esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[14] established a mathematical model for fracture width and fracture conductivity by studying the columnar placement of proppants and the mechanical properties of formation rocks, taking into account the elastic deformation of proppant and fracture walls. Some scholars have introduced auxiliary software, including Computational Fluid Dynamics (CFD), the Finite Element Method (FEM), the Discrete Element Method (DEM), and Particle Flow Code (PFC), among others [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], to investigate the transport process of proppant and the impact of proppant particles on fracture conductivity. [PERSON] et al. [16] used a DEM-CFD coupled model to establish a calculation model for fracture conductivity by considering parameters such as the fracture closure pressure, sand concentration, and elastic modulus. In summary, preliminary indoor experiments indicated that physical experimental methods suffer from prolonged testing times, high costs (particularly for fracture conductivity experiments), and exhibit unstable or even significant errors in test results under identical conditions. The phenomenon of repeatedly conducting experiments with different results is common, and the reasons behind these discrepancies and the underlying laws governing them are not clearly understood. The methods of physical experiments and numerical simulations mainly use empirical and semi-empirical models with poor generalizability, and some mathematical models also ignore some key parameters (such as the crushing rate). If there is also the need for rock triaxial mechanical testing to obtain parameters such as the elastic modulus and Poisson's ratio of rock samples and proppant, this will greatly increase time and economic costs. According to the limitations of the above research methods, this paper proposes a theoretical calculation method that can replace repetitive physical experiments to obtain the key performance parameters of proppant. By studying the micro-arrangement of proppant particles through computer simulation, both the randomness and deterministic patterns of the micro-arrangement are revealed. On this basis, a mathematical model of the mechanical relationship between proppant particles is established to grasp the force situation of each proppant particle in the fracture under a given closure pressure. Starting from the micro-arrangement simulation and mechanical analysis of proppant particles, we combine micro and macro perspectives, numerical calculations, and simple proppant crushing experiments to obtain the key performance parameters of the proppant and reveal the possible reasons and patterns behind different repeated experimental results. This approach provides theoretical support for the engineering application of hydraulic fracturing proppant materials.
# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Vertical Federated Learning (VFL)
#### 6.2.2 Fully Centralized vs. vFL  
For the scenario without the meanBitrate; according to the Dunn post hoc test, the average \(p\)-value was higher than 0.05 (0.34, 0.15, 0.74 for split index 2, 4, and 6 respectively), rejecting the hypothesis that Fully Centralized and \(vFL\) are coming from different distribution, thus they all can be considered on-par.  
For the scenario with the meanBitrate; according to the Dunn post hoc test, the average \(p\)-value was lower than 0.05 (0.01, 0.05, \(8\times 10^{-4}\) for split indices 2, 4, and 6 respectively), accepting the hypothesis that _Fully Centralized_ and \(vFL\) stem from different distribution, thus yielding a conclusion that the \(vFL\) result is slightly better than _Fully Centralized_ scenario. We attribute the reason for the latter is due to a larger gap between the feature sets of the two groups when the _meanBitrate_ is included, that might be forcefully addressed by the last few layers of neural network at the master node. In contrast in the _Fully Centralized_ model, the bias (potentially caused by feature quality gap) is introduced in the early hidden layers of the neural network on the worker nodes. We will investigate this evidence further in future work.
## 3 Results
### Effect of PBMT-sMF in H\({}_{2}\)O\({}_{2}\)-Exposed Environment
#### 3.2.1 Biochemical Effects of PBMT-sMF in H\({}_{2}\)O\({}_{2}\)-Exposed Environment  
A significant difference was found in the viability between groups, F (3,4) = 33.59, _p_! 0.002. All tested doses (9 J, 30 J, and 60 J) showed higher viability levels compared to the cytotoxicity induced by H\({}_{2}\)O\({}_{2}\) (102.5 \(\pm\) 9.19%, \(p\) = 0.007; 104.5 \(\pm\) 0.71%, \(p\) = 0.005; and 101 \(\pm\) 2.82%, \(p\) = 0.01, respectively; Figure 6A), and they were similar to the control. However, no significant difference in dsDNA release was observed at any dose compared to the control group or H\({}_{2}\)O\({}_{2}\) group (Figure 6B).  
Regarding the release of NO (Figure 6C) and the production of ROS (Figure 6D), there was a significantly higher production of both markers (131.34 \(\pm\) 8.64%; \(p\) = 0.041 and +47%; _p_! 0.05, respectively) in cells treated with H\({}_{2}\)O\({}_{2}\) compared to the control group (F (4,5) = 10.83, _p_! 0.011, and F (4,5) = 13.36, _p_! 0.007, respectively).  
The reduction in NO was 34%, 42%, and 38% for the 9 J, 30 J, and 60 J doses, respectively, compared to the H\({}_{2}\)O\({}_{2}\)-treated group (Figure 6C), with statistically significant differences  
Figure 6: Effects of Photobiomodulation Therapy Combined with Static Magnetic Field (PBMT-sMF) and simultaneous exposure to hydrogen peroxide (H\({}_{2}\)O\({}_{2}\); 700 uM) on cell viability (**A**), dsDNA release (**B**), NO (**C**), and ROS (**D**) in C2C12 muscle cells. \({}^{*}\) Statistical differences were determined by analysis of variance (ANOVA) and [PERSON]’s post hoc test (_p_! 0.05) compared to the control. # Statistical differences were determined by analysis of variance (ANOVA) and [PERSON]’s post hoc test (_p_! 0.05) compared to H\({}_{2}\)O\({}_{2}\). J = Joules.  
(\(p\) = 0.026, \(p\) = 0.011, and \(p\) = 0.017). Similarly, reductions of 49%, 52%, and 49% in ROS production were observed for the 9 J, 30 J, and 60 J doses, respectively, compared to the H\({}_{2}\)O\({}_{2}\)-treated group (Figure 6D), with statistically significant differences (\(p\) = 0.005, \(p\) = 0.004, and \(p\) = 0.005).  
An overall significant difference was found in COX activity between groups, F (4,5) = 38.59, \(p\) < 0.0006. Specifically, a significant increase was observed in cells exposed to H\({}_{2}\)O\({}_{2}\) compared to the control (240.36 \(\pm\) 3.493; \(p\) = 0.0004, Figure 7). The PBMT-sMF-irradiated group demonstrated an average reduction of approximately 1.5-fold in COX activity at all doses compared to the H\({}_{2}\)O\({}_{2}\) group, with the maximum reduction (nearly 60%) observed at 9 J, returning to levels similar to baseline (no statistical difference compared to the control; \(p\) = 0.083; Figure 7).
# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
###### Abstract
Those segmentation methods can be classified under three categories: statistical [6; 7; 8; 9; 10; 11; 12; 13], atlas-based [2; 14; 15; 16; 17; 18; 19; 20; 21; 22], and deformable model-based methods [23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Statistical methods are the fastest compared to other techniques; however, they cannot handle all real distributions of data as they rely on pre-defined statistics. While atlas-based methods are more accurate than statistical methods, they require heavy computations and are very dependent on the selection of atlases. Deformable-model-based methods outperform other techniques in segmenting connected objects, though these techniques depend on the design of the statistical and geometric guiding forces as well as the model initialization. In this study, we focused on addressing the limitations of the current segmentation techniques that deal with magnetic resonance (MR) infant brains, with a special focus on those which incorporate diffusion tensor imaging (DTI) measurements in their framework. To overcome these limitations, we propose in this study an adaptive, atlas-based technique for segmenting different structures of infant brains automatically from DTI scans (see Figure 1). Details of the proposed segmentation framework will be presented in the following section. Figure 1: The basic steps of the proposed framework for segmenting different structures from infant DTI brain data.
# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
###### Abstract  
Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.  
connected and autonomous vehicles; deep learning; traffic knowledge sharing; navigation +
Footnote †: journal: Sensors  
2023  
2323653
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 5 Conclusions  
The licensing process in Brazil requires an environmental impact study (EIA) [118]. However, environmental impact studies are generally not standardized and none of the studies carried out on the dams of the Teles Pires assessed the structural complexity of the river substrate and the potential loss of micro-habitats. We advocate that standardized freshwater habitat classifications generated from remotely sensed data should be included in every hydroelectric EIA. The studies should not rely on pre-existing classifications which are too coarse and often inaccurate in quantifying the aquatic-terrestrial boundaries and precise land covers in the planned lentiic zones [5, 16]. Frequent revisit period, high spatial resolution satellite imagery provides a better choice of data from which to derive landscape classifications for use in dam construction planning and EIAs.  
While the negative effects of the ongoing expansion of hydroelectric dams in Brazil and elsewhere in the Amazon on migratory fishes has been well documented, dams have other equally devastating effects on biodiversity such as the loss of specialized habitat for rheophile species, or those restricted to the unique conditions of the small Creek tributaries. Their contributions to long-term GHG emissions have also become increasingly popular areas of study for installations around the world. Multi-scale remote sensing from satellite imagery to UAS platforms can provide data products to facilitate long-term monitoring and assessment of hydropower dams and their impacts on the regional landscape, as well as unique micro-habitats important for many species.  
The following are available online at [[https://www.mdpi.com/article/10.3390/earth2020018/s1](https://www.mdpi.com/article/10.3390/earth2020018/s1)]([https://www.mdpi.com/article/10.3390/earth2020018/s1](https://www.mdpi.com/article/10.3390/earth2020018/s1)).  
Conceptualization, O.L. and M.K.; methodology, O.L., M.K., J.P.A.-M.; formal analysis, O.L. and M.K.; investigation, O.L., L.S., L.N.C.; writing--original draft preparation, all authors; writing--review and editing, all authors; visualization, O.L., M.K. and J.P.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ohio Cichlid Association, the 2018 Jim Smith Fund Grant (Lucanus) and a CNPq Productivity Grant #309815/2017-7 (Sousa). The RapidEye and PlanetScope imagery (Planet Labs Inc.) was provided by the Department of Geography, McGill University.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Methodology
### Sliding Window-Based Reconstruction Strategy
The sliding window-based reconstruction strategy significantly improves the overall quality of dynamic scene reconstruction, particularly in complex environments where dynamic objects constantly interfere with static background reconstruction. The ability of this strategy to aggregate information across multiple frames helps in handling both small-scale and large-scale dynamic interference. The accuracy of the background restoration is further enhanced by the use of temporal and spatial weighting, which ensures that the most relevant frames are prioritized during the reconstruction process. Moreover, the sliding window approach ensures that dynamic objects are effectively suppressed without sacrificing the accuracy or completeness of the static scene. Algorithm 1 provides the pseudocode for the sliding window-based reconstruction strategy, outlining the key steps involved in the dynamic background restoration process. It highlights the iterative nature of the approach, through which each window of frames is processed sequentially, dynamic regions are verified, and static regions are reconstructed. The use of weighted averaging in the background restoration step ensures that the final result accurately reflects the static scene without artifacts caused by dynamic interference.
# Pilot-Aided Frame Synchronization in Optical OFDM Systems
## 1 Introduction
We propose a frame synchronization timing metric based on the use of the mirror symmetry in the time domain pilot symbol and disparity between the average power of the O-OFDM symbols and pilot symbol to estimate the start of a PA O-OFDM frame. The pilot symbol is inserted within each frame since the symbol timing needs to be periodically monitored to ensure the transmitter and receiver are synchronized. The synchronization algorithm has low complexity and does not require prior knowledge of the transmitted pilot symbol at the receiver. In comparison with the modified Park's synchronization method [5; 9], our proposed PA synchronization scheme has additional capability of using the pilot signal for PAPR reduction in O-OFDM transmitter and the timing metric has relatively low complexity with the ability to give precise synchronization at signal-to-noise ratio (SNR) as low as 4 dB in an indoor VLC channel. The rest of the paper is structured as follows: description of the proposed frame synchronization algorithm is presented in Section 2. The simulation results and discussions on the performance of the proposed PA frame synchronization timing metric in the optical communication channel are presented in Section 3. Finally, concluding remarks are given in Section 4.
## 4 Results
### Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{**CNN–Transformer**} & **(1) Enhanced global feature capture capability: Compared to single CNN, it can extract long-range dependencies through Transformer’s self-attention mechanism. (2) Attempt at multi-modal feature fusion: Integrates TFR with shallow statistical features to improve recognition accuracy for complex modulations.** & **(1) High design complexity: The hybrid architecture requires coordinating parameters between convolutional and Transformer modules, increasing training complexity and potentially necessitating larger datasets or more sophisticated parameter tuning strategies. (2) Unresolved gradient degradation: Training difficulties persist in deep networks due to the absence of residual connections.** \\ \hline \multirow{6}{*}{**DFR**} & **(1) Effective gradient degradation mitigation: The ResNet residual connections ensure training stability in deep networks. (2) Enhanced phase feature extraction: HOS directly captures signal non-Gaussianity and phase information, compensating for the insensitivity of TFR to phase. (3) Global-local feature collaboration: Transformer models long-range dependencies, while ResNet extracts local details, jointly enhancing robustness in complex scenarios. (4) Dynamic feature fusion: The multi-head attention mechanism automatically adjusts feature weights according to task requirements, avoiding the limitations of fixed fusion.** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 6: Classification accuracy of different models. features increases the classification accuracy to 95.84%, an improvement of 2.92% compared to the input of single features (92.92%). Through analysis, it can be concluded that HOS compensates for the insensitivity of time-frequency domain analysis to phase information by capturing the non-Gaussian characteristics of signals, especially retaining key phase information under low-signal-to-noise ratio conditions. To visually observe the impact of HOS on signal feature enhancement, Figure 8 displays the confusion matrices of different inputs at 18 km. Rows represent predicted modulation types, and columns represent true modulation types. Labels 0-7 denote 2 FSK, 4 FSK, 2 PSK, 4 PSK, OFDM, DSSS, 4 DSSS, and 9 DSSS, respectively. The results show that after fusing HOS features, the DFTM model improves the recognition accuracy for multiple modulated signals such as 2 FSK, 2 PSK, and OFDM.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 4 Architectures
### Inter-Operator Coordination Mechanisms  
The proposed architectures are specifically designed to accommodate multi-stakeholder scenarios involving multiple SNOs and MNOs, each with distinct operational requirements and business objectives. The CMS framework facilitates coordination among these diverse operators through several key mechanisms that ensure efficient resource utilization while respecting individual operator constraints and priorities.  
In integrated operations architectures, the CMS serves as a neutral coordination entity that aggregates operational criteria from multiple SNOs and MNOs through standardized interfaces. The multi-objective optimization engine within the scheduler module enables simultaneous consideration of different operator priorities, using weighted optimization functions that can be configured to balance competing requirements. For example, multiple MNOs sharing satellite capacity can specify their respective QoS requirements and service priorities, while multiple SNOs can define their satellite resource constraints and operational windows.  
The coordinated operations architecture extends this capability by enabling multiple MANO systems from different MNOs to coordinate with the CMS while maintaining their individual network policies and orchestration strategies. This allows for scenarios where  
Figure 10: Control diagram using adaptive operations for a persistent connectivity constellation. Unlike coordinated operations, this architecture features an evolved NTN-MANO with enhanced autonomy and intelligence. The CMS manages SatOps including satellite OM and GS control, while the NTN-MANO independently manages NetOps through onboard SDN controllers and VIM. The two systems adapt to each other through a bidirectional interface that exchanges operational constraints and requirements, enabling autonomous optimization of both satellite operations and network orchestration while maintaining operational coherence.  
constellation resources are shared among multiple mobile network operators, each maintaining their own network management systems while coordinating satellite operations through the unified CMS framework.  
In adaptive operations architectures, the CMS interfaces with multiple NTN-MANO systems, enabling dynamic coordination between various operator domains. The bidirectional communication interface allows different operators to share operational constraints and requirements while maintaining operational autonomy. This mechanism supports complex scenarios such as multi-operator constellation sharing, cross-constellation handovers, and coordinated spectrum utilization.
## 2 Materials and Methods
### DSG Proximate Composition Analysis
#### 2.2.3 Fat Content Determination  
The fat content of dried distiller's spent grain (DSG) samples was estimated after extraction with _n_-hexane in a Soxhlet system, as described by [PERSON] et al. [31], with some modifications. Briefly, 5 g of milled dried DSG (DDSG) was placed into a tared extraction thimble, and the combined weight of the sample and thimble was recorded. The thimble was then inserted into the Soxhlet tube. Next, 150 mL of _n_-hexane was transferred into a pre-dried and pre-weighed 250 mL receiving round-bottom flask, which was then connected to the Soxhlet extractor. The heating mantle was set to 55 \({}^{\circ}\)C to provide reflux for 6 h, extracting the lipids into the solvent.  
After extraction, the solvent was removed from the lipid extract using a rotary evaporator (Rotavapor(r) R-250, BUCHI Labortechnik, Flawil, Switzerland) equipped with a vacuum. Following hexane recovery, the flask containing the extracted lipids was placed in a convection air oven set to 60 \({}^{\circ}\)C for 4 h to remove any residual hexane. This process was repeated by returning the flask to the oven and cooling it until a constant weight was achieved. The mass of the extracted lipid was determined as the difference in weight of the flask before and after extraction and drying. The fat content was calculated as the mass of the extracted lipid divided by the initial dry mass of the DSG sample (Equation (3)).  
\[\text{Fat content}=\frac{\text{Weight of extracted fat}}{\text{DSG Dry weight}}\ \times\ 100 \tag{3}\]