esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
[20] developed a ProsailH-BGC model and assimilated flux data and MODIS NDVI into the model, resulting in good accuracy of daily and annual carbon flux predictions. [PERSON] et al. [21] assimilated MODIS LAI into a process-based model, improving the performance of carbon flux predictions. Nevertheless, a series of issues occurred in existing reports. Firstly, current model calibration or parameter optimization neglected the requirement of spatial and temporal variation in the ecophysiological parameters of the process-based model. Secondly, quantifying the uncertainties in process-based models and remote sensing data remained difficult. The uncertainty caused by models included systematic errors and parameter errors [17]. The uncertainty in the remote sensing data stemmed from sensor error, cloud and aerosol contaminants, and inversion algorithms [17]. The technique used in this study offers a strategy of simulating forest carbon fluxes with temporal and spatial continuity and making full use of time series remote sensing data, the optimized MOD_17 model, and the Biome-BGC model. The results were checked using EC measurements at the forest flux tower, and global calibration of the model may be used on a local or regional scale for long-term simulation in future studies. Specifically, the original MOD_17 model was optimized first, using refined input data, specific parameters, and Global LAnd Surface Satellite (GLASS) /PAR at a ChangbaiMountains forest site dominated by broadleaf Korean pine forest, during 2003 to 2007. Considering the complicated ecophysiological parameters of Biome-BGC, a sensitivity analysis was conducted using the Extend Fourier Amplitude Sensitivity Test (EFAST). The Biome-BGC model was then calibrated by adjusting the most sensitive ecophysiological parameters to fit the simulated eight-day GPP of the selected 10 broadleaf Korean pine forest plots representing different meteorological conditions (i.e., temperature, precipitation) to those obtained from the optimized MOD_17 model. The best agreement confirmed the high accuracy obtained by regenerating the values of sensitive ecophysiological parameters. During the simulation, errors emanating from the model, the input data and the observations were corrected by assimilating GLASS LAI into the calibrated Biome-BGC using the EnKF. Finally, the simulated carbon fluxes were validated against EC measurements.
## 2 CFD Simulation Method
### Governing Equation and Solution Method
\tag{7}\]  
When calculating the rotor flow field in the forward flight, the initial conditions as shown below:  
\[\left\{ \begin{aligned} &\rho=\rho_{\infty}\\ & p=p_{\infty}\\ & u=u_{\infty}\\ & v=v_{\infty}\\ & w=w_{\infty}\end{aligned} \right. \tag{8}\]  
The boundary conditions involved in the numerical simulation of the helicopter rotor flow field mainly include the object surface boundary conditions and the far field boundary conditions. The boundary of the object's surface is mainly the surface of the blade. When the N-S equations are adopted, the relative velocity between the airflow at the object surface and the motion of the blade is zero, as shown below:  
\[\overset{\rightharpoonup}{v}-\overset{\rightharpoonup}{v}_{ blade}=0 \tag{9}\]  
where, \(\overset{\rightharpoonup}{v}\) and \(\overset{\rightharpoonup}{v}_{ blade}\) are the velocity of the fluid at the surface of the blade and the velocity of the surface of the blade, respectively. The normal gradient of the temperature and pressure on the surface of the blade is zero, as shown below:  
\[\frac{\partial T}{\partial n}=0 \tag{10}\]  
Figure 1: Flow diagram of the dual-time stepping method for overset grid. \[\frac{\partial p}{\partial n}=0 \tag{11}\]  
The inflow and outflow boundaries of the helicopter flow field are generally subsonic. \(R\) is Riemann invariants. For the subsonic inflow and outflow, \(R^{+}\) is calculated by interpolation between the inner and outer fields, and \(R^{-}\) is calculated by the value of the incoming flow at infinity, as shown below:  
\[R^{+}=V_{re}+\frac{2c_{e}}{\gamma-1} \tag{12}\]  
\[R^{-}=V_{r\infty}-\frac{2c_{\infty}}{\gamma-1} \tag{13}\]  
The normal velocity \(V_{r}\) and sound velocity \(c\) on the boundary:  
\[V_{r}=\frac{1}{2}\big{(}R^{+}+R^{-}\big{)} \tag{14}\]  
\[c=\frac{\gamma-1}{4}\big{(}R^{+}-R^{-}\big{)} \tag{15}\]  
Subsonic inflow boundary (\(Ma_{r\infty}<1,V_{r}<0\)):  
\[\left\{ \begin{array}{c}V_{r}=\frac{1}{2}(R^{+}+R^{-})\\ c=\frac{\gamma-1}{4}(R^{+}-R^{-})\\ V_{t}=V_{t\infty}\\ s=s_{\infty}\end{array} \right. \tag{16}\]  
Subsonic flow boundary (\(Ma_{r\infty}<1,V_{r}>0\)):  
\[\left\{ \begin{array}{c}V_{r}=\frac{1}{2}(R^{+}+R^{-})\\ c=\frac{\gamma-1}{4}(R^{+}-R^{-})\\ V_{t}=V_{t\varepsilon}\\ s=s_{e}\end{array} \right. \tag{17}\]  
where, \(V_{r}\) is the outward normal velocity of the far field boundary, \(c\) is the local speed of sound, and \(\gamma\) is the specific heat capacity. \(V_{t}\) is the tangential velocity, \(s\) is the entropy. The subscripts \(\infty\) and \(e\) represent the incoming flow value and the internal field value, respectively. A non-dimensional wall distance for a wall-bounded flow can be defined in the following way:  
\[y^{+}=\frac{\mu_{*}y}{\
u} \tag{18}\]  
where \(\mu_{*}\) is the friction velocity at the nearest wall, \(y\) is the distance to the nearest wall and \(\
u\) is the local kinematic viscosity of the fluid.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 5 Discussions
### Ablation Experiment  
In this paper, an improved collaborative filtering recommendation algorithm based on the TF-IDF model and user characteristics model is proposed. To discuss the influence of the two main improvements of the proposed method, two ablation experiments are carried out. The experiment is carried out on ML-100k data set, and the results of the proposed method (ICFTU) in Section 4.2 are used as reference. The method which is only based on the TF-IDF model is called ICFTU-TI, and the method which is only based on the proposed user characteristics model is called ICFTU-UC. The results of this ablation experiment are shown in Tables 3 and 4 and Figure 9.  
The results show that: (1) The ICFTU algorithm has the best performance and the smallest error, which shows that the method using both the TF-IDF and the improved user characteristics models are effective; (2) The error of ICFTU-TI is smaller than that of ICFTU-UC, and it is close to that of ICFTU. This shows that the TF-IDF method is the main factor to improve the accuracy of the model, and the improved user characteristics are the secondary factor, which is consistent with the discussion results on similarity weighted fusion parameters \(\xi\) and \(\mu\) in Section 5.1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**RMSE**} \\ \cline{2-11}  & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** & **45** & **50** \\ \hline ICFTU-UC & 1.174 & 1.118 & 1.097 & 1.069 & 1.052 & 1.023 & 1.01 & 1.004 & 1.001 & 0.999 \\ ICFTU-TI & 1.087 & 1.045 & 1.015 & 0.998 & 0.985 & 0.976 & 0.969 & 0.959 & 0.957 & 0.952 \\ ICFTU & 1.090 & 1.038 & 1.002 & 0.988 & 0.976 & 0.968 & 0.957 & 0.951 & 0.945 & 0.941 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the ablation experiment of RMSE.  
Figure 8: The influence of the model weights \(\xi\) and \(\mu\) on the proposed method: (**a**) of MAE; (**b**) of RMSE.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 3 Constraints of Stiffeners
### Constraint of Uniform Width  
The uniformity of stiffener width holds substantial significance for practical engineering manufacturing because in some applications, stiffeners with uniform widths are easier to manufacture. One effective approach to achieve this goal is to ensure that the norm of the gradient vector of the fundamental level set functions is equal to 1 across nearly all points. The norm of the gradient vector of \(\gamma(\mathbf{x})\) is provided by  
\[\left\|\
abla\gamma(\mathbf{x})\right\|=\sqrt{\left(\sum_{i=1}^{m}\alpha_{i} \frac{\partial\varphi}{\partial x}(\mathbf{x},\mathbf{q}_{i})\right)^{2}+\left(\sum_{ i=1}^{m}\alpha_{i}\frac{\partial\varphi}{\partial y}(\mathbf{x},\mathbf{q}_{i})\right)^{2}} \tag{14}\]  
where \(\partial\varphi/\partial x\) and \(\partial\varphi/\partial y\) are, respectively, the partial derivatives of \(\varphi\) with respect to \(x\) and \(y\). According to Equation (11), they are defined as [37]  
\[\frac{\partial\varphi}{\partial x}(\mathbf{x},\mathbf{q}_{i}) =-20\cdot t(\mathbf{x},\mathbf{q}_{i})\cdot(1-t(\mathbf{x},\mathbf{q}_{i}))_{+}^{ 3}\frac{\partial t}{\partial x}(\mathbf{x},\mathbf{q}_{i}) \tag{15}\] \[\frac{\partial\varphi}{\partial y}(\mathbf{x},\mathbf{q}_{i}) =-20\cdot t(\mathbf{x},\mathbf{q}_{i})\cdot(1-t(\mathbf{x},\mathbf{q}_{i}))_{+}^{ 3}\frac{\partial t}{\partial y}(\mathbf{x},\mathbf{q}_{i}) \tag{16}\]The \(\|\
abla\theta(\mathbf{x})\|\) of the function \(\theta(\mathbf{x})\) can be obtained similarly.  
The subsequent two constraints can ensure that at the center point \(\mathbf{x}_{e}\) of each element, \(\|\
abla\gamma(\mathbf{x}_{e})\|\) and \(\|\
abla\theta(\mathbf{x}_{e})\|\) are close to \(1\). Let \(d_{e}^{\theta}\) and \(d_{e}^{\psi}\) denote the constraints of the \(e\)-th element on \(\gamma(\mathbf{x})\) and \(\theta(\mathbf{x})\). Following the methods in [39; 40; 41], equality constraints can be transformed into inequality constraints as  
\[d_{e}^{\gamma} =\left(\|\
abla\gamma(\mathbf{x}_{e})\|-1\right)^{2}\leq\xi,\quad e=1 \ldots n \tag{17}\] \[d_{e}^{\theta} =\left(\|\
abla\theta(\mathbf{x}_{e})\|-1\right)^{2}\leq\xi,\quad e=1 \ldots n \tag{18}\]  
The \(p\)-norm approach [42] is utilized to consolidate the constraints as  
\[g_{\gamma} =\left(\sum_{e=1}^{n}\left(d_{e}^{\gamma}\right)^{p_{2}}\right)^{ 1/p_{2}}\leq\xi \tag{19}\] \[g_{\theta} =\left(\sum_{e=1}^{n}\left(d_{e}^{\theta}\right)^{p_{2}}\right)^{ 1/p_{2}}\leq\xi \tag{20}\]  
where \(\xi\) is the upper limit of the two constraints; \(n\) is the amount of finite elements; and \(p_{2}>0\) is the parameter of the \(p\)-norm.
# Experimental Seaborne Passive Radar
## 4 Results of Moving Target Detection
### Scenario 1: CASA C-295 and Fishing Boat (DVB-T Illumination)  
In Figure 14, we can see a situation map with the military ship (carrying the PCL receiver) marked with a red square, a cooperative CASA C-295 aircraft [25] passing around 10 km from the ship (green mark), a cooperative fishing boat in the close surroundings (yellow mark), and an accidentally passing cruise plane SAS756 (white square). The boresight directions of the surveillance (S) and reference (R) antennas have been illustrated with triangles of different colors.  
A signal from the DVB-T (191.5 MHz) transmitter was selected as a source of illumination with receiving antennas with horizontal polarization and the reference antenna from the side of the ship. After filtering out the clutter, the echoes from the CASA aircraft can be seen as a dot moving consequently in the bistatic \(v/R\) plane (around \(R=9\) km), marked with red rectangles in Figure 15 (the consecutive \(v/R\) maps are separated by 4 s). The fishing boat's echoes from the close surrounding cannot be separated from the clutter image due to the negligible Doppler frequency. The echoes from the cruise plane SAS756 that was moving more than 20 km northeast from the ship are not visible on the plots.
###### Abstract  
Multiples can cause artifacts in imaging; however, they contain information about underground structures. If the internal multiples are removed as a noise, the information contained by the internal multiple will also be removed. This will cause loss of some useful structures in the image. If the multiples and the primary can be separated from the recorded seismic data for imaging, the information contained by the multiples can be used and the artifacts can be attenuated. Here we developed a method to separate the primary and internal multiples and use them in least squares reverse time migration (L8 RTM). This method first separates the primary and the internal multiples in the data residual and predicts the wavefield of the primary and internal multiples in a forward-propagated wavefield. We use the high-order Born modeling method to predict the internal multiples. In this method, the internal multiples can be achieved by forward modeling of three times in the time domain. In the internal multiple prediction process, we get the wavefield of the primary and internal multiples in the forward-propagated wavefield. Then, by introducing the weighting matrix, we established the objective function for imaging of the primary and the internal multiples separately. In the calculation of gradient, we use the correlation of primary in the forward-propagated wavefield with the backward-propagated wavefield of the primary in the data residual, and internal multiples in the forward-propagated with the backward-propagated wavefield of internal multiples in the data residual. In this method, the multiple prediction process provided the internal multiples to suppress the artifacts, and L8 RTM constructed the model for the multiple prediction process. Finally, we performed numerical tests using synthetic data, and the results indicated that the L8 RTM without the internal multiple can suppress not only the artifacts of internal multiples but also some useful structures below the salt dome. L8 RTM with primary and internal multiples can suppress the artifact of internal multiples, and the useful structures below the salt dome are compensated in the image.  
seismic data analysis; internal multiple; Born modeling; least-squares reverse time migration +
Footnote †: journal: Applied Sciences
## 3 Controller Design
### Proposed Controller Design  
Figure 7 is the proposed controller, which simultaneously combines the adaptive radial basis neural network (ARBNN) [19], time delay estimator (TDE) [20], and non-singular fast terminal sliding mode control (NFTSMC) [21]. The ARBNN function is to estimate the payload part, using adaptive control to adjust the weights of neurons in the hidden layer. The TDE is used to deal with system uncertainties, external disturbances, friction, and backlash. Finally, the NFTSMC is used to ensure that the entire system remains stable. The proposed controller is to control the output torque of the motor of each joint based on the predefined desired trajectory in the joint space. The predefined trajectory is based on the dynamic model of the manipulator in Section 2. The force and position relationship could be found in [22].  
Figure 6: Structure of sliding surface.  
Figure 7: Schematic diagram of proposed controller.
## 4 Discussion and Limitations  
Throughout the paper, an isotropic PDL is assumed [18], and the orthodontic force was modeled as having a constant modulus of elasticity (cf. [51]).  
Under these assumptions, it was noted that the von Mises stresses grow after the remodeling process is initiated. This is so as the tooth continues to move. Our results indicate that the remodeling process decreases the normal stress, allowing the tooth to further move; this can increase the shear stress faster than the relaxation rate. After a significant movement, the accumulated relaxation of the remodeling process compensates for the increased shear stress, resulting in the overall von Mises stress decrease. Figures 6 and 7 suggests that after a significant remodeling process occurs, the main contributors to the von Mises stresses' distribution are the shear stresses since the remodeling process relaxes the normal stress faster than it relaxes the shear stresses. This claim should be further examined; as the PDL is not an isotropic material, this may also imply updating Equation (6).  
The importance of this study lies beyond that of engineering: as the algorithm provided here is rather fast (for example, calculating Figure 9 took five minutes on a MATLAB platform), one can create a \"motion planning\" algorithm to optimize the treatment time/forces applied, etc. [52]. In this regard, although there are studies in the field of biology, it is unknown to science what exactly governs the time duration of the remodeling process (this may depend, for example, on the exerted stress, the extent of the movement and the biological state of the tissue). Therefore, the time duration of each remodeling iteration in our scheme is obviously unknown. Nevertheless, having the model in hand, one may calibrate the time axis of the model by a set of controlled clinical OTMs.  
It is worth noting that the current model can be utilized to address similar cases involving other teeth that are anchored using miniscrew-guided mechanics, while taking into account various diameters [53] and lengths [54]. In the general case, the forces applied on a treated tooth by neighboring teeth should be incorporated; we shall consider this in future work. The effect of both surrounding tissues--other teeth and bone--could be modeled by the same method under different conditions, as both could be calculated by the stresses of the PDL springs introduced. Lastly, a set of clinical conclusions may be derived from our method, for example, that when a tooth is vertically loaded (extrusion), the resulting stresses are lower than in the case where horizontal forces of the same magnitude are applied. This will be the focus of our future work.
## 4 Conclusions  
This paper presented an investigation into the relationship between the printing process parameters and the elastic properties of PLA. The study offers insights into how printing parameters--printing temperature, speed, and layer height--influence the printed material's characteristics such as Young's modulus, shear modulus, and Poisson's ratio. The experimental data showed that nozzle temperature, layer height, and nozzle speed had different effects on the density and stiffness of the PLA specimens. Increasing the printing temperature corresponded to an increase in material density and the elastic properties of the material. In contrast, increasing the layer height led to a decrease in both density and the elastic properties of the material, suggesting that thicker layers result in less dense and less stiff structures. However, the variations in nozzle speed had a negligible effect on density and did not show a notable effect on the elastic moduli.  
Also, the examination of the correlation between the printing process parameters and internal structure was performed for two opposing cases of the minimum and maximum printing layer height. Through the analysis of the CT scans, pores were clearly visible in the samples, regardless of the height of the layer used in the printing process. However, the pore size was different for different layer heights.  
The study demonstrated that ultrasonic testing is effective in measuring the elastic properties of PLA materials. One significant advantage of this method is its ability to be used in real time, which enhances the potential for quality control in additive manufacturing.  
Conceptualization, A.Z., B.D. and M.P.; methodology, M.P. and F.B.A.; software, M.P. and F.B.A.; validation, M.P. and F.B.A.; writing--original draft preparation, M.P., F.B.A., B.D. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research is based upon work supported by the National Aeronautics and Space Administration under the NASA Cooperative Agreement No. NM-80 NSSC20M0215 and the Army Research Office under grant No. W911 NF2110138.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 11: Cross-sections obtained via X-ray computed tomography showing PLA specimen printed with layer height of 0.32 mm. Z-axis is the build direction; the XY plane is the layer plane. Scale bar length is 1 mm.
# A Self-Supervised Detail-Sensitive ViT-Based Model for COVID-19 X-ray Image Diagnosis: SDViT
## 4 Experiment
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Attention** & **Self-Supervised Learning** & **DCP** & **Domain Transfer** & **Test Set Accuracy** & **K-Fold Accuracy** \\ \hline ResNet101(Baseline) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 80.4333\% & 80.4733\% \\ Attention-VGG (SOTA) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 82.5\% & 82.5733\% \\ ViT & ✓ & \(\times\) & \(\times\) & \(\times\) & 81.2667\% & 81.3333\% \\ ViT + Self-supervised learning & ✓ & ✓ & \(\times\) & \(\times\) & 82.9667\% & 82.9333\% \\ ViT + DCP & ✓ & ✓ & ✓ & \(\times\) & 83.0333\% & 83\% \\ DCP + Self-supervised learning + ViT & ✓ & ✓ & ✓ & \(\times\) & 85.5333\% & 85.4933\% \\ SDViT(Our Model) & ✓ & ✓ & ✓ & ✓ & 86.9\% & 86.93333\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of different model configurations on synthetic-covid-cxr dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet101(Baseline) & 0.726307 & 0.808958 & 0.765408 \\ Attention-VGG(SOTA) & 0.750712 & 0.84375 & 0.794517 \\ ViT & 0.739040 & 0.831667 & 0.782621 \\ ViT + Self-supervised learning & 0.758279 & 0.858542 & 0.805302 \\ ViT + DCP & 0.758839 & 0.858958 & 0.805801 \\ ViT + Self-supervised learning + DCP & 0.784603 & 0.888333 & 0.833252 \\ SDViT(Our Model) & 0.799044 & 0.90375 & 0.848178 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical metrics of different model configurations on synthetic-covid-cxr dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet101(Baseline) & 0.726307 & 0.808958 & 0.765408 \\ Attention-VGG(SOTA) & 0.750712 & 0.84375 & 0.794517 \\ ViT & 0.739040 & 0.831667 & 0.782621 \\ ViT + Self-supervised learning & 0.758279 & 0.858542 & 0.805302 \\ ViT + DCP & 0.758839 & 0.858958 & 0.805801 \\ ViT + Self-supervised learning + DCP & 0.784603 & 0.888333 & 0.833252 \\ SDViT(Our Model) & 0.799044 & 0.90375 & 0.848178 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical metrics of different model configurations on X-ray Image dataset. proposed model can better extract the features of COVID-19 and fit its corresponding data distribution, which is extremely promising for future medical applications.