esa-sceva/satcom-chunk-collection huggingface dataset:
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 1 Introduction
The contribution of this paper is as follows:  
* According to our knowledge, PSO has already been applied for solving the FTP. However, the conducted experiments lack depth in their data analysis, as they predominantly focus on individual instances of the problem rather than a diverse range of scenarios. Introducing a novel approach, TrigAC-PSO is applied for the first time for solving the FTP, highlighting exceptional performance across a comprehensive set of instances;
* Nevertheless, the selected problem instances encompass various types of fuzzy numbers such as triangular and trapezoidal fuzzy sets. Moreover, the fuzzy numbers extend beyond conventional representations, encompassing both classic fuzzy numbers and fuzzy generalized numbers, thereby adding depth and complexity to this study. It is the first time that fuzzy generalized numbers are applied to a particle swarm optimization variation;
* The evaluation of results, from each method, is not based entirely on their attainment of the optimal solution. Instead, this study estimates the method's performance according to the degree of membership in which their solutions approach optimality as determined by membership functions. Once more, the TrigAC-PSO method demonstrates remarkable completeness against other methods. The remainder of this paper is organized as follows: Section 2 presents the mathematical formulation of the TP. Definitions for fuzzy logic and fuzzy numbers are briefly described in Section 3. Section 4 presents the FTP. PSO as well as TrigAC-PSO are represented in detail in Section 5. An extensive and intriguing study case is conducted in Section 6, solving the FTP by using the TrigAC-PSO algorithm. Subsequently, a comparative analysis is presented, comparing results obtained by the application of TrigAC-PSO against well-established methods documented in the respective literature. Lastly, conclusive remarks and future recommendations are presented in Section 7.
## 3 Results
### The Spatial Characteristic of SIUS
#### 3.2.2 Spatial Autocorrelation Analysis of SIUS  
In this paper, [PERSON]'s index was employed to test the spatial correlation of per capita SIUS. Initially, a global Moran's index analysis was conducted on per capita SIUS in Henan Province in 2023, based on prefecture-level cities. The results indicated that [PERSON]'s index was 0.47 and the Z-value was 2.75 (\(p\) < 0.001), suggesting a significant spatial positive correlation of per capita SIUS among prefecture-level cities. The overall distribution was clustered, with high- and low-growth areas neighboring other cities with high and low growth levels, respectively.  
Next, local spatial autocorrelation of per capita SIUS was tested. As shown in Figure 7, the high-high agglomeration pattern of per capita SIUS was clear: regions with large per capita SIUS were surrounded by other regions with similarly high per capita SIUS. High-high agglomeration areas were primarily clustered in JZ and JY, while low-low agglomeration areas were mainly found in ZK.
## 3 Results
### Particle Characteristics  
Particles of an aluminum adjuvant labeled with lumogallion (Alhydrogel) and particles of PLGA were included in the study as reference particles, both model particles with well-documented use both _in vivo_ and _in vitro_. Some general properties of SA-MIPs and the two reference particles are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SA-MIP** & **Alhydrogel** & **PLGA** \\ \hline \multirow{2}{*}{**Overall surface charge**} & Negative & Positive & Negative \\  & [28; 29] & [30] & [14] \\ \hline \multirow{2}{*}{**Single particle diameter**} & -0.2 \(\upmu\)m & -0.05 \(\upmu\)m & -2 \(\upmu\)m \\  & [4] & [31] & [22] \\ \hline \multirow{2}{*}{**Particle size in medium** (form aggregates)} & \multirow{2}{*}{0.2-3 \(\upmu\)m *} & 0.5-4 \(\upmu\)m * & \multirow{2}{*}{No aggregation **} \\  & & [30] & \\ \hline \multirow{2}{*}{**Protein adsorption in medium**} & \multirow{2}{*}{-50 kDa**} & -50 kDa–198 kDa** & \multirow{2}{*}{Not investigated} \\  & & Several bands & \\ \hline \multirow{2}{*}{**Fluorescence dye**} & Nitrobenzoxidazole & Lumogallion & \multirow{2}{*}{None} \\  & [4] & [27] & \\ \hline \end{tabular}  
* Determined by Coulter LS230 Laser Diffraction Particle Size Analyzer equipped with a Small Volume Module (Beckman Coulter, CA, USA) ** determined by flow cytometry *** determined by SDS-PAGE.  
\end{table}
Table 1: Particle characterization of sialic acid molecularly imprinted polymers (SA-MIPs), Alhydrogel and poly(lactic-co-glycolic acid) (PLGA).  
The prepared SA-MIPs form micron-sized aggregates when the stock solution is diluted in the culture medium and are expected to be negatively charged at pH 7 [28; 29]. A similar aggregation of nanoparticles to micron-sized features is observed for the positively charged reference particle Alhydrogel. PLGA is a negatively charged microparticle that does not aggregate as seen by flow cytometry. The size of the unaggregated PLGA particles is close to the size of aggregated SA-MIPs and Alhydrogel particles.
## 3 Motivation  
This paper investigates the restoration of sandstorm images from the perspective of the physical model to improve the reliability of visual tasks under sandstorm conditions. Unlike general image restoration tasks, the degradation factors in sandstorm images are complex and diverse, making it challenging to eliminate their negative impacts through a single process. Therefore, it is essential to account for degradation factors such as color shift, detail blurring, and low contrast and strike a balance between different modules during the restoration process. Recently, promising developments in underwater image enhancement and image dehazing technologies have provided valuable insights. Specifically, the scattering medium in underwater scenes causes color attenuation in the opposite order compared to sandstorm images. This characteristic allows us to adapt the color correction methods used for underwater image enhancement to sandstorm images. Moreover, the broad application of dark channel prior-based algorithms in both underwater image enhancement and sandstorm image restoration not only highlights the robustness of the dark channel prior but also motivates researchers to investigate novel prior knowledge. As such, dividing the restoration process into color correction and dust removal appears to be a rational and promising strategy.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 3 System Model
### Network Model  
This paper considers the vehicular network environment based on intelligent transportation system. As shown in Figure 1, the network consists of a central server, RSUs, and various smart vehicles, allowing any edge node to cache content. We assume that the central server stores all the available content in the network, RSUs communicate with each other through optical fiber, and edge servers for computing and caching are deployed, so the RSU can cache all kinds of content to meet the content service needs of vehicle users. Since we have built a dynamic vehicle cluster, when an intelligent vehicle issues a content request, the flow of the network system is as follows:  
The intelligent vehicle detects whether the requested content is cached by itself and the cluster head of the cluster, and if so, it can be obtained directly and the delay is generally ignored. Otherwise, the intelligent vehicle checks whether the request content is cached in the local RSU, and if so, the local RSU sends the content directly to the user; otherwise, the next step is to find out if there is any requested content in the nearby RSU. Second, the central server finds out whether the collaborative RSU has cached the content. If so, the cooperative RSU sends the requested content to the user through the retransmission of the local RSU, otherwise, the requested content can only be obtained from the central server. The central server forwards the request to the local RSU, which then sends it to the user by the local RSU. It is worth noting that obtaining request content from a central server increases delivery latency and data traffic compared with obtaining it from an RSU or local node. Nevertheless, the cache capacity on local or edge nodes is limited, and user preferences vary. Therefore, it is challenging to design the optimal caching strategy.  
Figure 1: Road model of vehicle edge network.
## 5 Numerical Simulation Activities
### ENVISAT Collision Scenarios  
The third set of simulations investigated potential collisions involving ENVISAT as target, see Figure 15[54]. In this case, collision scenarios included two impactors (a 100 kg satellite and a 3-ton rocket stage) at two impact velocities (1 and 10 km/s), colliding with ENVISAT on its central body or its main appendage. Among the main results collected by this campaign of simulations, it was found that the 40 kJ/kg threshold was not representative of the transition to catastrophic impact and that the current NASA SBM significantly overestimates the number of fragments larger than 1 m. More details are reported in [54].  
Figure 14: Simulated collision scenarios: central impacts (**left**) and collisions on the appendages (**right**).
## 3 Numerical Model Tests with FEA
### Finite Element Analysis Model and Assumptions  
The 2D finite element models were established using the ANSYS[19, 9] software to further analyze the mechanical and sealing performance of the gasket-in-groove in the circumferential joint. The dimensions of the gasket-in-groove in the finite element model were the same as those used in the model test (Figure 1). As shown in Figure 4, the gasket grooves in FEA were set up as rigid bodies, and the gaskets were discretized by utilizing three-node triangular plane elements. To address non-convergence problems caused by mesh distortions, nonlinear adaptivity for meshing was used in the simulations. More specifically, the grid is subdivided or redivided when the defined distortion criterion (the energy-based criterion used in this paper) is triggered in the solving process, thus avoiding grid distortion as much as possible and achieving the convergence of the solution.  
The simulations consist of two steps. In step (i), the gasket is compressed by the rigid body (i.e., the gasket groove), resulting in initial contact pressure on the contact surface; in step (ii), the vacuum pressure (i.e., fluid pressure penetration shown in Figure 4) is applied to one side of the gasket.  
To simplify the numerical model and improve computational efficiency, the following assumptions were considered in the simulations:  
1. Gasket sealing failure at the joint when the contact surfaces are completely separated from each other; i.e., air leakage occurs at the joint;
2. The gasket grooves are considered to be rigid; i.e., the deformation may be almost negligible, and they would be used as constraints on the gasket boundary;
3. The gaskets are considered to be isotropic and almost incompressible ideal elastic materials with a highly nonlinear stress-strain relationship, i.e., the hyper-elastic materials;
4. The gasket grooves and the gaskets are impermeable to air;
5. A plane strain condition is assumed.
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## 2 Numerical Modelling  
To model the gas flow and particle in-flight behavior, the Eulerian-Lagrangian approach implemented in ANSYS FLUENT V23.2 was employed. The gas phase was initially solved from a steady-state perspective. Once convergence was achieved, the particles were injected and tracked using the transient Lagrangian solver, the Discrete Phase Model (DPM).
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 3 Ultrasonic Signal Database  
### Database Construction  
The ultrasonic A-scan data were acquired using the equipment and ultrasonic transducers introduced earlier. A total of 2463 A-scan flaw signals were acquired and used to create a database of various A-scans acquired from standard block specimens. The database is organized by the type of flaw each A-scan includes, with 630 different crack signals, 378 different lack of fusion signals, 623 different slag inclusion signals, 344 different porosity signals, and 488 different incomplete penetration signals. The number of flaw signals acquired from the standard block specimens using UT is presented in Table 2.  
All 2463 A-scan signals consist of a mixture of the background signal from the weld interface and internal geometry, as well as the flaw signal. Figure 7 displays the mixed ultrasonic A-scan data for each type of flaw. During the acquisition process, the gates where each flaw signal is located in the mixed signal are marked. To train the DAE, the background signals for all 2463 data points were zero-padded based on the previously marked gates.  
Figure 7 illustrates the five types of flaw signals used in this study, which are typical flaws in weldments. (a) Shows the typical crack signal in welding obtained by UT, which is the most fatal type of flaw that could occur in welding. (b) Presents the conventional lack of fusion signal in welding obtained by UT. (c) Shows the ordinary slag inclusion signal in welding obtained by UT. (d) Displays the typical porosity signal in welding obtained by UT, which is often difficult to evaluate as the amplitudes are relatively low. (e) Presents the typical incomplete penetration signal in welding obtained by UT. Flawless signal samples are not included as they do not contribute to the network's goal of flaw signal extraction.
## 4 Results and Analysis
### Spatial Patterns and Trends of GPP  
Based on V2, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015. The spatial pattern of mean annual GPP and its standard deviation (sd) were shown in Figure 7. Generally, the mean annual GPP (Figure 7a) gradually decreased from southeast to northwest, consistent with the hydrothermal gradient of the TP. At the same time, the sd of GPP (Figure 7b) showed a decreasing trend from east to west, indicating that GPP in the eastern region has experienced greater fluctuations than GPP in the western region in the past 34 years. According to the statistical results, the mean annual GPP of alpine grasslands on the TP was 289.8 gC/m\({}^{2}\)/yr (305.8 TgC/yr), of which alpine meadows and alpine steppes accounted for 461.6 gC/m\({}^{2}\)/yr and 87.9 gC/m\({}^{2}\)/yr, respectively. In addition, most regions (65.3% of the alpine grassland area) had low sd of GPP within 60 gC/m\({}^{2}\)/yr over the study period, mainly distributed in the western region of the TP. Meanwhile, only 12.4% of the alpine grassland areas had sd greater than 120 gC/m\({}^{2}\)/yr, mainly located in the eastern region of the TP.  
The spatial distribution of the trends of GPP between 1982 and 2015 was provided in Figure 8. Obviously, the GPP trends on the TP showed a large spatial heterogeneity, which may be mainly attributable to the different changes in climatic conditions. Overall, the mean annual GPP in most regions (96.5%) showed an increasing trend, with 63.5% of the alpine grassland area increasing significantly. This trend was also reflected in its interannual variations (Figure 9a). As depicted, the mean annual GPP increased significantly from 1982 to 2015, with a rate of 2.91 gC/m\({}^{2}\)/yr, and it varied from 250.9 gC/m\({}^{2}\)/yr in 1982 to 347.5 gC/m\({}^{2}\)/yr in 2015. Meanwhile, its changing trend was closely related to the changes in precipitation and temperature, as shown in Figure 9b,c. In addition, it should be noted that the mean annual GPP in 1998 had a significant increase as compared to the mean annual GPP of the previous year. This anomaly may be attributed to the EI Nino and La Nina events in 1998. Specifically, the average temperature and precipitation in 1998 were obviously higher than those in 1997, and the SOS of most alpine grasslands was also advanced [23], all of which directly contributed to the significant increase of GPP in 1998.  
Figure 7: Spatial distribution of mean and standard deviation of GPP of alpine grasslands on the TP from 1982 to 2015. (**a**) Mean annual GPP; (**b**) Standard deviation of GPP.
## 5 Conclusions  
In this study, a 3D MS monitoring system was created to monitor the micro-fracture signals of the rock mass during the excavation and unloading process of the spillway tunnel in the Jinchuan hydropower station. The system was used to pick up, identify, denoise, and locate the waveforms in real time. Moreover, the temporal and spatial evolution characteristics and the fracture mechanism of the MS concentration zone were analyzed with geological and construction conditions taken into account; the fracture or damage area of the spillway tunnel was circled; the following conclusions were reached:  
1. For MS signals with non-stationary noise, the CEEMD-CS-ST-based noise reduction method was applied, and the SNR, SD, and CC were introduced to quantitatively demonstrate the reasonableness and effectiveness of this method.
2. In order to examine the temporal and spatial evolution characteristics of MS events induced by blasting, excavation, and unloading of the spillway tunnel, the temporal and spatial distribution, moment magnitude, and other source parameters of MS events  
Figure 16: Distribution of the ES/EP value of MS events controlled by excavation unloading intensity.  
Figure 15: Distribution of the moment magnitude and density of MS events controlled by excavation unloading intensity.  
were analyzed. With the site construction conditions taken into account, the causes of MS event concentration under different working conditions were elaborated, and it was determined that the construction disturbance and adverse geological structures were the main causes of micro-fracture events, which damaged the surrounding rock and made it prone to macroscopic deformation.
* Analysis of the fracture mechanism of MS events indicated that tensile failure and mixed failure predominated in the surrounding rock of the spillway tunnel, with traces of shear failure.  
Methodology, X.Z.; resources, G.L.; data curation, J.C.; writing--original draft preparation, Z.L.; writing--review and editing, H.M.; project administration, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (42277461) and the Science Foundation for Distinguished Young Scholars of Sichuan Province (2020 JJQ0011).  
Not applicable.  
Data Availability Statement: Data are based on field monitoring and can be obtained from the author via email if necessary.  
The authors declare no conflict of interest.
## 5 Experimental Results and Cases Analysis
### Cases Analysis
By dynamically balancing exploration and exploitation, it effectively uncovers high-potential solutions from the non-dominated solution set, thereby offering a more stable and reliable formulation scheme for practical production. In summary, compared with manual formulation schemes and other multi-objective optimization algorithms, the advantages of the IMOAHLO algorithm in solving the RHF formulation optimization problem lie not only in its ability to adapt to different production conditions and resource constraints but also in its capacity to achieve a superior multi-objective balance at a lower adjustment cost, especially in maintaining solution diversity when handling multiple conflicting objectives. The outstanding flexibility and remarkable performance of the IMOAHLO algorithm validate the design concept of synergistic en \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Bin** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Weight (t) & 35.3 & 40.0 & 63.3 & 37.1 & 87.2 & 109.1 & 70.6 & 82.7 & 69.6 & 23.4 & 17.5 \\ \hline C content & 8.450 & 84.400 & 0.000 & 4.840 & 4.268 & 4.268 & 4.268 & 4.268 & 20.531 & 3.549 & 8.450 \\ Cl content & 0.453 & 84.400 & 0.000 & 0.080 & 0.008 & 0.008 & 0.008 & 0.008 & 6.130 & 0.320 & 0.453 \\ Zn content & 2.757 & 0.000 & 0.000 & 0.279 & 3.977 & 3.977 & 3.977 & 3.977 & 3.485 & 12.547 & 2.757 \\ Fe content & 42.027 & 0.000 & 0.000 & 63.593 & 44.444 & 44.444 & 44.444 & 44.444 & 38.066 & 39.735 & 42.027 \\ FeO content & 22.439 & 0.000 & 0.000 & 14.011 & 42.137 & 42.137 & 42.137 & 42.137 & 6.606 & 16.737 & 22.439 \\ \hline Original & 0.10 & 0.10 & 0.00 & 0.20 & 0.25 & 0.09 & 0.10 & 0.10 & 0.14 & 0.02 & 1.00 \\ \hline IMOAHLO & 0.10 & 0.09 & 0.00 & 0.19 & 0.32 & 0.14 & 0.10 & 0.13 & 0.03 & 0.00 & 1.00 \\ NSGA-II & 0.10 & 0.08 & 0.00 & 0.22 & 0.35 & 0.08 & 0.10 & 0.13 & 0.04 & 0.00 & 1.00 \\ MOHLO & 0.10 & 0.09 & 0.00 & 0.23 & 0.26 & 0.13 & 0.12 & 0.17 & 0.00 & 0.00 & 1.00 \\ SMPSO & 0.10 & 0.07 & 0.00 & 0.33 & 0.39 & 0.03 & 0.03 & 0.03 & 0.09 & 0.03 & 1.00 \\ Manual & 0.10 & 0.07 & 0.00 & 0.20 & 0.25 & 0.09 & 0.12 & 0.11 & 0.14 & 0.02 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimization results of different algorithms in Case II. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SchemeFunction** & **Element** & **Scheme** & **Operation** & **Start–Stop** \\  & **Content** & **Similarity** & **Time** & **Switching** \\ \hline Original & 1.23 & 0.00 & \(-\)9.78 & 0 \\ IMOAHLO & 0.00 & 0.30 & \(-\)10.30 & 1 \\ NSGA-II & 0.00 & 0.30 & \(-\)8.89 & 1 \\ MOHLO & 0.00 & 0.34 & \(-\)8.51 & 2 \\ SMPSO & 0.00 & 0.56 & \(-\)5.93 & 0 \\ Manual & 0.13 & 0.06 & \(-\)9.78 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The objective function results of different algorithms in Case II. hancement through neighborhood search and adaptive mechanisms, providing a solution that balances stability and flexibility for complex industrial optimization problems.