esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Combining Prediction Intervals
#### 2.6.2 Median PI Combination Method  
A method that is less sensitive to outliers and has a considerable ease of use is expressed as Equation (28) given in [31].  
\[L_{Md}=Median(L_{1},\cdots,L_{m})\quad U_{Md}=Median(U_{1},\cdots,U_{m}) \tag{28}\]
# Hybrid Ring- and Tree-Topology RoF Transmission System with Disconnection Protection
## 3 Experimental Results and Discussion
By contrast, the uplink optical signal transmitted CCW through SBOADM1 and SBOADM2 and those transmitted CW through SBOADM3 and SBOADM4 can be transmitted successfully to the CO end. The optical spectra of the uplink signals measured at the CO end are overlapped and presented in Figure 11, and the error rate graphs of each BS-Group measured at the CO end are shown in Figure 3:1 The power levels of the received uplink optical signals generated from the BSs at BS-Group1, BS-Group2, BS-Group3, and BS-Group4 were \(-10.5\), \(-13.8\), \(-12.7\) and \(-10.9\) dBm, respectively. Compared with normal conditions, the uplink optical signals transmitted from BS-Group1 and BS-Group2 suffer slightly larger power attenuation in the transmission process because these signals have to pass through an additional \(1\times 2\) optical splitter. Nevertheless, the transmission losses of the uplink optical signals transmitted from BS-Group3 and BS-Group4 are reduced because the routing path is shorter than that in normal conditions. The overall results presented in the downlink and uplink transmissions when a breakpoint occurs were roughly similar to those under normal conditions except for the slight variation in power. These results demonstrate that the proposed hybrid ring- and tree-topology RoF transmission system can integrate TDM and WDM techniques properly into a signal network architecture to increase the supportable BS number and provide robust fiber-link disconnect protection to ensure quality of services. Figure 3:2 Spectrum measured at the receiving end of BS when the downlink optical signal is captured by SBOADM to the BS in (**a**) BS-Group1, (**b**) BS-Group2, (**c**) BS-Group3 and (**d**) BS-Group4 when a breakpoint occurs.
# Computational Study on Rogue Wave and Its Application to a Floating Body
## 2 Computational Methods
### Wave Generation and Absorption  
A bull's-eye wave was simulated based on the snake-type wave maker theory [16]. A series of monochromatic waves from a circular-shaped inlet boundary were propagating toward a focal point. Figure 1 shows a wavemaker and a focal point.  
Figure 1: Bull’s-eye wave with directional focusing.  
A multi-directional waves generation and absorption library has been developed and added to OpenFOAM [17; 18; 19]. The generated wave velocity and profile on the inlet boundary were written as follows:  
\[U_{H}=\frac{H}{2}\omega\frac{\cosh(k(z+h))}{\sinh(kh)}\cos(k_{x}x+k_{y}y- \omega t-\epsilon)\frac{\overset{\rightarrow}{n}}{|\overset{\rightarrow}{n}|} \tag{5}\]  
\[U_{V}=\frac{H}{2}\omega\frac{\sinh(k(z+h))}{\sinh(kh)}\sin(k_{x}x+k_{y}y- \omega t-\epsilon)\frac{\overset{\rightarrow}{S}}{|\overset{\rightarrow}{S }|} \tag{6}\]  
\[\eta=A\cos(k_{x}x+k_{y}y-\omega t-\epsilon) \tag{7}\]  
where \(U_{H}\) and \(U_{V}\) are the horizontal and vertical velocity components of the wave, respectively. \(\eta\) is the wave elevation, \(h\) is the water depth of numerical wave tank, and \(t\) is time. \(H,\ k,\ \omega\) and \(\epsilon\) are wave height, wave number, angular frequency, and phase difference of the wave, respectively. \(\overset{\rightarrow}{g}\) is the gravitational acceleration vector. \(\overset{\rightarrow}{n}\) is the normal vector which is perpendicular to \(\overset{\rightarrow}{g}\). \(x\) and \(y\) are x- and y-coordinates of numerical wavemaker on inlet boundary, respectively. The subscripts \(x\) and \(y\) indicate the x- and y-directions, respectively. The normal vector (\(\overset{\rightarrow}{n}\)) could be written as follows:  
\[\overset{\rightarrow}{n}=\left(x_{f}-x\right)\overset{\rightarrow}{t}+ \left(y_{f}-y\right)\overset{\rightarrow}{f}+0\overset{\rightarrow}{k} \tag{8}\]  
The phase difference (\(\epsilon\)) could be written as follows:  
\[\epsilon=k\sqrt{\left(x_{f}-x\right)^{2}+\left(y_{f}-y\right)^{2}} \tag{9}\]  
where \(x_{f}\) and \(y_{f}\) are the coordinates of the focal point.  
The relaxation zone technique [18; 19] was applied to absorb reflection waves. The velocity field in relaxation zone could be written as follows:  
\[\overset{\rightarrow}{v}=\left(1-w_{R}\right)\overset{\rightarrow}{v}_{ \text{target}}+w_{R}\overset{\rightarrow}{v}_{\text{computed}} \tag{10}\]  
where \(w_{R}\) is the weighing function could be written as follows:  
\[w_{R}=1-\frac{\exp\sigma^{3.5}-1}{\exp 1-1} \tag{11}\]  
where \(\sigma\) is the local coordinates system in the relaxation zone. Where the relaxation zone started, \(\sigma\) was set to \(1\) and at the outlet boundary where the relaxation zone ended, \(\sigma\) was set to \(0\). In this study, \(|\overset{\rightarrow}{v}_{\text{target}}|\) was set to \(0\) m/s.
## 5 Conclusions  
The wireless signals of UAVs are identifiable and can be used for detection of illegal UAVs. The AC-WGANs model has a high recognition rate and can be applied to the real-time classify system of the UAV signals in a long distance from 10 m to 400 m. The real-time classification system of the UAVs implemented in this paper can be used in the scene of multiple UAVs to separate the illegal ones. When combined with multiple pieces of acquisition equipment, this system can protect some important areas such as the airports and nuclear power stations. It should be very interesting to extend this framework to other domains such as video and audio classification. In the future work, the scenario of large datasets and the classification system of large datasets would be considered, and the case of similar devices (same model or same RF front-end), multiple UAVs would be analyzed, too. In this classification system, the influence of different models, SNRs and pre-processing methods would be detailed in the future work.  
[PERSON] presented the idea and designed the proposed algorithm; [PERSON] built the simulation model and performed the simulation; [PERSON] wrote the first draft of the manuscript; and [PERSON] revised the manuscript.  
This research was founded by the National Natural Science Foundation of China under Grant No. 91638204, the NSF Award 1748494 and Ohio Federal Research Network.  
The authors would like to express their acknowledgement for the support from the National Natural Science Foundation of China under Grant No. 91638204.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AFHSS & Adaptive Frequency Hopping Spread Spectrum \\ Attn-GAN & Attentional Generative Adversarial Networks \\ BEGAN & Boundary Equilibrium Generative Adversarial Networks \\ CGAN & Conditional Generative Adversarial Nets \\ Cycle-GAN & Cycle-Consistent Adversarial Networks \\ DSMX & Digital Signal Multiplex Equipment \\ DSSS & Direct Sequence Spread Spectrum \\ FASST & Flexible Audio Source Separation Toolbox \\ \end{tabular}  
Figure 11: The classification of different distance in the outdoor environment.
## 4 PD Recognition System
### Extension Recognition Method  
Extension theory contains the concepts of matter-elements and extension sets and its main application is in solving contradiction and incompatibility problems [PERSON] (1965). The matter-element can  
Figure 6: Procedure for computing fractal dimension and lacunarity.  
easily represent the nature of matter, and the extension set is the quantitative tool of the extension theory which represents the correlation degree of the matter-element by the designed correlation function. The membership function of the traditional fuzzy set describes the value of matter at interval [0, 1]. The extension set extends the fuzzy set from [0, 1] to [\(-\infty\), \(\infty\)]. As a result, it allows for us to define a set that includes any data in the domain [25]. The proposed extension-based recognition method is described as follows.  
**Step 1**: Formulate the matter-element \(R_{i}\) for each defect type as  
\[\begin{array}{rl}R_{i}&=(T_{i},C_{j},V_{j})\\ &=\left\{\begin{array}{ccc}T_{i}&c_{1}&\langle a_{i1},b_{i1}\rangle\\ &c_{2}&\langle a_{i2},b_{i2}\rangle\end{array}\right\}\end{array} \tag{23}\]  
where  
\(T_{i}\): the \(i\)th defect type of PD pattern,  
\(C_{j}\): the \(j\)th input feature,  
\(a_{ij}\): the lower bound of classical domains related to the \(j\)th input feature of the \(i\)th defect type,  
\(b_{ij}\): the upper bound of classical domains related to the \(j\)th input feature of the \(i\)th defect type.  
The classical domain \(V=\sphericalangle a\), \(b\)\(>\) of each value falls between the lower and the upper bounds on PD records. The neighborhood domain \(\hat{V}=\sphericalangle f\), \(g\)\(>\) of classical domains, the possible range of each characteristic, can then be determined by setting \(f=(1-\alpha)\times a\) and \(g=(1+\alpha)\times b\), where \(\alpha\) represents an extend factor. The extension correlation function concept is shown in Figure 7.  
**Step 2**: Extract the PD features, i.e., the fractal dimension \(D\) and the lacunarity \(\Lambda\), as referred to in Section 4.3.  
**Step 3**: Calculate the degree of the correlation  
\[K(x)=\left\{\begin{array}{cc}\frac{-2\rho(x,X_{0})}{b-a},&x\in X_{0}\\ \frac{\rho(x,X_{0})}{\rho(x,X)-\rho(x,X_{0})}&x\
otin X_{0}\end{array}\right. \tag{24}\]  
where  
\[\rho(x,X_{0})=\left|x-\frac{a+b}{2}\right|-  
**Step 4**: Set the weights of respective features \(W_{1}\), \(W_{2}\), \(W_{3}\) In this work, the weights of all features are set equally.  
**Step 5**: Evaluate the correlation index related to each defect type  
\[\zeta_{i}=\sum_{j=1}^{2}W_{j}K_{ij} \tag{27}\]  
**Step 6**: Normalize the correlation index  
\[\lambda_{i}=\frac{2\zeta_{i}-\zeta_{\text{min}}-\zeta_{\text{max}}}{\zeta_{ \text{max}}-\zeta_{\text{min}}} \tag{28}\]  
which falls between \([-1,1]\) and is seen as a significant quantity for pattern recognition  
**Step 7**: Find the maximum normalized correlation index (or 1). In the case of \(\lambda_{k}=1\), then the tested model is categorized to the \(k\)th defect type. The flow chart of the recognition method based on extension theory is shown in Figure 8.
## 4 Results and Discussion
### Risks Associated with the Safety of IFM  
In this section, the results of the risk verification survey questionnaire are presented. We identified 160 risks that will affect the safety of the IFM from the literature, and after filtering out duplicates, 100 were deemed valid. After that, we combined the ones with a similar meaning, resulting in 50 risks. A total of 10 risks were found under the environmental risk event, 24 under the operational risk event, and 16 under the quality risk event. Based on the survey questionnaire, we calculated the expert score opinion for each risk. The selection was focused mainly on risks, with the highest score being considered the most severe risks marked as '5 = crisis'. The geometric means (\(\mathbb{T}\)) for the reoccurrence were calculated based on all decision makers' weighted criteria, as shown in Table 4. Therefore, those risk agents were selected for further analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Event** & **Risk Agent** & **Total Risks** & \(\Pi\) \\ \hline Environmental Risk & Lack of an assessment of environmental and natural disasters & 10 & 7 \\ \hline Operational Risk & Lack of visibility and potential traceability of products & 24 & 11 \\ \hline Quality Risk & Contamination (microbial/biochemical) & 16 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Score of the selected risk agents.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline RCI & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Random consistency index.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Frequency** & **Percentage (\%)** \\ \hline Gender & & \\ \hline Male & 23 & 51 \\ Female & 22 & 49 \\ Total & 45 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Demographics of participants.
## 2 Results and Discussion
### E-Nose and E-Tongue Analyses  
The e-nose is a high-precision instrument that can detect subtle differences in the volatile components of a sample [33]. In this study, the e-nose was used to analyze the comprehensive flavor characteristic of three durians. The response intensity of the electronic nose of volatile compounds depends not only on the type of compound, but also on the concentration. Figure 1a,b shows the radar images of the electronic nose under non-polar and polar column separation, respectively. The results show that the radar fingerprint chart of the aroma of the three durians almost overlapped and that the concentrations of some volatile compounds were different. The score plot of PCA of three physicochemical indexes of different cultivars is shown in Figure 2. The results were statistically analyzed using PCA. The variance contribution rates of the first and second PCs were 60.88% and 38.83%, respectively. The total variance in contribution of the first two PCs was 99.71%. The research showed that the total variance greater than 85% indicated a rigorous classification
## 5 Conclusions  
In this paper, a bridge deflection prediction method based on cascaded residual smoothing and a multiscale spatiotemporal attention network is proposed. Firstly, the bridge deflection signal is preprocessed by the designed cascade residual smoothing method, which solves the problem of the traditional smoothing method ignoring the short-term fluctuation information, and at the same time, the long- and short-term trend characteristics are paid attention to by the quadratic exponential filtering of the residuals after the Gaussian filtering. Finally, the model prediction of bridge deflection is made by the designed multiscale spatiotemporal attention network. The model can obtain the spatiotemporal information of bridge deflection at multiple scales and filter the important fluctuation features by self-attention. Compared with the prediction methods that require observational data for physical modeling, the proposed method can automatically learn multilayer features from raw data and make predictions, avoiding the complicated manual feature design process. In addition, compared with the BiLSTM, the CNN-GRU, and CNN-Attention-LSTM, the mean MAE values of the proposed method were reduced by 28.17%,  
Figure 14: Average value.  
37.37%, and 25.79%, respectively. The average RMSE values decreased by 27.14%, 38.41%, and 24.69%, respectively. R\({}^{2}\) increased by 7.29%, 9.59%, and 2.36%, respectively.  
In future work, we hope to overcome the problem of low prediction accuracy of the proposed method over the long-term and will try to perform feature extraction from the perspective of the frequency domain to make it acquire better features.  
Conceptualization, X.W.; Methodology, J.L.; Data collection, H.-M.Q.; Writing--original draft preparation, X.W., L.-S.H. and C.-Q.W.; Writing--review and editing, X.W. and H.-M.Q.; Funding acquisition, X.W., J.L. and L.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Scientific Research Foundation of Hangzhou City University under Grants No. X-202107 and X-202109 and the Zhejiang Engineering Research Center of Intelligent Urban Infrastructure under Grant No. IUI2022-YB-05.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors thank everyone helping with this study and express their gratitude to all contributing to this study.  
The authors declare no conflicts of interest.
## 5 Results and Discussion
### Cyclic Elasto-Plastic Response
As a way of evaluating the deviation of the numerical model with respect to the experiments, the variation of the relative error is shown in Figure 15. In order to evaluate the maximum relative error observed throughout the tests, the instant corresponding to 50% of the total failure lifetime was assumed as a reference value. This approach allowed us to understand that, for the applied strain ranges, the maximum relative errors occurred up to a lifetime of 10%, with error values that could vary between \(-\)10.0% and 5.0%. For lifetimes greater than 10%, the error was compressed into a range of \(-\)2.5 to 2.5%.
## 6 Discussion
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Ref** & **Method** & **Dataset** & **Accuracy (\%)** & **Recall (\%)** & **F-Measure (\%)** & **Year** \\ \hline
[47] & Hybrid firefly algorithm & Orange & 86.38 & 80 & 85 & 2017 \\
[46] & FW-ECP & Orange & 79.4 & 74.1 & 72.7 & 2017 \\
[48] & PSO-FSSA & Orange & 94.08 & 84.01 & 80.28 & 2019 \\
[46] & FW-ECP & Cell2 Cell & 84.9 & 80.2 & 81.02 & 2017 \\
[50] & LSTM & Cell2 Cell & 72.7 & 78 & 80.65 & 2021 \\
[51] & Deep-BP-ANN & Cell2 Cell & 92 & 81.74 & 77.47 & 2022 \\
[52] & HKD-PFLICM & Cell2 Cell & 76.51 & 79 & 78 & 2021 \\
**Proposed** & Stacking-based ensemble model & Orange & 96 & 91.61 & 90.23 & 2022 \\
**Proposed** & Stacking-based ensemble model & Cell2 Cell & 93.6 & 85.45 & 83.72 & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art approaches. Figure 7: Illustration of accuracy comparison among all experiments using Orange and Cell2 Cell datasets.
## 2 Basic Theory of HSDTPEs
### Imposing Displacement Boundary Condition  
As illustrated in Figure 4, along boundary 1-2 of element \(e_{k}\), rotations \(\left(\overline{\theta}_{s},\overline{\theta}_{n}\right)\) in the local coordinate \((s,n,z)\) or the displacements \((\overline{u}_{0},\overline{v}_{0},w_{0})\) in the local coordinate \((s,n,z)\) are fixed, where \((\overline{u}_{0},\overline{v}_{0},w_{0})\) represents the in-plane and transverse displacements at the mid-plane. A fictitious thin layer \(e_{b}\) over boundary 1-2 shown in Figure 4 is also introduced to enforce the displacement boundary condition. We divide the displacement approximation in Equation (5) into two parts to the follow to separately enforce the rotation and mid-plane displacement boundaries.  
\[\overline{\mathbf{u}}^{\text{p}_{r}}\big{(}s_{p},n_{p},z_{p}\big{)} \approx\mathbf{\lambda}^{b}\mathbf{N}^{t}\big{(}x_{p},y_{p},z_{p}\big{)} \mathbf{a}^{r} \tag{25}\] \[\overline{\mathbf{u}}^{\text{p}_{r}}\big{(}s_{p},n_{p},z_{p}\big{)} \approx\mathbf{\lambda}^{b}\mathbf{N}^{t}\big{(}x_{p},y_{p},z_{p}\big{)} \mathbf{a}^{t} \tag{26}\]  
where \(\overline{\mathbf{u}}^{\text{p}_{r}}\) represents the displacement function of point p for rotations in triangular element \(e_{k}\), \(\overline{\mathbf{u}}^{\text{p}_{r}}\) represents the displacement function of point p for mid-plane displacements in triangular element \(e_{k}\), and \(\mathbf{a}^{r}\) and \(\mathbf{a}^{t}\) are the corresponding DOFs of element \(e_{k}\).  
\[\mathbf{N}^{r}=\left[\begin{array}{ccccc}\mathbf{0}&\mathbf{0}&\alpha_{z} \mathbf{P}^{2}&\mathbf{0}&g(z)\mathbf{P}_{x}^{3}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\alpha_{z}\mathbf{P}^{2}&g(z)\mathbf{P}_{y}^ {3}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}&0\end{array}\right] \tag{27}\]  
\[\mathbf{N}^{t}=\left[\begin{array}{ccccc}\mathbf{P}^{2}&\mathbf{0}&\mathbf{0 }&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{P}^{2}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{P}^{3}\end{array}\right] \tag{28}\]  
where \"0\" in bold in Equations (27) and (28) represents zero matrix, and \(\mathbf{\lambda}^{b}\) is the transformation matrix similar to Equation (20) where  
\[\mathbf{\lambda}^{b}=\left[\begin{array}{ccc}\cos\omega&\sin\omega&0\\ -\sin\omega&\cos\omega&0\\ 0&0&1\end{array}\right] \tag{29}\]Using the same derivation process as in Equation (24), the strain energy of the thin layer \(e_{b}\) is derived as:  
\[\Pi^{b}=\frac{1}{2d}\int_{-h/2}^{h/2}\int_{-l/2}^{l/2}\left[\left(\mathbf{a}^{r} \right)^{T}\left(\overline{\mathbf{N}}^{r}\right)^{T}\mathbf{D}^{r}\overline{ \mathbf{N}}^{r}\mathbf{a}^{r}+\left(\mathbf{a}^{t}\right)^{T}\left(\overline{ \mathbf{N}}^{t}\right)^{T}\mathbf{D}^{t}\overline{\mathbf{N}}^{t}\mathbf{a}^{t }\right]dsdz \tag{30}\]  
where \(\mathbf{D}^{b}\) is calculated using Equation (23), \(\overline{\mathbf{N}}^{r}=\lambda^{b}\mathbf{N}^{r}\), and \(\overline{\mathbf{N}}^{t}=\lambda^{b}\mathbf{N}^{t}\).  
Please refer to [PERSON] and [PERSON] [55] for the detailed derivation of the displacement boundary condition fixed at a point or the given displacement boundary condition.
# Mapping with Pleiades--End-to-End Workflow
### Outline  
This paper is organized as follows: Related work is reviewed in detail in Section 2. Then, the test sites and test data are described in Section 3. Next, Section 4 reports on the methodologies proposed in this work, followed by the results given in Section 5. All insights gained are discussed in Section 6. Finally, conclusions are drawn in Section 7.  
Figure 1: Proposed end-to-end 3D mapping workflow for multi-view stereo Pleiades images. The four core tasks are subdivided into according sub tasks.
## 3 Performance Evaluations
### Fiberless Channel  
Let us consider a scenario where we aim to transmit data at a speed of 50 Mbits/s, which corresponds to a bit duration of 20 ns. Our task is to identify an appropriate random sequence for modulating these data. In the sequel, we consider a standard PN sequence with seven chips per bit. This sequence can be generated using the diagram shown in  
Figure 4: \(q\)-stage shift registers with linear feedback.  
Figure 3: Block diagram of MF-based DSSS spread spectrum receiver.  
Figure 4 with three shift registers (flip flops). In the simulation, the code-generating polynomial \(1+X+X^{3}\) is considered.  
To produce seven chips for each bit, expanding the signal by a factor of 7, each chip should occupy one-seventh of the total bit duration. Therefore, the chip duration would be 2.86 ns for a bit duration of 20 ns. The final step in creating the communication waveform involves multiplying the intended transmission data by the PN sequence. This process generates the spread signal waveform. In practice, the resulting DSSS signal is often scrambled before transmission [10].