esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Successive Interference Cancellation  
The drawbacks of SIC might also be addressed using the DL technique. Due to the SIC receiver's poor cancellation, overall capacity decreases [42]. As a result of different hardware limitations, decoding and canceling may be faulty in real-world systems, making SIC possible. The performance gain of NOMA may be enhanced by using SIC at cell-edge users, as demonstrated by the authors in [72]. The creation of an easy-to-use, effective SIC receiver is essential to NOMA. Multi-stage SIC lowers multi-path fading and BER. The performance of the system is impacted by the signal's decoding sequence. High signal-to-noise ratio (SNR) signals are initially deciphered. The performance of the SIC receiver is enhanced by a low complexity, highly effective power allocation algorithm [73]. A real implementation's non-idealities and flaws cause error propagation in SIC, which is utilized to decode and identify desirable signals. Due to the signal processing required for SIC, receiver complexity increases as user equipment (UE) numbers rise. A deep neural network (DNN) is used in [74] to approximate the SIC receiver. In the MIMO-NOMA system, the combined optimization of precoding and SIC decoding minimizes the total mean square error between the user's intended signal and their decoded signal. Users and their sub-bands are grouped according to the status of the channel, ascending. The binary dislocation principle pairs them (BDP). As a result, users with excellent and bad channel conditions will be paired.  
A sub-band that satisfies user demands is selected. EP at the receiver can be removed if the signal-to-interference-plus-noise ratio (SINR) difference among a pair of users in the sub-band is sufficiently great. Users that share a band are given authority by BS. The minimum mean square error (MMSE)-SIC method with Interference Rejection Combining (IRC), which analyzes noise and interference independently and enhances average channel capacity and, therefore, system performance, can be used to attain the best performance at the receiver [75]. Some recent works are based on the theory that most EP-related problems in SIC may be handled by appropriately grouping or clustering users. Additionally, by concurrently optimizing precoding and SIC decoding using DNN and domain-specific information, the mean square error (MSE) value between the intended and decoded signals would be reduced to the absolute minimum. A comparison of reported studies on NOMA is presented in Table 3.
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 4 Proposed Work
### Results and Analysis of GFA
#### 4.2.5 Discussion  
In all steps of the previous simulation, the GFA mechanism retains the advantages of GF and solves the problem of end-to-end delay divergence noted in the GF mechanism. The divergence is caused by a stationary vehicle. GFA considers the speed of each vehicle and prioritizes the mobile vehicles compared to stationary ones.  
The reaction of GFA is noted when candidate vehicles are close and the speed of the one that is selected by GF is less than the speed of the closer vehicle to the source.  
We note that GFA reacts better than GF because of the score \(t_{p}\), especially when the score of the closer vehicle is less than that of the further one.  
GFA and GF have the same result, if the difference between the speeds of candidate nodes does not influence the score \(t_{p}\) in such a way that the score of the furthest node is lower than that of another closer node.  
This mechanism is used in the next section to collect information related to the segment delimited by the source junction and delimited junction.  
In the following paragraph, we present the simulation between the classic OLSR and the new version that integrates the GFA mechanism.
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.4 Fourth Generation (4G)  
The Long-Term Evolution (LTE) 4G technology was unveiled in 2009. Following its launch, it became widely used, allowing millions of users to transmit high-quality videos. 4G's fast mobile web connection offers up to 1 gigabit per second for static users, making high-quality video conferences, high-definition movies, and gaming services possible. The movement of all communication services to all-IP (Internet Protocol) was made possible to create a common platform for all the existing technologies. A crucial feature of 4G technology is terminal mobility which allows automatic roaming between other wireless networks [42].
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 3 The Clutter Cancellation and Proposed Long Time Integration Algorithm
### The Proposed Long Time Integration Algorithm
Detailed implementation procedures of the proposed processing technique are given, as follows:  
Step 1: Remove the direct and multipath signal from the target echo signal;  
Step 2: Calculate the cross-correlation between the target echo signal and the reference signal;  
Step 3: Estimate the coarse RA based on FrFT;  
Step 4: Estimate the accurate RA through maximizing the EIR; and,  
Step 5: Compensate the quadratic phase terms that are caused by the motion of the target and long time integration in the LIFT matrix. In summary, Figure 3 illustrates the flowchart of the proposed processing technique. Figure 3: Flowchart of the new imaging approach.
# Simplified Model Predicts Binder Behavior in Sand Mold Printing
###### Abstract  
Binder jetting is a crucial process in additive manufacturing (AM) and is widely used in sand mold casting. This study explores the challenges of simulating binder droplets in ANSYS Fluent, including complexity and computational time. To overcome these challenges, we propose a geometric approach that models the binder droplet as a circular shape instead of an actual droplet. Additionally, the dynamic mesh feature is employed to transform the initial boundary condition into a wall condition at a specified time interval (\(\Delta\)t). This simplified approach eliminates the need to simulate actual droplets, leading to significant computational resource and time savings. By adopting this geometric approach, we can accurately predict the diffusion and penetration behavior of binder droplets with varying materials and volumes in porous media with different porosities. Through data analysis, it was found that the main variables affecting the diffusion diameter and penetration depth are binder volume and porosity. The successful implementation of this simplified model enables researchers and engineers to expedite the simulation of binder behavior, facilitating process optimization and enhancing the understanding of binder jetting technology in the field of additive manufacturing.  
binder jetting; ANSYS Fluent; simulation; geometric approach; circular shape; dynamic mesh; diffusion; penetration; porous media; process optimization Article
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 5 Conclusions  
In this study, since the literature lacks an effective analysis method of collapse mechanisms and optimisation design theory for progressive collapse resistance of cable dome structure, a structural performance-based optimisation approach was proposed to improve the progressive collapse resistance for cable dome structures. First, the dynamic response and collapse model of a cable dome structure were analysed after the failure of its members by using Ansys LS-DYNA and the full dynamic equivalent load-based instantaneous unloading method. The importance coefficients of the members were then calculated to determine each member's importance in the structure's resistance to progressive collapse. Finally, a stepwise optimisation model was developed by combining global and local optimisation. The results were as follows:  
1. The removal of different members resulted in a variety of dynamic responses and collapse models within the structure. For instance, the removal of the IHC and OHC resulted in the largest dynamic responses, with the largest importance coefficients,  
Figure 11: Initial shape and optimised shape.  
Figure 10: Optimisation iteration progress.  
and thus they were classified as key components. The removal of the IUS resulted in the third-largest dynamic response, with the third-largest importance coefficient, and thus it was classified as an important component. The other members were associated with relatively small dynamic responses, with low importance coefficients, and thus they were classified as common components. These results indicated that the importance of various members in the structure's resistance to progressive collapse varied.
2. A global performance index was proposed to describe the global resistance to progressive collapse. The progressive collapse resistance of the cable dome structure decreased as the number of members decreased. The structure based on the Levy topology was more resistant to progressive collapse than that based on the Geiger topology.
3. Local optimisation based on the genetic algorithm effectively reduced the maximum member importance coefficient (i.e., that of the OHC) by 60.26%. The optimisation process performed by changing the hoop cable radii was also much more effective than changing other shape parameters.  
Conceptualization, L.-M.C.; methodology, L.-M.C. and Y.-J.L.; software, Z.-B.L. and K.-Y.H.; validation, Y.-J.L., K.-Y.H. and Y.-H.Z.; resources, L.-M.C., Y.-Y.Z. and S.-L.D.; writing--original draft preparation, L.-M.C. and Y.-J.L.; writing--review and editing, L.-M.C. and K.-Y.H.; supervision, L.-M.C., Y.-Y.Z. and S.-L.D.; project administration, L.-M.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (Grant No. 51578422, 51678082).  
Not applicable.  
The authors declare no conflict of interest.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Anonymity  
Anonymity is assured only by entities' temporary identities (\(\mathrm{TID_{H}}\), \(\mathrm{TID_{P}}\), \(\mathrm{TID_{D}}\)), while messages are exchanged on an insecure channel during the authentication procedure, which protects their real identities. The identity of the cloud server is protected because it is not used in the authentication procedure, hence, not exchanged on an insecure channel.
## 3 Theoretical Analysis and Numerical Model
### Boundary Condition Settings  
The generated mesh was imported into Fluent for fluid simulation, solved using a pressure-based steady-state solver affected by elevation factors, and the turbulence model was selected as an achievable equation model, and the parameter properties and value settings are shown in Table 2. The air flow demand of the advance side tunnel is \(Q_{a}\), and the air flow demand of the undercut side tunnel is \(Q_{u}\). In order to reflect the difference of the air flow demand of the double-hole tunnel: when \(Q_{a}>Q_{u}\), inlet1 = 8.16 m/s and inlet2 = 16.18 m/s; when \(Q_{a}<Q_{u}\), inlet1 = 16.18 m/s and inlet2 = 8.16 m/s.
## 3 Methodological Framework
### Mapping Layer
#### 3.3.1 Case-Based Reasoning  
CBR is any problem-solving strategy that employs previous solutions to a similar problem. It assumes that information can be gained from previous experience, that this helps to avoid paths leading to failure, and that an established solution can be tailored to the current challenge. CBR can be found in many contexts. Google Maps, for example, employs it to estimate the duration of your journey from point A to point B by looking at the patterns of previous users to determine how long it took them. It creates conclusions about how long your journey will take, even if it starts from two slightly different sites. By tackling memory, learning, planning, and problem solving, CBR lays the framework for a new generation of intelligent computer systems that can solve challenges and adapt to new situations. The \"intelligent\" utilisation of knowledge from previously solved problems (or cases) in CBR is based on the assumption that the more similar the two issues are, the more similar will be the solution. In general, the CBR procedure involves the following steps:  
1. _Retrieve_--retrieve from memory the event most like the current situation;
2. _Reuse_--propose a solution based on prior experience, modifying it to the new situation's requirements;
3. _Reuse_--re-evaluate the solution's relevance and usefulness in the new situation;
4. _Retain_--keep this new approach to problem solving in the memory system.  
Figure 2: Architecture of fastText embedding.  
CBR calculates a similarity score, using weighted averages to measure the likeness between the various cases to map them against the same solution. Equation (2) shows the formula to calculate the weighted average between cases:  
\[\text{Similarity (new case, old case)}=\frac{1}{W}\sum\
olimits_{i,j=1}^{n} \text{Similarity Score }\big{(}f_{i},\,f_{j}\big{)}\times W_{i} \tag{2}\]  
where \(W=W_{1}+W_{2}\,+W_{3}+\cdots W_{n}\).
## 6 Reliability Analysis for Transposition System
### Reliability Evaluation
\begin{array}{l}\frac{\partial R}{\partial\mu_{X1}}=[\partial R/\partial\mu _{X2},\partial R/\partial\mu_{X2},\partial R/\partial\mu_{X2},\partial R/ \partial\mu_{X2},\partial R/\partial\mu_{X2},\partial R/\partial\mu_{X2}, \partial R/\partial\mu_{X2},\partial R/\partial\mu_{X2},\partial R/\partial\mu _{X2},\partial R/\partial\mu_{X2}]\\ =[-0.7941,-0.1093,-5.359,-1.013,-0.04327,-1.974,-0.4001,-0.009105]\end{array}  \tag{23}\]  
In Figure 5, the parameters of dual gear 2 have greater impacts on system reliability than other gears in the transposition system of power tool servo SFW2512. Considering the plus or minus sign, the increases in mean values \(X_{1}\), \(Y_{2}\), \(X_{3}\), and \(Y_{4}\) can improve system  
Figure 4: Results between proposed method and Monte Carlo simulation with different samples. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **FOSM** & **AFOSM** & **Proposed Method** & **Monte Carlo** \\ \hline \(R\) & \(-\) & 0.9674 & 0.9734 & 0.9838 & 0.9831 \\ \(\partial R/\partial\mu_{X11}\) & \(X_{1}\) & \(-0.0981\) & \(-0.1027\) & \(-0.1079\) & \(-0.1081\) \\ \(\partial R/\partial\mu_{X21}\) & \(Y_{1}\) & 0.0400 & 0.0314 & 0.0401 & 0.0399 \\ \(\partial R/\partial\mu_{X31}\) & \(X_{2}\) & \(-0.2743\) & \(-0.2160\) & \(-0.2789\) & \(-0.2599\) \\ \(\partial R/\partial\mu_{X41}\) & \(Y_{2}\) & 0.1300 & 0.1281 & 0.1214 & 0.1218 \\ \(\partial R/\partial\mu_{X51}\) & \(X_{3}\) & 0.0227 & 0.0228 & 0.0250 & 0.0250 \\ \(\partial R/\partial\mu_{X61}\) & \(Y_{3}\) & \(-0.1667\) & \(-0.1677\) & \(-0.1695\) & \(-0.1699\) \\ \(\partial R/\partial\mu_{X71}\) & \(X_{4}\) & \(-0.0682\) & \(-0.0695\) & \(-0.0763\) & \(-0.0787\) \\ \(\partial R/\partial\mu_{X81}\) & \(Y_{4}\) & 0.0097 & 0.0102 & 0.0115 & 0.0113 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of results of four methods. reliability. However, the decreases in mean values \(Y_{1}\), \(X_{2}\), \(Y_{3}\), and \(X_{4}\) can reduce system reliability. In Figure 6, both the proposed method and the Monte Carlo simulation are considered to calculate the indices of the covariance reliability-based sensitivity, representing the effects of the parameters' dispersions on system reliability. Considering the minus sign, the increases in each parameter can decrease system reliability, which makes it difficult to guarantee the system's performance and accuracy. Also, the positions of dual gear 2 and dual gear 3 are more important than those of the other gears in the SFW2512.
## 1 Introduction
[PERSON] et al. [30] compared JET-cooling with hydraulic spray-cooling (WS). Researchers showed that WS-cooling provided ideal cooling uniformity, higher heat transfer efficiency and a lower cooling medium consumption than JET-cooling. A comparison of heat transfer during cooling by a hydraulic spray nozzle and JET was presented by [PERSON] et al. [6]. JET-cooling allowed for the dissipation of more heat from the cooled surface compared to WS-cooling. However, the authors demonstrated that the spray nozzle provided a more uniform distribution of water over the cooled surface. [PERSON] et al. [12] made a comparison between hydraulic and pneumatic spray-cooling. During cooling with a pneumatic spray, higher values of HTC than during cooling with hydraulic spray were obtained. The authors demonstrated that the biggest impact on HTC is the spray impingement density. In the available literature, there is a lack of papers comparing all three methods of water cooling (JET, WS, AAS) under high-temperature conditions, considering both single-phase and two-phase heat transfer. The main purpose of this paper was to identify and compare the boundary conditions of heat transfer on a cooled surface of a cylindrical sensor made of Inconel 600 during JET-, WS- and AAS-cooling. Experimental tests were carried out using these three cooling methods. All tests were carried out on the same test stand, with the use of the same sensor and under the same process conditions. To ensure a suitable cooling method, only the nozzle type was changed. These activities aimed to obtain as similar cooling conditions as possible for all the tested methods. The temperature measurements obtained from the experimental test were used as input data for an inverse method build in a computer program. The results of the calculation allowed for determining the boundary conditions of heat transfer on the cooled surface of the sensor. In this paper, the results of the calculation are presented in the form of average values of HTC and HF; also, the local values of HTC are shown. Additionally, the amount of energy dissipating from the sensor surface during boiling was compared to the energy dissipating during forced single-phase convection. The conducted analysis can be useful for researchers dealing with the intensification of heat transfer during water-cooling under two-phase heat transfer conditions, and for entrepreneurs which are looking for solutions that can meet their requirements.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 4 Results
### Example 1  
For the first experiment, video steganogram No. 1. was selected. As a result of the decoding function's operation, successive values of the functions \(IIF_{(0,0)}\), \(IIF_{(0,1)}\), \(IIF_{(1,0)}\), \(IIF_{(1,1)}\), and \(IIF\) were calculated. The plots of these functions are presented in Figure 1.  
Figure 1 highlights, with a blue vertical line, the characteristic values, \(chIIF^{s}\) and \(chIteration^{s}\), which indicate the minimum threshold at which the information gain becomes sufficient for accurately decoding the hidden message.
# A Survey on Satellite Communication System Security
## 6 Policies and Strategies in Satellite Communication
Consequently, it appears that worldwide strategies and policies are focused on protecting each country's satellite communication systems while imposing checks on the satellite communication systems of adversary nations. Especially now that satellite communication systems are playing an important role in various fields, the strategies and policies of each country will become more important elements that directly influence the security of satellite communication systems.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods  
### Sentinel-1 Data and DInSAR Methodology  
#### 2.1.1 DInSAR Processing and Post-Processing  
In the current study, we proposed a radar signal processing chain in five main steps (Figure 1):  
* Step 1: Classical cumulative DInSAR processing,
* Step 2: Quality assessment of the radar signal,
* Step 3: Unification of the results in a common vertical datum by trend removal,
* Step 4: Decomposition from displacement in the direction to the satellite LOS into vertical and horizontal directions, and
* Step 5: Geospatial analysis for extraction of the area affected by the mine subsidence and the lower point of the subsidence trough.  
The results were compared with vertical displacements (such as grid, area, and extremal point) derived from Knothe-Budryk modelling (Section 2.2) and were verified with geodetic levelling data (Section 2.3.2).  
The current study covered the time span of one year from the beginning of January 2017 until the end of December 2017. Sentinel-1 single look complex (SLC) images in VV polarisation and interferometric wide (IW) mode from orbits 175 and 51, respectively, from ascending and descending passes over the area of USCB were used (Figure 2).  
Figure 1: Differential satellite radar interferometry (DInSAR) processing and post-processing flow.  
The standard procedure of consecutive DInSAR using the ESA Sentinel Application Platform (SNAP) was applied for the whole stack of the available 60 ascending and 54 descending images for the period of interest. Within this procedure, the interferograms with the shortest temporal baselines were generated from consecutive pairs of SAR images. Most of the pairs comprised a time span of six days for the combination of the acquisitions from the two satellites (Sentinel-1A and Sentinel-1B), while some images were missing from the ESA data hub in a few cases, causing several 12-day interferograms to be produced: One from ascending and seven from descending sequences. The average perpendicular baselines were 79 m and 73 m for the ascending and descending groups of interferograms, respectively, with the longest baselines, 179 m from the ascending set and 196 m for the descending set. The shorter temporal and perpendicular baselines of the interferograms predisposed lower possibilities of decoherence. The Shuttle Radar Topography Mission (SRTM) 1 arc-sec (30-m resolution) DEM was provided by the US Geological Survey [38], (which is directly accessible through the platform) and was used to remove the topographic phase. The Goldstein filter was applied to support the Minimum Cost Flow (MCF) phase unwrapping procedure [39]. The phase difference \(\Delta\phi\) in the LOS from 59 ascending and 53 descending unwrapped interferograms were converted to displacement values in metric units using the incidence angle \(\theta\) at each pixel and by considering the wavelength \(\lambda\) of the C-band:  
\[d_{LOS}=\frac{\lambda\cdot\Delta\phi}{4\pi\cdot\cos\theta} \tag{1}\]