esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 3 Human-Robot Cooperative Control Based on Virtual Fixture
### Human-Robot Cooperative Control Based on Virtual Fixture  
The velocity, \({}^{F}V_{F}=[v_{x}\ v_{y}\ v_{z}\ \omega_{x}\ \omega_{y}\ \omega_{z}]^{T}\), obtained based on Equation (22) is the velocity vector in the force sensor coordinate system. Considering the VF is generally designed in the image coordinate system, it is necessary to transform the velocity of the endoscope from the force sensor coordinate system to the image coordinate system before performing the VF calculation. Assuming that after the navigation system is registered, the expression of the force sensor coordinate system in the image coordinate system is:  
\[\begin{split}\text{Image}_{\text{Robot}}T=\left[\begin{array}{ cc}\text{Image}_{\text{Robot}}R&\text{Image}_{\text{Robot}}P_{Org}\\ 0&1\end{array}\right]\end{split} \tag{23}\]  
The expression of the endoscope velocity in the image coordinate system can be obtained as follows:  
\[{}^{1}V_{F}=\left[\begin{array}{cc}\text{Image}_{\text{Robot}}R&0\\ 0&\text{Image}_{\text{Robot}}R\end{array}\right]^{\mathrm{F}}V_{F} \tag{24}\]  
For \({}^{1}V_{F}\), using the composite VFs in Figure 6 for constraint calculation, the endoscope velocity after VF restraint can be obtained in the image coordinate system as \({}^{1}V_{out}\). Then, the endoscope velocity is transformed back from the image coordinate system to the robot base coordinate system:  
\[\begin{split}\text{Robot}V_{out}=\left[\begin{array}{cc}\text{ Image}_{\text{Robot}}R^{-1}&0\\ 0&\text{Image}_{\text{Robot}}R^{-1}\end{array}\right]^{1}V_{out}\end{split} \tag{25}\]  
According to the robot Jacobian matrix, \(J_{0}\), the robot joint velocity, \(\dot{q}\), can be obtained as follows:  
\[\dot{q}=J_{0}^{-1.\ Robot}V_{out} \tag{26}\]
## 3 Analysis of Results  
### Diffusion Behavior  
Figure 6 illustrates the grout diffusion distance at different time points. The grouting port is positioned at \(90^{\circ}\), and the boundary condition at the fluid outlet is a pressure boundary condition. In the figure, the blue region (\(\phi=1\)) represents grout, while the red region (\(\phi=0\)) represents air. The interface between the cement mortar and air forms a transition zone (\(0<\phi<1\)), which is depicted in green and yellow in the figure.  
Figure 5: Geometric model and boundary conditions: (**a**) geometric model; (**b**) boundary conditions; (**c**) isolated zone.  
Figure 6: Grout diffusion (\(\alpha=90^{\circ}\)).  
At the beginning of grouting, the transition zone is relatively short, and the interface between the grout and air is well-defined. As the grout continues to diffuse, the interface becomes irregular, and the transition zone extends.  
Figure 7 illustrates the variation in grout diffusion distance over time when the grouting port is positioned at 30\({}^{\circ}\) and 150\({}^{\circ}\). Due to the influence of gravity, the downward diffusion velocity of the grout is higher than the upward diffusion velocity. Additionally, during downward diffusion, the interface between the grout and air is irregular, resulting in a longer transition zone. In contrast, during upward diffusion, the interface is well-defined, and the transition zone remains shorter.  
Figure 8 illustrates the grout flow in a specific region of the backfilling layer when the grouting port is positioned at 90\({}^{\circ}\) and the grouting time reaches 3 s. This region is located above the grouting port, with its exact position marked by the red dashed line in Figure 6. Figure 8a presents the fluid pressure contour map, where the fluid pressure gradually decreases along the direction of grout diffusion. Regions with a larger pressure drop gradient are typically found in narrow particle gaps, whereas in larger pores, the pressure distribution is more uniform.  
Figure 8b is the fluid velocity contour map, where regions with higher flow velocity exhibit a slender, tubular distribution. Due to the highly heterogeneous nature of the pore space, certain regions with larger pores and better connectivity serve as primary flow channels, allowing the fluid to preferentially flow through these pathways. As a result, elongated high-velocity regions are formed.
## 3 Results  
### Selection of the Studies  
As mentioned earlier, the above-mentioned keywords were used for the study search. Initially, 247 articles emerged from the searches across all bibliographic databases. Subsequently, the removal of duplicate entries and articles that did not meet the selection criteria resulted in a final count of 12 articles deemed suitable for inclusion in the review. A flowchart illustrating the study selection process is presented in Figure 1. It is worth noting that all the studies included in the review are recent, although no limitations relating to the year of publication of the articles were applied to our research.
## 2 Background Study
### D-Value  
The D-value is merely a distance and is based on the concept of the centroid. The centroid is the center of mass of the data and is calculated using Equations (1) and (2) on both the x-axis and y-axis for OP and PV, respectively. As discussed in the Introduction, the plot of controller output versus process variable generates a pattern that changes based on the S and J values. Instead of considering the pair of process variable and controller output, calculating the distance of each data point from the centroid reduces the number of input neurons required in the network. Once the centroid of the values in each axis is calculated, the distance of every single data point from the centroid is calculated as represented in Equation (3).  
\[x_{c}=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{1}\]  
\[y_{c}=\frac{1}{N}\sum_{i=1}^{N}y_{i} \tag{2}\]  
\[D_{i}=\sqrt{\left(x_{i}-x_{c}\right)^{2}+\left(y_{i}-y_{c}\right)^{2}} \tag{3}\]  
where \(N\) is the total number of time samples, \(x_{i}\) is the \(i^{th}\) OP value, and \(y_{i}\) is the \(i^{th}\) PV value.
## 5 Conclusions  
The algorithm employs multiple watermarking bits that are embedded into distinct quantization intervals. A multiple watermarking scheme for vector geographic data based on multiple QIM is devised and implemented, exhibiting superior robustness, a high capacity for multiple watermarking, and adjustable precision. The theoretical and experimental results demonstrate that: (1) the proposed algorithm enables the embedding of multiple watermarks into the same coordinate vertices, with a large capacity (theoretically unlimited). Notably, it resolves the challenge of embedding multiple watermarks in datasets containing a substantial number of coordinate vertices. (2) The algorithm exhibits robustness against common deletion attacks in vector geographic data processing and demonstrates resilience against data addition attacks. However, the adoption of multiple quantization index modulation results in an unequal quantization step for various watermarks. Consequently, these watermarks exhibit inconsistent robustness. This characteristic is particularly prominent in the event of random noise attacks. In addition, due to the need for multiple quantization based on the number of watermarks before embedding multiple watermarks, although this algorithm is a blind detection algorithm, it requires knowledge of how many watermarks will be embedded. In our future research, we aim to address these challenges by integrating statistical-analysis-based watermark detection techniques to complete the embedding of multiple watermarks in carrier data that already contain watermark information.  
[PERSON] and [PERSON] have worked jointly to develop this paper. [PERSON] and [PERSON] reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Geo-Information Engineering (grant No. SKLGIE2019-M-4-2), the National Natural Science Foundation of China (grant No. 42101428), the Natural Science Fund for Colleges and Universities in Jiangsu Province (grant No. 20 KJD170002) and the Research Foundation of Jinling Institute of Technology (No. jit-b-201913 and No. jit-fluxm-201905).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Data Size**} & \multicolumn{3}{c}{**Multiple Watermark Capacity**} \\ \cline{3-5}  & & & **Proposed Algorithm** & **Reference**[36] & **Algorithm** \\ \hline
1 & Point & 437 & \(\geq\)16 & 1 \\
2 & Point & 511 & \(\geq\)16 & 1 \\
3 & Point & 756 & \(\geq\)16 & 2 \\
4 & Point & 1158 & \(\geq\)16 & 3 \\
5 & Point & 1514 & \(\geq\)16 & 4 \\
6 & Point & 1911 & \(\geq\)16 & 5 \\
7 & Polygon & 2108 & \(\geq\)16 & 5 \\
8 & Polygon & 2301 & \(\geq\)16 & 6 \\
9 & Point & 2797 & \(\geq\)16 & 7 \\
10 & Polygon & 3246 & \(\geq\)16 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Multiple watermark capacity of experimental data with different types and sizes.
# Analysis of Polygonal Vortex Flows in a Cylinder with a Rotating Bottom
### Numerical Modeling
This was first attributed to the assumption of the flat free surface, but as was shown in [44] adding the deformation of the free surface did not improve the results. In [43], it was suspected that surface tension might be the cause for this disagreement. Following the definition given in [45] for the critical Reynolds number (\(Re_{\text{NM}}=VL/v\)) that governs the instability, they showed that, for every Reynolds number, \(Re_{\text{NM}}\) remains constant by taking the characteristic velocity, \(V\), as maximum velocity of the disk and the characteristic length, \(L\), as the thickness of the shear layer, estimated as \(L=(E_{h}/4)^{1/4}h\), where \(E_{h}=v/(\omega h^{2})\) is the Ekman number. The base flow, before the first symmetry breaking, was numerically simulated in [46] by direct solution of the Navier-Stokes equations, assuming a stream function. The equations were coupled to the free surface deformation and solved by an iterative process. The free surface deformation results agreed with those reported in [47] for \(Re=900\), but there was disagreement with the results of [36] for \(Re=900\) and \(1500\). In addition, the free surface deformation reported in [4; 8], was qualitatively compared in [46] for the case before the symmetry breaking and without reaching the rotating disk. It was found that, due to the solid body rotation near the axis, the free surface deformed as a parabola and the free surface level decreased as the Reynolds number increased. To validate the results, experiments with \(Re\) of the order 1000 and \(Fr\)-1 were also conducted in [46]. It was shown that the experimentally found free surface deformation agreed with the numerical results. Most recently, the base flow was studied numerically in [48], using the experimental conditions of [21]. First, the effects of the \(Fr\) and \(Re\) on the flow field were investigated [48]. It was found that for Froude number in the range of 0.01 to 1 (where the fluid almost touches the rotating disk in the latter case), the flow field far from the cylinder axis is not significantly influenced. Thus, beyond some exploratory computations dealing with perfectly symmetrical free surface vortices, the work in [48] concentrated on flow with flat free surface, thus reducing computational efforts. Accordingly, the attention focuses on meridional circulation, providing further confirmation of the computed boundary layers at \(Re=1500\)[49; 50] and at higher \(Re\) (>10,000) [27]. The discussion presented above suggests that a detailed numerical analysis of rotating polygons should be used to shed new light on this challenging problem. Such simulation, which encompasses the appropriate ranges of the governing parameters, is reported in the present study. This work focuses on conditions where stable states were observed experimentally. The meaning of \"stable\" in this context is that for a given set of conditions, a certain shape is attained, and though it may vary, it always remains within the same polygon type (constant \(N\)).
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Summary of Results  
In this section, we have demonstrated that it is possible to implement all three suggested approaches for group communication (i.e., unicast, multicast and hybrid) on constrained devices. Our experimental results on two testbeds confirm our previous results that were obtained by simulations. These results show that it is essential to have mechanisms in place to avoid congestion in the LLNs. The mechanisms suggested by CoAP are a good starting point, but leave plenty of room for improvements. In general, the multicast-based group communication approach has less network overhead and low reasonable latency, but lacks reliability and security. On the other hand, our unicast-based solution is flexible, reliable and can be easily secured using standard CoAP methods at the cost of increased network overhead and latency. We have shown that our hybrid solution can be used to build customized groups that benefit from the pros of each approach to achieve a better overall group communication solution.
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 3 Methodology
### Ontology Development  
In this study, sea level rise ontology (i.e., OntoSeaLevel) is introduced to provide semantic definitions to sea level projection scenarios and its connecting impact to elements defined by other ontologies, as seen in Figure 1. OntoSeaLevel is developed to closely align with the underlying data of future sea level projection attributes [44]. These attributes include Shared Socioeconomic Pathway (SSP), Representative Concentration Pathway (RCP), confidence level, percentage quantile, sea level change height, and projection year. ontosl:SeaLevelChange describes the central ontological class that represents sea level change. Its associating properties such as projection year and sea level rise height are described by ontosl:hasProjectionYear and ontosl:hasHeight, respectively. The ontosl:ImpactedSite class describes vulnerable sites, the corresponding subclasses of which can be concepts defined in other ontological classes. ontosl:ImpactedSite is connected with ontosl:SeaLevelChange through the ontosl:hasPotentialImpact object property. The subclasses of SSP follow the socioeconomic development narratives in the IPCC guideline [46]. At the same time, the subclasses of RCP follow the IPCC guideline definition [47; 48]. The confidence level follows the IPCC AR5 Uncertainties guideline definition [49]. In OntoSeaLevel, the percentage quantile defines the statistical likeliness of the projection outcome.  
OpenStreetMap ontology [50] is used to instantiate OpenStreetMap road network data that includes attributes such as road type, one-way property, road name, and road length. Land plot ontology (i.e., OntoZoning [51] and OntoPlot [52]) defines the plot area and its designated land use type. Building environment ontology (i.e., OntoBuiltEnv [53]) defines building attributes such as estimated construction cost, property usage, usage share, number of floors, and gross floor area.
## 3 Results and Discussion
### Perspectives on Use  
Barotric pressure can affect the response of different living organisms. Control of pressure variation inside a chamber enables the simulation of several pressure conditions that reproduce real conditions. Most studies correlate behavioral patterns with natural barometric pressure variation, while studies of laboratory simulation with pressure control are scarce [33]. Using a barometric pressure chamber in those correlational studies would bring more reliable information about the barometric pressure effects since in natural conditions other abiotic factors such as temperature, humidity, wind, and photoperiod might also influence the individual response. A barometric pressure chamber might be used in future studies focusing on the association of arthropod behavior with the response of plants to simulated adverse weather, which has not yet been elucidated, and/or on arthropod-plant interaction.  
Only a few studies have manipulated and controlled barometric pressure using a barometric chamber. In [9], a pressure chamber designed by [24] for insect behavioral experiments was used. Studies [16; 23] conducted behavioral experiments inside a controlled pressure chamber at the Advanced Facility for Avian Research (AFAR) at the University of Western Ontario, London, Canada. Study [23] reports that AFAR uses a hypobaric chamber which cannot increase barometric pressure above ambient values. Research work in [10; 17] used custom-made manual pressure chambers. As presented here, the design of an automated controlled barometric pressure chamber allows the maintaining, increasing, and decreasing of the barometric pressure, whatever the ambient pressure and without depressurization. The proposed design of the barometric chamber might extend this line of research since no similar equipment can be found in research facilities working with entomology.
## 5 Results and Discussion
### Fault Prediction Phase
#### 5.1.3 Algorithm Model Selection  
According to the equipment operation parameter data set, an AF data item is selected to optimize the established algorithm prediction model. In this experiment, a total of 12 model methods were cross-verified, and the prediction results were shown in Figure 17, where curves of different colors respectively represented the prediction results of different models.  
Table 6 shows the prediction evaluation indexes of different models. The results show that the Attention-GRU method proposed in this paper can more accurately predict the operating parameters of the machine tool servo drive system. When the operating parameters are abnormal, Attention-MLGRU can predict them in advance. Therefore, Attention-MLGRU is chosen as the prediction model.  
By introducing attention mechanisms and multi-layer GRU structures, the Attention-MLGRU model can dynamically focus on important information at different locations in the input sequence, while better controlling the complexity of the model and reducing the risk of overfitting, which makes the model more generalized and better able to adapt to different data sets and application scenarios.  
Figure 16: MLGRU prediction results with (**E1**) [PERSON] optimizer, (**E2**) SGD optimizer, (**E3**) Adagrad optimizer, (**E4**) RMSprop optimizer, (**E5**) Nadam optimizer. (**E6**) Comparison of training results with different optimizers.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Prediction Evaluation Index**} \\ \cline{2-7}  & **MSE** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **EVS** \\ \hline SLSTM & 0.01598 & 0.12642 & 0.08161 & 1.72489 & 0.99918 & 99.94\% \\ MLSTM & 0.01694 & 0.13014 & 0.05334 & 0.11771 & 0.99913 & 99.92\% \\ Bi-LSTM & 0.00478 & 0.06911 & 0.06299 & 1.49578 & 0.99975 & 99.99\% \\ \hline SLSTM-Attention & 0.01149 & 0.10719 & 0.08338 & 1.71055 & 0.99941 & 99.97\% \\ MLSTM-Attention & 0.00657 & 0.08107 & 0.06687 & 1.35809 & 0.99966 & 99.99\% \\ Bi-LSTM-Attention & 0.01142 & 0.10687 & 0.0634 & 1.50937 & 0.99941 & 99.96\% \\ \hline GRU & 0.00274 & 0.05234 & 0.04463 & 1.02071 & 0.99986 & 99.99\% \\ MLGRU & 0.00135 & 0.03678 & 0.01738 & 0.30526 & 0.99993 & 99.99\% \\ Bi-LGRU & 0.0013 & 0.03599 & 0.01867 & 0.27339 & 0.99993 & 99.99\% \\ \hline GRU-Attention & 0.00519 & 0.07205 & 0.03083 & 0.72028 & 0.99973 & 99.98\% \\ MLGRU-Attention & 0.00127 & 0.0356 & 0.01683 & 0.39899 & 0.99993 & 99.99\% \\ Bi-LGRU-Attention & 0.00247 & 0.0497 & 0.02308 & 0.302 & 0.99987 & 99.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of results of the evaluation indexes of the algorithm prediction.  
Figure 17: **(F1)** Summary of prediction results. Prediction results with **(F2)** SLLSTM, **(F3)** MLLSTM, **(F4)** Bi-LSTM, **(F5)** SLSTM-Attention, **(F6)** MLSTM-Attention, **(F7)** Bi-LSTM-Attention, **(F8)** GRU, **(F9)** MLGRU, **(F10)** Bi-LGRU, **(F11)** GRU-Attention, **(F12)** MLGRU-Attention, **(F13)** Bi-LGRU-Attention.
### The Transformation of Result in the of Zero Height International Standard Atmosphere  
The transformation of the calculated average vertical parachute speed in the specified sector under the conditions of zero height international standard atmosphere (ISA) is performed. It is necessary to check temperature data and air pressure in the measured zone. Then, the air density \(\rho\) is calculated in the measured sector according to the formula:  
\[\rho=\frac{p}{R\cdot T}\left[\mathrm{kg\cdot m}^{-3}\right] \tag{10}\]  
where:  
\(p\)--The average value of air pressure in the measured sector [Pa];  
\(T\)--Average air temperature in the measured sector [K];  
\(R\)--Universal gas constant; \(R=287\) J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\).  
From the calculated air density \(\rho\), we can calculate the relative air density \(\rho_{r}\) according to the formula:  
\[\rho_{r}=\frac{\rho}{\rho_{0 ISA}} \tag{11}\]  
where:  
\(\rho\)--Air density in the measured sector [kg\(\cdot\)m\({}^{-3}\)];  
\(\rho_{0 ISA}=1.2257\) kg\(\cdot\)m\({}^{-3}\)--Air density in the height 0 m according to ISA.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.  
Then, we calculated the average vertical parachute speed \(v_{vertMSA}\) in the sector, which is demarcated by the reference points \(\mathrm{RP_{n}}\) and \(\mathrm{RP_{n+1}}\):  
\[v_{vertISA}=v_{vert}\cdot\sqrt{\rho_{r}}\left[\mathrm{m\cdot s}^{-1}\right] \tag{12}\]