esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spatial Data Infrastructure  
Any SDI is made up of data, metadata, processes to transform and query the data, and standardized web services for interacting between them. These software components are distributed according to a client-server architecture model (Figure 1). Specifically, the SDI INSPECTORMAP is developed under the language of Php and JavaScript using \"Laravel\" as a framework.  
Behind the server-side of INSPECTORMAP (Back End, Figure 1) there is a Python script that, in combination with a PostgreSQL database, allows characterizing the areas to be studied as well as downloading and processing on a GeoServer the corresponding set of Landsat-8 and Sentinel-2 images. Furthermore, this script integrates an LNG [20] to ensure a more efficient integration of both data sets. From the downloaded images, the script automatically calculates the NDVI and NDWI spectral indices to analyze the presence of vegetation and water bodies, as explained in the previous section. The result is stored on the server and consumed as Web Map Services (WMSs) to map the affected areas with different color scales and, as Web Feature Services (WFSs), to quantify the affected areas in m\({}^{2}\).  
The graphical interface of INSPECTORMAP (Front End, Figure 1) shows the results obtained in the server-side. It is a graphical interface that is mainly composed of three elements (Figure 2): (i) a geospatial information manager, (ii) a calendar, and (iii) a results viewer.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 3 System Model  
### Secure Beamforming in 5G-Based CRN  
Beamforming [24] is a fast and an accurate process that is properly focused on the target user equipment (UE). The process of beamforming is demonstrated Figure 1. The process, in which a UE has a rapid movement, is not an easy task. It is able to adapt the radiation model of an antenna to a certain structure. It steers a power in a specific direction toward a user. This is the actual process of beamforming, which is shown in Figure 2.  
Figure 1: Beamforming of user equipment (UE).  
Let \(\mathrm{P(d_{u}^{l_{C}})=(d_{u}^{l_{C}})^{v}}\) indicate a large-scale path loss and \(v\) denote the path loss exponent. \(d_{u}^{l_{C}}\) specifies the distance between the BS and a \(u^{th}\) user. Likewise, the inter-cell channel-gain for the PU in the \(l^{th}\) cluster of a \(c^{th}\) cell is denoted by \(l_{pu}^{l_{C}}\).  
The licensed PUs need to be served first in the presence of a several SUs, but they are shared at the same time and frequency. The SU and the PU data in a cluster are retrieved using a SIC. The existing (L\(-\)1) clusters are denoted as an intruder in the fundamental clusters. In a multi-cell framework, all the PUs in a cell are specified as an intruder by another cell user. This does not occur in the case of SUs, which are within the same cell. This says that each of the clusters need to be secured from all other cell clusters, as well as in the PUs. This is solved by a proposed technique in the transmitter. The proposed technique consists of an incorporation of a ZFBF and an MMSE method. Nevertheless, the proposed technique implementation in the BS is not possible. This is because of the number of antennas in the BS is lesser than the entire number of antennas present in the clusters. This is also solved by a proposed technique.
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 5 Results & Discussion
### Application: Orthogonal Metal Cutting  
After validating the implementation of the higher-order Laplacian model, a 2D simulation of the orthogonal cutting of a Ti6 Al4V titanium alloy is carried out using the process parameters and problem dimension listed in Table 1. For this material, the JC parameters \(A=862\) MPa, \(B=331\) MPa, \(C=0.01\), \(m=0.8\), and \(n=0.35\) are chosen, according to the works in [17; 19; 25] for very similar applications. Figure 4 shows the geometry and boundary conditions for this problem, where a Dirichlet temperature boundary condition of \(T_{c}=T_{\infty}=300\) K is imposed on the fixed layers (see Figure 2). The SPH simulation runs until 2/3 of the workpiece length is cut with a speed of \(v_{c}=318.5\) m/min. This choice of the cut distance is made to ensure that the solution is not affected by the fixed boundary condition (see the right side of the Ti6 Al4V workpiece in Figure 4). Moreover, it leads to a cut distance of 2 mm, which is much longer than what is needed to reach the steady-state forces at this high cutting speed [37].  
Figure 3: Reconstruction of the Laplacian operator acting on a 2D quadratic function.  
A total of 41 particles (with regular spacing) along \(l_{y}\) are used to discretize the workpiece. Clearly, using SPH particles of different sizes leads to computationally more efficient discretizations. Due to the ease of computer implementation and for more simplicity, however, the present work considers a uniform spacing of SPH particles in all simulations. For implementing the thermal model throughout the system, it is necessary to discretize the cutting tool as well. In this paper, the size of SPH particles in the tool is set to be equal to the size of SPH particles in the workpiece. It is important to note that the heat equation on the tool side can also be solved by the finite-difference or finite-element methods. While these mesh-based approaches are generally more efficient than SPH in thermal modeling, they are not implemented here as the utilization of a hybrid particle-element technique is beyond the scope of this paper.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 2 Theoretical Analysis and Methods
### Methods
#### 2.2.2 Area Ratio of the Low Energy Zone and High Energy Zone  
The second observable named EDR is the area ratio of the low energy zone and high energy zone  
\[EDR=\frac{Area_{l}}{Area_{h}} \tag{7}\]  
where \(Area_{l}\) and \(Area_{h}\) are the areas of low energy zone and high energy zone, respectively.  
\[\left\{\begin{array}{l}Area_{l}=\int_{\tau_{1}}^{\tau_{2}}Y_{r}(\tau)d\tau \\ Area_{h}=\int_{\tau_{0}}^{\tau_{1}}Y_{r}(\tau)d\tau\end{array}\right. \tag{8}\]
###### Abstract  
This study investigated the influence of instability on the interaction between sub-millimeter liquid droplets and shock waves. Experiments were conducted using 0.42 mm diameter droplets with varying shock wave Mach numbers. The investigation quantified the effects of Weber numbers and initial diameters on the development of Rayleigh-Taylor and Kelvin-Helmholtz instabilities at the shock wave-sub-millimeter liquid droplet interface. Three-dimensional numerical simulations were performed to investigate the deformation and breakup behaviors of sub-millimeter liquid droplets under the impact of a shock wave with a Mach number of 2.12. The post-shock gas flow environment in this condition was in a supersonic state. The simulations utilized the volume-of-fluid method to model the gas-liquid interface, employed unsteady Reynolds-averaged Navier-Stokes methods to simulate turbulence, and incorporated grid gradient adaptive technology to enhance computational efficiency. The results revealed that by increasing the Weber number or decreasing the initial diameter, both the growth rate and the wavenumber extremum of the Rayleigh-Taylor and Kelvin-Helmholtz instability waves increased. The variation in the K-H instability's growth rate extremum increasing Weber number surpassed that of the R-T's instability. This indicated that both the R-T and K-H waves on sub-millimeter liquid droplets tended to exhibit increased growth rates and reduced scales. Moreover, as the Weber number increased, the K-H instability became dominant in the aerodynamic fragmentation. The numerical simulations showed good qualitative agreement with the experimental data, affirming the viability of numerical methods for addressing such challenges. The evolution of the sub-millimeter liquid droplets was marked by two primary stages, flattening and shear stripping, signifying that the K-H instability-driven SIE mechanism governed the aerodynamic breakup in the supersonic post-shock airflow.  
sub-millimeter droplets; shock wave; aerodynamic breakup; instability; supersonic post-shock airflow
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400
### Application of the Extended Kalman Filter Algorithm
Based on the noise measurement \(z(k)\) and the change of the reactor period during \(\Delta t,dT_{e}(k)\), at time \(k\Delta t\), the best estimates of \(E(k)\) and \(T_{e}(k)\) can be obtained by EKF as:  
\[\hat{\mathbf{x}}(k+1|k)=\mathbf{F}\mathbf{x}(k)+\mathbf{B}\mathbf{u}(k) \tag{32}\]\[\hat{z}(k+1|k)=\textbf{H}\hat{x}(k+1|k) \tag{33}\]  
\[\textbf{P}(k+1|k)=\textbf{F}\textbf{P}(k)\textbf{F}^{T}+\textbf{Q} \tag{34}\]  
\[\textbf{K}(k+1)=\textbf{P}(k+1|k)\textbf{H}^{T}\Big{[}\textbf{H}\textbf{P}(k+1| k)\textbf{H}^{T}+\textbf{R}\Big{]}^{-1} \tag{35}\]  
\[\hat{x}(k+1|k+1)=\hat{x}(k+1|k)+\textbf{K}(k+1)\{z(k+1)-\hat{z}(k+1|k)\} \tag{36}\]  
\[\textbf{P}(k+1)=\{\textbf{I}-\textbf{K}(k+1)\textbf{H}\}\textbf{P}(k+1|k) \tag{37}\]  
where \(\textbf{K}(k)\) is the Kalman gain, \(\textbf{P}(k)\) is the estimation error variance, and \(\textbf{P}(k+1|k)\) is the prediction error variance.
### The Existing Numerical Tools and Simulation Capabilities  
Given the context, the research work needed first to establish which simulation tools represent the industry standard for predicting composite distortion. One of the most-cited software packages is ESI PAM DISTORTION [19], along with a more comprehensive software package frame simulation module called PAM COMPOSITES. As claimed on the ESI web page, the simulation tool allows the prediction of manufacturing-induced residual stresses and shape distortion of composite parts made of continuous fibers and thermoset matrices. One of the newer simulation solutions, credited with a similar approach and similar capabilities to PAM DISTORTION, is ALTAIR Advanced Cure Simulation [20], which promises to allow users to predict polymerization, residual stress buildup, and deformations during composite processing.  
However, it was unclear to what extent both the ESI and ALTAIR tools would allow for the tuning of curing parameters and which mechanisms causing distortion significantly influence the computed results derived according to their respective simulation strategies.  
We explored the specific capabilities of other simulation environments: ANSYS Pre-Post [21], ABAQUS/Standard [22], COMSOL Multiphysics Nonlinear Structural Materials Module [23], and MSC Marc Advanced Nonlinear Simulation Solution [24]. Each simulation environment offers the means for developing a custom numerical tool for predicting composite distortion. The choice was made to use the MARC simulation solution due to our previous expertise with this product and the existence of a complete element library, material models, and robust non-linear algorithms. We previously used MARC to simulate large deformations, general contact, and coupled problems.  
Although several simulation environments can be chosen, the ESI and ALTAIR tools were the only dedicated numerical tools for simulating thermoset distortion.  
The ELADINE work supported the development of a specific airframe part for which distortions needed to be evaluated and compensated through the geometry design of the tools. The authors accept that the exploratory work was carried out for a particular scope, but the ambition and potential of a numerical tool capable of predicting distortion goes far beyond a unique part. A numerical tool can consistently simulate the general behavior of thermoset resins and apply them to most distortion analysis scenarios under the condition that a thorough assessment of the curing parameters of the employed materials is achieved. Severe challenges for predicting distortion are met when scaling simulations from small coupons to full-scale parts.  
One important factor to mention is that the methods and tools described in the article only apply to thermoset resins and not to thermoplastics. The article does not account for thermoplastics, although several methods, primarily experimental investigations, also apply to this particular class of polymers.
## 8 Network Setup, Results, and Discussion
### Simulation Setup and Limitations
Also, one of the main challenges is to lower the control and signaling overheads during base station handovers.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 3 Topological Structure Mesh Routing
### Initial PAN Construction
The _leaf-ID_ field refers to an ID assigned by the leaf node from the router, the _leaf-ID_ fields of the base station and router being set to 0. If the base station is not able to associate the device with its PAN, the _short-address_ field is set to 0 xff, and the _association status_ field contains the reason for association failure. If the base station or router can associate the device with its PAN, this field contains the short address that the device may use in its communications on the PAN until it is disassociated. A _short-address_ field value equal to 0 xfe indicates that the device has been successfully associated with a PAN but has not been allocated a short address. In this case, the device communicates with the PAN by using only its extended address. Figure 6: Format of the proposed association-request frame.
## 3 Methods
### Loss Function Combining Cross-Entropy and Dice Loss  
During the training phase, we used the combined loss function CE-Dice, which integrates the cross-entropy loss [27] and Dice loss [28] to guide the training of PSANet to minimize prediction errors. The Dice loss function measures the overlap between the predicted and true regions, effectively addressing class imbalance. The cross-entropy loss function measures the deviation e in relation to the true and predicted probability distributions and is commonly used in classification tasks. The calculation formulas are as follows:  
\[Diceloss=1-\frac{2\times\sum\limits_{i=1}^{N}p_{i}g_{i}+\epsilon}{\sum \limits_{i=1}^{N}p_{i}^{2}+\sum\limits_{i=1}^{N}g_{i}^{2}+\epsilon} \tag{12}\]The _cross-entropy loss_ is defined as:  
\[Cross-Entropyloss=-\frac{1}{N}\times\sum_{i=1}^{N}\left[g_{i}\times log(p_{i})+(1- g_{i})\times log(1-p_{i})\right] \tag{13}\]  
The _CE-Dice loss_ is defined as:  
\[CE-Dicloss=a\times Dicloss+b\times Cross-Entropyloss \tag{14}\]  
where \(g_{i}\) denotes the ground truth value, \(p_{i}\) denotes the predicted value, \(\varepsilon\) denotes a small constant added to prevent division by zero, \(N\) denotes the total number of samples, \(a\) denotes the weight assigned to _Dice loss_, and \(b\) denotes the weight assigned to _cross-entropy loss_.
## 4 Experimental Analysis
strain gauge SG2 (Figures 8 and 9) of the same type. Compensation strain gauge is applied on the small, unloaded part, which is connected to fixator tree close to active strain gauge (Figures 7 and 8). The unloaded part is manufactured from the same material as the fixator tree. Using basic equation of the Wheatstone bridge:  
\[\frac{\text{V}_{0}}{\text{V}_{\text{S}}}=\frac{\text{K}_{\text{t}}}{4}(\epsilon _{1r}-\epsilon_{2r}+\epsilon_{3r}-\epsilon_{4r}) \tag{5}\]  
If Equation (12) is used for quarter bridge deformation is:  
\[\epsilon=\frac{4}{\text{K}_{\text{t}}}\frac{\text{V}_{0}}{\text{V}_{\text{S}}} \tag{6}\]  
where: \(\text{V}_{0}\) i \(\text{V}_{\text{s}}\) are output voltage and power voltage of Wheatstone bridge, \(\text{K}_{\text{t}}\) factor of strain gauge, \(\epsilon_{1}\),..., \(\epsilon_{4r}\) deformations measured using strain gauges. Active strain gauges are applied at the diametrically opposite places of the fixator tree, at the nearest and farthest place from the bone model (Figures 7 and 8), its longitudinal axis are lined up with the directions of principal deformations (\(\epsilon_{1}\) i \(\epsilon_{3}\)) at measuring places. Using this method, it is possible to measure the intensity of principal stresses at measuring places. Using the already carried up FEM numerical analysis, the direction and intensity of principal stress are determined. It can be noticed that the other two principal stresses are negligible in comparison to the biggest one (\(\sigma_{1}\) at MP+) and smallest one (\(\sigma_{3}\) at MP\(-\)) (Table 4). According to this, principal stresses at measuring places (MP+ and MP\(-\)) can be calculated using Hook law for one axis stress condition:  
\[\sigma_{1}=\epsilon_{1}\text{E};\ \sigma_{3}=\epsilon_{3}\text{E} \tag{7}\]  
For acquisition, processing and monitoring of measured results DAQ software Catman (HBM) were used. Table 3 shows results for interfragmentary displacement and displacement at loading place from FEM numerical analysis and experimental analysis. Using Equation (2) and data from Table 3 relative axial \(\text{r}_{\text{D}(x)}\), craniocaudal \(\text{r}_{\text{D}(x)}\) and lateromedial \(\text{r}_{\text{D}(y)}\) displacements can be calculated. Relative axial displacements are dominant. Lateromedial have significantly smaller values, and craniocaudal are equal to zero. Table 4 shows intensity of principal and von Mises stresses at measuring places for FEM and an experimental analysis. It can be seen that von Mises stress at measuring place MP+ correspond to the intensity of principal stress \(\sigma_{1}\) at same place. In addition, von Mises stress at measuring place MP- corresponds to the intensity of principal stress \(\sigma_{3}\). Figure 11: Comparative diagram of the axial displacement d at the point of load. Figure 10: Comparative diagram of the principal stresses (\(\sigma_{1}\) on MP+) and (\(\sigma_{3}\) on MP\(-\)).
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Decision-Making Modeling  
One of the primary challenges in wildfire evacuation modeling lies in capturing the intricacies of decision-making processes during evacuations. Existing models predominantly employ binary choice methods, simply considering whether to evacuate or not, with a few studies incorporating the option of \"wait and see\". However, this limited approach overlooks various critical factors influencing evacuation decisions, such as past experiences, situational variables, and individual risk perceptions. To bridge this gap, further  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scale** & **Software** & **Main Features** & **Potential Usability for Wildfire Scenarios** \\ \hline \multirow{4}{*}{Microscale} & EXODUS & Agent-based evacuation modeling, fire-people interactions & - & Analyzing detailed traffic behavior at the individual vehicle level \\ \cline{2-4}  & Quadstone Paramics & Microscopic traffic behavior simulation, & - & Assess the cause of congestion and the effects of individual vehicles \\ \hline \multirow{4}{*}{Mesoscale} & Transportation Analysis and Simulation System (TRANSIMS) & Traffic simulation with individual agents, regional transportation study & - & Forecast vehicle movements at specific times \\ \cline{2-4}  & Cube Avenue & Traffic flow analysis over time, scenario library creation & - & More precise assessment of evacuation timelines \\ \cline{2-4}  & DYNASMART & Dynamic traffic modeling, real-time simulation, forecasting vehicle movement & - & Traffic flow analysis \\ \hline \multirow{4}{*}{Macroscale} & TransCAD & GIS and macro-level simulation, evacuation analysis procedure & - & Analyzing the overall evacuation process in response to wildfire \\ \cline{2-4}  & Oak Ridge Evacuation Modeling System (OREMS) & Predicting evacuation timelines, assessing different scenarios, traffic factor calculations & - & Analyzing the overall evacuation process in response to wildfire \\ \cline{2-4}  & Evacuation Traffic Information System (EITS) & Internet-based platform for sharing evacuation and traffic data, cross-state traffic flow forecasting & - & Threats affecting wide geographic areas, \\ \cline{2-4}  & EAME4.6 & Traffic and transportation modeling capabilities, trip forecasts, scenario analysis & - & Implementing traffic strategies during evacuation, like contraflow or use of road shoulders, \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evacuation software comparison on different scales.  
research is required to explore the impact of these factors on decision-making. Additionally, pre-evacuation factors, such as the perception of risk, information processing, and risk assessment, play a vital role in shaping evacuation decisions. Integrating these aspects into the models is imperative for a more accurate representation of the decision-making process during wildfire evacuations.
## 3 Experimental Results
### Stiffness Degradation  
Secant stiffness of the walls in each load cycle can be calculated as follows,  
\[K_{i}^{+}=\frac{V_{i}^{+}}{\Delta_{i}^{+}},\ \ K_{i}^{-}=\frac{V_{i}^{-}}{\Delta_{i}^{ -}} \tag{1}\]  
where, \(K_{i}^{+}\) and \(K_{i}^{-}\) are the secant stiffness during positive and negative loading in the \(i\)th cycle, respectively; \(V_{i}^{+}\) and \(V_{i}^{-}\) are the maximum lateral forces during positive and negative loading in the \(i\)th cycle, respectively; and \(\Delta_{i}^{+}\) and \(\Delta_{i}^{-}\) are the deformation corresponding to the maximum lateral forces.  
The secant stiffness of the walls decreased at different pace along with the increase of lateral forces, as shown in Figure 9. The secant stiffness dropped dramatically before state 1, indicating the existing damages in the bricks, the mortar, and the brick-mortar interface were very sensitive and could develop significantly under very small lateral forces. After dropping to a low level at state 1, the secant stiffness degraded gradually.  
The strengthened walls retained their stiffness in a rather long deformation procedure before they failed, indicating the UHPC layers and the reinforced polymer mortar layer took obvious effect after state 1 and the walls' integrity was rehabilitated.