esa-sceva/satcom-chunk-collection huggingface dataset:
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 5 Numerical Results
### Test Problem
#### 5.1.1 DWR Estimate  
The exact solution is \(\mathbf{u}\in V=\mathcal{C}([t_{0},t_{e}];\mathbb{R}^{2})\). We get the weak formulation  
\[\underbrace{\int_{t_{0}}^{t_{e}}\mathbf{\phi}^{T}\dot{\mathbf{u}}+\mathbf{\phi}^{T}\mathbf{B} \mathbf{u}\,\text{d}t+\mathbf{\phi}^{T}(t_{0})\mathbf{u}(t_{0})}_{=A(\mathbf{u},\mathbf{\phi})}= \underbrace{\mathbf{\phi}^{T}(t_{0})\mathbf{u}_{0}}_{=F(\mathbf{\phi})},\quad\forall\ \mathbf{\phi}\in V.\]  
We use the FE space \(V_{h}:=\left\{u\in V:u\big{|}_{I_{n}}\in\mathcal{P}^{q}(I_{n};\mathbb{R}^{2})\right\}\) denoting the space of continuous piece-wise \(q\)-th order polynomials \(I_{n}=[t_{n},t_{n+1}]\to\mathbb{R}^{2}\). Let \(\mathbf{z}\) be the exact adjoint solution for a given \(\mathcal{J}\) and \(\mathbf{z}_{h}\) its FE approximation, we get \(e^{\mathcal{J}^{\prime}}=A(\mathbf{u}_{h},\mathbf{z}-\mathbf{z}_{h})-F(\mathbf{z}-\mathbf{z}_{h})\). We approximate \(\mathbf{z}\) by \(\mathbf{z}_{h}^{+}\) to get Defining \(R_{1}(t):=\left(\dot{u}_{1}+u_{1}-u_{2}\right)\left(z_{1}^{+}-z_{1}\right)\) and \(R_{2}(t):=\left(\dot{u}_{2}+k\,u_{2}\right)\left(z_{2}^{+}-z_{2}\right)\), we get the final error estimate \(\eta_{h}(\mathbf{u}_{h})\) using the composite trapezoidal rule  
\[\eta(\mathbf{u}_{h}):=\sum_{n=0}^{N-1}\frac{\Delta t_{n}}{4}\left|\sum_{i=1}^{2}R_ {i}(t_{n})+2R_{i}(t_{n}+\Delta t_{n}/2)+R_{i}(t_{n+1})\right|.\]
## 2 POD Strategy and Data Usage
### Data Usage  
The twin GRACE-FO satellites fly a polar orbit with an altitude of 490 km. Over the mission lifetime, the two satellites will remain in co-planar orbits and the along-track separation is about 220 km. For POD, the GRACE-FO satellites are equipped with the new generation of the GNSS space science receiver, the TriG receiver. The receiver upgrades the capabilities offered by the BlackJack receiver, which was carried on the GRACE mission. GPS C/A, P1, and P2 pseudoranges, and associated carrier phase observations, namely LA, L1, and L2 can be provided by the receiver [54]. The GPS P1 and P2 pseudoranges and L1 and L2 carrier phase observations are used in this research.  
One year of data in 2019 is processed. The data can be obtained from the GRACE-FO level 1B RL04 products, which are available at [[ftp://isdcftp.gfz-potsdam.de/grace-fo/](ftp://isdcftp.gfz-potsdam.de/grace-fo/)]([ftp://isdcftp.gfz-potsdam.de/grace-fo/](ftp://isdcftp.gfz-potsdam.de/grace-fo/)) (accessed on 17 October 2021). The products also include the satellite attitude (SCA1B), precise orbits provided by JPL (GNV1B), biased inter-satellite ranges measured by KBR system (KBR1B), and the positions of the GPS antenna phase center (VGN1B). The GNV1B and KBR1B data can be used to assess the orbit quality. PCOs of ionosphere free carrier phase observations and SLR reflector positions in the science reference frame (SRF) are listed in Table 2. CODE's GPS final orbits and 5 s clock products are used, and the associated OSB products are also applied to allow for SD AR [55,56].
## 5 Experimental Results
### Speed Evaluation  
As we developed a system for real-time use, one of our focuses was to keep the processing speed high. The proposed method for object tracking in 360\({}^{\circ}\) panoramic videos was implemented on an Intel Core i7-7700k CPU @ 4.20 GHz. We implemented the code of this algorithm in Python along with OpenCV functions. This code was nonoptimized, and by running on a standard machine, it achieved approximately real-time performance with an average speed of 24.1 fps. Resolution of the immersive videos was very high, which resulted in slow computation speed. To further enhance the computation speed, we down-sampled the frame size and tracked the object at this resolution. Instead of down-sampling to a fixed size of frame, a down-sampling rate (25%) was used, which overcame the loss of the content's important information. After tracking the object's location in a down-sampled 360\({}^{\circ}\) frame, we remapped the location of the object in the 360\({}^{\circ}\) frame with the original resolution. In this way, it boosted the overall speed of the algorithm with no effect on tracking performance. Figure 11 shows a comparison of our method's speed performance with the performances presented by [PERSON] et al. [18]. These methods also presented simple online arbitrary object trackers by using the conventional approaches in their techniques for fast object tracking in 360\({}^{\circ}\) videos. Figure 11 shows information on computation time per frame for each method along with information on speed in fps.  
Figure 11: Comparison of tracking methods in terms of average computation time and frames per second (on top of the bars).  
Figure 10: Bar chart of recall and precision values for object tracking in each 360\({}^{\circ}\) panoramic video.
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.5 MFLCM Experimental Results  
The Random Forest (denoted as Random Forest2) is used to integrate four base classifiers, and MFLCM is the combination of four base classifiers and Random Forest2. The weight of class \(i\) of samples (i.e., samples in Figure 14) is the ratio of the total number of samples to the number of class \(i\) samples. The best parameters of Random Forest2 are shown in Table 13, and are obtained by random search. The weights of Random Forest2 to each base classifier are shown in Figure 30. Figure 31 shows the confusion matrixes of Random Forest2 on the validation set (which is also the confusion matrixes of MFLCM). Comparing the performance of MFLCM with the base classifiers in Table 5, MFLCM has higher total accuracy and F1 score than those of the base classifiers. Specifically, MFLCM reduces the confusion between passenger ships and fishing boats, as well as that between tankers and cargo ships. When evaluating the performance of MFLCM by samples, MFLCM improves the total accuracy by 1.57% and the F1 score by 1.63%, which is equivalent to a 24.61% reduction in misclassification over the best base classifiers (Random Forest1). When evaluating the performance of MFLCM by ships, MFLCM improves the total accuracy by 3.14% and F1 score by 3%, which is equivalent to a 24.08% reduction in misclassification over the best base classifiers (Random Forest1). Different classifiers focus on different ship features and they have different classification tendencies. By integrating multiple features,  
Figure 29: (**a**) Learning curve (XGBoost3); (**b**) Confusion matrix of samples (XGBoost3); (**c**) Confusion matrix of ships (XGBoost3).  
MFELCM reduces the bias effectively. Furthermore, the classification results of ships can be refreshed by updating the dynamic information regularly (e.g., using satellites to transmit and update data regularly), which enables near real-time online classification.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 1 Introduction  
### Terminology and Definitions  
This study uses a range of symbols and parameters related to water level dynamics, ship draught, and navigational safety. The following table summarizes the key terms (Table 1):  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol/Term** & **Definition** \\ \hline \multirow{2}{*}{_UKC_} & Under-Keel Clearance—the minimum vertical distance between the \\  & bottom of a ship’s keel and the seabed during passage. This value \\  & ensures the ship can move safely without grounding. \\ \hline \multirow{2}{*}{\(\Delta h\)} & Permissible drop in water level—the acceptable decrease in water level \\  & (relative to reference level) which still allows a vessel to enter the fairway \\  & without exceeding the minimum UKC \\ \hline \multirow{2}{*}{\(T\)} & Draught of the vessel—the vertical distance between the waterline and \\  & the bottom of the ship’s keel. \\ \hline \multirow{2}{*}{\(D\)} & Fairway depth—the total water depth of the fairway, measured from the \\  & reference level to the bottom. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of Symbols and Abbreviations.
## 3 Methodology
### Workflow  
The digital twin model can be divided into two stages: online training and offline analysis. During the online training, through the cooperation of four sub-models, the nonlinear fitting of the neural network is performed on the flow field of the big data. In the offline analysis, it is only necessary to input the condition parameters into the trained model, and the twin model can follow the field data of the physical world for real-time updates to provide real-time monitoring and evaluation of the equipment.  
Figure 2: General framework of the CFD digital twin.  
The workflow of the system is shown in Figure 3. The process of creating the CFD digital twin follows the following steps:  
1. Geometric model  
The geometric model of the physical entity is created and meshed using the ANSYS ICEM CFD pre-processing software to restore the physical entity 1:1 ratio and make it reflect its shape, size, boundaries, etc. In the subsequent simulation, the geometric model achieves a complete mirror image of the actual device. The model finally outputs a mesh file.  
2. Rule model  
The production operation has a certain process. Based on the historical data from the sensors during the operation of the real physical entity, the process curve for the field operation of the push-plate kiln can be obtained. Then, summarize the normal values of each variable in the production process to determine the variation interval of each parameter.  
3. Physical model  
Based on the geometric model and the rule model, use the ANSYS FLUENT solver to simulate the physical process, select an appropriate physical solution model, and set the behavior rules for the parameters, according to the rule model. The behavior rules are reflected in various parameter settings in the software, such as boundary conditions. In this part, the FLUENT automation script is used to generate the flow field data and the TECPLOT is used to post-process the data.  
Figure 3: Proposed digital twin workflow.  
4 Neural Network Model (POD-MCNN)  
The neural network model is the key module to realize CFD offline dynamic analysis. In this part, the flow field big data generated by the physical model are split into training and test sets for the design and training of the neural network model. A well-trained model can respond quickly to the input parameters and output the corresponding CFD results. The detailed process of building the neural network model is described in Section 3.3.
## 3 Proposed Hybrid Precoding-Companding Technique
### Precoding  
Precoding techniques achieve large PAPR reduction gain with small computational complexity. It does not require Side information or distort the transmitted signal. However, most of the precoding matrices lead to more distortion in nonlinearity than the original signal. Fortunately, the SRC matrix does not increase the distortion. However, it causes some data rate loss [11]. Precoding matrix \(\mathbf{R}\) is given as:  
\[\mathbf{R}=\left[\begin{array}{cccc}R_{1,1}&R_{1,2}&\cdots&R_{1,(N_{T}-N_{R}) }\\ R_{2,1}&R_{2,2}&\cdots&R_{2,(N_{T}-N_{R})}\\ \vdots&\vdots&\ddots&\vdots\\ R_{N_{T},1}&R_{N_{T},2}&\cdots&R_{N_{T},(N_{T}-N_{R})}\end{array}\right] \tag{15}\]  
SRC precoding matrix spreads \((N_{T}-N_{R})\) subcarriers on \(N_{T}\) subcarriers, where \(0\leq N_{R}<N_{T}\) is the loss in terms of subcarriers. Losses in subcarriers become zero if \(N_{R}\) equals zero, this causes SRC matrix to be DFT matrix. Values of \(R_{n,m}\) are defined as follows [40]:  
\[R_{n,m}=R_{n,\rho}e^{j\left(\frac{2\pi nm}{N_{T}}\right)} \tag{16}\]  
where \(R_{n,\rho}=\begin{cases}\dfrac{(-1)^{n}}{\sqrt{2}}\sin\left(\dfrac{\pi n}{2N _{R}}\right),&0\leq n\leq N_{R}\\ \dfrac{(-1)^{n}}{\sqrt{2}},&N_{R}\leq n\leq(N_{T}-N_{R})\\ \dfrac{(-1)^{n}}{\sqrt{2}}\cos\left(\dfrac{\pi(n-N_{T})}{2N_{R}}\right),&(N_{ T}-N_{R})\leq n\leq N_{T}\end{cases}\)  
The computational complexity of the SRC precoding is characterized by the number of real multiplications (\(RM_{s}\)) and the number of real additions (\(RA_{s}\)) which are given as follows:  
\[RM_{s}=4N_{DSC}(N_{DSC}-N_{R}) \tag{17a}\] \[RA_{s}=2N_{DSC}(2(N_{DSC}-N_{R})-1) \tag{17b}\]  
where \(N_{DSC}\) is the number of data subcarriers.  
Figure 2: General block diagram of SRC-Log-based OFDM system.
## 4 Applications of Intelligent Acoustic Monitoring
### Industry and Predictive Maintenance
Edge computing and lightweight ML models are increasingly used here.
## 3 Results
### Effect of the Strain Rate  
Vinyl nitrile foams displayed strain rate dependent behavior. In compression, the stress at the onset of densification was about twice as high at 100 s\({}^{-1}\) than at 0.01 s\({}^{-1}\) for the VN-A and VN-B samples, and almost three times higher for the VN-C samples (Table 1). At maximum efficiency, the energy absorbed by the three VN foams was about four times higher at 100 s\({}^{-1}\) than at 0.1 s\({}^{-1}\), and the transmitted stress was about three times higher (Table 1 and Figure 5). Between 50 and 200 s\({}^{-1}\), the stress of the plateau also increased by about 1.4 times in the three VN foams. This increase in the stress of the plateau with strain rate was also seen in the combined loading, in both the compression and shear directions. Strain rate also affected the maximum energy absorption efficiency, with values of 35% in quasi-static and 51% at 100 s\({}^{-1}\) for the VN-B in compression.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & E* (**Mpa)** & \(\epsilon_{\text{pl}}\) & \(\sigma_{\text{pl}}\) & \(\epsilon_{\text{eff.}}\) & \(\sigma_{\text{eff.}}\) & \(\mathbf{W_{eff.}}\) & \\  & **(Mpa)** & **(Mpa)** & **(Mpa)** & **(/cm\({}^{3}\))** & & \\ \hline \multicolumn{8}{c}{Quasi-static Compression (0.01 s\({}^{-1}\))} \\ \hline VN-A & 1.60 & 0.03 & 0.05 & 0.63 & 0.25 & 0.08 \\ VN-B & 2.89 & 0.04 & 0.12 & 0.62 & 0.33 & 0.12 \\ VN-C & 4.81 & 0.05 & 0.23 & 0.57 & 0.56 & 0.19 \\ \hline \multicolumn{8}{c}{**Impact Compression (100 s\({}^{-1}\))**} \\ \hline VN-A & 7.71 & 0.049 & 0.38 & 0.62 & 0.68 & 0.28 \\ VN-B & 16.30 & 0.052 & 0.85 & 0.64 & 1.05 & 0.52 \\ VN-C & 22.24 & 0.054 & 1.21 & 0.57 & 1.82 & 0.73 \\ \hline \multicolumn{8}{c}{**Impact Compression and Shear (100 s\({}^{-1}\))**} \\ \hline VN-A\_comp. & 6.75 & 0.053 & 0.36 & 0.58 & 0.48 & 0.20 \\ VN-A\_shear & 2.31 & 0.050 & 0.12 & 0.61 & 0.27 & 0.10 \\ VN-B\_comp. & 8.02 & 0.066 & 0.53 & 0.64 & 0.58 & 0.30 \\ VN-B\_shear & 2.83 & 0.058 & 0.16 & 0.65 & 0.29 & 0.13 \\ VN-C\_comp. & 12.64 & 0.085 & 1.08 & NA & NA & 0.32 & 0.36 & NA & NA \\ VN-C\_shear & 5.61 & 0.062 & 0.35 & NA & NA & NA & 0.32 & 0.36 & NA & NA \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the stress-strain curves of the three VN foams in quasi-static compression (strain rate 0.01 s\({}^{-1}\)), impact compression, and impact combined compression and shear loading (strain rate 100 s\({}^{-1}\)). Abbreviations: E\({}^{*}\) Young’s modulus; \(\varepsilon_{\text{pl}}\) and \(\sigma_{\text{pl}}\) strain and stress at yield; \(\varepsilon_{\text{eff.}}\)\(\sigma_{\text{eff.}}\) And W\({}_{\text{eff.}}\) Strain, stress, and energy per unit volume at the point of maximum efficiency (at the onset of densification); \(\sigma_{\text{s}}\)/\(\sigma_{\text{c}}\) ratio between the stress in shear and in compression at a given strain; W\({}_{\text{s}}\)/W\({}_{\text{c}}\) ratio between the energy per unit volume in shear and in compression at a given strain.
## 6 Airtightness Sealing Performance and Failure Analysis
### Separation Pressure and Failure Pressure  
The foregoing analysis indicates that the contact pressure at the gasket-gasket contact surface varies with the vacuum pressure. It is understandable that when the vacuum pressure exceeds \(p_{imax}\) on the contact surface, which does not mean that the gasket sealing has failed but rather that only sections of the contact surface may be separated. That is, the non-separated sections of the contact surface are still capable of providing sealing. Hence,  
Figure 11: The contact pressure distribution along the gasket–gasket contact surface with different vacuum pressure.  
the entire air-leakage process should be analyzed to determine the failure pressure. The separation pressure \(p_{s}\), defined as the fluid pressure corresponding to the beginning of contact surface separation, will be used in the following subsequent discussions.  
Figure 12 illustrates the relationship of \(p_{imax}\) versus \(p_{s}\) and \(p_{f}\). It can be observed that \(p_{f}\) and \(p_{s}\) show an increasing trend with increasing \(p_{imax}\), which implies that a higher \(p_{imax}\) would provide a better sealing effect. In all cases, \(p_{s}/p_{imax}\) is below 1.0, and \(p_{f}/p_{imax}\) is over 1.0, indicating the fact that the contact surfaces begin to separate as the vacuum pressure is lower than \(p_{imax}\), while the contact surface sealing begins to fail as the vacuum pressure is higher than \(p_{imax}\). This is due mainly to the redistribution of contact stresses on the contact surfaces induced by the gasket deformation caused by the vacuum pressure. On the one hand, the gasket deformation causes the outside contact pressure to decrease, and the contact surfaces start to separate, thereby reducing the contact length at the contact surface. On the other hand, the gasket deformation causes the inside contact pressure to increase, which means the sealing capacity is increased. Moreover, it is worth noting that a higher \(p_{imax}\) would result in a lower \(p_{s}/p_{imax}\) and \(p_{f}/p_{imax}\), of which the latter means that a higher \(p_{imax}\) would lead to a lower safety factor (\(p_{f}/p_{imax}\)).
## 2 Observer-Based Estimation
### Kalman Filter
Estimation results have been accurate, since they show a low RMS error in steady state manoeuvres, and low maximum error at peaks as well. However, this approach still has potential issues in terms of computational effort. Also, some observer-based studies can be found in the literature, which describes the vehicle behaviour using a kinematic model instead of a more common dynamic model and using a KF observer and its variants [43; 44; 45; 27; 40; 46; 61]. The major advantage is that the kinematic model has no sensitivity to physical vehicle parameters and uncertainty. However, as discussed in Section 2.1, this kind of model is unobservable if the yaw rate is zero. To overcome this problem, [PERSON] et al. [48] proposed a solution to avoid unobservability during zero or near-zero yaw rate conditions by combining the approach proposed by [PERSON] et al. in [45] with a dynamic model-based approach (see Section 2.1). Nonetheless, even if the kinematic model approach can be considered accurate enough to estimate VSA in transient manoeuvres, progressive drifting in results due to integration is still a potential issue. Other observer-based methods are mainly based on hybridising the KF with various artificial intelligence (AI) strategies [16; 37; 46; 47] to improve the observation model determination. The authors in [37] and [47] use ANFIS (Adaptive Network Fuzzy Inference System) respectively with UKF and EKF, while in [46] and [16], the modelling ability of a simple fuzzy logic inference system has been used to improve EKF estimation performances. In particular, [PERSON] et al. [16] proposed a combined approach using a kinematic model in transient conditions, and a dynamic model in steady-state conditions. Specifically, for the kinematic model, a high pass filter is applied to the measured signals, in order to avoid progressive drifting during integration. An appropriate steady-state index is defined to discern the degree of regime condition in the range 0-1, based on a real-time fuzzy logic-based analysis of the history of three signals: steering angle, lateral acceleration, and yaw rate, with a larger weight on the latter.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
###### Abstract  
Crude oil price forecasting contributes to global economic development. This study proposes a hybrid deep learning model for crude oil price forecasting. First, empirical wavelet transform decomposes raw data into multiple. Then, three neural networks generate preliminary forecasts, which are subsequently refined by a reinforcement learning-based ensemble method. Finally, an error correction module handles residuals, further enhancing the forecasting outcomes. Three West Texas Intermediate datasets and additional emergency scenarios were used to validate the hybrid model. The findings indicate that the proposed model achieves superior predictive performance compared with sixteen benchmark methods and three advanced models.  
crude oil price forecasting; empirical wavelet transform; neural networks; error correction module; hybrid model 2025 15 1055
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 3 Methodology  
### Electron Density Models  
The electron density in this study is obtained from two three-dimensional and time-dependent electron density models: the Neustrelitz Electron Density Model (NEDM2020) [18] and the NeQuick 2 model [15]. For both models, the input values depending on solar activity are the solar radio flux index F10.7, month, geographic latitude and longitude, height, and universal time (UT). The output obtained is the electron concentration at the specified location and time.  
The NeQuick 2 model was developed at the Aeronomy and Radiopropagation Laboratory of The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy, and at the Institute for Geophysics, Astrophysics, and Meteorology (IGAM) of the University of Graz, Austria. This model comprises vertical profiles consisting of multiple Epstein layers, and it derives essential electron peak density and height parameters through spatial and temporal interpolation from a comprehensive set of global maps. Consequently, NeQuick 2 incurs significant computational demands in terms of time and processing power [14].  
On the other hand, the NEDM2020 model was developed at the German Aerospace Center in the Institute for Solar-Terrestrial Physics (DLR-SO), Neustrelitz, Germany. Including the NTCM model, this model relies on about 100 model coefficients and a set of empirically fixed parameters. Remarkably, the electron density values can be directly computed for any specified location and time without the requirement for the specialized temporal or spatial interpolation of parameters, making it faster than the NeQuick 2 model in terms of computational efficiency [21].  
A model comparison of electron density profiles is presented in Figure 5 featuring one example at very low (first row) and mid-low (second row) elevation angle events. The first column displays the electron density per ray mapped along the specular point change using the NEDM2020 model. The subsequent columns (second, third, and fourth) illustrate the electron density profile comparison between the NEDM2020 and NeQuick 2 models for the incident, reflected, and direct rays, respectively.  
Figure 5: Electron density variations along the change and model profile comparison for the incident, reflected, and direct rays at very low and mid-low elevation angles (F10.7 = 75).