esa-sceva/satcom-chunk-collection huggingface dataset:
# Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
## 3 Granular Uncertainty Description
### One-Dimensional Data with Two Categories
To realize these requirements, the well-known information entropy [31] function is introduced here, as  
\[h(u)=-u\cdot\log(u)-(1-u)\cdot\log(1-u) \tag{10}\]  
where \(u\) is the membership value of a given testing data point and \(-u\cdot log(u)\) and \(-(1\)\(-\)\(u)\cdot log(1-u)\) represent the uncertainty belonging and not-belonging to the cluster, respectively. By summing them up, the total uncertainty of a testing point is expressed as \(h(u)\). Moreover, the specificity could be endowed with the size parameter \(\tau_{1}\) (\(0\leq\tau_{1}\leq x_{0}\)), as seen below. \[spe(\tau_{1})=(1-\tau_{1})^{\beta} \tag{11}\]  
where \(\beta\) is a parameter determining the variance rate of specificity. It is seen that the coverage function in (9) describes the total uncertainty of data points within ClusterA, which is an increasing function to \(\tau_{1}\). The specificity function in (11) is a decreasing function to \(\tau_{1}\). To optimize the information granule, both two functions are required to be at maximum. However, it is obvious these two functions are in conflict. Therefore, we define the optimization criterion by a product [32], as below. \[Q(\tau_{1})=\text{cov}(\tau_{1})\cdot spe(\tau_{1}) \tag{12}\]  
The uncertain granular interval in ClusterA could be determined by the optimal parameter \(\tau_{1}\), as seen below. \[\tau_{1,opt}=\text{argmax}_{\tau_{1}}Q(\tau_{1}) \tag{13}\]  
where argmax implies the operator is seeking the maximum values from the given function. For determining the value of \(\tau_{2}\) (\(x_{0}\leq\tau_{2}\leq 1\)), the same steps are implemented. The two functions are defined as below. \[B(x) = 1/\sum_{i=A,B}\left(\frac{\left|x-v_{\rm B}\right|}{\left|x-v_{i} \right|}\right)^{2}=\frac{\left|x-v_{\rm A}\right|^{2}}{\left|x-v_{\rm A} \right|^{2}+\left|x-v_{\rm B}\right|^{2}} \tag{6}\]0  
\[B(x) = 1/\sum_{i=A,B}\left(\frac{\left|x-v_{\rm B}\right|}{\left|x-v_{i} \right|}\right)^{2}=\frac{\left|x-v_{\rm A}\right|^{2}}{\left|x-v_{\rm A} \right|^{2}+\left|x-v_{\rm B}\right|^{2}} \tag{6}\]1  
Similarly, the uncertain granular interval in ClusterB could be determined by the optimal parameter \(\tau_{2}\) through the product \(\tau_{2,opt}=\text{argmax}_{\tau_{2}}Q(\tau_{2})\). After construction of the uncertain granule, we could form classification rules according to (4). While considering the particularity of one-dimensional data, both the membership and the size of uncertain regions could be reflected by data values. Therefore, we can rewrite the classification rules as seen below. \[\left\{ \begin{array}{ll}Rule\ 1:&\text{IF}\ x_{k}\in[x_{0}-\tau_{1},\ x_{0}+ \tau_{2}],\ \text{THEN}\ y_{k}\ \text{needs}\ \text{\"clerical}\ \text{review}\text{\"};\\ Rule\ 2:&\text{IF}\ x_{k}<x_{0}-\tau_{1},\ \text{THEN}\ y_{k}\ \text{is in Cluster}_{\text{A}};\\ Rule\ 3:&\text{IF}\ x_{k}>x_{0}+\tau_{2},\ \text{THEN}\ y_{k}\ \text{is in Cluster}_{\text{B}}. \end{array} \right. \tag{16}\]
## 6 Conclusions  
Based on analyses of the isotopic ages, mineralogy, and geochemistry of the Dahongliutan pluton in the Western Kunlun orogen, the following conclusions were drawn:  
(1) Combined with the zircon CL images and U and Th data, zircon U-Pb dating indicates that the emplacement age of the Dahongliutan pluton is 220 \(\pm\) 2.2 to 205.99 \(\pm\) 0.79 Ma, and was the result of Late Triassic magmatism.  
(2) The Dahongliutan pluton is enriched in silica, alkali elements, and potassium. The A/CNK ranges from 1.01 to 1.43, indicating a strongly peraluminous rock type. LREEs and HREEs are highly fractionated. The negative Eu anomalies of 20 samples are relatively pronounced. The pluton is enriched in HFSEs (e.g., Th) and LLEEs (e.g., Rb); however, it is depleted in Sr, Ta, and Nb. Thus, the pluton belongs to the high potassium calc-alkaline peraluminous rock series.  
(3) The Dahongliutan pluton is an S-type granite; however, a small amount of upper mantle material was involved during early magma formation. The pluton also exhibits I-type granite characteristics. The negative Eu anomalies, low Y and Yb contents, high Sr/Y ratios, low Nb, Ta, and Sr contents, and high Nb/Ta ratios (2.89-14.40) indicate that garnet and rutile residue were present in the source. Thus, the Dahongliutan pluton may have formed via the partial melting of crustal rocks at 1-1.5 GPa, with the contribution of some mantle-derived material.  
(4) The U-Pb ages and regional geology indicate that the Dahongliutan pluton had a composite emplacement mechanism that was dominated by tectonic expansion and magma intrusion, which may have occurred in the same collisional tectonic setting. Duringthe Late Triassic, the Western Kunlun region entered the post-collisional stage of the orogen and intrusive rocks formed in an extensional setting.  
(5) Late Triassic magmatism provided important heat and ore-forming materials for the mineralization of Pb-Zn, rare metals, Cu, Fe, and other minerals in the study area. Mineralization in this region is closely related to magmatism, which is important for geologic prospecting.  
Conceptualization, B.Z., Z.Z. and Z.S.; methodology, B.Z., Z.Z., Z.S. and H.S.; writing--original draft preparation, B.Z., and Z.Z.; writing--review and editing, B.Z., Z.Z. and H.S.; visualization, B.Z. and Z.Z.; supervision, Z.S., H.S. and L.Y.; funding acquisition, B.Z. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the China Geological Survey Project (Grant No. 1212011220655) and the Xinjiang Uygur Autonomous Region Geological Exploration Fund Project Management Center Project (Grant No. K15-1-LQ04).  
Not applicable.  
We would like to thank the editor and reviewers for valuable comments and suggestions, which have greatly improved this manuscript.  
The authors declare no conflict of interest.
# Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions
## 3 Results
### Task 2--ISO Standards for Industrial Robots
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach Direction**} & \multirow{2}{*}{**Approach Direction**} & \multicolumn{4}{c}{**LIGHT WIND**} & \multicolumn{4}{c}{**STRONG WIND**} \\  & & **N [m]** & **E [m]** & **A [m]** & **3D [m]** & **N [m]** & **E [m]** & **A [m]** & **3D [m]** \\ \hline \multirow{4}{*}{COM-1} & N & 0.76 & \(-\)0.68 & \(-\)1.79 & 2.06 & \(-\)1.10 & 0.55 & \(-\)2.11 & 2.44 \\  & S & 0.98 & \(-\)0.53 & \(-\)1.66 & 2.00 & \(-\)1.65 & 0.21 & \(-\)2.57 & 3.06 \\  & E & 0.86 & \(-\)0.90 & \(-\)1.81 & 2.20 & \(-\)1.33 & 0.36 & \(-\)2.75 & 3.08 \\  & W & 0.50 & \(-\)0.55 & \(-\)2.24 & 2.36 & \(-\)1.80 & 0.47 & \(-\)2.15 & 2.84 \\ \hline \multirow{4}{*}{COM-2} & N & 1.59 & 0.03 & \(-\)1.99 & 2.55 & 0.69 & \(-\)0.68 & \(-\)2.50 & 2.68 \\  & S & 1.50 & 0.96 & \(-\)1.70 & 2.46 & 0.99 & \(-\)0.94 & \(-\)2.70 & 3.02 \\ \cline{1-1}  & E & 0.50 & \(-\)0.19 & \(-\)1.35 & 1.45 & 0.43 & \(-\)0.13 & \(-\)3.69 & 3.71 \\ \cline{1-1}  & W & 1.66 & 0.69 & \(-\)1.29 & 2.21 & 0.74 & \(-\)0.62 & \(-\)3.05 & 3.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of UAV positioning accuracy test in task 2.  
does not affect the repeatability of the device positioning. However, it should be noted that, for the tests carried out in strong winds, these parameters are greater. Just as it occurred in the previous case, the variability of the positioning accuracy attained from many directions in windy conditions is greater. The parameters for each direction are similar within one on-board computer. COM-1 has a higher accuracy of about 0.3 m in strong wind conditions. The variability of the positioning accuracy that was achieved from multiple directions is represented by one parameter, which in the discussed case has been summarised in the last column of Table 4 denoted as _vAPP_. The parameters of deviation and repeatability of the distance implemented for each direction of reaching the central point have been summarised in Table 5. For the parameter of the distance deviation for each direction of approach to the central point, it is noticeable that the performed length of the segment has been shorter than the assumed one for all directions of the flight. For COM-1, these mean values of the distance deviation are slightly lower. For classic industrial robots, this indicates the error of the scale. However, when analysing the repeatability of the performed distance, it follows that this parameter exceeds 1 m. Therefore, it can be assumed that the previously discussed values of the distance deviation can be considered to be negligible. It is worth noting that the mean value of this parameter is higher in strong winds. The results of such tests may form the basis for testing in multi-UAV routing [37; 38], as well as tests of new algorithms implemented in on-board computers while using the extended Kalman filter (EKF) [65].
# An Improved SIFT Underwater Image Stitching Method
## 2 Related Work
Therefore, researchers have conducted further research on underwater mosaics of arbitrary images by using only the intrinsic information of the images. [PERSON] et al. [22] introduced a fully automated feature-based method for creating seamless, panoramic 2D mosaics from a sequence of images. Their texture-based approach, combined with statistical filtering, ensures accurate correspondences between adjacent frames and facilitates the removal of outliers. [PERSON] et al. [8] proposed an enhancement to the SIFT algorithm to improve the automatic recognition of underwater images. Their method incorporates a Gabor filter as a pre-filter and utilizes the Hausdorff distance to compute the distance between key points, resulting in the detection of more key points and improved matching. [PERSON] et al. [27] presented an approach for large-area underwater mosaicking, addressing challenges related to low overlap, unstructured motion, and varying lighting conditions in underwater images. Their method involves three key steps: determining image homography by estimating and compensating for radial distortion, topology estimation through feature-based pairwise image registration using a multiscale Harris interest point detector and a Zernike moment-based feature descriptor, and global alignment of all images based on the initial alignment obtained from the pairwise estimation. However, this approach heavily relies on effective feature detection and matching phases. As far as our knowledge extends, there remain significant challenges in detecting a sufficient number of accurate feature pairs when the image feature contours are blurred. Insufficient feature matches can lead to errors in the mosaic.
## 4 Simulation Results
### Partial Retrieval  
The variations in cloud horizontal scale are often quite significant [28]. Some cloud covers stretch over hundreds of kilometers and it may be difficult to measure the entire field at the same time. It would be of interest to see how the retrieval method would perform if only part of the cloud is reachable by the signal links. Thus, simulations were carried out for scenarios where ground receivers are restricted to smaller areas. For example, 11 receivers placed from \(x=35\) km to \(x=45\) km (marked by the green bar at the bottom of Figure 8a) are used for retrieving the attenuation field. We firstly identify the targeted area (marked by the red dashed line in Figure 8a) and define it using \(120\times 30\) (\(u=250\) m) rectangular basis functions. The final retrieved field after the PL function is shown in Figure 8b. It can be seen that the middle section directly above the receivers has been retrieved successfully while for other areas the retrieved field is erroneous. The limited number of receivers with restricted locations cannot provide enough information to retrieve the left and right sections of the targeted area. Secondly, we redefine the target area (marked by the purple dashed line in Figure 8a) with \(30\times 15\) Gaussian basis functions. The final retrieved field after the PL function is shown in Figure 8c. The retrieved field in the middle section also agrees with its counterpart in the reference field, but the left area outside the 35 km mark and the right area outside the 45 km mark have a high level of errors.  
The results above indicate that partial retrieval is possible when a limited number of receivers or signal links are available. Because most of the signal links have high elevation angles, the best retrieval performance is found in the area directly above the receivers. It is also implied that tomographic retrieval can be carried out in a sectioned manner. For a targeted area within the attenuation field, only the signal links relevant to it are needed for its retrieval.  
Figure 8: Simulation results for partial retrieval. Panel (**a**) shows the reference field and the location of 11 receivers (green bar). Panel (**b**) shows the retrieved field using \(120\times 30\) rectangular basis functions defined in the area marked by the red dashed line in panel (**a**). Panel (**c**) shows the retrieved field using \(30\times 15\) Gaussian basis functions defined in the area marked by the purple dashed line in panel (**a**).
## 2 Experimental
### Preparation of RRCA and RRAC  
Figure 1a reveals the preparation process of three generations of RRCA and RRAC. A design service life of 50 years for RAC was defined as a generation in this study. The first-generation recycled aggregate (4.75-26.5 mm) marked as RCA-1 was provided from Jiangsu Lyhe Environmental Technology Co. Ltd. (Changzhou, China) to prepare the first generation RAC (RAC-1). To achieve the equivalence of a 50-year service life of concrete and prepare the second (RCA-2)- and third (RCA-3)-generation recycled aggregates by a laboratory acceleration process, the multi-environmental time similarity theory [34] was adopted. The cured RAC-1 was carbonated for 17 d indoor under 30% of its ultimate tensile stress, which was equal to concrete carbonated for 50 years in the natural environment. The loading procedure of RAC-1 followed the previous study [35], and the loading device is shown in Figure 1b. Then the RAC-1 was crushed by a two-grade crushing technique and sieved to obtain the RCA-2 (4.75-26.5 mm). Similarly, RCA-3 and the second (RAC-2)- and third (RAC-3)-generation RAC were prepared. The physical properties, phase compositions, and morphologies of three generations of RRCA had been described in the previous research [21]. All RRAC could meet the requirements for medium quality aggregates. The grading curves of NCA and RRCA are shown in Figure 2. All the cumulative sieve residual rates of NCA and RRCA conformed to the continuous grading requirements of a 5-20-mm aggregate diameter in accordance with GB/T 25177-2010. Three generations of RRAC with 40 MPa of target strength were prepared using the corresponding RRCA at different replacement rates (25%, 75%, and 100%), which were labeled as RRAC-25, RRAC-75, and RRAC-100.  
The mixed proportions of NAC and RRAC are given in Table 1. Cube specimens (100 \(\times\) 100 \(\times\) 100 mm) of RRAC were prepared for measuring compressive strength. Twenty-seven 100 \(\times\) 100 \(\times\) 400 mm prism samples for each group of RRAC and NAC were cast for the freeze-thaw cycle and accelerated carbonation test. All specimens were demolded after 24 h and preserved in a standard curing room (20 \(\pm\) 3 \({}^{\circ}\)C, RH \(>\) 90%) until the age of 28 d. The specimens for freeze-thaw cycles needed to be taken out 4 days in advance and soaked in (20 \(\pm\) 2 \({}^{\circ}\)C) water until the freeze-thaw cycle began. The freeze-thaw cycles (100-800 times) of RRAC were carried out by the quick-freezing method in the laboratory. After subjecting to the freeze-thaw cycles, the NAC and RRAC specimens were carbonated in a standard carbonation box (20 \(\pm\) 3% of CO\({}_{2}\) concentration, 70 \(\pm\) 5% of RH and 20 \(\pm\) 2 \({}^{\circ}\)C of temperature) for 28 d.  
Figure 1: (**a**) Schematic for preparation process of recycled concrete aggregate (RCA) and repeatedly recycled aggregate concrete (RRAC) and (**b**) loading device of RRAC.  
Figure 2: Grading curve of natural aggregate concrete (NCA) and RRCA.
# Computational Dynamics of Multi-Rigid-Body System in Screw Coordinate
## 5 Numerical Experiment and Discussion
### Inverse Kinematics Simulation for Gough-Stewart Platform  
When the velocity screw of the reference point \(P\) on the moving platform is specified in Appendix A Table 1, and the initial conditions of the Gough-Stewart platform are also provided in Tables 2 and 3, the inverse velocity and the inverse displacement (through one-step numerical integration) and acceleration (through one-step numerical differentiation) of the Gough-Stewart platform could be programmed in accordance with the algorithms deduced in Section 3. With the discrete conditions in Table 2 for the numerical calculations, the kinematics parameters can be output by programming in MATLAB. In Figures 7-11, the kinematics of each kinematic chain for the Gough-Stewart platform are all plotted.  
Figure 6: Kinematics analysis of the Gough–Stewart platform.  
Figure 7 illustrates the relative displacements of all joints. Figure 7a shows the angular displacements of all rotational joints, and Figure 7b represents the angular displacement of all prismatic joints. They are all solved by Equation (15) with the one-step numerical algorithm.  
Figure 8 illustrates the relative velocities of all joints. Figure 8a shows the angular velocities of rotational joints, and Figure 8b shows the angular velocities of prismatic joints. They are all represented by Equation (14) with the one-step numerical algorithm.  
Figure 9 illustrates the absolute angular velocities of all legs. Figure 9a shows the absolute angular velocities of the fixed legs \(L_{AB}^{p}\), \((p=1,2\cdots 6)\), and Figure 9b shows the absolute angular velocities of the moving legs \(L_{BC^{\prime}}^{p}\), \((p=1,2\cdots 6)\).  
Figure 10 illustrates the absolute accelerations of the fixed legs \(L_{AB}^{p}\), \((p=1,2\cdots 6)\) at their individual mass centers. Figure 10a shows the absolute linear accelerations of point \(D\) on the fixed legs while Figure 10b illustrates the absolute angular accelerations.  
Figure 11 illustrates the absolute accelerations of the moving legs \(L_{BC^{\prime}}^{p}\), \((p=1,2\cdots 6)\) at their individual mass centers. Figure 11a shows the absolute linear accelerations of the mass centers of the moving legs, and Figure 11b shows the absolute angular accelerations of the moving legs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Condition** & **Parameters** & **Value** \\ \hline Total time of the path & \(t(s)\) & \(10\) \\ \hline Interval of iteration & \(\Delta t\) & \(0.001\) \\ \hline Steps of iteration & \(k\) & \(10,000\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discrete conditions for numerical algorithm.  
Figure 7: Relative displacements of the joints in each link: (**a**) relative angular displacements; (**b**) relative linear displacements. They are all solved by Equation (15) with the one-step numerical algorithm.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
###### Abstract
By constructing scene graphs and performing image matching and retrieval based on scene graph similarity, this method more fully utilized the high-level semantic information in images, including the categories and spatial relationships of objects, to improve retrieval accuracy and efficiency, which fundamentally aligned with human behavior in image retrieval. 3. Several scene graph construction strategies for target spatial relationships have been developed to meet various retrieval needs, including fully connected, randomly connected, nearest neighbor, star-shaped, and circular connections. These strategies can flexibly adapt to various data features and retrieval requirements, providing a variety of options for scene-graph-based remote sensing image retrieval. 4. The RSSG dataset, which contains a corresponding scene graph for each image, was created by using various scene graph construction strategies. To our best knowledge, RSSG is the first remote sensing dataset in the academic community. The experimental results of the performance evaluation using RSSG showed that the selection of appropriate construction strategies according to different scene characteristics obviously enhanced retrieval performance. The remainder of the paper is organized as follows. Section 1 introduces the background, significance, content, main work, and organizational structure of the research. Section 2 discusses the progress of related works. Section 3 describes the developed remote sensing image retrieval algorithm based on scene graph similarity, consisting of three sub-algorithms: a one-shot object detection algorithm, a scene graph construction algorithm using detection results, and a graph neural network-based similarity calculation algorithm. Section 4 presents the experimental results, demonstrating that the retrieval accuracy of the algorithm developed in this research is superior to similar algorithms. Finally, Section 5 provides a summary and outlook.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 4 Conclusions
### Future Directions  
The concepts, also called CUIs, were successfully detected to accurately label the radiology images. The future direction to this work is categorized into two main types as given below.  
Future research could focus on training separate models for each imaging modality represented in the dataset. Given the high diversity among modalities, such as x-ray, MRI, and CT scans, specialized models may capture modality-specific features more effectively than a unified model. After training, the outputs from each modality-specific model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Method** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & \begin{tabular}{c} **Standard** \\ **Deviation (\%)** \\ \end{tabular} \\ \hline DenseNet121 (20 CUIs) & \
\begin{tabular}{c} Transfer learning \\ (ImageNet) \\ \end{tabular} & 90.2 & 88.0 & 89.0 & \(\pm\)1.5 \\ ResNet101 (20 CUIs) &
\begin{tabular}{c} Transfer learning \\ (ImageNet) \\ \end{tabular} & 89.1 & 87.0 & 88.0 & \(\pm\)1.8 \\ VGG19 (3047 CUIs) & No transfer learning & 68.0 & 65.0 & 27.0 & \(\pm\)2.5 \\ ImageCLEF baseline 2020 & Official baseline & 39.0 & 36.0 & 37.0 & \(\pm\)3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of model performance with ImageCLEF baseline.  
could be combined through ensemble learning strategies to improve overall classification performance. This approach could also help identify which models perform best for each modality type, providing deeper insights into how modality characteristics affect learning outcomes and guiding the development of more targeted diagnostic tools.  
In this study, we reduced the number of concepts to the 20 most frequent ones to address extreme class imbalance and ensure a stable experimental framework. However, future work will focus on expanding the classification task to include a broader and more diverse set of UMLS concepts. This will allow for a more realistic evaluation of model performance in clinical applications. Considering the severe imbalance and the few-shot learning challenges associated with rare medical concepts, future directions also involve exploring advanced learning strategies such as few-shot learning, meta-learning techniques, and specialized data augmentation methods tailored for multilabel classification problems.