esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Hybrid Flower Pollination Resource Consolidation Algorithm
#### 4.2.4 Virtual Machine Migration Phase  
The _VM_ migration technologies have proven to be a very effective tool for data center resource management in a non-disruptive manner, and their migration incurs SLA violations. Based on the above modeling analysis, the _VM_ migration pseudocode has been presented as Algorithm 2. The proposed MOH-FPRC algorithm uses a _VM_ migration strategy with a minimum number of migrations to reduce energy consumption and minimize the number of SLA violations. The main idea of the _VM_ migration algorithm, after a request has been received, is to identify a target resource for in-cluster scaling. Then, the two PMs, one sending the request and the other accepting to be the target, negotiate the _VM_ migration. Once an agreement has been reached, the two PMs carry out the operation without disrupting the power of the former for efficient resource utilization. Some of the main improvements introduced into this new approach to achieving the MOH-FPRC algorithm include (i) the introduction of the DC technique that helps to reflect on the  
Figure 3: Dynamic clustering.  
most accurate number of available resources in the Cloud system. It also helps to divide the submitted request \(\Xi_{n}\) into an equal number of clusters as the number \(n\) of available resources, and (ii) with the incorporation of the LNS strategy to balance the global and local search procedure for efficient optimization. In addition, (iii) the algorithm integrates the resource migration strategy in case of resource over-utilization during the consolidation. The proposed algorithm migrates resources to available target resources to reduce the number of migrations that results in fewer SLA violations.  
```
0: Combination of resource request
0: CPU Cluster, Memory Cluster, and Storage Cluster
1:Initialization
2:Get \(n\) from the Cloud management system
3:\(\Xi_{n}=\{A_{1}+A_{2}+A_{3}+\dots\dots\dots\dots\dots\ \ \ A_{n}\}\)
4:\(A_{i}=\{J\}\ i=1,2,3,\dots,n\);
5:\(k=0\)
6: Let \(\Xi_{n}=\Xi_{n-k/(n-k)}\)
7:Current Step:
8:\(\textbf{While}(n-k\geq 1)\) or \(\big{(}\Xi_{n}\
eq\bar{\mathbb{C}}\big{)}\). Do
9:\(\textbf{Select }A_{i}\mathcal{L}_{i}\in\Xi_{n-k}\)
10:\(\Xi_{n-k}=\big{(}\Xi_{n-k}\cup\ \mathcal{L}_{i}\big{)}/(n-k)\)
11:\(k=k+1\)
12:Get current \(n\) from Cloud management system
13:End While
```  
**Algorithm 1**_Dynamic Clustering Algorithm_  
```
0: Active PMs in \(n\) cluster, VM migration \(VM_{m}\), Migration time, Migration data
0:SLA violation due to PM migration
1:Initialization
2:For each PM in PM list do
3:For each data of PM component (CPU, memory, storage) do
4: Select best VM migration strategy
5: Estimate the \(SLAV=(PDM)+(PM_{active})\)
6: Compute the predicted resource utilization of PM
7:If utilization \(<1\)then
8:Repeat step 2-step 7 Else
9:VM is migrated
10:
11:VM get allocated
12:VM started on targeted PM
13:PM state change according to current utilization
14:End
15:Return (Number of VMs migration)
16:SLAV
```  
**Algorithm 2**_VM Migration Algorithm_
## 5 Proof of Concept  
Traditional radar systems typically rely on dedicated hardware components for signal processing, waveform generation, and data analysis. However, by leveraging software-based processing in radar systems, such as in software-defined radar (SDR), multiple radar functions can be consolidated onto a single hardware platform, reducing equipment and maintenance costs [25; 26; 27; 28]. This offers increased flexibility and adaptability, allowing for the rapid development and deployment of new radar functionalities. SDR platforms can be easily reprogrammed to support different radar modes, waveforms, and signal processing algorithms, enabling radar systems to be highly configurable [29; 30; 31].  
In this section, we present a proof of concept and a prototype for our proposed module in the radar system using SDR. The system uses two SDRs, one for transmitting the radar signal and holding the selected Doppler value and the other for receiving the transmitted signal. We also incorporated a synchronizer in our system by adding a marker signal, such as LFM or hyperbolic frequency modulation (HFM), to accurately estimate the beginning of the bi-phase signal at each CPI. We chose the LFM signal as our synchronizer because of its Doppler-tolerant nature. It is worth noting that we did not extract any information from the LFM signal; it was only used as a synchronization marker.  
We provide the setup parameters of our experiment in Table 1, with the following adjustments based on the specifications of the SDRs used:  
* SDR type: 2 USRPs N210;
* Sampling rate: 50 MHz;
* Band: 2.45 GHz--S-Band;
* Doppler values: 8, 10, and 15 KHz;
* LFM Bandwidth: 1 MHz.  
We evaluated the performance of our module at the three different Doppler values of 8, 10, and 15 KHz, and we obtained the results shown in Figure 16. Note that we selected these specific values to test the system under challenging conditions within the medium- and  
Figure 15: Probability of detection curve for the proposed algorithm with low (800 Hz), medium (3500 HZ), and high Doppler (13 KHz) values.  
high-Doppler-frequency ranges. The results indicated that our module accurately estimated the Doppler values with minor differences from the actual values. More specifically, for the Doppler value of 8 KHz, the estimated value was 7.987 KHz, with a difference of only 12 Hz; for 10 KHz, the estimated value was 10.031 KHz, with a residue of 31 Hz; for 15 KHz, the estimate was 15.02 KHz, with a difference of 20 Hz. These results align with the simulation results recorded in Figure 13. Additionally, our module successfully recovered the well-known shape of the signal with all Doppler values, as observed in the MF output shown in Figure 16b,d,f. This result reflects the robustness and effectiveness of our proposed module in real-world applications, making it a valuable addition to any radar system.  
Figure 16: The received signal and MF output (in black) and the associated CFAR (in red) of the proposed module for different Doppler values. Top at 8 KHz, middle at 10 KHz, and bottom at 15 KHz.
## 3 Results and Discussion  
### Morphological Characterizations of CQDs/TiO\({}_{2}\) Particles  
The synthesized CQDs/TiO\({}_{2}\) particles were first characterized with morphological (SEM and TEM) techniques.  
Figure 1: Scanning electron microscope (SEM) images and particle-size distribution diagrams of carbon quantum dots(CQDs)/TiO\({}_{2}\) particles obtained at different hydrothermal reaction periods: (**a–c**) 10 h; (**d–f**) 12 h; (**g–h**) 14 h; and (i) SEM image of CQDs/TiO\({}_{2}\) particles surface.  
Figure 1 shows the typical SEM images and size analysis of the synthesized CQDs/TiO\({}_{2}\) particles with different reaction periods. Nano-titanium and nano-carbon, obtained by titanium sources and furfural, were fused together due to functional groups on the TiO\({}_{2}\) surface and grew to form a core-shell structure. It can be seen that the obtained CQDs/TiO\({}_{2}\) particles show uniform spheres with an average diameter of 0.91 \(\upmu\)m after 10 h reaction (Figure 1a-c). When the reaction time was increased to 12 h, the formed CQDs/TiO\({}_{2}\) particles remained in a spherical shape, however, their average size increased to about 1.41 \(\upmu\)m, as shown in Figure 1d-f. It is clear that CQDs/TiO\({}_{2}\) particles continue to grow with the increasing reaction period. By comparing the SEM images of Figure 1b and 1e, it can be found that the size of the particles with a 12 h reaction are more uniform than that with a 10 h reaction. When the reaction time continued to increase to 14 h, besides spherical particles, some rod-like substances were produced, as shown in Figure 1g and h. The thickness of the rods was measured to be about 0.5 \(\upmu\)m. Therefore, we suggest that the synthesized CQDs/TiO\({}_{2}\) was not a solid sphere with uniform density and composition, but a multilayer core-shell cladding structure. Furthermore, it can be observed from Figure 1b and e that the surface of the particles became smoother with the increasing reaction time. The higher resolution image of CQDs/TiO\({}_{2}\) particles shows that a lot of nanoparticles with a size of 10 nm were created on the surface of particles.  
To further prove the formation of CQDs on TiO\({}_{2}\) surface, TEM and related EDS elemental analysis were utilized.  
The formation process of CQDs/TiO\({}_{2}\) particles can be further explained through Figure 2a,b. Titanium sources were first hydrolyzed in ethanol to generate nanoscale TiO\({}_{2}\). Due to abundant functional groups on the TiO\({}_{2}\) surface, furfural was carbonized and deposited on TiO\({}_{2}\). Therefore, a large number of particles grew up and form microspheres. In this process, some particles first come into contact to form a thin shell, and the particles inside the thin shell formed the core. Figure 2c-e present the element analysis of the obtained CQDs/TiO\({}_{2}\) particles that are shown in Figure 2a, and it can be clearly found that the elements of Ti, O, and C (Figure 2c-e) are dominant in the tested materials. The EDS analysis (Figure 2f) further proves the existence of Ti, O, and C elements.
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
###### Abstract  
A gas and coal dust explosion is potential hazard in majority coal mines. A coal mine mobile refuge chamber is a new class of device for miners those who are unable to escape after an accident which can provide basic survival conditions. In this paper, in order to study the propagation law of an underground methane/air mixture explosive wave, and check the failure mode of a coal mine mobile refuge chamber, a full-sized underground tunnel model and a refuge chamber model have been established in ANSYS/LS-DYNA (LSTC, R7.0.0, Livermore, CA, USA, 2014). The simulation results show that the reflected wave pressure on the front surface of the refuge chamber was about twice as high than the incident wave. The pressure on various locations of the chamber was also analyzed. When the peak pressure of the explosive shockwave reached 0.64 MPa, the maximum displacement and stress occur at the center of the front door and the joint of stiffeners and the front plate, respectively. Most parts of the coal mine mobile refuge chamber were in a plastic failure state and the refuge chamber could be defined as damaged. The front door, the front plate, the connecting flange, and the stiffeners on each side were the primary key components. In the end, suggestions were proposed for the refuge chamber.  
refuge chamber; failure mode; blast impact; dynamic response; gas explosion Article
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 3 Results and Discussion  
This section presents the experimental setup, results and analysis of the proposed method. First, we explain the experimental setup and methods, followed by data analysis using both qualitative and quantitative methods. Finally, we compare the proposed method with the existing literature for a fair assessment.  
The experiments are conducted on a PC with Intel(R) Core (TM) i5-5200U [EMAIL_ADDRESS] GHz and RAM 8 GB using MATLAB2018b. We have considered a whole-brain atlas website ([[http://www.med.harvard.edu/AANLIB/home.html](http://www.med.harvard.edu/AANLIB/home.html)]([http://www.med.harvard.edu/AANLIB/home.html](http://www.med.harvard.edu/AANLIB/home.html)), accessed on 1 September 2021) to conduct our experiments. For this purpose, 23 MRI-CT medical image data sets are taken from this database. All these data sets are registered with a resolution of 256 \(\times\) 256. Image registration [55] is a necessary step prior to image fusion. It is defined as the process of mapping the input images with the help of a reference image. Such mapping aims to match the corresponding images based on specific features to assist in the image fusion process. The database contains various cross-sectional multimodal medical images, such as MRI (T1 and T2 weighted), CT, single-photon emission computed tomography (SPECT), and positron emission tomography (PET).  
Furthermore, it has a wide range of brain images ranging from healthy to different brain diseases, including cerebrovascular, neoplastic, degenerative, and infectious diseases.  
We have considered 23 pairs of MRI-CT from fatal stroke (cerebrovascular disease) to validate our proposed approach (Supplementary Materials). Interested readers can find more details of this database in [56].  
The efficacy of any image fusion algorithm can be verified using subjective (qualitative) and objective (quantitative) analysis. In Section 3.1, we first verified the subjective performance of various fusion algorithms and then performed objective analysis using fusion metrics in Section 3.2.
## 3 Results and Analysis
### Experiments Comparing the Improved Model with the Baseline Model  
Under identical experimental conditions, the mAP and loss values of the improved YOLOv8n model (VEW-YOLOv8n) were compared with those of the original YOLOv8n model during training. Curve comparison graphs for mAP and loss values are depicted in Figure 13, with (a) illustrating the [EMAIL_ADDRESS] value, (b) showing the [EMAIL_ADDRESS]:0.95 value, and (c) presenting the loss values. Figure 13 elucidates that both the [EMAIL_ADDRESS] [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 values of VEW-YOLOv8n surpassed those of the original YOLOv8n, while the loss value was lower. The paper demonstrates that the proposed VEW-YOLO8n has superior convergence and detection accuracy in detecting apple diseases. Furthermore, the performance of VEW-YOLOv8n and YOLOv8n on the test set was verified, and the Precision-Recall curves (P-R curves) are delineated in Figure 14, which shows that the VEW-YOLOv8n model achieved a map value of 95.6%, while the YOLOv8n model achieved a map value of 92.9%. The improved model demonstrated an average accuracy improvement of 2.7% compared to the YOLOv8n model. This comparison indicates that the VEW YOLOv8n model also outperformed the YOLOv8n model on the test set, substantiating the superiority of the improved VEW-YOLOv8n over YOLOv8n.
## 3 Results and Discussion
### FTIR Characterization of Nanolubricant Stability
\begin{table}
\begin{tabular}{c c c c} \hline
**Wavelength (cm\({}^{-1}\))** & **Mistral 15W40** & **PLANTO MOT SAE 10W40** & **Interpretation** \\ \hline
3649.1 & - & Peaks present & O-H stretching (hydroxyl groups) \\
3458.9 & - & Peaks present & O-H stretching (hydroxyl groups) \\
3402.1 & Peaks present & - & O-H stretching (hydroxyl groups) \\
2729.4 & - & Peaks present & C-H stretching (hydroxonyms or additives) \\
2728.1 & Peaks present & - & C-H stretching (hydroxonyms or additives) \\
1740.0 & - & Peaks present & C=O stretching (esters) \\
1739.9 & Peaks present & - & C=O stretching (esters) \\
1707.4 & Peaks present & - & C=O stretching (esters) \\
1605.6 & - & Peaks present & C=C stretching (unsaturated components) \\
1515.9 & - & Peaks present & N-O asymmetric stretching (additives) \\
1464.4 & - & Peaks present & CH\({}_{2}\) bending (hydrocarbon chains) \\
1456.0 & Peaks present & - & CH\({}_{2}\) bending (hydroxcarbon chains) \\
1377.9 & - & Peaks present & CH\({}_{3}\) bending (hydroxcarbon chains) \\
1377.4 & Peaks present & - & CH\({}_{3}\) bending (hydroxcarbon chains) \\
1305.6 & Peaks present & - & C-O stretching (esters) \\
1230.9 & Peaks present & - & C-O stretching (esters) \\
1167.8 & - & Peaks present & C-O stretching (esters) \\
1157.0 & Peaks present & - & C-O stretching (esters) \\
1118.6 & - & Peaks present & C-O stretching (esters) \\
1026.9 & - & Peaks present & C-O stretching (esters) \\
974.97 & Peaks present & - & Various hydrocarbon-related vibrations \\
889.35 & Peaks present & - & Various hydrocarbon-related vibrations \\
814.84 & Peaks present & - & Various hydrocarbon-related vibrations \\
768.05 & - & Peaks present & Aromatic or unsaturated components \\
722.16 & Peaks present & Peaks present & CH\({}_{2}\) rocking (long hydrocarbon chains) \\
667.99 & - & Peaks present & Aromatic or unsaturated components \\
655.18 & Peaks present & - & Aromatic or unsaturated components \\ \hline \end{tabular}
\end{table} 
Table 7: Comparative FTIR Spectral Analysis of Conventional Lubricant (Mistral 15W40) and Biolubricant (PLANTO MOT SAE 10W40).
## 4 The Proposed Key Distribution Framework
### A Spatiotemporal Diversification-Based Multipath-Multi-Key Distribution
#### 4.1.1 Selection Phase  
Upon receiving a key distribution request, the central server of the CP determines optimal routing paths and key reconstruction strategies based on the current state of the network and predefined security requirements, as detailed in Section 4.2.1. This phase involves selecting paths that minimize the risk of interception and ensure efficient key distribution. Algorithms such as the K-shortest path (KSP) and enhanced versions of [PERSON]'s algorithm are utilized to identify candidate paths with minimal correlation, thereby reducing the likelihood of common vulnerabilities across these paths [37]. After path selection, the server sends the MTD configuration deployment pattern (MTD-CDP) to the network relays.
## 3 Simulation and Results
### Discussions  
For both simulation areas, improvements in the RSS values are approximately 30%, and SINR values reached a range of 20 dB. In addition, the channels for all the SNs are LoS, and the ASE values are almost doubled. Although such improvements are achieved considering UAVs at an altitude of 50 m in area-A, area-B requires an altitude of 145 m for the UAVs. This is due to the geometry of area-B, which has higher buildings than area-A. In terms of average DL throughput, area-A achieved more than 40% improvement, while achievement in area-B was slightly less than 30%. A variation in DL throughput improvement is expected because it is subject to the number of SN attached to each cell and the scheduling strategy for the allocation of resource blocks among UTs. Other scheduling strategies, such as best-CQI, can be considered to improve the overall network throughput at the expense of fairness. For example, in best-CQI scheduling, resource blocks will not be allocated to UTs with low CQI indices, such as those at the cell edges. It is worth noting that the results show that the advantages of replacing some terrestrial APs with UAVs are not limited to performance improvements; they can also reduce costs because a smaller number of remaining terrestrial APs is required. After replacing 50% of the terrestrial APs with UAVs in area-A, one of the remaining terrestrial APs can be removed without affecting the performance. In contrast, UAVs in area-B can eliminate three of the remaining terrestrial APs.
## 4 Evaluation  
### Frequency Shifting Effect Testing  
First, we conducted tests on the frequency-shifting functionality of the tags. The deployment of the reader and the tags was similar to that illustrated in Figure 10a. In this experiment, we utilized a single tag to construct the system with the reader. The reader transmitted a carrier signal at 910 MHz, while the tag generated an 8 MHz adaptive pulse-width square wave for the frequency shifting. The results of the frequency shifting are depicted in Figure 11, where it is evident that the incident 910 MHz signal has been shifted to a frequency of 918 MHz. Due to the achievement of the frequency-shifting functionality, the excitation signal and the backscattered signal operate at different frequency points. This arrangement enables the parallel transmission of multiple tags within the overall system.
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 2 Engineering Background, Site Investigation, and Laboratory Test
### Site Investigation of Anti-Slide Pile Deformations  
Electronic Total Station displacement monitoring and site investigation were conducted to further study the deformation behavior of anti-slide piles. Several obvious cracks were discovered at the back edges of anti-slide piles during our site investigation of the Zhenzilin landslide. The widest crack was approximately 13 cm (see Figure 3), which is consistent with the monitoring results of the pile head by the Electronic Total Station. The cracks in the landslide body would provide channels for surface water seeing into the ground [5], which would weaken the strength of mudstone below the slip surface and increase the landslide risk. Consequently, the causes of the deformation of the anti-slide pile require further study to guide the design of anti-slide piles in similar bedrock areas.  
Figure 2: Geology conditions pertaining to the Zhenzilin landslide obtained via engineering geological survey: (**a**) a geological map of the Zhenzilin landslide; (**b**) a cross-section AA′ of the Zhenzilin landslide.  
Following the landslide treatment, the highway toll station and four-story dormitories were built on the landslide. According to the load design code, an extra 50-kN/m load was imposed on the anti-slide piles.