esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of Anti-Inflammatory Activity of Limonoids
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{3}{*}{**Compound (\#)**} & \multirow{3}{*}{
\begin{tabular}{c} **Concentration** \\ \(\upmu\)**M** \\ \end{tabular} } & \multicolumn{3}{c}{**Mean Release of TNF-\(\upalpha\)**} & \multirow{3}{*}{**SD Value**} \\  & & **by RAW264.7 Cells** & & \\  & & **(pg/mL)** & & \\ \cline{2-4}  & **Cell Control** & **70.5** & **9.0** & **Release (\%)** \\  & **Stimulation Control** & **3602.5** & **461.1** & \\ \hline
2 & 20 & 2592.3 & 140.1 & 28.6 \({}^{*}\) \\
3 & 20 & 2266.4 & 172.6 & 37.8 *** \\
4 & 20 & 2506.6 & 59.8 & 31.0 *** \\
5 & 20 & 2504.3 & 152.2 & 30.5 \({}^{**}\) \\
6 & 20 & 2501.6 & 155.0 & 31.2 \({}^{**}\) \\
7 & 20 & 2398.1 & 161.0 & 33.4 \({}^{**}\) \\
8 & 20 & 2432.7 & 147.2 & 32.5 \({}^{*}\) \\
9 & 20 & 1956.1 & 174.6 & 55.3 *** \\
**10** & 20 & 3379.4 & 173.8 & 18.0 \\
**11** & 20 & 4395.4 & 290.9 & \(-\)8.6 \\
**12** & 20 & 4179.9 & 478.5 & \(-\)3.0 \\
**13** & 20 & 636.3 & 127.1 & 90.0 *** \\
**14** & 20 & 3416.5 & 834.2 & 17.0 \\
**15** & 20 & 4686.7 & 489.3 & \(-\)16.3 \\
**16** & 20 & 2770.8 & 287.4 & 34.0 \({}^{**}\) \\ \hline \end{tabular}  
* Significance: \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\); ***\(p<0.001\) vs. LPS control. N = 4.  
\end{table} 
Table 1: Experiment results of anti-inflammatory activity screening of limonoids.
## 3 Results
### The Experimental Preparation
#### 3.1.3 Algorithms Compared  
The suggested approach aims to acquire the geometric characteristics of the data and select features using an autoencoder-based approach. For the purpose of assessing the efficacy of DGA, the suggested algorithms are compared with a set of seven representative algorithms:  
* Baseline: Select all features;
* LS ([2]): The Laplacian Score approach, which chooses the features with the greatest variance while effectively preserving the local manifold structure of the data;
* SCFS ([12]): Unsupervised feature selection for subspace clustering, using self-expression models to learn cluster similarity to select discriminant features;
* UDFS ([38]): Uses the discriminant information of local structure and l_2,l-norm regularization discriminant to select features;
* MCFS ([13]): Clustering feature selection grounded in spectral analysis and sparse regularization;
* DUFS ([39]): Applies the dependency information among features to the unsupervised feature selection process based on regression;
* NLRL-LE ([10]): A feature selection method via non-convex constraint and latent representation learning with Laplacian embedding;
* LLSRFS ([40]): A unified framework for optimal feature combination combining local structure learning and exponentially weighted sparse regression.
# Degradation of Hydraulic System due to Wear Particles or Medium Test Dust
## 4 Discussion  
### Comparison of Hydraulic Gear Pumps Tested with Wear Particles and Test Dust  
Initially, a test with wear particles at cleanliness level 22/21/19 and a test at cleanliness level 21/21/21 with MTD was performed. A comparison of the volumetric efficiencies of the pumps shows that the hydraulic system tested with medium test dust failed significantly faster than the hydraulic system tested with wear particles (Figure 17). The flow rates on the far left of Figure 17 show the internal leakage of the pump tested with test dust at four different pressures, and the rest of the figure shows the measured flow rates at four different pressures of the pump tested with wear particles. From the measurements, although the oil cleanliness was better when tested with test dust, the pump wore out much faster. Particles when trapped between two relative moving surfaces cause three-body abrasion which can be further divided into micro-ploughing, micro-rolling, and micro-cutting wear mechanisms [8]. The hardness of particles plays a crucial role because wear particles found in real hydraulic systems have lower hardness than the test dust, which is intended for testing hydraulic components [20; 21; 22; 23].  
Figure 16: Measured internal leakage of a hydraulic motor tested at attachment point 1—test with wear particles.  
Evaluation of the degradation and prediction of hydraulic gear pump life is closely related to leakage characteristics and thus volumetric efficiency. As wear increases, leakage increases, causing volumetric efficiency to decrease [20]. The volumetric efficiency of the pump tested with wear particles was 0.83 at the beginning of the test at 100 bar, but only 0.65 at the end of the test after 624 h of operation. The volumetric efficiency of the pump tested with test dust decreased from 0.87 to 0.11 at 100 bar in only 5.3 min of operation. The reduction in volumetric efficiency at pressure 100 bar was 0.18 for the gear pump tested with wear particles and 0.76 for the gear pump tested with test dust. Wear can be minimized by designing pump leakage to be distributed over a large area. If leakage is confined to small areas, wear can be rapid and the flow degradation rate will be correspondingly high. Designing the pump to distribute leakage over a larger area may result in reduced efficiency. However, this will be compensated by a reduction in the wear rate and, consequently, a longer life [23]. By using more wear-resistant materials, pump housing wear should be reduced.
## 4 Numerical Simulation
### Casting Blasting Simulation  
#### 4.2.1 Establishment of the Casting Blasting Model  
Based on the actual rock accumulation range and coal layer fragmentation requirements, simulation schemes of two charge structures were designed using the optimized charge structure to verify the effect of cast blasting. Using the SPH-FEM algorithm [47, 48], a bench model with four rows and a total of 12 blast holes was established for coal-rock mixed blasting numerical simulation. This analysis focused on the damage to the coal layer near the coal-rock interface and the rock blasting fragments' throwing effect under different stemming lengths. The model size is 100 \(\times\) 31 \(\times\) 30 m, as shown in Figure 14, with the model scheme and parameters listed in Table 7.  
Figure 13: Coal damage curves at each measuring point.  
1. The SPH-FEM coupled analysis method was adopted, where explosives and the core blasting region (coal and rock mass) were modeled as SPH particles in the bench model, while the coal-rock boundary rock mass and coal bottom rock mass were modeled using FEM grid elements [48].
2. The *LOAD_BODY_Y command was employed to apply gravitational acceleration in the Y-direction, and fixed boundary conditions were applied at the bottom of the model.
3. Non-reflective boundary conditions were applied on all sides of the bench model, except for the free surface, to eliminate the influence of stress wave reflections on the accuracy of the simulation results.
4. The detonation time for each explosive SPH particle in the boreholes was set sequentially to achieve hole-by-hole detonation.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 2 Background
### Basics of Kubernetes
#### 2.1.1 Container  
Before introducing Kubernetes, it is necessary to introduce containers and why containers are widely used in cloud computing.  
Containerization is operating system-level virtualization. Unlike the hypervisor, which creates virtual hardware resources, a container only creates a separate isolated space for processes. In Linux, this isolation space is achieved through namespaces [12] and cgroups(Control Groups) [13]. Namespace provides running environment isolation for processes, including network, file system, PID space, etc. Cgroup provides isolation and limits on resources required by processes, including CPU, Memory, Disk I/O and network usage.  
Compared to hardware virtualization, containers are much more lightweight and can package applications and required libraries in a self-container component. Therefore, they can be quickly ported and deployed in different computing environments and provide elasticity needed by cloud applications [14]. As containers run in isolated namespaces, the process in one container typically cannot interfere with or monitor processes in other containers or the host OS, providing proper use of Linux-based security access controls such as AppArmor and Secomp.
# Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery
## 2 Materials and Methods
### Mathematical Models
* FENE-P Model (Finitely Extensible Nonlinear Elastic--Peterlin): Based on molecular theory, it describes the elasticity of long polymer chains while incorporating a finite extensibility factor. This makes it particularly suitable for modeling high-stress conditions, such as blood flow through stenosed arteries or within mechanical circulatory devices. The governing equation is expressed as follows: \[\tau+\lambda\bigg{(}\frac{\partial\tau}{\partial t}-\mathbf{L}\cdot\tau-\tau \cdot\mathbf{L}^{T}\bigg{)}=\eta_{0}\dot{\gamma}(f)+\frac{\lambda\eta_{0}}{ \eta_{p}}(f\tau),\] where \(f\) is the finite extensibility factor (set to 100), \(\eta_{p}\) is the polymer viscosity, \(\eta_{0}\) is the zero-shear viscosity (set to 0.03 Pa-s), and \(\lambda\) is the relaxation time (set to 0.001 s). The FENE-P model captures the nonlinear elastic behavior of polymers, making it highly effective for applications involving significant stress and deformation. These models were implemented in COMSOL Multiphysics(r) (Version 6.3) software [11], allowing for detailed simulations of blood flow in both large (aorta) and small (right coronary artery, RCA) vessels. The ability of each model to replicate shear stress, velocity profiles, and pressure distributions was systematically analyzed, highlighting their respective strengths and limitations.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 6 Future Works  
The scenarios studied in this work address only the single-user, single-service scenario. Future research will concentrate on the task-scheduling problem of the multi-user, multi-service satellite scenario. Furthermore, in practical engineering, the arrival of tasks is continuous. Dynamic scheduling for continuous tasks also requires further research.  
In terms of the algorithm, optimization objectives and heuristic algorithms are combined through node mapping and algorithmic coding approaches in this study. Using the algorithm's own search to find the optimal solution will consume more time, and combining some a priori methods can effectively improve the search velocity. In addition, the decision matrix and the criteria defined in this paper will take up a lot of space. If these problems are sufficiently improved in future work, they will have high engineering value.  
Conceptualization, Y.H.; data curation, Y.H.; formal analysis, Y.H.; investigation, Y.H.; methodology, Y.H.; project administration, W.G.; resources, Y.H.; software, Y.H.; supervision, W.G.; validation, Y.H.; visualization, Y.H.; writing--original draft, Y.H.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 2 Inverse Halftoning Technology for Digital Halftoning
### End-to-End Learning Method Based on Training of Multiple Sub-Networks (EEBMN)
#### 2.2.1 Deep Learning Without Pre-Training (DLOPT)  
In reference [20], the authors proposed a Progressively Residual Learning network (called PRL method) which can synthesize the global tone and subtle details in a progressive manner. It consists of two modules: content aggregation module and detail enhancement module. The network architecture is briefly depicted in Figure 6. For the content aggregation, the sub-network looks like a U-Net structure with skip connection. It starts with a convolutional layer and a residual block, followed by two downscaling blocks, and then four residual blocks for constructing the content and tone features; finally, two up-scaling blocks are used to make the feature maps back to the input resolution. For the detail enhancement module, the output of the content aggregation module and the input halftone image are concatenated and then fed into the detail enhancement module. The detail enhancement module consists of eight residual blocks, which are used to extract structural details. For the loss function, the MSE and perceptual loss are both adopted to guide the image recovery. The advantage of the method in [20] is that the image details are restored in a progressive manner, as well as the application of residual learning for extracting structural details. However, halftone dot noises still exist when it is used to restore historical photos.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 5 Performance Evaluation
### Discussion and Comparative Summary  
Sensor nodes are noticed to work smartly and collect information from the surroundings. However, they are constrained with their resources such as processor, memory, and battery life. Therefore, security provisioning becomes a difficult task due to restricted resources.  
As long as RSU is equipped with a modern CPU, the computation overhead of the proposed scheme will be negligible for RSU. However, in absence of such a unit, it may rise invariably. The security analysis of our scheme shows that it is more efficient in meeting more functional requirements. Hence, the proposed scheme can be implemented to exchange relevant information rapidly between vehicles directly for smart city applications.  
To summarize, we tabulate the performance of different schemes along with the proposed scheme in Table 3. It is observed that the proposed protocol gives optimum performance under the defined scenario and assumptions being made.  
In Table 4, we expand the security performance analysis for some more available schemes [43; 44; 45] and tabulate the findings. Such an analysis is believed to help future researchers to identify the protocols specific to their area of study. Moreover, it presents a comprehensive survey along with a diversified list of criteria available for further exploration.
## 3 Validation and Experimentation
### Code Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**File**} & \multicolumn{4}{c}{**RhoModel**} & \multicolumn{4}{c}{**JavaScript Generated Code**} & \multicolumn{2}{c}{**Conciseness**} \\ \cline{3-10}  & & **MRho** & **MIRho** & **RHOLOC** & **LOC** & **SLOC** & **LLOC** & **CLOC** & **BLOC** & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \hline \multirow{2}{*}{_RhoEngine_} & Rho.js (client) & \multirow{2}{*}{237(1)} & \multirow{2}{*}{1578(6)} & \multirow{2}{*}{1815} & 2375 & 1347 & 1350 & 714 & 314 & \multirow{2}{*}{2.19} \\  & Rho.js (sever) & & & & 1598 & 847 & 809 & 510 & 241 & \\ \hline _RhoModel_ & MRho.js & 277(1) & 949(6) & 1226 & 2140 & 1518 & 1055 & 516 & 106 & 1.75 \\ \hline WebIDERI & WebIDERho.js & 129(1) & 910(3) & 1039 & 1232 & 812 & 886 & 287 & 133 & 1.19 \\ \hline RestRho & RestRho.js & 86(1) & 717(2) & 803 & 1186 & 1177 & 782 & 302 & 118 & 1.47 \\ \hline ServerRest & ServerRestRho.js & - & - & - & 21 & 11 & 11 & 4 & 3 & - \\ \hline Handlebar & Handlebars-all.js & - & - & - & 3351 & 2575 & 1643 & 311 & 465 & - \\ \hline Express & Express-all.js & - & - & - & 4054 & 1820 & 1379 & 1575 & 659 & - \\ \hline MariaDB & MariaDB-all.js & - & - & - & 14,318 & 11,531 & 8434 & 1635 & 1152 & - \\ \hline \multicolumn{1}{c}{} & **Total** & 729(4) & 4118(17) & 4847 & 30,275 & 21,638 & 16,350 & 5859 & 3189 & 1.65 \\ \hline \hline \end{tabular} Note: X(Y) MRho/MIRho; X number of Rho lines, Y: number of Psi files; RIOLOC: total Rho lines of code; LOC: JavaScript-generated lines; SLOC: physical executable code lines; LLOC: logical executable code lines; CLOC: comments code lines; BLOC: blank code lines.  
\end{table} 
Table 2: Components of the RhoArchitecture (in yellow), ServerRestRho (in blue), and frameworks using RestRho (in violet): JavaScript generation code summary. (HD = 97), the shortest implementation time (HT = 207,299), the lowest estimated error per module (HB = 12.8), and the lowest comprehension effort (HEF = 1439). In addition, according to [39; 42], Figure 9 confirms the lowest average values for CRR: the lowest complexity and associated functionality (HS = 237, HV = 19.370), the lowest level of difficulty (HD = 51), the shortest implementation time (HT = 103.691), the lowest error estimate per module (HB = 6.5), and the lowest comprehension effort (HEF = 1.471). In comparison, the HD difficulty level of CRR is about one-third the difficulty of CRA and one-half the difficulty of FRR. On the other hand, it is relevant to see that CRR's HEF comprehension effort is approximately fourteen times lower than FRR and three times lower than CRA. The difference in CRR is also noticeable in the lower estimation error per module, eight times lower than FRR and three times lower than CRA. Another fact to highlight is that in the MariaDB framework, most of Halstead's metrics are higher than the rest of the frameworks and components. Figure 8: Comparison of the SLOC, LLOC, CLOC, and BLOC metrics between components RhoArchitecture CRA, ServerRestRho CRR, and frameworks used RestRho FRR.
## 3 Results
### Common Mode Error Analysis  
The CME is extracted using PCA, and the number of principal components is determined according to different principal component contribution rates, greater than 80% in this study [27]. The effects of CME on the reference station SCMN, baseline SCML-SCMN, are shown in Figures 4 and 5. The residual series of the reference station and baseline without CME removed are periodic. The CME is more intuitive to reflect the main trend of the residual series, and the time series with CME removed becomes smaller and stable in all directions by orders of magnitude and do not have obvious periodicity.  
Figure 4: Comparison of residual SCMN series before and after removing CME. (**a**) Residual sequence of SCMN without removing CME; (**b**) CME extracted from SCMN; (**c**) Residual sequence of SCMN after removing CME.  
Table 1 shows the impact of CME on each reference station and each direction of the baseline, including the maximum value, the minimum value and the average value of the absolute value. The impact of CME reaches the millimeter scale. Using the average of absolute values as the main reference, the CME impact on the plane of the reference station is about 2.5 mm. The impact on the vertical direction is larger, with an average impact of about 4.3 mm. The CME impact on each direction of the baseline decreased compared to the reference station, with a reduction of 0.14 mm (13.9%) and 0.69 mm (15.9%) in the N and U directions, respectively, and the most significant reduction in the E direction, with 1.06 mm (47.3%).
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Analysis and Insights from Performance Metrics  
#### 5.8.1 Accuracy  
The accuracy metric shows a steady upward trend, peaking around 90%. This indicates that the model becomes increasingly effective at predicting shot outcomes based on QE and biomechanical metrics as training progresses.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 4 Estimation of Forwarder Search Delays  
The critical question in opportunistic routing is how to choose the forwarder and how long a node should wait for potential forwarders. We can identify a tradeoff: waiting short time would decrease delays, but for applying QoS routing metrics (such as considering energy efficiency), a sufficient number of replies should be collected. In this section, we answer the question \"how long shall nodes wait for potential forwarders\" while focusing uniquely on the delay point of view. We elaborate a simplified theoretical model and we proceed as follows:  
* We determine the average progress towards the final destination when taking the first available forwarder.
* We determine the delay between the \(k\)-th and the (\(k+1\))-th available replies (assuming exponential and uniform distributions of wake-up intervals of nodes.)
* We will show that selecting the first available forwarder is a delay saving strategy. In the rest of this section, we detail these steps.
## 4 Results and Discussion  
### Compressive Deformation Evolution  
Figure 4 provides the stress-strain curves of the gangue specimens in the loading process. It is observed that, as stress increases, the strain sharply increases at first and then gradually stabilizes. The deformation process of each specimen can be mainly divided into three stages: 1 the rapid deformation stage, 2 the slow deformation stage, and 3 the stable deformation stage.  
The axial stress applied to the specimen is the immediate cause of the axial deformation of the specimen, and the loading stress is the factor directly affecting the axial strain. Theoretically, the stress-strain curves of the three specimens i-1 to i-3 partially display the stress-strain curve of Specim i-4. It can be seen in Figure 4a that the four curves almost coincide. A good fitting effect is obtained when a logarithmic function is used to describe the stress-strain relationship of samples i-1 to i-4. The fitting equation is \(y=0.074\ln(4.95x+0.99)\), and the details are given in Figure 5a.  
As shown in Figure 4b, the loading velocity has a significant influence on the compressive deformation of the specimen. With increasing loading velocity, the strain in the specimen increases, but it gradually stabilizes when the loading velocity is larger than  
Figure 4: Stress–strain curves of the specimens during the test: (**a**) is the stress–strain curve for specimens i-1 to i-4; (**b**) is the stress–strain curve for specimens i-5 to i-8.  
Figure 5: Fitting curves between the main control factors and strain: (**a**) is the fitted curve of loading stress with strain for specimens i-1 to i-4; (**b**) is the fitted curve of loading velocity with strain for specimens i-5 to i-8.  
5 kN/s. Meanwhile, the corresponding stress amplitudes in stages 1 and 2 also increase. This is mainly because, under a small loading velocity, the relative movement among the particles is relatively slow, a stable contact structure can be easily generated, and the supporting force of the specimen is increased as a whole. However, under a large loading velocity the relative movement becomes faster due to the impact effect, which destroys the contact structures among the particles and increases the overall deformation of the specimen. When the loading velocity is larger than 5 kN/s, its influence on the overall deformation of the specimen is gradually weakened. Therefore, the loading velocity of 5 kN/s can cause greater damage to the contact structures among the particles. A logarithmic function is used to describe the relationship between loading velocity and strain (at the stress level of 20 MPa). The fitting equation is \(y=0.0085\ln(x-0.98)+0.37\), as illustrated in Figure 5b.  
Generally, the loading stress directly affects the axial strain of the gangue specimen, and the axial strain gradually increases with loading velocity and eventually stabilizes.