esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
## 2 Methods
### Elliptic Fourier Analysis (EFA)
#### 2.2.2 Normalization of EFDs  
Because the position, direction and scale of different product profiles are different after picture preprocessing, their EFDs are inconsistent and needs to be normalized to get the normalized elliptic Fourier descriptor (NEFD) \(F^{*}\)[23]. Let the normalized coefficients of \(n\)th harmonic are \(a_{n}^{*}\), \(b_{n}^{*}\), \(c_{n}^{*}\) and \(d_{n}^{*}\), then:  
(1) Since the DC component of EFD only reflects the position of the profile, it is not useful in describing the form. Let the DC component \(a_{0}^{*}=c_{0}^{*}=0\). The center of the first ellipse of the profile can be moved to the coordinate origin to achieve the normalization of the position.  
(2) Transform the elliptic Fourier coefficients according to the following equations to achieve the normalization of direction:  
\[\left[\begin{array}{cc}a_{n}^{*}&b_{n}^{*}\\ c_{n}^{*}&d_{n}^{*}\end{array}\right]=\frac{1}{E^{*}}\left[\begin{array}{cc} \cos\phi_{1}&\sin\phi_{1}\\ -\sin\phi_{1}&\cos\phi_{1}\end{array}\right]\left[\begin{array}{cc}a_{n}&b_{ n}\\ c_{n}&d_{n}\end{array}\right]\left[\begin{array}{cc}\cos n\theta_{1}&-\sin n \theta_{1}\\ \sin n\theta_{1}&\cos n\theta_{1}\end{array}\right] \tag{5}\]\[\theta_{1}=\frac{1}{2}\text{arctan}\Bigg{[}\frac{2[a_{1}b_{1}+c_{1}d_{1}]}{a_{1}^ {2}+c_{1}^{2}-b_{1}^{2}-d_{1}^{2}}\Bigg{]}(0\leq\theta_{1}<2\pi) \tag{6}\]  
\[\Bigg{[}\begin{array}{c}a_{1}^{*}\\ c_{1}^{*}\end{array}\Bigg{]}=\Bigg{[}\begin{array}{cc}a_{1}&b_{1}\\ c_{1}&d_{1}\end{array}\Bigg{\|}\begin{array}{c}\cos\theta_{1}\\ \sin\theta_{1}\end{array}\Bigg{]} \tag{7}\]  
\[\phi_{1}=\text{arctan}\Bigg{[}\frac{c_{1}^{*}}{a_{1}^{*}}\Bigg{]}(0\leq\phi_{ 1}<2\pi) \tag{8}\]  
where \(\theta_{1}\) is the angle between the starting point of the profile and the main axis of the ellipse; \(\varphi_{1}\) is the sequence of rotated ellipse; \(a_{1}\), \(b_{1}\), \(c_{1}\) and \(d_{1}\) are the four coefficients of the first ellipse.  
(3) Calculate the size of the first ellipse \(E_{1}^{*}\) and divide each descriptor by \(E_{1}^{*}\) to achieve the normalization of scale:  
\[E_{1}^{*}=\sqrt{\Big{(}a_{1}^{*}\Big{)}^{2}+\Big{(}c_{1}^{*}\Big{)}^{2}} \tag{9}\]
## 2 Antenna Structure  
### Antenna Design  
Figure 1 shows the 3-D exploded view of the proposed low-SLL, differential-fed, SIW-based SAA. The antenna consists of a \(6\times 16\) slot array, two identical six-way unequal PDs located at both ends of the slot array, a two-way hybrid WG-to-SIW \(E\)-plane PD, an antenna fixture, and metallic decoupling walls. The slot array and the PDs are realized on a \(1.52\)-mm-thick Taconic TLY-5 dielectric substrate (\(\varepsilon_{r}=2.2\) and tan\(\delta=0.0009\)). The six-way unequal PD provides tapered amplitude and in-phase excitation along the \(Y\)\(\!\)oz-plane for six SIWs. Each SIW consists of \(16\) radiating slots. The \(1\times 16\) linear slot array is excited using a differential feed to avoid undesired beam squinting over the entire operating band. To realize the differential feed, excitations from both ends of the \(1\times 16\) slot array need to be equal in amplitude and \(180^{\circ}\) phase difference. Therefore, the two-way hybrid WG-to-SIW \(E\)-plane PD is then developed to provide equal-amplitude and out-of-phase excitation for two six-way unequal PDs. The input port of the proposed antenna is a WR-90 waveguide. Finally, metallic decoupling walls are implemented to suppress the external mutual coupling in the \(E\)-plane (\(Y\)\(\!\)oz-plane) between adjacent linear slot arrays [19]. In order to simplify fabrication, the metallic decoupling walls are integrated into the antenna fixture.
## 3 Experiments and Results
### Performance Comparison of Different Models
#### 3.7.3 Comparison of the Inference Speeds of Alternative Models  
When building a real-time segmentation model, the inference speed of the model is a crucial influencing factor. We compared the inference speed of the proposed network with the alternative models in Section 3.7.1, and the results are shown in Table 4.  
Since cracks are narrow, linear structures with a small percentage of pixels in the whole image, higher resolution means that more details of the cracks can be represented and higher accuracy of segmentation can be achieved, but the inference speed is reduced.  
We tested the inference speed of the model at different resolutions of 416 \(\times\) 416, 800 \(\times\) 640, 1280 \(\times\) 1024, and 2048 \(\times\) 1024 in an inference environment based on an Nvidia Geforce RTX 2080 ti. In terms of speed, BiseNet was the fastest, followed by RIIAnet, meaning that both of these models can meet the demands of real-time segmentation under conditions of 2048 \(\times\) 1024 high resolution; however, this is difficult for the U-Net, Deepcrack network models with good performance to achieve. Combined with the results of Section 3.7.2, in the trade-off between speed and accuracy, we think RIIAnet is more efficient.
# An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
## 5 Results
### Simulation Study
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Case** & **K** & **N** & **Success Rate (\%)** & **Manip. Mean \(\hat{\omega}\)** & **Manip. Max.** & **Comp. Time (Sec.)** \\ \hline I A & & & & & & \\ (Simple\_Planner) & - & 2000 & 44 & 1.64 \(\pm\) 0.48 & 1.95 & 0.99 \(\pm\) 0.01 \\ \hline I B & 1 & 30 & 76 & 1.96 \(\pm\) 0.16 & 2.04 & 4.32 \(\pm\) 0.11 \\ I B & 5 & 30 & 90 & 1.98 \(\pm\) 0.11 & 2.04 & 13.38 \(\pm\) 0.17 \\ I B & 10 & 30 & 96 & 2.01 \(\pm\) 0.05 & 2.04 & 24.27 \(\pm\) 0.62 \\ I B & 20 & 30 & 100 & 2.02 \(\pm\) 0.03 & 2.04 & 45.92 \(\pm\) 2.13 \\ I B & 50 & 30 & 100 & 2.03 \(\pm\) 0.02 & 2.28 & 114.18 \(\pm\) 2.25 \\ \hline II A & & & & & & \\ (Simple\_Planner) & - & 2000 & 26 & 1.29 \(\pm\) 0.21 & 1.41 & 0.56 \(\pm\) 0.01 \\ \hline II B & 1 & 30 & 56 & 1.47 \(\pm\) 0.12 & 1.52 & 4.99 \(\pm\) 0.12 \\ II B & 5 & 30 & 80 & 1.49 \(\pm\) 0.13 & 1.52 & 17.18 \(\pm\) 0.19 \\ II B & 10 & 30 & 100 & 1.50 \(\pm\) 0.06 & 1.52 & 34.66 \(\pm\) 0.74 \\ II B & 20 & 30 & 100 & 1.52 \(\pm\) 0.02 & 1.53 & 64.74 \(\pm\) 3.33 \\ II B & 50 & 30 & 100 & 1.53 \(\pm\) 0.006 & 1.53 & 156.64 \(\pm\) 4.62 \\ \hline III A & & & & & & \\ (Simple\_Planner) & - & 2000 & 12 & 1.20 \(\pm\) 0.43 & 1.61 & 0.77 \(\pm\) 0.01 \\ \hline III B & 1 & 30 & 33 & 1.58 \(\pm\) 0.05 & 1.60 & 6.37 \(\pm\) 0.18 \\ III B & 5 & 30 & 80 & 1.58 \(\pm\) 0.06 & 1.61 & 23.63 \(\pm\) 0.66 \\ III B & 10 & 30 & 97 & 1.60 \(\pm\) 0.05 & 1.61 & 44.85 \(\pm\) 0.77 \\ III B & 20 & 30 & 100 & 1.60 \(\pm\) 0.03 & 1.61 & 88.99 \(\pm\) 3.15 \\ III B & 50 & 30 & 100 & 1.61 \(\pm\) 0.009 & 1.61 & 219.88 \(\pm\) 4.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of case studies I, II and III. Table 1 also presents computation times of each simulation carried out on an Intel(tm) Core i7 CPU @ 2.90 GHz x 16 running Ubuntu 20.04 and Matlab(tm) R2018a. Mean computation times (seconds) and 2\(\sigma\) intervals are collected. As expected, a linear trend is observed in the computational effort.
# Atomic Layer Deposition of Inorganic Thin Films on 3D Polymer Nanonetworks
## 5 Summary and Outlook  
In this review, we focused on the experimental realization and application of functional 3D nanonetworks based on polymer template-assisted ALD. Previous studies have shown that ALD and 3D polymer nanonetwork templates have excellent compatibility. Particularly, as the pore size gradually decreases to the submicron level, ALD is a unique technique that can uniformly coat the surface without clogging the pores. However, the extremely low GPC values of conventional ALD are not suitable for mass-production. In this regard, it is worth attempting to apply the recently proposed spatial ALD (SALD), which allows a much faster deposition rate, to complex nanostructures [101; 102; 103]. The remaining technical issue is to ensure the deposition of inorganic thin films at low temperatures. It is preferable to deposit a high-quality thin film at room temperature, considering the heat resistance of the polymer. In this regard, the potential of PEALD, which is capable of depositing a variety of high-quality thin films at lower temperatures than thermal ALD as a result of the presence of highly reactive ions and radicals, is very high. However, with the exception of a very few cases, most of the studies related to polymer template-assisted ALD reported so far depend on thermal ALD. This suggests that we should pay more attention to PEALD in the future to implement more useful and functional 3D nanonetworks. In particular, PEALD can deposit noble metals such as Pt at temperatures below 200 \({}^{\circ}\)C, which is difficult to achieve by thermal activation alone. By combining this capability with a nanoporous network structure, high-performance catalysts can be realized, which are very important in today's energy and environmental communities. In summary, continuous efforts to combine ALD with actively evolving advanced nanofabrication techniques will provide opportunities for new levels of materials with exceptional properties.  
This work was supported by the Low-dimensional Materials Genome Development funded by the Korea Research Institute of Standards and Science (KRISS-2018-18011084).  
The authors declare no conflict of interest.
# LoRaWAN Network for Fire Monitoring in Rural Environments
###### Abstract  
The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet of Things (IoT) industry, solutions for early fire detection should be developed. The assessment of the fire risk of an area and the communication of this fact to the population could reduce the number of fires originated by accident or due to the carelessness of the users. This paper presents a low-cost network based on Long Range (LoRa) technology to autonomously evaluate the level of fire risk and the presence of a forest fire in rural areas. The system is comprised of several LoRa nodes with sensors to measure the temperature, relative humidity, wind speed and CO\({}_{2}\) of the environment. The data from the nodes is stored and processed in a The Things Network (TTN) server that sends the data to a website for the graphic visualization of the collected data. The system is tested in a real environment and, the results show that it is possible to cover a circular area of a radius of 4 km with a single gateway.  
L 2020, 9, 531; doi:10.3390/electronics9030531
## 4 Discussion
### Model Evaluation  
The performance metrics vary over time; however, for a brief evaluation, we can refer to the mean (across all time windows) performance metrics for the main realistic practical scenario: 0.798 for accuracy, 0.704 for F1 score, and 0.8868 for AUC. Although it is important to note that these results can vary a lot depending on the specifics of the data--for example, in other research, where different real data were used [47], the author achieved an F1 score of 0.989, which is well above the values achieved in other research using similar methods. Therefore, these values are only valid reference values in our research context. Therefore, the results cannot be considered comparable to those obtained by other authors using different data.
## 5 Analysis and Verification
### Results of FDM-DEM Coupling  
Figure 7 presents the contours of soil displacement around the tunnel induced by shield tunnelling with and without isolation piles. It can be seen that the affected zone of soil displacement above the top of the tunnel is wider and the displacement variation is more uniform without isolation pile. The affected zone of soil displacement above the tunnel top is restricted under the influence of isolation piles. The soil around the pile is restrained to some extent under the impact of friction, which blocks soil displacement to progress to the other side of the isolation pile.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Macroscopic** & **Young’s** & **Density** & **Poisson’s** & **Friction** & **Cohesion** \\
**Parameters** & **Modulus (MPa)** & **(kg/m\({}^{3}\))** & **Ratio** & **Angle (\({}^{\circ}\))** & **(kPa)** \\ \hline Soil mass & 75 & 2250 & 0.25 & 30 & 40 \\ \hline
**Microscopic** & **Effective** & **Normal** & **Tangential** & **Normal** & **Shear** \\
**Parameters** & **Modulus (MPa)** & **Stiffness** & **Stiffness** & **Stiffness** & **Bond (kPa)** & **Bond (kPa)** \\ \hline Soil mass & 18.5 & \(7.5\times 10^{6}\) & \(4.5\times 10^{6}\) & 40 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Macroscopic and microscopic physical and mechanical parameters of soil.  
Figure 5: FDM-DEM Coupling Numerical Model.  
The comparisons of surface settlement trough calculated by FDM-DEM coupling is shown in Figure 8. There is an asymmetric phenomenon in the surface settlement trough without isolation piles due to the randomness of particle generation and particle distribution after initial stress balance. The overall results are highly similar to those calculated by Loganathan formula. The maximum surface settlement with isolation piles is slightly less than that without isolation piles. In this case, the maximum surface settlement without isolation piles is 8.937 mm, and the maximum surface settlement with isolation piles is 8.225 mm, with a decrease of 7.96%. The numerical results indicate that if isolation piles are pre-installed on the right side of the tunnel, the soil displacement will be restrained on the right side, but the soil displacement will transfer to the side without pile.  
Figure 6: Results of analytical solution.  
Figure 7: Contours of soil displacement: (**a**) without isolation pile and (**b**) with isolation pile.
## 1 Introduction
However, as can be seen in Figure 2c, because the shear-reinforcing bar is anchored to cast-in-place (CIP) topping concrete and additional flexural reinforcing bars to integrate members on the PC member, an intact anchorage cannot be expected prior to the placement and curing of the CIP topping concrete (i.e., at the construction stage). Therefore, at the construction stage, the structural safety of the dapped end may be vulnerable unless additional shear-reinforcing bars are placed. Currently, to improve the vulnerable structure of the dapped end, due to the unanchored shear reinforcement at the construction stage, Z-shaped shear-reinforcing bars are placed inside the PC slab (Figure 3a). However, the Z-shaped shear reinforcement is not anchored in the flexural rebars. Therefore, in place of the Z-shaped shear reinforcement, clip-type shear reinforcement (Figure 3b) was proposed. The clip-type shear reinforcement, as a closed type, was arranged in the direction of the tensile stress generated at the dapped end for an optimal design to improve the anchorage performance. The structural efficiency of the diagonal and continuous shear reinforcement can be found in the existing literature [32; 33; 34]. In this study, to investigate the structural safety of a multi-tee PC slab at the construction stage, three multi-tee PC slab specimens were tested. The major parameters were the details of the dapped end reinforcement and the shear span ratio. The load-deflection relationship, failure mode, tested to predicted strength ratios, and shear reinforcement strains were analyzed. This study specifically focused on the performance characteristics of the existing Z-shaped shear reinforcement and the proposed clip-type shear reinforcement installed at the PC slab's dapped end. Furthermore, the insulation material (EPS) attached to the lower part of the multi-tee PC slab at the construction stage was investigated. Based on the test results, design considerations were proposed.
Sensors_**2012**, _12_, 13907-13946; doi:10.3390/s121013907  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Telematics Engineering Department, Carlos III University of Madrid, Avda. Universidad 30,  
28911 Leganes, Madrid, Spain; E-Mails: [PERSON][EMAIL_ADDRESS] (G.L.); [PERSON].I.M.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON];  
Tel.: +34-91-624-8798.  
_Received: 16 July 2012; in revised form: 9 October 2012 / Accepted: 10 October 2012 / Published: 16 October 2012_  
**Abstract:** Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in \"LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments\", published in the _IEEE Transactions on Information Technology in Biomedicine_, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.  
**Keywords:** Wearable Health Monitoring Systems (WHMS); Body Area Networks (BAN); Wireless Sensor Networks (WSN); ubiquitous health; pervasive health; e-Textile; indoor location
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 3 Basic Principles of Servomotor Control
### Integration with Industry 4.0 Principle in Servomotors  
Industry 4.0 enables the use of smart technologies that alter the behavior of mechanisms in the industry, with servomotors being one of the key elements in smart technologies due to their provision of precision and efficiency. Thanks to integration with Industry 4.0, new opportunities have emerged in predictive management and seamless communication of production processes and mechanisms. Based on [16; 73; 74; 75; 76; 77], it is possible to identify the key aspects in the construction of this principle and draw conclusions about its advantages and disadvantages.  
The main points of integration with Industry 4.0 are:  
1. Internet of Things (IoT) connectivity. Servomotors connected to Industry 4.0 are part of a unified Internet of Things network. This connection allows real-time monitoring of motor conditions, collecting a wealth of data such as operational parameters, temperature, vibration, etc.
2. Data analysis and predictive maintenance. Modern methods of data analysis allow the collection of data streams from servomotors into a unified database. This systematic organization of data enables the prediction of motor behavior and planning maintenance and repairs, thus avoiding unjustified equipment downtime.
3. Remote monitoring and control. Servomotors connected to Industry 4.0 can be remotely controlled. This is beneficial in large-scale manufacturing where engines are distributed over a large area, requiring remote control and monitoring of engine conditions for timely management adjustments without on-site intervention. Remote monitoring reduces delays and increases overall production efficiency.
4. Standardization. The use of specific standards, such as Open Platform Communications Unfired Architecture (OPC UA), facilitates the integration of servomotors into a unified network with other production components.  
Adaptive control. Integration with Industry 4.0 allows the development and use of flexible servomotor control systems. Control systems can adapt promptly to changing conditions and respond to disturbances, thereby reducing setup time and downtime.
6. Energy efficiency. By connecting servomotors to a unified network using Industry 4.0 protocols, it is possible to reduce the energy consumption of production and optimize processes to use a more logical distribution of energy resources.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 4 Parameter Estimation and Controller Design Based on EDM System
### Controller Design  
Adaptive control can make the system work automatically in the optimal or near-optimal operating state and obtain high-quality control performance under the condition that the model knowledge or environmental knowledge of the controlled object is incomplete or little known. The minimum-variance self-tuning controller adapts a recursive least-squares method to estimate the system parameters and uses the variance of the output error of the system as a performance index function by minimizing the performance index to calculate the control law. Finally, according to  
Figure 8: Order identification result.  
the calculated control law, the parameters are adjusted to realize the process control. Consider the following system:  
\[A(z^{-1})y(k)=z^{-d}B(z^{-1})u(k)+C(z^{-1})\xi(k) \tag{22}\]  
where \(u(k)\) and \(y(k)\) represent the moving position of the electrode and interelectrode gap, respectively. \(C(z^{-1})\) is a Hurwitz polynomial, \(\xi(k)\) is white noise with variance \(\sigma^{2}\), \(d=1\) is the number of pure delays, and  
\[\left\{\begin{array}{l}A(z^{-1})=1+a_{1}z^{-1}+a_{2}z^{-2}\\ B(z^{-1})=b_{0}+b_{1}z^{-1}\\ C(z^{-1})=1\end{array}\right. \tag{23}\]  
The interelectrode gap at iteration \(k+d\) is based on the electrode position and gap measurement at iteration \(k\) and the previous iteration. This predicted gap at iteration \(k+d\) is denoted as \(\hat{y}(k+d|k)\), and the prediction error is  
\[\widetilde{y}(k+d|k)=y(k+d)-\hat{y}(k+d|k) \tag{24}\]  
The interelectrode gap prediction error variance is  
\[J=E\Big{\{}\widetilde{y}^{2}(k+d\Big{|}k)\Big{\}} \tag{25}\]  
The minimum d-step optimal prediction output with \(y^{*}(k+d|k)\) in the above performance index Formula (25) needs to satisfy the equation below:  
\[C(z^{-1})y^{*}(k+d\Big{|}k)=G(z^{-1})y(k)+F(z^{-1})u(k) \tag{26}\]  
where  
\[\left\{\begin{array}{l}C(z^{-1})=A(z^{-1})E(z^{-1})+z^{-d}G(z^{-1})\\ F(z^{-1})=B(z^{-1})E(z^{-1})\end{array}\right. \tag{27}\]  
and  
\[\left\{\begin{array}{l}E(z^{-1})=1\\ G(z^{-1})=g_{0}+g_{1}z^{-1}=-a_{1}-a_{2}z^{-1}\\ F(z^{-1})=f_{0}+f_{1}z^{-1}=b_{0}+b_{1}z^{-1}\end{array}\right. \tag{28}\]  
Equation (26) is called the optimal output prediction equation, and Equation (27) is called the Diophantine equation.  
Therefore, the minimum-variance control law is  
\[C(z^{-1})y_{r}(k+d)=G(z^{-1})y(k)+F(z^{-1})u(k) \tag{29}\]  
From Equation (29), the control law is  
\[u(k)=\frac{C(z^{-1})y_{r}(k+d)-G(z^{-1})y(k)}{F(z^{-1})} \tag{30}\]  
where \(y_{r}\) are the reference gaps, \(y\) are the measurement gaps, and the control variable at the current moment is calculated by the above formula and acts on the system, which completes the control of current time. Therefore, the minimum-variance self-correcting control is realized by such reciprocal execution.