esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
## 6 Conclusions  
Considering the use of automatic welding of T-profile plates by robots in the shipbuilding process, it is necessary to reduce the point cloud of T-profile plates obtained by laser scanners. In this paper, a point cloud simplification method based on octree coding and the threshold of the surface curvature feature is proposed in order to reduce the point cloud of the T-profile steel plate in shipbuilding. Based on the point cloud characteristics of the T-profile plate in this study, octree coding is used for dividing the point cloud into sub-cubes, and then the curvature features are obtained based on the \(k\)-neighborhood search and the curvature calculation. Finally, by combining the two simplification algorithms, the point cloud data of the T-profile plate can be simplified. Through the experiment described in this study, it was found that the proposed simplification method is faster than the other three traditional simplification methods including the bound box method, the method based on regional gravity center, and the curvature sampling method. When the reduction rates are approximately the same, the simplification method proposed in this paper can retain more feature points than the other traditional methods. The proposed method can combine the advantages of both the random sampling simplification method and the regional center of gravity simplification method in different curvature regions, and is able to better preserve the surface characteristics of the T-profile plate under testing.  
The study in this paper may be beneficial for shipyards to develop their automatic welding systems. In the future, further research of the point cloud simplification methods are needed for automatic welding of different plates with other profiles. In addition, more point cloud data sets of shipbuilding plates with different profiles will be needed to verify the proposed method.  
Conceptualization, Y.G., C.P., L.W.; methodology, Y.G., C.P.; software, Y.G., C.P., validation, Y.G., C.P.; formal analysis, Y.G., L.W.; investigation, Y.G.; resources, Y.G., C.P., L.W., and B.W.; data curation, Y.G., and C.P.; writing--original draft Preparation, Y.G. and C.P.; writing--review and editing, Y.G., C.P., L.W. and B.W.; visualization, C.P.; supervision, L.W. and B.W.; project administration, B.W.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Analysis and Test Research Project of Public Welfare Technology Application of Zhejiang Province (No. LGC21F030001), National Key Technologies Research & Development of China (No. 2018 YFB2101004) and the Zhejiang Province Key R&D Program Project (No. 2019C03114).  
Not applicable.  
The authors are grateful to [PERSON] for carefully reviewing this paper.  
The authors declare no conflict of interest.
# Wireless Control Combining Myoelectric Signal and Human Body Communication for Wearable Robots
###### Abstract  
In this study, a communication module based on human body communication was developed to wirelessly control a wearable robot hand based on myoelectric signals. The communication module adopts 10-60 MHz band and an impulse radio multi-pulse position modulation method to achieve low transmission loss and high data rate. A technique to reduce the module size was developed by sharing the myoelectric signal detection electrode and transmitting electrode, and three receiving electrode structures were investigated to improve signal transmission performance. As a result, the developed communication module provides a packet detection rate of 100% and a bit error rate of less than \(10^{-6}\) up to at least 110 cm along the arm, and a wearable robot hand was demonstrated to be properly controlled based on a human subject's myoelectric signals.  
wearable robot; wireless control; human body communication; communication module +
Footnote †: journal:  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 5 Results and Discussion
### Index of Social Transport Needs Covered
Results show that the transport need of less than a quarter of the population are covered by the SITEUR. It is worth noting that 50.3% of the inhabitants have a very high level of STN, while only 6.8% of the population have very low social transport needs. Despite this, SITEUR only contributes 3% of the city's modal split (Figure 2). Furthermore, the SITEUR does not adequately serve more than half of the population. Notwithstanding that mass transport is scarce in the city, this population ratio is striking, given the high construction and operation costs of SITEUR.
# Blistering Mechanism Analysis of Hydraulic Asphalt Concrete Facing
## 1 Introduction  
Asphalt concrete facing is widely used as the water proofing lining for reservoirs [1; 2]. However, various defects may occur in the project for various reasons [3] including cracking, slope flowing, blistering etc., which may led to the failure of the impervious layer. Blistering is a common defect in asphalt concrete facing and is the result of the facing being under internal pressure. Blistering can damage the integrity of the asphalt concrete facing, and in severe cases, the surface may be cracked and water leakage may occur. Due to the different internal pressure causes of blistering, the corresponding engineering treatment measures also differ. Therefore, once blistering of the asphalt concrete facing is found in a project, the cause should be first identified to provide a basis for treatment.  
The upper reservoir of Zhanghewan Pumped-Storage Power Station is totally lined with asphalt concrete facing slab; it was constructed in 2006 and began operation in December of 2007. The asphalt concrete facing has a slope of 1:1.75. The elevation of the upper reservoir is 812 m, the normal water level is 810 m, and the dead water level is 779 m. The asphalt concrete facing has a complex structure, and a cross section is as shown in Figure 1. From top to bottom there is a 2 mm thick asphalt mastic sealing, followed by a 10 cm thick impervious layer, an 8-10 cm thick drainage layer (void content of 16-20%), and an 8 cm thick impervious leveling layer (permeability coefficient \(5\times 10^{-5}\) cm/s, voidcontent 5%). Each layer was paved at one time. The mix ratio of the impervious layer and the drainage layer are as shown in Table 1. The aggregate is pure limestone and the bitumen is B90. Since September 2009, blisters have been found on the asphalt concrete facing. The diameter of the blisters is generally from 10 to 40 cm. From 2009 to 2014, a total of more than 500 blisters were treated. At first, it was conjectured that the blistering was due to choking of the drainage layer at the local area, with the water in the drainage layer forming steam pressure at high temperature and causing the blister. Accordingly, in November of 2014, exhaust pipes 76 mm in diameter were inserted from the drilling holes into the drainage layer on the top level of the facing to reduce the steam pressure. Unexpectedly, new blisters still kept appearing after the installation of the exhaust pipes, indicating the ineffectiveness of these pipes.  
In order to manage further potential blisters in the operation of the reservoir, the present blisters were investigated and cored on-site, and models were experimented with in the laboratory and studied, as described in this paper, to clarify the cause of the blisters. This information could then also be referenced by other projects.
## Appendix B Special Case  
Expansion of (60) results in  
\[\begin{array}{rl}\mathbb{H}_{4}&=\hbar\beta\bigg{[}\frac{1}{2\gamma}\Big{(} \dot{b}^{4}-\dot{b}\Big{)}^{2}\Big{(}\dot{a}^{2}+\dot{a}^{2}\Big{)}+\Big{(}1+ \frac{1}{2\gamma}\Big{)}\Big{(}\dot{m}+\frac{1}{2}\Big{)}\Big{(}\dot{n}+\frac {1}{2}\Big{)}+\Big{(}\dot{b}^{4}+\dot{b}^{2}\Big{)}\Big{(}\dot{n}+\frac{1}{2} \Big{)}\bigg{]}\\ &=\hbar\beta\bigg{[}\frac{1}{2\gamma}\Big{(}\dot{b}^{4}+\dot{b}^{2}\Big{)} \Big{(}\dot{a}^{2}+\dot{a}^{2}\Big{)}+\Big{(}1+\frac{1}{2\gamma}\Big{)}\Big{(} \dot{m}+\frac{1}{2}\Big{)}\Big{(}\dot{n}+\frac{1}{2}\Big{)}+\Big{(}\dot{b}^{4} +\dot{b}^{2}\Big{)}\Big{(}\dot{n}+\frac{1}{2}\Big{)}-\frac{1}{2\gamma}\Big{(} \dot{m}+\frac{1}{2}\Big{)}\Big{(}\dot{a}^{2}+\dot{a}^{2}\Big{)}\Big{]},\end{array} \tag{15}\]where \(\hat{n}=\hat{a}^{\dagger}\hat{a}\) and \(\hat{m}=\hat{b}^{\dagger}\hat{b}\) are respectively photon and photon number operators. Retaining only the interacting terms, gives the expression  
\[\mathbb{H}_{4,int}=\ \hbar\beta\bigg{[}\frac{1}{2\eta}\left(\hat{b}^{\dagger 2}+ \hat{b}^{2}\right)\left(\hat{a}^{\dagger 2}+\hat{a}^{2}\right)+\left(1+\frac{1}{2 \eta}\right)\hat{m}\hat{n}+\left(\hat{b}^{\dagger 2}+\hat{b}^{2}\right)\hat{n}- \frac{1}{\eta}\hat{m}\left(\hat{a}^{\dagger 2}+\hat{a}^{2}\right)\bigg{]}. \tag{100}\]  
In the limit \(\omega\gg\Omega\) with \(\eta\rightarrow\infty\), \(\mathbb{H}_{4}\) as in (53) is recovered. With linearization of the electromagnetic field operators, and removing the non-interacting terms the following is found  
\[\mathbb{H}_{4,int}=2\hbar\beta\big{[}\frac{1}{2\eta}\left(\hat{b}^{\dagger 2}+\hat{b}^{2}\right)(\overline{a}^{ \dagger}\hat{a}^{\dagger}+\overline{a}\hat{a})+\left(1+\frac{1}{2\eta}\right) \hat{m}(\overline{a}^{\dagger}+\overline{a}^{\dagger}\hat{a})+\left(\hat{b}^{ \dagger 2}+\hat{b}^{2}\right)(\overline{a}\overline{a}^{\dagger}+\overline{a} \hat{a})-\frac{1}{\eta}\hat{m}(\overline{a}^{\dagger}\overline{a}^{\dagger}+ \overline{a}\hat{a})\big{]}, \tag{101}\]  
By continuing the work on the linearized quadratic interaction one obtains the expression  
\[\begin{array}{rl}\mathbb{H}_{4,int}&=2\hbar\beta|\overline{a}|\Big{[}\frac{1 }{2\eta}\left(\hat{b}^{\dagger 2}+\hat{b}^{2}\right)\left(e^{-i\varphi}\hat{a}^{ \dagger}+e^{-i\varphi}\hat{a}\right)+\left(1+\frac{1}{2\eta}\right)\hat{m} \left(e^{-i\varphi}\hat{a}^{\dagger}+e^{-i\varphi}\hat{a}\right) \tag{102}\]  
which for \(\varphi=0\) simplifies to  
\[\mathbb{H}_{4,int}=2\hbar\beta|\overline{a}|\bigg{[}\left(1+\frac{1}{2\eta} \right)\left(\hat{b}^{\dagger 2}+\hat{b}^{2}\right)+\left(1-\frac{1}{2\eta} \right)\hat{m}\bigg{]}\times\left(\hat{a}^{\dagger}+\hat{a}\right). \tag{103}\]
# Design of a Digital Array Signal Processing System with Full Array Element
## 3 Implementation and Verification of the DBF Algorithm
### Hardware Implementation of the MIMO Phased Array DBF Algorithm
Turning the angle of the receiving antenna turntable in Figure 19 to point to different angles shows the received multibeam spectrum in Figure 20. The corresponding transmit beam spectrum obtained when the turntable is turned to \(-45^{\circ}\) is shown in Figure 20a. The energy of the carrier frequency of 8.1 GHz, which corresponds to \(-45^{\circ}\), is the largest. Similarly, Figure 20b shows that when the receiving antenna turntable is turned to \(15^{\circ}\), the energy of the carrier frequency of 8.3 GHz corresponding to \(15^{\circ}\) is the maximum. Therefore, the beam can be pointed in the correct direction, consistent with the simulation results, and realize the MIMO phased array DBF algorithm.
## 2 Materials and Methods
### Normal Estimation  
For an industrial product, one 3D bounding box (BBX) with six surfaces is available from its CAD model. The method starts with matching the supporting plane and its normal direction from the six surfaces of the BBX. In practical condition, the supporting plane of the product is determined on one surface of the bounding box but is invisible in the camera captured image \(\mathcal{I}_{c}\). Therefore, the supporting plane and its normal is searched using features extracted from the visible part of the object. Given the  
Figure 4: The outline of the offset matrix calibration method.  
marker pose \(T_{M}\) calculated from \(\mathcal{I}_{c}\), the CAD model is projected six times in a virtual environment using \(T_{M}\) as the view matrix. In each time of projection, the LCS is transformed to the center of one BBX surface to align the surface with the marker. The projected images are denoted as the template images \(\{\mathcal{I}_{t1},\mathcal{I}_{t2},\cdots,\mathcal{I}_{t6}\}\), which contains one true solution of the normal direction. One example of the projection results is shown in Figure 5.  
In the projected template image, four corner points on the top surface of the BBX are recorded to construct the region of interest (ROI) area. After the projection, all six template images are cropped into their projected BBX size to form the new template images \(\{\mathcal{I}_{R1},\mathcal{I}_{R2},\cdots,\mathcal{I}_{R6}\}\) that only contain information about the target object. Then, dominant orientation templates (DOT) [24] of strong image gradient is extracted from \(\mathcal{I}_{c}\) and \(\{\mathcal{I}_{R1},\mathcal{I}_{R2},\cdots,\mathcal{I}_{R6}\}\) to perform the template matching. The template matching slides the template image on \(\mathcal{I}_{c}\) to find the position with maximum similarity. The similarity score is computed using the following equation:  
\[\varepsilon(\mathcal{I}_{c},\mathcal{I}_{R},c)=\sum_{r\in P}\big{|}\cos(\text{ ori}(\mathcal{I}_{R},r)\text{-ori}(\mathcal{I}_{c},c+r))\big{|}, \tag{4}\]  
where \(\text{ori}(\mathcal{I}_{R},r)\) is the gradient at the pixel coordinate \(r\) on \(\mathcal{I}_{R}\) and \(c\) is the location of the center of \(\mathcal{I}_{R}\) on the target image \(\mathcal{I}_{c}\). The template image that has the highest similarity score is recognized to have the most approximate normal direction, and the corresponding BBX transformation matrix respective to the MCS(MDCS) is recorded as T\({}_{o1}\). The matching position of the bounding box projection on \(\mathcal{I}_{c}\) is recorded as \(C_{t}=(x_{t},y_{t})\).
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 4 Experiments
### Performance on Common Datasets
#### 4.4.1 The ShanghaiTech Dataset  
The ShanghaiTech crowd counting dataset is a diverse and crowded dataset introduced by Reference [16]. It has two parts, Part A and Part B. Part A is collected from the Internet. We can see from the Table 1 that it is mostly congested while Part B is taken from busy streets in Shanghai and is comparatively less dense with crowd counts. For Part A, 300 images are used for training and the remaining 182 images for testing. Similarly, 400 images of Part B are for training and 316 for testing.  
Table 2 reports the performance of our model along with other methods. It can be seen that our method got good estimations in the Part A dataset, nearly close to the result of CSRNet [11]. Furthermore, in the Part B dataset our method estimates better than other existing methods. It reflects that our model works well in extremely dense crowd scenes. Additionally, it may reflect that more experiments are required on this large dataset to improve the accuracy.
## 3 The Proposed Method
### Pose Estimation
#### 3.2.2 Pose Estimation for a Single Human  
Based on human detection, a single human body in the video can be cropped out according to the coordinates of the bounding box \((x,y,w,h)\). Here we leverage the stacked hourglass model (SHM) [36] for single human pose estimation. The SHM uses a full CNN to output precise pixel locations of keypoints of the human body in cropped images, and multi-scale features are employed to capture spatial position information of various joint points of the human body. The single hourglass network structure is shaped like an hourglass, and the top-down to bottom-up is repeated to infer the position of the joint point of the human body. Each top-down to bottom-up structure is a stacked hourglass  
Figure 2: The entire process of human pose estimation. Convolutional neural network (CNN).  
module. As the SHM only involves convolution calculations, it essentially only needs to complete the multiplication of the matrix, which also ensures the speed of the pose estimation.
## 2 Materials and Methods
### Performance Test for RTV-SWCNTs Composite
#### 2.3.1 RTV-SWCNTs Composite Tensile Test  
The RTV-SWCNTs composites are subjected to tensile experiments to measure their elastic modulus, shear modulus, and Poisson's ratio. The tensile test machine used in this experiment is the Inspekt Table 100 tensile testing machine produced by Hegewald & Peschke, Germany, as shown in Figure 3. The tensile properties of the RTV-SWCNTs composites are tested at room temperature.  
The mechanical properties of the RTV-SWCNTs composites with CNT contents of 0 vol%, 0.25 vol%, 0.5 vol%, 1 vol%, 2 vol%, and 4 vol% are tested. Three tensile specimens are prepared for each component group to reduce errors caused by measurement or other factors.  
The stress-strain relationship of the RTV-SWCNTs composite material is non-linear. Because the elastic layer of the BHP has small deformation, the Mooney-Rivlin (M-R) constitutive model is used to describe the constitutive model of RTV-SWCNTs composites [20; 21; 22; 23; 24; 25].  
\[\sigma_{\theta}=2C_{01}\left(3\varepsilon_{\theta}-6\varepsilon_{\theta}^{2} \right)+2C_{10}\left(3\varepsilon_{\theta}-3\varepsilon_{\theta}^{2}\right) \tag{1}\]  
where \(C_{10}\) and \(C_{01}\) are temperature-dependent material parameters. Then Equation (1) is simplified as:  
\[\frac{\sigma_{\theta}}{6\left(\varepsilon_{\theta}-\varepsilon_{\theta}^{2} \right)}=C_{01}\left(1-\frac{\varepsilon_{\theta}^{2}}{\varepsilon_{\theta}- \varepsilon_{\theta}^{2}}\right)+C_{10} \tag{2}\]  
Among them, \(\sigma_{\theta}\) is the stress of composite, and \(\varepsilon_{\theta}\) is the strain of the composite. If, \(x=1-\varepsilon_{\theta}^{2}(\varepsilon_{\theta}-\varepsilon_{\theta}^{2})\), \(y=\sigma_{\theta}/6\left(\varepsilon_{\theta}-\varepsilon_{\theta}^{2}\right)\) Equation (2) can be abbreviated to,  
\[y=C_{01}x+C_{10} \tag{3}\]  
Figure 3: The tensile test bench and data acquisition program.  
The tensile test data is substituted into Equation (2), and after linear fitting, the composite parameters \(C_{10}\) and \(C_{01}\) are obtained.  
When the elastic material RTV is subject to small and medium deformation, the relationship between the elastic modulus \(E\) and the shear model \(G\) is as follows [26],  
\[G=E/2(1+\mu) \tag{4}\]  
Due to the incompressibility of RTV, [PERSON]'s ratio \(\mu\) = 0.5 can be substituted into Equation (4) to obtain \(E\) = 3\(G\). Among them, the relationship between the shear modulus \(G\) and the parameters \(C_{10}\) and \(C_{01}\) is,  
\[G=2(C_{10}+C_{01}) \tag{5}\]  
So, \(E=6(C_{10}+C_{01})\)
## 2 System Model and Problem Description
### Time-Evolving Graph with Limited Capacity  
Since all inter-layer links except PGLs show the characteristic of intermittent connection, the network topology is time-varying. In view of this, a time-evolving graph is introduced to characterize the time-evolving nature of the network and illustrate an example of CP.  
As shown in Figure 2, the network topology over time is characterized by TEG. Where \(g_{n},v_{n},p_{n},s_{n}\) represent Earth stations, UAVs, HAPs, and satellites, respectively. The subscript of the node indicates the node identifier, and the superscript is the state number of the node. For example, \(s_{2}^{1}\) indicates the satellite 2 at the state 1. \(C_{1},C_{2},\cdots,C_{n}\) represent the state of the network, \(CT_{1},CT_{2},\cdots,CT_{n}\) are the corresponding contact time of each state.  
In the contact topology of Figure 2, the change of network topology in TEG is clarified from two perspectives. In the perspective of time, the network topology updates when the connection status between nodes changes. In the other perspective of space, the intra-layer links of HAPs are stable due to the stable position while the intra-layer links of the satellite layer are intermittent connected due to the movement of satellites. Considering the limitation of the number of transponders and the energy budget, only one contact can be established by spatial nodes at the same time, which is shown in Equation (1):  
\[\sum_{n=1}^{N_{s}+N_{u}+N_{v}}Y_{c,n,m}=\{0,1\}\ \ \forall c,\ m=s,h,v \tag{1}\]  
Figure 2: A contact topology and a contact plan in TEG manner.  
where \(s,h,v\) indicate the satellites, HAPs, and UAVs respectively, and \(N_{s},N_{h},N_{v}\) represent the number of satellites, HAPs and UAVs, respectively. \(Y_{c,n,m}\) represents the number of links that can be established between nodes \(n\) and \(m\) at the state \(c\).  
To avoid wasting link resources, the MLSCN prohibits data from being transferred back to the lower space nodes from higher space nodes, which is shown as follows:  
\[X_{c,n,m}^{y,z}=0\ \ \forall y,\ z,\ (n=s,m=h),\ (n=h,m=v) \tag{2}\]  
where \(X_{c,n,m}^{y,z}\) represents the flow from node \(y\) to \(z\), and traversing from node \(n\) to node \(m\) at state \(c\). The changes of the node load in the network must satisfy the flow balance as shown in Equation (3):  
\[B_{c,n}^{y,z}-B_{c-1,n}^{y,z}=\sum_{m=1}^{N_{s}+N_{h}}X_{c,m,n}^{y,z}-\sum_{m= 1}^{N_{u}+N_{h}}X_{c,n,m}^{y,z} \tag{3}\]  
where \(B_{c,n}^{y,z}\) represents the number of packets from node \(y\) to \(z\) which is temporarily stored in node \(n\) at state \(c\).  
Assuming that the bandwidth of each contact is constant and equal, then the contact time of each state can also be called the contact capacity [17]. Since the contact capacity of each state is limited, the amount of each flow delivered at state \(c\) should be less than the contact capacity \(CT_{c}\), which is shown in Equation (4):  
\[X_{c,n,m}^{y,z}\leq CT_{c} \tag{4}\]
## 6 Simulation and Parameters  
### Transmitter and Receiver Parameters  
The transmitter parameters were designed to be arbitrary inputs for which the UOWC performance was simulated. The model determines the O-SNR outcome of a link at a given power and beam divergence angle (for D-LOS and P2P-LOS links) for all wavelengthsin the 400-700 nm spectra (2.5 nm bandwidth resolution) for any correlations between the parameters and noise influences, while also varying the depths and link distances as detailed in Section 6.2. Section 7 discusses the UOWC performance for selected parameters of beam divergence angles of 120\({}^{\circ}\), 60\({}^{\circ}\) (D-LOS), and 5\({}^{\circ}\) (P2P-LOS). The transmission powers were set to 1 W and 10 W, as made possible by off-the-shelf emitters.  
The receiver photodiode was a blue-green wavelength enhanced First Sensor PIN-PD (Model: PS13-6b TO PD) with hardware parameters as given in Table 3[61]. The spectral responsivity curves were extracted from the datasheet. This device was selected due to the high 3 dB frequency bandwidth of approximately 5 MHz, large active area, and broad spectral responsivity. Given that large active regions correlate to lower response times, this device seemed to provide a good trade-off between the received power while maintaining a frequency response that could still accommodate for multimedia transmissions.