esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of Residual Life Prediction for Electrical Connector  
### Degradation Process of Electrical Connector  
The failure modes of electrical connectors mainly include contact failure, insulation failure and mechanical failure, where the contact failure accounts for more than 45.1% of all failures. The contact surface of the connector is the key component of delivery current and signal. With the degradation of the metal surface, the contact resistance will increase. When the contact resistance reaches the failure threshold, the electrical connector fails [20].  
Together with the equipment, the electrical connectors installed in the equipment are always subjected to various environmental stresses. Under environmental stress, the metal surface suffers fretting wear, which results in the oxidation and corrosion of the contact surface. The oxidized layer is composed of insulating materials. It grows thicker around the contact interface and gradually covers most of the area of the interface. The growth of the oxidized layer on the surface leads to increased contact resistance and easily causes contact failure. The growth process is shown in Figure 1. Initially, the film is formed around the contact surface. With the contact surface being continuously oxidized, the oxidized layer accumulated gradually. Finally, the oxidized layer, which is an insulating material, will cover the original contact surface. When the whole contact surface is covered by oxidized film, the electrical connector fails.  
With the degradation of the contact surface, the contact surface is gradually covered by the insulating layer and some of the conductive spots disappear and some of the conductive spots become smaller. The number and size of conductive spots can reflect the using state of electrical connector. Since the conductive spots are difficult to observe, the contact resistance related to the conductive spot is usually used to analyze the using state of the electrical connector. However, the contact resistance has some limitations in the degradation analysis of electrical connectors.  
For the sake of analysis, it is assumed that the shape of the conductive spots is circular with a radius of \(a\). The contact resistance of a conductive spot is \(R_{a}=\rho/4a\), where \(\rho\) is the resistivity of the metal material [21]. Considering the parallel structure between the conductive spots, the contact resistance \(R_{c}\) can be expressed as:  
\[R_{c}=\frac{R_{a}}{N_{c}} \tag{1}\]  
where \(N_{c}\) denotes the number of conductive spots.  
The common degradation curve of contact resistance is as shown in Figure 2. In the early and middle stages of electrical connectors, the contact resistance remains low. Only in the later stage does the contact resistance increase rapidly, showing significant failure features. Therefore, simply extracting contact resistance as the feature is difficult for accurately predicting the residual life during the whole life cycle of the electrical connector.  
Figure 1: The degradation process of the contact surface.  
Figure 2: Degradation curve of contact resistance.
## 4 Results and Discussion
### Electrochemical Impedance Spectroscopy Measurements
#### 4.1.1 Solar Salt (KNa)NO\({}_{3}\) Testing
The adjustment of the data obtained was correlated with the different equivalent circuit models for molten salts, which allowed us to find a suitable and concise model for the case study. The fit corresponds perfectly to a protective layer model. This can be explained by the sufficiently high chemical stability in molten salt systems, which for our case represents solar salt. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Time** & \(R_{s}\) & \(C_{all}\) & \(\beta_{all}\) & \(R_{t}\) & \(C_{ox}\) & \(\beta_{ox}\) & \(R_{ox}\) \\ \hline  & \(\Omega\)**-cm\({}^{2}\)** & **F s (\({}^{\Theta}\)**m\({}^{-1}\)**) & & \(\Omega\)**-cm\({}^{2}\)** & **F s (\({}^{\Theta}\)**ox\({}^{-1}\)**) & & \(\Omega\)**-cm\({}^{2}\)** \\
2 h & 2.218 & 0.089 & 0.436 & 34.67 & 0.178 & 0.813 & 47.96 \\
4 h & 2.204 & 0.077 & 0.468 & 41.59 & 0.208 & 0.828 & 46.11 \\
6 h & 2.154 & 0.072 & 0.478 & 46.70 & 0.203 & 0.788 & 52.84 \\
8 h & 2.151 & 0.313 & 0.889 & 34.97 & 0.067 & 0.496 & 53.01 \\
24 h & 2.283 & 0.661 & 1.000 & 17.65 & 0.069 & 0.503 & 68.04 \\
48 h & 2.154 & 0.299 & 0.834 & 31.10 & 0.086 & 0.465 & 37.04 \\
72 h & 2.105 & 0.089 & 0.493 & 34.15 & 0.430 & 0.881 & 21.91 \\
96 h & 2.313 & 0.114 & 0.402 & 23.67 & 0.177 & 0.737 & 38.75 \\ \hline \end{tabular}
\end{table} 
Table 3: Fitting results of the impedance spectra of stainless steel 304 during corrosion in the presence of (KNa)NO\({}_{3}\) (solar salt) at 550 \({}^{\circ}\)C.  
Figure 7: Nysquist diagram of stainless steel 304 layer in (KNa)NO\({}_{3}\) (solar salt) at 2 h, 4 h, 6 h, 8 h, 24 h, 48 h, 72 h and 96 h at 550 \({}^{\circ}\)C.  
Figure 8: Nysquist diagram of stainless steel 430 layer in (KNa)NO\({}_{3}\) (solar salt) at 2 h, 4 h, 6 h, 8 h, 24 h, 48 h, 72 h and 96 h at 550 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Time** & \(R_{s}\) & \(C_{dl}\) & \(\beta_{dl}\) & \(R_{t}\) & \(C_{ox}\) & \(\beta_{ox}\) & \(R_{ox}\) \\ \hline  & \(\Omega\)-cm\({}^{2}\) & F s\({}^{\rm(\beta}\)\({}_{\rm dl}\)\({}^{-1}\)) & & \(\Omega\)-cm\({}^{2}\) & F s\({}^{\rm(\beta}\)\({}_{\rm ox}\)\({}^{-1}\)) & & \(\Omega\)-cm\({}^{2}\) \\
2 h & 1.843 & 0.0516 & 0.5125 & 75.72 & 0.310 & 0.890 & 62.12 \\
4 h & 2.049 & 0.1500 & 0.6500 & 45.00 & 0.013 & 0.618 & 10.19 \\
6 h & 1.922 & 0.1021 & 0.6900 & 106.37 & 0.089 & 0.479 & 18.78 \\
8 h & 2.398 & 0.2848 & 0.8778 & 55.21 & 0.071 & 0.534 & 35.78 \\
24 h & 1.927 & 0.2411 & 0.8122 & 56.00 & 0.092 & 0.480 & 22.00 \\
48 h & 2.046 & 0.2247 & 0.8032 & 61.00 & 0.080 & 0.500 & 23.00 \\
72 h & 1.970 & 0.0789 & 0.5047 & 64.52 & 0.092 & 0.990 & 17.00 \\
96 h & 1.836 & 0.0096 & 0.5900 & 3.271 & 0.078 & 0.557 & 10.00 \\ \hline \end{tabular}
\end{table} 
Table 4: Fitting results of the impedance spectra of stainless steel 430 during corrosion in the presence of (KNa)NO\({}_{3}\) (solar salt) at 550 \({}^{\circ}\)C.  
Figure 9: Nysquist diagram of Nickel alloy HR224 layer in (KNa)NO\({}_{3}\) (solar salt) at 2 h, 4 h, 6 h, 8 h, 24 h, 48 h, 72 h and 96 h at 550 \({}^{\circ}\)C.
## 1 Introduction
With this model, we can rigorously study the time-varying network capacity trends and limitations of the remote sensor and satellite network. We combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to study the major factors influencing network capacity on short-term and long-term potential. Our objective in analyzing the capacity trends is to provide insights and design guidelines for optimizing the IWSSN network schedules. The rest of this article is organized as follows. Section 2 summarizes related works in integrated sensor-satellite networks and existing capacity models/tools. Section 3 gives a formal definition of the network model and notations. In Section 4, we propose a general capacity definition and an analytical capacity model for the IWSSN. Then, we analyze the influence of major capacity-impacting constraints on the network capacity in Section 5. Finally, we make a conclusion in Section 6.
# Domination and Independent Domination in Extended Supergrid Graphs +
## 1 Introduction
For other relevant works and comparisons, the reader is referred to [19; 20; 21]. Supergrid graphs have been proposed for computing the stitch traces for computerized embroidery machines [22]. Unfortunately, the Hamiltonian cycle and path problems on supergrid graphs have been shown to be NP-complete in [22]. Thus, we studied the complexities of the Hamiltonian and longest paths for the special classes of supergrid graphs in [23; 24; 25; 26; 27]. In this paper, we expand supergrid graphs to a generalized graph class called extended supergrid graphs. In general, a supergrid graph is not a grid graph, and vice versa. Extended graphs contain supergrid, grid, diagonal supergrid, triangular supergrid graphs as graph subcalsses. Generally, rectangular grid graphs form a subclass of grid graphs, rectangular supergrid graphs form a subclass of supergrid and diagonal supergrid graphs, and rectangular triangular-supergrid graphs are a subclass of triangular supergrid graphs. However, the intersection of rectangular grid graphs, rectangular supergrid graphs, and rectangular triangular-supergrid graphs is empty, except for certain special paths. In this paper, we study the complexities of domination and independent domination problems for extended supergrid graphs and their subclasses. The domination problem on grid graphs is known to be NP-complete [2]. Thus, the studied problems are NP-complete as well for extended supergrid graphs. In [28], we provided a rough proof to claim that the domination and independent domination problems on supergrid graphs are NP-complete. In this paper, we present a full proof to show that the domination and independent domination problems on diagonal supergrid graphs are NP-complete. By a simple extension, we verify that these two problems for supergrid and triangular supergrid graphs are NP-complete as well. On the other hand, the domination and independent domination numbers of rectangular grid graphs have been computed in [18; 29; 30; 31]. In [28], we solved the domination and independent domination problems on rectangular supergrid graphs in linear time. However, the complexities of the domination and independent domination problems on rectangular triangular-supergrid graphs remain unknown. In this paper, we provide a tight upper bound of the domination and independent domination numbers for rectangular triangular-supergrid graphs. The remainder of this paper is organized as follows. Section 2 describes the symbols used in this paper, definitions, and several known related results. In Section 3, we first prove the domination and independent domination problems on diagonal supergrid graphs to be NP-complete in detail. We then extend this result into supergrid and triangular supergrid graphs. In Section 4, we provide a tight upper bound of the domination and independent domination numbers for rectangular triangular-supergrid graphs. Finally, concluding remarks are made in Section 5.
## 3 Results and Discussion
### Effect of Fine Fraction Content on Cracking Resistance
#### 3.1.4 Fracture Energy (G\({}_{\text{F}}\))  
The evolution of fracture energy, which is shown in Figure 8, measures the work required for crack initiation and propagation. As expected, PMB 45/80-65 mixtures present an improved cracking response.  
However, in terms of fine fraction content, the results differ from those obtained in the above-mentioned cracking parameters. As can be observed F2 gradation requires the higher energy amount to achieve cracking followed by F3 and F1 gradation, in this order. Evaluating the statistical results, the coefficient of determination (R2) for the data set obtained is less than 0.500, showing a poor correlation between the fracture energy and the input parameters.  
\[\begin{array}{c}G_{F}=58.855+16.743\times BG-5.890\times T-4.931\times F\\ R^{2}=0.403\end{array} \tag{5}\]  
This may be explained by the fact that the fracture energy parameter is influenced by the maximum load as well as the post-peak curve. As the fine fraction content increases, the maximum load tends to increase but at the same time, the toughness tends to diminish. For this reason, the fine fraction content that represents a balanced combination between the maximum load and maximum toughness will result in a peak value of the fracture energy.
## 4 Results and Discussion
### Alarm Signals, Key Causal Factors, and Decisions  
Most sites were healthy and there were no alarm signals during the first day. However, at approximately 11:00 a.m. on July 12, the blue alarm signal first appeared in the second reach of the Feiyun River. After this time, blue or stronger alarm signals appeared in more and more reaches. For instance, at 1:00 p.m. on July 12, HI values ranged from 0.853 to 0.45 as shown in Figure 6. Figure 6 provides a spatial distribution of the variation in HI at 1:00 p.m. on July 12. Figure 6 indicates that orange alarm signals appeared in Guixi reach while blue alarm signals appeared in Zhaoshandu Reservoir, Yuquan downstream reach, Huantanken reach, etc. These data revealed a wide variation in health across the 48 reaches. We computed the _VIPs_ of the main causal factors as shown in Figure 7. Figure 7 shows that the turbidity _VIP_ was the largest. Usually, turbidity is caused by particles suspended or dissolved in water, which can be algae or sediment. During this event, the intense rain eroded a large amount of sediment flowing into this water body, which made the water appear cloudy or murky. This result indicated that turbidity was the key causal factor that threatened river health. From the derived warning information, the key causal factor were identified and delivered to policy makers at local and provincial levels. Then, the tool searched the knowledge base to find response decisions, which were then sent to managers who were reminded to improve the ability to treat water.  
Figure 5: Health index (HI) prediction over the period of Maria: (**a**) HI trend thematic map of all reaches and (**b**) HI trend line chart of Zhaoshandu Reservoir.  
Figure 6: HI warning thematic maps.
## 2 Materials and Methods
### In Silico Validation  
We examined the quantification accuracy of the perfusion parameters through an in silico validation procedure (Figure 3). We generated a total of 30 digital phantoms, and each phantom was characterized by the total hemoglobin concentration and oxygen saturation. Thirty phantoms were divided into 5 sets, with 6 individual phantoms in each set. The hemoglobin concentration was increased from 2.25 to 20.15 g/L with an increment of 4.5 g/L from set 1 to set 5. In each set, the oxygen saturation levels were increased from 50% to 100% with an increment of 10% for 6 phantoms. Absorption coefficients at 660 and 850 nm were calculated based on the total hemoglobin concentration and oxygen saturation using Equations (5) and (6) for all phantoms. The absorption coefficients at both wavelengths were used to quantify hemoglobin concentration and oxygen saturation using the stage-2 LUT. The quantified hemodynamic properties were compared to the designed hemodynamic properties to assess the quantification accuracy.
## 5 Conclusions  
In summary, two new sesquiterpene synthases, _Pm_STPS1 and _Pm_STPS2, which were identified from _P. minus_ leaf transcriptomics analysis, were cloned and characterised. Both of the enzymes produced industrially important acyclic sesquiterpenes, \(\upbeta\)-farnesene, \(\upalpha\)-farnesene, and farnesol. _Pm_STPS2 also produced nerolidol as the major product from FPP conversion. This study demonstrated the production of _P. minus_ characteristic fragrance-related sesquiterpenes, by both _Pm_STPS1 and _Pm_STP, as well as the potential of further metabolic engineering in _E. coli_, using _Pm_STPS2 for the microbial production of nerolidol.  
**Supplementary Materials:** The following are available online, Figure S1: Nucleotide and predicted amino acid sequence of _Pm_STPS1 and from _P. minus_; Figure S2: Nucleotide and predicted amino acid sequence of _Pm_STPS2 from _P. minus_; Figure S3: SDS-PAGE analysis of recombinant pQE2_Pm_STPS1 and pQE2_Pm_STPS2 proteins is marked with red box and molecular mass markers are indicated; Figure S4: The expression analysis of _Pm_STPS1 and _Pm_STPS2 in _E. coli_ M15 after 0.5 mM IPTG induction at 1, 3, and 5 h; and Figure S5: Mass spectra of major three sesquiterpenes produced by recombinant _Pm_STPS1 and _Pm_STPS2 in comparison with the mass spectra from authentic standards.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed all of the experiments; [PERSON] performed the experiments and analysed the data; S.N.B. and H-H.G. contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote and edited the paper. All of the authors reviewed the manuscript.  
**Funding:** This research was funded by Research University Grants from Universiti Kebangsan Malaysia [UKM-GUP-2013-028 and DLP 2013-024] awarded to [PERSON]. We also acknowledge the Centre for Research and Instrumentation Management (CRIM), UKM for a Research Instrumentation Development Grant 2010 (PIP-CRIM). The publication fee was supported by DPP-2018-010 awarded to INBIOSIS, UKM.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] for their technical assistance and the reviewers for constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Aiming to alleviate traffic congestion, many congestion avoidance and traffic optimization systems have been proposed recently. However, most of them suffer from three main problems. Firstly scalability: they rely on a centralized server, which has to perform intensive communication and computational tasks. Secondly unpredictability: they use smartphones and other sensors to detect the congested roads and warn upcoming vehicles accordingly. In other words, they are used to solve the problem rather than avoiding it. Lastly, infrastructure dependency: they assume the presence of pre-installed infrastructures such as roadside unit (RSU) or cellular 3G/4G networks. Motivated by the above-mentioned reasons, in this paper, we proposed a fully distributed and infrastructure-less congestion avoidance and traffic optimization system for VANET (Vehicular Ad-hoc Networks) in urban environments named DIFTOS (Distributed Infrastructure-Free Traffic Optimization System), in which the city map is divided into a hierarchy of servers. The vehicles that are located in the busy road intersections play the role of servers, thus DIFTOS does not rely on any centralized server and does not need internet connectivity or RSU or any kind of infrastructure. As far as we know, in the literature of congestion avoidance using VANET, DIFTOS is the first completely infrastructure-free congestion avoidance system. The effectiveness and scalability of DIFTOS have been proved by simulation under different traffic conditions.  
congestion avoidance; traffic optimization; congestion avoidance; VANET; intelligent transportation systems; ITS; path planning; distributed server 2018 182567 doi:10.3390/s18082567
## 5 Experiments and Results  
Two experiments were conducted to compare the proposed method performance. The first experiment used the K-EmoDB database developed in Section 3. We chose six emotions for the experiment: neutral, anger, happiness, sadness, fear, and excitement. Randomly selected records (630 of 900, i.e., 70%) were used as training data, and the remaining 170 records as test data. We used 5-fold cross-validation, i.e., repeated the sample selection five times. The second experiment compared the proposed method with previous emotion recognition studies using various international databases. We used the various databases employed in the previous studies and compared accuracy directly and indirectly.  
Figure 6: Proposed emotion recognition network structure.
## 2 Results and Discussion
### Taxonomic Misdeterminations  
#### 2.3.1 Eremophila mitchellii  
The two species, _E. mitchellii_ and _E. sturtii_ have a history of misidentification. Serrulatic acids were isolated from _E. mitchellii_ but misdetermined as _E. sturtii_[44]. Conversely, a new class of sesquiterpenes, the mitchellenes, were isolated from _E. sturtii_ but misdetermined as _E. mitchellii_, then named as mitchellenes rather than'sturtienes' [45]. A revision to the phytochemistry of the two species was published later which increased the number of mitchellenes [4]. New mitchellenes maintain the same naming system.  
Figure 7: Principal component analysis of essential oil components from _Eremophila longifolia_.  
A study of the chemical variation of hydrodistilled essential oils from eight specimens demonstrates that myomontanone is consistently present in the profile of volatiles from specimens widely distributed across NSW and south Qld (Table 8). However, from limited sampling, ngaione is expressed by some specimens but not others, with no clear geographical pattern. Mitcheltene B is present in all hydrodistilled essential oils. Other mitchellenes, such as mitcheltene G, and mitcheltene isomers or epimers that have not been chemically assigned yet, were realised by examination of mass spectral data. Principal component analysis created two major groups (Figure 8) that are divided according to presence or absence of ngaione.  
Leaves from four specimens of _E. mitchellii_ were sampled to investigate possible chemical variation by comparison with the profiles reported by [PERSON] et al. [7]. A type of variation that is familiar to Australian species was evident, wherein the monoterenoid components were absent from one specimen (Table 9). While most specimens expressed predominantly \(\alpha\)-pinene and bicyclogermacrene, no \(\alpha\)-pinene was expressed by the one specimen, Emi-181. Furthermore, timber essential oil (Emi-Wood) from a private collection received posthumously ([PERSON], 1934-2015) was fractionated and components assigned by NMR, revealing a chemical character identical to the specimen studied by [PERSON] et al. [7].  
This has inspired a new view on phenoplasticity of volatiles in Australian species. Due to the perishable nature of leaves, accumulation of volatiles therein creates a chemical profile that records short term expression patterns. However, due to efficient storage of metabolites in timber, the metabolome therein is a record of long-term expression patterns, disguising the effects of phenoplasticity and improving metabolomic reproducibility. Because phenoplasticity of volatiles antagonises the taxonomic agreement in chemotaxonomic studies, it may be better to use timber, bark, or fragments of branches to create more precise chemical profiles within taxa.