esa-sceva/satcom-chunk-collection huggingface dataset:
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 6 Conclusions  
In this paper, we introduced EDC-Net, a novel framework for edge detection based on capsule network structures. We designed a point-wise segmentation pipeline to classify the edge and non-edge points. We formulated a loss function specifically tailored to extract edge points in highly unbalanced point clouds where the number of non-edge points is often much larger than the number of edge points. We proved that our proposed loss function significantly boosts the training process in terms of time and accuracy. Moreover, we built a weakly-supervised transfer learning structure to set up a flexible and robust pipeline applicable to unlabeled datasets.  
We provided extensive experimental results on ABC and ShapeNet datasets. We demonstrated that our proposed EDC-Net performs accurately on edge detection tasks, and we furthermore proved its robustness on the noisy data. Moreover, we proved that our proposed weakly-supervised transfer learning improved the performance on the ShapeNet dataset, for which there are no available edge labels. Experimental results demonstrated that our proposed EDC-Net performs efficiently in terms of the execution time while maintaining high accuracy. Moreover, EDC-Net provides a compressed model with a small model size, which is important for a better deployment to fit high-performing models into devices with constrained resources.  
Our contribution does not focus merely on surpassing the edge detection state-of-the-art techniques, but we also introduced a novel learning approach for edge detection tasks based on capsule network structures. Furthermore, we proposed a new approach that tackles the challenge of a lack of annotated data for training models in this community.  
Methodology, D.B.; Writing--review and editing, D.B. and M.E.P. All authors have read and agreed to the published version of the manuscript.  
This research was done in the framework of \"Geodesia i Navegacio (GEON)\" project (reference: 2017 SGR 820) by AGAUR, Generalitat de Catalunya, through the Emerging Research Group.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Modeling the VLC Networks: Architecture, System Model and Scenarios
### Single-Carrier Modulations  
Single-carrier modulations encode the information in either the position, width or amplitude of the pulses. The modulation scheme referred to as on-off keying (OOK) is one of the simplest and most used, which makes switch input current, and therefore the optical power of the LED, between high and low levels much faster than the human eye can detect [16]. Although it is a very simple modulation, yet is energy- and spectrum-inefficient, it is usually implemented in low cost and medium data-rate applications. There are other simple single-carrier schemes, such as pulse width modulation (PWM), where the information is encoded in the duration of the pulse [60]. Managing the position of the pulse, multi-level pulse position modulation (M-PPM) is proposed in [61]. On the other hand, assuming a constant duration of the pulse while encoding the information in the amplitude of the pulse leads to multi-level pulse amplitude modulation (M-PAM) [62]. In Figure 5, an example of the 4-PAM, OOK, PWM and 4-PPM transmission schemes is de  
Figure 5: Dynamic range of the LED optical transmitter. Single carrier modulations: 4-PAM, OOK, PWM and 4-PPM. \(T_{S}\) denotes the symbol period.  
picted considering the dynamic range of the optical transmitter. Each of these single-carrier modulation schemes is subject to a trade-off among distortion, flickering effects, linear dynamic range of the LEDs, transmission bandwidth or bit error rate (BER). In [63], the application of single-carrier modulation schemes in VLC systems is analyzed considering these issues.
## 3 Results
### Accuracy Test
#### 3.1.1 Standard Cube Measurement  
In the experiment, a standard cube with black and white grid is designed. The size of the cube is 200 mm \(\times\) 200 mm \(\times\) 200 mm, the mesh of which is 20 mm \(\times\) 20 mm, and the uncertainty is 0.01 mm. Six positions of the cube are chosen randomly, where 13 feature distances on the laser line are selected, as shown in Figure 9. The standard values are measured by the verifier caliper (0.02 mm), and the result is shown in Table 1, where \((d_{x}d_{y}d_{z})\) and \((d^{\prime}x,d^{\prime}y,d^{\prime}z)\) represent the values in \(X\), \(Y\) and \(Z\) directions of the standard value \(d\) and measurement value \(d^{\prime}\) respectively in global coordinates.  
The RMS error of the feature distance is 0.125, and (0.049, 0.147, 0.113) in \(X\), \(Y\) and \(Z\) directions. The mean error of the feature distance is 0.046 mm, and the maximum errors in \(X\), \(Y\) and \(Z\) directions are 0.099 mm, 0.409 mm and 0.313 mm, respectively.  
Figure 9: Standard cube measurement; (**a**) 13 feature distances selected on the standard cube; (**b**) 13 feature distances on the 3D point cloud.
The industry has mastered the production of fiber-optic sensors for monitoring different physical quantities. These sensors have some advantages over electrical ones. Existing monitoring systems have a rather high cost per measurement point and a limitation in the distance from the data processing unit to the sensor. The use of OF will allow for an increase in this distance up to 30 km, while the power consumption for data transmission is only a few milliwatts. The fundamental difference between the idea and the existing analogs in the world is in the development of a hardware-software complex able to work with single-mode fiber optic fiber and analyze the data obtained by changing the pixels of the light spot falling on the surface of the TV matrix. It should be noted that the fiber-optic system for monitoring the technical condition of buildings, structures, and any extended objects has high noise immunity and low power loss in the measuring channels. At the same time, the length of measurement channels can be tens of kilometers. Thus, the purpose of this research work is to conduct experimental studies of intelligent fiber-optic systems for monitoring damage to reinforced concrete foundation structures with the development of conceptual foundations for the physical and mathematical models of processes in optical fibers.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
In Figure 3, one can observe the variables of greatest influence and relationships between them by way of the graph of interaction between the 17 chemical elements, organic matter (total organic carbon--TOC), and pH, categorized as per the different seasons of the year--summer (gray), spring (yellow), autumn (blue), and winter (red).  
To verify if there was a significant difference in the chemical elements' concentration between different tropical climatic conditions, we submitted the data with categories of summer and spring (hot and rainy) and winter and autumn (cold and dry), similar to the study of [80]. Therefore, we observed that chemical elements, as well as organic matter content and pH, were not sensitive to the tropical climate conditions and did not significantly vary in their concentration. Fourteen chemical elements and pH are observed to cluster; the exception is K, P, and S (Figure 3).  
Furthermore, we observed (Figure 3) that the contribution of variables of weight higher than 7.5%, that is, with the greatest statistical contribution, were the Cu and Ba metals, as well as the Ca macronutrient. Ref. [34], by analyzing diverse samples of biosolids from WWTPs in the state of Sao Paulo (Brazil), also found the Cu, Ba, and Ca elements as high-contribution variables through the PCA method. With a contribution from 3% to 4%, we have the As metal and the N macronutrient, which met with a low percentage of the need for the related crops. The K, P, and S parameters also had a low contribution, lower than 2.5%. With median contribution values between 5% and 7.5% the Se, Mg Zn, Ni, Cr, Pb, Hg, Cd, and Mo elements, pH, and total organic carbon (organic matter); furthermore,elements, TOC, and pH clustered. The same substances were significant in the study of [81], along with Cd, Zn, Ca, Mg, Ni, and TOC, and substances and TOC also clustered with pH. According to [82], pH is a way of controlling the concentrations of metals in biosolids.  
At last, we can document with this PCA analysis that the concentrations of chemical elements in biosolids are not sensitive to tropical climatic variations, as well as intense weathering. Yet the pH is the key element and should be used as a basic parameter for environmental control.  
In summary, besides the sludge potential for energy and renewable-gas production [83] and other processes such as: incineration, gasification, anaerobic digestion, and nutrient recovery by using the struvite production pathway [84], our results show the possibility of using biosolids in a safe and simplified way. In-depth research on water-energy-food, focusing on biosolids, by business models, will improve rural-urban water and sanitation planning and decision making with a focus on the circular economy and support the interest in wastewater management and pave the way for future feasibility studies [84; 85].
## Appendix A Review on the Conversions between the Integers and the Corresponding Binary Sequences
Hence, the incremented integer is \(1+\xi^{T}\Big{(}\frac{\mathbf{x}(n)+1}{2}\Big{)}\) and the corresponding incremented binary sequence is \(\mathfrak{H}\Big{(}1+\xi^{T}\Big{(}\frac{\mathbf{x}(n)+1}{2}\Big{)}\Big{)}\). As a result, we can define a nonlinear discrete time dynamical system as follows:\(\mathbf{x}(n+1)=\mathfrak{H}\Big{(}1+\xi^{T}\Big{(}\frac{\mathbf{x}(n)+1}{2} \Big{)}\Big{)}\) and \(y(n)=\psi^{T}\mathbf{x}(n)\) for \(n\geq 0\). Here, \(\mathbf{x}(n)\) and \(y(n)\) are the state vector and the output of the nonlinear discrete time dynamical system, respectively, where \(\mathbf{x}(n)\) is the binary sequence and the \(k\)th element of \(\mathbf{x}(n)\) is \(c_{k}\). Moreover, \(y(n)\) is the correspondingcomplex number. However, it is worth noting that \(Y\) is not the whole complex plane. That means, some complex numbers cannot be represented by the binary combinations of the elements in \(\Gamma\). Since \(\mathfrak{I}\) involves the modulo operator, the nonlinear discrete time dynamical system may exhibit both the fractal phenomenon and the chaotic phenomenon [22]. Figure 11 shows some computer numerical simulation results of \(Y\) with \(\angle\lambda=\frac{\pi}{10}\) for \(|\lambda|=1.1\), \(|\lambda|=1.3\), \(|\lambda|=1.5\) and \(|\lambda|=1.9\). On the other hand, Figure 11 shows some computer numerical simulation results of \(Y\) with \(|\lambda|=1.5\) for \(\angle\lambda=0\), \(\angle\lambda=\frac{\pi}{4}\), \(\angle\lambda=\frac{\pi}{2}\) and \(\angle\lambda=\pi\). It can be seen from Figure 11 that the size of \(Y\) becomes smaller as \(|\lambda|\) increases. This is because the distance between the complex numbers \(\lambda^{-k}\) and \(-\lambda^{-k}\) decreases as \(|\lambda|\) increases. Besides, it is worth noting that \(Y\) does not contain a neighborhood around the origin as \(|\lambda|\) increases. In addition, if \(\angle\lambda\) is an integer multiple of \(\pi\), then \(\lambda\) is a real number. In this case, \(Y\) can only contain the real numbers in the range \(\left(-\frac{\lambda}{\lambda-1},\frac{\lambda}{\lambda-1}\right)\). If \(\lambda\) is a real number with \(|\lambda|\geq 2\), then \(Y\) does not contain all real numbers in the range \(\left(-\frac{\lambda-2}{\lambda-1},\frac{\lambda-2}{\lambda-1}\right)\). This investigation will be applied to derive a stability condition for the sigma delta modulator. Moreover, it will be used for formulating a frequency-domain-based bit flipping control strategy for stabilizing the sigma delta modulator. **Figure A2. **_Cont.
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 4 Simulation Results  
The proposed WPD layout, which operates at 2 GHz, is simulated with the assistance of ADS software using the Rogers/5880 substrate. The simulated S-parameters of the designed WPD are illustrated in Figure 9a,b.  
As seen from the simulated results, at this operating frequency, the isolation and insertion losses are approximately 30 dB and 0.1 dB, respectively. Moreover, the input and output return losses are approximately 28 dB and 44 dB, respectively. In addition, the third and second harmonics are suppressed with a high attenuation level.  
Moreover, the equivalent lumped-elements circuit of the proposed WPD and its simulation results are depicted in Figure 10a and b, respectively. As can be seen, two transmission zeros near 4 GHz and 6 GHz are created to suppress the second and third harmonics of the proposed WPD.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Param.** & **Value (mm)** & **Param.** & **Value (mm)** & **Param.** & **Value (mm)** \\ \hline L\({}_{1}\) & 6.8 & L\({}_{5}\) & \(11\) & L\({}_{9}\) & 12.5 \\ L\({}_{2}\) & 3.5 & L\({}_{6}\) & 8.5 & L\({}_{10}\) & 8.7 \\ L\({}_{3}\) & 23.6 & L\({}_{7}\) & 5.8 & W\({}_{1}\) & 2.1 \\ L\({}_{4}\) & 9.2 & L\({}_{8}\) & 3.4 & W\({}_{2}\)/W\({}_{3}\) & 1.3/2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The applied transmission line values in the proposed power divider.  
Figure 9: Simulated S-parameters of the designed WPD. (**a**) \(|\)S\({}_{12}\)\(|\) and \(|\)S\({}_{11}\)\(|\); (**b**) \(|\)S\({}_{22}\)\(|\) and \(|\)S\({}_{32}\)\(|\).  
The surface current distribution in the proposed divider is depicted in Figure 11. As seen in Figure 11a, the current is distributed uniformly at the output ports in the main frequency. Furthermore, Figure 11b,c show that the current has not reached the output at the second and third harmonics, which proves the harmonic suppression ability of the proposed WPD.  
Figure 11: _Cont._  
Figure 10: (**a**) The equivalent lumped-elements circuit of the proposed WPD and (**b**) its simulation results.
## 3 XNAV for Spacecraft in Formation
### Procedure of Pulsar-Based Navigation Using Pulse Phase Delay
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## 2 Design of the 3.7 kW WCS
### Design of Resonance Coupling Coils
#### 2.2.2 Design of Compensation Topology  
The compensation network adopts a bilateral \(LCC\) topology (Figure 4). A satisfying feature of \(LCC\) is that it can behave as a constant current source over a large load range when working at or close to the resonant state. It can reduce the difficulty of controlling when the battery is charging.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coil Attribute Name** & **Value/Type** & **Ferrites Attribute Name** & **Value/Type** \\ \hline Coil geometry & Planar spiral & Ferrite material & Ferroxcube 3C94 \\ Outer diameter & 600 mm & Long ferrite size & 240 mm \(\times\) 15 mm \(\times\) 9 mm \\ Inner diameter & 300 mm & Short ferrite size & 180 mm \(\times\) 15 mm \(\times\) 9 mm \\ Number of turns & 16 & Long ferrite quantity & 18 \\ Conductor diameter & 3.9 mm & Short ferrite quantity & 18 \\ Litz conductor & II(16 AWG4\(\times\)5X24/36) & Adjacent ferrite angle & \(10^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of coils and ferrites.  
Figure 3: The coupling coils: (**a**) 3D model of coupling coils; (**b**) parameter measurement of coils.  
The transmit power \(P\) of system can be expressed as follows:  
\[P=\dot{U}_{AB}\cdot\dot{I}_{Lf1}=\frac{U_{AB}U_{ab}M}{2\pi fL_{f1}L_{f2}}, \tag{6}\]  
where \(U_{AB}\) is the input voltage and \(U_{ab}\) is the output voltage of compensation topology, and \(f\) is stands for resonant frequency. \(LCC\) circuits on both sides will magnetically resonate at \(f\) which is generally 85 kHz. The compensation inductor \(L_{f1}\) and two capacitors \(C_{f1}\)\(C_{1}\) are calculated by Equations (7)-(9). Parameters of the compensation topology are equivalent, and the values are shown in Table 4[23]:  
\[L_{f1}=L_{f2}=\sqrt{\frac{kU_{AB}U_{ab}L_{1}}{\omega_{0}P}}, \tag{7}\]  
\[C_{f1}=C_{f2}=\frac{1}{\omega_{0}{}^{2}L_{f1}}, \tag{8}\]  
\[C_{1}=C_{2}=\frac{1}{\omega_{0}{}^{2}\Big{(}L_{1}-L_{f1}\Big{)}}. \tag{9}\]
## 3 Analysis of Jet Flow Field in Pipe
### Flow Field Analysis of 40\({}^{\circ}\) Tilt Angle Rear Nozzle  
In Figures 9 and 10, the flow field analysis outcomes for the rear nozzle configured with an axis angle of 40\({}^{\circ}\) and apertures measuring 3.5 mm and 2 mm, respectively, have been captured along the \(x\)-\(y\) plane for in-depth vertical plane analysis.  
Upon observation of Figure 9, it becomes evident that Region I signifies the rear nozzle generating a more expansive vortex area upon impacting the wall. Despite making contact with the pipe wall over a shorter stroke, this configuration enhances the local wall cleaning capability within a smaller attenuation range. However, the diffusion effect becomes more significant when the jet comes into contact with the pipe wall, causing the dispersed jet to collide with the jet emanating from the front nozzle along the pipe wall. This collision results in a larger vortex area, as depicted in Region III. The enlargement of the vortex area in Region III encroaches on the space where the jet from the front nozzle converges with the high-velocity jet emitted by the rear nozzle, thereby intensifying the backflow problem in Region II and impeding effective drainage. Furthermore, the accumulation of water and debris in Region II, resulting from the obstruction of prompt flow into the annulus outflow pipeline, can hinder the front nozzle's ability to effectively remove blockages, thereby compromising overall efficiency. This scenario is detrimental to effective cleaning  
Figure 9: Aperture 3.5 mm.  
and waste removal, as increasing the angle of the rear nozzle would further diminish the self-feeding efficiency. Figure 10 vividly demonstrates how an increase in the inclination angle of the rear nozzle exacerbates this issue. The size of the vortex area in Region III, as evidenced by Figure 10 with a 2 mm aperture, expands significantly. This finding underscores that the vortex area in Region III is not solely influenced by the angle of inclination of the rear nozzle, but is also significantly dependent on the nozzle diameter and flow rate.
## 4 Platform Development of Key Technologies
### O&M Service Path Optimization Analysis Technology  
As shown in Figure 6, based on the analysis of information from multiple sources, such as fleet demand, network distribution, personnel, and spare parts reserves, and in order to determine the number and type of service orders, technologies for scheduling resources such as service outlets, spare parts, personnel, and mobile service vehicles in the regional environment were studied. Operation and maintenance service work requires the maximization of time efficiency, so it is crucial that the scheduling of maintenance services be performed in the shortest possible time. Depending on the reports and determination of service information obtained from the agricultural machine operator, information is  
Figure 4: Functional architecture of the inter-regional operation and maintenance service O&M platform.  
sent to the operation and maintenance service platform, and an optimized maintenance service order will then be sent to maintenance personnel. This allows the optimal service path to be provided to the maintenance personnel, and the function takes into account the distance between the fault demand point and the service station and the maintenance service vehicle, so that emergency relief materials can be transported to the destination in the shortest possible time and at the lowest cost.
## 2 Information Fusion Process Based on D-S Evidence Theory
### Application Principle of D-S Evidence Theory for MIFMS  
In MIFMS, each sensor receives specific information. This information needs to be transmitted to the microprocessor for fusion. Then, the microprocessor makes a decision in cooperation with a central control system. In the end, the system makes a final decision according to the results from subsystems. Therefore, it is appropriate to use the D-S evidence theory as a convergence algorithm. Each subsystem has five sensors. Figure 1 shows the system structure of a MIFMS in which the D-S method is applied. The whole system works as an information fusion process.  
In Figure 1, \(M_{1}\left(A_{i}\right)\), \(\ldots\), \(M_{5}\left(A_{i}\right)\), \(i\) = 1,2, \(\ldots\), \(n\) are basic probability assignments to proposition \(A_{i}\) from five sensors. \(M_{j}\left(A_{i}\right),j=1\), \(\ldots\), 5 are the combined basic probability assignments according to Dempster-Shafer rules. The set of all fire cases is denoted \(\Theta\) and is called the alarm framework. Every subset of \(\Theta\) is independent.