esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusion  
A fuzzy logic control based QoS management approach has been proposed for WSANs. With this approach, the sampling period of each source sensor node is adjusted dynamically so that the deadline miss ratio associated with the relevant data transmission from the sensor to the actuator is maintained at a desired level. In this way, QoS requirements with respect to timeliness, reliability, and robustness can be satisfied. Simulation results have demonstrated the effectiveness of the proposed approach.  
Our future work in this direction includes: 1) improvement of the FLC-QM scheme for large-scale WSANs through, e.g., developing a unified framework; 2) extensive simulation studies on WSANs with more complex network topology; and 3) experimental studies and practical implementation of the FLC-QM scheme in WSANs.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 3 Proposed Method  
Figure 2 illustrates the proposed architecture for classifying situational awareness based on EEG signals. In the first step, the signals sampled from 14 electrodes located on a subject's head (as depicted in Figure 2a) are converted into pulses in the timeline using delta-sigma modulation (DSM) coding. Classifying an EEG signal using an SNN requires encoding the raw EEG data into spikes. We utilize the well-known delta-sigma modulation and an SCTN-based integrate-and-fire (IF) neuron to convert the analog input sensor voltages into a binary stream of spike trains with firing rates that are linearly proportional to the input voltage [27]. After that, the frequency characteristics are extracted for each channel using a dedicated array of resonators. The feature extraction phase is carried out using the SNN-based resonator architecture and by applying a supervised STDP learning rule [43]. The weights and biases of the resonator networks are adjusted using the proposed supervised algorithm, aiming to detect the targeted EEG sub-band frequencies. In the next step, we propose a unique mapping of the extracted features using five SCTN-based networks. Finally, another network is used for classification. The proposed process for feature extraction, feature mapping, and classification is detailed below.  
Figure 1: (a) SNN-based resonator architecture and (b) the neurons’ output.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 7 Conclusions  
This study presents a comprehensive and comparative investigation into roll stability control for both on-road and off-road vehicles, offering several key contributions to the field. First, by establishing a unified analytical framework, the paper enables a clearer understanding of the fundamental differences and shared challenges in roll stability control across diverse vehicle categories. Second, the study identifies and compares the effectiveness of core technologies--such as active suspension, direct torque control, and adaptive leveling--within distinct operational environments, providing a solid foundation for informed system design. Third, the analysis reveals that while on-road vehicles benefit from high-speed responsiveness and sensor fusion accuracy, off-road vehicles demand enhanced adaptability to environmental uncertainties, prompting a shift toward more robust and terrain-aware control strategies.  
A notable contribution of this work is the detailed synthesis of experimental validation approaches tailored to each vehicle type, bridging the methodological gap between simulation-heavy on-road studies and field-based off-road evaluations. Furthermore, the study highlights potential directions for integrating emerging technologies--particularly machine learning, predictive algorithms, and intelligent terrain perception--into roll stability systems, paving the way for more intelligent, adaptive, and universally applicable control solutions.  
By clarifying the technological divergences and convergence points between vehicle categories, this research provides a foundation for future cross-domain innovation in vehicle dynamics and control. It not only enhances the theoretical understanding of roll stability, but also contributes practical insights into system implementation under real-world conditions, supporting the development of safer and more adaptable intelligent vehicles.  
Conceptualization, J.C. and R.W.; methodology, J.C. and R.D.; validation, J.C. and W.L.; investigation, J.C. and D.S.; writing--original draft preparation, J.C.; writing--review and editing, R.W. and R.D.; visualization, D.S. and Y.J.; supervision, R.W. and R.D.; funding acquisition, R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52472410; the National Key Research and Development Program of China, grant number 2023 YFB2504500; and the \"Unveiling the List and Taking Command\" Tackling Project of Nantong, grant number JB2022003.  
Not applicable.  
The data presented in the study are available on request from the corresponding authors.  
The authors would also like to thank the anonymous reviewers for their constructive comments.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Catalyst Preparation  
All of the catalysts tested in this work, except for the commercial P25 that was used as a benchmark, were synthesized via FSP. In order to synthetize the pure WO3 and TiO2 and the mixed oxide WO3/TiO2, ammonium metatungstate hydrate (AMT) and titanium tetraisopropoxide (TTIP) were used as precursors, respectively, for WO3 and TiO2. AMT is a water-soluble compound which cannot be dissolved in alcohols. On the other side, TTIP is strongly hygroscopic, and requires the use of an anhydrous organic solvent to avoid hydrolysis directly in the flask. DMF (dimetiformamide) was used as a solvent, since it is an anhydrous organic solvent able to solubilize AMT and due to its boiling point (153 \({}^{\circ}\)C). Both a low-boiling and a very high-boiling solvent must be avoided to prevent, respectively, the sintering of the produced particles or the formation of egg-shell phenomena, which lead to the formation of hollow particles. Solutions with a concentration of the organic precursor equal to 3% wt. compared to the solvent were used for injection in the FSP apparatus. This concentration was taken as a reference from a previous work [40] to avoid the vaporization of the solvent and the precursor directly into the tip of the needle, which can lead to clogging and to a twisted flame. Furthermore, increasing the concentration above a certain value again incurs the phenomenon of particle sintering, due to the higher probability of collision of the formed particles. All of the instrumentation was well dried in the oven to eliminate any moisture. The prepared solutions were injected into the FSP apparatus with a flow of 2.7 mL/min. The flowrates for the methane, external and the internal oxygen flows were, respectively, 0.5 L/min, 1 L/min and 5 L/min. During all of our syntheses, an overpressure of 1.5 bar of oxygen used for dispersion was maintained. After synthesis, all of the powder obtained was collected from the wall of the glass chamber. The composite catalyst obtained had final mass and molar ratios of the two oxides equal, respectively, to TiO\({}_{2}\)/WO\({}_{3}\) = 60/40 (wt/wt) and TiO\({}_{2}\)/WO\({}_{3}\) = 81.3/18.7 (mol/mol).
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 4 Results and Experimental Validation of the Developed Model
### Experimental Validation of Conventional Vehicle Model  
The conventional vehicle model was obtained by disabling the ECMS controller, Electric Machine, and Battery Electro-Thermal model subsystems. This vehicle model was designed based on a backward model was developed in Matlab/Simulink environment using all characteristics and parameters of the selected vehicle. The model estimates the fuel  
Figure 12: Penalty functions on temperature (**a**) and on temperature change rate (**b**).  
consumption of the vehicle by computing the required ICE speed (\(\omega_{ice}\)) and torque (\(T_{ice}\)) for a given driving cycle. To validate the model, experimental test data of the powertrain components and of the vehicle from ANL were used. The data includes measurements of the vehicle speed (\(v\)), the ICE torque (\(T_{ice}\)) and its angular speed (\(\omega_{ice}\)), the selected gear (\(i_{gr}\)), and the instantaneous fuel consumption (\(m_{ico}\)) profile. A Mazda CX9 2016 SUV was selected for further analysis. The complete data of the ICE map and the vehicle parameters are available in [32]. For the validation, the simulated value of the torque, the angular speed and the fuel consumption rate of the ICE, and the engaged gear of the gearbox were compared to those from the experiments reported by ANL.  
As is shown in Figure 13, the input is the speed profile of the UDDS driving cycle. The gearshift in the model is defined on the basis of the vehicle velocity and the result of the gear ratio is almost overlapping with the ANL experimental data (Figure 13b). Moreover, the simulated values of the ICE angular speed (Figure 13c) and the torque (Figure 13d) reported at the output of the gearbox show a good match with experimental data. For the calculation of the tire radius (Equation (3)), the coefficient \(\epsilon\) was chosen to be equal to 0.95. The simulated and experimental values for the fuel rate (Figure 13e) have slight differences, due to the fact that transient fuel consumption is not taken into account. Figure 13e visualizes this fact, where the differences are mainly in the transient phases, which was not modelled. For the total fuel consumed during the UDDS cycle, a simulated value of 699 g vs. an experimental value of 739 g was obtained. This corresponds to a difference of about 5%. However, if transient fuel consumption behaviour of the engine is excluded this difference is within just 2% (713 g), while the other 3% of the difference is due to transient phenomena which is not included in the developed model as mentioned above.
# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 2 Materials and Methods
### PadGAN
#### 2.3.4 Loss  
The loss function consists of both the primary network loss and the auxiliary network loss. Both are trained together but independently backpropagated. The loss function equations of the primary network and the auxiliary network are the same and include both generator adversarial loss, discriminator adversarial loss, and pixel reconstruction loss. Equation (11) represents the generator adversarial loss:  
\[\mathcal{L}_{G\_adv}=E[D(x,G(x))-1]^{2} \tag{11}\]  
where \(\mathcal{L}_{G\_adv}\) represents the generator adversarial loss, \(E(\cdot)\) denotes the expectation, \(D(\cdot)\) represents the discriminator's output result, \(x\) denotes the input image, and \(G(x)\) represents the generator's output result. Theoretically, the generator's adversarial loss is minimized when the discriminator identifies the generated result as 1. Equation (12) shows the discriminator adversarial loss:  
\[\mathcal{L}_{D\_adv}=E[D(x,y)-1]^{2}+E[D(x,G(x))]^{2} \tag{12}\]  
where \(\mathcal{L}_{D\_adv}\) represents the discriminator adversarial loss and \(y\) represents the real image. The discriminator adversarial loss consists of two parts: the first part minimizes when the concatenated real image with the source image dimension, after being passed through the discriminator, approaches 1; the second part minimizes when the concatenated generated image with the source image dimension, after being passed through the discriminator, approaches 0.  
The generator loss and discriminator loss have opposite objective functions, and, during training, one should be fixed while the other is trained in an alternating manner to achieve the adversarial goal. Furthermore, to enhance the authenticity of the generated images, pixel-wise reconstruction loss should be introduced, as shown in Equation (13):  
\[\mathcal{L}_{1}=E[\|y-G(x)\|_{1}] \tag{13}\]  
where \(\mathcal{L}_{1}\) represents the pixel-wise reconstruction loss and \(\|\cdot\|_{1}\) represents the \(L1\) norm. Therefore, the overall loss for both the main network and the auxiliary network is represented as Equation (14):  
Figure 3: The structure of the two generators. The upper part represents the primary generator (PG), while the lower part represents the auxiliary generator (AG). In the primary generator, the letter “F” represents the feature fusion layer, UC(N) represents the up-sampling transpose convolution operation, and DC(N) represents the down-sampling convolution operation. N represents the number of convolutional channels.  
\[\mathcal{L}=\lambda_{L1}\mathcal{L}_{1}+\lambda_{adv}(\mathcal{L}_{G\_adv}+ \mathcal{L}_{D\_adv}) \tag{14}\]  
where \(\mathcal{L}\) represents the overall loss, \(\lambda_{L1}\) represents the pixel-wise reconstruction loss coefficient, and \(\lambda_{adv}\) represents the adversarial loss coefficient.
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 4 Results  
The thermal orthophotos resulting from the UAV data acquisition and photogrammetric processing are presented in Figure 3, illustrating the spatial distribution of LST across the study area during four diurnal periods. It should be noted that the results presented herein represent thermal conditions specific to January 2025 (winter season) in a semiarid climate, characterized by relatively cool ambient temperatures and minimal vegetation activity. These orthophotos reveal distinct thermal patterns across different LULC classes, with notable variations in temperature distribution between morning, afternoon, evening, and midnight acquisitions. In conjunction with Figure 2, these thermal orthophotos facilitate the identification of LST differentials across distinct land cover types. The thermal imagery provides the foundation for the subsequent quantitative analyses of LST variations.
# The Applications of Plasma Techniques
### Operating Problems of Arc Plasma Reactors Powered by AC/DC/AC Converters  
[PERSON] and [PERSON] in [6] reported the results of research on the cooperation of three-electrode plasma reactors with gliding arc discharge powered from multi-phase AC/DC/AC converters. To achieve the scientific and practical goal of the project, a test stand was designed and built which included: a multi-electrode GlidArc type plasma reactor; a power-electronic AC/DC/AC converter working as a source of voltage or current with regulated parameters of energy transferred to the discharge space; a reactor operation diagnostics systems; and a process gas feeding and flow control system. The GlidArc Plasma Reactor demonstrated high sensitivity to changes in many electrical, gas chemical, gas-dynamic, and mechanical parameters. The AC/DC/AC converter system was sensitive to interference generated by the plasma reactor. It was found that the operation of the reactor in certain conditions causes larger interferences on the converter.  
However, it is difficult to systematize the influence of specific parameters of the reactor's operation on the operation of the AC/DC/AC converter and vice versa due to mutual correlations of many parameters. On the one hand, the correct operation of a plasma reactor depends on the characteristics of the power supply system; on the other, the power supply system reacts to such an untypical receiver as a plasma reactor.
## 3 Methodology
### Navigation Network Generation and Model Reconstruction  
After obtaining the information of building components and navigation elements in the indoor space, building components can be integrated based on the hierarchical relationship in Section 3.1 to reconstruct the navigation element models. The reconstructed results can generate not only models with geometric information, but also vector 3D models with semantic information to satisfy the applications of GIS and BIM.  
For indoor navigation and route planning, we set the geometric center of the navigation element as a node and the connection relationship obtained from the label information in the connecting transition space or on both sides of the door as an edge to construct the navigation network, as shown in Figure 10a. However, navigation elements connected by only one edge may have a navigation path through the wall [30]. Therefore, some geometric features of indoor spaces need to be incorporated into the construction of navigation networks.  
The concavity of the corner point of the navigation element is judged. If the angle of the corner point on the indoor side is \(\partial\) (\(180^{\circ}\leq\partial<360^{\circ}\)), the corner point is a concave point. When two wall segments on either side of the concave point intersect with the navigation network, these two wall segments are extended to intersect with other wall segments. Then, the local navigation network is reconstructed based on the result of segmentation, as shown in Figure 11. Besides, the structural units connected with more than three other units are set as corridors. As shown in Figure 10b, the navigation network of the corridor is also reconstructed according to the literature [10].  
Figure 10: Corridor navigation network generation and adjustment. The purple dots are corridor nodes. The red dots and green dots are abstract nodes. The thick blue lines indicate the connection relationship between nodes. (**a**) represents the initial navigation network. (**b**) represents the corridor node optimized navigation network.
## 3 Permeability Test Results and Discussion  
### Effect of \(T_{\rm{hl}}\)  
In this study, effect of different \(T_{\rm{hl}}\) foaming agents on the permeability of standard sand was investigated. The permeability results (FER = 10 and FIR = 50%) shown in Figure 5a indicates that the permeabilities of ZD10, ZD20, and ZD30 (\(T_{\rm{hl}}\) \(<\) 30 min) were significantly greater than those of ZD40, ZD50, ZD60 (\(T_{\rm{hl}}\)\(>\) 30 min). In 30 min, the permeability coefficients of the ZD10 and ZD20 samples increased from \(10^{-5}\) cm/s to \(6\times 10^{-4}\) cm/s, while the permeability coefficient of the sample group with a large \(T_{\rm{hl}}\) (ZD30-ZD60) was always less than \(5\times 10^{-5}\) cm/s. In addition, the larger \(T_{\rm{hl}}\) Is, the lower the permeability coefficient. Figure 5b presents the changes of in sizes for ZD10 and ZD30 foams when FER = 10 within 5 min. The bubble image results of ZD30 foams shows that the size and shape changes are significantly smaller than those of the ZD10 foams. This phenomenon also indicates that a foam with a high \(T_{\rm{hl}}\) has better structural stability and a lower liquid flow rate [22; 23].  
In the permeability experiments, when the voids of the standard sand were completely filled by foam, the sand particle skeleton and the foam jointly resisted the external water pressure. High stability foams that have a long \(T_{\rm{hl}}\) were also more resistant to external water pressure due to their more stable liquid film structure and slower liquid loss rate. These results indicated the permeability performance of conditioned sand was closely related to the stability of foam.
# Effect of ElectroSpark Process Parameters on the WE43 Magnesium Alloy Deposition Quality
## 1 Introduction
According to the set of process parameters the microstructure of the deposits has been observed both amorphous or extremely fine grained [22; 23]. For example, [PERSON] and [PERSON] [29] show the feasibility of using the ESD process to deposit amorphous electrode material (Zr\({}_{41.2}\)Ti\({}_{13.8}\)Ni\({}_{10}\)Cu\({}_{12.5}\)Be\({}_{22.5}\)) onto an amorphous substrate of the same composition without crystallizing the deposit or the substrate. [PERSON] et al. [30] investigated the ESD coating structure of as-cast Fe\({}_{48}\)Cr\({}_{18}\)Mo\({}_{7}\)B\({}_{16}\)C\({}_{4}\)Nb\({}_{7}\) crystalline alloy on 316L stainless steel substrate. In particularly, their results showed that the coating structure is amorphous at low total heat input values, but at high values of total heat input, an extremely fine-grained or likely nano-crystalline structure was obtained. Some studies have analyzed both microstructure and defects of 2024 aluminum alloy coatings deposited onto a homologue substrate by ESD process [19; 31]. Moreover, the effect of the ESD process as a repair technology was studied by [PERSON] et al [32] and [PERSON] et al. [33] respectively for a 2024 aluminum alloy and Ni-Superalloy. Especially, regarding the aluminum alloy, a comparative study between deposits performed on substrates supplied both in natural aged and artificial aged state has been performed. Results showed that the as-repaired samples had low tensile properties due to defectiveness and residual stress. However, it has been demonstrated that the tensile performances can be improved by post repair heat-treatments. Others researchers studied in detail the mass transfer mechanism in the ESD process. In particular, [PERSON] and [PERSON] have developed a method to determine the efficiency of mass transfer, also called transfer coefficient, constantly monitoring the erosion of the anode and the mass gain of the cathode [34]. Until now, the opportunity of applying the ESD technology to WE43 magnesium alloy has not been studied, despite the huge interest in many sectors, first of all the aeronautic one. This work is the first study regarding the application of ESD process on WE43 magnesium alloy. Especially, the ESD technique was used to deposit WE43 coatings on homologue substrate. Five depositions at increasing levels of spark energy were processed. The discussion will involve the effect of the spark energies on the microstructural and mechanical features for the production of high performance and reliable deposits. Additionally, since during the WE43 alloy deposition process different defects could occur, the relationship between the processing energies and the area fraction and morphology of these defects was investigated. The identification and examination of these defects is a very important topic since their presence may exert significant influence on the deposits' mechanical properties.