esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 1 Introduction
Motivated by the pros and cons of the virtual linkage approach and consensus algorithm, a decentralized virtual linkage approach is presented in this paper. Instead of using hierarchical architecture, the proposed method instantiates a local copy of the virtual linkage's state implements the same consensus algorithm on each agent to facilitate the reconfigurable formation control of a team of agents. The decentralized virtual linkage approach has several advantages as compared to the original virtual linkage approach. First, the decentralized architecture overcomes the limitations of the hierarchical architecture. In details, this approach scales well with the number of agents in the group and only requires each agent to communicate with its local neighbors. Second, with the introduction of expansion/contraction rates for each virtual link, this approach has a stronger reconfiguration ability than the traditional virtual linkage approach. The paper is organized as follows. In Section 2, the preliminary knowledge is presented. Section 3 illustrates the control strategy for the formation movement. Simulation results are in Section 4. Finally, in Section 5, some concluding remarks of this paper are given.
## 2 Materials and Methods
### Characterization  
The crystalline phases and microstructures of the scaffolds were analyzed using X-Ray diffractometer (XRD; Rigaku, TTRAXIII, Tokyo, Japan). An X-ray diffractometer with a monochromatic Cu K\(\alpha\) radiation (45 kV and 40 mA) was used and XRD patterns were recorded between the 20 interval ranges from 20\({}^{\circ}\) to 60\({}^{\circ}\) (scan speed: 4\({}^{\circ}\)/min, step scan: 0.03\({}^{\circ}\)). The crystalline phases were identified using a Joint Committee on Powder Diffraction Standards (JCPDS) card. The structure of the scaffold was observed by scanning electron microscopy (SEM; SU3500, HITACHI, Tokyo, Japan). After copper coating for 1 min, the microstructure of the scaffolds was observed at 40 kV. Additionally, the scaffolds were dried and placed in isotherm system under 120 \({}^{\circ}\)C for 4 h to degas. The porous classification of the bioglass scaffolds was measured with a nitrogen (77 K) adsorption/desorption isotherm system (Belsorp max, Microtrac, Montgomeryville, USA). On the other hand, the pore volume and diameter were determined using the Barett-Joyner-Halenda method (BJH) method from the adsorption branches.
# Passive Multi-Layer Composite Damper of Flat Belt Tensioner Idler
## 3 Results
#### Damping at Discontinuities  
Interfaces and Cells  
A passive damper consists of materials with different stiffness and density; thus, the longitudinal waves generated by the vibrations of the belt and the pulley partly pass through the damper layers (transmission) and are partly reflected (reflexion), as shown in Figure 9. The interface between layers are discontinuities at the macroscale level and the closed cells of foam structures are discontinuities at the microscale level. At each discontinuity, the equilibrium of stresses (internal forces) and the continuity of mechanical vibration velocities apply.  
Figure 8: Rotational speed changes of roller bearings at resonance peak.  
Figure 9: Velocity and stress at discontinuity.  
One of our goals was to reduce the amplitude of mechanical vibrations, and this can be achieved both through discontinuities of the structure (between components) and through discontinuities of used materials, preferably viscoelastic materials. In these areas, some of the incident wave energy goes through the interface and propagates further, which is expressed by the transmission coefficient \(T\) (Table 3), while some of the wave energy is reflected back. At discontinuities, the law of momentum must apply, i.e., the momentum of an element of an incident wave is equal to the sum of the momentum of the element from the transmitted and reflected wave (Figure 9). The velocities are as follows:  
\[v_{\rm I}=v_{\rm T}+v_{\rm R}\qquad\Rightarrow\qquad\quad v_{\rm T}=v_{\rm I }-v_{\rm R} \tag{5}\]  
where \(v_{\rm I}\), \(v_{\rm T}\), and \(v_{\rm R}\) are the velocity of the incident, transmission, and reflection wave, respectively.  
Analogously for stresses:  
\[\sigma_{\rm T}=\sigma_{\rm I}-\sigma_{\rm R} \tag{6}\]  
Using the relation for mechanical impedance \(Z\):  
\[Z=\frac{\sigma}{v} \tag{7}\]  
and substituting into (6) and using (5), the transmission coefficient \(T\) is obtained as  
\[T=\frac{v_{\rm T}}{v_{\rm I}}=\frac{2Z_{1}}{Z_{1}+Z_{2}} \tag{8}\]  
The mechanical impedance \(Z\) in (8) is calculated by  
\[Z=\rho c_{\rm L} \tag{9}\]  
where \(\rho\) is the density of the material and \(c_{\rm L}\) is the longitudinal wave speed in that material.  
The calculated transmission coefficient \(T\) is in accordance with Equations (8) and (9). The values of densities \(\rho\) (\(\rho=m/\)volume) and longitudinal wave speed \(c_{\rm L}\) are calculated according to [20,21]. Having several layers, the values of the transmission coefficient \(T\) at the individual interfaces were averaged for each passive damper and are shown in Table 3. Our results confirm that passive damper type 2 is the best absorber of vibration energy (the lowest \(T\)).
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
## 3 Calculation
### Generative Adversarial Network and Convex Hull-Based Clustering of Discriminator Output  
The generative adversarial network for anomaly detection used in this study consisted of two deep neural networks: the generator and the discriminator (Figure 20).  
The generator consisted of three hidden dense layers with a rectified linear unit activation function and aims to generate the fake output based on real input data (periodogram). The discriminator predicts whether the input is real or fake by mapping the input into two-dimensional probability space. As in the case of the generator, the discriminator was designed as a deep neural network with three hidden dense layers with rectified linear unit activation function. As a loss function for the generator and discriminator while training, the binary cross entropy was used. The network was trained only on periodograms for a normal state of operation. The probability space (output) of the discriminator, along with convex hull-based clustering, is shown in Figure 21.  
The data points for both the normal
## 3 Results
### Conductivity Measurements  
Thermal annealed sludge biochar was analyzed for electrical conductivity (DC mode) and compared with carbon black. The results are shown in Figure 8.  
The value of conductivity obtained in the case of carbon black shows a gradual increase with increasing pressure. The values of conductivity for low pressure values in Figure 8 are comparable to the ones reported previously [34]. Pressure values similar to the values used in this study were adopted in [26]. The values of conductivity obtained in [26] were higher because of a higher temperature (>2000 \({}^{\circ}\)C) treatment on the carbon black.  
Thermally annealed sludge biochar reaches an electrical conductivity value of around 300 S/m, and remains quite constant after a pressure of 200 bar. This was likely due to the higher graphitization degree reached at high temperatures [41; 42]. Non-thermally annealed sludge biochar did not show any appreciable electrical conductivity, and was therefore not analyzed with this system.  
Figure 8: Conductivity measurement (DC) of thermal annealed sludge biochar and carbon black (*Vulcan® XC72R) as a function of applied pressure (0-1000 bar).  
Figure 7: FESEM on composites with 10 wt.% (**left**) and 20 wt.% (**right**).  
Table 2 summarizes the electrical conductivity values obtained for biochar coming from various sources and treated at different temperatures. Irrespective of the origin of the biochar, the conductivity value increases with the treatment temperature.
## 3 Results and Discussions
### Volatile Compounds (VOCs)  
There were seven alcohols, six aldehydes and six ketones observed in HTS fillets samples during storage, without taking into account aromatic compounds, hydrocarbons, and amines compositions. For the fresh HTS fillets (day 0), some aldehyde compounds characterized the fresh fish flavor as their flavor thresholds are lower compared to alcohols and ketones compounds (Table 3). The most abundant VOCs were ethanol and benzaldehyde isolated from HTS fillets. Ethanol, 2-decanol, benzaldehydehyde, 2-methylbutanal, and 2-octanone showed tendencies to decrease gradually. Conversely, 1-octen-3-ol, 1-penten-3-ol, 3-methyl-butanal, hexanal, propanal, octanal, 2-nonanone, 2-pentanone, 3-octanone, 2-heptanone, and 2,3-pentanedione showed upward trends of HTS fillets in all samples during storage. The accumulation of 3-pentanone was due to the metabolites produced by spoilage microorganisms, and chemical reactions could be further metabolized [19]. Benzaldehyde was derived from the degradation of amino acid and has a desirable effect on fish odor owing to its pleasant stone fruit and almond nutty aroma, and a higher percentage of benzaldehyde could be identified at the beginning [31]. 1-Octen-3-ol was usually derived from the oxidation of 12-hydroperoxy arachidonic acid, which was basically perceived as off-flavor due to the low odor threshold and usually classified as an oxidative spoilage marker [32]. The accumulation of propanal could be due to the increase of 16-hydroperoxide formed by autoxidation of methyl linolenate and 15-hydroperoxide from auto-oxidation of methyl linolenate [19]. The high levels of hexanal could be closely associated with oxidation of linoleic acid. VOCs generated from the process of lipid auto-oxidation are generally recognized as one of the leading causes of quality deterioration in aquatic products [33]. Furthermore, the treated HTS fillets (C1, C2, C3, and C4) showed lower increases compared to CK1 and CK2 at the same storage time. Obviously, the relative contents of 1-octen-3-ol, 1-penten-3-ol, benzyl alcohol, phenylethyl alcohol, propanal, octanal, 2-nonanone, 2-pentanone, 3-pentanone, 3-octanone, 2-heptanone, and 2,3-pentanedione in CK1 and CK2 were higher than that of the treated HTS fillets, and some of them had putrid odors above certain concentrations, such as octanal and 1-octen-3-ol, having grassy or mushroom-like odors [4]. A low number of off-odor compounds in treated HTS fillets samples could be attributed to the inhibition of lipid oxidation and microbial growth by \(\varepsilon\)-PL and RA protective coatings [34; 35].
## 5 Discussion
### About the Analysis of the Natural Frequencies and Vibration Modes
#### 5.4.2 Possible Correction of Values  
Another issue already noted by some authors, as indicated in Section 2, is the variation in the values obtained for the natural frequencies and, consequently, the possible need to correct them to implement an SHM system, given that the mentioned variations may not be negligible and would give rise to false positives.  
Indeed, the referenced studies state that the values of the natural frequencies can be affected by temperature and that they can also be correlated with the maximum accelerations recorded since it is an indirect measure of the load that is supporting the structure and, therefore, of the internal mechanisms that are activated in the materials to resist such actions.  
In the case of the E-1 structure, an in-depth study was carried out on the influence of several variables: the arch temperature, the ambient temperature, the average wind, and the maximum accelerations, both vertical and transversal. Regarding the role of temperature, the analysis of individual correlations showed a greater correlation in the case of ambient temperature and not for the arch temperature. In the case of the average wind, no correlation was observed, and in the case of maximum accelerations, it was seen that there was a certain correlation in both the vertical and transverse cases, although they were manifested to a greater or lesser extent depending on the vibration modes. These results were in coherence with the behaviour suggested by other authors in the case of various bridges [15; 16; 17; 18; 20; 21; 22; 23].  
Consequently, a model that took into consideration the ambient temperature and the maximum accelerations, both vertical and transverse, was proposed. It allowed achieving correlations with a coefficient of determination (\(R^{2}\)) of up to a value of 0.90 in the case of the natural frequency \(f_{\exp 6}\), as shown in Figure 6, where the relationship with the ambient temperature is represented, as it is the variable that, independently, had a greater correlation.  
Figure 5: Analysis of the natural frequencies in an event of the E-2 structure, accelerometer AV011: (a) Accelerogram; (b) Spectrum.
## 4 Experiments and Analysis
### Comparative Analysis of the Results of Different Domain Adaptation Methods
#### 4.2.2 Comparative Analysis of Diagnostic Results  
Figure 7 illustrates the confusion matrix of the output categories after performing fault diagnosis for tasks A-C\(\rightarrow\)B. The horizontal coordinates represent the predicted categories of the target domain samples and the vertical coordinates represent the true labels of the target domain samples. From the confusion matrix, it can be seen that if the four methods (a-d) are utilized for fault diagnosis, there are 110, 59, 71, and 51 target domain samples that have been misclassified, respectively. However, after using the proposed method, there are only 17 sample classification errors in 3200 target domain samples. Nine rolling element fault samples were identified as normal state, three rolling element fault samples were identified as outer ring faults, one inner ring fault sample was identified as normal, two normal state samples were identified as outer ring faults, two normal state samples were identified as rolling element faults, and the remaining target domain samples were all correctly classified. The overall diagnostic accuracy was 99.47%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Test** & **Adaptive Method** & **JMMD** & **CORAL** & **MK-MMD** & **LMMD** & **MSDSA** &
\begin{tabular}{c} **Increase** \\ **Percentage (\%)** \\ \end{tabular} \\ \hline B-C\(\rightarrow\)A & 93.87\% & 94.00\% & 89.34\% & 95.37\% & 97.40\% & 2.03–8.06\% \\ \hline A-C\(\rightarrow\)B & 97.81\% & 98.53\% & 98.75\% & 98.81\% & 99.65\% & 0.84–1.84\% \\ \hline A-B\(\rightarrow\)C & 97.03\% & 97.47\% & 97.50\% & 97.37\% & 99.34\% & 1.84–2.31\% \\ \hline Average & 96.24\% & 96.67\% & 95.20\% & 97.18\% & 98.80\% & 1.62–3.6\% \\ \hline \end{tabular}
\end{table}
Table 4: Multi-source domain fault classification accuracy of different adaptive methods.  
Figure 6: Feature visualization: (**a**) JMMD; (**b**) CORAL; (**c**) MK-MMD; (**d**) LMMD; (**e**) MSDSA.
# Influence of Aging Time on Vertical Static Stiffness of Air Spring
## 5 Simulation on Vertical Static Stiffness of Air Springs with Aging Time  
### Effects of Cord Aging  
The simulation analysis results of the air spring vertical static stiffness, only considering rubber aging, are shown in Figure 6, and the static stiffness after 12 days of aging increased by 11.9% compared with that before aging. As the figure shows, as the rubber ages, the vertical static stiffness of the air spring decreases after one day of aging, and it increases between 1 and 12 days.  
The effective area of the air spring is the theoretical equivalent area [24]. The effective bearing area of the air spring is the effective area, which refers to the bearing area that can just offset the current vertical load. It should be emphasized that the effective area of the air spring is not the maximum cross-sectional area of the airbag or the contact area with the cover plate, because there is always uniformly distributed tension in the longitudinal section of the rubber airbag, and some pressure bearing areas should be used to offset the tension of the airbag itself. The greater the effective area, the greater the stiffness [25]. The vertical static stiffness of the air spring is mainly proportional to the air pressure and effective area. As the aging time increases, the rubber becomes hard and brittle. When subjected to a vertical displacement load of the same size, the deformation of the air spring airbag is smaller, making the effective area of the air spring decrease and the air pressure in the airbag increase. The effective sectional area reduces the stiffness of the air spring, and the air pressure increases the stiffness. When the effect of the effective area is greater than the effect of increasing pressure, the vertical static stiffness of the air spring decreases; when the effective area is less than the increasing pressure, the vertical static stiffness of the air spring increases. According to the above analysis, the effective area plays the main role when aged up to one day; in 1-12 days, air pressure plays the main role.
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 3 Solution Algorithms
### Key Element Design of Hybrid Metaheuristic Algorithms
#### 3.1.1 Chromosome Representation  
Each chromosome in the GA contains important information regarding various aspects, including the location of DCs, the suppliers responsible for servicing the DCs, the assignment of DCs to retailers, and the scheduling and routing plan for vehicles from the DCs to retailers. In Figure 1, an example of a chromosome string is presented, representing a scenario involving four suppliers, three DCs, seven retailers, four vehicles with different capacities, two perishable products, and two periods. Each chromosome is divided into four distinct strings, each representing a specific aspect of the solution.  
The information encoded in each of the four strings of the chromosome is described below (refer to Figure 1 for visualization).  
The first string (Figure 0(a)) represents the manufacturers-to-DC service decision for each perishable product and period. Each gene value indicates the number of operating DCs served by a specific manufacturer for a perishable product and period. For instance, in Period 1 for Perishable 1, Manufacturer 1 serves DC 3, while Manufacturer 2 serves DC 2.  
The second string (Figure 0(b)) represents the total number of retailers served by each DC for a specific perishable product and period. The gene value indicates the number of retailers served by an operated DC. For example, in Period 1 for Perishable 1, DC 1 is not operating, DC 2 serves three retailers, and DC 3 serves four retailers.  
Figure 1: Chromosome representation.  
The third string (Figure 1c) represents the allocation of vehicles to DCs. Each gene represents the number of the DC, and the gene values are filled randomly with the number of vehicles. DCs that are not in operation are assigned the value zero. In Figure 1c, for Perishable 1 in Period 1, Vehicle 8 is allocated to DC 2, and Vehicle 9 is allocated to DC 3.  
The fourth string (Figure 1d) indicates the allocation of vehicles(blue) to retailers for the purpose of routing. Retailers are assigned sequential numbers starting from 1, while vehicles are numbered consecutively, starting from the last retailer. By combining Figures 1c and 1d, the route for Vehicle 8 is DC2-3-2-6-DC2, and the route for Vehicle 9 is DC3-4-1-5-7-DC3. If two vehicles are adjacent, the leading vehicle is considered idle. For example, in Period 2 for Perishable 2, Vehicle 10 is idle (Figure 1d).
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Dataset
#### 4.1.2 KITTI Dataset  
The KITTI Dataset was founded in 2012 through a collaboration between the Karlsruhe Institute of Technology (KIT) and the Toyota Technological Institute at Chicago (TTI-C) to support research in the area of autonomous driving. This dataset includes diverse sensor data types, including images, LiDAR point clouds, GPS, and IMU data. It functions as a fundamental dataset for computer vision applications such as 3D object detection and depth estimation. In the context of depth estimation tasks, the KITTI Dataset offers a collection of 90,000 RGB-D images. In order to ensure consistency with the size of the indoor scene dataset, this study randomly obtained 20,000 images from 90,000 raw images for use as the training set, 1000 images for use as the validation set, and 1000 images for use as the test set.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 3 Results and Discussion
### Adhesion Evaluation  
In order to evaluate the adhesive strength between the deposited diamond coatings and the drill's substrate after the initial and reconditioning cycles, indentation tests were carried out using a Rockwell hardness testing machine with a diamond indenter, which had a conical angle of 120\({}^{\circ}\) and a radius of curvature at the tip 0.2 mm. Each diamond-coated drill was tested under a load of 980.7 N. Figure 8 shows the SEM images of the surface morphologies of the coated drills after the indentation tests. For the TLC0 and the TLC1, as shown in Figure 8a,b, respectively, cracks of the diamond coatings around the indenter trace were observed. On the other hand, Figure 8c reveals the peeling of the diamond film of the TLC2 after indentation. A magnified SEM image of the film-peeled area, presented in Figure 8d, showed the substrate voids partially filled with the diamond material. The  
Figure 7: Raman spectra of the CVD0, CVD1, and CVD2 coatings, using a green (\(\lambda=532\) nm) excitation.  
existence of the voids decreased the mechanical strength of the interface and created stress concentration regions. Thus, the adhesion force was significantly reduced.  
The voids originated from the WCE2 step, as shown in Figure 5h. Evidently, the diamond growth started inside the voids but did not completely fill them out before the continuous film coalesced and incapsulated the voids, blocking the gas-phase C transport and deposition in those areas. The findings suggested that the WCE2 and further step conditions should be fine-tuned in order to prevent the formation of excessively large craters and subsequent film voids. Another possible way is to apply the selective seeding process that stimulates an in-depth diamond growth, as has been reported by [PERSON] et al. [25].
## 2 Materials and Methods
### Statistical Analysis  
Continuous variables were expressed as mean \(\pm\) standard deviation or median and range, as appropriate. Categorical data were reported as percentages. Normality of the data distributions was verified with the Shapiro-Wilk test. Statistical difference between data distributions obtained by Horos and LIFEx software was evaluated using Wilcoxon matched-pairs signed-rank test. To assess the agreement between the measurements obtained by the two software programs, for muscle area we used the classical Bland-Altman analysis, supplemented by [PERSON]'s exact 95% confidence interval (CI) for the limits of agreement [46] and 95% CI for the bias was reported. In addition, for CAC score, due to a trend in the linear relationship between average values and differences using the two software programs, we used the regression method proposed by [PERSON] and [PERSON] [47]. Correlations between measurements obtained from the two software programs  
Figure 1: Horos and LIFEx pipelines.  
were evaluated by the concordance correlation coefficient, introduced by [PERSON] to measure the agreement between methods when the response is continuous. Ninety-five percent confidence intervals (95% CIs) were supplemented, while two-sided \(p\) values \(<\) 0.05 were considered statistically. Statistical analysis was performed using the R software, version 3.6.3 (The R Foundation for Statistical Software, Vienna, Austria). For our analysis, we used Bland Altman Leh, DesTools, and ggplot2 R packages.