esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Eddy Simulation of Droplet Deformation and Fragmentation Under Shock Wave Impact
## 3 Computational Model
### Flow Geometry  
The compressible flow governing equations were solved within a rectangular computational domain defined as \(\Omega=[-80D_{0},120D_{0}]\times[-30D_{0},30D_{0}]\times[-30D_{0},30D_{0}]\), where \(D_{0}\) represents the initial diameter of the spherical droplet. The domain size was determined based on prior studies to minimize boundary condition effects while maintaining affordable computational complexity. Practically, a sufficiently large computational domain was chosen to ensure that no significant reflections occur at the boundaries (sides, inlet, and outlet) within the time interval of interest.  
Differently from analogous aerobreakup studies, e.g., [16; 27], fully three-dimensional simulations were performed without imposing any symmetries or simplifications to the computed flow field, which allows us to accurately capture the non-symmetric complex behavior of the deforming liquid droplet surface and the surrounding airflow. The simplified flow geometry is illustrated in Figure 1, along with a schematic of the physical model, including the traveling normal shock front, before the impact on the droplet. The reference coordinate system \((x,y,z)\equiv(x_{1},x_{2},x_{3})\) is oriented with the first axis in the stream-wise direction, with the origin being located at the leading edge of the spherical water droplet in its initial position. Regarding boundary conditions, pressure inlet and pressure outlet conditions were imposed in the streamwise direction, while the four lateral sides were treated as symmetry boundaries, thus limiting artificial reflections of waves or disturbances, which could otherwise affect the simulation accuracy.  
In contrast to previous studies [21; 32], the flow within the shock tube was not explicitly simulated. Rather, the discontinuous airflow conditions across the moving shock front were directly imposed. This simplified simulation setup expedited the calculations, where the reduced computational complexity allowed us to allocate more resources to resolving the droplet dynamics, thus focusing on the primary research interest. However, it should be mentioned that such a procedure can lead to losing detailed information about the ambient airflow, while not exactly reproducing the initial conditions for the breakup process, which potentially implies discrepancies between simulation results and experimental observations [25; 33].  
The simulations began with the shock front located one diameter away from the droplet's leading edge (\(x/D_{0}=-1\)), thus separating the domain into post-shock and pre-shock sections. It is important to note that the shock front, and consequently the post-shock airflow, moves in the positive \(x\)-axis direction.
## 3 Results and Discussion
### 3D-EEM Spectra of Standards Phenolic Compounds  
Standard phenolic compounds exhibited excitation with \(\lambda\)ex ranging between 260 and 360 nm and emission with \(\lambda\)em ranging from 315 to 420 nm. Moreover, some flavonoids presented a low fluorescence intensity [24]. Detailed results are shown in Table 2 and the spectra of the standard phenolic compounds are presented in Figure S1.  
### 3D-EEM Spectra of Standards Phenolic Compounds  
Standard phenolic compounds exhibited excitation with \(\lambda\)ex ranging between 260 and 360 nm and emission with \(\lambda\)em ranging from 315 to 420 nm. Moreover, some flavonoids presented a low fluorescence intensity [24]. Detailed results are shown in Table 2 and the spectra of the standard phenolic compounds are presented in Figure S1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Potanical Source** & **Aggregate** & **Fructose +** & **Sucrose** & **Electrical Conductivity** & **Moisture (\%\%/\%)** \\ \hline \multirow{3}{*}{Thyme honey} & Min & 60.1 & 0.0 & 251 & 14.3 \\ \cline{2-5}  & Max & 86.4 & 3.7 & 600 & 17.9 \\ \cline{2-5}  & Average & 68.2 & 0.7 & 435 & 15.8 \\ \hline \multirow{3}{*}{Pine honey} & Min & 46.4 & 0.0 & 911 & 14.6 \\ \cline{2-5}  & Max & 77.2 & 1.0 & 1431 & 17.7 \\ \cline{2-5}  & Average & 58.9 & 0.1 & 1122 & 16.1 \\ \hline \multirow{3}{*}{Fir honey} & Min & 46.3 & 0.0 & 1041 & 13.7 \\ \cline{2-5}  & Max & 64.5 & 2.4 & 2000 & 18.4 \\ \cline{2-5}  & Average & 55.0 & 0.2 & 1526 & 15.5 \\ \hline \multirow{3}{*}{Citrus honey} & Min & 66.2 & 0.0 & 181 & 15.3 \\ \cline{2-5}  & Max & 76.2 & 4.8 & 324 & 18.7 \\ \cline{2-5}  & Average & 71.9 & 1.0 & 275 & 17.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of physicochemical analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenolic Compound** & \(\lambda\)**ex (nm)** & \(\lambda\)**em (nm)** \\ \hline Caffeic acid & 310–360 & 410 \\ \hline Chlorogenic acid & 300–360 & 416 \\ \hline p-Coumaric acid & 320–340 & 380 \\ \hline Ferulic acid & 310–360 & 400 \\ \hline Sinapic acid & 300–360 & 415 \\ \hline Ellagic acid & 280–380 & 400 \\ \hline Homogentisic acid & 280–320 & 335 \\ \hline Gallic acid & 265–315 & 345 \\ \hline Protocatechuric acid & 265–315 & 335 \\ \hline Syringic acid & 250–315 & 335 \\ \hline Vanillic acid & 260–315 & 330 \\ \hline Methyl syringae & 250–320 & 340 \\ \hline Gentisic acid & 290–360 & 400 and 475 \\ \hline Kaempfer & 280–320 & 430 and 500 \\ \hline Catechin & 250–310 & 315 \\ \hline Chrysin & & Low intensity \\ \hline Hesperetin & & Low intensity \\ \hline Isorhamentin & & Low intensity \\ \hline Naringenin & & Low intensity \\ \hline \hline \end{tabular}
\end{table}
Table 2: The wavelengths of \(\lambda\)ex and \(\lambda\)em of standards phenolic compounds.
## 4 Experiments
### Ablation Studies  
We have demonstrated that several improvements to DQN can successfully achieve better performance. Then we conducted additional experiments to test combinations of these extensions and assess how the removal of each DQN extension affects the performance of model extraction. To better comprehend the influence of each component on the DQN agent, we conducted ablation studies. In each study, we systematically removed one component from the complete set of these extensions. Note, that ERE is implemented based on PER, so when PER is removed in our ablation study, ERE is also omitted. In this context, we focus on the scenario without PER. Figure 7 shows a comparison of the accuracy score of the full combinations to five ablated variants.  
The findings indicate that Prioritized Experience Replay (PER) was the most critical element among DQN enhancements, as its removal significantly reduced performance. The soft update and double DQN followed in importance, according to the results of other ablations. It is noteworthy that in the initial stages of learning, spec  
Figure 6: Performance of different synthetic data generation techniques with PER method on surrogate models. The figure above illustrates the Jacobian-based methods, Linf-projected Gradient Descent (LinfPGD), Fast Gradient Sign Method (FGSM), Entropy, and a data-free model extraction method. The X-axis indicates training budgets from 1000 to 10,000 in 1000 intervals, and the Y-axis displays the model’s accuracy. For comparison, we also included the PER method trained using the DQN model. Since a budget of 3000 was utilized to train the DQN model, its performance is plotted to start from a budget of 3000 up to 10,000.  
Figure 7: Performance of different ablation methods trained on surrogate models. The figure above compares our combination method with five different ablations. The X-axis indicates training budgets from 100 to 1000 in 100 intervals, and the Y-axis displays the model’s accuracy. Since a budget of 3000 was utilized to train the DQN model, the total budget is 4000.  
200 queries, there was a noticeable difference between the ablations and the full agent. Overall, removing the dueling network and Noisy net from the combined setup did not result in a significant performance change. Both the dueling DQN and Noisy net tend to introduce instability, complicating the maintenance of a stable learning trajectory and convergence to an optimal policy.
## 4 Result and Analysis
### Characteristic Distribution of Groundwater Storage Capacity
#### 4.2.1 Distribution of Groundwater Reserves Under Annual Scale Change  
The groundwater reserves are assessed by the water balance method, and a yearly distribution map of these reserves is created based on the varying groundwater levels in each sub-basin. The groundwater reserves in sub-basins Nos. 18, 19, 21, and 22 are evidently the most plentiful. The primary cause of the rich groundwater reserves in the aforementioned sub-basins is the comparatively ample yearly rainfall in these areas. The study indicates a strong correlation between rainfall levels and local groundwater reserves, with this disparity being most pronounced during the wet years of 2010, 2013, 2016, and 2018. The distribution of water storage in the aquifers of the research area is uneven, with Mudanjiang City at the center and a marked decrease in water storage from west to east and from south to north. Groundwater is predominantly found in mountainous regions, with basins Nos. 18, 19, 21, and 22 also situated in such areas. The western region predominantly features Zhangguangcai Mountain, Jiaodong Mountain, and Taiping Mountain. Consequently, the groundwater distribution in the aforementioned sub-basins will be markedly greater than in other sub-basins, while the reserves in the plain region are more limited compared to those in the hilly region. Figure 5 illustrates the annual distributions of the groundwater reserves.  
Figure 4: Runoff rate determination and verification of the model.  
Beneath the surface, groundwater exists within the pores, fissures, and cavities of soil and rock formations. This subsurface water constitutes an essential component of water resources. Groundwater possesses several advantages, including stable volume, low temperature, superior quality, resistance to contamination, and relatively low extraction costs. It serves as a dependable source for agricultural irrigation, industrial production, and domestic use. The annual groundwater reserves in the study area can be determined using the water balance method, and the average annual groundwater reserves in the study area from 2010 to 2018 amount to 2.413 billion m\({}^{3}\)/a, indicating a sustainable level of exploitation. In 2013, the groundwater reserves reached their peak at 3.8 billion m\({}^{3}\)/a, with recharge exceeding depletion, thus maintaining a positive equilibrium state.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### Proposed Parent Selection Algorithm
#### 3.3.1 Trust Computation in RPL  
Trust computation assumes that all nodes are reliable in the beginning depending on the ICMPv6 and DIO packet exchange. As ContikiRPL assumes that nodes overhear neighbour nodes and their packet transmission [6], SMTrust also assumes the same. When RPL is initialized, preferred parents and routing decisions are determined, based on the specification in OF for trustworthy parent selection. Rank is computed for the nodes, according to the normal operations of RPL [51]. Once the topology is constructed and neighbour list is established, the trust values are computed according to the quantification of trust metrics, and the trust index is calculated by (1). Algorithm 1 illustrate the procedure for trust computation and parent selection facilitating the isolation of malicious nodes.  
When a node receives DIO messages, the information communicated is used for routing table updates. The node calculates the trust values for its neighbors via the SMTrust trust computation mechanism. A set of trusted potential parents is then selected for an optimal path towards the root node. The selection of a trustworthy preferred parent ensures traffic routing reliability in the network. Finally, a new DIO message is generated and broadcasted to its neighbours, which contains the computed trust values, via DAG metric container. This method is followed by all the neighbouring nodes for the construction of DODAG. Afterward, the DODAG maintenance relies on the Trickle timer [6], limiting the transmission of control messages.  
Figure 3: Process Flow of SMTrust.  
algorithm, Algorithm 1, is O(1) in the best case except the trust calculation for neighbouring nodes required as input for parent selection, which is O(n). Moreover, there can be n number of potential parents for a particular node, so in order to check the trustworthiness of the potential parents one by one, the complexity of Algorithm 1 is O(n) in the worst case. Similarly, the computational complexity for attack detection is O(1) in the best case, and O(n) in the worst case. Therefore, it can be deduced that the overall complexity for the added procedures in SMTrustOF is O(n). For SMTrust, the computational complexity increases with the calculation of trust metrics and aggregation.
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 2 Finite Element Modeling and Static Aeroelasticity Calculation for Airfoils  
The finite element software Nastran is employed to perform static aerodynamic elasticity calculations on the wing of a composite fixed-wing aircraft currently in the design phase [24; 25]. The wing's design parameters are as follows: a chord length of 1.45 meters, a wingspan of 10.46 meters, and a total airfoil area of 15.167 square meters. According to the design requirements for the air-to-air aircraft, under ultimate load conditions, the vertical displacement of the wing tip must not exceed 6% of the wingspan, and the stress around the bolt holes at the root of the wing beam must not exceed 60% of the material's compressive strength [26; 27].  
A 3D finite element model of the wing and the wing ribs is shown in Figure 2. Fixed constraints are applied to the bolt holes at the beam root. The wing is meshed using quadrilateral shell cells for the airfoil, wing ribs, and beam. To ensure calculation accuracy, grid independence was verified during the initial design stage using 18,000, 32,000, and 56,000 meshes, with equivalent stress used as the evaluation criterion. For a flight condition of zero altitude, a flight speed of 150 km/h, and an angle of attack of 0\({}^{\circ}\), the grid correlation verification results are shown in Table 1. The findings reveal that the maximum equivalent stress difference between 18,000 and 56,000 grids is less than 2%, indicating that grid independence has been achieved. To reduce computational cost, 32,000 grids are selected for further numerical simulations. The detailed grid cell distribution is presented in Table 2, with the airfoil containing 19,040 cells, the wing ribs containing 3048 cells, and the beam containing 8716 cells. The maximum cell size for the entire wing grid is 27 mm, while the minimum cell size is 2 mm. Based on the design specifications of the aircraft, with flight conditions at zero altitude, a speed range of 150 to 240 km/h, and an angle of attack range of 1 to 10 degrees, the wing stresses are solved under varying conditions.
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 4 Experimental Results and Analysis
### Block Generation Efficiency  
The 2 nd task of evaluating the efficiency of federated learning is to analyze the efficiency of block generation in ETFL, which includes two parts: the efficiency of parameter validation and the efficiency of parameter aggregation. As shown in Figure 12, the validation time of ETFL is shorter than that of the other two FL schemes. This is because they do not optimize the validation mechanism. In contrast, ETFL is able to ignore part of the non-essential parameter evaluation process and achieve equivalent legitimacy verification. Thanks to the proof-of-contribution consensus mechanism, the verification time of ETFL will continue to decrease as the system continues to run.  
As shown in Figure 12, in each iteration of the parameter aggregation process, ETFL adopts a contribution-based consensus mechanism to select the node with the highest contribution as the aggregator at the beginning of each iteration, which will greatly reduce the additional aggregation delay due to the mining competition during the block generation process, and exhibits a more excellent parameter aggregation efficiency compared to several other schemes.
## 3 Result and Discussion  
### Hyperspectral Image Preprocessing  
Firstly, in order to remove the background region, all the corrected images were then spatially cropped down to 451(W) \(\times\) 451(H) (Figure 1a). Additionally, only 141 spectral bands ranging from 400 to 920 nm were reserved, because the wavelengths 350-498 nm and 923-1000 nm mainly contributed to noise. Median filter (kernel size 5) was then applied to image at each spectral band to reduce pixel random noise. Moreover, in order to suppress the unwanted background outside of each Petri dish and the shallow at the edge of the dish, all the hyperspectral images were masked by binary plate masks (Figure 1b), which were little smaller than those of the Petri dishes. The masks, i.e., the selection of the ROI area, were created manually by ROI selecting tool in the ENVI software (Exelis Visual Information Solutions, Boulder, CO, USA). In order to apply same algorithms on different images synchronously, pixel-based mosaic for each type of fungi was constructed. Two mosaics were composed of _A. flavus_ and _A. pansitius_ incubated from 1 to 6 days, respectively. PCA and inverse PCA were conducted to obtain the denoised hyperspectral image data. The first 6 PCs contained the majority of information in the hyperspectral data cube and very little noise. Thus, the first 6 PCs were used in the inverse PCA. The cleaned images were used for subsequent spectral extraction and multivariate classification. Pixel and spectral approach methods were used to find characteristics of fungi growth and distinguish different fungi.  
Figure 1: (**a**) Resized hyperspectral image; (**b**) mask for the hyperspectral image.
# Impact of Transparency in the Teamwork Development through Cloud Computing
## 4 Results
### Homogeneity of the Exyzerimental and Control Groups  
The homogeneity of the control and experimental groups was established using a survey. Homogeneity is investigated from two perspectives: the student profile and teacher treatment.  
The survey was completed by 83 students out of a total of 96 students, representing an 86.45% response rate. Out of a total of 44 people, 36 joined in the experimental group, accounting for 81%. In the control group, 47 people out of 51 took part, accounting for 92% of the total.  
1.1 Homogeneity of The Experimental and Control Groups with Respect to Student Profile and Team Creation  
The equivalence is investigated to ensure that both groups are homogeneous by asking the following questions:  
* Q1--university entrance qualification
* Q2--the total number of times the student has enrolled in the course
* Q3--gender
* Q4--the method by which you joined the team. In this case, the possible values are as follows:  Some of the members of the team were friends of mine
* I knew a few of the team members
* I did not know anyone on the team
* I was the team's first sign-up  
To determine if the distribution is normal, the Shapiro-Wilk test is used first. The \(p\)-value of the data series for both the control and experimental groups is shown in Table 1.  
Table 1 shows that all of the values have a \(p\)-value of less than 0.01; hence, none of the data series are normal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(p\)**-Value** & **Experimental** & **Control** \\ \hline Q1 & \(1.409\times 10^{-10}\) & \(4.554\times 10^{-13}\) \\ Q2 & \(4.043\times 10^{-13}\) & \(3.614\times 10^{-15}\) \\ Q3 & \(0.000000005812\) & \(9.647\times 10^{-10}\) \\ Q4 & \(0.0007028\) & \(0.0001633\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shapiro-Wilk test for experimental and control groups.  
Equivalence has been investigated using the Wilcoxon \(p\)-value for unpaired samples to see if there are any major differences. All samples are categorized by group (experimental and control) and compared for the following data sets: university entrance qualification, gender, and team creation (Table 2).  
All of the \(p\)-values obtained are greater than 0.01; thus, the student body profile is equivalent.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 3 The Time-Division Multiplexing Problem  
Aimed at improving the spectrum utilization of the unlicensed bands by following LTE-Unlicensed (LTE-U), this article contributes to the determination of the optimal time ratio, \(\delta\), for the time-division multiplexing of LTE and Wi-Fi over unlicensed bands. We explain the typical environments this study intended and formulate the finding of the optimal \(\delta\) value as a one-dimensional mixed integer programming problem.
## 3 Proposed Protocol: ELEACH-DFL  
We propose the ELEACH-DFL which is applied two fuzzy logics for CHs selection and cluster configuration, based on LEACH algorithms, for an energy-efficient network. In the process of selecting CHs, we consider the residual energy and local distance as fuzzy inputs and make rules on which nodes with more residual energy and higher centrality are selected as CHs. After selecting CHs, another FIS is applied to clusters configuration. The remaining energy of Non-CH nodes, distances from CHs, and distances between CHs and BS are used as fuzzy inputs. These two logics allow a network to increase its lifetime.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.4 Control Plane Design  
The control plane in the KDN architecture is also logically centralized, similar to the originally proposed logically centralized SDN architecture. It should be capable of handling large amounts of requests from network devices and applications, and should be able to process generated knowledge or rules from the knowledge plane. Thus, the processing capability of the controller must be very high. Controllers such as the NOX-MT use multi-threading and optimization techniques such as input-output batching to improve the controller's performance [312]. McNettle is another computationally powerful controller that can be used for KDN, as it can achieve a throughput of 14 million flows per second and reduce the number of system calls to optimize cache usage, operating system processing, and runtime system overhead [563]. In order to handle scalability issues and due to a single controller being a single point of failure, a physically distributed control architecture with multiple distributed controllers is a better design choice for the KDN. If KDN uses a set of distributed controllers with different platforms, SDN hypervisors can be used to achieve interoperability [564]. Alternatively, for distributed control systems, one may use the same controller platform to achieve maximum interoperability. For distributed controllers, state consistency using a hybrid approach, having both full consistency for critical operations that affect a large portion of the network and eventual consistency for less critical changes, is a much more desirable design approach for KDN [375].