esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 6 Conclusions  
In this work a Neural Network based Quality Control filter for CYGNSS wind retrieval is developed. The inputs to this filter are the 13 diagnostic variables that broadly represent instrument related, measurement geometry related and surface related attributes. Of these diagnostic tools, the surface related attributes (NBRCS, LES, and SNR) and instrument related attributes (azimuth angle, star tracker status, PRN, satellite roll) play a dominant role in distinguishing outliers from good sample population. The Neural Network is trained over two different training datasets at two different CYGNSS wind regimes based on the behavior of the GMF. The operating range of the filter is between 5-18 m/s. Within this range the probability of outlier detection is > 75% and the false alarm rates is <20%. In total \(\sim\)20.5% of the data is removed as outliers by this filter. Atleast 75% of the outliers with wind speed difference of at least 5 m/s is removed while \(\sim\)100% of the outliers with wind speed difference of at least 10 m/s is removed. This filter has significantly reduced the scatter in the data. The quality filtered dataset has a standard deviation of <=2 m/s over a wind range of wind speeds. The design space for this filter is also analyzed in this work to identify trade-offs between PD and FAR. The choice of PD and FAR will depend on the application. For example, a low FAR may be especially important for applications in which good spatial and temporal sampling are very important (e.g., to image rapidly changing weather systems) whereas a high PD may be especially important for applications in which the lowest possible uncertainty in wind speed is important (e.g., to detect small trends over long time intervals, such as are associated with global change).  
As the next steps in this work, the higher wind regime will be the focus of interest. Strategies to improve the performance of this filter at higher winds while retaining as many samples as possible will be considered. Also, currently this filter is developed only for fully developed seas, in the future the feasibility of extending to young seas will also be studied.  
Conceptualization, R.B. and C.R.; methodology, R.B. and C.R.; software, R.B.; validation, R.B.; formalism analysis, R.B.; investigation, R.B. and C.R.; resources, R.B.; data curation, R.B.; writing--original draft preparation, R.B.; writing--review and editing, R.B. and C.R.; visualization, R.B.; supervision, C.R.; project administration, C.R.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
The work presented was supported in part by NASA Science Mission Directorate contract NNLL13 AQ00C with the University of Michigan.  
The MERRA-2 data used in this study/project have been provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center through the online data portal in the Goddard Earth Sciences Data and Information Services Center, 10.5067/3Z173 KIE2 TPD.  
The authors declare no conflict of interest.
## 2 System Description and Modelling
### Mathematical Modelling
#### 2.2.1 Flash Tank  
The flash tank is modeled by a custom Simscape block. The mass and energy equilibria are expressed assuming that the feed brine enters the flash tank, while the vapor and the liquid phases flow out of this, with the corresponding specific enthalpy in the following equations:  
\[\sum_{i=1}^{N}\dot{m}_{o}=\sum_{i=1}^{N}\dot{m}_{in} \tag{2}\]  
\[\frac{\partial Q}{\partial t}-\frac{\partial W}{\partial t}=\sum_{i=1}^{N} \dot{m}_{o}h_{o}-\sum_{i=1}^{N}\dot{m}_{in}h_{in} \tag{3}\]  
\[\frac{\dot{m}_{cap}}{\dot{m}_{b,\;total}}=\frac{h_{brine,\;in}-h_{liq,sat}}{h _{cap,\;sat}-h_{liq,sat}} \tag{4}\]  
where \(\dot{m}_{in}\) and \(\dot{m}_{o}\) are the mass flow rates that flow into and out of the tank in kg/s, respectively, \(i\) the number of components at the inlet and outlet of the tank, \(Q\) the heat provided to the flash tank (if adiabatic \(Q=0\)), \(W\) is the work consumed in the tank (zero in this case) and \(h_{in}\) and \(h_{o}\) are the specific enthalpies of the inlet and outlet streams, respectively, in kJ/kgK. The specific enthalpy and other thermodynamic properties of the feed brine and the pressure in the flash tank are considered as inputs for the model. Applying the energy equilibrium, the outlet sp. enthalpy for the vapor and liquid parts of the flow and their respective fractions are estimated, according to Equation (4). In this equation, \(\dot{m}_{cap}\) and \(\dot{m}_{b,\;total}\) are the mass flow of the generated vapor and the total brine at the inlet of the flash in kg/s, respectively, \(h_{brine,\;in}\) is the specific enthalpy (kJ/kg) of the inlet brine coming from the brine heater, \(h_{cap,\;sat}\) is the specific enthalpy of the saturated vapor (kJ/kg) at the flash tank pressure, and \(h_{liq,sat}\) is the specific enthalpy of the saturated liquid brine (kJ/kg), based on the brine salinity. The performance of the flash evaporator was validated with the results presented by the experimental study of [PERSON] et al. (18) for the superheat of the inlet brine more than 10 K, compared to the flash evaporator saturation temperature (as function of pressure).  
Figure 3: Flowchart of the numerical process.
## 3 Experiments and Results
### Morphology Measurement Experiment  
The dual-camera DIC system can be used to measure regular morphology. However, for objects with complex surfaces, good results are difficult to obtain due to field of view limitations. Similarly, for big objects, the field of view of the two cameras cannot completely cover the area to be measured. The presented system involves three perspectives, and the left and right views can be adjusted independently to satisfy the requirements of complex topography measurements or of big objects, thus enabling it to achieve better results than the two-perspective DIC. The specimen used for this test was a flat aluminum plate with a cylindrical bulge in the center, and the presented system was used to measure the surface profile of the specimen. The results of three-perspective DIC calculation were compared with those of two perspectives. The left and right parts of Figure 9 show the results of DIC calculation using three and two perspectives, and the middle green channel was not included in the calculation of two perspectives. Similar parameters were used in both calculations. Figure 9 illustrates that the morphology result under three perspectives is better than that under two perspectives. Clear defects exist at the boundary between the cylindrical protrusion and plane in the morphological cloud map calculated by two channels. The reason is that the boundary line has a large error in the DIC calculations, whereas the middle channel can provide redundant information to eliminate errors. In our previous work, the DIC calculation of three fields of view had considerable advantages in measuring complex morphologies compared with two fields of view. In the calculation of double views, remarkable concave and convex regions cannot be obtained accurately through correlation calculation, whereas the three-perspective system can obtain complete information. The system proposed in this study also has this advantage. The statistical error maps in Figure 10 show that the topographical errors calculated from the three perspectives (left) are considerably smaller than the results of the two-perspective calculations (right), which represents the uncertainty of the 3D coordinates of each point.  
Figure 8: X direction displacement error map (the final step).  
Figure 10: Contour statistical error map/mm. (**a**) contour statistical error calculated under three perspectives; (**b**) under two perspectives (green channel was not included).  
Figure 9: Cloud map of the measured contour/mm. (**a**), morphology result calculated under three perspectives; (**b**), morphology result calculated under two perspectives (green channel was not included).
## 3 Theoretical Framework and Dataset
### Mix Network  
In computer networks, sending and receiving messages is a nonlinear process. Nevertheless, it is easy to track the senders and recipients of messages in a communication system. To avoid this problem, it is possible to employ a mix network [15], which collects messages from multiple senders, shuffles them, and then sends them to their original destinations.  
Two different types of mix networks are used in anonymous communication systems:  
* A threshold-mix network stores a fixed number of messages, \(n\). After this number of messages is reached, they are mixed and then sent to their corresponding destination.
* A timed-mix network waits a certain amount of time, \(t\), before all stored messages are mixed and delivered.  
The objective is to break the link between senders and recipients by making end-to-end communications difficult to track. Regardless of the shuffling procedure of mix networks, if someone is gathering the messages, he will not be able to know the source or the destination of the message. He will only be able to see the amount of messages sent and received by each user. Other systems contain more than just one mix network. That is, after the first mix is complete, the messages enter another mix network.  
An anonymous communication system is made of three elements: A set of senders \(S\), a set of recipients \(R\), and the mix network, as shown in Figure 1. If a sender \(s\in S\) sends a message to a recipient \(r\in R\), then we say \(s\) is related to \(r\) or \(s\) is a friend of \(r\).  
Figure 1: Basic elements of an anonymous communication system.
## 4 Experiments
### Diversity Evaluation  
This section aims to quantitatively evaluate the diversity of the generated results from both single and multiple experiments. Firstly, we evaluate the differences among the results generated in a single experiment based on one-shot generation. A total of 100 bubble diagrams are generated based on the input nodes, and the GED is calculated for each pair of these results. The GED values are normalized to represent similarity coefficients, where a GED value closer to 1 indicates a greater structural difference, and a value closer to 0 indicates a higher structural similarity between two bubble diagrams. The similarity distribution is illustrated in Figure 14a, where most differences exceed 0.2, indicating significant distinction among the generated results. We visualized a similarity matrix to  
Figure 11: The loss curve of DBGNN-VGAE.  
Figure 12: Diversity generation of DBGNN-VGAE.  
intuitively display the variation between the generated outputs, as shown in Figure 14b. The results demonstrate that VGAE successfully projects bubble diagrams from high-dimensional to low-dimensional space, achieving an abstract representation of structural information. Furthermore, the dissimilar outputs confirm the model's capacity for diverse generation, effectively addressing the challenge of producing diverse bubble diagrams.  
We conduct one hundred generation experiments to verify how the diversity of generated results varies under a high-intensity continuous generation. We test one hundred groups of input room nodes and generate one hundred bubble diagrams for each group by randomly sampling latent variables. In each experiment, we calculate the GED between each generated bubble diagram and the reference diagram, and then we sum these values to measure the total diversity for a single experiment. Higher total GED values indicate greater diversity, while lower values suggest less differentiation. The reference GED value is set to 100, signifying that, on average, each generated diagram has at least one link differing from the ground truth. The diversity trend curve is shown in Figure 15a, revealing a fluctuation with a peak reaching up to 1600 total GED, and most experiments display diversity above the mean line. Additionally, we recorded the bubble diagram with the lowest GED for each experiment, as illustrated in Figure 15b. Remarkably, 55% of the generated diagrams exhibit a GED of 0, affirming their alignment with the ground truth. As the sampling of latent variables increases, the proposed model can produce outputs fully consistent with actual layouts. The experiments demonstrate that the model main  
Figure 14: (a) GED similarity distribution. (b) GED similarity matrix.  
Figure 13: Generation results from random noise in one-shot experiment.  
tains robust diversity even under high-intensity continuous use, verifying its reliability in practical applications. Furthermore, the excellent diversity results confirm the effectiveness of integrating VGAE.
## 3 Results and Discussions
### Flame Length, Height, and Tilt Angle
#### 3.3.2 Variation of Flame Length, Height, and Tilt Angle  
In windy conditions, variations of pool fire length are mainly determined by the coupling of the pool size, flame lift, fuel evaporation rate, and airflow environment. The interactions between wind force and flame buoyancy affect air entrainment and lead to irregular flame length variations [31].  
Figure 8 shows the flame characteristics of length, height, and tilt angle under a 0-5 m/s wind velocity and a 6 cm-high baffle. It can be seen that as the wind velocity increased from 0 to 5 m/s, the flame was more inclined and the flame base area was larger. The flame height decreased when the wind velocity increased.  
Figure 9a,b shows the trends of the average flame lengths and heights with the increase in wind speed. The flame length increased first and then decreased with increasing crosswinds. The flame height decreased with an increase in wind speed.  
When the wind velocity increased to 1 m/s, the flame length first increased due to the wind, which increased the spread of flammable gas and maintained the combustion. When the wind velocity exceeded 2 m/s, the speed of fuel vapor diffusion and combustion could not keep up with the wind speed in the mainstream zone. Under the condition of the high baffle (4-10 cm), the flame buoyancy and gas cycle in the recirculation region were the most important influencing elements of bottom flame length \(\mathrm{L}_{\mathrm{f}}\). Therefore, the flame length decreased when the wind speed increased. The flame height of the heptane pool fire with a baffle at various crosswind velocities is shown in Figure 9b. The flame height reduced considerably when the crosswind velocity was less than 2 m/s. The downward trend gradually slowed down as the crosswind velocity increased to 5 m/s. What distinguished this result from the pool fire in the crosswind was that the flame height was limited by baffle blockage, as seen in Figure 2. We compared the flame height with the wind-fire experiment when the baffle-fire distance was 20 cm. As for the difference in the flame height, we refer to [PERSON]'s study [13] when there is a distance between the flame and upstream baffle. The bottom part of the flame inclines to the baffle side causing there to be no more fuel vapor to maintain the upper flame, which finally influences the flame height.  
Figure 8: Flame characteristics under a 0–5 m/s wind velocity and a 6 cm-high baffle.  
Figure 7: Definition of flame length and tilt angle in the presence of a baffle and crosswind: (a) \(\mathrm{H}\) = 2 cm and (b) \(\mathrm{H}\) = 4, 6, 8, and 10 cm.  
Figure 9: (**a**) Flame length of the pool fires in the experiments. (**b**) Flame height of the pool fires in the experiments and [PERSON]’s study [13] of flame height.
# Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling
## 4 Hierarchical RL Scheduling Scheme
### Upper-Level Agent  
The decision interval of the upper agent is one time step, and there are \(T\) steps in each episode. For \(t=1,\ldots,T\), the environment stochastically provides the supply volume of crude oil from each oil field to each transfer station or refinery \(w^{t}_{e_{f}}\) and \(w^{t}_{e_{r}}\), the supply volume from each import port to each transfer station or refinery \(w^{t}_{e_{f}}\) and \(w^{t}_{e_{r}}\), and the demand volume for gasoline or diesel in the transfer station and refinery \(n^{k,t}_{i}\). The RL agent observes status \(s_{t}\) from the environment and determines the expected inventory volume of each refinery and transfer station at the end of this time step \(t\), noted as \(a_{t}\). The upper-level agent will provide these end-stage inventory volumes to the lower-level agent, which will search out an actual execution strategy. The environment moves to the next time step \(t+1\), based on the actual strategy. The reward \(R_{t}\) is calculated by combining the inventory alert penalty \(\mathcal{P}^{t}\) and the feedback \(J^{t}\) from the lower-level agent.  
It is worth noting that at the beginning of training, the lower-level agent may not find feasible solutions if it intends to strictly meet the expected inventory \(a_{t}\). Therefore, at the lower layer, these expected inventory values are not used as hard constraints (see Section 4.3 for specification), and the inventory volumes caused by the execution strategy (including processing volume and transportation plan) are not necessarily equal to the expected inventory. In other words, from the perspective of the upper-level agent, although the action is the expected inventory at the end of the time step \(t\), i.e., the expected inventory at the beginning of the next time step, the actual observed inventory from the environment at time step \(t+1\) is not necessarily equal to the action \(a_{t}\). In the next part, we provide the Markov decision processes (MDP) corresponding to the description.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 3 Results  
### Feedback and Transportation Analysis  
Bamboo processing waste is spatially concentrated in several southeastern provinces of China, reflecting the maturity and industrial intensity of regional bamboo processing sectors. As shown in Figure 2, the most resource-rich province is Fujian, followed by Guangxi, Guangdong, and Jiangxi. These provinces jointly account for a significant share of China's total bamboo residue, making them ideal candidates for large-scale hard carbon production.  
The ten provinces with the highest estimated bamboo residue availability were selected for scenario-based techno-economic analysis. As detailed in Section 2.2, average transportation distances from feedstock sources to centralized carbonization facilities were estimated using a simplified geometric model combined with empirically derived detour correction factors to reflect road-based transport conditions.  
Table 4 summarizes key geographic and logistical indicators, including provincial area, estimated average straight-line and road-based transport distances, and total bamboo residue availability. Fujian stands out with 13.8 Mt of residues and a relatively short transport distance of 167.4 km. Other provinces such as Guangxi and Guangdong also exhibit favorable combinations of feedstock supply and logistical feasibility. In contrast, more remote regions such as Sichuan and Yunnan face longer average transport distances, potentially raising delivery costs.  
Figure 2: Spatial distribution of bamboo processing residues across China [18].
## 2 Analytical Models
### Indirect Models  
Unlike the previous methods, where the bulk parameter \(\beta\) accounting for the overall pressure ratio across the seal is used, indirect models do not employ any bulk parameters, but they solve the one-dimensional continuity equation for each cavity. Hence, the mass flow leakage across the \(i\)-th constriction can be calculated by means of the following equation [15]:  
\[\dot{m}_{i}=\mu_{i}\cdot K_{i}\cdot A\cdot\sqrt{\frac{p_{i}^{2}-p_{i+1}^{2}}{R \cdot T_{0,in}}} \tag{6}\]  
Since the leakage from a cavity to the next one stays constant, it is possible to recast Equation (6) by imposing \(\dot{m}_{1}=\dot{m}_{2}=...=\dot{m}_{N}=\dot{m}\). Thus, according to the number \(N\) of sealing points, a system of \(N\) equations and \(N\) variables (\(m\), \(p_{2}\), \(p_{3}\),..., \(p_{N-1}\)) can be written and solved through iterative methods; \(p_{1}=p_{0,in}\) and \(p_{N}=p_{s,N}\) are known terms as inlet/outlet pressures across the seal. In this way, both the leakage mass flow and the pressure distribution across the labyrinth can be retrieved.
# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
## 4 Results
### Limitations  
In this study, we have conducted preliminary explorations and research into the prediction method of long-term solar wind series, considering the irregularity and uncertainty of time. However, due to the limitations of the authors' abilities, the optimal approach has not yet been identified. Further exploration and research are required to address the remaining issues.  
(1) The datasets included in this study encompass seven feature variables. It should be noted, however, that the solar wind is affected by a multitude of factors that extend beyond the scope of this study. Moreover, the datasets employed in this study have an hourly temporal resolution. Nevertheless, there is a plethora of more granular information on solar wind features currently available. Consequently, future studies may wish to consider incorporating this additional detail on solar wind forecasts as an input variable that affects the forecast results.  
(2) During the experimental process, it was observed that the long-term series prediction exhibited reduced sensitivity to outliers. This indicates that the dataset contains a certain number of outliers, and the proposed model is unable to predict these outliers effectively. This is an issue that requires further in-depth research and exploration. Consequently, future research should focus on differentiating between erroneous values and outliers in solar wind data and investigate how to enhance the detection and processing capabilities of the model in the presence of a considerable number of outliers in the dataset.  
(3) Solar wind features encompass a diverse array of information types, including data, images, and frequencies. Future research will explore the potential of multimodal studies to fully leverage the diverse feature resources available and facilitate information sharing across different modalities. Integrating multiple information sources can lead to more accurate prediction and analysis of the solar wind sequence, thereby enhancing the performance and reliability of prediction models.  
Figure 10: Visualization of the MAE results for different subsequence length selections in the SelectSeg module of the Mode Independence Attention experiment based on the omni-IMF dataset experiment.
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 3 Methodology
### RouteNet Model with Attention  
The RouteNet model with the integration of the Graph Attention Mechanism is depicted in the block diagram shown in Figure 6. This enhanced architecture combines the power of the original RouteNet model with the Graph Attention Mechanism to improve its predictive capabilities.  
Figure 5: Detailed RouteNet model architecture with iterative message passing.  
A significant improvement in this model architecture is the incorporation of an attention layer, represented by the yellow-highlighted block in Figures 6 and 7. The attention layer introduces a dynamic and adaptive weighting system to the exchange of information between link and path updates. In simpler terms, it allows the model to assign varying degrees of importance to different aspects of the information being exchanged. This dynamic weighting mechanism represents a more sophisticated and flexible approach, enabling the model to better process and prioritize data based on their relevance, which, in turn, contributes to an overall improvement in the model's capabilities.