esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 3 Results
### Characterization of Nafion Membrane
The deconvolution of the C1s peak of each Nafion membrane (Figure 6) shows a comparison of their respective carbon structures, indicting no significant changes in the base CF\({}_{\rm x}\) chemical structure. The high-resolution scans of the F1s peaks for the commercial and modified Nafion membranes are at 689.4 eV and 689.42 eV, respectively. The F1s peaks further show the similarity between the two materials. The F1s peaks are also visible in the \(\alpha\)-\(\beta\) plane, which is the case for the \(\alpha\)-\(\beta\) plane. The F1s peak of each of the two materials is visible in the \(\alpha\)-\(\beta\) plane, which is the case for the \(\alpha\)-\(\beta\) plane. the two Nafion films from the perspective of the base CF\({}_{\rm x}\) (CF\({}_{2}\) and CF\({}_{3}\) peak differences are negligible) backbone. A NaF peak is evident at 684.40 eV, as indicated from the F1s high-resolution scan of the modified Nafion film. Figure 6: XPS high resolution scans of the commercial Nafion (**left**) and modified Nafion (**right**). From top to bottom: carbon—C1s, fluorine—F1s, oxygen—O1s and sulfur—S2p. The black curve in all sub-figures is the scan data; the blue curve is the fitted data; the red curves are the deconvoluted peaks, and the green curve is the baseline. The O1s high-resolution peak comparison shows slightly higher peak broadening for the modified Nafion material compared with the commercial Nafion material. This is likely due to the exchange in solvent system between the two Nafion materials; however, key features from the deconvoluted peaks show a similar base structure. It is also evident from the O1s peak deconvolution that trapped water molecules at 536 eV are present in both materials. The S2p centered at 170.55 eV (commercial) and 170.88 eV (modified) indicates the sulfonate (SO3) group in both Nafion materials. Figure 7 provides the Na1s peak centered at 1070.74 eV, indicating a NaF chemical state. Table 2 provides film thickness values and the surface roughness (Sa) for 3 screen-printed Nafion membranes characterized by LSCM. The fabricated films showed good repeatability in thickness. Sa is the average surface area roughness of the Nafion membranes. The overall Sa accounts for approximately 20% of the film thickness over the area of the deposited films. The level of roughness is likely due to the amorphous morphology of the cured Nafion membranes.
## 4 Verification and Validation
### Validation of the Stability Improvement
#### 4.2.1 Tension Stiffness  
The tension stiffness represents a material's ability to revert to the original state when it is subjected to tension and compression. In this section, an identical case of a relaxing membrane to that in Section 3.1 is simulated by the AWI approach. The Figure 7 shows the maximal tension stiffness simulated with different iteration steps. With the explicit method, the maximal tension stiffness is \(\overline{K}_{s}=13.6\), whereas it can be increased with different slopes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & & \multicolumn{2}{c}{**Enclosed Area, \(A\)**} & \multicolumn{2}{c}{**Pressure Jump,**} \\ \cline{3-6}  & & & & \(\Delta p\) \\ \cline{3-6}  & & **t = 30,000** & **t = 100,000** & **t = 30,000** & **t = 100,000** \\ \hline \multirow{3}{*}{Simulated} & (Explicit) & 1.1532 & 1.1461 & 0.00607 & 0.00596 \\  & (AWI) & 1.1544 & 1.1469 & 0.00601 & 0.00581 \\ \multirow{3}{*}{**Analytical**} & & 1.1576 & 1.1576 & 0.00589 & 0.00589 \\  & (Explicit) & 0.38 & 0.99 & 3.1 & 1.2 \\ \cline{1-1}  & (AWI) & 0.28 & 0.92 & 2 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the nearly relaxed membrane compared with analytical solutions.  
Figure 6: Oscillating histories of the enclosed area and inside pressure simulated by the explicit and AWI methods. (**a**) Oscillating histories of the enclosed area. (**b**) Oscillating histories of the inside pressure.  
using the proposed method. According to the curve in Figure 7, when the numbes of iterations are chosen as 5, 7, 10, and 20, the slopes of the increase are 2.88, 4.84, 1.69, and 0.07, respectively. Therefore, the iteration is highly efficient when the iteration number \(N\leq 10\); if \(N=10\), the maximal tension stiffness \(\overline{K}_{s}=42.7\) is 3 times that obtained by the explicit method. However, when the iteration number is larger than 10, the maximal \(\overline{K}_{s}\) almost stops increasing.
## 5 Discussion  
Compared to previous binary segmentation methods, our proposed Cauchy-TB demonstrates comprehensive superiority on the metrics of accuracy, JC, and DSC, while performing comparably in sensitivity and specificity. When extended to solve the multilevel image segmentation problem, similar superiority can be observed. Overall, our method exhibits the best performance. We selected eight popular methods in the field as a standard and compared their performance on eight images from the BrainWeb dataset. Our method showed significant improvements on PSNR and FSIM metrics. The advantages of  
\begin{table}
\begin{tabular}{c c c} \hline
**Slice** & **2 Thresholds** & **3 Thresholds** \\ \hline \hline \multicolumn{3}{c}{Original Image} & Thresholded Image \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._Tsallis entropy mainly lie in its flexibility and adaptability. By adjusting the parameter \(q\), Tsallis entropy can handle more complex, nonlinear images that are significantly affected by noise, providing more accurate segmentation results. The strength of Bayesian posterior probability lies in its ability to address the issue of two distributions becoming isolated during thresholding. By offering a more accurate probability distribution, it enhances the overall segmentation performance. The Cauchy distribution we introduced has better tolerance for outliers, which becomes more pronounced as image noise increases. Additionally, by extending our method to multilevel segmentation tasks based on a recursive approach, we greatly reduce the computational resources and time required.  
In the case of multi-class problems with complex tissue structures, as the number of required categories increases, our method may show certain limitations. This is due to the higher uncertainty in estimating parameters when the pixel count for certain categories is low in the ground truth distribution, which could lead to significant estimation errors. To address this issue, non-parametric methods, such as kernel density estimation, can be used to avoid making strong assumptions about the distribution, thereby providing more robust and accurate results in such situations. In practical medical applications, automated brain imaging segmentation tools can significantly reduce the time and effort required by doctors. With automated segmentation, doctors can focus more on analysis and decision-making, rather than spending extensive time manually segmenting images. Although our method does not show a clear advantage in terms of time efficiency, we have achieved improvements in segmentation accuracy. Furthermore, it is evident that there is a limit to the effectiveness of global thresholds for image segmentation. In future research, our focus will be on improving time efficiency and finding local thresholds for regions prone to misclassification.
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 4 Experimental Setup and Simulation Analysis  
### Experimental Setup  
This paper designs four UAV autonomous obstacle avoidance environments, categorized into training and testing environments with static and dynamic obstacles. For clarity, the diagram for static obstacles is shown in Figure 2. The model's performance was evaluated using different initial positions and obstacle layouts in various configurations of the training and testing environments. The reward values, loss values, path lengths, and success rates were recorded during the testing process, and the UAV paths were plotted. The computer configuration for the simulation includes a GeForce RTX 3090, Windows 10 OS, CUDA 11.1, PyTorch 1.9.1, and Python 3.8.  
The sensitivity analysis of the model's main parameters is presented in Figure 3, highlighting significant variations in performance based on different parameter settings. For the learning rate, setting it to 0.0001 results in a gradual reward increase with slow convergence. A rate of 0.01 causes sharp fluctuations and a decline in rewards later in training. Conversely, a learning rate of 0.001 facilitates quicker reward increases early on, balancing exploration with the use of existing information, leading to optimal performance. Regarding batch size, using 64 leads to high reward fluctuations and instability in the learning process. A batch size of 256 incurs high computational costs and may cause overfitting. A batch size of 128 provides a smooth and steady reward curve, effectively balancing computational efficiency and gradient stability. For the discount factor, a setting of 0.90 overemphasizes immediate rewards, limiting long-term growth. A value of 0.95 strikes a good balance between short- and long-term rewards, producing solid performance. While 0.99 emphasizes long-term rewards, slowing early-stage growth, it allows significant development in later stages. Although variations in these parameter settings can cause the model to perform differently, within their respective reasonable ranges, such as a learning rate of 0.0001-0.01, a discount factor of 0.9-0.99, and a batch size of 64-256, the model still demonstrates good performance, ensuring the superiority of the method with practicality and effectiveness. The experimental parameter settings for DRL are shown in Table 2.  
Figure 2: Drone simulation environment diagram ((**a**) is the training environment; (**b**) is the test environment).
# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 1 Introduction
The main contributions of this research are the introduction of novel alternatives for fingerprint classification, combining the best descriptors presented in the literature with the M-ELM approach. This method demonstrates a 4% improvement in performance compared to approaches using commercial computers [13], although it is accompanied by a slight decrease in the penetration rate (PR) of 0.0003. Additionally, the training time is significantly reduced by approximately 17 s compared to ELM-based approaches. These results emphasize the scalability of the proposed method, as it evaluates the impact of varying the number of hidden neurons across different classifier configurations, including one hidden layer for the ELM and two or three hidden layers. The remainder of this article is structured as follows: Section 2 provides a review of the state of the art, summarizing the most significant contributions from the past decade that are directly relevant to this study, focusing on the two dominant trends: CNNs for image-based classification and ELMs for descriptor-based classification. Section 3 discusses the descriptors, along with the theoretical and mathematical foundations employed for feature extraction and fingerprint classification using multilayer ELMs. Section 4 describes the validation process and performance metrics applied in this study, along with detailsabout the database, including the quality and quantity of fingerprints. Section 5 presents the heuristic optimization process, considering different hidden layers for the multilayer ELM algorithm (one, two, and three hidden layers) using various descriptors and databases. It also includes tables summarizing the results, offering a comparison of completeness and computational cost in terms of the general and specific performance of the proposed approach relative to the most recent and high-performing studies. Finally, Section 6 concludes the manuscript by summarizing the findings and outlining potential directions for future research.
# Fire Tests of Load-Bearing, Light-Steel-Framed Wall Systems Insulated with Polyurethane Foam
## 4 Discussion
### Comparison between Nominally Identical Specimens (P1 and P2)  
Since fire tests are expensive and time-consuming, the results are usually analysed, and conclusions are drawn from individual measurements. On the other hand, studies dealing with repeated fire tests with the same specimens have shown that fire tests are difficult to reproduce in terms of their physical quantities even though they were designed to be repeated under the same conditions [37]. Therefore, two nominally identical LSF panels (P1 and P2) are tested for comparison. Figure 12a,b show the comparison of the time-temperature curves recorded between the two GBFs (positions HT1 and LT1) and between the GFB base layer and the PUR insulation (positions HT2 and LT2) for specimens P1 and P2, while Figure 12c,d show the comparison of the time-temperature curves obtained at the steel studs at both thermocouple positions.  
Although specimens P1 and P2 were made from the same components, tested under the same conditions and exhibited similar failure modes, the degradation of specimen P2 began earlier. In general, the time-temperature curves recorded at GBFs at the lower positions for both specimens (Figure 12b) show greater differences compared to the analogous results recorded at the upper positions (Figure 12a). The location where the cracks are believed to have formed comes from the observations made from the thermograms (Figure 11) and it is located directly above nodes N6 and N7, where local buckling was observed during the post-test inspection. About 40 min after the start of the test, severe horizontal cracking and opening of the joints were observed on both specimens. The differences between the time-temperature curves in the steel members of specimens P1 and P2 are also high (Figure 12c,d), especially at lower position from the 50 th minute onwards (Figure 12d). Consequently, the observed vertical deflection was more pronounced for specimen P2  
Figure 12: Comparisons of time–temperature curves for specimens P1 and P2: (**a**) average time–temperature curves recorded at GBFs (higher positions—HTs); (**b**) average time–temperature curves recorded at GBFs (lower positions—LTs); (**c**) average time–temperature curves recorded on steel studs at higher positions (HTs); (**d**) average–time-temperature curves recorded on steel studs at lower positions (LTs).  
(Figure 10). These discrepancies in the obtained results indicate the need to repeat the fire tests to draw strong conclusions and provide a broader basis for the results if modelling is used. Thus, the results obtained do not allow clear conclusions to be drawn regarding the uniformity of the results obtained and the subsequent FRR. If FRR is concerned, since the load was removed before the structural failure criteria were reached, it is hard to predict the behaviour of panels from test end onward. However, the results obtained on both specimens suggest that an FRR of at least 60 min can be achieved for LSF with combustible cavity insulation protected with two layers of GBF with reaction to fire A1.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 4 Results  
In this study, the RLV flight numerical simulation from the deorbit to the initial reentry phase was used to verify the feasibility and performance of the proposed method. The state of the vehicle in the example was as follows: the Mach number \(M_{a}\) = 25, AoA initial state was 0\({}^{\circ}\), AoA command was \(\alpha\) = 40\({}^{\circ}\), thrust generated by a single thruster \(F_{i}\) = 3.87 kN [12], distance from the moment vector to the center of gravity \(L_{d}\) = 26.86 m, and moment of inertia \(I_{v}\) = 651,470 kg\(\cdot\)m\({}^{2}\). The nonlinear flight dynamics model in Equation (1) was employed for verification. The 1796 U.S. standard atmosphere model [24] was used in this study and the example vehicle data were comparable to the space shuttle [25].  
Figure 6: Thruster control recovery evaluation based on fault detection.
# Zebrafish Larval Melanophores Respond to Electromagnetic Fields Exposure
###### Abstract  
Groups of zebrafish (_Dunio rerio_) embryos receive radiations of different frequencies and intensities by means of new prototype devices. They are exposed to static (B\({}_{0}\), 0 Hz), extremely low-frequency (ELF, 0.2 Hz), low-frequency (LF, 270 kHz), very-high-frequency (VHF, 100 MHz), and ultra-high-frequency (UHF, 900 MHz) field irradiations. The applied magnetic field intensities are 40 mT at 0 Hz, 40 mT at 0.2 Hz, 470 \(\mu\)T at 270 kHz, 240 nT at 100 MHz, and 240 nT at 900 MHz. Such combinations are meant to cover environmental radiations from geomagnetic fields and cosmic magnetism to electromagnetic radiation of electronic instruments such as GSM and UMTS transmission-mode mobile systems. For each frequency, fish are monitored for up to 5 days. Unexpected embryos are used as controls. Notably, exposure to the different radiations brings alterations of body pigmentation in zebrafish embryos and larvae in terms of total number, area, and morphology of (black) melanophores. This research may contribute to evaluating the roles and effects of magnetic radiation on living matter.  
magnetic field; living matter interaction; melanophores; pigmentation pattern formation; radio frequency; living matter interaction; teleost fish; zebrafish +
Footnote †: journal: Applied Physics Letters  
Article
Consequently, the RV is divided into two portions: a functional and an atrialized ventricle. Tricuspid regurgitation (TR) occurs with consecutive volume overload of the right heart [1,2].  
The latest surgical approach for EA is [PERSON]'s Cone repair, which, for the first time, enables the anatomical repair of the TV [3, 4, 5]. This procedure can be performed in various anatomical forms of the disease and significantly reduces TR, with a low mortality rate of only 2.5% [3,6]. In this technique, the enlarged anterior leaflet is detached from the ventricular wall and rotated to create a cone shape. The remnants of the posterior and septal leaflet may be incorporated into this cone-shaped valve, the base of which is then reattached to the anatomic annulus [3].  
Several studies have reported positive clinical outcomes after Cone repair with improved postoperative NYHA class and reduced TR. To assess the precise impact of Cone repair on RV function, several echocardiographic and cardiovascular magnetic resonance (CMR) studies have been conducted. However, identifying a parameter that reflects actual myocardial changes is difficult and complex, as most functional parameters are influenced by altered postoperative hemodynamics [4,5,7,8].  
CMR Feature Tracking (CMR-FT) is a relatively new method for quantifying myocardial deformation [9]. Initially developed for the left ventricle, CMR-FT may also apply to the RV despite its thin wall and trabeculations [10,11]. Several studies have demonstrated that this method can evaluate subtle alterations in myocardial contraction that routine cardiac imaging cannot detect [12,13]. We are the first to perform CMR-FT of the right ventricle (RV) before and after Cone repair with the objective of assessing its value for the perioperative assessment of myocardial function in Ebstein patients undergoing Cone repair.
## 1 Introduction
Their results revealed that the rotational frequency increases significantly when considering the mass eccentricity induced by the tooth breakage defect. [PERSON] et al. [22] studied the vibrational mechanisms and frequency features of a single-stage parallel gearbox under several fault states using a rigid multibody dynamic model, and they explained the cause of high-order mesh frequency harmonics and the asymmetrical modulation sidebands. These tooth breakage fault research works focused on parallel gear stages, while the vibration of the planetary gear is much more complex, because the driving flanks consist of multiple external contact (sun-planet) pairs and internal contact (ring-planet) gear pairs [23]. [PERSON] et al. [24] established a rigid multibody model of a planetary gear system with tooth breakage defect and pointed out the effects of the length of tooth breakage on the dynamic response. [PERSON] et al. [25] investigated the tooth breakage fault on the wind turbine planetary gear system considering the gear manufacturing errors by numerical and experimental research. They found that the sidebands of the planetary gear system become much more complex when manufacturing errors are taken into consideration and that different tooth breakage faults could be located by monitoring the values of their characteristic frequencies. Among these research works, few of them studied the dynamics of the planetary gearbox with tooth breakage fault under different rotational speed conditions. Due to the unstable wind conditions, frequent low speed start and emergency brakes, the rotating speed is nonstationary [26]. The planetary gear rigid multibody model adopted in these works uses a rotating frame as its reference frame [27], so in order to use this model, the premise of constant rotating speed was applied first based on the assumption that if the concerned time period is small, the work condition of the gearbox could be considered as quasi-stable [25]. The solutions of the traditional model are referred to the rotatory reference frame and need to be decomposed into a fixed reference frame. Methods are varied; for example, [PERSON] and [PERSON]\(\tilde{\text{n}}\)a proposed a frame decomposition methodology [7], while [PERSON] et al. [28] use the summation of the weighted vibration of each planet gear as the sensor measurements. We proposed an improved rigid multibody model for the planetary gearbox, which can handle the varying rotational speed directly [29]. In this paper, a dynamic model for a planetary gearbox with tooth breakage fault is built based on this model and validated by experiment. The effects of the tooth breakage size and the rotational speed on the vibration of the planetary gearbox are investiaged by numerical simulation and experiment.