esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Study on Rogue Wave and Its Application to a Floating Body
## 3 Results and Discussion  
### Regular Wave Simulation  
A regular wave was simulated to validate the numerical. The parameters of flow are listed in Table 1. The density and kinematic viscosity of water are 998 kg/m\({}^{3}\), 1.004 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, respectively. The density and kinematic viscosity of air are 1.204 kg/m\({}^{3}\), 1.506 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s, respectively.  
The Stokes second wave with the wavelength (\(\lambda\)) of 3.2375 m and the wave period (\(T\)) of 1.44 s was generated from the inlet boundary. For the wave height (\(H\)), 0.02 m was considered. The domain extent and boundary conditions of a numerical wave tank are shown in Figure 2. The Cartesian coordinate was selected and the generated wave from the inlet boundary were propagated in the x-direction. The whole domain extent was 19.4 m in the x-direction and 5 m in the z-direction. The wave absorption zone was located in front of the outlet boundary. The x-directional length of the absorption zone was set to 6.475 m, twice the wavelength [18,19].  
For the velocity, the Dirichlet boundary condition was applied on the inlet boundary while the Neumann boundary condition was applied on the outlet boundary. At the side boundary, a no-slip boundary condition was considered. An atmospheric condition was applied on the top boundary and a no-slip condition was applied on the bottom boundary.  
Figure 2 shows a typical structured grid of 27,000 meshes. In a wave, 100 meshes were used in the one wavelength and 20 meshes were used in the wave height. Figure 3 shows the nondimensionalized wave elevations and the analytic solution. The simulated amplitude and period of a regular wave were well matched with the analytic solution.  
\begin{table}
\begin{tabular}{c c} \hline \hline Parameters & Value \\ \hline \(\rho_{\text{water}}\) & 998 kg/m\({}^{3}\) \\ \(\
u_{\text{water}}\) & 1.004 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s \\ \(\rho_{\text{air}}\) & 1.204 kg/m\({}^{3}\) \\ \(\
u_{\text{air}}\) & 1.506 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s \\ \(U_{H\text{max}}\) & 0.0445 m/s \\ Re\({}_{\text{wave amplitude}}\) & 445 \\ Re\({}_{\text{floating body}}\) & 88,900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flow parameters.  
Figure 2: Domain extent and boundary condition of the regular wave simulation.
## 3 Performance Analysis of WCwEH
### Asymptotic Analysis  
This subsection analyzes the upper bound in the performance of WCwEH in the range of high transmit power (i.e., \(\bar{P}\rightarrow\infty\)). It should be noted that the energy scavenger becomes saturated completely when \(\bar{P}\rightarrow\infty\). In other words, \(\bar{P}\to J\) when \(\bar{P}\rightarrow\infty\). Therefore, the CDF of \(w=gJ\) reduces to \(F_{w}(z)=F_{g}\Big{(}\frac{\tilde{z}}{\tilde{J}}\Big{)}\) as \(\bar{P}\rightarrow\infty\). As a result, the OP of WCwEH is derived as  
\[\mathcal{O}^{\infty}=\left\{\begin{array}{ll}F_{g}\Big{(}\frac{\Lambda_{0} \sigma}{|1-\Lambda_{0}\rho|\tilde{J}}\Big{)}&,1\geq\Lambda_{0}\rho\\ 1&,1<\Lambda_{0}\rho\end{array}\right. \tag{17}\]
## 1 Introduction
Subsequently, SPH has been successfully applied to various fields such as open channel [18; 19; 20] and pipe flows [21], fluid-structure interaction [22; 23; 24; 25], fluid sloshing [26; 27; 28; 29; 30], soil mechanics [31], rock mechanics [32] and solid mechanics [33; 34; 35]. Particle-based methods eliminate the disadvantage of mesh-based methods, which needs mesh regenerations for large deflections and crack propagations, by discretizing the domain with particles. Thus, it is preferred to simulate flows including free surfaces, large deflection problems and crack propagation problems with particle-based methods. As a result of a large deflection problem including rigid body motion due to small transverse stiffness, cables can be considered as a very suitable structural element to be modeled by particle-based methods. In the present study, the SPH method is implemented with an additional damping term to simulate the dynamic behavior of a structural cable element. As far as the authors' best knowledge, this is the first study in which a cable is simulated with the SPH method. In addition, a series of novel experiments are conducted for validation purposes. In the experiments, the dynamic response of a cable element under harmonic motion is investigated. The paper is organized as follows:  
* First, the numerical model in which SPH is formulated for the simulation of cable is presented in Section 2. * The numerical model is validated by comparing the results with different experiments found in the literature in Section 3. Novel experiments conducted for this study are also presented in this section. * Damping parameters are briefly discussed in Section 4. * Conclusions are drawn in Section 5.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 2 Background and Related Work
Typical FL solutions prioritize the privacy and security of all the parties involved. In fact, the server does not need to access individual information from each party involved to perform SGD. The server only requires the weighted averages of the update vectors. In [17], the authors proposed the secure aggregation protocol to compute these weighted averages. This method ensured that the server became aware of what types of data were present in the dataset, but without knowing the data that each user contained. In mobile contexts, devices have limited resources in terms of energy and network connectivity. This introduces some unpredictability in terms of the number of parties that can participate in each round of updates, and the system must be able to respond to disconnections and new participants. Since ML models can be parameterized with millions of different values, model updates can involve a large quantity of data, which imposes a direct cost on users on metered network plans. Secure aggregation proposes to work with high-dimensional vectors, uses efficient communication strategies, even considering the addition of new parties at each round and being robust to users dropping out, and finally, provides the strongest possible security measures under the constraints of a server-mediated, unauthenticated network model. [PERSON] et al. [18] introduced the use of the robust federated averaging (_RFA_) algorithm. RFA replaces the mean aggregation of _FedAvg_ with a geometric median (GM) based robust aggregation oracle. Similar to _FedAvg_, _RFA_ trades off communication for local computation by running multiple local steps. The communication efficiency and privacy preservation of _RFA_ follow from computing the GM as an iterative secure aggregate.
## 6 Discussion
#### 6.1.2 Gas Steady States  
Table 2 lists the relations of \(M\) with the reconstruction error and the iteration number (i.e., the computation complexity) during the reconstruction of gas data under steady states, and Figure 7 illustrates the effect of the number of measuring points (\(M\)) on reconstruction performance under gas steady states. It can be observed that since the gas signal varies steadily, the reconstruction accuracy exceeds the value under gas mutation states at the same compression ratio. When the compression ratio is equal to or greater than 0.3, the reconstruction error is smaller than 1.3% and the iteration number is smaller than 100.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(M\) & **51** & **102** & **154** & **205** & **256** & **307** & **358** & **410** & **461** & **512** \\ \hline Iteration number & 158 & 115 & 98 & 88 & 74 & 77 & 79 & 72 & 66 & 63 \\ Error (\%) & 6.74 & 4.98 & 3.66 & 2.39 & 1.94 & 1.35 & 1.12 & 0.99 & 0.73 & 0.63 \\ \hline \end{tabular}
\end{table}
Table 1: The relations of \(M\) with the reconstruction error and the computation complexity during the reconstruction of gas mutation data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(M\) & **51** & **102** & **154** & **205** & **256** & **307** & **358** & **410** & **461** & **512** \\ \hline Iteration number & 174 & 102 & 99 & 77 & 68 & 65 & 59 & 52 & 51 & 47 \\ Error (\%) & 6.61 & 3.03 & 1.28 & 0.99 & 0.90 & 0.76 & 0.76 & 0.69 & 0.59 & 0.60 \\ \hline \end{tabular}
\end{table}
Table 2: The relations of \(M\) with the reconstruction error and the computation complexity during the reconstruction of gas data under steady states.  
Figure 7: Compression perception and the reconstruction error of the gas steady signal (TVS-MH).
## 3 Analysis of Success Probability
### Success Probability in Hybrid Cognitive Networks
#### 3.1.2 Analysis of the Secondary Network  
The success probability at any test SR under the hybrid spectrum access mode can be obtained at any given switching rate \(\varepsilon\) as  
\[\mu_{s}^{h}=p_{q}(1-p_{f})(1-P_{o,s}^{O})+(1-p_{q})p_{m}(1-P_{o,s}^{OI})+(1- \rho)\varepsilon[p_{q}p_{f}(1-P_{o,s}^{UI})+(1-p_{q})(1-p_{m})(1-P_{o,s}^{UI})], \tag{10}\]  
where \(P_{o,s}^{O}\) is the SU outage probability as in the conventional overlay mode provided there was no misdetection, \(P_{o,s}^{UI}\) is the SU outage probability as in the underlay mode with a false alarm (at least from PU's perspective), \(P_{o,s}^{OI}\) is the SU outage probability in overlay mode when there was misdetection, \(P_{o,s}^{UI}\) is the SU outage probability in the underlay mode with interference from PTs, \(p_{f}\) is the probability of false alarm, \(p_{q}\) is the probability that there is no PT in the channel and \(\rho\) represents the penalty term due to periodic monitoring of the interference channel in underlay mode.  
The outage probability at any typical SR \(y_{k}^{s}\) is obtained from (3) as  
\[P_{outage}=P(SINR_{y_{k}^{s}}\leq\theta_{s})=1-\exp(-z\sigma^{2})\mathcal{L}_{I_{ \text{ss}}}(z)\mathcal{L}_{I_{\text{ps}}}(z), \tag{11}\]  
where \(z=\frac{\theta_{s}r_{k}^{s}}{P_{s}^{s}}\). Similarly, the parameter \(\theta_{s}=2^{\eta}-1\), where \(\eta\) is known as the data rate for secondary transmission.  
1. [label=0., ref=0]
2. Underlay model: In the presence of at least one PT, STs coexist with active PTs as in the conventional underlay mode, hence the SU's outage probability at any typical SR \(y_{k}^{s}\) at \(z=\frac{\theta_{s}r^{s}}{P_{s}^{s}}\) is given as \[P_{o,s}^{III}(y_{k}^{s})=1-\exp(-z\sigma^{2})\mathcal{L}_{I_{\text{ss}}}(z) \mathcal{L}_{I_{\text{ps}}}(z).\] (12) Similar to (7), we also know that \[\mathcal{L}_{I_{\text{ss}}}(z)=\mathcal{L}(\lambda_{s}^{I},P_{s}^{LI},z),\] \[\mathcal{L}_{I_{\text{ps}}}(z)=\mathcal{L}(\lambda_{p}^{I},P_{p},z).\] Finally, when STs transmit in the underlay mode as a result of a false alarm, the only interference generated is from other active STs, hence, \[P_{o,p}^{LI}(y_{k}^{s})=1-\exp(-z\sigma^{2})\mathcal{L}(\lambda_{s}^{I},P_{s}^ {LI},z).\] (13)
3. Overlay mode: In overlay mode, the interference from PTs can be assumed to be zero when there is no active PT on the channel. With this, \(\mathcal{L}_{I_{\text{ps}}}=1\) and \[P_{o,s}^{O}(y_{k}^{s})=1-\exp(-z\sigma^{2})\mathcal{L}(\lambda_{s}^{I},P_{s}^ {O},sz=\frac{\theta_{s}r_{s}^{s}}{P_{s}^{o}}).\] (14) When there is misdetection of PTs signal, STs transmit with full transmit power \(P_{s}^{O}>P_{s}^{LI}\) thereby generating higher interference at any active PR, while also receiving interference from active PTs. Hence, at any typical SR, \[\mathcal{L}_{I_{\text{ps}}}(sz)=\mathcal{L}(\lambda_{p}^{I},P_{p},sz),\] \[P_{o,s}^{OI}(y_{k}^{s})=1-\exp(-sz\sigma^{2})\mathcal{L}(\lambda_{s}^{I},P_{s} ^{O},sz)\mathcal{L}(\lambda_{p}^{I},P_{p},sz).\] (15)
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 2 Methodology
### Deep Learning-Based Surrogate Models  
This study first evaluates the use of a general convolutional neural network (CNN) to construct a surrogate for groundwater transient flow models (Cases 1 and 2). Subsequently, long short-term memory (LSTM) and gated recurrent unit (GRU) are integrated to enhance the training efficiency and prediction accuracy of surrogate models, especially in replacing complex flow models (Case 3). Finally, the GRU is combined with a depthwise separable convolutional neural network (DSCNN) in lieu of the original CNN, aiming to further improve training efficiency. Additionally, various optimizers, activation functions, and convolutional layers are assessed to determine their effects on the DSCNN-GRU surrogate model, with the goal of identifying the optimal hyper-parameter combination (Figure 5).
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 4 Experiments and Analysis
### Experimental Results
#### 4.2.3 Results of Audio-Visual Fusion  
Table 6 shows the performance of the AVSR system after modifying the fusion methods. Because changing the fusion method in the decoder part does not cause convergence, we only show the recognition results for changing the fusion method in the encoder part. Note that, when using the self-attention to fuse the audio and video information in the encoder part, we do not need to strictly align the two modalities in time series, so we can apply the speed perturbation to the audio-visual data, namely 'CAT + SP' in the 'Data Processing' column. As shown in Table 6, our modified fusion methods do not bring improvement. We suspect that this is because the difference in the amount of information between these two modalities is too large, which leads to the result that fusion methods that need to learn parameters cannot extract useful information effectively.
## 2 Methods
### PPP-RTK/INS/Vision Integration Model with a Cascading Filter  
The PPP-RTK/INS/vision integration model with a cascading filter is realized by integrating the output of the tightly coupled integration of PPP-RTK/INS with the tightly coupled integration of INS/vision. The difference between the position provided by the PPP-RTK/INS and the position predicted by INS/vision constitutes the observation of position. Based on Equations (14) and (23), the measurement model of the PPP-RTK/INS/Vision integration model can be expressed as  
\[\begin{split}\hat{\mathbf{x}}_{GNSS}^{\varepsilon}-\widetilde{\mathbf{x} }_{GNSS}^{\varepsilon}&=\delta\mathbf{x}_{\text{INS}}^{\varepsilon}+ \mathbf{C}_{b}^{\varepsilon}\mathbf{l}^{b}\times\mathbf{\phi}+\mathbf{\varepsilon}_{\mathbf{x}}\\ z_{a,i}&=\mathbf{H}_{a,x_{i}}x_{i,c}+\varepsilon_{a,i}\end{split} \tag{28}\]  
in which \(\hat{\mathbf{x}}_{GNSS}^{\varepsilon}\) is the position predicted by INS/vision and \(\widetilde{\mathbf{x}}_{GNSS}^{\varepsilon}\) is the positioning results of PPP-RTK/INS. \(\mathbf{\varepsilon}_{\mathbf{x}}\) is the measurement noise.  
The integration model of multi-GNSS PPP-RTK/INS/vision with a cascading filter is derived from the description above. An overview of the proposed model is shown in Figure 1. First, the position is obtained by the GNSS to assist the navigation initialization, e.g., IMU alignment with GNSS. Then, the INS begins to provide high-rate navigation. When the INS synchronizes with the GNSS, tightly coupled integration is performed based on either PPP/INS or PPP-RTK/INS. As for the latter, high-precision atmospheric correction is applied and AR is carried out with the UPD products. Furthermore, feature points are extracted and tracked in each image. When the time of the camera synchronizes with the INS, the state vector is augmented and the MSCKF is adopted to calculate the relative position of the vehicle platform. The positioning results of the GNSS/INS are then integrated with the MSCKF to produce the final navigation information. There are two Kalman filters: the filter of PPP-RTK/INS and the filter of INS/Vision. The positioning results of PPP-RTK/INS are added into the filter of the INS/vision so it becomes a cascading Kalman filter. All the IMU sensor errors are fed back in time in the process.
## 3 Experimental and Analysis
### Evaluation Metrics  
This study selected average precision (AP) and mean average precision (mAP) as the main performance evaluation metrics for the model. Average precision considers the relationship between precision and recall, represented as the area under the P-R curve. mAP is the average of the AP values for all categories. Additionally, FPS was introduced to represent the model detection speed, GFLOPs and Params measured the model's time and space complexity, and the model size was used to reflect memory usage. The calculation formulas were as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]  
\[mAP=\frac{1}{N}{\sum_{i=1}^{N}AP_{i}} \tag{9}\]  
where \(TP\) represents true positives; \(FP\) represents false positives; \(FN\) represents false negatives (the number of missed detections); \(N\) denotes the total number of categories.
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 4 Results
### Model Prediction  
The trained RBF neural network model can be used to predict the insured loss rate of rural housing in each township under different design wind speeds in Ningbo. The corresponding design wind speeds in Ningbo (input layer data) were calculated according to the wind pressures of 10, 50, and 100 year return periods as specified in the current Building Structure Load Code (GB50009-2012) [34], and their values were 21.9 m/s, 28.3 m/s, and 31.0 m/s, respectively. The output layer results are shown in Figure 15. Overall, the RBF neural network model showed a positive correlation between the insured loss rate of rural housing and the design wind speeds (i.e., the higher the wind speed, the higher the disaster loss). Under the same design wind speed, the insured loss rate of rural houses in the townships of Fenghua District, Ninghai County, and Xiangshan County in the south of Ningbo was much higher than that in the northern townships. Under the design wind speed of a 100-year return period (31.0 m/s), the insured loss rate of rural housing in more than 100 out of 139 townships would be greater than 0.1%, which indicates that rural houses in Ningbo will suffer great damage. According to the above quantitative typhoon loss prediction results, the local emergency management bureau can carry out timely and targeted reinforcement and repair of rural housing in key townships before the typhoon arrives to minimize casualties and economic losses caused by wind collapse.  
Figure 13: Comparison of mean square error of prediction results between the BP and RBF models.  
Figure 14: Average insured loss rate by township due to five typhoons in 2019. (**a**) Actual value; (**b**) RBF prediction; (**c**) BP prediction.  
Figure 15: RBF neural network prediction results. (**a**) Loss rate under the design wind speed of 10 year return period (21.9 m/s); (**b**) Loss rate under the design wind speed of 50 year return period (28.3 m/s); (**c**) Loss rate under the design wind speed of 100 year return period (31.0 m/s).
# An Intelligent Condition Monitoring Approach for
## 3 Condition Monitoring Approach for Shearing Machines
### Hybrid HMM-ANN Model for Fault Diagnosis  
The HMM possesses a strong dynamic-modeling ability because it can be applied to analyze a time series of states. When using it for classification, the method gives the categorizing result according to the model producing the highest likelihood. If the signal to be recognized is contaminated by noise, the output probabilities of several HMMs may be very close and can lead to an incorrect recognition. As mentioned above, an ANN exhibits strong robustness, fault tolerance, learning ability, and self-adaptability. It is not sensitive to incomplete input information or characteristic defects. However, during recognition, the samples are classified according to the signal characteristics of the current time series, without considering the relation between \"front and back time series\". Thus, the ANN model may lead to unrealistic results. In conclusion, the advantages of HMM and ANN are complementary. In this study, a hybrid HMM-ANN model was constructed by combining HMM's dynamic-modeling ability and ANN's high robustness, fault tolerance, learning ability, and self-adaptability. The hybrid model consists of two layers: the HMM layer and the ANN layer. The structure is shown in Figure 4.  
Figure 4: Principle and structure of a hybrid HMM-ANN model (solid line: the training process, dashed line: the recognizing process).  
The HMM layer is in the upper layer, thereby enabling the hybrid mode's strong dynamic-modeling ability. The HMM layer consists of a modeling module and an output probability calculation module. The modeling module is used to obtain the HMMs by learning the samples, while the output probability calculation module is used to calculate the output probabilities of samples under each of the HMMs. The ANN layer is in the lower layer, taking the advantage of ANN's fault tolerance, learning ability and self-adaptability. It contains an input layer, several hidden layers and an output layer. The hybrid HMM-ANN model is trained by the learning process and produces the predicted outputs.
## 2 Methods and Experiment
### Experimental Details  
The experimental setup of the spectrometer for the determination of the HCl pressure broadening coefficients is shown in Figure 1. A similar setup was used in our previous thermal boundary layer effects study [20]. A 3.6 \(\upmu\)m interband cascade laser (ICL) (Nanoplus GmbH) is used to scan across the P5 HCl absorption line in the HCl 1-0 fundamental band. In order to perform spectrometric measurements, the wavelength of the laser was swept at 138.9 Hz across the transition line by means of a linear saw-tooth-shaped current ramp which was generated by a function generator (Agilent, 33220A). The laser injection current is supplied by a low-noise laser current driver (Thorlabs, LDC8002), while the laser temperature is stabilized by a Peltier-element operated by a Peltier driver (Thorlabs TEC8020). The laser beam was directed through a 77.4 cm long single pass gas cell which was made of stainless steel and was equipped with Brewster-angled plane-parallel sapphire windows and then focused onto a photovoltaic photodetector (VIGO, PVI-4 TE-5). The inner surface of the gas cell was passivated by a commercial SilcoNert 2000 coating (Silcotek Corporation). The detector signal was sampled with 18 bit resolution at 600 kHz by a DAQ (National Instruments, PXI-6289). For gas pressure measurements, a pressure sensor (MKS Baratron) was used. A Pt100 resistor inserted into the gas cell measured the gas temperature. Measured pressures and temperatures were traceable to the SI via PTB's national pressure and temperature standards.  
The measurements used high-quality premade HCl/CO\({}_{2}\) (110 \(\pm\) 5 \(\upmu\)mol/mol HCl in CO\({}_{2}\), HCl purity 5.5, CO\({}_{2}\) purity 5.3, Linde GmbH) and HCl/N\({}_{2}\) (500 \(\pm\) 30 \(\upmu\)mol/mol HCl in N\({}_{2}\), HCl purity 5.5, N\({}_{2}\) purity 6.0, Linde GmbH) gas mixtures for HCl-CO\({}_{2}\) and HCl-N\({}_{2}\) pressure broadening coefficients, respectively. All other pressure broadening coefficients (HCl-Ar, HCl-O\({}_{2}\) and HCl-Air) were measured by first \"coating\" the cell walls with HCl by filling it with premix HCl gas mixture, then quickly replacing the premix gas with pure matrix gases Ar (purity 5.0, Linde GmbH), O\({}_{2}\) (purity 5.0, Linde GmbH), or synthetic air (N\({}_{2}\):O\({}_{2}\) = 80:20, 1% uncertainty of the ratio, O\({}_{2}\) purity 5.0, N\({}_{2}\) purity 5.0, Linde GmbH) and taking advantage of the delayed HCl desorption from the gas cell inner walls into the pure matrix gas.