esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Key Technology
### Fault Diagnosis  
When the health monitoring service issues a data abnormality warning, a possible fault of the constellation system requires an intervention of the fault diagnosis technique to confirm the cause of the fault. As a data-driven fault diagnosis technology, deep learning is used to extract fault characteristics from the training samples by learning from multiple hidden layers of network. Its strong characteristic learning and extraction capability can greatly improve the efficiency of fault diagnosis [24; 25]. However, the success of deep learning in fault diagnosis requires a large number of labeled data for training. For satellite constellations, although there are many data sources, such as test data of individual equipment in the constellation system, data obtained during ground tests and telemetry data generated during operation in orbit, due to the design principles of the high reliability and high safety of the constellation, most of the satellites are operating under normal operating conditions and the high cost and long production period of the constellation, making it impossible to perform system-level fault tests. This will lead to abundant normal operation data and scarce failure data of the constellation, which are unfavorable for the training of neural networks.  
In solving the problem of data imbalance, fault simulation technology based on system model would be of great value. The fault diagnosis workflow is shown in Figure 9. Based on the reliable system model described in Section 2.1, which has been corrected several times, after the fault modes are injected into the model as parameters, the model can generate the labeled fault data required for deep neural network training. However, the representation of fault modes in the model and the correlation between fault modes and model parameters still require a lot of preliminary work by designers. It is possible that not all fault modes can be expressed by the model. In addition, the more the fault modes are associated with model parameters, the more refined the granularity of model is, which will greatly reduce the efficiency of simulation. However, the time cost and economic cost are still quite advantageous compared with physical tests.  
There are many deep learning algorithms used for fault diagnosis, such as auto-encoder (AE) [26], convolutional neural network (CNN) [27; 28] and recurrent neural network (RNN) [29], which have their advantages and disadvantages. How to obtain high diagnosis rates in engineering applications with the aid of the above deep learning algorithms is the focus of the subsequent research.
## 1 Introduction
Then the reflection parameter \(S_{11}\) shows distinct minima, each of them can be attributed to distinct resonance modes and the corresponding resonance frequencies,\(f_{\mathrm{res}}\). A typical \(S_{11}\)-spectrum around a resonance mode is shown in Figure 1b. Further details on the microwave cavity perturbation method applied in the automotive exhaust can be found in [15,18,19]. Prior investigations with this method on the state observation of three-way catalysts with the size of \(\varnothing\) 4.66\" \(\times\) 6\" (\(\varnothing\) 11.84 cm \(\times\) 15.24 cm; full size as it is installed in the exhaust pipe) [11,12,20] and \(\varnothing\) 1\" \(\times\) 3\" (\(\varnothing\) 2.54 cm \(\times\) 7.62 cm; typical lab sample) [6,8] have shown that there is a good correlation between the resonance parameter \"resonance frequency\" and the catalysts' oxidation states. This work reports for the first time on an initial study to investigate whether the microwave-based technique can also be used to detect the aging state of the catalyst. Figure 1: (**a**) Sketch of the setup. (**b**) Typical resonance spectra in the reduced and in the oxidized state (data: 500 \({}^{\circ}\)C, lean: \(\lambda\) = 1.02, rich: \(\lambda\) = 0.98). The resonance frequency is indicated by an arrow. Peak width (FWHM) or reflection parameter at \(f_{\mathrm{res}}\) were not considered further.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 4 Experiments
### Experimental Results
#### 4.2.2 Results on the Airborne LiDAR Data  
The detection process was carried out on LiDAR data collected from outdoor airborne surveys for automated inspections of electrical grid assets. The accuracy of the detection was assessed by measuring the proportion of the true TT points within the detection outcomes. The recall rate was determined by calculating the ratio of true TT points to the total 3D point clouds of TTs. Furthermore, the average ground sample distance (GSD) of the 3D point clouds for TTs was also listed for each scenario. The outcomes for the three respective scenarios are detailed in Table 3.  
Table 3 shows that the proposed method is capable of extracting complete TTs in all three real-world scenarios (with recall rates approaching 100% in all three scenarios). However, there is still room for improvement in detection accuracy. This is primarily due to the fact that the current 2D detection boxes are larger than the actual range of the TTs, encompassing point clouds of nearby ground objects.  
The 2D detection results on the virtual view are shown in Figure 8. It can be seen from the figure that all three TTs in the scene have been detected on the virtual view, despite the complex topographic relief and distribution of clutters near the TTs.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Scheme** & **mAP\({}_{50}\)** \\ \hline
1 & YOLOv7 & 0.90 \\
2 & YOLOv7 + local path & 0.93 \\
3 & YOLOv7 + dual path & 0.95 \\ \hline \end{tabular}
\end{table}
Table 2: Results of the ablation study.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Accuracy** & **Recall Rate** & **GSD/m** \\ \hline
1 & 85\% & 100\% & 0.22 \\
2 & 80\% & 99\% & 0.21 \\
3 & 83\% & 100\% & 0.17 \\ \hline \end{tabular}
\end{table}
Table 3: Results on the airborne LiDAR data.  
The proposed method projects the 2D detection results into the 3D scene according to the projection matrix of the virtual view, obtaining the accurate position and point cloud information of each TT in the 3D scene. The results are shown in Figure 9. All 3D points belonging to the TTs are colorized in red in the figure, and the details of the three towers are displayed with local magnification. It can be seen from the figure that the algorithm can very well extract the 3D point cloud of the TT, but it can also be seen that a small number of point clouds of trees and bare ground near the TT are wrongly extracted.  
It is also worth noting that the extracted 3D TT point clouds can be directly used for the 3D model reconstruction of the towers. Furthermore, the proposed algorithm is capable of comprehensively extracting the entire point cloud of the TT, thereby preserving the high resolution of the point cloud data to the greatest extent. For instance, when applied to airborne LiDAR data, the resolution of the target point cloud detected using the proposed method can reach within 0.2 m. This level of detail ensures sufficient 3D base map accuracy for the automated inspection of electrical grid assets.
## 3 Fragility Curves and Damage States  
A fragility curve (\(FC\)) represents the probability that an engineering demand parameter \(EDP\) will exceed a specific capacity \(C_{DS_{i}}\) associated with a damage state \(DS_{i}\), as function of a seismic intensity measure \(IM\), such as Peak Ground Acceleration (\(PGA\)) or spectral acceleration or displacement, as follows:  
\[P\big{(}EDP\geq C_{DS_{i}}|IM\big{)}=\Phi\Bigg{(}\frac{\ln(EDP)-\ln\big{(}C_{ DS_{i}}\big{)}}{\beta_{DS_{i}}}\Bigg{)} \tag{18}\]  
where \(\Phi\) is the standard normal cumulative distribution function, and \(\beta_{DS_{i}}\) is the logarithmic standard deviation of the capacity \(C_{DS_{i}}\), representing the variability in structural capacity due to material, geometric, and modeling uncertainties.  
The seismic performance of liquid storage tanks is evaluated through four critical damage states, each corresponding to the specific failure modes identified as critical. Table 1 summarizes these damage states, along with their associated capacities and fragility functions.  
Incremental dynamic analysis (IDA) is used to derive the fragility curves for each damage state. The IDA process involves (1) selecting a suite of representative earthquake ground motions (Section 4.2); (2) scaling the ground motions incrementally to different intensity levels \(IM\); and (3) simulating the tank response to each scaled ground motion using the 2 DoF model developed in Section 2.  
For each ground motion and \(IM\), the \(EDPs\) (\(\delta_{s},FS,OM,\sigma_{m}\)) are computed and compared to their respective capacities \(\big{(}H_{f},FS_{cr},\ OM_{cr},\ \sigma_{ep}\big{)}\) to determine whether a specific damage state is exceeded. The resulting fragility curves are plotted for each damage state, showing the probability of exceeding the capacity (\(C_{DS_{i}}\)) for varying \(IM\) levels. The flowchart of the procedure to obtain FCs using IDA is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Damage State** & **Description** & **Capacity** & **Demand** & **Fragility Function** \\ \hline \(DS_{LO}\) & Liquid overspill & \(H_{f}\) in Equation (16) & \(\delta_{s}\) in Equation (17) & \(P(\delta_{s}\geq H_{f}|IM)\) \\ \hline \(DS_{BS}\) & \begin{tabular}{c} Exceedance of critical \\ base shear \\ \end{tabular} & \(FS_{cr}\) in Equation (12) & Base shear (\(FS\)) & \(P(FS\geq FS_{cr}|IM)\) \\ \hline \(DS_{OM}\) & \begin{tabular}{c} Exceedance of \\ overturning moment \\ \end{tabular} & \(OM_{cr}\) in Equation (11) & \
\begin{tabular}{c} Overturning moment \\ (\(OM\)) \\ \end{tabular} & \(P(OM\geq OM_{cr}|IM)\) \\ \hline \(DS_{EFB}\) &
\begin{tabular}{c} Exceedance of critical \\ buckling limit \\ \end{tabular} & \(\sigma_{ep}\) in Equation (14) & \(\sigma_{m}\) in Equation (15) & \(P\big{(}\sigma_{m}\geq\sigma_{ep}|IM)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the damage states.
## 4 Numerical Example Preliminaries  
In order to solve compressible and incompressible flows with the same code, in this work, the set of pressure-primitive variables is chosen:  
\[\mathbf{Y}=\begin{cases}p\\ \mathbf{u}\\ T\end{cases}\]  
For the numerical examples presented below, the full Navier-Stokes (full NS) model uses the perfect gas equation of state. For the Boussinesq model, this equation of state is substituted by constant density. Note that the equation of state \(\rho=\rho(T)\), would yield approximated acoustically filtered compressible equations [4].  
In this work, the following stabilization matrices are analyzed and compared:  
* The compressible \"classic\" tau matrix transformed into pressure primitive variables [20; 27];
* The incompressible diagonal tau presented in [20], which is an extension of [45];
* The incompressible non-diagonal tau [20], referred to here as HH, also an extension of [45];
* The incompressible Polner tau, which is an extension of the above definition, with the parameter \(\omega=0.6\)[29] ;
* The compressible Polner tau, an extension of the above definition [33].  
The incompressible taus defined above have been updated with the newer continuity-stabilization parameter, \(\tau_{c}\), based on the inverse of the momentum-stabilization parameter, \(\tau_{m}\), which was presented and proposed in these works [36; 37; 38; 39; 40; 41]. See Appendix A for the structure of the above incompressible stabilization matrices.  
Other stabilization matrices are possible. See, for instance, the stabilization matrix derived from residual-free bubbles [13; 35], where the off-diagonal terms come up naturally. These terms will be shown later to be fundamental in buoyancy-driven flows.
_Article_  
**Techno-Economic Evaluation of Hand Sanitiser**  
**Production Using Oil Palm Empty Fruit Bunch-Based**  
**Bioethanol by Simultaneous Sacharification and**  
**Fermentation (SSF) Process**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 2,**  
**[PERSON] 1, [PERSON] 1, [PERSON] 3 and**  
**[PERSON] 1,* [PERSON] 1,*  
1 Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia; [EMAIL_ADDRESS] (A.F.P.H.); [EMAIL_ADDRESS] (C.A.C.); [EMAIL_ADDRESS] (M.Y.A.R.)  
2 Chemical Engineering Program, Institut Teknologi Sumatera, Lampung 35365, Indonesia; [EMAIL_ADDRESS]  
3 Chemical Engineering Department, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +62-857-811-76292  
Received: 22 July 2020; Accepted: 26 August 2020; Published: 29 August 2020  
**Featured Application: The results of this work can be applied as the basis of consideration during the feasibility study step of plant design supposed to produce biomass-based bioethanol for commercial-scale hand sanitiser production. The results can contribute in the effort of converting agricultural waste into more valuable alternative product such as hand sanitiser which is urgently needed in global pandemic situation.**  
**Abstract:** Oil palm empty fruit bunch (OPEFB) is a potential raw material abundantly available for bioethanol production. However, the second-generation bioethanol is still not yet economically feasible. The COVID-19 pandemic increases the demand for ethanol as the primary ingredient of hand sanitisers. This study evaluates the techno-economic feasibility of hand sanitiser production using OPEFB-based bioethanol. OPEFB was alkaline-pretreated, and simultaneous saccharification and fermentation (SSF) was then performed by adding _Saccharomyces cerevisiae_ and cellulose enzyme. The cellulose content of the OPEFB increased from 39.30% to 63.97% after pretreatment. The kinetic parameters of the OPEFB SSF at 35 \({}^{\circ}\)C, which included a \(\mu\) max, ks, and kd of 0.018 h\({}^{-1}\), 0.025 g/dm\({}^{3}\), and 0.213 h\({}^{-1}\), respectively, were used as input in SuperPro Designer(r) v9.0. The total capital investment (TCI) and annual operating costs (AOC) of the plant were $645,000 and $305,000, respectively, at the capacity of 2000 kg OPEFB per batch. The batch time of the modelled plant was 219 h, with a total annual production of 32,506.16 kg hand sanitiser. The minimum hand sanitiser selling price was found to be $10/L, achieving a positive net present value (NPV) of $108,000, showing that the plant is economically feasible.  
**Keywords:** bioethanol; economic analysis; hand sanitiser; oil palm empty fruit bunch (OPEFB); simultaneous saccharification and fermentation; SuperPro Designer(r)
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 2 Design of Docking Logic and Kinematic Analysis for LNG Loading Arm  
In the traditional LNG loading-arm docking process, operations primarily rely on manual visual judgment and the experience of operators, who manually adjust the arm's rotating joint to align with the target flange. This method not only places high technical demands on operators but also suffers from interference caused by wind and waves in complex marine environments. As a result, docking efficiency is reduced, stability is compromised, and the process fails to meet the high efficiency and safety requirements of modern ports. The actual manual docking scenario is shown in Figure 1.  
To address these issues, this paper proposes an automated docking logic based on a staged control strategy, which divides the docking process into coarse docking and fine docking phases. The coarse docking phase focuses on rapid positioning and trajectory planning to achieve preliminary approach, while the fine docking phase ensures precise alignment through subtle adjustments and real-time error correction.  
During the docking process, the system dynamically adjusts the posture of the loading arm based on integrated sensor feedback, including distance measurements and visual information. This enables the system to compensate for deviations caused by external factors such as wind and waves, ensuring accurate and stable docking.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Introduction to CNN Principles
#### 2.2.1 Normalization and Training Test Splitting  
Before applying CNN for feature extraction, the data needs to be normalized to the interval \([-1,1]\) with the formula:  
\[X_{norm}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}\times(b-a)+a \tag{1}\]  
where a = \(-1\), b = 1, and \(X_{min}\) and \(X_{max}\) are the minimum and maximum values of the data, respectively.  
After that the data is split into a training set and a test set.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 2 DFT/FFT Algorithm: A Brief Overview  
The DFT of a sampled time-domain signal is described by:  
\[X_{k}=\sum_{n=0}^{N-1}x_{n}\cdot W_{N}^{kn}\quad k=0,1,\ldots,N-1 \tag{1}\]  
where \(x_{n}\) is the \(n\)-th time sample, \(X_{K}\) represents the DFT frequency output at the \(k\)-th spectral point, \(N\) is the number of sample points in the DFT data frame, and  
\[W_{N}=e^{-j\frac{2\pi}{N}} \tag{2}\]  
is the twiddle factor, which is a complex value.  
Conceptually, the DFT performs complex matrix multiplication. Stated \(\bar{X}=[X(0),X\)\((1),\ldots,X(N-1)]^{T}\), the DFT-transformed complex-output vector, and \(\bar{x}=[x(1),x(2),\ldots,\)\(x(N-1)]^{T}\), the input time-domain vector, the DFT is represented by the matrix product:  
\[\bar{X}=D_{N}\cdot\bar{x} \tag{3}\]  
where the matrix \(D_{N}\) is  
\[D_{N}=\begin{bmatrix}1&1&1&...&1\\ 1&W_{N}&W_{N}^{2}&...&W_{N}^{N-1}\\ 1&W_{N}^{2}&W_{N}^{4}&...&W_{N}^{2(N-1)}\\.&.&.&...&.\\.&.&.&...&.\\.&.&.&...&.\\ 1&W_{N}^{N-1}&W_{N}^{N-2}&...&W_{N}\end{bmatrix} \tag{4}\]  
To reduce the computation complexity of the DFT, a family of algorithms has been developed, named FFT. FFT algorithms are based on the mathematical properties of the matrix \(D_{N}\). They partition the calculation of an \(N\)-sample-long DFT into smaller DFTs by applying the strategy of divide and conquer [24]. Depending on the way the DFT is mapped into subproblems, several algorithms have been conceived. Among them are the popular Cooley-Tukey and prime-factor algorithms [25].  
In this paper, the limited bit word length of the coefficients of the sequence to be transformed is exploited, instead of exploiting the intrinsic symmetries of matrix \(D_{N}\).
## 3 Experimental and Numerical Simulation Setup
### Numerical Simulation Setups  
In order to obtain more wind speed data for different lanes, the computational fluid dynamics (CFD) simulation software FLUENT (Canonsburg, Pennsylvania, U.S.) was used. In order to validate the numerical simulation method, the numerical simulation results are compared with the wind tunnel experiment results in the following section. The error between these two results are acceptable, and the reason that might cause the differences is also discussed. Through the grid independency study and the validation, the most suitable set of mesh is adopted for this study. The computational domain is shown in Figure 4. The top and the bottom parts of the pylon, which are vertically far away from the main girder are not considered in the numerical model, since these almost have no effect on the wind environment at the bridge deck. Moreover, the size of the flow field should be large enough that the boundary conditions will not affect the flow pattern at the bridge deck. In order to ensure that the flow field can be fully developed without squeezing the air flow, the dimension of the calculation domain is determined.  
The steady Reynolds-averaged Navier-Stokes (RANS) simulation method is applied, and the discretization numerical method is used to solve the flow field. The turbulence model is the Realizable k-\(\varepsilon\) model. The semi-implicit method for pressure-linked equations (SIMPLE) of the pressure-velocity coupling algorithm is used to solve the discretized problem. The discrete format is second-order upwind difference.  
The inlet is uniform inflow with the speed of 8 m/s, and the inlet is set to be the same as that of the wind tunnel test. The outlet is set to be pressure outlet, the other four boundaries are set to be symmetry boundary condition. The pylon, bridge deck, wind barriers, and guardrails are all set to be no-slip wall boundary conditions (see Figure 4).  
The number of grids is approximately 20 million, and the geometric model is shown in Figure 5. The mesh near the pylon and bridge deck is locally refined, which is shown in Figure 6. The near-wall mesh is quite dense, but it is relatively sparse in a less important area, which improves the accuracy of the calculation and saves on limited computer resources at the same time.  
Figure 4: Boundary conditions of computational domain (B is the girder width 41 m).
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Area Division and Overlapping  
The continuous radio propagation makes it difficult to divide whole areas into distinct clusters. An overlapping between adjacent clusters is required to reduce classification error [12]. We utilized the same concept of area overlapping as given in [12]. The whole area is divided into sub-areas as shown in Figure 2. In the classification/coarse positioning phase, the uncertainty in finding the relevant sub-area is the highest at the intersection of different sub-areas. For example, let the actual position of some object, e.g, \"T\", be in area 1, but because of the sharp boundary, the classifier might predict it in area 2. This problem would cause an error later, as the regression algorithm, in the accurate positioning phase, tries to find the object's localization coordinates in the wrongly predicted area. To reduce this uncertainty, it is worthwhile to increase the margin or overlapping between interconnected areas. Furthermore, if there is no overlapping, the reference points lying at the boundary would have to belong to only one sub-area but not the other one, which in turn reduces the localization accuracy in that particular area. To avoid these problems, we need to introduce an overlapping among sub-areas. Neighboring sub-areas would share all the RPs lying in the overlapped area. In Figure 2, the red squares show the RPs and both red diamonds and green circles represent the test points. The classification algorithm will run on the preprocessed dataset to find the relevant sub-areas of the online fingerprints.  
Figure 1: Architecture of the system’s framework.
## 2 Methodology
### Noise Reduction of Prototype Dam Displacement Data
#### 2.1.2 Sample Entropy  
The SE algorithm is derived from approaches developed by [44]. The SE algorithm can be used to measure the complexity of a time series; the higher the entropy value, the higher the sequence complexity and the stronger the randomness; the lower the entropy value, the lower the sequence complexity and the stronger the regularity [45]. The length of the time series \(\  
Denote the length of the dam deformation sequence \(\mathbf{x}\{x(1),x(2),\cdot\cdot\cdot,x(n)\}\) to N. Then \(\mathbf{x}\) is decomposed and can be expressed as: \[x(n)=\sum\limits_{i=1}^{D}IMF_{i}(n)+r(n)\] (6) where \(D\) is the number of decomposed IMFs; IMF\({}_{i}(n)\) is the decomposed subsequence; \(r(n)\) is the residual series.
2. Define the c-filtering reconstruction \(y_{c}(n)\) as \[y_{c}(n)=\sum\limits_{i=c}^{D}IMF_{i}(n)+r_{D}(n)\] (7) where \(c=1,2,\cdot\cdot\cdot,D\); \(r_{D}(n)\) is the residual series.
3. Eliminate the IMF by taking the minimum value from the following equation \[\text{SMSE}(y_{c}(n),y_{c+1}(n))=\frac{1}{\text{N}}\sum\limits_{n=1}^{\text{ N}}\left[y_{c}(n)-y_{c+1}(n)\right]^{2}=\frac{1}{\text{N}}\sum\limits_{n=1}^{ \text{N}}\left[IMF_{c}(n)\right]^{2}\] (8) where \(c=1,2,\cdot\cdot\cdot,D-1\).