esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 2 Measurement Principle
### Relative Altimetric Accuracy  
For marine applications such as geostrophic current inversion or gravity anomaly inversion, the relative altimetric accuracy is being more concerned than absolute accuracy, since the physical variables are inversed from the OST gradient instead of the absolute value. Apart from phase noise, all parameter errors of IRA will introduce spatial correlated altimetric error along the range direction, as indicted by variable \(X\) in Equations (4)-(6). Figure 3 gives the example of altimetric error introduced by baseline inclination error. The error increases linearly along the range direction, the absolute altimetric error is defined as the difference between True OST and OST measured from IRA, and the relative altimetric error is defined as the difference between two adjacent points, A and B.  
The relative altimetric error related to baseline length error, baseline inclination error, and phase noise, are as follows:  
\[\delta_{h}^{B}=\frac{\Delta X}{B\cot(\theta-\alpha)}\cdot\sigma_{B} \tag{8}\]  
\[\delta_{h}^{\alpha}\approx H\tan\theta\cdot\sigma_{\alpha}=\Delta X\cdot\sigma _{\alpha} \tag{9}\]  
\[\delta_{h}^{\phi}=\frac{\Delta X\lambda}{4\pi B\cos(\theta-\alpha)}\cdot\sigma _{\phi} \tag{10}\]  
where \(\Delta X\) is the horizontal distance between two points; in this paper, it stands for the grid resolution after multi-looking. Comparing Equations (8)-(10) to Equations (4)-(6), it appears that the relative altimetric errors are usually much smaller than the absolute errors since most of the common part has been cancelled out by differentiation. Therefore, the relative altimetric accuracy of IRA is determined by systematic parameter errors and the final resolution of the OST product.
# Wind-Load Response and Evacuation Efficiency Analysis of Marine Evacuation Inflatable Slide
## 3 Wind-Load Response Analysis of Inflatable Slide under Fluid-Solid Coupling
### Validation and Simulation of the Inflation Process of Emergency Evacuation Slide  
The AIRBAG module in LS-DYNA was used to define the gas in the chamber of the inflatable evacuation slide, so that the gas in the slide can satisfy the ideal gas state equation [23]. According to the response analysis results of the emergency evacuation slide under different inflatable internal pressures, the evacuation efficiency of the inflatable slide is simulated by the inflatable internal pressure values of 3000 Pa, 3500 Pa, 4000 Pa, 4500 Pa, and 5000 Pa. It is expected that through the simulation of the inflatable internal pressure range, the ideal internal pressure value which meets the criterion of the evacuation system sliding efficiency can be found, and the analysis of the stability part of the inflatable slide can be considered comprehensively. Next, we take 4000 Pa inflatable internal pressure as an example to simulate the inflatable process of the slide, extract the slideway part of the inflatable slide, set the inflatable time to 0.5 s, and magnify the inflatable deformation scale by three times.  
From the 0 s stress-free state, the displacement of the inflatable state at each time point of 0.1 s, 0.2 s, 0.3 s, 0.4 s, and 0.5 s is 0.158 m, 0.179 m, 0.187 m, 0.189 m, and 0.190 m respectively. According to Figure 16, the volume of the slide increases and tends to be stable with the increase of inflation time. The maximum displacement appears on the sliding surface, which proves that the inflation simulation method used in this paper can simulate the pressure process of the slide more realistically. Figure 17 shows the maximum expansion displacement and surface stress curves of the slide under five kinds of inflatable internal pressures.  
Figure 15: Displacement deformation curves of the inflatable slide under different inflatable internal pressures.
## 4 Branch-and-Price-and-Cut Algorithm
### Generation of Constraints on Critical Times  
Since we reduce the size of the RLMP by relaxing empty container related constraints (24) and (25), the resulting optimal solution of the RLMP might not ensure that the stock of empty containers at the depot falls within the range of [0, \(Q\)] at any time. To ensure that the empty container constraints are respected, they are dynamically added to the RLMP. The main procedure of generation of empty container constraints on critical times is shown below.  
**Step 1: Identifying critical times:** After solving the RLMP to optimality, we identify the critical times associated with the selected routes that form the optimal solution of the RLMP.
**Step 2: Checking empty container constraints:** We check if the empty container constraints regarding all critical times are respected according to constraints (24) and (25). If empty container constraints on critical times are satisfied, then the procedure stops; otherwise, go to Step 3.  
**Step 3: Adding constraints on critical times:**: We augment the RLMP with empty container constraints on critical times when the depot's empty container stock is out of the range [0, Q]. In other words, a new RLMP augmented with constraints on newly included critical times is obtained.
**Step 4: Re-solving with column generation:**: The new RLMP is re-solved via the column generation method; then, go to Step 1.
## 3 On-Boarding Process for Distributed Analysis
### On-Boarding Workflow
#### 3.3.1 Station Registry  
As the FAIR principles have suggested (see Section 2), to make a digital asset (in our case: the station) findable, it should have an identifier and should be findable in a searchable resource. Therefore, we have decided to extend the architecture with a so-called station registry. The station registry is the leading component of the on-boarding process.  
It is a web-based application that hosts all available stations characteristics and their correspondence to the institution they belong to. In this way, it is similar to the Domain Name Service (DNS) of the internet and the authority for providing a list of available stations. The CS (and other software as well) can then reuse the information about available stations to let the scientist configure the route an analysis task should take.  
Therefore, the station registry is the place where new stations can be added as well as available stations are de-registered before they will be de-installed in their corresponding institutions. We combine the action to register a new station with the on-boarding process. A status (online state) reflects whether a station is already available and can be included in a distributed analysis. While all users (including any software clients) can list available stations, only registered users can modify this list, i.e., adding new, deleting available and modifying characteristics (e.g., station name) of stations.  
To register a station, the station admin has to input basic information about the station, such as a responsible person, name, and contact information. Further, the station is assigned to an organisation or consortium, and one can select whether the station is publicly available or private (within the organisation). We have carefully taken into consideration that the described data model is easily extendable.  
Finally, an on-boarding endpoint of a specific DA infrastructure can be selected to on-board the station to this ecosystem. Therefore, we assume that each DA ecosystem provides an on-boarding interface, which can be triggered by the station registry. This further makes our registry compatible with multiple ecosystems by simultaneously keeping all necessary information about the stations in one place. The way we have designed such an on-boarding procedure in the CS is part of the next section.
## 4 Experiments and Results Analysis
### Performance Comparison of Different Deep Learning Models
#### 4.3.3 Single Model Performance Comparison Results
Deep learning models excel on MAPE, while shallow learning models (particularly random forest) perform better on RMSE. Figure 4: Visualization of RMSE values for all models on test set. (**a**) RMSE of each model on test set (sliding time window = 60 min). (**b**) RMSE of each model on test set (sliding time window = 30 min). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RMSE, Sliding Time Window = 60 min**} & \multicolumn{3}{c}{**RMSE, Sliding Time Window = 30 min**} \\ \hline Prediction Horizon & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ (mins) & 2 & 10 & 20 & 30 & 60 & 2 & 10 & 20 & 30 & 60 \\ Linear Regression & 5.922 & 10.980 & 12.487 & 13.029 & 13.318 & 5.915 & 10.950 & 12.426 & 12.981 & 13.426 \\ Ridge Regression & 5.922 & 10.980 & 12.487 & 13.029 & 13.318 & 5.915 & 10.950 & 12.426 & 12.981 & 13.426 \\ LASSO Regression & 5.919 & 10.965 & 12.465 & 13.011 & 13.313 & 5.919 & 10.946 & 12.421 & 12.964 & 13.421 \\ Random Forest & 5.924 & 10.763 & 12.359 & 12.543 & 12.768 & 5.945 & 10.724 & 12.332 & 12.628 & 12.978 \\ LSTM-DNN & 18.124 & 11.758 & 13.286 & 13.082 & 13.418 & 5.854 & 11.012 & 12.216 & 12.958 & 13.154 \\ DNN64\_2 & 6.099 & 11.039 & 12.441 & 13.075 & 13.943 & 6.035 & 11.530 & 12.489 & 13.054 & 13.486 \\ DNN84\_4 & 6.248 & 12.230 & 12.497 & 13.137 & 13.423 & 6.615 & 11.117 & 12.484 & 13.071 & 13.463 \\ DNN128\_2 & 6.035 & 11.383 & 12.587 & 13.079 & 13.440 & 6.031 & 11.024 & 12.492 & 13.025 & 13.550 \\ DNN128\_4 & 6.153 & 11.098 & 12.555 & 14.393 & 13.388 & 6.160 & 11.014 & 12.783 & 12.955 & 13.493 \\ DNN256\_2 & 6.107 & 11.230 & 12.673 & 13.452 & 14.234 & 6.002 & 11.179 & 12.548 & 13.138 & 13.591 \\ DNN256\_4 & 5.992 & 11.660 & 12.504 & 13.194 & 13.409 & 6.365 & 11.342 & 12.521 & 14.226 & 13.582 \\ DNN512\_2 & 5.966 & 11.025 & 12.666 & 12.987 & 13.400 & 5.996 & 11.606 & 12.807 & 13.079 & 13.624 \\ DNN512\_4 & 5.957 & 11.338 & 12.574 & 13.123 & 13.653 & 6.352 & 11.068 & 12.619 & 13.966 & 13.472 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE values of all models on test set.
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 3 Parametric Study
### Multi-Objective \(H_{2}\) Norm Optimum Design
The energy dissipation enhancement mechanism is described by the factor \(\psi\)[32] (shown in Table 1), which equals the ratio of the response mitigation of an SDOF structure with CBIS to that of an SDOF structure with a viscous element having the same additional damping coefficient as the CBIS, that is:  
\[\psi=\frac{\sigma_{U_{0}}(\zeta)-\sigma_{U}(\mu,\xi,\kappa)}{\sigma_{U_{0}}( \zeta)-\sigma_{U_{0}}(\zeta+\xi)}. \tag{29}\]  
The degree of the energy dissipation enhancement of the CBIS can be adjusted by adding the following supplementary constraint condition to the optimization problem by:  
\[\psi\geq\psi_{0}, \tag{30}\]  
where \(\psi_{0}\) is a constant during and the recommended range 1\(<\psi_{0}\leq\)2, according to the many numerical case studies [32]. Here, \(\psi\) is 8.7517, which means that the inerter element has fully played its role, and, in the case of the same additional damping ratio, the energy dissipation efficiency of CBIS is 8.7517 times that of a purely viscous element. To illustrate the effects of CBIS on the seismic performance in the time domain, dynamic time-history analyses were conducted to further verify the design results under harmonic excitation and seismic excitations. Four seismic waves are used as external excitations--the El Centro record (1940, NS), the ground motion recorded at Tohoku University during the 2011 Tohoku earthquake (M = 9.0, PGA = 3.33 m/s\({}^{2}\)), Kobe record (1995) and Chi-chi record (1999). The Tohoku wave occurred on 11 March 2011 and was part of the most powerful known earthquake in Japan. For the SDOF structure, the inherent damping ratio is \(\zeta\) = 0.02. The natural period on the rigid base is 1.00 s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Optimized Design Parameters**} & \multicolumn{2}{c}{**Displacement Response Ratio**} & \multicolumn{2}{c}{**Force Ratio**} & \multicolumn{2}{c}{**Energy Dissipation Efficiency**} \\ \hline \hline Intertance-mass ratio & Damping ratio & Stiffness ratio & \(\gamma u\) & \(\gamma_{F_{4}}\) & \(\psi\) \\
0.0198 & 0.1065 & 1.2728 & 0.40 & 2.1375 & 8.7517 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of performance-based design optimization. Figure 8: Pareto optimal front.
# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 4 NMN Regulates Physiological Metabolism
### Anti-Aging  
With the aging of the global population, there is an increasing demand for anti-aging healthcare products with the function of prolonging the life span and delaying the onset of aging-related diseases [85; 86; 87]. As a precursor for the synthesis of NAD\({}^{+}\), NMN slows the process of NAD\({}^{+}\) depletion with age by increasing NAD\({}^{+}\) levels in vivo and thus may serve as a potential anti-aging healthcare product [1; 19; 88]. In recent years, researchers have carried out extensive studies on the anti-aging effect of NMN in animal models and clinical trials (Table 1). A large body of evidence suggests that NMN has important beneficial effects in regulating physiological metabolism, such as protecting endothelial cells [89], improving blood supply [90], repairing metabolic dysfunction [91], and protecting the nervous system [92]. In 2021, researchers conducted a small randomized, double-blind clinical trial of 25 prediabetic postmenopausal women who were overweight or obese and received oral NMN (250 mg daily) for 10 weeks. The results showed that NMN increased muscle sensitivity to insulin in women in the treatment group compared with those in the placebo group [93]. In 2023, a similar study included 36 healthy adults (40-59 years, 38.9% male) who were treated with oral NMN (250 mg daily) for 12 weeks. The results showed that the serum levels of the NAD\(+\) metabolite nicotinamide were significantly higher in the treatment group than in the placebo group, and the arterial stiffness index showed a decreasing trend.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Human Clinical Trials Focusing on Ageing**} \\ \hline
**Compound** & **Study Design** & **Dose and Duration** & **NcT/uMiN No.** \\ \hline \multirow{8}{*}{NMN} & Healthy volunteers aged & Oral administration & \\  & from 40 to 65 years & Long-term NMN administration & NCT04228640 \\  & & 300 mg daily for 60 days & \\ \cline{2-4}  & Healthy male volunteers aged & Long-term NMN administration for 8 weeks & UMIN000030609 \\  & & Dose is not described & \\ \cline{2-4}  & Double-blind study in & Oral administration & \\  & postmenopausal and prediabetic & Long-term NMN administration: 250 mg & NCT03151239 \\  & women aged 55–75 years & daily for 8 weeks & \\ \cline{2-4}  & Healthy male volunteers aged & Oral administration & \\  & from 40 to 60 years & Single administration of 100, 250, & UMIN000021309 \\  & & or 500 mg NMN & \\ \hline \multirow{2}{*}{NMN} & Healthy volunteers aged & Oral administration & \\  & from 50 to 70 years & Long-term NMN administration: 100 mg or & UMIN000025739 \\ \cline{1-1}  & & 200 mg for 24 weeks & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The metabolic effects of NMN in mouse models and human clinical trials.
## 3 Proposed Algorithms Explanation
### Localization Free Energy Efficient Routing Protocol (LFEER)
#### 3.1.3 Destination Nodes Selection and Data Forwarding  
This part briefly explains the selection criteria of the DSNs and data forwarding mechanism. When the source nodes receive information about neighbors, the residual energy, number of hops and BER of each node is present in the field of the source nodes according to the manner discussed above. The node which has the maximum residual energy, fewest hops and the least bit error rate is chosen as the DSN. The selected DSN forwards the information bag to the next DSN via multi-hopping until the information packets reach to the final DNN. The next DSN selection criterion is the same as the first DSN selection. If the final DNN is in the range of the source node, then the information bag is directly transmitted to the final DNN. The proposed LFEER protocol checks the residual energy and number of hops of each node and selects such a DSN which is best in these parameters. As a result, energy consumed by the node reduces. Thus, the proposed LFEER algorithm can be practically implemented in scenarios where the communication for the longtime is needed, such as undersea monitoring. The forwarder selection is based on assigning a cost function or fitness function to each node based on residual energy (\(R_{e}\)), number of hops (\(n\)) and BER as  
\[f=R_{e}/n\times BER. \tag{1}\]  
A node having the highest value of the function \(f\) is chosen as a DSN.
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method
### Multi-Strategy Genetic Algorithm
#### 2.3.2 Posterior Probability Penalty  
The proposed method delivers a low number of false positives and high accuracy because it uses a posterior probability penalty. The modified posterior probability density is also called the responsiveness of the component to the data. It is calculated as follows:  
\[\gamma_{ik}=\frac{\alpha_{k}f\left(x_{i},\mathbf{\mu}_{k},\mathbf{\sigma}_{k} ^{2}\right)}{P(x_{i}|\Theta)}*L_{i}(k) \tag{8}\]  
where \(\gamma_{ik}\) is the responsiveness of sample \(i\) to sub-model \(k\)\(L_{i}(k)\) is the robust correction function, which indicates how well the \(i\)-th sample matches the \(k\)-th component.  
\(L_{i}(k)\) is defined as follows:  
\[L_{i}(k)=\begin{cases}\frac{P(x_{i}|\Theta)}{k-1}*p_{i}&k<K\\ \frac{k-1}{w\left(x_{i},\mathbf{\mu}_{k},\mathbf{\sigma}_{k}^{2}\right)}*(1-p _{i})&k=K\end{cases} \tag{9}\]  
where is \(p_{i}\) the robustness factor, which indicates the confidence level of the \(i\)-th sample.  
The main role of the robustness factor \(p_{i}\) is to prevent model overfitting. According to the TLE prediction accuracy, the robustness factor was divided into three categories: (1) the normal segment refers to data in the expected accuracy range; (2) the uncertain segment refers to data with a wide accuracy range; (3) the segment to be eliminated refers to data outside the expected accuracy range; it has a low probability of occurrence. Weight estimation methods include the [PERSON], Danish, the IGG (Institute of Geodesy and Geophysics, Chinese Academy of Sciences) III weight function, one-time paradigm minimization, and Turkey's methods [32]. Only the IGGIII method provides the above three segments [33]. Therefore, it was used to determine the robustness factor as follows:  
\[p_{i}=\begin{cases}\begin{array}{cc}1&v<c_{0}\\ \frac{c_{0}}{v}\left(\frac{c_{1}-v}{c_{1}-c_{0}}\right)^{2}&c_{0}\leq v<c_{1} \\ 0&c_{1}\leq v\end{array}\\ \\ v=\frac{d_{ik}}{\sigma}\end{cases} \tag{11}\]where \(v\) is the normalized distance; \(d_{ik}\) denotes the distance between the \(i\)-th sample point and the best Gaussian component \(\mu_{k}\); \(\sigma\) is the arithmetic square root of the variance of the \(k\)-th Gaussian component \(\sigma_{k}^{2}\); \(c_{0}\) and \(c_{1}\) are experience constants related to the scenarios, with ranges of 1-1.5 and 2.5-8, respectively [33; 34; 35].
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 3 Methods and Techniques
### Speech Data Pre-Processing  
Any successful SEC model begins with speech database preparation. Most speech emotion corpus are influenced by speakers' age, gender and cultural background, among others. It then becomes necessary to diligently carry out adequate preparation of the dataset. Unlike other image processing sub-fields, applying transfer learning to a speech-based task requires substantive data preprocessing in order to make the input image suitable for the deep learning network. Specifically, speech signals carry information such as the sampling ratio, frequency and speed that must be enhanced before their usage for training and the corresponding feature map.  
The required input to our proposed model is log mel-spectrogram. This is extracted from the speech signal. Mel-spectrogram has proven to be efficient and rich in emotional content for speech emotion classification. In generating the mel-spectrogram, a series of operations such as pre-emphasis, framing and windowing must need to be performed. The speech signal is first subjected to pre-emphasis for amplification of the frequency. Because of the continuous nature of the speech signal, framing is performed to disintegrate the signal into a fixed-length size, after which a hamming window of 20 ms size function is applied on the frame with 10 ms shift, as computed in Equation (1)  
\[w(n)=0.54-0.46 cos\bigg{(}\frac{2\pi n}{M-1}\bigg{)}0\leq n\leq M-1 \tag{1}\]  
where \(M\) represents the window size for the hamming function \(w(n)\).  
The mel-filter bank used in this work is 40, while the sample rate is 16 kHz and FFT (Fast Fourier Transform) is 512. A speech signal (\(x\)) of length (\(N\)) can be converted into a signal in the frequency domain (\(X\)) using the FFT technique as computed in Equation (2). The size of FFT corresponds to the size of the last convolution layer of our pretrained model. FFT is adopted [32] in generating the complete mel-spectrogram of the speech sample. As part of preprocessing, the mel-spectrogram generated using FFT is resized to the input specification (size) of our proposed model, which is 224 \(\times\) 224 \(\times\) 3. This represents the size (height \(\times\) width) and the number of channels, respectively.  
\[X(t)=\Sigma_{r=1}^{N-1}\bigg{(}x[r]W\cos\frac{n\pi x}{L}+b_{n}\sin\frac{n\pi x }{L}\bigg{)} \tag{2}\]  
Figure 2: Structure of the Proposed Model.  
where \(x(r)\) represents a signal, \(W\) represents an \(N\)-point window function and \(h\) = 0, 1, 2, \(\ldots,N-1\).
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 4 Relative Poses between the Camera and IMU Sensors
Figure 6: Effects of the weighting parameter on downtilt (**a**) azimuth (**b**) estimation for three calibration configurations (best viewed on screen): (1) no calibration, (2) calibration without accelerometer-magnetometer DoFs and (3) calibration with accelerometer-magnetometer DoFs. Combining Equations (3) and (4), we obtain:  
\[\underset{\underset{\mathbb{C}}{R},\,\overset{\mathbb{G}}{\alpha R}}{argmin}\sum_{i=1 }^{Q}\frac{1}{Q_{i}}\sum_{j=1}^{Q_{i}}\bigg{(}\big{(}1-w\big{)}\delta_{t}\Big{(} t_{i},h_{t}\big{(}\overset{\mathbb{B}}{C}R_{i}^{j}\overset{\mathbb{C}}{I}R_{i}^{j} \big{)}\Big{)}^{2}+w\delta_{a}\Big{(}a_{i},h_{a}\big{(}\overset{\mathbb{B}}{C} R_{i}^{j}\overset{\mathbb{C}}{I}R_{i}^{j}\big{)}\Big{)}^{2}\bigg{)};0<w<0.5. \tag{5}\]  
Equation (5) is written in a standard least-mean-square form, and it can be effectively solved using the Levenberg-Marquardt algorithm [54].
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Optical Cavity Boundary Layer Control
#### 4.1.4 Experimental Results of Orifice Plate  
Figure 10 shows the pressure distribution along the optical cavity at different pumping rates in the orifice plate state. Because of the opening of the hole, only the middle four measurement points along the pressure measurement point were retained. The pressure distribution curve was similar to previous results, showing a trend of increasing pressure in the front part of the optical cavity and decreasing pressure in the back part. With an increase in pumping volume, the pressure distribution decreased further. Furthermore, the orifice plate was effective in controlling the growth of the boundary layer.  
Table 5 lists the chemical reaction platform power of the perforated plate at different pumping rates. It can be seen that when chemical reaction platform upper and lower wall plates are used, the pumping capacity increases from 2% to 8%, and the impact on the chemical reaction platform power is not apparent.