esa-sceva/satcom-chunk-collection huggingface dataset:
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 4 Feudalistic Platooning
### Messaging  
All vehicles within a platoon (and a vassalage) send CAMs according to [34]. Depending on the driving context, the time interval may vary between 0.1 s and 1.0 s. In case special events occur, DEN messages may be sent according to [35]. If vehicles further want to enable cooperative sensing, CPMs may be exchanged according to [36]. For platoon management (join, leave, split, merge), messages following the proposal in [20] are exchanged. Once a platoon is formed, platoon management mainly consists of periodic messages that keep the platoon updated. While performing _Feudalistic Platooning_, no further periodic messages are needed in terms of management. Instead, _Feudalistic Platooning_ introduces _FeudalMsg_, the core of the communication within a vassalage.  
_FeudalMsg_:  
The _FeudalMsg_ (see Figure 2) is a modular message that currently encapsulates one of two different messages: _SyncMsg_ and _VassalMsg_. From the latter, multiple messages may be present within one _FeudalMsg_. Which of the two messages is included within _FeudalMsg_ is indicated by _type_. Furthermore, the message contains the ID of the transmitter (_txStationId_), a time stamp (_timestamp_), and the platoon ID (_platoonId_). The latter helps to resolve ambiguities in the event that more than one platoon is within communication distance.  
_SyncMsg_:  
It is part of _FeudalMsg_ and included once _FeudalMsg_ is of type ::_Sync_. _SyncMsg_ is used during the synchronization process (see Section 4.4) to successfully devolve from an ordinary platoon to a feudal platoon. As illustrated in Figure 3, _SyncMsg_ consists of a reference number (_reference_), a Boolean sync array (_sync_), and an array for cellular LTE addresses (_lteAddresses_). Both arrays have the same size as the number of vehicles that are part of the platoon.  
_VassalMsg_:  
It is part of _FeudalMsg_ and included once _FeudalMsg_ is of type ::_Vassal_. Including one _VassalMsg_ is then compulsory. _VassalMsg_ is exchanged to share information about a vassalage. Many _VassalMsg_ may be included, e.g., once information from other vassalages of the platoon is shared. As shown in Figure 4, _VassalMsg_ consists of the vassal ID of the originating vassalage (_vassalageId_) and arrays for CAMs and DENMs containing all the vehicle status and event information from the vassalage members (_cams_, _denms_).
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Thermal-Fluid Finite Volume Model of the TPMLG
#### 3.2.1 Fundamentals of Thermal-Fluid Analysis  
Heat conduction in the solid region and the heat balance equation on the fluid-solid interface are considered in the solution domain. Heat transfer equations in steady-state form ignoring the time term for the 3-D temperature field of the TPMLG are expressed as follows:  
\[\frac{\partial T}{\partial x}\bigg{(}\lambda_{x}\frac{\partial T}{\partial x }\bigg{)}+\frac{\partial T}{\partial y}\bigg{(}\lambda_{y}\frac{\partial T}{ \partial y}\bigg{)}+\frac{\partial T}{\partial z}\bigg{(}\lambda_{z}\frac{ \partial T}{\partial z}\bigg{)}+\dot{q}=0 \tag{5}\]  
\[-\lambda\frac{\partial T}{\partial n}=\phi=-h(T_{w}-T_{f}) \tag{6}\]  
where \(T\) refers to temperature; \(\lambda_{x}\), \(\lambda_{y}\), \(\lambda_{z}\) indicate thermal conductivities in three directions of the rectangular coordinate system, respectively; \(\dot{q}\) indicates heat generated per unit volume; \(n\) indicates the normal direction of the wall surface; \(\phi\) is heat flux; \(h\) is the convective heat transfer coefficient; \(T_{w}\) represents wall temperature; and \(T_{f}\) represents fluid temperature.  
The fluid flow problems are solved by the Navier-Stokes equation, shown as follows:  
\[\frac{\partial V}{\partial t}+(V\cdot\
abla)V=f-\frac{1}{\rho}\
abla p+\frac {\mu}{\rho}\
abla^{2}V \tag{7}\]  
where \(V\) is velocity vector;\(f\) is body force per unit volume of fluid; \(\rho\) is fluid density; \(p\) is pressure; and \(\mu\) is fluid dynamic viscosity.
## 2 Governing Equation and Calculation Setting
### Model Validation  
In this study, four different grid numbers were used to prove the insensitivity of the simulation results to grid counts. Figure 3 shows the effect of the number of grids on the gas entrainment rate. When the number of grids is greater than 470,048, the change in the entrainment rate does not exceed 0.11 %. Considering both computational accuracy and time efficiency, a meshing strategy entailing a grid number of 568,417 is identified as the optimal choice. This selected strategy features a maximum mesh size of 2.25 mm and a first-layer mesh height of 2.18 mm.  
Experiments are conducted to verify the gas entrainment rate at different nozzle outlet velocities, in order to demonstrate the reliability of the numerical model. Figure 4 shows a schematic of the experimental system, which includes a water tank that is large enough to ensure minimal leakage effects. The system consists of a centrifugal pump, a regulating valve, a turbine flow sensor, a Venturi injector, and a thermal gas flow sensor. The centrifugal pump provides high-pressure liquid for the system, with flow controlled by the regulating valve. The turbine flow sensor monitors the liquid flow, while the thermal gas flow sensor tracks gas flow at the gas inlet in real time. For the Venturi injector, the nozzle is made of stainless steel, and the remaining parts are constructed of smooth acrylic. Both materials have minimal surface roughness, making it negligible in our study. To prevent leakage, Teflon tape and gaskets were applied at key connections, and the overall system was thoroughly checked for leaks prior to experimentation to ensure integrity. Liquid and air flow rates were monitored through liquid and gas flow meters, respectively, and no leakage was observed during the experiments. The outlet of the Venturi injector is connected to the atmosphere.  
Figure 3: Variation in the gas volume flow rate under different mesh numbers.  
Figure 5 illustrates the variation in the experimental entrainment rate and the simulated entrainment rate concerning the nozzle outlet flow rate. It is evident that both the experimental entrainment rate and the predicted entrainment rate exhibit an increase with the escalation of the nozzle outlet flow rate, following a similar trend. Notwithstanding, there exist numerical discrepancies between them, likely arising from experimental inaccuracies, simplifications in the simulation models, and control equations. As the nozzle outlet flow rate increases, the experimental and simulated entrainment rates tend to converge. At a nozzle outlet flow rate of 16.78 m/s, the error between the experimental and simulated entrainment rates reduces to a mere 2.17%, indicating the reliability of the model. This study delves into exploring the impact of the mixing section structure on gas entrainment under the specified condition of a nozzle outlet flow rate of 16.78 m/s.
## 6 Conclusions  
This study concludes the following:  
1. Based on FY-3C satellite data and auxiliary environmental variables, and by employing machine learning methods (RFRR stacking model), this study successfully generated 1062 days of all-weather surface temperature (FM-LST) from 2017 to 2019. An analysis showed that Dow_LST in the microwave data downscaling product, as the dominant feature in the model, increased in importance over the years and significantly impacted the prediction accuracy of surface temperature under cloud cover conditions. Other environmental variables, such as the DEM and NDVI, while showing stable influence, played a relatively smaller role in surface temperature prediction.
2. Through a comparative analysis of FM-LST with in situ site measurements of surface temperature, it was found that different land cover types affected the model's accuracy. Over several years of data, the model reflected the seasonal variation of surface temperature. Although there was some fluctuation in prediction accuracy for certain land cover types, the model's prediction accuracy was high at most sites, demonstrating the effectiveness of the method. This trend was reflected in the accuracy validation bar charts for the different land cover types, with particularly significant prediction accuracy in the marshland and desert environments.  
Compared to previous studies, this research is the first to fully rely on Fengyun satellite series data, including vegetation index, infrared, and microwave temperature, to achieve a comprehensive reconstruction of surface temperature. Through precise machine learning techniques, we successfully filled the data gaps caused by cloud cover, enhancing the accuracy and reliability of surface temperature estimation. This pioneering work not only improves the spatial continuity of surface temperature data but also establishes a solid foundation for the application of Fengyun satellite series data in surface temperature monitoring.  
[PERSON] contributed to searching the literature, describing the model, analyzing the data and model performances, providing the figures and maps, and writing the manuscript. W.Z. and Q.Z. contributed to designing the research. T.S. and Z.C. contributed to remote sensing data processing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2022 YFE0113900) and the National Natural Science Foundation of China (Grant No. 42301420).  
Not applicable.  
Data Availability Statement: Data presented in this research are available upon fair request.  
**Acknowledgments:** The remote sensing data for this experiment were provided by the Fengyun Satellite Meteorological Innovation Center (FYSIC), and the site validation data were sourced from the Third Pole Environment Data Center (TPDC) ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 27 March 2024). We sincerely thank the FYSIC and the TPDC for their support in providing the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 2 Related Works
### Block-Matching-Based Estimation  
Reference [10] use a different method than the others to find the affine parameters. Because they use less parameters, the complexity increase is lower. However, even with only three parameters, the search around neighbors, if using a standard diamond or square pattern, goes from 8 transform computations to 26, and affine prediction is also more costly to compute.  
Their idea is to decouple the search for the parameters. As with other methods, they start with an initial estimation based on the classical translation-based motion estimation. Then, they try values in the entire search range, with a step size of \(4\Delta\), where \(\Delta\) represents the quantization step for the affine parameter. They use the best value they found during this search for the next iterations. The first iteration checks the neighbors at a distance of \(2\Delta\), then the second with a distance of \(\Delta\). This will give the best affine parameter for the given translation parameters. But the best translation parameters might be different in case of affine prediction, so the second step, the parameter refinement, is performed.  
The parameter refinement works by alternating translation parameter refinement and affine parameter refinement. In both cases, the encoder will look for the closest neighbors, at a quarter sample distance for the translation and \(\Delta\) for the affine parameter. The refinement stops when either a maximum number of iterations or no more improvement happens.
## 4 Development of Proposed Method
### Proposed Method  
In this section, we present the proposed denoising method, which integrates VMD, DFA, and SWT in a novel way to enhance denoising performance. The method aims to effectively separate signal components from noise by the adaptive thresholding of the modes obtained from VMD based on their scaling exponents computed via DFA and then applying SWT-based denoising to the identified noise-dominant modes.  
Figure 3 illustrates a block diagram of the DFA-based metric calculation along with the overall proposed method. As shown in Figure 3, the proposed method consists of the following main steps:  
1. **Signal Decomposition using VMD**: Decompose the noisy signal into BLIMFs using VMD.
2. **Scaling Exponent Computation using DFA**: Compute the scaling exponent for each BLIMF using DFA.
3. **Adaptive Threshold Determination**: Determine an adaptive threshold that relies on the higher frequency content of each mode using changes in frequency and long-range correlations.
4. **Denoising of Noise-Dominant Modes using SWT**: Classify the BLIMFs into signal-dominant and noise-dominant modes based on whether the scaling exponent is lower or greater than the adaptive threshold.
5. **Signal Reconstruction**: Reconstruct the final denoised signal using only the denoised noise-dominant modes obtained from SWT.  
Figure 2: Block diagrams for VMD-based benchmark algorithms: (**a**) VMD-DFA [18] and (**b**) VMD-DFA-DWT [19]. (The thresholding parameter \(\theta\) is selected independently for the respective method, as discussed in the manuscript).
# EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs
## 2 Materials and Methods  
Electric discharge machining (through die-sinking) of D2 steel has been carried out in this research. D-grades of tool steel are widely used materials in die-making industry. Typical applications include forging dies, die-casting blocks, and drawing dies. D2 tool steel is a widely used material in many applications. It has a high content of carbon and chromium as can be seen from its chemical composition provided in Table 1. Since electric discharge machining is a process in which electrical energy is converted into thermal energy and the material is eroded by melting of the electrodes (tool and work), the thermal and electrical properties of substrate materials are derived from the erosion phenomenon. The important properties of D2 steel, including thermal, electrical, and physical properties, are shown in Table 2. A D2 steel plate of 4 mm thickness has been used as a substrate and through-holes of 8 mm diameters are machined using copper electrodes.
## 6 Simulation Results and Analysis
The SRCR consequently declines rapidly and may even fall below that of the HEAA algorithm once a specific service threshold is reached. Taking a number of services of 160 as an example, the network experiences a moderate demand pressure for service resources. In comparison to HEAA, the SRCR of NRA-PPJC has an improvement of 20.4% and a 4.17% increase when compared to the PBRA, which means it can effectively allocate based on business resource requirements and onboard resource status. Figure 8 shows the comparison of convergence speed of the proposed ADMM algorithm with other common initialization scenarios. From the graph, it can be seen that both  
Figure 6: Comparison of SRCR for various datasets. Figure 7: Comparison of SRCR for different methods. zero initialization and random initialization [29] have residual values greater than those based on the compensation data. Moreover, since the initialization method may initially align local and global variables closer to a local optimal solution, its convergence speed tends to be faster than that of the zero-initialization approach. The dynamic initialization approach proposed in this paper, which is based on compensated data, facilitates the rapid update of local variables, subsequently expediting the convergence of global variables. When compared to the zero-initialization method, this approach enhances convergence speed by 60%, and when compared to the random initialization method, it improves by 41%.
## 2 Distribution System Testbed
### Time Clock System  
In the testbed, the time clock system is an important tool. It has allowed researchers to synchronize the time of the data collected from relays and meters for the electrical fault and disturbance events. Consequently, the recorded events from the IEDs (meters and relays) could be identified easily after running the use case scenario tests, comparing the time of the IED events at the disturbance initiation. The satellite-synchronized network clock is connected to all IEDs [48]. Figure 5 shows the time clock system with hardware. The primary and backup time frames are defined by the GPS antenna and SEL2488 internal clock [48], respectively. In the testbed, the synchronized time for the metering devices is generated by the internal clock of the time clock system. It performs the time synchronization of all relays/meters with the same time-stamps to identify the recorded events for the electrical fault test use case scenarios.
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 4 Discussion  
The likelihood of failure, Pf, of an open pit increases as the slope angle increases. Geotechnical, economic, technical, strategic and regulatory compliance are all important factors in slope optimization [59; 60; 61; 62]. Generally, the slope stability of an open pit deteriorates as shear strain increases. However, it is difficult to set a threshold for maximum shear strain. Thus, it cannot be used as a failure evaluation criterion for an open pit (e.g., probability of failure, Pf, cannot be estimated). For incremental horizontal and vertical displacements, the findings revealed that the rock mass moves towards ore direction (e.g., negative lateral displacement due to mining activity) as the slope angle increases. The probability of failure due to rock mass movement, on the other hand, will not be estimated because establishing a threshold is difficult. The use of rock mass deformation would be beneficial if multi-point hole extensometers (MPBHXs) were installed to monitor and read the rock mass deformation. As a result, it can be used to validate the readings with numerical analysis. Alternatively, incremental displacement won't be employed as a stability indicator (e.g., no threshold has been decided). The Inverse Velocity (IV) method has been successfully employed in conjunction with slope-monitoring radars to predict slope failure. However, the application of this method is limited due to inherent uncertainty associated with rock mass [60].  
The probability of failure is rare (e.g., Pf \(<\) 5%) when overall slope angles are small (e.g., 20\({}^{\circ}\) and 25\({}^{\circ}\)). Then, the probability of instability becomes unlikely when overall slope angles are 30\({}^{\circ}\) and 35\({}^{\circ}\) (e.g., 5%\(<\) Pf \(<\) 20%). When slope angles lie between 40\({}^{\circ}\) and 55\({}^{\circ}\), the probability of failure becomes possible (e.g., 20% \(<\) Pf \(<\) 60%). At steep overall slope angles (e.g., 60\({}^{\circ}\) and 65\({}^{\circ}\)), the probability of failure is likely (e.g., 60% \(<\) Pf \(<\) 85%). However, the probability of failure becomes certain (e.g., Pf \(>\) 85% at very steep angles (e.g., 70\({}^{\circ}\)) [63]. Therefore, it is recommended not to design an open pit at steep angles (e.g., \(\geq\)60\({}^{\circ}\)), as the probability of failure is likely to certain. When the slope angle is 60\({}^{\circ}\), the mine net profitability and cost of overburden removal are $ 2,158,300 and $190,70\({}^{\circ}\), respectively. However, the probability of instability is likely (e.g., Pf \(=\) 65.91%), whereas at a slope angle of 20\({}^{\circ}\), the mine net profitability cost of overburden removal and the probability of instability are $1,441,400, $ 907,600 and 0.89% (e.g., rare). Therefore, it is necessary to combine these three design parameters together when selecting/designing the overall slope angle of the open pit [64].  
Figure 12: The combined three design parameters at different overall slope angles.
## 3 System Architecture, Software Services and Deployment
### Data Engineering on the Fog Layer
#### 3.3.2 Continuous Data Analysis  
Once all values within the predefined timeslot are processed, regardless of the sensor stream they belong to, an aggregated output for a variety of functions, as well as time intervals is produced. This output contains information regarding minimum, maximum or average values in time intervals spanning from one hour up to one month, whereas customization is also possible for producing even more precise results. Additional analysis can be applied to each separate stream, allowing the framework to identify local spikes and perform basic measurements of the data slope. The resulting dataset is stored in the integrated memory of the EPL-A and is also forwarded to the cloud layer.
## 2 Materials and Methods  
### Overview  
Figure 2 provides an overview of this study. First, the gross tumor volume (GTV) region on the CT image and dose distribution (DD) in the GTV region were cropped based on the GTV contour information. Radiomics (\(R\)), local binary pattern (\(L\)), and topological (\(T\)) features were extracted from the cropped CT and DD images. Moreover, deep features were extracted from the deep learning-based prediction model. A total of 22 models--that is, \(R\), \(L\), \(T\), \(RL\), \(RT\), \(LT\), and \(RLT\) in CT-only, DD-only, combined CT with DD (CD), and deep learning-based (DL) models--were compared. Second, significant features associated with treatment failure were explored using the Cox proportional hazards (CPH) model with the least absolute shrinkage and selection operator (LASSO) penalty. The treatment failure prediction model was then constructed with the Rad-score calculated using the significant features. Finally, the performance of the prediction model was assessed using the C-index and Kaplan-Meier curves.  
Figure 1: Example CT and binary images of non-treatment failure: Non-TF (**upper**) and treatment failure: TF cases (**lower**). Red circle represents the cancer region.  
Figure 2: Overall study procedure.