esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 2 Materials and Methods  
### Reliability Prediction  
Reliability prediction is to predict the possible reliability index of the equipment based on the functional structure of the equipment, the expected working environment and the reliability data of the parts or components of the equipment [16]. Reliability prediction of products can find the weak links of the system in advance, and through repeated reliability prediction, more appropriate design schemes can be obtained. On the contrary, if the reliability prediction is not carried out, the reliability index will not reach the standard after the successful design and manufacturing of the product due to the failure to implement the necessary reliability work, resulting in the loss of economic benefits of the enterprise. Therefore, it is of great significance to predict the reliability of the loading and unloading truss robot for CNC punch.  
For the loading and unloading truss robot for CNC punch, the main purpose and significance of reliability prediction are as follows:  
1. Evaluate whether each system of the loading and unloading truss robot for CNC punch can reach the specified reliability index;
2. Lay the foundation for reliability distribution of the loading and unloading truss robot for CNC punch;
3. According to the reliability prediction results, the weak links of the loading and unloading truss robot for CNC punch are discovered in advance to reduce the research and development cost and time;
4. Develop preventive maintenance plan, parts reserve and update plan for the loading and unloading truss robot for CNC punch.
# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Evaluation of the Recovery Phase in GHR
When \(\beta\) decreases, the increment in the average of hops is statistically significant (see the \(p\)-values \(<\)0.05 in the average number of hops section in Table 6). In fact, the are only two \(\beta\) values that reach the same number of hops, the SA recovery (\(\beta=3\)) and the aggressive recovery (\(\beta=0\)) for classical forwarding (\(\alpha\to\infty\)) in the intermediate density. Notice that the differences in the average number of hops between the classical approach (\(\beta\to\infty\)) and the other \(\beta\) values examined in this section is up to 1.5 hops at maximum. Regarding the percentage of idle time depicted in Figure 6d, it follows exactly the same behavior described for the average number of hops. That is, while the probability to forward a packet instead of keeping it in the buffer (low \(\beta\) values) increases, the percentage of idle time decreases. Only in the high density scenario and for SA forwarding (\(\alpha=3\)), the idle times sensed by SA recovery (\(\beta=3\)) and aggressive recovery (\(\beta=0\)) are the same. Nevertheless, the maximum difference between the conservative carry and forwarding and the other mechanisms is at maximum 2% for the three vehicle densities. To summarize the performance evaluation of our Geographical Heuristic Routing (GHR) protocol, we have found that SA forwarding with carry and forwarding (\(\alpha=3\), \(\beta\to\infty\)) is the best option for low density areas, because it shows the best percentage of packet losses in this vehicle density. In the intermediate density, SA forwarding and recovery (\(\alpha=3\), \(\beta=3\)) obtains the same level of packet losses as traditional forwarding (\(\alpha=\to\infty\), \(\beta\to\infty\)) with a lower delay. For this reason, we choose this configuration for medium vehicle density. Finally, we consider that SA forwarding with aggressive recovery (\(\alpha=3\), \(\beta=0\)) is appropriate for high vehicle density scenarios. This GHR setup gets the lowest average delay and only decrements around 2% the best packet delivery ratio in this density, achieved by SA forwarding (\(\alpha=3\), \(\beta\to\infty\)). We will use these specific configurations, which depend on the vehicle density, to compare GHR with MMMR, greedy-DTN and iAODV in the next section.
# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
## 4 Numerical Example
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **G-mean** & **AUC** & **MCC** \\ \hline \multirow{6}{*}{Lenses} & K-mean & 0.6200 & 0.5520 & 0.6215 & 0.4215 & 0.8525 & 0.8952 \\ \cline{2-7}  & K-Boosted & 0.7520 & 0.6020 & 0.7010 & 0.6025 & 0.7980 & 0.8821 \\ \cline{2-7}  & K-SMOTE & 0.8625 & 0.6250 & 0.8215 & 0.8520 & 0.8678 & 0.8150 \\ \cline{2-7}  & SMOTE-Boosted & 0.8920 & 0.6525 & 0.8950 & 0.9010 & 0.8960 & 0.8320 \\ \cline{2-7}  & HH-PSO-K & 0.9700 & 0.7020 & 0.9320 & 0.9250 & 0.9525 & 0.8980 \\ \hline \multirow{6}{*}{Lymphography} & K-mean & 0.7150 & 0.6420 & 0.6315 & 0.5415 & 0.9025 & 0.9002 \\ \cline{2-7}  & K-Boosted & 0.7620 & 0.6520 & 0.7216 & 0.6225 & 0.8180 & 0.8920 \\ \cline{2-7}  & K-SMOTE & 0.8832 & 0.6750 & 0.8415 & 0.8820 & 0.8978 & 0.8255 \\ \cline{2-7}  & SMOTE-Boosted & 0.8920 & 0.6820 & 0.9050 & 0.9100 & 0.9060 & 0.8610 \\ \cline{2-7}  & HH-PSO-K & 0.9805 & 0.7120 & 0.9410 & 0.9360 & 0.9620 & 0.9080 \\ \hline \multirow{6}{*}{Arrhythmia} & K-mean & 0.5600 & 0.5320 & 0.6015 & 0.5015 & 0.7525 & 0.8650 \\ \cline{2-7}  & K-Boosted & 0.7120 & 0.6120 & 0.7210 & 0.5025 & 0.8080 & 0.8620 \\ \cline{2-7}  & K-SMOTE & 0.8425 & 0.6040 & 0.8415 & 0.7520 & 0.8378 & 0.8050 \\ \cline{2-7}  & SMOTE-Boosted & 0.8420 & 0.6425 & 0.8990 & 0.8080 & 0.8460 & 0.8220 \\ \cline{2-7}  & HH-PSO-K & 0.9200 & 0.8020 & 0.9120 & 0.9150 & 0.9225 & 0.8900 \\ \hline \multirow{6}{*}{Bach-Chorales} & K-mean & 0.8250 & 0.7520 & 0.8215 & 0.7200 & 0.8500 & 0.9052 \\ \cline{2-7}  & K-Boosted & 0.8520 & 0.8080 & 0.8010 & 0.8024 & 0.8180 & 0.9021 \\ \cline{1-1} \cline{2-7}  & K-SMOTE & 0.9025 & 0.8650 & 0.9215 & 0.8720 & 0.8878 & 0.8950 \\ \cline{1-1} \cline{2-7}  & SMOTE-Boosted & 0.9120 & 0.8925 & 0.9250 & 0.9110 & 0.9060 & 0.8920 \\ \cline{1-1} \cline{2-7}  & HH-PSO-K & 0.9800 & 0.9020 & 0.9420 & 0.9350 & 0.9625 & 0.9080 \\ \hline \end{tabular}
\end{table} 
Table 2: The performance parameter results of hybrid algorithms. Through experiments, we found that the performance of HHCE-MOPSO clustering algorithm could be evaluated using some synthetic and real-life datasets with some validity metrics (cluster numbers, intra cluster distance, intercluster distance, G-mean, and MCC), which was also compared with some prevalent state-of-art automatic clustering algorithms. Furthermore, we found that big data clustering is completed in a reasonable time and is sustainable as well over the dynamic data. The impact of well-defined distance functions on the objective function of different clustering algorithms has been compared with the results of the proposed algorithm. To further verify the hybrid algorithm, we used the UCI, such as Hayes-Roth, for experimental simulation and the results are shown in Figures 46-48 as follows.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.3. Palu, Indonesia  
The 2018 Palu earthquake struck on 28 September 2018 in Palu City and the Central Sulawesi region of Indonesia. This earthquake triggered four massive flowslides and generated tsunami waves in coastal areas, which caused around 4340 fatalities [85]. As shown in Figure 11c, all of the reported ground failures, coastal areas impacted by tsunami waves, and most of the areas with a high probability of landslide and liquefaction occurrence were located in grids with an \(MDI>0.04\) cm\({}^{3}\)/cm\({}^{3}\). Moreover, the DPM and USGS liquefaction and landslide hazard maps, shown in Figure 12, raise the possibility of other ground failures in areas that experienced a high soil moisture increase but were not assessed by the GEER team.  
Figure 10: Damage proxy map of Samos, Greece 2020 earthquake, identifying areas in which considerable deformations occurred, overlayed on seismic records and \(\Delta SM_{SMAP}\) (9 km \(\times\) 9 km) in grids with \(MDI>0\). The DPM surface changes range from minor (yellow pixels) to major (red pixels) for potential damage.  
Figure 11: \(\Delta\)_SMAMAP_ (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) _MDI_ \(>\) 0.08, and (**c**) _MDI_ \(>\) 0.04 cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Palu, Indonesia 2018 earthquake.  
Figure 12: Damage proxy map of Palu, Indonesia 2018 earthquake, identifying areas in which considerable deformations, overlayed on seismic records and \(\Delta\)\(SM_{SMAP}\) (9 km \(\times\) 9 km) in grids with \(MDI\) > 0.04 cm\({}^{3}\)/cm\({}^{3}\). The DPM surface changes range from minor (yellow pixels) to major (red pixels) for potential damage.
# The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming
## 4 Model Application, Validation, and Testing
An algorithm was developed to implement the tool, which performs the function of generating plans for precision agriculture activities from historical and satellite data. Figure 5: Field view with linked information after data conversion. Figure 6: Field map functionality: (**a**) overlay; (**b**) comparison. Figure 7: Different digital field layers generated according to the desired indicator.
## 4 Discussion  
### Influence of Lateral Pressure Coefficient  
#### 4.1.1 Stress Field  
The lateral pressure coefficient can affect the stress distribution of the model in two main ways. The first option is to alter the magnitude of the stress. As the vertical load remains constant, increasing the lateral pressure coefficient causes the overall stress level of the model to increase. The second approach is to modify the stress distribution. As the lateral pressure coefficient increases in the model, the occurrence of the first type of problem also increases, and the stress gradually decreases due to the angle's influence. The stress curves with various lateral pressure coefficients are depicted in Figure 7.  
In terms of the magnitude of stress, the radial and circumferential stresses at the interface increase as the lateral pressure coefficient increases. However, the shear stress at the interface decreases. When the lateral pressure coefficient tends to 0, the shear stress also tends to 0. It can also be observed from the variation curves of the stress paths that both the radial and circumferential stresses increase as the lateral pressure coefficient increases. The shear stress decreases as the lateral pressure coefficient increases. The stress variation rule mentioned above indicates that as the lateral pressure coefficient increases, the shear stress on the ice-rock decreases continuously. This decrease is beneficial for preventing shear damage to the ice-rock.  
In terms of the distribution of stress according to the stress distribution law, when the distance from the center of the circle is constant, the stress varies periodically with the angle. As the lateral pressure coefficient increases, the shape of the radial and circumferential stresses changes and eventually forms a circle. However, the shape of the shear stresses does not change. This is because the shear stress is zero in the first type of problem. With an increasing polar longitude, the shear stress and radial stress exhibit a non-linear decreasing trend, while the circumferential stress demonstrates a non-linear increasing trend. This suggests that the destruction of ice-rock is most likely to occur within the interior of the rock rather than at the interface between the ice and rock.
## 3 Joint Angle and Delay Estimation (JADE)  
### Derivation of the CLF  
At any given pilot transmission period, we can derive the log-likelihood function (LLF) that depends on all three unknown parameter vectors \(\theta\), \(\tau\), and \(\gamma\) as follows:  
\[\mathcal{L}\big{(}\theta,\tau,\gamma\big{)}=\ \big{\|}\mathbf{X}-\mathbf{H} \big{\|}^{2}=\big{\|}\mathbf{X}-\mathbf{A}(\theta)\mathbf{D}(\tau)\gamma\big{\|} ^{2}. \tag{10}\]  
Hence, we can estimate \(\gamma\) using the least squares (LS) solution as follows:  
\[\widehat{\gamma}_{\text{LS}} = \Big{[}\mathbf{A}(\theta)\mathbf{D}(\tau)\Big{]}^{\tau}\mathbf{x} =\mathbf{B}^{\dagger}\mathbf{x}, \tag{11}\]  
where \(\mathbf{B}^{\dagger}=\big{(}\mathbf{B}^{H}\mathbf{B}\big{)}^{-1}\mathbf{B}\) is the Moore-Penrose pseudo-inverse of \(\mathbf{B}\). By injecting \(\widehat{\gamma}_{\text{LS}}\) into (10), we obtain the CLF:  
\[\mathcal{F}_{c}\big{(}\theta,\tau\big{)}=\mathbf{x}^{H}\mathbf{B}(\mathbf{B} ^{H}\mathbf{B})^{-1}\mathbf{B}^{H}\mathbf{x}. \tag{12}\]  
Hence, we can obtain the joint ML estimates of \(\theta\) and \(\tau\) as the optimal solution \(\Xi_{opt}\) to the following optimization problem:  
\[\Xi_{opt}\triangleq[\theta_{opt},\ \tau_{opt}]=\underset{\Xi\triangleq[ \theta,\tau]}{\operatorname{argmax}}\big{(}\mathcal{F}_{c}\big{(}\theta,\tau \big{)}\big{)}. \tag{13}\]
## 3 The Proposed CNN-SNN Conversion Method  
This section presents the proposed CNN-SNN conversion method, which enhances the inference latency and performance of the SNN models. It first describes the training strategy for a CNN model whose weights are later transferred to an SNN model. Next, it proposes a new threshold balancing technique for CNN-SNN conversion. Then, it addresses the inference in the SNN models.
## 2 Materials and Methods
### Cleaning Test Methods  
#### 2.3.1 Solubility Parameters  
In order to explore the range of solubility of the original paint layers and the varnish film, some preliminary solubility tests were carried out by using pure solvents or mixtures of them embedded in a cotton swab. The cotton swabs were rolled on the surface for a few seconds and then checked by observation under both visible and UV light to evaluate the presence of any paint or varnish residues. Solubility tests allowed us to evaluate the behavior of pictorial materials and to build effective alternative methods of solubilization.  
Based on the Teas graph, solvent blends with increasing polarity were prepared considering their solubility parameters (Fd, dispersion force; Fp, polar force; Fh, hydrogen bonding force) [3]. The solvents used, either pure or in binary blends, were iso-octane (Fd 100, Fp 0, Fh 0), ethanol (Fd 36, Fp 18, Fh 46) and acetone (Fd 47, Fp 32, Fh 21).  
Figure 3: Schematic representation of the three series of mock-ups.  
More specifically, the Fd value, which describes the polarity of the solvent, is used to compare and assess different solvent blends. In accordance with the literature [3], the range of solubility of the materials of interest for _Spitz-Rund_ are as follows:  
* Aged drying oil films: Fd 45-70.
* Aged natural resin films (shellac): Fd 30-54.
* Aged ketone varnish: Fd 36-96.  
Therefore, based on these values and aiming to remove the ketone varnish without any interaction with the underlying painting layers, a safe range of solubility with Fd 70-96 should be selected.  
However, it is important to remark that the above-mentioned parameters should be referred only to free solvents. The practice suggests to us that whether using solvents loaded into gel systems, the safety range can be slightly reconsidered.
## 4 Case Study
### Discussion
#### 4.2.4 Analysis of Carbon Emission  
The carbon emissions and carbon trading costs of VPP under the operation of the four Strategies are shown in Table 6. Compared with Strategy 1 (our proposed strategy), Strategy 2 considers less cooperation among lower-level PVP alliance members than Strategy 1. And, carbon emissions and carbon trading costs in Strategy 2 are slightly increased, increasing by 119.33 kg and CNY 74.77, respectively, which is not a big increase. Compared with Strategy 1 and Strategy 3, Strategy 3 takes less consideration of the demand response of the internal VPP load and the lower PVP alliance load in Strategy 1. Compared with Strategy 1, Strategy 3's carbon emission and carbon transaction costs are significantly increased, with an increase of 4122.85 kg and CNY 1236.56, respectively. Compared with Strategy 1, Strategy 4 takes less consideration of the cooperation between members of the lower-level PVP alliance, the internal load of VPP, and the demand response of the load of the lower-level PVP alliance. And, the carbon emission and carbon transaction cost of Strategy 4 increased by 4477.76 kg and CNY 1343.03, respectively. The above results show that the cooperative  
Figure 11: Interaction tariffs between PVP members.  
operation of low-level PVP alliance load has a small impact on the carbon emissions of VPP, but its role in promoting low-carbon operation cannot be ignored, and demand response has a greater impact on the overall low-carbon economic operation of VPP. Therefore, fully tapping the potential of flexible load in VPP is of great significance to promote energy saving and emission reduction and reduce operating costs.
## 6 Future Challenges by Intelligent Ship PHM
### Engineering Self-Healing and Immune System  
MSAE is highly uncertain, and some equipment is only suitable for non-invasive methods. Therefore, more advanced solutions than preventive maintenance are needed to optimize resource allocation and improve O&M efficiency. Engineering self-healing and the immune system could be a suitable approach to this problem.  
System self-healing includes many methods. The most common strategy is to use the redundant equipment in the system to replace the recovery function of the failed equipment. This function must be considered during MSAE design and manufacturing. The second is to unload non-essential equipment and maintain the main output function. In addition, when a single or partial component has a problem affecting the system function, use the interaction relationship among subsystems, equipment, and components in the system to adjust the function output of normal equipment and make up for the impact of faulty equipment on the system.  
The engineering immune system is an analogy of the biological immune system, which protects against invasion and infection by identifying and killing the pathogens [4]. The system's goal is to achieve efficient near-zero breakdown performance with as little human intervention as possible. The system has a high degree of autonomy and can recover and maintain system functions without external interference. At present, this technology has been applied to the computer field. To further improve the health management of MSAE, further development of engineering self-healing and immune systems are essential.
## 4 Conclusions  
This paper systematically studied the repairing process of super gear of 20 CrMnTi by LC Ni60 alloy powder. Process parameters' analysis and optimization, deposition strategies' optimization, and performance testing and analysis of repaired teeth were carried out. The conclusions can be drawn up as follow:  
1. By laser cladding technology, the damaged gear of 20 CrMnTi can be repaired with Ni60 powder using the optimal parameters: Orthogonal and interaction experiments were carried out to optimize the parameter of LC Ni60 alloy powder on 20 CrMnTi substrates. The optimized process parameters are laser power of 370 W, scanning speed of 9.5 mm/s, powder feeder rate of 0.6 r/min, and carrier gas rate of 7.5 L/min. Moreover, the overlap rate and \(Z\)-increment obtained by theoretical calculations and experimental analysis are 23.96%, and 0.3 mm, respectively. Based on the results of the optimization of the deposition strategy, the hot overlap of the same layer (strategy I-1), the reverse scanning method (strategy II-1), and the staggered overlap mode (strategy III-3) can reduce the error accumulation and improve the surface quality and precision.
2. The XRD result indicates that the repaired gear contains \(\gamma\)-Ni, Ni3B, Cr7C3, Cr23C6, CrB, and Cr5B3. Moreover, the repaired gear forms a good metallurgical bond with the substrate. The microstructure of the metallurgical bond zone forms plane crystal, columnar crystal, and dendrite in turn. The number of Cr-C-B compounds increases from the dedendum to the addendum. The precipitates exhibit bulk-shaped, butterfly wing-shaped, and dendritic-shaped. Moreover, these precipitates have gone through processes from thin to coarse and from variable to less.
3. The hardness of the repaired gear teeth is 60.63 HRC, which is similar to that of other gear teeth. The friction coefficient and volume loss of repaired gear teeth are 0.49  
Figure 19: Optical images of worn surfaces (**a-1**) 2D worn surface morphologies of repaired gear teeth, (**a-2**) height graph of a-1, (**a-3**) enlarged view of a-1. (**b-1**) 2D worn surface morphologies of 20 CrMnTi substrate, (**b-2**) height graph of b-1, (**b-3**) enlarged view of b-1.  
and 1675.596 \(\mu\)m\({}^{2}\) respectively, which is similar to that of the substrate. The wear mechanism of the substrate is concluded to be adhesive wear, and the repaired gear is dominated by abrasive wear.  
Conceptualization, C.G. and T.Y.; methodology, Y.Z. and L.C.; software, Y.C.; validation, C.G.; formal analysis, C.G.; investigation, C.G.; resources, T.Y.; data curation, C.G.; writing--original draft preparation, C.G.; writing--review and editing, C.G., Y.Z. and L.C.; visualization, C.G., L.C. and Y.C.; supervision, T.Y.; project administration, C.G.; funding acquisition, T.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (N2003026); 111 Project (B16009).  
The authors declare no conflict of interest.
## 2 Antenna Design Approach
### Structure of the Proposed Antenna Unit  
This section introduces the structure of the antenna unit, considering the fabrication and measurement requirements. A top view of the unit structure is shown in Figure 12a,  
Figure 11: Full-wave simulated results for Antenna I to III. (**a**) S\({}_{11}\); (**b**) boresight gain.  
Figure 10: Characteristic currents and far fields of the stacked patch. (**a**) J1 at 29.3 GHz; (**b**) J2 at 30.7 GHz.  
Figure 9: (**a**) MS and (**b**) MWC of Antenna III with feed.  
and Table 1 lists the relevant parameters for the antenna design. In the exploded view in Figure 12b, the antenna consists of three layers of silicon and four layers (M1 to M4) of gold, with a total size of 0.47\(\lambda_{0}\)\(\times\) 0.47\(\lambda_{0}\)\(\times\) 0.07\(\lambda_{0}\). The top patch is on M1 and the bottom patch fed by TSV is on M2. M3 and M4 are grounded with slotted isolation rings. The ball grid array (BGA) with a pitch of 0.8 mm interconnected with the print circuit board (PCB) is under the M4 layer, where the signal is fed from the central solder ball. It should be noted that, in order to meet the manufacturing rules for metal bonding between silicon wafers and ensure the mechanical stability of products, two strip metal shapes are added to the M2, which has little impact on the antenna performance, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Size** & **Parameter** & **Size** \\ \hline Lp1 & 1.09 & ws1 & 0.05 \\ Wp1 & 3.67 & ws2 & 0.1 \\ Wp2 & 2.45 & Lt & 0.4 \\ Ls1 & 1.16 & Wt & 3.5 \\ Ls2 & 0.83 & Wg & 5 \\ Ls3 & 0.81 & df & 0.15 \\ Ls4 & 0.57 & & \\ \hline \end{tabular}
\end{table}
Table 1: Dimensions of the proposed antenna. (Unit: mm)  
Figure 12: Configuration of the proposed antenna unit. (**a**) Top view; (**b**) exploded view.