esa-sceva/satcom-chunk-collection huggingface dataset:
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 2 Materials and Methods
### Ex-Situ Characterizations  
#### 2.2.1 Chemical Stability Testing  
The accelerated chemical stability of the selected membranes was tested by immersing the membrane samples into the V5+ solution prepared from an operating VRFB cell. Specifically, the chemical stability tests were conducted by soaking 3 \(\times\) 3 cm membranes in 1.6 M V5+ solution with a 4 M total sulfate at 40 \({}^{\circ}\)C for 10 days. Oxidation of the membranes was monitored by VO2+ concentration change in the soaking solution, as VO2+ ions are released when oxidation of a membrane occurs by V5+ solution. The concentration of VO2+ in the soaking solution was periodically measured by UV-Vis spectroscopy (Varian 50 Conc UV-Visible Spectrophotometer) during the chemical stability test. To establish the calibration curve, mixtures of 0.1 M VO2+ solution and 0.1 M V5+ solution were prepared at different ratios. The absorbance was determined for each mixture at a wavelength of 760 nm at which the maximum absorbance of VO2+ takes place and the plot of the absorbance of mixtures of VO2+ solution and V5+ solution against the concentration of VO2+ was shown in Figure S1. It is worthwhile to mention that all of the membranes were directly used without any pre-treatment for this chemical stability test. Some of the degradation might be related to the oxidation of trace solvents or the exchangeable ions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Membranes** & **Company** & **Type** & **Thickness (\(\mu\)m)** & **IEC (mmol g\({}^{-1}\))** \\ \hline
1 & N212 & Nafion & & CEM & \\  & N212 & & CEM & PFSA & 50 & 0.9 \\ \hline
2 & FS-930 & & Fumapem & & CEM & \\  & FS-930 & & Fumatech GmbH, St. Ingbert, Germany & & PFSA & 30 & 1.14 \\ \hline
3 & DF & & Dongyue Chemical Ltd., Zibo, China & & CEM & 50 & 1.57 \\  & DF & & D\(\ddot{\text{e}}\)gue Chemical Ltd., Zibo, China & & PFSA & 254 & – \\ \hline
4 & VAN & VANADionTM-20 & & National Store & & \\  & & & (Ion Power), Umm Ramool, Dubai & & PFSA & \\ \hline
5 & FAP-450 & & Fumasep & & AEM & \\  & FAP-450 & & Fumatech GmbH, St. Ingbert, Germany & & Hydrocarbon & 50 & 2.18 \\ \hline
6 & AHA & & Neospeta & & AEM & \\  & AHA & & AIM & Hydrocarbon & 220 & 0.35 \\ \hline
7 & AMV & Selemion AMV & Asahi Glass Co., Ltd., Tokyo, Japan & & AEM & \\  & & & Hydrocarbon & & 110 & 1.6 \\ \hline
8 & CMV & Selemion CMV & Asahi Glass Co., Ltd., Japan & & CEM & \\  & & & Hydrocarbon & & 2.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Investigated membranes for vanadium redox flow batteries (VRFBs).
## 1 Introduction
The pairwise potential is modeled to consider the spatial correlation of the given imagery, and adjacent pixels can usually be assumed to be the same class. The pairwise potential aims to solve the misclassified categories by utilizing the shape, texture, and spectral features. The spatial, spectral, and emissivity information are fused efficiently in the SSECRF algorithm by the potential terms. The DFC dataset was adopted to confirm the effectiveness of the proposed SSECRF algorithm, and it was found that the proposed algorithm outperforms the comparative methods in both visual performance and quantitative assessment. The rest of this paper is organized as follows. Section 2 provides some information related to the experimental datasets. Section 3 introduces some of the basic concepts of emissivity retrieval and CRF, and the proposed algorithm for fusing the visible and thermal infrared hyperspectral imagery data. Meanwhile, we also describe the features extracted from these two types of imagery. In Section 4, we discuss the classification results and parameter sensitivity. Finally, the conclusion is drawn in Section 5.
## 3 Results
### Results of Sound Propagation Modeling
The use of modeling based on the BS 5228 standard allowed not only for the spatial representation of noise levels but also for the assessment of the relationship between anthropogenic traffic noise and the natural acoustic background of the forest. By integrating field data with digital terrain models, it was possible to obtain analytical results without the need for expensive expert software. In comparison to ISO 9613-2, the BS 5228 standard proved to be a more flexible, accessible, and suitable solution for the environmental conditions of the study area. Its application in forest environments enables the execution of realistic and  
Figure 5: Results of sound propagation modeling on research area P4, with direct measurement values of \(L_{Aeq}\) (dB) overlaid on a map base with a digital terrain model (own elaboration based on map data obtained from [54]). spatially differentiated analyses of the acoustic climate, providing a valuable foundation for further research employing more advanced modeling methods and tools. However, significant discrepancies were identified between the modeled results and the actual values recorded deeper within the park. In many locations, particularly those farther from the road, the measured noise levels were higher than those predicted by the spatial model. This phenomenon was primarily due to the presence of other acoustic events characteristic of the natural environment. Notably, loud bird songs, especially in areas with high avian activity, played a significant role. Natural sound sources were not incorporated into the propagation model, which partially explains the elevated values observed in direct measurements. These observations highlight the need to account for the full, complex soundscape of natural environments in acoustic analyses, particularly in the context of protected areas.
## 6 Conclusions  
In this paper, a numerical study was conducted on the influence of turbulence intensity and Reynolds number on the mean topology and transition characteristics of flow separation to provide better understanding of the unsteady jet flow of TeDP UAV. Through the comparative analysis of the aerodynamic performance of the NACA0012 airfoil with experimental results, the numerical calculation method was verified. The effects of turbulence intensity and Reynolds number on aerodynamic performance of different airfoils, transition characteristics and evolution of LSBs were analyzed. The following conclusions are made.  
The increasing freestream turbulence intensity results in earlier transition and reattachment. At small angle of attack and higher turbulence intensity levels, airfoil encounters a minor decrease in lift, whereas at pre-stall angle of attack, lift increases and stall is delayed. Similarly, the increasing Reynolds number results in higher local vorticity Reynolds number and smaller flow energy dissipation, contributing to a stronger effect of the overall surface shear and earlier transition. The shear layer around airfoil can resist an adverse pressure gradient for a longer distance so that aerodynamic performance of the airfoil improves. However, the mechanism of earlier transition is different. The increase of turbulence intensity leads to an earlier transition because of the higher velocity fluctuations that penetrate within the boundary layer. However, the increase of the Reynolds number alters the state of the boundary layer at separation, reducing the shear layer thickness and consequently its stability characteristics.  
The generation of the LSB in laminar flow usually occurs at a Reynolds number of 50,000 and the evolution of the LSB structure on the suction side of the airfoil is closely related to Reynolds number and turbulence intensity. As the Reynolds number or turbulence intensity increases, the LSB will emerge earlier and its length will become shorter. The generation of the LSB changes the effective shape of the airfoil, and increases the curvature of the suction side of the airfoil, which causes the nonlinear phenomenon of the lift coefficient at small angle of attack. In addition, different airfoils have different sensibility to turbulence intensity and Reynolds number. Compared with the low-speed symmetrical airfoil, the low-Reynolds-number airfoil not only has superior performance in lift characteristics, but also in delaying flow separation. This research provides guidance for further airfoil design of the TeDP UAVs.  
Conceptualization, Y.Z. and Z.Z.; methodology, Y.Z. and X.L.; software, Y.Z.; validation, Y.Z., Z.Z. and K.W.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Z.Z.; data curation, K.W., X.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; supervision, K.W.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EQUIPMENT PRE-RESEARCH PROJECT, grant number 41411020401.  
The authors declare
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 4 Experiments and Results
### Performance on Large-Scale Navigation Tasks  
Finally, we evaluate the performance of JPS-IA3C on large-scale navigation tasks. JPS-A3C+ replaces IA3C with A3C+ in the proposed method. As shown in Figure 9, JPS-IA3C can navigate the nonholonomic robot with continuous control by dealing with both complex static obstacles and moving obstacles. Furthermore, navigation policies learned by JPS-IA3C have the notable generalization ability and can handle unseen tested environments that are almost three orders of magnitude larger than the training environment (Figure 6). We do not show trajectories generated by JPS-A3C+ for clear representation.  
In Figure 9(a), JPS-IA3C can achieve success rates of more than 94% in all of the tested environments and even 99.8% in environment 3, while the success rates of JPS-A3C+ are below 42% in all of the evaluations and even 0% in environment 2. Guided by the same subgoals, JPS-A3C+ only considers current observations and ignores hidden states (i.e., moving obstacles' velocities and  
Figure 9: (a–c) Given the start position and the goal position, the purple line is the path planned by the Jump Point Search+ (Prune) (JPS+ (P)), and the blue line is the trajectory generated by JPS-IA3C. Black regions are unpassable, while light white regions are traversable for robots. Yellow regions close to obstacles denote warning areas. Moving obstacles appear in pink square areas, where there are 30 random moving obstacles.  
changes of map topology) from history observations, leading to collisions with complex static obstacles or moving obstacles. However, JPS-IA3C can use the LSTM-based network to construct accurate belief states by storing historical information over a certain length, which can handle environmental changes. In Figure 10b,c, compared with JPS-A3C+, JPS-IA3C can navigate robots in large-scale environments with shorter path lengths and less execution time, which concludes that constructing the LSTM-based network architecture can greatly improve the performance of the proposed algorithm by endowing the robot with certain memory.
## 5 Results and Discussion
### Selection of Available Satellites  
Based on combining EMD and the IMF determination method to separate the modulation terms of each satellite, the original phase of each satellite was determined by the LLS fitting. Following the steps in satellite selection, satellites with continuous phases were preliminarily selected, with 17 satellites in both the ascending and the descending segments. The statistics of these satellites relative to the threshold ranges (\(r_{n}\)) of different cross-correlation coefficients are shown in Table 5. It can be seen that there were 14 satellites having lifting tracks with \(r_{n}\) greater than 0.400. Among them, seven satellites had \(r_{n}\) greater than 0.600; they were PRN 14 (S), PRN 30 (S), PRN 23 (S), PRN 07 (S), PRN 09 (J), PRN04 (J), and PRN 14 (J). In addition, the statistical correlation (\(r\)) between the phases of the 17 satellites that were in the elevation segment and SM is shown in Figure 8. It can be observed that for the satellites with \(\overline{r_{a}}\) less than 0.400, their corresponding \(r\) were below 0.550, which means that the correlations were low or non-existent. Thus, these satellites could not be used for SM retrieval and needed to be eliminated. For the satellites with \(\overline{r_{a}}\) greater than 0.400, their corresponding \(r\) were greater than 0.600, implying that these satellites had a strong correlation relevant to SM and could be selected for SM retrieval. Among them, when \(r_{n}\) was above the threshold of 0.700, the correlations of the selected satellites were all greater than 0.750. Figure 7: The comparison of the SNR trend terms. The fitting results of different IMF combinations of PRN14 and PRN32 are shown. In the Figure, the black line represents the original SNR, and the colored line is the fitted trend term. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite Number** & **Number of Decomposition** & **Number of Layers of** & **Number of Layers of** \\  & **Layers of EMD** & **Combined Modulation Term** & **Combined Trend Term** \\ \hline PRN 01 & 10 & IMF1–4 & IMF5–10 \\ PRN 02 & 10 & IMF1–8 & IMF9–10 \\ PRN 03 & 10 & IMF1–8 & IMF9–10 \\ PRN 04 & 10 & IMF1–8 & IMF9–10 \\ PRN 05 & 10 & IMF1–9 & IMF10 \\ PRN 06 & 10 & IMF1–8 & IMF9–10 \\ PRN 07 & 10 & IMF1–9 & IMF10 \\ PRN 09 & 10 & IMF1–6 & IMF7–10 \\ PRN 10 & 10 & IMF1–7 & IMF8–10 \\ PRN 11 & 9 & IMF1–6 & IMF7–10 \\ PRN 12 & 10 & IMF1–7 & IMF8–10 \\ PRN 13 & 10 & IMF1–7 & IMF8–10 \\ PRN 14 & 10 & IMF1–6 & IMF7–10 \\ PRN 15 & 10 & IMF1–8 & IMF9–10 \\ PRN 16 & 10 & IMF1–6 & IMF7–10 \\ PRN 18 & 10 & IMF1–8 & IMF9–10 \\ PRN 19 & 9 & IMF1–8 & IMF9–10 \\ PRN 20 & 10 & IMF1–5 & IMF6–10 \\ PRN 21 & 10 & IMF1–8 & IMF9–10 \\ PRN 22 & 10 & IMF1–8 & IMF9–10 \\ PRN 23 & 10 & IMF1–8 & IMF9–10 \\ PRN 24 & 10 & IMF1–6 & IMF7–10 \\ PRN 25 & 10 & IMF1–8 & IMF9–10 \\ PRN 27 & 10 & IMF1–7 & IMF8–10 \\ PRN 28 & 10 & IMF1–6 & IMF7–10 \\ PRN 29 & 10 & IMF1–8 & IMF9–10 \\ PRN 30 & 9 & IMF1–8 & IMF9–10 \\ PRN 32 & 10 & IMF1–6 & IMF7–10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Separation results for the trend and modulation terms from each satellite (DOY98).
## 2 Experimental Apparatus and Procedure
### The LIF-PIV Setup  
Figure 5 shows a schematic of the LIF-PIV setup. For future research, a two-color setup was configured. This setup allows for one- and two-color flow measurements with dichroic filter replacement. In this study, the one-color setup was used. In thick laser sheets, defocused particles can be observed and the particles overlap, which either disables or lowers the accuracy of the calculation. To observe particles with 1.2 \(\upmu\)m diameters, the laser diameter was reduced from 5 mm to 0.254 mm through a combination of optical lenses. The reduced-diameter laser took on a sheet form with a 30 degree angle laser-line-generating lens and passed through an objective lens (Mitutoyo, 10 \(\times\) F 20 mm, numerical aperture (N.A.) 0.28, working distance (WD) 34 mm). The laser was then bent at a right angle through a prism and mirror before illuminating the nozzle. In this experiment, the fluorescent signal area to be reflected was widened, and the laser illuminated both sides of the nozzle to prevent the creation of shadows from N\({}_{2}\) bubbles or particles.  
For particle detection, the same objective lens used for the thin light sheet was used. The laser wavelength band reflected from N\({}_{2}\) bubble surfaces was eliminated by installing a notch filter in the back of the object lens. A camera with high quantum efficiency (QE) was used because the laser exposure time was very short (several tenths of nanoseconds) and the fluorescent signal area was small. An electron multiplying (EM)-CCD camera ([PERSON], C9100-23B ImagEm X2, Iwata city, Japan), 512 \(\times\) 512 pixels[2], QE 99%, max 1076 frame/s) was used in this experiment. Figure 6 shows the timing used in the experiment. Since the EM-CCD camera does not have a double shutter mode, the first and second lasers were illuminated while the camera shutter was open. To synchronize the camera and the laser, a digital delay pulse generator (BNC Model 505, CA, USA) was used. The synchronization cycle was 10 Hz, and the time interval for the first and second lasers was 15 \(\upmu\)s. Table 2 shows a list of the optical parts used for the LIF-PIV setup in Figure 5a.  
Figure 5: Schematics of (**a**) the laser-induced fluorescence LIF-PIV setup, (**b**) the beam path setup, (**c**) the beam reducing setup, and (**d**) the particle detection setup.
## 3 Proposed Design
### Residual Learning  
The residual network has been proposed to overcome the aforementioned challenges. The residual network adds the input data to the result of certain layers, and the accumulated result is transmitted through the output. Therefore, the network as a whole only needs to learn to differentiate between input and output, which reduces learning challenges, speeds up network convergence, and preserves data integrity. This direct connection is often referred to as a shortcut or skip connection. Residual learning approaches have received a lot of attention since they can extract discriminating features and perform better under various channels and noise configurations. Strong representational capabilities allow the deep residual learning network (ResNet) to continually learn latent information from the signals it receives, therefore increasing classification accuracy.  
Considering the benefits of residual learning, we designed a CNN with a residual block to identify the modulation scheme of the Radio ML dataset. The proposed residual learning approach is shown in Figure 2. As depicted, the input data \(x\) have two transmission paths: one that transmits the information directly into the output, and another that is evaluated by three convolution layers to produce the output.  
This process is formulated as follows:  
\[f_{1}:g(x)=CONV2D(x) \tag{1}\]  
\[f_{2}:p(x)=CONV2D(g(x)) \tag{2}\]  
\[f_{3}:q(x)=CONV2D(p(x)) \tag{3}\]  
The residual block takes \(x\) as the input and forwards it to a stack of convolution layers represented as \(f_{1},f_{2},\) and\(f_{3}\), and produces the corresponding outputs \(g(x)\), \(p(x)\), and \(q(x)\). The residual block can be represented as  
\[f(x)=x+q(x) \tag{4}\]  
The inputs to the model are in-phase and quadrature (I & Q) samples of raw data with a shape of \(2\times 128\). The convolution layers consist of a filter size of 32 followed by a kernel size of \(5\times 5,3\times 3\), and \(1\times 1\). The output of the convolution is passed through an activation function, which adds non-linearity to the neural network. The leaky rectified linear unit (Leaky ReLU) is employed in this network, which is a popular activation function due to its simplicity. It is useful for preventing gradient vanishing problems. The padding is kept the same in each layer, and kernels are initialized with a Glorot uniform initializer [48]. The output of the convolution layer is followed by batch normalization, which normalizes the input and speeds up the training process. The final layer is the max pooling layer, which determines the maximum value of the target region, reduces the training parameter, and extracts the important characteristics found in the dataset. This layer is helpful in enhancing the model's reliability and effectiveness. The pool size in this network is \(1\times 2\). To avoid overfitting, we employed a regularization technique of dropout with a value of 0.5. The final output of the network is represented as  
\[Y=Maxpool(LeakyReLU(BN(f(x)))) \tag{5}\]
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics
### Underwater Passive Surveillance  
With SCHs, the Petrel II glider can passively monitor underwater acoustic targets. The data from the sea trial in August 2018 are studied. In the experiment, various acoustic signals were transmitted. Both the multitone signal and the broadband signal are chosen to simulate the unknown target. The transmission loss and the signal direction of arrival (DOA) are estimated, to show the potential of the glider in underwater target detection.
## 1 Introduction  
Due to rapid changes in medical imaging technologies, a vast amount of visual information is now available to radiologists in the form of multi-slice images on flat-panel displays [1; 2; 3; 4]. Numerous cross-sections of anatomical structures ensure a more detailed and comprehensive representation of visual information when compared to X-ray images [3; 5], however, the complexity of three-dimensional visualization comes with benefits and costs. On the one hand, radiologists have the opportunity to detect the smallest details [2; 4], which positively influences the identification of abnormal lesions [6]. On the other hand, the limited processing capacity of the human visual system [7] is systematically challenged by the presentation of complex visual information on flat-panel displays. The obtained images are still viewed in a conventional slice-by-slice approach. As a result, radiologists experience high cognitive load with increased human-computer interaction [8], which may negatively influence decision-making accuracy [9]. Moreover, when compared with two-dimensional images, considerably longer time is spent on volumetric image reading [4; 10]. Prolonged high cognitive load causes mental fatigue [11], which in turn negatively influences visual attention [12]. The current situation leads to growing interest in improving visual ergonomics in radiology with the help of new visualization methods that would provide medical professionals with a three-dimensional representation of information.
## 4 Results and Analysis
### Semiconductor Wearout  
The semi-conductor wear tear is used to predict the failure rate and end of life of integrated circuits using degradation algorithms. Analysis includes electro-migration, time dependent dielectric breakdown, hot carrier injection, and negative bias temperature instability and is carried out in accordance to SAE ARP 6338. Four components on the board were subjected to this analysis, for both thermal cycle and events. In each case, the activation energy was defined as 0.9 and the appropriate operating voltage for each device was assumed. In each case, all of the semiconductors exceeded the life span of the board for the thermal cycle and event temperatures, as shown in Figure 17.  
Figure 16: (**a**). Solder joint fatigue life prediction for SAC305 lead-free solder (**b**). Fatigue failure distribution for SAC305 lead-free solder (**c**). Solder joint fatigue life prediction for PB90 SN10 lead solder. (**d**). Fatigue failure distribution for PB90 SN10 lead solder.