esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this work, a multi-view stereo (MVS) method based deep learning was first applied to assist in assessing the damage level of buildings in post-earthquake areas. The method was tested in aerial UAV images from Yangbi County, Yunnan Province. The time consumption, memory consumption, and the performance of 3D reconstruction of different models were compared. In addition, the applicability of different 3D reconstruction models was discussed. A number of conclusions can be made as follows: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, and the memory consumption is the lowest, which can meet the timeliness requirement of post-earthquake rescue and damage assessment; (2) CasMVSNet exhibited the best 3D reconstruction result in both high and small buildings; (3) the deep-learning-based 3D reconstruction method can provide the detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The assessment results were very close to the results of the field survey.  
The main contributions of our study can be summarized as follows: (1) we first attempted to use deep-learning-based MVS to UAV aerial 3D reconstruction and building damage assessment research, which can provide 3D information for post-earthquake rescue and loss assessment; (2) the applicability of different MVS models for 3D reconstruction of UAV images have been analyzed and discussed.  
The limitation of the proposed method is that the damage level of the buildings is determined by visual interpretation. In the future, we will devote efforts to construct a network to realize the automatic classification of buildings, based on the results of the 3D reconstruction.  
Conceptualization, Z.H., J.L. and S.J.; data curation, Y.Y., Z.H., J.L. and C.Z.; methodology, Z.H., Y.Y., S.J. and J.L.; validation, Y.Y., H.P., R.Z., Y.Z., Y.H., J.W. and S.Y.; formal analysis, Z.H. and Y.Y.; investigation, Y.Y. and C.Z.; writing--original draft preparation, Z.H., Y.Y. and H.P.; writing--review and editing, Z.H., Y.Y., H.P., J.L. and S.J.; supervision, J.L. and S.J. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China, grant number 2017 YFC1500906; the National Natural Science Foundation of China, grant number 41871325, 42061073; and the Natural Science and Technology Foundation of Guizhou Province under Grant [2020]1Z056.  
Not applicable.  
The _DTU_ datasets are freely available online, and can be found at [[https://github.com/YoYo000/MVSNet](https://github.com/YoYo000/MVSNet)]([https://github.com/YoYo000/MVSNet](https://github.com/YoYo000/MVSNet)) (accessed on 29 August 2022).  
We thank our team's graduate Master [PERSON] for providing UAV images so that our work could be successfully completed.  
The authors declare no conflict of interest.
## 3 Algorithm Description
### Overall MOWPS Algorithm  
By incorporating the aforementioned designs, the complete process of the proposed MOWPS can be summarized as follows:  
**Complexity Analysis** : Recall that \(N_{p}\), \(\xi\), and \(\varepsilon\) represent the number of individuals in \(\Psi_{all}\), the elite proportion, and the selection proportion during the roaming process, respectively. The loop of MOWPS repeats \(K_{max}\) times. In each loop, the SDRS with complexity \(O(\,|\,\Psi_{all}\,|^{2})\) is first adopted to evaluate individuals in \(\Psi_{all}\). Then the roaming process generates \(2\varepsilon\,|\,\Psi_{e}\,|\,\) individuals for each elite individual in \(\Psi_{e}\), and the total complexity is \(O(2\varepsilon\,|\,\Psi_{e}\,|^{2})\). After that, multiway attacking with complexity \(O(\,|\,\Psi_{all}\,|)\) is performed. Finally, LMS is applied to individuals in \(\Psi_{e}\), with a complexity of \(O(\,|\,\Psi_{e}\,|)\). Thus, the complexity of MOWPS is \(O(K_{max}\times(\,|\,\Psi_{all}\,|^{2}+2\varepsilon\,|\,\Psi_{e}\,|^{2}+\,|\, \Psi_{all}\,|+\,|\,\Psi_{e}\,|))=O(K_{max}\times(N_{p}{}^{2}+2\varepsilon\, \xi^{2}N_{p}{}^{2}+N_{p}+\varepsilon N_{p}))\).
# Gaussian Process Modeling of Specular Multipath Components
###### Abstract  
The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Process regression (GPR) is used as a tool to analyze and predict the SMC amplitudes and phases based on a measured training data set. In this regard, the dependency of the amplitude (and phase) on the angle-of-arrival/angle-of-departure of a multipath component is analyzed, which accounts for the incident angle and incident position of the signal at a reflecting surface--and thus for the reflection characteristics of the building material--and for the antenna gain patterns. The GPR model describes the similarities between different data points. Based on its model parameters and the training data, the amplitudes of SMCs are predicted at receiver positions that have not been measured in the experiment. The method can be used to predict a UWB channel impulse response at an arbitrary position in the environment.  
gaussian process regression; multipath radio channels; geometric-stochastic channel model +
Footnote †: journal: Applied Sciences
# Quantum Coding via Quasi-Cyclic Block Matrix
## 4 Long-Length Quantum Coding via Quasi-Cyclic Jacket Matrix
### The Construction Method of the Exponent Matrix
For example, if we decompose the length \(n=156\) of the QC code as \(J_{156}=J_{13}\otimes J_{3}\otimes J_{2}^{\otimes 2}\) with the most basic method, the cycles will be smaller. However, if we take another decomposition method \(J_{156}=J_{7}\otimes J_{11}\oplus J_{43}\oplus J_{23}\oplus J_{13}\), the cycle length is obviously increased, and, hence, the obtained quantum codes have the advantage of simple implementation of an iterative decoder and low-complexity encoding. According to information coding theory, the sparse parity-check matrix \(H\) of code \([N,k]\) for decoding can be gained from the generator matrix. Based on the character of our constructed matrix with prime number \(n\), it is easy to check that the variable and check node degrees in \(H\) or the corresponding bipartite Tanner graph at most are \(\eta=n-1\) and \(\rho=[k/n]+1\), where \([\cdot]\) is an integral function. For Example 2, the row and column weights of the parity-check matrix for constructed codes \([110;80]\) are \(\eta=9\) and \(\rho=8\), respectively. It is obvious that the bound can be met with its 6-cycle for \(x=1\). Furthermore, according to the family of proposed codes from the circulant permutation matrix in Equation (14), the obtained quantum QC code of dimension \(O(N)\) contains its upper bound on the minimum distance \(O(N/\log N)\) as the code length \(N\rightarrow\infty\). Thismeans that, the adopted parameter \(n\) in the proposed coding method should correspondingly approach  
\[n\approx\sqrt{\sum_{i=2}^{x+1}\eta(\eta-1)^{2(i-2)}},\ \ \ 1<x\in\mathbb{Z} \tag{41}\]  
with the long-code length, where \(\mathbb{Z}\) is an integer set. In practical applications, quantum codes with long length may potentially be used in some future quantum information fields involved in large-scale data, such as quantum machine learning, which uses quantum computers and aims to enhance the power of machine learning. However, some major obstacles still limit the use of quantum hardware for practical applications of machine learning. One of the bottlenecks is that the data has to be encoded into the quantum computer in an efficient manner to generate its useful quantum kernel. Although some encoding methods have been investigated [43; 44; 45], the features are limited by the number of qubits [46]. Additionally, the inherent noise of quantum computers also influences the quality of the experimental results. Generally, rapid processing of the large datasets is vital because the operation timescale relies linearly on the size of the dataset. Therefore, to provide a useful kernel for machine learning, a good coding manner with long code length can efficiently load a high-dimensional feature vector into a quantum computer. As a consequence, the proposed coding method in this paper provides a tentative coding scheme for ML based on large-scale data.
## 2 Method
### Model Development and Validation
#### 2.2.2 Model Validation under Steady State Operation  
The 1D model used for this investigation was validated using the previously explained experimental data. For the validation, information related to exhaust manifold pressure, BMEP, BSFC, and compressor outlet temperature are shown in Figure 8. The X-axis refers experimental data, while the Y-axis shows the modeling predictions. Figure 8 includes a \(\pm\)5% error in the dotted line (a continuous black line corresponds to the perfect agreement). This model validation was carried out for TC\(-\)1, taking advantage of the working points in Figure 3.  
Figure 8: Simulation data compared to testing data at 1000 and 1500 RPM: turbine inlet pressure (**A**), BMEP (**B**), BSFC (**C**), and compressor outlet temperature (**D**).  
First, Figure 8A, dealing with turbine inlet pressure (p3), shows a high accuracy in prediction, which is an indication of good practice in predicting the turbine and compressor efficiencies and their corresponding temperature boundaries. Any overpredicted turbocharger efficiency would lead to systematic underpredictions in the required p3 for a given boost pressure. Another indicator of accurate compressor efficiency is the compressor outlet temperature (T2), which, in any case, is close to the perfect agreement, as seen in Figure 8B.  
BMEP and BSFC were selected as the primary variables for the assessment of modeling practices. BMEP and BSFC are good combustion modeling indicators and allow for the validation of the developed ANN. Overall, observing the results depicted in Figure 8, it can be concluded that the model developed is thermofluid-dynamically stable and representative of the engine performance; hence, it is acceptable for use in further prospective studies, such as in this study.
###### Abstract  
Considering the demand for vehicle stability control and the existence of uncertainties in the four-wheel steering (4 WS) system, the mixed H2/H\(\infty\) robust control methodology of the 4 WS system is proposed. Firstly, the linear 2 DOF vehicle model, the nonlinear 8 DOF vehicle model, the driver model, and the rear wheel electrohydraulic system model were constructed. Secondly, based on the yaw rate tracking strategy, the mixed H2/H\(\infty\) controller was designed with the optimized weighting functions to guarantee system performance, robustness, and the robust stability of the 4 WS vehicle stability control system. The H\(\infty\) method was applied to minimize the effects of modeling uncertainties, sensor noise, and external disturbances on the system outputs, and the H2 method was used to ensure system performance. Finally, numerical simulations based on Matlab/Simulink and hardware-in-the-loop experiments were performed with the proposed control strategy to identify its performance. The simulation and experimental results indicate that the handling stability of the 4 WS vehicle is improved by the H2/H\(\infty\) controller and that the 4 WS system with the H2/H\(\infty\) controller has better handling stability and robustness than those of the H\(\infty\) controller and the proportional controller.  
four wheel steering vehicle; handling stability; H2/H\(\infty\) controller; yaw rate tracking; hardware-in-the-loop experiments +
Footnote †: journal: Applied Sciences
## 1 Introduction
This is mostly because there is a concern that the mitigation focus will shift from agricultural measures to internal measures, which in turn could potentially increase the agriculture-based nutrient input. Other concerns are competition of space and the fear of potential negative side effects that cannot yet be foreseen [24]. In order to dispel misinformation and concerns of internal mitigation measures, early and comprehensive stakeholder involvement is necessary. Stakeholders typically include governmental, non-governmental, and business representatives, experts (e.g., scientists), landowners, and local users of natural resources. The benefits of early involvement of stakeholders are well-known and include a higher acceptance of measures, which accelerates planning and implementation processes [25]. The WFD encourages active involvement of stakeholders and the public. However, in practice, participatory processes are often restricted to the legally required information supply and consultation. However, especially for the implementation of new sea-based measures, an early and active participation process is crucial to take into account stakeholder concerns and increase acceptance. Here, a holistic approach would be most beneficial to capture the perception of the different stakeholders. The ecosystem service (ES) concept can be a suitable approach to obtaining a holistic view. While the operationalization of the ES concept in EU environmental policy is already recommended [26; 27], no concrete application is provided within the terms of the WFD. As a first step to improve the participation and communication between target groups, ecosystem service assessments (ESAs) have been conducted and used to understand changes caused by new measures to fulfill the WFD [28; 29; 30]. The ESA conducted by [PERSON] et al. [25] captured experts' views, disagreements among them, as well as potential misunderstandings of mitigation scenarios. A final discussion and the opportunity for participants to modify the ESA values ensure that it serves as a promising tool in participation and target group involvement within the coastal planning processes [28]. While former studies gave the first insights into the potential usage of ESAs for the implementation of water quality improvement measures, they lack concrete information about the application within the WFD itself. Therefore, this study aims to evaluate how and at which stage a participatory mapping approach or ESA can be used as a tool to engage stakeholders actively and successfully promote the implementation of new internal mitigation measures within the planning and implementation process of the WFD. To accomplish this, we (1) first use a participatory mapping approach to identify stakeholder groups' perceptions of and preferences for supportive internal measures, (2) test the use of an ESA with different target groups and different mussel mitigation scenarios, (3) evaluate different assessment methods and their suitability for different target groups, and (4) define recommendations for the use of the tested stakeholder tools within the planning and implementation process of the WFD.
## 4 Application in Chenxi Bridge
### Identification for Test Data  
Figure 18 shows the identification results with the measurement data and Table 7 presents the identification results of the three methods applied to the measured data. As shown, the accuracy of the IL method depends on the location of the data points. It should be noted that there is a mass meter at the end of the bridge, so the weight of the truck can be obtained directly. In general, accuracy is proportional to the data range. The maximum RMSE errors for the IL and AIL methods at different points are 15.2% and 10.3%, respectively. It can be seen that the AIL method is better than IL. In contrast, the error of the AIL-Modified method is 3.4%, which is significantly lower compared to the other two methods. From the results of Table 7, it can be seen that the AIL-Modified method has a better performance compared to the other two methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** \\ \hline IL & 42.7 & 43.0 & 39.2 & 42.4 & 42.2 & 38.3 & 37.9 & 41.1 \\ AIL & 42.6 & 42.3 & 44.6 & 41.1 & 42.9 & 42.8 & 42.7 & 43.8 \\ AIL-Modified & & & & & 43.1 & & & \\ Measured value & & & & 44.6 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The identification results for the different methods (ton).  
Figure 17: The displacement data for when the truck was passing the bridge (red rectangle in Figure 15).
## 5 Discussion  
### Stem Volume Empirical Modeling  
In Section 4.1, an empirical backscattering model was developed to estimate stem volume information across the forests of Japan. The challenge is that backscattering can have multiple causes within a forested area, and can be affected by species, structures, soil moisture [44], density of forests [18], _etc._ A simple empirical modeling method was used by taking a correlation analysis between the backscattering intensity and the ground reference data. We have avoided most of the possible distracting factors affecting the difference in the backscattering to make a more accurate trend between the parameters and SAR image, which allowed a better interpretation when compared to many studies performed in similarly rugged areas [24]. We could discuss about the limitation of L-band SAR for the estimation of biomass, due to its sensitivity which gives saturation at a certain biomass level [45], however the most important variable in our study is the topographic effect. Although various radiometric corrections have been developed to normalize the SAR imagery [19,26,27,46] the radiometric accuracies of those correction methods can always be challenged. However, without their application, the topographic effects caused by rugged terrain will remain a crucial issue, especially for the study area here in Japan, and can result in higher errors compared to not applying them. There might be solutions to improve the interpretation of the modeling, but in this first study on national-scale estimation using remote sensing, we believe that the result can serve as a standard. Note that during the relationship analysis, speckle filtering was not applied to the PGM data. This decision was made because the data were downscaled to 25 m resolution from the original, which averaged out the area, reducing the noise. Further filtering might lose the actual information from the surface objects. However, in the future, a different filtering method might be applied to evaluate whether the difference affects the results.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 1 Introduction
[28] presented the design and analysis of securely-entrusted multi-topology routing (SEMTOR), a set of routing-protocol mechanisms that enable the cryptographically secured negotiation and establishment of concurrent and individually trusted routing topologies for infrastructure-less networks without relying on any central management. [PERSON] et al. [29] proposed the framework that deals with the security flaws through hyper elliptic curve based public key cryptosystem, which combines Digital Signature Algorithm (DSA), ElGamal approaches that ensure the entity authentication, and secure group communication. The UAV information transmission is a complex task. It is important to design a routing protocol that can provide efficient and reliable node to node packet transmission. In this paper, an efficient digital signature algorithm based on elliptic curve cryptosystem is applied to routing protocol,  
Figure 1: The attacked unmanned aerial vehicle (UAV) communication network. and an improved security method suitable for on-demand routing protocol is proposed. Through the simulation of three routing protocols (AODV, SAODV and ISAODV), the performance indicators such as packet delivery rate, throughput, end-to-end delay, and routing overhead are compared and studied. The simulation results show that the performance of the ISAODV and SAODV routing protocols in terms of packet delivery rate, throughput, and routing overhead is very close to the AODV routing protocol. This shows that the ISAODV and SAODV routing protocols inherit the characteristics of the AODV routing protocol and maintain the route discovery and route maintenance capabilities of the AODV routing protocol to the greatest extent. In addition, because each node on the active path of the SAODV protocol must be authenticated and signed based on the certification authority (CA) certificate, the complexity of information transmission is much higher than that of ISAODV based on the elliptic curve cryptosystem. The ISAODV routing protocol proposed in this paper effectively reduces the complexity of the algorithm on the basis of improving network security, and provides a powerful guarantee for the security of UAV communication networks. The remaining sections of this paper are organized as follows. Section 2 describes the principle of security AODV routing protocol. Section 3 describes the principle of improved security AODV routing protocol. Section 4 describes the simulation and numerical results, and the performance indicators such as the packet delivery rate, throughput, and end-to-end delay of the UAV communication network are compared and analyzed. Finally, Section 5 summarizes the paper.
## 3 Observation Models in Single or Multi-GNSS
### Other Typical Observation Models  
In PPP, the most widely used models are conventional ionosphere-free model (CIF) and uncombined model (UC), including uncombined model with triple-frequency (UC3), uncombined model with dual-frequency (UC2), conventional ionosphere-free model with triple-frequency (CIF3), conventional ionosphere-free model with dual-frequency (CIF2). The Table 1 summaries the differences of the models, where each equation in CIF has IFB or no IFB. Namely, IFBs in CIF can not be divided from receiver clock, there is no IFB in estimated parameters.  
The observation equations in CIF are respectively either the combining pseudoranges or the combining carrier phases as follows.  
\[P_{CIF3}^{sys}=k_{1,P}P_{1}^{sys}+k_{2,P}P_{2}^{sys}+k_{3,P}P_{3}^{sys} \tag{32}\]  
\[L_{CIF3}^{sys}=k_{1,L}L_{1}^{sys}+k_{2,L}L_{2}^{sys}+k_{3,L}L_{3}^{sys} \tag{33}\]  
where \(k_{1,P}\), \(k_{2,P}\), \(k_{3,P}\), \(k_{1,L}\), \(k_{2,L}\), and \(k_{3,L}\) are combining coefficients of the pseudorange and carrier phase. The six coefficients satisfy the following Formula (34) to make sure the pseudorange and carrier phase measurements have the properties of geometry preserving, ionosphere-free, and the lowest noise propagation. The coefficients of the pseudorange are equal to the coefficients of the carrier phase. Table 2 gives dual-frequency the specific values of coefficients for GPS and BDS systems.  
\[\left\{\begin{array}{l}k_{1}+k_{2}+k_{3}=1\\ k_{1}+k_{2}f_{1}^{2}/f_{2}^{2}+k_{3}f_{1}^{2}/f_{3}^{2}=0\\ k_{1}^{2}+k_{2}^{2}+k_{3}^{2}=MIN\end{array}\right. \tag{34}\]  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Model** & **System** & **Observations** & **Parameters** & **Parameter Description** \\ \hline \multirow{3}{*}{UC3} & Single & 6n & 4n+5+1 & X, Y, Z, _dt, Tw_, IFB, n*(\(I\),\(N_{1}\),\(N_{2}\),\(N_{3}\)) \\  & Multi & 6n & 4n+5+m+1 & X, Y, Z, _dt, Tw_, ISB, m*(\(I\),\(N_{1}\),\(N_{2}\),\(N_{3}\)) \\ \cline{1-1} \multirow{3}{*}{CIF3} & Single & 2n & n+5 & X, Y, Z, _dt, Tw_, n*(\(I\),\(N_{1}\),\(N_{2}\),\(N_{3}\)) \\  & Multi & 2n & n+5 & X, Y, Z, _dt, Tw_, n*(\(I\),\(N\),\(N\)) \\ \cline{1-1} \multirow{3}{*}{UC2} & Single & 4n & 3n+5 & X, Y, Z, _dt, Tw_, n*(\(I\),\(N_{1}\),\(N_{2}\)) \\  & Multi & 4n & 3n+5+1 & X, Y, Z, _dt, Tw_, ISB, n*(\(I\),\(N\),\(N\)) \\ \cline{1-1} \multirow{3}{*}{CIF2} & Single & 2n & n+5 & X, Y, Z, _dt, Tw_, n*(\(I\),\(N\),\(N\)) \\  & Multi & 2n & n+5+1 & X, Y, Z, _dt, Tw_, ISB, n*\(N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precise point positioning (PPP) Models comparison with multi-satellites n and multi-systems m. Uncombined model with triple-frequency (UC3), uncombined model with dual-frequency (UC2), conventional ionosphere-free model with triple-frequency (CIF3), conventional ionosphere-free model with dual-frequency (CIF2), inter-system clock bias (ISB), inter-frequency clock bias (IFB).