esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hybrid Membranes
However, carbon nanomaterials have also been used as fillers for fuel cell membranes, e.g., the CNTs introduction can improve their mechanical properties [237; 238]. Some authors also reported a decrease in the membrane permeability to methanol as well as its crossover in direct methanol fuel cells with CNTs introduction [237]. The use of carbon nanotubes with a sulfonated surface for this purpose is especially effective, since they can increase the proton conductivity of membranes due to additional charge carrier introduction [239; 240; 241; 242]. Moreover, the negatively charged surface of sulfonated CNTs creates an additional double electric layer around them and increases the membrane selectivity. This leads to a decrease in the methanol permeability of such hybrid membranes and an increase in the fuel cell power [239; 243]. For example, a decrease in the methanol permeability of the obtained hybrid membrane by more than 3 times was shown [244]. At the same time, the authors of [245] noted that at very high sulfonation levels a morphological transition caused a decrease in transport properties. There is information about increase in the conductivity of sulfonated poly (etheretherketone) membranes due to their doping with CNTs coated with silica [246] as well as CNTs with imidazole groups on their surface [247; 248]. Other carbon materials as dopants for proton-conducting membranes are less studied. However, in recent years, composite materials with graphene have received much more attention [249]. Among the advantages of composite Nafion/graphene membranes, increased proton conductivity is noted [250]. At the same time, the authors of [251] reported a decrease in conductivity and methanol permeability of such membranes. The proton conductivity of sulfonated graphene-Nafion composite membranes at low humidity (20-25% RH) was found to be five times higher than that of a pristine Nafion membrane, while a peak power density of fuel cells based on them is 1.5 times higher [252; 253].
## 5 Conclusions  
This study presents an optimized approach for emergency evacuation route planning in dam failure-induced flooding scenarios, emphasizing the integration of flood dynamics and real-time road network conditions. Using UAV-based photogrammetry and flood data, a coupled flood-road network model was developed, which forms the foundation for evacuation route planning with [PERSON]'s algorithm. This methodology was validated through a case study of the Shanmei Reservoir, offering insights into improving evacuation strategies.  
1. A coupled flood-road network information coupling model was developed by simulating the dam breach and integrating the downstream flood evolution with the road network. The [PERSON] algorithm was used to identify optimal evacuation paths, resulting in an effective method for dam breach path planning.
2. The flood-road network coupling model for the downstream area of Shanmei Reservoir enabled the analysis of the road network's failure process. Maximum floodwater levels of 18.65 m and flow velocities of up to 22.18 m/s were observed, demonstrating the dynamic nature of flood's impacts on evacuation routes.
3. Four evacuation strategies were developed for eight affected locations in the downstream area of Shanmei Reservoir, identifying 13 emergency flood evacuation paths. This approach improved evacuation planning for flood prevention, mitigation, and disaster relief.  
In conclusion, the proposed methodology enhances evacuation route planning by integrating dynamic flood evolution and road network data. The case study results from Shanmei Reservoir show that all evacuees were able to reach designated shelters using optimized routes. This research has important implications for flood risk assessment, hazard mitigation, and emergency planning, suggesting that similar methods can be applied to other regions at risk of dam failure, improving public safety and disaster preparedness.  
G.A.: conceptualization, methodology, investigation, formal analysis, writing--original draft. Z.W.: methodology, investigation, writing--original draft. M.Q.: conceptualization, methodology, investigation, writing-review and editing. S.H.: conceptualization, methodology, writing-review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This study was supported by financial supports from the National Natural Science Foundation of China (No. 42271026).  
**Institutional Review Board Statement:** No applicable.  
**Informed Consent Statement:** No applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Numerical Simulation Study of the Iced Wind Turbine Flow Field  
### Calculation Settings  
#### 5.1.1 Numerical Model and Grid  
In order to study the effects of different icing conditions of blades on the aerodynamic characteristics of wind turbines, the wind turbine blade model obtained from icing experiments is used for simulation. The iced wind turbine model is imported into ANSYS 19.2 software for the creation of the inner and outer basins. Since the wind turbine model has rotational motion, the inner and outer basins were divided into the rotational domain and the stationary domain. The diameter of the wind turbine is 1.95 m. The inner basin is created as a cylinder of 3 m in diameter and 7 m in length, which is 2 m from the front surface of the wind turbine. The outer basin is a cylinder of 5 m in diameter and 35 m in length, which is 5 m from the front surface of the wind turbine. Through continuous debugging and irrelevance verification, it was determined that a total mesh number of about 2.6 million was appropriate. The grid division is shown in Figure 12.  
Figure 11: Changes in icing thickness trends along the blade spanwise direction, the windward side icing: (**a**) 0.5 kg; (**b**) 1.0 kg; (**c**) 1.8 kg and the leeward side icing: (**d**) 0.5 kg; (**e**) 1.0 kg; (**f**) 1.8 kg.
To the best of our knowledge the closest work to our manuscript lies in [18], where the authors proposed a modified code matched interleaver for distributed turbo coded cooperative non-OFDM systems using single antenna at each node and employed binary phase shift keying (BPSK) as the only modulation technique. However, the literature [18] did not consider the distributed turbo coded OFDM with multiple antennas at each cooperating node. It has also not considered higher-order modulation in its design. Therefore, addressing these matters are the main motivation and contribution of this manuscript. The remaining part of the manuscript is organized as follows. Section 2 presents the basic theory of turbo coded OFDM with MIMO antennas. Section 3 discusses the brief overview of CMI. Section 4 primarily deals with proposed turbo coded OFDM with MIMO antennas scheme in cooperative scenario. Joint iterative SISO decoding for turbo coded cooperative OFDM is explained in Section 5. In Section 6, the BER performance comparison of CMI with uniform random interleaver (RI) for turbo coded OFDM with different modulation techniques over AWGN channel are discussed. Furthermore, turbo coded OFDM with MIMO (\(2\times 2\)) antennas over fast Rayleigh fading channels under different modulation schemes are also discussed. Monte Carlo simulated results of turbo coded-OFDM with MIMO (2 \(\times\) 2) antennas in cooperation with different modulation schemes under CMI are presented and discussed in this section. Finally, the paper comes to an end with the conclusion drawn in Section 7.
# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 4 IST Model Based on GAN
### Building a Style Transfer Product Image Generation Model  
The car front-end generation task presented in this study entails generating car front-end images that are coherent with both a given reference image and a form scheme diagram of the car front end. This task necessitates meeting two primary criteria: (1) authenticity, which ensures that the generated car front-end images possess a visually realistic appearance; (2) consistency, which necessitates that the form and intricate details of the generated car front end correspond precisely with the descriptions outlined in the form scheme diagram. Authenticity guarantees the practical relevance of the generated images to users, whereas consistency ensures that the users' requirements for the form of the generated images are fulfilled, striving for a close alignment with the visual contour specifications delineated in the form scheme diagram. The methodology introduced in this study employs authenticity discriminators and conditional skip connection (CSC) modules to fulfill these two criteria.  
As illustrated in Figure 2, the proposed framework comprises two modules: the generator module and the authenticity discriminator. The generator module encompasses an encoder and a decoder. By initiating with a real car front-end image, the encoder is employed to extract multi-scale features of the corresponding form scheme diagram. Subsequently, the high-level style features of the reference image are extracted by inputting the reference car front-end image. The decoder integrates the encoding results from both encoders to synthesize the desired car front-end image. The authenticity discriminator evaluates whether the input car front-end image is generated by the generator or is authentic, from a holistic perspective, thereby guiding the generator to produce realistic car front-end images. The training process is governed by five loss functions: image reconstruction loss, perceptual loss, style loss, edge loss, and authenticity discriminator loss.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.2 Stain Normalization
\begin{table}
\begin{tabular}{l l l l l} \hline
**Work** & **Task** & **Model** & **Setting** & **Performance** \\ \hline [PERSON] et al. & & & & \\ (2024) [84] & Virtual Staining & Fix2 Pix-GAN CUT & - & _Pix2 Pix: FID_\(=11.16,CUT\): FID_\(=38.52\) \\ \hline [PERSON] et al. & Data augmentation, & & GPU NVIDIA A100, & \\ (2022) [69] & Stain Normalization & CycleGAN & 10 GB VRAM & - \\ \hline [PERSON] et al. & Data augmentation, & & & \\ (2021) [93] & Stain normalization & CycleGAN & - & - \\ \hline [PERSON] et al. & & & & \\ (2022) [66] & Synthetic image generation & GAN & \begin{tabular}{l} GPU NVIDIA \\ GTX TITAN X single, \\ 12 GB RAM \\ \end{tabular} & \(FID=142.84\) \\ \hline [PERSON] et al. & & & & \\ (2024) [62] & Data augmentation & SynCLay & - & \begin{tabular}{l} CoNiC-\(FID=60,15\pm 107\), \\ \(SSIM=0.86\pm 0.002\), \\ PANINE-\(FID=100.80\pm 1.83\), \\ \(SSIM=0.92\pm 0.02\) \\ \end{tabular} & Data augmentation & SynCLay & - \\ \hline [PERSON] et al. & & & & \\ (2025) [70] & Stain normalization & P2P-GAN, & & \\ DSTGAN & & GPU NVIDIA RTX 4090 & \begin{tabular}{l} TUPAC: \(SSIM=0.988, PCC=0.992,PSNR=39.130\pm 0.844\); \\ MITOS: \(SSIM=0.984, PCC=0.991,PSNR=37.698\pm 2.635\); \\ ICIAR: \(SSIM=0.984, PCC=0.992,PSNR=38.399\pm 1.750\); \\ MICCA: \(SSIM=0.975, PCC=0.990,PSNR=34.174\pm 0.892\) \\ \end{tabular} & \begin{tabular}{l} TUPAC: \(SSIM=0.988, PCC=0.992,PSNR=39.130\pm 0.844\); \\ MITOS: \(SSIM=0.984, PCC=0.991,PSNR=37.698\pm 2.635\); \\ MICCA: \(SSIM=0.985, PCC=0.992,PSNR=38.399\pm 1.750\); \\ MICCA: \(SSIM=0.975, PCC=0.990,PSNR=34.174\pm 0.892\) \\ \end{tabular} & \begin{tabular}{l} \(TUPAC: \(SSIM=0.988, PCC=0.992,PSNR=39.130\pm 0.844\); \\ MITOS: \(SSIM=0.984, PCC=0.991,PSNR=37.698\pm 2.635\); \\ ICIAR: \(SSIM=0.984, PCC=0.992,PSNR=38.399\pm 1.750\); \\ MICCA: \(SSIM=0.975, PCC=0.990,PSNR=34.174\pm 0.892\) \\ \end{tabular} \\ \hline [PERSON] et al. & & & \\ (2024) [62] & Color normalization & CycleGAN & - & - \\ \hline [PERSON] et al. & & & \\ (2019) [97] & Stain normalization & CycleGAN & GPU NVIDIA GTX 1080 Ti & - \\ \hline [PERSON] et al. & & & \\ (2024) [56] & & & \\ \hline [PERSON] et al. & & & \\ (2022) [61] & Synthetic Image generation & cGAN & \
\begin{tabular}{l} GPU NVIDIA TITAN RTX, \\ 24 GB VRAM \\ \end{tabular} &
\begin{tabular}{l} \(SSIM=0.9066\pm 0.065\), \\ \(MSE=0.0076\pm 0.003\) \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of GAN models for histological image processing tasks.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 4 Analysis of Test Results of the Basic Component of the Stabilizer
### Basic Component Analysis of the Stabilizer
#### 4.1.1 The S Content of Basic Components  
The SBS samples with different contents of S were prepared based on the aforementioned procedure and used in the 48-h segregation softening point test (a measured quantity of polymer-modified bitumen in a sealed aluminum tube was conditioned in a vertical position for 48 h at a temperature of 163 \(\pm\) 5 \({}^{\circ}\)C. At the end of the conditioning period, the top and bottom portions were separated and subjected to further testing to determine the degree of separation. The softening point was the test used for this purpose in this article.). Component Nos. 1, 2, 3, 4 did not show the gelation phenomenon; Component Number 5 showed an obvious gelation phenomenon after shearing for 20 min; component number 6 showed a serious gelation phenomenon after shearing for 10 min. The test results are shown in Figure 4 below.  
Figure 4: The 48-h segregation softening point with different S content.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **S** & **TMTD** & **BHT** & **ZnO** & **Carbon-White** & **Kaolin** \\ \hline Molecular formula & S & C\({}_{\mathrm{c}}\)H\({}_{\mathrm{2}}\)N\({}_{\mathrm{2}}\)S\({}_{\mathrm{4}}\) & C\({}_{\mathrm{3}}\)H\({}_{\mathrm{2}}\)O & ZnO & SiO\({}_{\mathrm{2}}\) & Al\({}_{\mathrm{2}}\)O\({}_{\mathrm{3}}\)-2 SiO\({}_{\mathrm{2}}\)-2H\({}_{\mathrm{2}}\)O \\ \hline Density & 2.3600 g/cm\({}^{3}\) & 1.43 g/cm\({}^{3}\) & 1.048 g/cm\({}^{3}\) & 5.6060 g/cm\({}^{3}\) & 2.2–2.6 g/cm\({}^{3}\) & 2.5 g/cm\({}^{3}\) \\ \hline Molecular weight & 32.07 g/mol & 204.14 g/mol & 220.36 g/mol & 81.39 g/mol & 60.08 g/mol & 87–88 g/mol \\ \hline \multirow{4}{*}{Characteristics} & Can be dissolved in benzene, inflammation & Can be dissolved in alcohol, benzene, with smell. & Can be dissolved in ethyl alcohol, benzene, ether. & White or pale yellow powder. & White powder. & White powder, can be slightly soluble in hydrochloric acid and acetic acid. \\ \hline \end{tabular}
\end{table}
Table 3: Technical indicators of S, TMTD, BHT, ZnO, carbon-white and kaolin.  
As shown in Figure 4, the 48-h segregation softening points' difference of modified bitumen decreases gradually with the increasing content of S compared to lower content of S. With the further increase of the content of S, the gelation phenomenon occurs in the bitumen modification process. The higher the content of S is, the earlier the gelation phenomenon occurs and the more serious the gelation phenomenon. This phenomenon can be explained by the vulcanization reaction process shown in Figure 2. It can be called over vulcanization or a high degree of vulcanization. With respect to the comprehensive test phenomenon, test results and economic factors, the content of S in Component No. 4 was the optimum content, which determines a = 60.
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Machine Learning Algorithms for Stress and Mental Health Management in Smartwatches  
This section provides a short summary of machine learning (ML) algorithms used in smartwatches for mental health monitoring and stress management. Specifically, smartwatch-based systems for mental health monitoring utilize a variety of machine learning algorithms designed to interpret physiological and behavioral signals. Common choices such as k-Nearest Neighbors (KNNs), Decision Trees (DTs), and Random Forests (RFs) offer interpretable and efficient classification, especially in structured biometric data. Support Vector Machines (SVMs), particularly with Gaussian kernels, are widely applied due to their accuracy in handling complex and nonlinear biosignals.  
Naive Bayes and Logistic Regression provide fast, lightweight models that are often used in early-stage or real-time detection tasks. In contrast, Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are suited for capturing deeper, nonlinear relationships in multi-modal sensor data. Boosting Neural Networks (BNNs) combine multiple weak learners to enhance predictive performance.  
Dimensionality reduction and feature selection techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Bayesian Networks (BNs) further support the modeling process. In long-term social sensing, heuristic methods like signal magnitude area (SMA) and short-term audio energy thresholding are also employed to infer behavior patterns relevant to mental health.  
Collectively, these algorithms support the accurate real-time inference of stress and mental states, leveraging data from wearable devices in both clinical and everyday settings.  
As shown in Figure 5, the reported precision values generally range from 70 to 85%, with AUROC values only occasionally provided. This lack of standardized reporting further highlights the challenge of quantitative comparison between studies.
## 3 Problem Formulations
### Imperfect Sensing Conditions  
Figure 6 depicts the ergodic capacities of cognitive satellite network versus the sensing time for different \(I_{av}\) in imperfect conditions. It should be noted that the ergodic capacities are the concave function of the sensing time. Along with the sensing time increasing, the ergodic capacities increase at the beginning and decrease after the optimal sensing time, since the longer the sensing time, the smaller the transmission time.  
The ergodic capacity of cognitive satellite network versus \(\tau\) with different \(P_{av}\) is illustrated in Figure 7. The trend of ergodic capacities is similar to Figure 6. However, the ergodic capacity gain  
Figure 5: Ergodic capacity versus \(I_{av}\) for different \(m_{sp}\).  
Figure 6: Ergodic capacity versus sensing time for different \(I_{av}\) with \(m_{sp}=1\).  
in Figure 7 is larger than that in Figure 6. It is because \(P_{av}\) has more impact on the ergodic capacity than \(I_{av}\).  
In Figure 8, we present the ergodic capacity of the satellite user versus sensing time \(\tau\) with different \(P_{av}\) and \(m_{sp}\). It can be clearly observed that the trend of ergodic capacities is consistent with the findings in Figures 6 and 7, and the achievable ergodic capacity being higher corresponds to a larger \(P_{av}\). Furthermore, it is noted that, with the difference of \(m_{sp}\), the optimal sensing durations are almost the same. However, along with the decrease of \(m_{sp}\), the ergodic capacity of the satellite user increases for the same \(P_{av}\), since the smaller the value of \(m_{sp}\), the more severe the fading conditions of the interference link become.
## 2 RF Features
### Received Signal Strength  
Received Signal Strength (RSS) is one of the features most frequently used in localisation due to its low hardware requirements and easy implementation compared to its counterparts. RSS is the received power in the receiver over the bandwidth. The main concept behind using RSS for localisation is that the power attenuation of the signal from transmitter to receiver depends on the distance. Extraction of the exact relation, however, in real environments seems infeasible due to the unknown channel model, and the majority of existing literature relies on using simple models for mapping RSS to distance or range. The most popular model is the log-distance one-slope propagation model:  
\[P_{d}[dB]= P_{0}[dB]-10\alpha log_{10}(d/d_{0})+X_{\sigma}[dB]+b[dB]\,, \tag{1}\] \[d= ||X-S||\,,\]  
where:  
* \(P_{0}\): power at the reference distance \(d_{0}\) from the transmitter (usually 1m).
* \(P_{d}\): received power at distance \(d\) from the transmitter.
* \(X_{\sigma}\): shadowing effect( mostly considered as Gaussian).
* \(\alpha\): Path loss exponent (PLE), the rate at which power decrease over distance.
* \(b\): bias error.  
A more complex form of propagation model has been used, such as two-slope, third-order, and higher up to the sixth-order model [18; 19]. [PERSON] et al. [20] propose finding the best model for each transmitter-receiver pair using the Genetic algorithm to search between multi-state path loss models with k states. It should be noted the transmission power is assumed to be fixed. When power control is applied at the transmitter, which might be the case in 5G, range inference based on distance is impossible. Transmission power and PLE are usually obtained by conducting a pre-test, collecting data from the environment, and matching the suitable values based on the model. Algorithms that deal with unknown PLE and transmission power thus offer the advantage of removing the need for an intensive pre-test phase.  
Another contributing factor for less accurate RSS-based localisation--in addition to channel modelling error--is that RSS is not stable and fixed over time and under changing environments. In [21], three main factors that play an important part in unstable RSS readings are discussed, and the effects of antenna orientation, foreground obstacles, and beacon density are explored.
## 3 Results and Discussion  
### Structure and Properties Description of B-L MILs  
Since the details of the structure (shown in [51]) and property characterization of the B-L MILs 5a-d with dual Brensted-Lewis properties were published in our previous paper [51], only a brief description is provided here. The B-L MILs 5a-d with close to a 100% atom economy were synthesized using three steps: Ullmann-type coupling, treatment with HCl, and coordination with FeCl\({}_{3}\) (seen in [51]). The  
Figure 1: The structures of magnetic ionic liquids with dual functions as a solvent–catalyst and dual Bronsted–Lewis acidity (B-L MILs).  
target samples, 5a-5d, with a 0.5 molar fraction of FeCl\({}_{3}\) in the B-L MILs, were characterized using an elemental analysis (the purity differences of 5a-d between the theoretical and experimental values were both within the expected ratios (within 0.3)), electrospray ionization mass spectrometry (ESI-MS, a fragment peak at m/z = 198.01 corresponding to [FeCl\({}_{4}\)]-, as shown in [51]), UV-vis spectroscopy (three characteristic bands at 534 nm, 619 nm, and 688 nm in [51]), and Raman absorption spectroscopy (a strong band at approximately 330 cm\({}^{-1}\) in [51]). The B-L MILs exhibited good solubility in various organic solvents with the exception of hexane, but they were less soluble in dichloromethane and ethyl ether (seen in [51]). B-L MILs with a sp2-substituted phenyl ring at the nitrogen atom and good charge distribution resulted in good thermal stability [51] and a low melting point (ranging from 65 to 85 \({}^{\circ}\)C, as shown in [51]). In terms of the most important tunable acidity, the electron-donating group (EDG) in the para-substitution of the aryl-imidazolium moiety had lower Bronsted acidity and higher Lewis acidity than the electron-withdrawing group (EWG) (as shown in [51]). These properties are related to their sp2-para-substitution effect because the electron-withdrawing and electron-donating groups can influence the charge distribution (calculated using the electrostatic surface potential model) of B-L MILs.