esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture  
The leg-arm hexapod robot is shown in Figure 1, and the architecture of the proposed system is shown in Figure 2. The results of hand posture and Chinese natural language recognition are transmitted to a control layer via a human-robot interaction layer. The hexapod robot is then controlled to perform a specific task using the control layer. Environmental information is also obtained through an environmental perception layer.  
A path planning module and a 3D SLAM module constitute the environmental perception layer. The former features an efficient hierarchical pathfinding algorithm based on a grid map of the indoor environment in which the integrated leg-arm hexapod robot operates [16]. The latter is an effective approach to SLAM based on RGB-D images for the autonomous operation of the robot [17].  
The speech module is responsible for semantic understanding, task planning, and data interaction based on the network's submodules. The semantic understanding submodule is based on a task-oriented method of semantic understanding [18] using the characteristics of the integrated leg-arm hexapod robot and Chinese instructions, a semantic understanding algorithm, and a structural language framework based on verbs. Natural language was thus transformed into regular commands in the structural language.  
A method of task planning based on answer-set programming was developed in the task planning submodule. Structural language commands can be used to express key information, but there is a deviation between the structural language commands and the corresponding instructions that the robot can execute. To solve this problem, a combination of information concerning the robot's state, environment, structural commands, executable actions, and the optimal objective was used. Furthermore, the answer-set rule was designed, and the commands in the structural framework are converted into a sequence of actions that a robot can perform.  
In data interaction based on the network submodule, a remote connection between the host computer and the robot is established for exchanging data through a Wide Area Network (WAN). In this way, the host computer can remotely obtain real-time information on the robot and send commands to guide the robot's movements. Furthermore, a real-time communication connection between the front and back ends of the network is established based on the Django framework in the host computer so that it can obtain services from the front end of the network and guide the robot to specific locations on the outdoor map. By designing the interface of the host computer, it becomes convenient for users to obtain information about the robot in real time.  
The system of interaction has three modes: the hand posture mode, the speech mode, and a combination of the two. In the hand posture mode, hand postures are used to control the robot. In the speech mode, Chinese natural language is used to control the robot. Information on both hand postures and speech is used to control the robot in the combination of the hand posture and speech modes.
# A Fully Automated Pipeline for a Robust Conjunctival Hyperenia Estimation
## 2 Materials and Methods
### Image Processing Pipeline  
Our image processing pipeline is composed by a series of independent and fully automated steps (ref. Figure 1):  
1. conjunctiva segmentation;
2. vessels network segmentation;
3. vessels network features extraction and color features extraction;
4.  
The first step of processing involves the segmentation of the conjunctiva area from the background, i.e., skin, eyelids, eyelashes, and caruncle. For each image of the datasets, we collected a manual annotation of the conjunctiva area performed and validated by the 3 experts. The manual annotation set includes a binary mask of the original image in which only the conjunctiva area is highlighted. The conjunctiva segmentation allows for  
Figure 1: Schematic representation of the pipeline. (**Step 1**) The image acquired by the slit lamp is used as input for the Neural Network (Mobile U-Net) model for the segmentation of the conjunctival area. The model was trained using manually annotated images validated by experts. (**Step 2(a)**) Focusing on the conjunctival area the image is standardized using a brighter-correction algorithm given by a combination of median filters and background subtraction. (**Step 2(b)**) The image standardization helps us to remove possible artifacts and color issues related to the non-rigid image acquisition procedure. The processed image is used as input for the vessels network segmentation algorithm given by a tuned implementation of the NEFI algorithm. (**Step 3,4**) Starting from the vessels’ network image, a set of features for the quantification of the conjunctival redness are extracted and used for the development of a predictive model. The full set of steps are performed automatically and thus without any human intervention.  
reducing the region of interest of our analysis on the most informative area for the redness evaluation.  
Several already published automated grading systems for the hyperemia quantification perform this step using semi-supervised methods, leaving to the final user the manual selection of the region of interest (ROI) [3, 8, 9, 10]. Our method automatizes this task using a semantic segmentation neural network model trained on a set of images manual annotated and validated by experts.  
In the second step, the pipeline performs a second segmentation for the vessels' network identification, starting from the segmented conjunctiva. The vessels network is automatically segmented using a customized version of the NEFI [19] algorithm. The vessels network segmentation provides a mask to apply to the original image.  
In step three, the pipeline extracts features starting from the selected areas based on quantities proposed in the literature and based on different color spaces (RGB and HSV, i.e., Hue, Saturation, and Value). In step four, the extracted set of features was used to feed a penalized regression model for the prediction of the final Efron scale value.
## 3 Results  
### Analysis of Protein Molecular Weight Distribution Using SE-UPLC  
SE-UPLC analysis was performed to estimate the apparent MW distribution of protein fractions in the WPHs (Figure 1). The SE-UPLC elution profile was roughly divided into five fractions, including one high-MW fraction (>13,500 Da), two medium-MW fractions (3500-13,500 Da and 500-3500 Da), and two low-MW fractions (150-500 Da and <150 Da). Intact whey proteins of the WPHs can be seen in the high-MW fraction, whereas most of the short peptides obtained from LC-HRMS analysis belong to the 500-3500 Da fraction.  
Table 1 represents the relative distribution (% of the area) of MW of these protein fractions in the WPHs.  
The analysis revealed that the H3 and H4 hydrolysates had a higher amount of proteins and large polypeptides (>13,500 Da), approximately 35% of the area of all fractions, than the H2 hydrolysate (6%). There were no \(\alpha\)-LA and \(\beta\)-LG (14.2 kDa and 18.3 kDa, respectively) in the H1 hydrolysate (Table 1). Looking at the second fraction, there was more or less the same amount of peptides with the MW of 3500-13,500 Da in all the WPHs, with the larger share in the H3 sample (24%). A large amount of peptides with MW in the range of 500-3500 Da was observed in the H1 and H2 hydrolysates, comprising 54% and 43% of the area of all fractions, respectively. In H3 and H4, this fraction showed nearly two times lower relative area % than in the H1 and H2 hydrolysates. Approximately 16% of the area was covered by short peptides with MW in the range of 150-500 Da in the H1 and H2 hydrolysates, conversely to only 8% of area covered by this fraction in H3 and H4. The last fraction (MW < 150 Da), which represents low-MW molecules, accounted for 11% and 16% of the area in the H1 and H3, vs H2 and H4 hydrolysates, respectively, and will not be considered in further detail in this study (Figure 1, Table 1).
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
###### Abstract  
A method called iTEKA, which stands for iterative time elastic kernel averaging, was successfully used for averaging time series. In this paper, we adapt it to GPS trajectories. The key contribution is a denoising procedure that includes an over-sampling scheme, the detection and removal of outlier trajectories, a kernelized time elastic averaging method, and a down-sampling as post-processing. The experiment carried out on benchmark datasets showed that the proposed procedure is effective and outperforms straightforward methods based on medoid or Euclidean averaging approaches.  
GPS trajectory; time series averaging; noise reduction; kernel methods; dynamic time warping +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Magnetic Field Finite Element Simulation  
Based on the simplified model of the variable thickness MRF transmission device shown in Figure 2, a two-dimensional axisymmetric finite element model is established, and the calculated results of the magnetic circuit analysis are adopted as the magnetic field excitation conditions of the finite element model.  
The area surrounding the MRF transmission device magnetic field analysis model is set to air with a relative permeability of 1. The number of turns of the coil is set to 100 turns, and the current directions of both coils are the same. At different working gap thicknesses, the magnetic field distribution in the variable thickness MRF transmission device derived from the finite element steady-state analysis is shown in Figure 8, where the magnetic lines of force all pass perpendicularly through the MRF working area, enabling the MRF to generate the maximum shear yield stress.  
As shown in Figure 8, at a current \(I=3.0\) A and a gap thickness, \(h\), of 1.5 mm, the magnetic flux passes through the MRF working gap from the left- and right-side guide discs, the flux in the gap varies relatively evenly with the radial distance, and the flux through the MRF is relatively low when it is close to the coil due to the relatively small magnetic resistance of the friction disc compared to the MRF. In Figure 8, we can see that  
Figure 8: Cloud chart of magnetic induction intensity and magnetic lines of force at the current of 3.0A. (**a**) 3D cloud chart, (**b**) streamline of the magnetic induction intensity, and (**c**) magnetic field distribution on an arbitrary cross-section.  
the local area of the squeeze disc is close to magnetic saturation, and the maximum current is set to 3 A to avoid the heating phenomenon caused by the excessive magnetic flux.  
To establish the relation between current, \(I\), and MRF shear yield stress before squeeze, \(\tau_{y}(B)\), the energizing current of the coil is varied and the magnetic field strength excited by the current is calculated at a current \(I=0\) to 3.0 A and a gap thickness of 1.5 mm, as shown in Figure 9.  
The radial distance from any plane of the MRF to the small diameter is defined as \(r\). As seen from Figure 9, the magnetic induction strength rises rapidly with the increase of current when the current is small, and the growth rate is slow at the current close to 3 A. Since the magnetic induction intensity is small at the two ends of the working gap, the pressurization process of the MRF in the whole gap needs to be considered.
## 3 Numerical Results and Complexity Analysis  
Here, we present a computer simulation to verify the performance of the proposed NPUSCH receiver scheme in terms of bit error rate and throughput under a specific SNR. In addition, the complexity of the entire system is analyzed in detail.
## 4 Simulation
### Scenario 2  
In this part, the performance results of the proposed UT-CLAT method are presented on the basis of 1000 Monte Carlo simulations. Specifically, the simulation in Scenario 1 was repeated 1000 times with \(1\leq k\leq 1200\). For each robot, the position root-mean-square errors (PRMSEs) of the local posterior estimates and target posterior estimates were computed for all trails. Moreover, to demonstrate the effectiveness in a nonlinear scenario, the proposed UT-CLAT method is compared to the EKF-CLAT method by extending the CL algorithm in [9] to the CLAT scenario. In Figure 7, the averaged PRMSEs of the collaborative localization results of 1000 Monte Carlo simulations are plotted using both the UT-CLAT and EKF-CLAT methods.  
As observed in Figure 7, both methods can realize stable self-localization in around 200 time instances. In general, the UT-CLAT method is able to achieve more accurate self-localization results. In Figure 8, the averaged PRMSEs of the target tracking results of different robots are plotted. Similar to the CL result  
Figure 6: Target tracking errors and covariance of robots 1–4.  
Figure 7: Self-localization estimation position root-mean-square errors (PRMSEs) of the unscented transformation-based collaborative self-localization and target tracking scheme (UT-CLAT) vs. the extended Kalman filter-based CLAT (EKF-CLAT) for robots 1–4.  
in Figure 7, the UT-CLAT is able to realize stable target tracking, and it outperforms the EKF-CLAT method for each robot.
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
## 4 Discussion  
A coaxial multifunctional endoscopic system for the diagnosis and treatment of laryngeal lesions was developed. The cross-sectional OCT imaging, superficial white light laryngoscope, and laser ablation function were integrated into one signal probe with a diameter of 8 mm and length of 250 mm. With this coaxial design, the imaging modes and laser ablation position could register more accurately and reduce the examination time compared to the single-modality probe [7; 17].  
However, some limits still need to be improved. The 8 mm diameter is suitable for the ex vivo study, but for the in vivo study, a smaller diameter is needed since the commonly used laryngoscope diameter is around 5 mm [33]. We plan to use smaller illumination fiber and thinner wall tubing to solve this problem. The lateral resolution of OCT was about 120 \(\upmu\)m. It was not as good as the single-channel OCT endoscope [34], which is usually around tens of microns. This was because we wanted to ensure a large field of view of the white light imaging. In the future, we hope to find a balance between the field of view and the lateral resolution. For the ex vivo experiments, the A-scan rate was 100 kHz. It was fast enough to acquire images without large motion artifacts. However, for in vivo studies, the A-scan rate could be further increased to 200 kHz, and a motion feedback function could further increase system stability [35].
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 1 Introduction
Then, the knowledge graph embedding method is used to train entity embeddings and relation embeddings. Figure 1: An example of NEL whose goal is to link each mention to an entity in the KB (e.g., “[PERSON]” is linked to [PERSON]; “Artificial intelligence” is linked to Artificial intelligence). Note that there are various relations between entities in the KB. Finally, according to the different characteristics of local and global models, we use the previously trained entity embeddings and relation embeddings only for the global model of entity linking; that is, the global scores are computed from the perspective of the graph structure and fused with the Ment-Norm [7] model. Existing methods have been able to achieve more than 90% F1 on the standard AIDA-CoNLL dataset; for example, Ment-Norm achieves 93.07% F1. Our KGEL method achieves an improvement of 0.4% F1 on the basis of Ment-Norm, and the average result of KGEL on the five out-of-domain datasets is also 0.2% higher than Ment-Norm, which indicates that our model also has better generalization. Our method can also further improve the performance when using a more superior baseline. The main contributions of our paper can be summarized as follows. (1) We propose to introduce knowledge graph structure information into the entity linking model, so as to complement the semantic information. (2) We obtain the Wikipedia-Wikidata mappings of entities and the required triples, and then obtain the entity and relation embeddings containing the graph structure through the knowledge graph embedding method. This provides a new idea for information fusion between different knowledge bases (graphs). (3) Extensive experiments on multiple datasets show the excellent performance of our method and demonstrate the effectiveness of the knowledge graph structure for entity linking.
## 3 Results and Discussion
### Seismic Performance Analysis for Other Structural Variables
The target displacements obtained for the structural model created by increasing the ground storey height and created relatively are greater than the target displacements obtained according to the reference building. An increase in target displacements occurred with the increase in frame opening. A decrease in target displacements was also observed for Model IV, where the height of each storey in the structure was reduced. Considering all of these parameters together in any structural  
Figure 11.3 The 2D models obtained for different storey heights. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil Class**} & \multicolumn{2}{c}{**Target**} & \multirow{2}{*}{**Model I**} & \multirow{2}{*}{**Model II**} & \multirow{2}{*}{**Model III**} & \multirow{2}{*}{**Model IV**} \\  & **Displacements (m)** & & & & & \\ \hline \multirow{4}{*}{ZA} & DL & 0.0369 & 0.0464 & 0.0473 & 0.0387 \\ \cline{2-6}  & SD & 0.0473 & 0.0595 & 0.0607 & 0.0496 \\ \cline{2-6}  & NC & 0.0821 & 0.1032 & 0.1053 & 0.0861 \\ \hline \multirow{4}{*}{ZB} & DL & 0.0553 & 0.0696 & 0.0710 & 0.0580 \\ \cline{2-6}  & SD & 0.0710 & 0.0893 & 0.0911 & 0.0745 \\ \cline{2-6}  & NC & 0.1231 & 0.1548 & 0.1579 & 0.1291 \\ \hline \multirow{4}{*}{ZC} & DL & 0.0637 & 0.0801 & 0.0817 & 0.0667 \\ \cline{2-6}  & SD & 0.0817 & 0.1027 & 0.1048 & 0.0856 \\ \cline{2-6}  & NC & 0.1416 & 0.1780 & 0.1816 & 0.1484 \\ \hline \multirow{4}{*}{ZD} & DL & 0.0932 & 0.1253 & 0.1278 & 0.1020 \\ \cline{2-6}  & SD & 0.1214 & 0.1607 & 0.1640 & 0.1315 \\ \cline{1-1} \cline{2-6}  & NC & 0.2152 & 0.2787 & 0.2842 & 0.2299 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Target displacements for different soil classes and different structural models. design will ensure that the earthquake performance of the structures is at the desired level. The comparison of the target displacement values attained for different structural models is given in Figure 16.  
Period, base shear force, elastic and effective stiffness values were also obtained for all structural models considered within the scope of the study. These results obtained from numerical analysis for all structural models are shown in Table 9.  
The study uses static pushover analysis, which fails to account for cyclic degradation, frequency-dependent responses, or long-term soil softening effects. The structural analyses performed by considering different damping ratios, building importance classes, and different soil classes for earthquake zones, period, base shear force, and elastic and effective  
Figure 16: Comparison of target displacements for different soil classes and structural models.
## 4 Discussion
### Effects of Loop Shape and Wire Size  
When the gable bend was increased, the repeated calculations required for the space closure, \(N_{b}\), decreased remarkably. This was because a moment acting on the anterior teeth was increased by the gable bend. The larger moment reduced the tipping angle that occurred early in the space closure. Thereby, the anterior teeth could be upright with a small number of repeated calculations.  
When decreasing the loop height or increasing the wire size or replacing the helical loop with the vertical loop, the spring constant, or stiffness of the loop increases. In these cases, to limit the retraction force to 2 N, it was necessary to reduce the amount of activation, which decreased the closing distance \(C\).  
When increasing the wire size to 0.018 \(\times\) 0.025 inches, the spring constant increased; thereby, the closing distance \(C\) decreased. In addition, because the elastic deformation of the whole archive became smaller, the tipping angle of the central incisor decreased.  
The mechanical properties of a spring depend on the flexural rigidity \(EI\), in which \(E\) is Young's modulus, and \(I\) is the moment of inertia of the cross-sectional area [29; 30]. For the rectangular wire of depth \(h\) and width \(b\), \(I=bh^{3}/12\). When the wire size increases from 0.016 \(\times\) 0.022 to 0.018 \(\times\) 0.025 inches, \(I\) increase 1.6 times. This effect is roughly equivalent to changing the material of the 0.016 \(\times\) 0.022-inch wire from TMA (\(E=69\) GPa) to a titanium alloy (\(E=1000\) GPa).
## 2 Materials and Methods
### Kinematic Model of the Robotic Antenna
#### 2.1.4 Velocity Kinematics  
The time derivative of the quadratic form of the inverse kinematics becomes \(q_{i}^{2}=l_{i}^{T}\;l_{i}=l_{i}\cdot l_{i}\) the generalized coordinates velocity expression \(q_{i}=\frac{1}{q_{i}^{2}}l_{i}^{T}\;l_{i}=\lambda_{i}^{T}(d,R)\;\dot{l}_{i}\), where \(\lambda_{i}(d,R)\) is given by (2). Using the angular velocity identity [17], \(R=[\omega\times]R\), where \([a\times]\in SO(3)\) is the cross product operator, then the time derivative of (1) becomes  
\[\dot{l}_{i}=\dot{d}-\big{[}(Ra_{i})\times\big{]}\omega_{p}^{(0)}=\Big{[}I_{3},\quad-\big{[}(Ra_{i})\times\big{]}\Big{]}\
u_{p}^{(0)} \tag{4}\]  
Figure 4: Closed kinematic chain of limb \(i\).  
with \(\
u_{p}^{(0)}=\left(\dot{d},\omega_{p}^{(0)}\right)^{T}\in\mathcal{M}\subset\mathbb{ R}^{6}\) the platform twist, in base coordinates. Then, generalized velocity coordinates can be written as follows  
\[\dot{q}_{i}=\left[\lambda_{i}^{T}(d,R),\quad-\lambda_{i}^{T}(d,R)[(Ra_{i})\times] \right]\
u_{p}^{(0)} \tag{5}\]  
In vector form, it becomes  
\[\dot{q}=A(d,R)\;\
u_{p}^{(0)} \tag{6}\]  
where \(A(d,R)\) is the full rank matrix given by  
\[A(d,R)=\begin{bmatrix}\vdots&\vdots\\ \lambda_{i}^{T}(d,R)&\left((Ra_{i})\times\lambda_{i}(d,R)\right)^{T}\\ \vdots&\vdots\end{bmatrix}, \tag{7}\]  
which maps the linear and angular velocities \(\
u_{p}^{(0)}\) of the platform to the full space of the generalized velocity vector \(\dot{q}\) for well-posed velocity kinematics. Notice that \(\lambda_{i}\) can be obtained either using (2) if the position and attitude of the platform are available or by direct measurement of the two angles in each of the base joints.