment using a leading edge threshold of 50% and 70%, for LRM and SAR modes, respectively, if fewer than 90% of the original 20 Hz waveforms are classified as representing an echo from sea ice covered seas. The downsampling reduces the number of retracking operations and reduces the speckle noise in each range bin, although potentially smearing the leading edge of the echo and producing artificially higher SWHs. However we find that our 1 Hz processed record performs well in relation to coincident 20 Hz records from our own processing (Section 3.1) and from the GPD system (Section 3.4). Each CS2 level 1-B data file that contains any open ocean identified SAR or LRM echo from the desired region is processed and saved in a matching SWH file along with the retracking fit.
## 3 Results and Discussion
### Interfacial Adsorption
#### 3.5.2 Radial Distribution Function (RDF)  
The RDF can analyze the correlation between the given particle and the surface in the system, that is, the adsorption type between the polymer and the iron surface are studied in this experiment to help understand the adsorption type of polymer on the Q235 surface. In general, the initial RDF peak is between 1 A and 3.5 A, indicating that the polymer and carbon steel surfaces are chemically adsorbed. The peaks appearing after 3.5 A may be caused by physical adsorption [32]. Figure 9 shows the radial distribution function between the N and O atoms and the Fe(110) surface in PCA and PCA-GLU. The Fe-O bond lengths in PCA and the Fe-N and Fe-O bond lengths in PCA-GLU were found to be between 1 A and 3.5 A, indicating that N and O atoms showed chemical adsorption on the two corrosion inhibitors' surfaces made of carbon steel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inhibitors** & **PCA-GLU** & **PCA** & **GLU** & **CA** \\ \hline Interaction & \(-\)224.23 & \(-\)182.01 & \(-\)83.15 & \(-\)77.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The value of the interaction energy between the corrosion inhibitor and Fe(110) surface acquired by molecular dynamics simulation in a 3.5 wt% NaCl aqueous solution system.  
Figure 8: Side and top views of the PCA and CA adsorbed on the Fe(110) surface in vacuum slabs.