esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper presents an intelligent evaluation model based on fuzzy logic for satellite thermal health analysis. A new evaluation index and topological system are proposed to evaluate satellite health status. Taking the temperature controller in the satellite thermal control subsystem as the target  
Figure 16: Health indicators of TCS (Compare Case I and Case IV). (a)Health degree. (b)Failure factor.  
Figure 14: Dynamic temperature of damaged components under Case I and Case IV.  
Figure 15: Health indicators of satellite subsystems (Case IV). (a) Health degree. (b) Failure factor.  
of thermal load, the health deterioration of the satellite under typical working conditions such as time-varying and maximum thermal load is analyzed. Based on the analytical investigations presented in this paper, the following conclusions may be made:  
1. The fuzziness of the relationship between temperature and failure probability is considered, and the relationship between temperature and failure probability is quantitatively described by intelligent analysis method (fuzzy reasoning).
2. The model can quickly and accurately evaluate the effects of different thermal conditions on satellite health. The health deterioration of the system is characterized by the change of health degree and failure factor.
3. Multi-period, high heat flux density, and low duty ratio have great influence on satellite health.  
The model solves the problem of collaborative solution of different models (satellite thermal analysis is continuous process simulation, satellite component failure is discrete probability event simulation), as well as the challenges of model absence and non-quantification in satellite thermal health analysis. The model can better reflect the dynamic deterioration process of the system health. In the future, it can be transported in many systems with strict life requirements, and only the topology system needs to be modified according to different systems.  
Conceptualization, Y.-Z.L.; methodology, E.-H.L.; software, E.-H.L. and T.-T.L.; validation, Z.-Z.Z. and T.L.; formal analysis, E.-H.L.; investigation, E.-H.L.; resources, Y.-Z.L.; data curation, T.-T.L. and Z.-Z.Z.; writing--original draft preparation, E.-H.L. and J.-X.L.; writing--review and editing, T.L.; visualization, E.-H.L.; supervision, Y.-Z.L.; project administration, Y.-Z.L.  
The project was funded by the Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences through grant no.LSU-JCJS-2017-1.  
The authors declare no conflict of interest.
## 3 Fatty Acids and Hydrocarbons
### Fatty Alcohols and Hydrocarbons  
Many species of cyanobacteria naturally produce small quantities of hydrocarbons [71]. The hydrocarbon production pathway and its enzymes were identified [72; 73]. Fatty acyl-ACP, the end product of fatty acid elongation, is reduced to fatty aldehyde by fatty acyl-ACP reductase. Fatty aldehyde decarboxylase then catalyzes the conversion of fatty aldehydes into alkanes or alkenes with formate as a byproduct.  
Engineered PCC6803 has been demonstrated to produce fatty alcohols with a titer of around 0.2 mg/L through the expression of fatty acyl-CoA reductase from jojoba [22]. While hydrocarbon biosynthesis is natural to many cyanobacteria, overexpression of the genes involved in alkane biosynthesis in cyanobacteria has been used to increase alkane productivity [65; 74; 75]. However, compared to the higher productivity of fatty acids, both fatty alcohol and hydrocarbon productions are relatively low, indicating the presence of bottlenecks. It is likely that the key enzyme fatty acyl-ACP reductase is limiting, as the flux to fatty acyl-ACP is capable of supporting higher flux production of fatty acids. As an alternative to using acyl-ACP reductase, liberation of fatty acids followed by activation to acyl-CoAs allows the use of CoA-acylating aldehyde dehydrogenase, which has been shown to reduce to fatty aldehyde more efficiently in _E. coli_[76]. However, this pathway has yet to be tested in cyanobacteria.
## 1 Introduction
### Literature Review
#### 1.1.1 Machine Learning-Based Methods  
[PERSON] et al. proposed a random forest (RF)-based tool wear prediction method using cutting force, vibration, and acoustic emission signals, and four time-domain statistical features were extracted from these signals to train the model [9]. [PERSON] et al. proposed a tool condition monitoring framework with linear regression (LR), Bayesian linear regression (BLR), decision forest (DF), boosted decision trees (BDTs), and artificial neural networks (ANNs), and several time and frequency domain statistical features were used [10]. [PERSON] et al. proposed a tool wear-state recognition algorithm based on wavelet threshold denoising (WTD), variational mode decomposition (VMD), and weighted random forest based on K-nearest neighbors (KWRF) using the triaxial vibration signals of the spindle [11]. WTD and VMD were used to denoise and decompose the original vibration signal; then, the Isomap nonlinear dimensionality reduction algorithm was used to feature dimensionality reduction from twenty types of time, frequency, and time-frequency domain features, and KWRF was used to identify the wear state. The model was tested under different working conditions, which showed good generalization capability and robustness. [PERSON] used the ANN, support vector machine (SVM), and Gaussian process regression (GPR) for tool wear estimation with five types of time-domain features and three types of frequency-domain features [12]. In addition, the process information is also input into these machine learning models, which has been shown to improve model prediction performance.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation
#### 4.2.3 Qualitative Evaluation of Nodes Identification
Note that the results are quite intuitive, and conform with our findings in the earlier analysis of the dataset in Appendix A, as authors with Asian names are more likely to share common names, due to shorter name length, and simplified transcription (from Mandarin to English for example). We also investigated the ranks of nodes that maps to more than 2 entities (highlighted with an asterisk in both tables). Again FONDUE-NDA outperforms NC, and ranks 3 out of 6 highly ambiguous names in the top 10. This confirms the results obtained in Section 4.2.2, as FONDUE-NDA outperforms NC in as a state-of-the-art method for identifying ambiguous nodes. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **FONDUE-NDA** & **NC**[16] \\ \hline AUC & 0.971 & 0.944 \\ TP & 17 & 11 \\ FP & 14 & 22 \\ F1-score & 0.55 & 0.35 \\ NDCG & 0.877 & 0.681 \\ NDCG\({}^{*}\) & 0.743 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of FONDUE-NDA compared to NC for the PubMed dataset containing 31 ambiguous author-names of which 6 are associated with more than 2 Orcid IDs (highly ambiguous). NDCG\({}^{*}\) reflects the ranking score of those highly ambiguous nodes.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 2 DW Family Code Construction
### Enhanced Double Weight (EDW) Code Construction  
EDW code is an enhanced version of the DW code. It is characterized as a low cross-correlation code, where cross-correlation (\(\lambda_{c}\)) = 1. Unlike DW code, where the code weight is always 2, the weight in EDW can be any odd number greater than 1. EDW has a different sequence and structure than DW and MDW, whereby it contains 2,2,1 rather than 1,2,1. The EDW sequence also preserves the cross-correlation of 1 and enables two overlaps within three columns. The link between the number of users (\(K\)) and the code length (\(N\)) is given in the equation below [5; 20].  
\[N = 2K\ +\ \frac{4}{3}\bigg{[}\sin\bigg{(}\frac{K\pi}{3}\bigg{)}\bigg{]}^{2} \frac{8}{3}\bigg{[}\sin\bigg{(}\frac{(K+1)\pi}{3}\bigg{)}\bigg{]}^{2}\ +\ \frac{4}{3}\bigg{[}\sin\bigg{(}\frac{(K+2)\pi}{3}\bigg{)}\bigg{]}^{2} \tag{2}\]  
The basic matrix for EDW weight (\(W\)) = 3 consists of a \(3\times 6\) matrix. Therefore, the basic EDW code sequence for \(W\) = 3 is shown in Table 2[20].  
The sequence differs from a basic code for \(W\) = 5 and above due to the new construction method (shifting technique). Table 3 shows the construction of the EDW code for \(W\) = 5 [20].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(K_{th}\) & \(C_{9}\) & \(C_{8}\) & \(C_{7}\) & \(C_{6}\) & \(C_{5}\) & \(C_{4}\) & \(C_{3}\) & \(C_{2}\) & \(C_{1}\) \\ \hline
2 & 0 & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 \\
3 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Basic MDW code sequence For \(W\) = 4 [17].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(K_{th}\) & \(C_{6}\) & \(C_{5}\) & \(C_{4}\) & \(C_{3}\) & \(C_{2}\) & \(C_{1}\) \\ \hline
2 & 1 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 0 \\
3
## 3 Results and Discussion
### Sheet Resistance  
Figure 10a shows the measured actual sheet resistance of the composite together with the calculated no contact, fully contact resistances. It is obvious that the measured sheet resistance of the composite locates between the upper and lower values calculated from the no contact and fully contact models. Therefore, the actual contact model is assumed to represent the average contact condition of the Gr/AgNW/Gr composite well.  
On the basis of Equation (3) and the measured sheet resistance of the composite, contact resistances with different AgNW concentrations can be derived and are shown in Figure 10b. Nonlinear regression of the sheet resistance \(R_{S}\) and contact resistance \(R_{C}\) is conducted and the correlation functions with various AgNW concentrations \(\phi\) are obtained as follows:  
\[R_{S}=-137.5\phi^{3}+534.3\phi^{2}-735.6\phi+382,\ R^{2}=0.98 \tag{6}\]  
\[R_{C}=-2410\phi^{3}+5920\phi^{2}-4883.8\phi+1384.2,\ R^{2}=0.96 \tag{7}\]  
The measured sheet resistance of the AgNW network, Gr/AgNW/Gr nanocomposite, and calculated contact resistance value with five AgNW concentrations is listed in Table 2. It can be seen that all the values decrease with the increased AgNW concentrations. This is because the AgNW network with a higher concentration has more connection joints within the network and a greater connection area with the two graphene layers that can increase the conductivity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AgNW** & **AgNW** & **Gr/AgNW/Gr** & **Gr/AgNW/Gr** & \\
**Concentration** & **Transmittance** & **Transmittance** & **Transmittance** & **Transmittance** & **Error** \\
**(mg/mL)** & **(Measured)** & **(Calculated)** & **(Measured)** & \\ \hline
0.2 & 87.7\% & 81.6\% & 85.7\% & 5\% \\
0.4 & 86.6\% & 80.5\% & 81.4\% & 1\% \\
0.6 & 85.0\% & 79.1\% & 80.6\% & 2\% \\
0.8 & 83.5\% & 77.6\% & 80.1\% & 3\% \\
1.0 & 79.3\% & 73.7\% & 78.3\% & 6\% \\ \hline \hline \end
## 4 Results and Discussion
### Fabricating Parameters  
There were many gaps around the staples, as shown previously, caused by the insertion process. The voids could not be avoided but could be minimized. Voids also occurred around the penetrated rebar [39] and embedded steel wire [13]. [PERSON] and [PERSON] observed that the voids were smaller at the deeper point of their penetrated rebar, indicating  
Figure 16: Simple models of the reinforcements. (**a**) Stapled beam under three-point bending test and (**b**) staple over the interface.  
better bonding status between the rebar and the composite [39]. Moreover, as shown in Figure 4a, the staples were packed with a film of glue that could further decrease the bonding (the packed steel fibers also have the outer films). The voids and films led to insufficient bonding (i.e., weak contact [13]). The first solution is to minimize the voids by optimizing the inserting parameters or introducing other techniques to the SIA, such as vibrating the staple ultrasonically. The second is to enhance the contact area between the staples and composite, similar to the coating technique. [PERSON] and [PERSON] coated rebar with sand and improved the residual bonding strength by 23% compared to rebar with a smooth surface [33]. The third is the cementitious material. A study [53] showed that the bond quality is highly dependent on the chemical interaction between the cable surface and matrix mortar and the flow behavior of the matrix around the cable. The mortar with high fluidity could fill the gaps, but high fluidity leads to low ability to build. Fortunately, it is possible for large-scale printing with a large nozzle and long loop time between two layers to achieve hardening, planted by large staples. Vibrations and the high-fluidity printable material can minimize the size of the voids.
## 3 LC-Tank Oscillator
### LC-Tank Layout Design  
The complete layout of the LC-VCO is shown in Figure 7, and it is composed of the simple current tail mirror, varactors, cross-coupled cell, inductor, and poly-silicon resistance from bottom to the top.  
The current mirror is designed as a single strip, and a common centroid technique is adopted for the cross-coupled cell. Moreover, the minimum space allowed by technology rules is used, helping to increase matching. About 85% of the total area is occupied by the differential inductor (\(177\times 198\)\(\upmu\)m\({}^{2}\)) that has a quality factor of 20. It has been chosen with an odd number of turns because the two output terminals are on the same side of the cell, thus making the routing shorter with MOSFETs devices. Moreover, the single resistor connected to the center tap helps to reduce the metal connection length between the inductor and the cross-coupled cell.  
Figure 6: Frequency response simulated for minimum (**red line**) and maximum (**blue line**) values of the control voltage. The vertical marker indicates the target frequency of 6.25 GHz.  
The oscillator is designed to work properly in the temperature range \(-55\)\({}^{\circ}\)C, \(+125\)\({}^{\circ}\)C with \(10\%\) variations of current bias and voltage supply. The total layout area of the proposed LC-VCO is \(308\times 198\)\(\upmu\)m\({}^{2}\).
# Querying on Federated Sensor Networks
## 3 L-FSN Protocol
### Routing Tables on Gateways
#### 3.1.2 Failure Detection and Recovery  
In a layered FSN topology, in case an EG fails, EGs can select alternative paths based on their routing table. After routing tables are established on each EG, each EG can provide multiple path information to each destination EGs. In case of path failures, EGs can forward packets through alternative paths.  
Each time a gateway receives a packet, it replies back with an acknowledgement message to the sender gateway. If a gateway does not receive an acknowledgement message, it initiates the failure-recovery mechanism as follows. If a failed node is an EG, failure information of the EG is propagated into FSN by routing table exchanges. If a failed node is an IG, this information is propagated among IGs based on the intra-island routing protocol.
## 3 Equations of Motion in Terms of Quasi-Velocities
### Controller  
The obtained results can be summarized in the following proposition.  
**Proposition 1**.: _Consider an underactuated vehicle described by Equations (1) and (4)-(12) where the control objective is to stabilize trajectory tracking errors, i.e., \(\lim_{t\rightarrow\infty}x_{e}=0\), \(\lim_{t\rightarrow\infty}y_{e}=0\), and \(\lim_{t\rightarrow\infty}\psi_{e}=0\). If Assumptions 1-3 are fulfilled then the output signals \(\tau_{u}\) (42) and \(\tau_{r}\) (51) of the vehicle controller enable it to follow a desired trajectory in the horizontal plane in the presence of parameter perturbations and external disturbances. The full control scheme consists of:_  
1. _Kinematic control algorithm which makes the velocity control subsystem uniformly ultimately bounded;_
2. _Dynamic control algorithm which moves the vehicle to a desired trajectory (and at least ensures uniformly ultimate boundedness)._  
**Proof of Proposition 1.** Taking into considerations presented in Section 4.1 (assumptions), Sections 4.2 and 4.3 it can be concluded using the kinematic controller the velocity control subsystem is uniformly ultimately bounded, while the dynamic controller moves the vehicle to a desired trajectory and ensures uniformly ultimate boundedness. The control signals \(\tau_{u}\) (42) and \(\tau_{r}\) (51) guarantee that a desired trajectory in the horizontal plane under parameter perturbations and external disturbances is tracked. Thus, it can be stated that the stability analysis is completed.
# A Review of Time-Scale Modification of Music Signals +
## 4 TSM Based on Waveform Similarity Overlap-Add (WSOLA)
### The Procedure
However, due to the constraint \(\Delta_{m+1}\in[-\Delta_{\text{max}}:\Delta_{\text{max}}]\), the adjusted frame \(x^{\prime}_{m+1}\) must be located inside of the _extended frame region_\(x^{+}_{m+1}\) (solid blue box in Figure 5b):  
\[x^{+}_{m+1}(r)= \begin{cases}x\left(r+(m+1)H_{\text{a}}\right),&\text{if }r\in[-N/2-\Delta_{\text{max}}:N/2-1+\Delta_{\text{max}}],\\ 0,&\text{otherwise}.\end{cases}  \tag{8}\]  
Therefore, the idea is to retrieve the adjusted frame \(x^{\prime}_{m+1}\) as the frame in \(x^{+}_{m+1}\) whose waveform is most similar to \(\tilde{x}_{m}\). To this end, we must define a measure that quantifies the similarity of two frames. One possible choice for this metric is the _cross-correlation_  
\[c(q,p,\Delta)=\sum_{r\in\mathbb{Z}}q(r)\ p(r+\Delta) \tag{9}\]  
of a signal \(q\) and a signal \(p\) shifted by \(\Delta\in\mathbb{Z}\) samples. We can then compute the optimal shift index \(\Delta_{m+1}\) that maximizes the cross-correlation of \(\tilde{x}_{m}\) and \(x^{+}_{m+1}\) by  
\[\Delta_{m+1}=\operatorname*{argmax}_{\Delta\in[-\Delta_{\text{max}}:\Delta_{ \text{max}}]}c(\tilde{x}_{m},x^{+}_{m+1},\Delta). \tag{10}\]  
The shift index \(\Delta_{m+1}\) defines the position of the adjusted analysis frame \(x^{\prime}_{m+1}\) inside the extended frame region \(x^{+}_{m+1}\) (Figure 5c). Finally, we compute the synthesis frame \(y_{m+1}\), similarly to OLA, by  
\[y_{m+1}(r)=\frac{w(r)\ x(r+(m+1)H_{\text{a}}+\Delta_{m+1})}{\sum\limits_{n\in \mathbb{Z}}w(r-nH_{\text{s}})} \tag{11}\]  
and use Equation (2) to reconstruct the output signal \(y\) (Figure 5d). In practice, we start the iterative optimization process at the frame index \(m=0\) and assume \(\Delta_{0}=0\).
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
## 2 Methods
### Subspace Approximation (PCA)  
If signals in a dataset can be well approximated within a subspace of lower dimension (\(R<N\)), we can find the optimal basis by applying the celebrated Principal Component Analysis (PCA) algorithm. This method was originally introduced in statistics by [PERSON] in 1901 [29], but developed later independently by [PERSON] [30] and [PERSON] [31]. PCA basis vectors \(\boldsymbol{\phi}_{i}\) and their associated coefficients are easily computed by means of a Singular Value Decomposition (SVD) of the data covariance matrix. Formally, given a set of normalized signals \(\{\mathbf{x}_{j}\in R^{N}\}\) (zero-mean and unit-variance samples \(n=1,2,\ldots,J\)), the rank-\(R\) PCA decomposition of any signal \(\mathbf{x}_{j}\) in the set is obtained by Equation (2), with orthonormal vectors \(\boldsymbol{\phi}_{i}\) corresponding to the first \(R<N\) dominant singular vectors  
Figure 1: Decomposition models. (**a**) **General linear model**: a collection of vector data samples organized as columns of a matrix dataset \(\mathbf{X}\) is approximated by the product of matrices \(\mathbf{\Phi}\) and \(\mathbf{A}\). (**b**) **Subspace** approximation: all vectors in the dataset are approximated by linear combination of few vectors (principal components). (**c**) **Sparse coding**: each vector in the dataset is approximated by the linear combination of atoms (columns of a dictionary \(\mathbf{\Phi}\)). In both, (**b**,**c**), the optimal choice of matrix \(\mathbf{\Phi}\) can be computed from the dataset itself by means of the SVD and an dictionary learning algorithm, respectively. (**d**) **EMD**: every single signal is decomposed as a sum of characteristic modes. Tensor decomposition models such as **Low-rank Tucker** (**e**), **Low-rank CP** (**f**) and **Sparse Tucker** (**g**) can be written as sum of rank-1 tensors (**h**).  
of the data covariance matrix and coefficients are computed by \(\alpha_{i,j}=\mathbf{x}_{j}^{T}\boldsymbol{\phi}_{i}\). In this case, the obtained decomposition model is also referred as low-rank matrix factorization as illustrated in Figure 1b.
## 3 Modeling and Results  
In this study, we employ NTL attenuation as the response variable, and the depth, magnitude, DorN, cityDistance, roadDistance, population and HP point as the predictor variables. The earthquake events considered for analysis span the years 2013 to 2022; following data processing and sample extraction, we obtained a dataset comprising 15,916 samples. The samples were classified utilizing machine learning techniques, with each model predicting the probability of an earthquake causing short-term irreparable damage to a city's power system.
###### Abstract  
Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time-frequency (TF) correlation analysis method of time series decomposition (TD) derived from synchrosqueezed S transform (SSST) is proposed in this paper. First, the two-dimensional time-frequency matrices of the signals is obtained by synchrosqueezed S transform. Second, time series decomposition is used to transform the matrices into the two-dimensional time-time matrices. Third, a correlation analysis of the local time characteristics is carried out, thus attaining the time-frequency correlation between the signals. Finally, the proposed method is validated by stationary and non-stationary signals simulation and is compared with the traditional correlation analysis method. The simulation results show that the traditional method can obtain the overall correlation between the signals but cannot reflect the local time and frequency correlations. In particular, the correlations of non-stationary signals cannot be accurately identified. The proposed method not only obtains the overall correlations between the signals, but can also accurately identifies the correlations between non-stationary signals, thus showing the time-varying and frequency-varying correlation characteristics. The proposed method is applied to the acoustic signal processing of an engine-gearbox test bench. The results show that the proposed method can effectively identify the time-frequency correlation between the signals.  
time-frequency correlation; synchrosqueezed S transform; time series decomposition; coherence 23 January 2019; Accepted: 6 February 2019; Published: 22 February 2019