esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Localization System Fusion Vision-CNN
## 4 Simulation
### Initialization of the Robot Global Positioning  
#### 4.2.1 Visual Localization Accuracy Analysis  
The simulation environment built in Gazebo is shown in Figure 6d. First, 665 images and their corresponding laser measurement data and pose information were collected in the simulation environment as the training data of the neural network. Figure 6a is the image information collected by the camera of the robot at the origin of the coordinate system. The red dot in Figure 6b represents the laser measurement information at the same position. The pose information of the robot can be obtained directly through the Gazebo backend, and then the same collected data can be used to train the posenetLSTM and the neural network proposed in this paper. Since the neural network directly outputs the pose only by inputting the image, the trained neural network is directly used to estimate the robot's motion trajectory to characterize its relocalization ability.  
Figure 6: (**a**) Obtained image information; (**b**) Laser measurement information; (**c**) The robot’s trajectory; (**d**) Simulation environment in Gazebo.  
The green line in Figure 6c is the trajectory of the robot used to evaluate the relocation accuracy of the algorithm. The green dot is the starting point of the trajectory and the red dot is the end of the trajectory. The estimated trajectory and the trajectory error based on the posenetLSTM and the method proposed in this paper are shown in Figure 7. It can be seen from Figure 7 that the amplitude of the error fluctuation of the original posenetLSTM method is significantly larger than that of the method proposed in this paper, both in translation and in the rotation direction. Moreover, in some positions, the error based on the posenetLSTM method also has a mutation. Since the proposed method introduces laser measurement as a structural constraint, the positioning accuracy and the stability are greatly improved. The statistical results including root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) are shown in Table 1. It can be seen that compared with the posenetLSTM method, the relocalization accuracy of the proposed algorithm in the \(x\), \(y\), and \(yaw\) directions can be improved by approximately 28.92%, 43.13%, and 46.83%, respectively.  
Figure 7: Visual pose tracking results based on neural network. (**a**,**c**,**e**) The comparison of pose estimation results in the \(x\), \(y\), and \(yaw\) directions, respectively; (**b**,**d**,**f**) The comparison of pose estimation errors in the \(x\), \(y\), and \(yaw\) directions, respectively.
## 3 Obstacle Avoidance Motion Planning Framework
### Compliance-Oriented Potential Field
#### 4.2.3 Speed-Limit Potential Field  
For violations of exceeding the upper speed limit and falling below the lower speed limit at intersections, the speed-limit potential field is constructed using increasing and decreasing exponential functions, respectively.  
Vehicle speed is used as the independent variable of the speed limit PF, which is defined as:  
\[U_{\text{speedlimit}}=\eta\left(\textit{exp}(v^{(t+k,t)}-v_{\text{limit}} )-1\right) \tag{27}\]  
where \(\eta\) is a scaling factor. \(v_{\text{limit}}\) is the speed limit of the road. This function results in less impact on vehicle speed when it is under the speed limit. When exceeding the speed limit, there is a rapid escalation in the cost incurred.  
Moreover, no-parking zones and no-reversing zones are distinctly designated within crosswalks and intersections. The speed constraint PF in no-parking and no-reversing zones is defined as:  
\[U_{\text{propel}}=\eta\cdot a^{\eta} \tag{28}\]  
where \(a\) and \(\eta\) are the base of the function and the scaling factor. In this study, \(a\) is set to 0.8. The value of the potential field increases as the vehicle approaches a stop.
## 2 Algorithm for Multi-Layer Interference Cylinder  
According to the Lame equation, the radial displacement of any point in the cylinder can be obtained [17]:  
\[u_{\varphi}=\frac{1-\mu}{E}\cdot\frac{a^{2}p_{1}-b^{2}p_{2}}{b^{2}-a^{2}}\cdot \rho+\frac{1+\mu}{E}\cdot\frac{a^{2}b^{2}(p_{1}-p_{2})}{b^{2}-a^{2}}\cdot\frac {1}{\rho} \tag{1}\]  
where \(a\) is the inner radius of the cylinder, \(b\) is the outer radius of the cylinder, \(\rho\) is the radius of any point of cylinder, \(p_{1}\) is the internal pressure of the cylinder, \(p_{2}\) is the external pressure of the cylinder, \(E\) is the modulus of elasticity, and \(\mu\) is Poisson ratio.  
Figure 2 is the schematic diagram of force on two cylinders with interference fit. \(C_{i}\) is defined as cylinder \(i\); \(C_{i+1}\) is cylinder \(i+1\); \(p_{i-1}\) is the internal pressure of \(C_{i}\); \(p_{i}\) is the contact pressure between  
Figure 1: Low speed coupling of wind turbine.  
\(C_{i}\) and \(C_{i+1}\); \(p_{i+1}\) is the external pressure of \(C_{i+1}\); \(r_{1,i}\) is the inner radius of \(C_{i}\); \(r_{2,i}\) is the outer radius of \(C_{i}\); \(r_{2,i+1}\) is the outer radius of \(C_{i+1}\); \(S\) is contact surface between \(C_{i}\) and \(C_{i+1}\).  
According to Equation (1), the relation between the radial displacement of contact surface of multi-layer interference cylinder can be obtained. Based on the radial displacement relationship between \(C_{i}\) and \(C_{i+1}\), a matrix model of multi-layer interference cylinder can be obtained as:  
\[\delta=\mathbf{Kp} \tag{2}\]  
Among them, \(\delta=[\delta_{1},\delta_{2},\cdots\cdots,\delta_{n}]^{\mathrm{T}}\), \(\mathbf{p}=[p_{1},p_{2},\cdots\cdots,p_{n}]^{\mathrm{T}}\),  
\[K=\left[\begin{array}{cccccc}(K_{1,2}+K_{4,1})&-K_{2,2}&0&\cdots&0&0&0\\ -K_{3,2}&(K_{1,3}+K_{4,2})&-K_{2,3}&0&\cdots&0&0\\ 0&-K_{3,3}&(K_{1,4}+K_{4,3})&-K_{2,4}&0&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&&\vdots\\ 0&\cdots&0&-K_{3,n-2}&(K_{1,n-1}+K_{4,n-2})&-K_{2,n-1}&0\\ 0&0&\cdots&0&-K_{3,n-1}&(K_{1,n}+K_{4,n-1})&-K_{2,n}\\ 0&0&0&\cdots&0&-K_{3,n}&(K_{1,n+1}+K_{4,n})\end{array}\right]\]
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 3 Proposed Methods
### Mathematical Formulation of the Multi-Objective Integer Linear Programming Model
#### 3.1.2 Mathematical Formulation
\] (11) Subject to the following constraints:
* Resource wastage limitation constraint: Ensure that total resource wastage does not exceed the minimum value, \(W_{min}\), determined in Stage 2: \[ \begin{split}\sum_{j=1}^{M}\left(3-\left(\sum_{i=1}^{N}\left( \frac{c_{i}\phi_{ij}}{C_{j}}+\frac{r_{i}\phi_{ij}}{R_{j}}+\frac{s_{i}\phi_{ij} }{S_{j}}\right)\right)\right)-3\left(M-\sum_{j=1}^{M}\kappa_{j}\right)\leq W_{ min}\end{split} \] (12) and (3)-(6) and (8)-(10). Computational Complexity Analysis  
In Stage 1, the number of variables is \(N\times M\). The number of constraints is \(N+3M\), giving a computational complexity of \(O(N\times M)\). In Stage 2, the same number of variables \(N\times M\) is used, with an additional \(M\) binary variables (\(\kappa_{j}\)), and the number of constraints remains \(N+3M\), with the complexity still \(O(N\times M)\). In Stage 3, the model retains the \(N\times M\) variables and the additional \(M\) binary variables (\(\kappa_{j}\)), with the total number of constraints remaining \(N+3M\), and the complexity is, again, \(O(N\times M)\). Overall, the entire model involves \(N\times M\) variables plus \(M\) additional binary variables across all steps. The total number of constraints across all steps is \(N+3M\). Thus, the model's computational complexity across all steps is \(O(N\times M)\).
## 1 Introduction
The inclusion of an electronic controller and sensors enables automated, precise control over translation and rotation, as shown in Figure 1.  
The path-planning algorithm described here incorporates an IK solver based on a NN, which provides a joint configuration solution that moves the MA to the desired pose given the current joint configuration, which is optimized for minimal traversal time between configurations for both the joints and the MA. These actions are minimized using a specially  
Figure 1: The newly developed 3D minimally actuated serial robot, 3D MASR, has a passive serial arm and a single mobile actuator (MA). It is configured with 4 rotational and 1 prismatic joint. designed regularization term tailored for every form of MASR. This approach leverages the unique advantages of the MA by enabling it to be positioned anywhere along the arm, thus requiring less joint actuation. Importantly, training the NN does not require extensive data collection from an actual or simulated robot since it relies on the computation of forward kinematics. This paper is organized as follows: Section 2 presents the robot design, and Section 3 covers its control. Section 4 deals with the kinematic analysis, and Section 5 focuses on the development of a path-planning algorithm that reduces action time. In Section 6, we present the results of multiple experiments conducted with the robot.
## 2 Method
### Edge-Based Finite Element Method  
The edge-based finite element method is employed to discretize Equations (8)-(10) using edge-based basis functions \(\mathrm{N_{i}(x,\ y,\ z)}\), which assign edge-based degrees of freedom to the nodes of each element to construct the edge-based finite elements. This approach is particularly well suited for simulating complex geometries and for practical reservoir dynamic monitoring. Moreover, it strikes an excellent balance between accuracy and the number of degrees of freedom, ensuring both computational efficiency and precision.  
In the finite element method, the electric field intensity \(\mathbf{E}\) can be expressed as a linear combination of vector basis functions \(\mathrm{N_{i}(x,\ y,\ z)}\) associated with each edge. The electric field intensity within the tetrahedral element depicted in Figure 1 can be expressed as follows:  
\[\mathbf{E}^{\epsilon}(x,y,z)=\sum_{i=1}^{6}\mathbf{N}_{i}^{\epsilon}(x,y,z)E_{i}^{\epsilon} \tag{11}\]  
where \(E_{i}^{\epsilon}\) represents the degree of freedom associated with the \(i\)-th edge, and \(\mathbf{N}_{i}^{\epsilon}\) is the vector basis function associated with each edge i.  
By applying the Galerkin weighted residual method and selecting the test function \(\mathrm{W_{i}(x,\ y,\ z)}=\mathrm{N_{i}(x,\ y,\ z)}\), by substituting the above expression of the electric field into the governing equation, and through derivation, a linear system of equations \(\mathrm{Kx}=\mathrm{b}\) is obtained. Among them are the following:  
\[K_{ij}=\int_{\Omega}\left[\mu^{-1}(\
abla\times\mathbf{N}_{i}^{ \epsilon})\cdot\left(\
abla\times\mathbf{N}_{j}^{\epsilon}\right)-\left( \omega^{2}\epsilon-i\omega\sigma\right)\mathbf{N}_{i}^{\epsilon}\cdot\mathbf{ N}_{j}\right]d\Omega+\int_{\partial\Omega}r_{t}(\hat{n}\times\mathbf{N}_{i}^{ \epsilon})\cdot\left(\hat{n}\times\mathbf{N}_{j}^{\epsilon}\right)d\Gamma \tag{12}\] \[b_{i}=\int_{\Omega}\mathbf{J}_{s}\cdot\mathbf{N}_{i}^{\epsilon}d\Omega \tag{13}\]  
where \(K_{ij}\) represents the \((i,j)\)-th element of the stiffness matrix, \(\mathbf{J}_{s}\) denotes the current density of the electric dipole within the tetrahedral element \(e\), and \(\hat{n}\) denotes the unit normal vector at the boundary. To reduce storage space, the large sparse matrix obtained is stored using the Compressed Row Storage (CRS) format. Subsequently, the direct solver from the Portable, Extensible Toolkit for Scientific Computation (PETSc) library is invoked, utilizing the Generalized Minimal Residual (GMRES) solver preconditioned with the Geometric Algebraic Multigrid (GAMG) method to solve the control equations, thereby obtaining the electric field intensity and magnetic induction intensity components on the edges of the mesh elements.
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 3 Experiments and Analysis
### Subjective Comparison and Analysis  
The visual comparison of different enhancement algorithms is shown in Figures 7-10. Figure 7a is the source CBCT image, and Figure 7b is the image enhanced by MSR, which shows that the brightness of the source image is improved but with low contrast. Figure 7c,f,g show similar enhanced results with improved brightness but lacking in details. The DCP method improves the contrast of the source image, but does not enhance the details. As shown in Figure 7e, the CBF-based method shows good detail, but it still contains some noise. Compared with these methods, the proposed method shows better visual effects with higher contrast and richer detail, and the important regions are more significant.  
Another three sets of experiments are shown in Figures 8-10. As can be seen from the enlarged regions and the yellow arrows in Figure 8, it is difficult to directly observe the presence of skeletal information at this position from the enlarged region in Figure 8a. The enhancement results of the comparison methods also did not improve the visual contrast in this region, but the skeletal information can be clearly observed in our result, which  
Figure 6: Experimental data. The first line is CBCT and the second line is the corresponding CT.  
The same conclusion can also be obtained in Figures 9 and 10. Especially in the enalrged region in Figure 10, our results provide a much clearer organizational structural and edge information, which is very helpful for disease diagnosis and treatment. Through comparison, it can be found that the rigidity informaiton in our results is more significant,  
Figure 10: Enhanced results of pair 4. (**a**) Source CBCT images. (**b**) MSR. (**c**) MSRCR. (**d**) DCP. (**e**) CBF (**f**) RRM. (**g**) SMIPC. (**h**) Ours.  
Figure 9: Enhanced results of pair 3. (**a**) Source CBCT images. (**b**) MSR. (**c**) MSRCR. (**d**) DCP. (**e**) CBE (**f**) RRM. (**g**) SMIPC. (**h**) Ours.  
the tissue edge in our results is clearer, and the contrast of the texture structure is also better than that of the comparison algorithms. The observation effect is greatly improved by our enhancement method, proving that our method can effectively enhance the CBCT images.
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 3 Methods
### RPN-Like Approach to Predict Faces  
A similar approach to RPN [17] is used to predict faces in our method. One \(1\times 1\) convolutional layer is used to classify the predefined anchors, but another \(1\times 1\) convolutional layer is used to regress the coordinates of anchors at the same time, which is the major difference from RPN. We make prediction on each scale of the final feature maps respectively. Then the predictions of each scale are integrated together. Finally, non-maximum-suppression (NMS) is used to obtain the best results. Only anchors with aspect ratio of 1 are used, and sizes of anchors are set to {16 \(\times\) 16, 32 \(\times\) 32}, {32 \(\times\) 32, 64 \(\times\) 64} and {64 \(\times\) 64, 128 \(\times\) 128} with respect to P9, P13 and P17.
## 3 Methodology
### Architecture Patterns
#### 3.2.1 Layered Architecture Pattern  
The layered architecture pattern was used to break down the tasks of the system into a series of interrelated subtasks. Each of these tasks was represented by a layer that had two communication interfaces, the upper interface providing services to its top layer, and the lower interface providing services to its lower layer [31; 32].
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 3 TET-1 Data Processing Chain
### Pre-Processing  
It is necessary to correct data for atmospheric effects. In our study, we applied MODTRAN [36] atmospheric transmittance and up-welling irradiance based on a mid-latitude summer model. The upwelling irradiance is a significant source of errors in the TIR band, but does not provide such significant errors at shorter wavelengths [37]. The reflected irradiance was also ignored, which is reasonable over hot bodies [37]. In addition to the atmospheric effects, the data were also corrected for emissivity. For Stromboli, a grey body assumption was applied, with a flat emissivity of 0.95 [4, 5].  
Data de-convolution is an important step in pre-processing. This is important in the case of a sub-pixel object that acts as an anomaly in a relatively homogeneous background--for instance a lava flow observed in a MIR band. In this case, the signal may be dispersed over several pixels although a typical lava flow is no more than a pixel wide in most satellite instruments. This effect is a result of the imperfect point spread function of the optics on satellite instruments [26, 38]. In the case of TET-1, this is a very important step as its spatial sampling is 160 m, but the effective pixel size is twice as large (Table 1). This results in a somewhat blurred image. In TET-1 this effect is most significant in the MIR channel, thus only the data for this channel were de-convolutioned using the Richardson-Lucy algorithm [39, 40]. Various iterations were tested using our data, and the differences in the maximum radiance values varied by 3% between five and 100 iterations. When a higher number of iterations was used, more artifacts became visible around the lava flow, thus the algorithm was run with only five iterations. The de-convolution increased the maximum MIR radiance values in an image from about 3.5 to 4.5 W/m\({}^{2}\)/sr/um.
###### Abstract  
The photoelectrochemical reduction of CO2 to syngas that is used for many practical applications has been emerging as a promising technique to relieve the increase of CO2 in the atmosphere. Si has been considered to be one of the most promising materials for photoelectrodes, but the integration of electrocatalysts is essential for the photoelectrochemical reduction of CO2 using Si. We report an enhancement of catalytic activity for CO2 reduction reaction by Ag catalysts of tuned morphology, active sites, and electronic structure through reducing anodic treatment. Our proposed photocathode structure, a SiO2 patterned \(p\)-Si photocathode with these reduced Ag catalysts, that was fabricated using electron-beam deposition and electrodeposition methods, provides a low onset-potential of \(-0.16\) V vs. the reversible hydrogen electrode (RHE), a large saturated photocurrent density of \(-9\) mA/cm\({}^{2}\) at \(-1.23\) V vs. RHE, and faradaic efficiency for CO of \(47\%\) at \(-0.6\) V vs. RHE. This photocathode can produce syngas in the ratio from 1:1 to 1:3, which is an appropriate proportion for practical application. This work presents a new approach for designing photocathodes with a balanced catalytic activity and light absorption to improve the photoelectrochemical application for not only CO2 reduction reaction, but also water splitting or N2 reduction reaction.  
photoelectrochemical; CO2 reduction reaction; Ag catalysts; Si photocathode; syngas production 2020 10 3487 10.3390/app10103487
## 3 Method
### Cost Volume Aggregation
#### 3.3.2 Cross-Scale Cost Volume Information Exchange  
Traditional multi-view stereo (MVS) algorithms often overlook the correlation between cost volumes at different scales, resulting in a lack of information transfer within each layer [5]. To address this limitation, our study introduces a cross-scale cost volume information exchange module, outlined in Figure 5. To address this, our module employs a portion of the Cascade Iterative Depth Estimation and Refinement (CIDER) [34], applying a lightweight regularization to coarsely regularize the stage-wise cost volume. Subsequently, through a separation operation, this volume is integrated into the next layer. This process eliminates noise and facilitates the fusion of information from small-scale cost volumes into the subsequent layer's cost volume, thereby enhancing the quality of depth map estimation. It separates the initially regularized stage-wise cost volume, fusing it into the next layer. This process not only eliminates noise but also enables the integration of information from small-scale cost volumes into the next layer, enhancing the quality of depth map estimation. Taking the \((n-1)\)-th layer as an example, the generated cost \(C_{agg}^{n-1}\in R^{B\times C\times D\times H\times W}\) undergoes initial regularization to acquire sufficient contextual information, followed by an upsampling operation, resulting in \(C_{agg}^{n-1}\in R^{B\times\frac{G}{2}\times D^{\prime}\times 2H\times 2W}\), where \(D\), represents the upsampled depth samples. This size is consistent with the subsequently generated cost volume \(C_{agg}^{n}\) in the next stage. The fusion of these volumes yields the final cost volume \(C^{n}\in R^{B\times C\times D^{\prime}\times 2H\times 2W}\) for that stage.  
Figure 5: Cross-scale cost volumes information exchange module structure diagram.
# Marine Microalgae for Potential Lutein Production
## 7 Extraction of Lutein from Microalgae and Challenges
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Microalgae** & **Biomass** & **Cultivation Conditions** & **Lutein Yield** & **Stress Conditions** &
\begin{tabular}{c} **Extraction** \\ **Methodologies** \\ \end{tabular} & **References** \\ \hline \multicolumn{6}{c}{**Marine cultures**} \\ \hline _Glumydomonas_ sp. JSC4 & 1271 mg L\({}^{-1}\) d\({}^{-1}\) & 1-L glass photobioreactor & 3.27 mg L\({}^{-1}\) d\({}^{-1}\) & Temperature (35\({}^{\circ}\)C) & Solvent extraction & [75] \\ \hline _Chlamydomonas_ sp. & 1500 mg L\({}^{-1}\) d\({}^{-1}\) & 1-L glass photobioreactor & 5.08 mg L\({}^{-1}\) d\({}^{-1}\) & Light intensity (625 umpl) photons m\({}^{-2}\) s\({}^{-1}\) & Solvent extraction & [50] \\ \hline _Chlamydomonas_ sp. JSC4 & 560 mg L\({}^{-1}\) d\({}^{-1}\) & 1-L glass photobioreactor & 3.42 mg g\({}^{-1}\) & Salinity gradient & Solvent extraction & [76] \\ \hline _Chlamydomonas_ sp. JSC4 & 490 mg L\({}^{-1}\) d\({}^{-1}\) & 1-L glass photobioreactor & 2.95 mg g\({}^{-1}\) & Light wavelengths (blue light) & Solvent extraction & [50] \\ \hline _Chlamydomonas_ & \multirow{2}{*}{-} & \multirow{2}{*}{Batch growth} & \multirow{2}{*}{20 mg L\({}^{-1}\)} & UV-A radiation (10 umpl photons \\ _acidophila_ & & & & m\({}^{-2}\) s\({}^{-1}\)), or heated at 40 \({}^{\circ}\)C & Solvent extraction & [77] \\ \hline _Chlamella salina_ & - & 3-L glass flask & 2.92 mg g\({}^{-1}\) & - & Microextraction \\ \hline _Dentalella salina_ & 2.2 g m\({}^{-2}\) d\({}^{-1}\) & Tubular photobioreactor & 15.4 mg m\({}^{-2}\) d\({}^{-1}\) & None & Solvent extraction & [79] \\ \hline _Mariellopsis_ sp. & 40 g m\({}^{-2}\) d\({}^{-1}\) & Outdoor tubular & 6 mg g\({}^{-1}\) & None & Solvent extraction & [80] \\ \hline _Mariellopsis_ sp. & 12.9 g m\({}^{-2}\) d\({}^{-1}\) & Open ponds & 100 mg m\({}^{-2}\) d\({}^{-1}\) & None & Solvent extraction & [57] \\ \hline _Tetrastnis_ sp. CTP4 & - & 5-L reactors & 3.17 mg g\({}^{-1}\) & Light intensity (170 and 280 umpl photons m\({}^{-2}\) s\({}^{-1}\)) and temperature (35 \({}^{\circ}\)C) & Solvent extraction & [49] \\ \hline \multicolumn{6}{c}{**Freshwater cultures**} \\ \hline _Chlamitisitis_ & 0.117 g L\({}^{-1}\) d\({}^{-1}\) & 2-L airlift photobioreactor & 5.58 mg g\({}^{-1}\) & None & Solvent extraction & [81] \\ \hline C. _cuspiters_, & 0.131, 0.122, & 3.86, 4.38 and & & & \\ C. _aofrigiensis_ and & 0.103 g L\({}^{-1}\) d\({}^{-1}\) & In indoor vertical alveolar & 3.59 mg g\({}^{-1}\) & None & Glass bead vortexing \\ C. _protherooides_ & (respectively) & panel photobioreactor & (respectively) & & and ball mill grinding & [61] \\ \hline _Chlamella protothecoides_ & 31.2 g L\({}^{-1}\) & Heterotrophic growth in a 3.7-L fermenter & 1.90 mg g\({}^{-1}\) & 80 g L\({}^{-1}\) glucose addition & Solvent extraction & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of lutein-producing microalgae and their lutein yield associated with tested cultivation and stress conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline