esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
###### Abstract  
Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation.  
machine learning; structural types; decision forest; self-training procedures; city-scale seismic damage simulation +
Footnote †: journal: Applied Sciences
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
## 2 Related Works
The conventional DCT is inefficient for floating-point implementation within the FPGA devices due to increased hardware complexity and lower-speed performance. Moreover, after developing a new DCT-type algorithm, the system just created must be embedded into the final product that uses the new DCT implementation. However, using a GPU to implement and test new algorithms has a significant advantage: these units are already implemented on all personal computers, laptops, and smartphones. So, all the steps from algorithm development to its physical use in real applications are considerably reduced.
## 8 Conclusions  
In this work, an adaptive rectifier concept was implemented to address the early breakdown voltage issue of conventional rectifiers while exhibiting high efficiency over a wide range of RF input power levels. The proposed rectifier achieved an efficiency of over 40% of the RF input power level ranging from \(-6\) to 25 dBm and accomplished 66% of maximum peak power efficiency at 15 dBm. To further improve the adaptive rectifier efficiency, an active load impedance block was added to expand the efficiency of the adaptive rectifier's performance. The results from the active load modulation topology demonstrated a 40% RF-DC power conversion efficiency over a wider range of input power levels (\(-6\) dBm to 32 dBm) while keeping the maximum peak power efficiency at 66% at 15 dBm. The rectifier circuit was designed to extend the power input range of RF WPT Harvesters operating at 915 MHz in the ISM band for Short/Long range WPT applications from controlled sources to operate with true spatial freedom and be insensitive to range and location.  
Conceptualization, A.M.A.; Methodology, A.M.A., R.E.A., J.A.L., and M.C.E.Y.; Supervision, R.E.A. and M.C.E.Y.; Validation, A.M.A., R.E.A., J.A.L., and M.C.E.Y. Writing (review & editing), A.M.A., R.E.A., and M.C.E.Y.  
This research received no external funding.  
The authors would like to express their thanks to the Saudi Cultural Bureau in Ottawa for their support. They would also like to thank [PERSON] and [PERSON], from the School of Electrical Engineering and Computer Science, University of Ottawa, for their help in PCB fabrication.  
The authors declare no conflict of interest.
# A Distributed Token Passing Protocol for Time Constrained Data Gathering in VANETs
## 2 Related Work  
Intelligent Transport System (ITS) covers a wide range of applications for vehicles, such as driving safety and warning, up to automated driving, information exchanges among vehicles and Internet applications. VANETs [20], a special class of MANETs [21], is a key component for developing current ITS. As in MANETs, the VANET network paradigm has no fixed infrastructure and, instead, the members of the network themselves, i.e., the vehicles, provide network services. VANETs devices are classified into two categories: Road Side Unit (RSU) (e.g., traffic lights) and On-Board Unit (OBU) (e.g., wireless device embedded in a vehicle) [22]. In general, communications in VANETs typically allow vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) wireless interfaces. VANET is a consolidated research topic because it has shown to be an excellent element to improve vehicle and road safety and traffic congestion avoidance. In order to implement these kinds of services, cooperative awareness plays a crucial role. WiFi-based Vehicle-to-everything (V2X) communications technology represents a mature candidate, whose capabilities have already been tested. Presently, IEEE 802.11p and the corresponding ITS-G5 in Europe are the current standards. As an alternative, 3 GPP considers the support to the V2X feature in cellular networks (i.e., LTE and the next generation 5G). Recently, D2D communications (or Proximity services) are introduced to avoid the control signalling overhead to register and synchronize all the vehicles with the cellular infrastructure. Indeed, D2D communications allow autonomous device discovery and the exchange of data without the presence (or with limited presence) of the cellular infrastructure (i.e., the eNodeB). An overview of standardisation directions for vehicular communications can be found in [23]. Furthermore, in the literature, several works presented the join use of IEEE 802.11p and infrastructured LTE, as in [24; 25].  
Several medium access control (MAC) layer protocols are designed for V2X for an efficient coordination among the nearby vehicles and for reliable transmission mechanism of safety message. Among these different proposals, the Token Ring (TR) protocol [26] has also been considered to arrange node topology in a ring. However, our proposal avoids the network formation, while a token passing scheme is instead adopted. In this section, the state-of-the-art MAC protocols based on the token ring principle, which may be adopted in VANET communications, are given an overview.
## 2 Preliminaries and Problem Description
### Problem Description  
Consider a class of nonlinear multi-agent systems consisting of \(N\) follower agents and one leader agent, the dynamics of the \(i\)th follower agent are given as  
\[\begin{array}{l}\dot{x}_{i,m}=g_{i,m}x_{i,m+1}+f_{i,m}(\overline{x}_{i,m}), \ m=1,\cdots,n-1\\ \dot{x}_{i,m}=g_{i,n}u_{i}(t)+f_{i,n}(\overline{x}_{i,n})\\ y_{i}=x_{i,1}\end{array} \tag{5}\]  
where \(\overline{x}_{i,m}=[x_{i,1},x_{i,2},\cdots,x_{i,m}]^{T}\in R^{m}\), \(i=1,\cdots,N\) and \(m=1,\cdots,n\); \(\overline{x}_{i,n}\in R^{n}\), \(u_{i}(t)\in R\) and \(y_{i}\in R\) are the state, control input, and output of the \(i\)th follower agent, respectively. In addition, \(f_{i,m}(\cdot)\) represents unknown uncertain dynamics and \(g_{i,m}\) represents unknown control gains, where \(i=1,\cdots,N\) and \(m=1,\cdots,n\).  
The objective of this paper is to design an adaptive neural network finite-time control law \(\mu_{i}(t)\) for the system (1), such that: (1) in the presence of unknown control gains, all signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and (2) the output of all follower agents can achieve asymptotic tracking of the leader agent's trajectory \(y_{d}\) in finite time, and tracking errors can converge to a small neighborhood of zero.  
**Assumption 2**.: _The reference signal \(y_{d}\) and its first and second derivatives \(\dot{y}_{d}\) and \(\ddot{y}_{d}\) are smooth, available, and bounded._  
**Assumption 3**.: _The unknown control gains \(g_{i,m}\), \(i=1,\cdots,N\) and \(m=1,\cdots,n\), are bounded. That is, there exist unknown positive constants \(g_{i,m}^{*}\) such that \(|g_{i,m}|\leq g_{i,m}^{*}\)._  
**Remark 1**.: _Regarding Assumption 2, this is one of the common conditions for adaptive control problems based on dynamic surface control design and can be seen in [25; 31]. Assumption 3 requires that the unknown control gain is bounded, and then we can apply the Nussbaum gain function technique to deal with the control problem of unknown control direction._
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 2 Free-Space Optical Communications  
FSO communications are a technology that uses light propagation in air and free-space to transmit information at data-rates higher than those usually achievable by radio-frequency (RF) transmissions. These systems are of great interest, in all application environments where it is not possible to deploy optical-fibers, including, of course, air or space borne systems [5], as shown in the Figure 1.  
Figure 1: Example of application environments of FSO technologies. Source: Reference [6].  
Compared to RF communications, which use electromagnetic waves at a frequency lower than infrared light, FSO provides a higher throughput. Other benefits of this technology are the wider available bandwidth and reduced sized antennas. Optical links have a shorter wavelength; thus, the beam divergence angles are smaller. The very narrow laser beams provide an inherent security and robustness to electromagnetic interference. These systems do not require license fees, and they have lower installation cost [7].  
On the other hand, the propagation of the laser beam requires a line of sight for transmission, which means that, if at least one of the end-nodes is on Earth, the communication link can be disrupted by clouds and affected by adverse weather conditions, such as rain.  
Transmission in clear-sky conditions can also suffer from some performance degradation because of the induced interference on the light beams, lessening the power received due to the scattering of photons. The wavefront distortion is another effect, as shown in Figure 2, which causes constructive and destructive interference [5].
## 5 Experiments and Results obtained
### Analysis of Results
#### 5.1.1 CNN without Regularization  
Experiment 1: The size of the image is 150 \(\times\) 150 pixels. The CNN consists of 11-hidden layers. We used Adam Optimizer. The size of the Kernel is 3 \(\times\) 3. The accuracy we got was 85.99 on the train set and 79.49 on the test set.  
Experiment 2: The size of the image is 128 \(\times\) 128 pixels. The CNN consists of 13-hidden layers. We used Adam Optimizer. The size of the Kernel is 2 \(\times\) 2. The accuracy we got was 85.61 on the train set and 82.91 on the test set.  
Experiment 3: The size of the image is 150 \(\times\) 150 pixels. The CNN consists of 13-hidden layers. We used Adam Optimizer. The size of the Kernel is 2 \(\times\) 2. The accuracy we got was 94.99 on the train set and 85.11 on the test set.  
Experiment 4: The size of the image is 128 \(\times\) 128 pixels. The CNN consists of 11-hidden layers. We used Adam Optimizer. The size of the Kernel is 2 \(\times\) 2. The accuracy we got was 94.51 on the train set and 85.41 on the test set.  
Effect of various parameters of CNN without regularization is shown in Table 1.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
###### Abstract  
Power wireless private networks (PWPNs) have been highly developed in China in recent years. They provide a basis for the energy Internet of Things, which enables the exchange of energy information between devices. Although the power wireless private network is an imitation of the public cellular network, a number of special challenges remain in power private networks. Due to the lack of general standards for PWPNs at the beginning of deployment, there are now two independent PWPN systems in China: long-term evolution (LTE) 230 and LTE 1800. Each has its own core and access networks with independent hardware. In this paper, we propose a high-level design of multinetwork integration to allow LTE 230 and LTE 1800 to coexist. For core network integration, we propose a protocol controller to select the active protocol according to the user's mode selection, since both LTE 230 and LTE 1800 evolved from the standard LTE system. For access network integration, we propose a multinetwork integration controller to help the device access the optimal cell. The simulation results show that the integrated system can retain the advantages of these two independent systems in terms of both capacity and coverage.  
power wireless private network; integration; core network; access network +
Footnote †: journal:  
_Future Internet_**2019**, _11_, _221_; doi:10.3390/f11110221
## 4 Mathematical Model of the Algorithm
### Mathematical Model for Describing Positional Relation between Two Vertical Parallel Surfaces  
#### 4.2.1 Extraction Algorithm for Combined Projection Waveform  
Three kinds of situations may occur during the measurement of two vertical parallel surfaces. As the detection model shown in Figure 14, when \(PD\) is measured surface with the \(PC\) as the datum surface, the measurements is \(X_{\text{s3}}\). When the contact line \(A\) is at \(P_{0}\) and the detection line \(C\) is at \(P_{1}\), the distance between the surface where the contact line \(A\) is located and the surface where the detection line \(C\) is located is the same as the distance between the two surfaces which are absolutely parallel, and both are \(L\). At this time, the measurement is fixed, defined as \(X_{1}\). Ideally, in the case where there is no positional relation between the two surfaces, the detection line \(C\) should be located at \(P_{2}\), and the contact line \(A\) should be located at \(P_{0}\). Since two absolutely parallel surfaces do not exist, the actual detection line \(C\) should be located at \(P_{3}\).  
Figure 13: Separation algorithm model of the combined projection curve.  
In the detection model shown in Figure 14a,b, the spring is in compression state, at this time, \(X_{\text{s3}}\)\(<X_{1}\). In the detection model shown in Figure 14c, the spring is in the stretching state, at this time, \(X_{\text{s3}}\)\(>X_{1}\). Then, the combined projection waveform can be defined as  
\[C_{3}=X_{\text{s3}}-X_{1} \tag{14}\]  
When measuring \(PC\) with \(PD\) as datum, the measurement is \(X_{\text{s4}}\), when the spring is in compression state, \(X_{\text{s4}}\)\(<X_{1}\), and when the spring is in stretching state, \(X_{\text{s4}}\)\(>X_{1}\). Then, the combined projection waveform can be defined as  
\[C_{4}=X_{\text{s4}}-X_{1} \tag{15}\]  
At this time, the spatial sequence waveform corresponding to the combined projection waveform \(C_{3}\) can be represented by \(R_{3}\), and the spatial sequence waveform corresponding to the combined projection waveform \(C_{4}\) can be represented by \(R_{4}\). After that, the trend curves \(\Delta_{3}\) and \(\Delta_{4}\) of \(R_{3}\) and  
Figure 14: Separation algorithm model of the combined projection curve: (**a**) Model 1; (**b**) Model 2; (**c**) Model 3.  
\(R_{4}\) are extracted respectively, and \(\Delta_{3}\) is defined as the combined projection curve of \(R_{3}\), while \(\Delta_{4}\) is defined as the combined projection curve of \(R_{4}\).
# A Second Harmonic Wave Angle Sensor with a Collimated Beam of Femtosecond Laser
Theoretical calculations are carried out to predict the sensor's performance, and the feasibility of the angle sensor is confirmed experimentally. In the experiment, the beams are aligned so that they propagated through the NLO crystal to produce SHG, where the NLO crystal is mounted on a rotary stage representing the target to be measured. Since the SHG in the crystal is strongly related to the angle and the wavelength, the small angular change of the crystal can be directly detected by the SHG spectrum change in a given wavelength range. The peak of the SHG spectral evolution as the angular position of the crystal changes is then evaluated to determine the sensitivity of the angle sensor. Finally, to confirm the feasibility of the measurement method where the target does not have to be placed in one position, experiments are carried out at several positions of the NLO crystal along the beam propagation direction.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 3 Proposed Method
### Loss Functions  
If the network only considers spectral information, it will lead to incomplete utilization of spatial information, and only considering spatial information will result in some missing spectral information. Therefore, this article constructs an end-to-end loss function from two aspects: spectral information preservation and spatial information injection, given that:  
\[L_{total}=L_{spectral}+L_{spatial} \tag{1}\]  
where, \(L_{total}\) represents the total loss of IFNPMH, \(L_{spectral}\) represents the spectral information loss between the fused and the reference images, and \(L_{spatial}\) represents the spatial information loss between the fused and the reference images.  
\[L_{spectral}=\frac{1}{M}\sum_{m=1}^{m=M}(y_{i}-\hat{y_{i}})^{2} \tag{2}\]  
Here, \(M\) is the number of training data, \(y_{i}\) is the reference image, and \(\hat{y_{i}}\) is the fused image generated by the IFNPMH network. The \(L_{spectral}\) loss is the basic loss, aimed at preserving spectral information in the generated image.  
\[L_{spatial}=\lambda\frac{1}{M}\sum_{m=1}^{m=M}(\forall y_{i}-\forall\hat{y_{ i}})^{2} \tag{3}\]  
where \(\forall\) represents the gradient operator extracting high-frequency spatial information, and \(\lambda\) is a regularization parameter used to balance the loss of spectral and spatial information. The purpose of \(L_{spatial}\) is to effectively inject spatial information into the fused image.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 4 Discussion and Conclusions
Although the time series of voltage and thermal features are not correlated, which is caused by different physical response times of measured features, they both possess relevant information for battery fault prediction. While the response time of the voltage feature is almost immediate and does not increase over time, the cell temperature can distinguish a defected cell after the heat appears on the surface. The image noise interference, lower quality of metering device, and imperfection involved by the assumption of linear relation between pixel value and a spot temperature could also play their roles in the decrease of relevance, which will be addressed in our future work. Further statistical tests were still able to confirm high clarity, valuable representation, and high relevance of the thermal data in the fault prediction task. Classification results proved the separability of thermal features in the multi-class and binary classification task. Thus, it is possible to create an infrared thermography monitoring system for battery fault detection able to replace the maintenance personnel. The disadvantage of this method is the necessity of free space in the UPS surrounding area. The camera angle of view is another limiting factor; it is unsuitable for container-type UPS, but it can be easily applied in accumulator rooms, where sufficient distance between batteries and camera can be reached. In this case, such a monitoring system could provide almost the same results as voltage monitoring, but with a lower investment. This work was supported by Project LO1404 \"Sustainable Development of Centre ENET\", VSB- Technical University of Ostrava and Grant Agency of the Czech Republic--TACR TH02020191 and VSB-TU internal grants SGS SP2018/58, SP2018/78 and SP2018/42. [PERSON] is the author of the concept. [PERSON] and [PERSON] performed the experiment and data annotation. [PERSON] processed the thermograms for the feature extraction and [PERSON] was responsible for design and execution of the statistical analysis. The authors declare no conflict of interest.
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 4 Discussion
### Comparison between the Kriging Method and HAS Method
Figure 9g,h shows the RRMSE distributions of the seven stations predicted by the HAS and Kriging methods, respectively, at 12:00 a.m. local time for the low solar activity year in July 2018. The RRMSE plots of the HAS method show that it was larger at the Perth station. All the other stations had smaller values than the predicted RRMSEs of the Kriging method, and as a whole, the HAS method was better. Overall, the HAS method is suitable for applications which are sensitive to local variations. However, it is important to note that some regions may have large RRMSEs. 5. Overall, the HAS method showed high accuracy and reliability in the spatial reconstruction of the foF2 at different times and locations. Compared with the Kriging interpolation method, the HAS method can more accurately capture the spatial variation characteristics of the ionospheric parameters with smaller prediction RRMSEs. Figure 9: _Cont._