esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Result
### Flickr30k
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **Phi** & **Llama** & **Pixtral** & **Qwen** & **Phi** & **Llama** & **Pixtral** & **Qwen** & **Phi** & **Llama** & **Pixtral** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**S-BERT**} & \multicolumn{4}{c}{**MoverScore**} & \multicolumn{4}{c}{**CIDEr**} \\ \hline B & 0.4049 & 0.706 & 0.6929 & 0.6868 & 0.5489 & 0.7277 & 0.7215 & 0.6654 & **0.0464** & 0 & 0.0353 & 0.1001 \\ \hline I & 0.5785 & 0.6291 & 0.3118 & 0.5564 & 0.7742 & 0.7533 & 0.7184 & 0.6632 & 0 & 0.0036 & 0.0458 & 0.0364 \\ \hline I, S & 0.383 & 0.6801 & 0.3212 & 0.5279 & 0.7289 & 0.7312 & 0.7093 & 0.6329 & 0.0016 & **0.0666** & 0 & 0.0758 \\ \hline I, T & 0.4057 & 0.6029 & 0.324 & 0.5385 & 0.7306 & 0.7052 & 0.7208 & 0.6491 & 0 & 0.0603 & 0.0159 & **0.1592** \\ \hline C & 0.7164 & **0.7108** & 0.6852 & 0.7366 & 0.7952 & **0.7539** & 0.7373 & 0.7916 & 0.0062 & 0.0287 & 0.0336 & 0.0004 \\ \hline C, I & 0.7232 & 0.702 & 0.6716 & 0.7423 & 0.7963 & 0.7422 & 0.7126 & 0.8 & 0.0054 & 0.0572 & 0.0201 & 0 \\ \hline C, S & 0.7206 & 0.6965 & 0.7012 & 0.7237 & 0.7951 & 0.74 & 0.7758 & 0.7908 & 0.0039 & 0.0115 & 0.0001 & 0.0021 \\ \hline C, T & 0.7187 & 0.6912 & 0.6456 & 0.7419 & 0.7959 & 0.7234 & 0.6532 & 0.7899 & 0.0089 & 0.0319 & 0.0001 & 0.0013 \\ \hline S & 0.7229 & 0.7047 & 0.697 & 0.7431 & 0.7989 & 0.7391 & 0.705 & 0.7939 & 0.0196 & 0.0323 & 0 & 0.0025 \\ \hline T & 0.7391 & 0.6152 & **0.7228** & **0.7586** & 0.7998 & 0.7057 & **0.7821** & **0.805** & 0.0063 & 0.0347 & 0.0002 & 0.0019 \\ \hline R(B) & **0.7334** & 0.6259 & 0.6601 & 0.3307 & **0.8118** & 0.7391 & 0.7545 & 0.5655 & 0 & 0.0442 & **0.1011** & 0.0404 \\ \hline R(C) & 0.702 & 0.6078 & 0.655 & 0.3846 & 0.7898 & 0.7379 & 0.7571 & 0.5795 & 0.0007 & 0.0093 & 0.0163 & 0.0918 \\ \hline R(S) & 0.2484 & 0.6191 & 0.6509 & 0.3645 & 0.4852 & 0.7047 & 0.7257 & 0.572 & 0.0005 & 0.0202 & 0.0146 & 0.0813 \\ \hline R(T) & 0.69 & 0.6092 & 0.659 & 0.3097 & 0.7826 & 0.7078 & 0.7567 & 0.557 & 0.0006 & 0.0098 & 0.0204 & 0.0694 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performances of S-BERT, MoverScore, and CIDEr according to MLLM models and prompt engineering techniques on the MSCOCO dataset (The boldfaced numbers indicate the highest performance for each language model across different PES variants).
# Stabilization of Underactuated Surface Vessels: A Continuous Fractional Power Control Method
## 2 System Modeling and the Objective  
### Modeling  
The complete configuration of an USV (the position and orientation) can be described by six independent coordinates, or six degree of freedom (DOF), i.e., surge, sway, heave, roll, pitch and yaw. It is common to reduce the six-DOF model to a three-DOF one with surge, sway and yaw, since the other three states (heave, roll and pitch) are open loop stable for most ships and hence are neglected. Define the state vector as \(\eta=[x,\ y,\ \psi]^{T}\), where \([x,y]\in R^{2}\) is the position of the vessel in the inertial frame, and \(\psi\in[0,2\pi]\) is the heading angle of the ship relative to the geographic north. Then model of the USV can be the same as that of [21; 22; 23]:  
\[\left\{\begin{aligned} \dot{x}=& u\cos\psi-v\sin\psi,\\ \dot{y}=& u\sin\psi+v\cos\psi,\\ \dot{\psi}=& r,\end{aligned}\right.\] (1a) and \[\left\{\begin{aligned} \dot{u}=&-\frac{d_{11}}{m_{11}}u+ \frac{m_{22}}{m_{11}}vr+\frac{m_{23}+m_{32}}{2m_{11}}r^{2}+\frac{\tau_{u}}{m_ {11}},\\ \dot{v}=&-\frac{d_{22}}{m_{22}}v-\frac{m_{11}}{m_{2 2}}ur-\frac{d_{23}}{m_{22}}r-\frac{m_{23}}{m_{22}}\dot{r},\\ \dot{r}=&\frac{m_{22}}{m_{22}m_{33}-m_{32}m_{23}} \bigg{\{}\tau_{r}+\frac{uv}{m_{22}}(m_{11}m_{22}-m_{22}^{2})\\ &+\frac{ur}{m_{22}}\left[m_{11}m_{32}-\frac{m_{22}(m_{23}-m_{32} )}{2}\right]\\ &+(m_{32}d_{22}-m_{22}d_{32})\frac{v}{m_{22}}\\ &-(m_{22}d_{33}-m_{32}d_{23})\frac{r}{m_{22}}\bigg{\}},\end{aligned}\right. \tag{1b}\]  
where \(u,\ v,\ r\) are, respectively, the forward velocity, the transverse velocity and the angular velocity in yaw, \(m_{11},\ m_{22},m_{23},m_{32}\) and \(m_{33}\) are the inherent and added mass of the vehicle, \(d_{11},d_{22},d_{23},d_{32}\) and \(d_{33}\) are the hydrodynamic damping coefficients, \(\tau_{u}\) and \(\tau_{r}\) are the force in surge and the torque in yaw. The USV considered has bilateral asymmetry, so \(m_{23}=m_{32}\
eq 0\) and \(d_{23}\
eq d_{32}\
eq 0\). The details are in Figure 1.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM  
### Soil Property Maps Using Remote Sensing on Bare Soils as Covariates in DSM  
Instead of using the soil spectral response to inverse a model prediction of a soil property, an alternative is to incorporate the map of that prediction as a covariate in a SCORPAN model [239; 240; 241]. These studies are limited by the spatial availability of bare soil images; the influence of perturbing factors such as soil roughness [157], moisture, and vegetation residues on the toposoil [102; 242]; mismatches between soil sampling and remote sensing acquisition dates (e.g., [186]); and, in some cases, the low variability and range of some soil property values [123; 126; 182]. Increasing the frequency of acquisition dates (e.g., with S1 and S2 products) now enables the detection of more bare soil fields and area expansion by mosaicking several images [128] and reduces the influence of soil moisture and crop residues on the prediction accuracy (e.g., [128; 129]). Figure 5, extracted from [243], shows an example of RS data on bare soil used as covariates in a DSM model to predict SOC in a region of central France. Interestingly, the covariates derived from RS imagery on bare soil are among the most important in SOC prediction.  
In this example, the question of how to combine these soil predictions with a model adapted to usually vegetated areas remains to be solved, in order to obtain a full DSM map of soil properties over a large area including various land uses. This general question applies for nearly all RS data obtained from bare soils. If one wishes a full map of a landscape, RS data on bare soils are almost never available on the whole area, except for deserts.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 3 Proposed Schemes
### Problem Definition  
In UWSNs, the frequent mobility of sensor nodes results in the non-uniform distribution of nodes. There are some local regions with high node density, and others are low node density regions, as shown in Figure 1. Low node density regions become sparse due to high node mobility and the void regions created. That is caused due to two possibilities: either the unavailability of sensor nodes in a region or due to the energy hole that creates the coverage hole. This problem leads to high energy consumption due to packet drops. In AHH-VBF, the forwarder node selection on the basis of the relative position to the virtual vector (desirabeless factor) and the maximum distance from the source node does not avoid void holes effectively. For example, as in Figure 2, Node S is the current forwarding node, and a circle named S1 Tx-range is the transmission range of Node S. Two parallel lines shaped as are virtual pipeline restrict transmissions within this area. Nodes A, B, C, D, E and F are above Node S in its upper hemisphere of transmission range. Some nodes are suppressed neighbors below Node S. When Node S sends a packet, Nodes A, B, C, D, E and F along with suppressed nodes receive the packet. Suppressed nodes discard the packet directly, whereas Node F also discards the packet because it is  
Figure 1: Network architecture.  
outside of the pipeline. In AHH-VBF, Node S selects Node A on the basis of the relative position of Node A with respect to the virtual vector, and its distance is the maximum from Node S; whereas Nodes B, C, D and E are distant from the sink as compared to Node A. Therefore, these nodes are not qualified for forwarding node selection. However, the selection criteria for forwarding the node within the one-hop neighbor range is not efficient enough to cater to void hole problem. As illustrated in Figure 2, the void region is created due to the unavailability of nodes above Node A in the A1 Tx-range. If Node A continues to forward the packet in the void region, the packet drop occurs. Hence, the probability of unsuccessful transmissions in this scenario shows that forwarding node selection needs to be optimized to improve packet delivery.
# Catalytic Evaluation of Hafnium Modified SiO\({}_{2}\) for the Dehydration of Alcohols
## 3 Results and Discussion
### Scanning Electron Microscopy  
Figure 4 shows the morphology of pure SiO\({}_{2}\), materials modified with 3.6 and 5% mol hafnium, and the material impregnated with 12 wt% sulfate ions. 5.0 Hf/SiO\({}_{2}\) and 12 SHf/SiO\({}_{2}\) exhibit irregular morphologies with a tendency to agglomerate particles of variable size, forming large cavities between the agglomerates. Only 3.6 Hf/SiO\({}_{2}\) shows a morphology of homogeneous particles similar to that observed in pure silicate. All the materials exhibited porosities classified as mesopores and macropores that correlate with the characterization results shown in Figure 3b.  
EDS analyses of 3.6 Hf/SiO\({}_{2}\) and 12 SHf/SiO\({}_{2}\) are shown in Figure 5, both samples comprising Si, O, and Hf. Additionally, sulfur (S) was identified in the spectrum of the 12 SHf/SiO\({}_{2}\) material, corresponding to the presence of SO\({}_{4}^{2-}\) ions used to increase the acid strength of 3.6 Hf/SiO\({}_{2}\). [PERSON] et al. (2016) carried out an EDS analysis on MCM-41 showing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Area (m\({}^{2}\)g\({}^{-1}\))** & **Average Pore Diameter (Å)** & **Pore Volume (cm\({}^{3}\)/g)** \\ \hline
3.6 Hf/SiO\({}_{2}\) & 784 & 26 & 0.47 \\
5.0 Hf/SiO\({}_{2}\) & 831 & 22 & 0.45 \\
12 SHf/SiO\({}_{2}\) & 152 & 64 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural properties of hafnium silicates.  
Figure 4: Scanning electron microscopy of hafnium silicates: (**a**) 5.0 Hf/SiO\({}_{2}\); (**b**) 12 SHf/SiO\({}_{2}\); (**c**) 3.6 Hf/SiO\({}_{2}\); (**d**) SiO\({}_{2}\).  
elemental compositions of silicon (Si) and oxygen (O) similar to those observed in these spectra. Table 2 shows the atomic percentage of the elements constituting the catalysts. This quantification of elements was carried out in different sections of the samples, showing that Hf and S are not homogeneously distributed. However, as the analysis confirmed the presence of Hf (3.42%), the amorphous structural form of HfO\({}_{2}\) is verified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & 3.6 Hf/SiO\({}_{2}\) & \multicolumn{2}{c}{**12 SHf/SiO\({}_{2}\)**} \\ \hline
**Element** & **Weight (\%)** & **Element** & **Weight (\%)** \\ \hline O & 40.03 & O & 40.48 \\ Si & 32.83 & Si & 36.24 \\ Hf & 27.14 & Hf & 4.56 \\  & & S & 18.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elemental composition of the catalysts.  
Figure 5: EDS analysis of hafnium silicate and sulfated hafnium silicate: (a) 3.6 Hf/SiO\({}_{2}\); (b) 12 SHf/SiO\({}_{2}\).
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 2 Experiment Facility and Model
### Heat Flux Calculating  
In this experiment, the heat flux distribution on the surface of the plate was obtained by solving the one-dimensional inverse heat-transfer equation [28]. The surface temperature of the model was recorded by IR image. A constant initial temperature was assumed through the PEEK. According to the one-dimensional heat conduction equation,  
\[\frac{\partial T}{\partial t}=\alpha\frac{\partial^{2}T}{\partial y^{2}} \tag{1}\]  
where \(\alpha\) is the thermal diffusivity, which is related to the materials. In Equation (1), the time term adopts the first-order forward difference, and the space term adopts the second-order central difference scheme. The finite difference scheme is presented as follows:  
\[\frac{T_{i+1,j}-T_{i,j}}{\triangle t}=\frac{\alpha(T_{i,j-1}-2T_{i,j}+T_{i,j+1} )}{\left(\triangle y\right)^{2}} \tag{2}\]  
\(\Delta y\) takes the appropriate value to ensure the numerical stability. Utilizing the temperature distribution within the PEEK model, the heat flux for individual pixels at each time step is computed through the application of [PERSON]'s law, as expressed below [28]:  
\[q=-k\frac{\partial T}{\partial y} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flow Condition** & **P\({}_{0}\)/kPa** & **T\({}_{0}\)/K** & **Re/m/10\({}^{6}\)** \\ \hline \multirow{3}{*}{Ma = 5} & Case1 & 200.1 & 501 & 2.52 \\  & Case2 & 490.5 & 491 & 6.38 \\ \cline{1-1}  & Case3 & 729.9 & 539 & 8.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flow condition.  
Figure 2: Schematic diagram of flat-plate installation in wind tunnel.  
The dimensionless Stanton number (\(St\)) is derived from the surface heat flux by utilizing the freestream parameters.  
\[St=\frac{q}{\rho_{\infty}U_{\infty}C_{p}(T_{0}-T_{w})} \tag{4}\]  
where the subscript \(\infty\) represents the freestream condition. \(C_{p}\), \(T_{0}\), and \(T_{w}\) represent the constant pressure specific heat, flow total temperature, and wall temperature, respectively.
# Influence of Printing Substrate on Quality of Line and Text Reproduction in Flexography
## 4 Results and Discussion
### Analysis of Text Deformation  
For the analyses of quality reproduction of text, the area and perimeter of lowercase characters \"r\" and \"s\" were measured for two text sizes, 6 pt (2.15 mm) and 8 pt (2.82 mm). The values of area and perimeter of characters together with their target values are shown in Table 2.  
Figure 5: Visual comparison of line edge noise 170 \(\upmu\)m in width printed in positive and negative shapes on three different printing substrates. The biggest change in text area for both observed text character, \"r\" and \"s\", was noticed on OPP film (similar to cases of previously considered line area characteristics) which can be seen in the highest value of character area in a positive shape (0.66 mm\({}^{2}\) for character \"s\"), and the lowest in a negative shape (0.50 mm\({}^{2}\) for the same character \"s\") (Table 2). Higher text character perimeter values indicate higher line raggedness. The highest values for this print quality parameter were obtained on uncoated paper (8.98 mm), then OPP film (8.24 mm) and coated paper (8.08 mm). Those text perimeter distinctions between the samples printed on OPP film and coated paper are extremely slight (0.16 mm in negative shape and 0.1 mm in positive shape). The relative values of deviation of text area and text perimeter in percentage from target value give a complete insight into the quality of reproduction (Figure 6). The dotted line shows the target value. Although the surface of the text character \"s\" is 20% bigger than the surface of the text character \"r\", the difference in area deviation percentage, but also in the perimeter between the analyzed letter characters (Figure 6), is completely negligible (on OPP film and coated paper it is up to 3%, and on uncoated paper it is up to 5%). This can be seen on line diagrams which are identical for individual parameters, text area and text perimeters.
## 3 Results and Discussion
### Distribution Characteristics of Design Variables and Their Formation Causes
#### 3.2.3 Effect of the Roof's Insulation Layer Thickness  
Figure 13 shows the value distribution of the roof insulation layer thickness in Pareto solutions. In the Xi'an area, the insulation layer thickness is mainly concentrated above 0.14 m, and the range of 0.16-0.19 m occupies the largest proportion (~88%). The value distribution in Yulin is more concentrated, mainly above 0.18 m, of which 0.2 m accounts for up to 64%. The mean thickness of the insulation layer in Yulin is 0.19 m, which is higher than that in Xi'an (0.17 m), revealing that the thickness of the roof insulation layer is more inclined to the upper limit area of the given interval.  
Based on the benchmark model, the relationship between roof insulation layer thickness and the four objectives is demonstrated in Figure 14. As shown, the change rule of the two regions is effectively the same; with the increase in insulation layer thickness, EC, TDHP, and LCCO\({}_{2}\) show a trend of obviously reducing at first, followed by a slow decrease; LCC displays a trend of first decreasing and then increasing, and the insulation layer thickness corresponding to the inflection point is ~0.04 m. In terms of objective values, under the influence of climatic conditions and solar radiation, the EC and TDHP of Xi'an are lower than those of Yulin with the same insulation layer thickness, but Xi'an's LCC and LCCO\({}_{2}\) are higher. This shows that the use of solar radiation can reduce the life-cycle cost and carbon emissions. The influence of the insulation layer thickness on the four objectives is contradictory, with different effect amplitudes, as well as being subject to the other design variables interacting to form the value distribution shown in Figure 13.  
Figure 12: Relationship between the south sunvisor overhang length and the four objectives in Xi’an: (**a**) case-1; (**b**) case-2; and (**c**) case-3.  
Figure 13: Value distribution of roof insulation layer thickness in Pareto solutions: (**a**) Xi’an and (**b**) Yulin.
## 3 Experiments and Results
### Experimental Results for Visualization  
This study presents a RTAB-Map based on experimental findings on the UAV's flight odometer and trajectory, as well as the 3D reconstruction of the pipeline in-wall. The drone flight's odometer utilizes data from UAV sensors to estimate the positions and trajectories  
Figure 4: Experimental pipe and environment: (**a**) experimental pipe and drone; (**b**) drone within pipe.  
Figure 3: RTAB-Map memory management model [13].  
of the camera. RTAB-Map can be localized using visual odometry or laser odometry. In this study, the visual odometry is selected and obtained using a depth camera. Figure 5a illustrates the visual odometry during real-time operation. The green dots represent ordinary environment feature points in the map, the yellow dots represent feature points found multiple times in the map, and the red dots represent features found in the dictionary for closed-loop detection.  
The 3D visualization using the RTAB-Map is created by generating a local map when a new node is created. This local map is generated using the depth image obtained from the depth camera, which is then converted into point cloud data. Once the global optimization is completed, the individual local maps are stitched together to obtain a global map based on the odometry poses of each node. Figure 5b shows the results of the 3D point cloud map, which includes the camera's position nodes and the motion trajectory.  
Figure 6 illustrates the sparse reconstruction results obtained using RTAB-Map. Specifically, Figure 6a showcases the inner wall of the pipe and its corresponding trajectory, while Figure 6b focuses on the in-wall textures of the joint and its trajectory. Among them, the blue line represents the trajectory, and the nodes represent the poses of these key frames. To enhance clarity, the dense point clouds of the obtained trajectories undergo a filtering process. The visualization of the pipeline's dense point cloud before and after processing by using the median filter can be seen in Figure 7.  
Figure 5: Odometry and visualization of 3D point cloud map: (**a**) camera odometry; (**b**) 3D point cloud map.  
Figure 6: The sparse reconstruction results using RTAB-Map: (**a**) pipe in-wall textures and UAV trajectory; (**b**) pipe interconnecting walls and UAV trajectory.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 3 Modeling
### Establishment of Finite Element Model to the System  
The finite element model is established using the module modeling strategy. According to the structure composition, the novel GRM mechanical system was divided into three modules: crystal assembly, electronic box assembly and bracket assembly. Each module is decomposed and modeled again according to its own auxiliary equipment. After the modular modeling was completed, the bonding and bolt connection were simulated using the connection constraint or multi-point constraints (MPC) algorithm, and then each module was assembled into a finite element model of the overall structure, as shown in Figure 3. It is necessary to simplify the actual structure, in order to make element calculations feasible in the process of establishing the model [27; 28; 29], such as the simplification of the SiPM components and the cover of the electronic box, the weight being applied to the electronic box. The complexity of the overall structure when meshing was also considered, in order to use more accurate hexahedral cells for finite element calculations. For parts that could not be meshed directly using the \"Structured\" mesh in ABAQUS, \"Sweep\" was used for meshing, but this resulted in malformed cells in transition zones or in narrow edges. FEM with deformed meshes were used to balance the following three factors: (1) They allow more detailed geometric features of the representation model. (2) The mesh size of FEM affects CPU computing time [30]. (3) When the contact area mesh is refined, the coarse mesh can better transition to the fine mesh. In order to avoid the distortion of the calculation results caused by the abnormal cells appearing in the key area, during the finite element calculation, the abnormal cells appear in the non-key area by adjusting the mesh density of the whole model and refining the mesh of the contact area. Through the above adjustment, it was found that the FEM used in this paper reached the convergence of the calculation results, and the FEM was effective. The FEM was also proved to be effective by comparing with the experiment in Section 4.2.1. Thus, in this case, although the model had deformed units, the accuracy of the results met the calculation requirements. The number of elements and deformation rate are shown in Table 1.