esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Progress on Membranes for Redox Flow Batteries
### Nafion (Perfluorinated Membrane)  
Nafion is a sulfonated tetrafluoroethylene based fluoropolymer-copolymer which has excellent ionic properties. Nafion's unique ionic properties result from the incorporation of perfluorovinyl ether groups terminated with sulfonate groups onto a tetrafluoroethylene (Teflon) backbone. The combination of the stable Teflon backbone with acidic sulfonic groups gives high cation conductivity as well as chemical stability. However, it is selectively and highly permeable to water [72].  
Nafion has commonly been used as an ion exchange membrane in direct methanol fuel cell (DMFC) [73,74] and in VRB studies [75, 76, 77, 78]. However with Nafion, there is a crossover of methanol when used in DMFC and vanadium ions in case of VRB, which results in a decrease in energy efficiency. While a few research groups have tried to find alternative membranes for VRB systems, that are available at a lower cost, most of the groups still use Nafion as a separator with several modifications to improve the stability. To reduce the methanol permeability in DMFC, surface modification using a layer-by-layer self-assembly polyelectrolyte [74] and in-situ sol-gel reaction (to incorporate inorganic oxide nanoparticles within the pores of Nafion [79, 80] have been carried out. The following sub sections give an overview of the work that has been carried out by modifying Nafion membrane.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 5 Significance of Each Machining Parameter and Their Interactions on the MRR
### Significance of Each Machining Parameter on the MRR  
According to Table 2, it can be seen that \(\triangle_{\mathit{1\,U}\,I}\) is 14.54 mg/min and \(\triangle_{\mathit{1\,U}\,I}\) is 8.57 mg/min. This means that the change of MRR caused by the change of current is large after fixing the value of voltage, and the change of MRR caused by the change of voltage is small after fixing the current value. Thus, the influence of current on MRR is more significant than that of voltage. This is represented by Equation (2). The symbol \(E_{Z}\) is used to represent the significance of the influence of processing parameter \(Z\) on MRR. For example, the influence of current is \(E_{I}\).  
In the same way, it can be concluded that the influence of pulse on time on MRR is more significant than that of voltage based on the interaction curve of pulse on time and voltage, as shown in Equation (3). Meanwhile, the influence of pulse off time is more significant than that of voltage based on the interaction curve of pulse off time and voltage,as shown in Equation (4). Furthermore, the influence of pulse off time on MRR is more significant than that of pulse on time according to the interaction curve of pulse on time and pulse off time on MRR, as shown in Equation (5).  
\[E_{I}>E_{U} \tag{2}\]  
\[E_{T_{on}}>E_{U} \tag{3}\]  
\[E_{T_{off}}>E_{U} \tag{4}\]  
\[E_{T_{off}}>E_{T_{on}} \tag{5}\]  
For the interaction curve of current and pulse on time on MRR and the interaction curve of current and pulse off time on MRR, the curves are parallel to the coordinate axes. In such cases, the above method is not suitable for analysing the significance of the machining parameter on MRR. Instead, the significance of the machining parameter on MRR can be found by the comparison of \(\triangle_{y}(x)\). For example, for the interaction curve of current and pulse on time on MRR, Table 2 shows that \(\triangle_{Ton}(I)\) is 26.87 mg/min and \(\triangle_{I}(T_{on})\) is 12.00 mg/min. Obviously, \(\triangle_{Ton}(I)\) is larger than \(\triangle_{I}(T_{on})\). This means that the change in MRR caused by the current when the pulse on time selected is larger than the change in MRR caused by the pulse on time when the current is fixed. Therefore, the influence of current on MRR is more significant than that of pulse on time, as shown in Equation (6). In the same way, the influence of current on MRR is more significant than that of pulse off time, as shown in Equation (7).  
\[E_{I}>E_{T_{on}} \tag{6}\]  
\[E_{I}>E_{T_{off}} \tag{7}\]  
To sum up, the significance of the influence of current on MRR is the most significant, followed by pulse off time, pulse on time, and then voltage, as shown in Equation (8).  
\[E_{I}>E_{T_{off}}>E_{T_{on}}>E_{U} \tag{8}\]
## 5 Results and Discussion
### Cubic Metric
#### 5.1.1 OFDM Signals' Case  
To evaluate the performance of our implementations, the first metric used is the cubic metric. For this purpose, a series of measurements of the cubic metric over 10,000 OFDM symbols are carried  
Figure 11: Hardware co-simulation.  
From these figures, it is clear that the results provided by implementing the proposed solutions are faithful to those obtained by simulation. Indeed, in the case of an implementation on Xilinx FPGA chip, the average error in reduction of the cubic metric is 0.002 dB, while, for an implementation on the Altera FPGA chip, it is equal to 0.003 dB. The small errors observed can be justified by the truncation errors caused by the fixed-point representation.  
Figure 14: Cubic metric reduction in the case of 16-QAM modulation (Xilinx FPGA).  
Figure 15: Cubic metric reduction in the case of 16-QAM modulation (Altera FPGA).
## 3 Data and Methodology
### Research Methods
#### 3.2.5 Accuracy Assessment Methods  
The assessment of the classification accuracy was based on several accuracy metrics, including general assessment, overall accuracy (OA), Kappa index, F1-score and class-specific assessment, confusion matrix, and average class accuracy. For the training area, the accuracy is calculated on 20% of the samples that were not used during the training. For the test area, the models that were trained in the training area were used to generate the maps of the test area, and the assessment was performed using the ground truth data of the test area.
## 5 Discussion
### Performance Comparison
#### 5.1.2 Random Training Process  
We also conducted studies on a randomized training process to verify the effect of this design on improving model performance. The experimental results are shown in the Table 11. The experiment used for comparison only trained the time series data with a missing rate of 20%. It was foreseeable that the performance of the model under the high missing rate was significantly worse than the random training process. For the informer-WGAN model, as the missing rate increased, the gap was more significant. Not only that, but under the 20% missing rate, the model using the random training process was also significantly better than the model trained with a fixed missing rate. The results show that random training made the model more focused on the time series features, while fixed missing rate training made it easier for the model stuck in a local optimum.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Missing Rate** & \
\begin{tabular}{c} **Informer-WGAN** \\ **(Linear Discriminator)** \\ \end{tabular} &
\begin{tabular}{c} **Informer-WGAN** \\ **(Informer Discriminator)** \\ \end{tabular} \\ \hline
20\% & 1.710 & **1.119** \\
40\% & 1.708 & **1.157** \\
60\% & 1.798 & **1.188** \\
80\% & 1.866 & **1.283** \\ \hline \hline \end{tabular}
\end{table}
Table 10: The ablation experiment results (PRMSE) of different discriminators on electricity dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Missing Rate** & **ARIMA** & **Infomer** & **Informer-WGAN** \\ \hline
20\% & **0.937**/0.345 & 1.337/0.747 & 1.029/**0.920** \\
40\% & 1.081/0.254 & 1.348/0.737 & **1.055**/**0.911** \\
60\% & **0.858**/0.186 & 1.404/0.696 & 1.073/**0.902** \\
80\% & 1.132/0.089 & 1.430/0.694 & **1.130**/**0.880** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The RMSE/Pearson correlation results of the informer-WGAN and other imputation results on electricity dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Missing Rate** & **ARIMA** & **Infomer** & **Informer-WGAN** \\ \hline
20\% & 2.066/0.762 & 1.795/0.773 & **1.796**/**0.774** \\
40\% & 2.240/0.737 & **1.804**/**0.767** & 1.847/0.756 \\
60\% & 2.311/0.732 & **1.832**/0.750 & 1.867/**0.751** \\
80\% & 2.439/0.652 & 1.903/0.739 & **1.883**/**0.748** \\ \hline \hline \end{tabular}
\end{table}
Table 9: The RMSE/Pearson correlation results of the informer-WGAN and other imputation results on EITH1 dataset.
## 4 Review: Slant Links Rain Fade Models
### Advantages  
The model proposed a fade slope determining technique with measured data from the tropical environment based on the nonlinear behavior of fade slope distribution.  
Limitations  
The model was tested only in Malaysia at 10.982 GHz. It has not been tested in other tropical climate conditions.
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 3 Experimental Results
It can be confirmed that the proposed quad-channel SerDes receiver (4 CTLEs + 4 CDRs + 1 MDLL) achieves the highest energy efficiency of 2.418 pJ/bit and the highest data rate of 25 Gb/s/channel, while using a small area of only 0.351 mm\({}^{2}\).
## 3 Results and Discussion
### Thermoelectric Properties
At room temperature, Mo\({}_{2}\)CF\({}_{2}\) and Mo\({}_{2}\)C(OH)\({}_{2}\) exhibited a low electronic thermal conductivity of 0 W m\({}^{-1}\) K\({}^{-1}\) s\({}^{-1}\) at the Fermi level, while this value was around 10 W m\({}^{-1}\) K\({}^{-1}\) s\({}^{-1}\) for Mo\({}_{2}\)CO\({}_{2}\) at the same position. This is consistent with the classical description that when the carrier density is small, the electronic thermal conductivity must  
Figure 6: Electrical conductivity as a function of the chemical potential at different temperatures from 300 K to 600 K for monolayer and multilayer Mo\({}_{2}\)CF\({}_{2}\), Mo\({}_{2}\)C(OH)\({}_{2}\), and Mo\({}_{2}\)CO\({}_{2}\), respectively. be small. Particularly, the electronic and thermal transport [38; 39] can be attributed to the surface functional groups in MXenes, in which -F and -OH terminated showed the largest electronic transmission compared to =O terminated MXenes. Overall, the results show that there was no significant difference between the monolayer structures and their corresponding multilayer structures in terms of electronic and thermoelectric properties. This indicates that the interlayer interactions in Mo\({}_{2}\)C-multilayers are relatively weak. Like the trend observed for electronic structure of Mo\({}_{2}\)CF\({}_{2}\) and Mo\({}_{2}\)C(OH)\({}_{2}\) from monolayer to multilayer, where increasing the number of Mo\({}_{2}\)C-layers led to a narrower band gap energy, the value of the Seebeck coefficient also decreased as a function of the number of layers. The electrical conductivity and electronic thermal conductivity of all Mo\({}_{2}\)C-structure when the number of Mo\({}_{2}\)C-layers was increased. The electrical conductivity and thermal conductivity of Mo\({}_{2}\)CF\({}_{2}\) were higher than those of Mo\({}_{2}\)C(OH)\({}_{2}\) due to its special energy band structure. As can be seen in Figure 2, the energy bands below the valence band maximum were relatively flat, but there was a part from the A-H midpoint to the K-G midpoint that was linearly dispersed. Such a band structure can create a relatively large Fermi surface and Fermi velocity, and is desirable for high-performance thermoelectric devices. Based on such special band shapes, a general rule for the design of high-performance thermoelectric materials is proposed as follows: When the Fermi level is near the energy boundaries, a combination of relatively flat and relatively dispersed parts in the band structure can create a large Seebeck coefficient, a low resistivity, and, thus, a  
Figure 7: Electronic thermal conductivity of Mo\({}_{2}\)CF\({}_{2}\), Mo\({}_{2}\)C(OH)\({}_{2}\), and Mo\({}_{2}\)CO\({}_{2}\) monolayer and multilayer structures as a function of chemical potential at different temperatures from 300–600 K.  
large thermal coefficient. It should be noted that it is not possible to directly compare the electrical conductivity and electronic thermal conductivity of monolayers with multilayers because these quantities depend on volume, and it is not possible to accurately determine the thickness of the monolayer.
## 3 Results and Discussion
### Influence of Solvent on the Resultant Effect of the Mixture's Antioxidant Activity  
Figure 2 shows changes in % inhibition as a function of concentration (volume) in systems containing single antioxidants (chlorogenic acid or kaempfer) and their binary mixtures. These data were obtained using different reaction solvents--methanol (Figure 2A) and ethanol (Figure 2B). At this point, it should be noted that the data in Figure 2A are a repetition of the data in Figure 1B, and this repetition was made intentionally. The methanolic system at 25 \({}^{\circ}\)C was taken as a reference system to facilitate the interpretation of the results.  
The analysis of the presented data allows us to conclude that while there are no clear differences in the antioxidant properties of kaempfer and chlorogenic acid determined in different solvents in one-component systems (compare the course of two red and two blue curves in both figures), the comparison of the location and course of the experimental and theoretical curves in Figure 2A,B for mixtures leads to the conclusion that in both cases these curves do not coincide, which indicates the lack of additive antioxidant activity. Moreover, this effect is more pronounced in the case of ethanol. The location of the theoretical curve over the experimental one indicates the antagonistic antioxidant behavior of the mixture. The antagonism is confirmed by the data in Table 3. In the case of analogous double systems (mixtures of antioxidants with the same composition), in which ethanol was used instead of methanol as the reaction solvent, the \(F\) values are higher, and the \(p\) values are lower (see Table 3).  
When analyzing the effect of different solvents, it should be taken into account that the influence of the alcohol type on the antioxidant activity of the binary mixture may be related to the structural differences of the bulk alcohols, which consist of hydrogen-bonded clusters. These clusters exist in thermodynamic equilibrium. [PERSON] et al. [25; 26] showed that hepta-, hexa-, penta-, tetra- and trimeric cluster structures predominate in methanol at room temperature, while monomeric structures are present in small amounts. On the other hand, in ethanol, pentamer structures are mainly present. It cannot be ruled out that different alcohol structures may affect the kinetics of the neutralization reaction of the ABTS radical cation. And although this effect is not so visible in one-component systems, it is more pronounced in two-component systems and different clustering is responsible for the kinetics of the reaction, and thus for the antioxidant antagonistic effect.
## 4 Results
### Packed Tower Air Stripping with Liquid Circulation
The gas flow rate and the mass transfer coefficient are not necessarily proportional, and the optimal point should be derived. The amount of NaOH consumed was 1.09 g of 100% NaOH/L liquid to maintain pH 8.5 at L/G 107.2 L/m\({}^{3}\) for 24 h. When compared to the results by pH at 20 \({}^{\circ}\)C, the ammonia removal efficiency is excellent despite less air supply and NaOH consumption for the unit liquid. Figure 7 shows the total air supplied against the ammonia removal rate at different L/G ratios. Here, the total air supplied refers to the total cumulative air for 1 L of artificial wastewater. To compare the total air supplied according to L/G ratio, it was revealed that the lower L/G ratio, the greater the total air consumed at the same removal rate. The total volume of air required was 240 L/L liquid at 53.6 L/m\({}^{3}\) and 212 L/L water at 107.2 L/m\({}^{3}\) to achieve 76.1%, where the ammonia removal rate was equal to the ammonia fraction (\(\eta/f_{NH_{3}}=1\)). At L/G ratios of 4.5-26.8 L/m\({}^{3}\), it is predicted that a greater volume of air will be required more than 1600 and 300 L/L liquid, respectively. From Figures 6 and 7, it was found that as L/G ratio increased, the mass transfer rate improved, and the more air supply was required to ensure the same ammonia removal rate. To increase the air supply, the size and power costs of the blower need to be increased. Therefore, it is necessary to determine the optimal L/G ratio considering removal efficiency, air supply, and time. In this study, the most efficient L/G condition was 26.8 L/m\({}^{3}\), and 53.6 L/m\({}^{3}\) also seemed to be appropriate when trying to lower the total air supply. In consequence, a liquid-gas ratio of 26.8-53.6 L/m\({}^{3}\) was selected as an optimal condition for packed tower operating at 70 \({}^{\circ}\)C, pH 8.5. Figure 6: Ammonia stripping performance according to the liquid-gas ratio at 70 \({}^{\circ}\)C and pH 8.5: (a) Ammonia removal rate; (b) logarithm of ammonia concentration ratio.
## 4 Discussion
### Limitations and Prospects  
This study explains the drivers of carbon allocation to leaves and their impact on the GPP in the boreal forests of China. However, the method used for the analysis of temporal changes using \(\Delta\)LAI as a proxy for leaf carbon has uncertainties. The cessation of the increase in \(\Delta\)LAI does not imply that the carbon allocation to the leaves will not increase. The carbon consumed by leaf respiration is also a part of the carbon allocation to leaves. Therefore, future studies should combine experimental data from field operations with long-term observational data on plant carbon allocation. Furthermore, our analysis was performed at a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), which is coarse and may lead to considerable uncertainty [55]. For example, for mixed pixels in the forest-grass boundary zone, changes in LAI may also be caused by changes in forest type rather than changes in climate or phenology. Therefore, additional influencing factors must be considered to explain the true driving mechanisms of forest carbon allocation to leaves. Although remote sensing-based methods provide new possibilities for studying leaf carbon allocation, the lack of long-term and large-scale field measurement datasets of leaf biomass attributable to the large scope of the study area will continue to prevent us from exploring long-term changes in leaf carbon allocation for many years. Thus, the progress of future research on ecosystem leaf C allocation may still be limited by the research basis of plant trait observation networks.
## 4 Applications, Results, and Discussion
### Comparison of Prediction Results for the Various Standard Classification Approaches
The \(\mathsf{S}_{\mathsf{TID}}\) is defined as the detection ability at the beginning of the \"under-attack\" conditions. Although the ANNs performed better in the detection of the initial cyber-attack event in comparison with the SVM, the overall detection ability of ANNs was worse. This shows that it lacks the ability to detect cyber-attacks consistently after the initial cyber-attack. Figure 3b,c illustrate the difference in the detection ability of the ANNs and SVM through visual detection comparison. In Figure 3, the red bars represent the prediction data for under-attack conditions and the black bars indicate the observed data. In the detection history of ANNs (Figure 3b), most of the red bars are located in the initial event corresponding to each attack scenario except two cases (i.e., 2 nd and 5 th attack scenarios). However, the SVM detected four initial events among seven scenarios, and subsequently, most of the under-attack events were predicted constantly. Based on the comparison among the machine learning algorithms, the performance of ELM is outstanding in all aspects, particularly in detection accuracy and initial attack event detection. As depicted in Figure 3d, the ELM detected all the cyber-attack scenarios and even detected most of the initial attack events in the seven scenarios. However, some of the attack events were not detected and there were some false detections around the attack events. This proves that although the ELM was able to consistently and accurately detect most of the attacks, it failed to do so consistently. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Indices** & **KNN** & **ANNs** & **SVM** & **ELM** \\ \hline S & 0.418 & 0.749 & 0.754 & 0.891 \\ \(\mathsf{S}_{\mathsf{CLF}}\) & 0.512 & 0.708 & 0.786 & 0.841 \\ \(\mathsf{S}_{\mathsf{TID}}\) & 0.323 & 0.789 & 0.722 & 0.941 \\ F1 & 0.048 & 0.558 & 0.694 & 0.764 \\ TNR & 1.000 & 0.952 & 0.967 & 0.959 \\ TPR & 0.025 & 0.464 & 0.604 & 0.722 \\ PPV & 1.000 & 0.700 & 0.815 & 0.810 \\ Accuracy & 0.810 & 0.857 & 0.876 & 0.913 \\ TP & 10 & 189 & 246 & 294 \\ FP & 0 & 81 & 56 & 69 \\ TN & 1682 & 1601 & 1626 & 1613 \\ FN & 397 & 218 & 161 & 113 \\ \hline \hlineFigure 3: Performance comparison between observed and prediction data for each detection algorithm: (**a**) KNN, (**b**) ANN, (**c**) SVM, and (**d**) ELM.
# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 3 Description of the Dataset  
We work with a small database of anonymized Digital Subtraction Angiography (DSA) from our research partner at the Department of Radiology at the University Hospital in Martin, Slovak Republic (33 patients), as shown in Figure 4. It contains angiograms from patients affected by different cerebral aneurysms. Each patient's record is in Digital Imaging and Communications in Medicine (DICOM) format. Every DICOM record has 122 X-ray images of the head from rotating angles. Each image has a resolution of 1024 \(\times\) 1024 pixels. The DICOM dataset pixels use 16-bit unsigned integer coding--grayscale mode. The dynamic range of our data was 10 bit (range 0-1024). This limitation is due to the capabilities of the used CTA. Informed consent was obtained from all subjects involved in the study (all have agreed to disclose the following data).  
In our paper, we work with real brain aneurysm data from the doctors who annotated the data (the data were annotated by experts). Annotated data are a crucial component for training and validating machine learning models, especially in the field of medical image analysis. We look at a patient's aneurysm from different angles. We have several different views of the aneurysm from one patient (and several aneurysms from one patient). The neural network learns from different data. The images from the same patient have no similarities between classes. The specific strategic approaches are:  
* Data augmentation: Generate additional training samples by applying various transformations to the existing images, such as rotation, scaling, flipping, and changes in brightness or contrast.
* Domain-specific data acquisition: We collaborate with other institutions or research groups to obtain additional brain aneurysm data.  
Figure 3: Structure of the ConvLSTM [21].  
* Active learning: We iteratively select and annotate samples about which the model is uncertain, gradually expanding the labelled dataset.  
It is important to note that the effectiveness of these strategies may vary depending on the specific characteristics of our dataset and the nature of the task.  
Figure 4 shows a sample of selected images from four patients. The patient number is the anonymized identifier in our database. The image number is the position in the sequence. Each represents one angle-position X-ray image. It shows scans of one patient per row. Several color mappings we were using improved the visualization of the grayscale data. The bottom row is a unique case where adaptively thresholding obtains the binary image of the vascular system. The computer displays grayscale images only at a depth of eight bits, since the human eye has limits in recognizing deep resolutions. Since the data use a 10-bit depth, we can extend the grayscale image in the false color mapping. Such a step improves the observer's ability to see more details.