esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Tilt Angle Dependencies
### Optimal Tilt Angle Dependence on the Height  
In this section, the variation in \(\gamma_{\rm opt}\) is observed when changing the module height. The simulations considered \(\alpha\) equal to 0.75, and the remaining operating conditions were as in the previous case.  
Since it was observed that changing the height of the module \(\gamma_{\rm opt}\) did not change (equal to 45\({}^{\circ}\)), in Figure 13 are reported the changes in \(G_{\rm front}\), \(G_{\rm rear}\), \(G_{\rm eq}\), and \(BG\), when the height changed from 1 m to 5 m and \(\gamma\) was \(\gamma_{\rm opt}\). The changes in \(G_{\rm front}\), \(G_{\rm rear}\), and \(G_{\rm eq}\), were evaluated through the energy gain \(EG\), obtained as  
\[EG_{\rm c,h}=100(y_{\rm c,h}-y_{\rm c,1})/y_{\rm c,1} \tag{10}\]  
where \(y_{\rm c,h}\) is the annual insolation of the specific component (front, rear, or equivalent) at height \(h\), and \(y_{\rm c,1}\) is the annual insolation of the specific component (front, rear, or equivalent) at 1 m of height. The solar irradiance of the back surface increased the most with the height compared to the other components. However, the equivalent component exhibited a significantly smaller change due to the absence of a corresponding change in the front component. In fact, even when \(EG_{\rm rear,5}\) was 21.6%, \(EG_{\rm eq,5}\) was only 5.5%. This was because the annual rear insolation magnitude was still much lower than the front one (i.e., at a hub height of 5 m, the annual front insolation was two times higher than the rear one).  
Figure 12: Bifacial gain (purple), optimal tilt angles for annual front insolation (green), rear (blue), equivalent (firebrick), maximum insolation magnitude at optimal tilt angle for the front (green), and rear (blue) surfaces, for different ground-albedo scenarios.  
Moreover, \(BG\) increased with the hub height, meaning that a higher height led to better exploitation of the rear surface.
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## Abbreviations  
The following abbreviations are used in this manuscript:  
AP Access Point  
BS Basic Service Set  
CA Collision Avoidance  
CSMA Carrier-Sense Multiple Access  
CST Carrier-Sense Threshold  
DCF Distributed Coordination Function  
DSC Dynamic Sensitivity Control  
MCS Modulation and Coding Scheme  
RSSI Received Signal Strength Indicator  
SNR Signal-To-Noise Ratio  
SR Spatial Reuse  
WLAN Wireless Local Area Networks  
WMA Weighted Moving Average
## 4 Results and Analysis
### Results of the Online Questionnaire of Public Space Design Stakeholders
The use of just a few 3D images in communication was only appreciated by one fourth of the non-designers, while almost half of the designers found it to be sufficient. There is more interest in 3D video; just under 50% of designers and about half of the non-designers favored the medium. Similar scores were observed for the use of interactive online 3D tools. 3D co-design in workshops was appreciated by half of the non-designers, whereas designers felt slightly less of a need for it. About one fifth of non-designers advocated for a 3D co-design, whereas half fewer designers advocated for it. One in three designers thought that a 3D co-design was too complicated, whereas half fewer non-designers thought the  
Figure 5: The use of 2D/3D and VR/AR in participation. same. There was more enthusiasm for using 3D, which was always advocated by slightly more non-designers compared with designers. Only one fourth of participants favored using VR and AR glasses in participation, with this view being shared slightly more by non-designers than designers. A third of all participants saw the benefits of AR on smartphones when communicating about new projects. There was no majority advocating the use of VR and AR in the communication and participation of the design of a public domain. The online survey included several questions that were particularly relevant for determining the designers' 3D expertise. For the preliminary design stage survey, more than half of the designers analyzed the surroundings in 2D, with only 10% using 3D scanning, although that number seems to be increasing. Remarkably, an overwhelming majority felt that 3D data on surrounding buildings was not crucial to the design. At the start of the design phase, most designers still designed in 2D; only 15% used a 3D symbol library. Half of the designers used 3D for volume studies and only a quarter used 3D for detail studies. Even further into the design process, 3D design is still not the standard. On the output side, a third of designers used static 3D images at the end of the design process. Only 7% used video in 3D design simulations and only 4% used interactive 3D models. A limited number of designers had experience with game engines. These results show that 2D is still the standard in the survey, design, and communication phases.
# Meteorological Conditions that Promote Persistent Contrails
## 2 Data and Methods
### Normalised Geopotential and Estimation of Lapse Rates  
In this section, we explain the normalisation procedure for geopotential. The geopotential height increases with decreasing pressure. If one wants to determine conditional distributions of geopotential height under different conditions, either this has to be done for each pressure level separately or one finds a way to normalize its value by a prescribed standard geopotential height for each pressure level. Such a standardisation is possible as follows: Using a mean tropospheric temperature, \(\overline{T}=255\) K, we determine a corresponding mean pressure scale height, \(\overline{H}=R_{d}\overline{T}/g=7452\) m, with the gas constant for dry air, \(R_{d}\), and the gravitational acceleration, \(g\), (which we simply assume constant). We then define standard geopotential heights:  
\[Z^{*}(p)=\overline{H}\,\ln(p_{s}/p). \tag{1}\]  
Here, the reference pressure \(p_{s}=1000\) hPa, and \(p\) is one of the selected pressure levels. The resulting values are \(Z^{*}(300,250,225,200\,\mathrm{hPa})=(8972,\,10,331,\,11,116,\,11,994)\) m. Normalisation of \(Z\) with the appropriate \(Z^{*}(p)\) results in unified ranges of about 0.9 to 1.1 for each level. Obviously, pressure levels vary quite considerably in altitude; the isobar at a nominal height of 10 km may actually vary between 9 and 11 km.  
Furthermore, we analyse conditional distributions for the lapse rate, i.e., \(\gamma=-\mathrm{d}T/\mathrm{d}z\). The procedure to estimate the lapse rate at the position of an aircraft is provided in a recent paper [16]. Hence, it suffices to give the final formula:  
\[\gamma=\frac{g}{R_{d}}\frac{\ln(T_{0}/T_{1})}{\ln(p_{0}/p_{1})}, \tag{2}\]where the \((T_{0},p_{0})\) are the temperature and pressure of the nearest pressure level below the flight position, respectively, and \((T_{1},p_{1})\) are the corresponding values on the closest level above that point. Note that the lapse rate cannot be estimated for flight positions at 300 hPa or below, nor at 200 hPa and above, since only the data at the four pressure levels from 300 to 200 hPa are used in the analysis.
## 4 Numerical Simulations
### TianQin Orbit Uncertainty Propagation  
To demonstrate the effectiveness and practicality of the proposed AGMM-VSG in solving uncertainty propagation of computationally expensive, black-box systems, as well as the application in real-world space-based GW observatories, a case of TianQin Orbit (TQO) is investigated.  
The state of the detector is defined as \(\mathbf{x}=\left[r_{x},r_{y},r_{z},\upsilon_{x},\upsilon_{y},\upsilon_{z}\right]^{T}\). The high-fidelity dynamics model includes an 8 \(\times\) 8 spherical-harmonic Earth gravity field (JGM-3), the point-mass gravity field from the Moon, and the Sun (the ephemeris [14]). The spacecraft of TianQin each have a disturbance reduction system (DRS) needed to reduce the effects of the non-gravitational forces [54]; thus, the solar radiation pressure and the atmospheric drag are neglected.  
The elements of the nominal orbit of one detector of TianQin are shown in Table 5. The orbit is propagated in one period, which is about 314,710.3177 s.  
Both the AGMM-VSG and the fifth-order CR are applicated to the above TQO problem of predicting the evolution of uncertainty. In employing the AGMM-VSG, the criterion for splitting the Gaussian components is set to be \(\upsilon>10^{-3}\), and the number of evaluations for constructing the surrogates in each iteration is set to 300. The final state uncertainty and their corresponding means and \(3\sigma\) ellipsoids, using proposed AGMM-VSG and MC, are projected in Figure 9. It is witnessed that the accuracy of AGMM-VSG and MC are almost the same when considering the first two moments. Moreover, the projection of the predicted PDF from the AGMM-VSG, together with the 1000 Monte Carlo samples, are shown in Figure 10. It is clearly observed that both the implementations of the position and velocity of the AGMM-VSG approach characterize the non-linearity of the MC samples-approximated distribution.  
Figure 8: Likelihood agreement measure for the AGMM, AGMM-VSG, and fifth-order CR of LEO case.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline _a/_km** & \(e\) & _i/_deg** & _\(\Omega\)/_deg_ & _\(\omega\)/_deg_ & _n/_deg_ \\ \hline
99,995.572323 & 0.000430 & 94.697997 & 210.445892 & 358.624463 & 61.329603 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Elements of the nominal orbit of TQO case.
## 2 Emergency State and Automatic Generation Control
### Framework of Automatic Generation Control  
A basic AGC/LFC model contains two control areas. Each control area contains an AGC controller, a governor, a non-reheat turbine generator, and a system power flow load \(\Delta P_{\rm LA}\) or \(\Delta P_{\rm LB}\)[33]. Three major features required for the AGC controller in an interconnected power system can be summarized as follows.  
1. The controller should provide generation commands to the AGC unit to balance the real-time active power flow between the generator and system loads;
2. The controller should reduce the frequency deviation in the control area;
3. The controller should decrease the scheduled tie-line power deviation between any two areas, i.e., mitigate the value of the ACE.  
Therefore, the frequency deviation \(\Delta f\), the ACE \(e_{\rm ACE}\), the scheduled tie-line power deviation \(\Delta P_{\rm T}\), CPS indices (including CPS index \(k_{\rm CPS}\), CPS1 index \(k_{\rm CPS1}\), and CPS2 index \(k_{\rm CPS2}\)) are the inputs to the AGC controller. The generation command provided to the AGC unit is then the output of the controller.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 2 IPT System Design  
Figure 1 illustrates the static IPT system setup specifically designed for battery electric locomotives. The system employs one or multiple transmitter pads integrated with magnetic concrete strategically placed between the railroad tracks and paired with receiver coils attached directly under the trains. The transmitter coils embedded within the track structure generate a magnetic field using magnetic coils which is supported by the magnetic concrete slab enhancing the magnetic coupling.  
While not a subject of the current paper, battery electric train technology employs lithium-ion batteries (LIBs) in racks that connect to the powertrain via power electronic controls, as shown in Figure 2. The front section of the train houses the power electronics units, highlighted in blue, responsible for handling power conversion, management, and efficient control during wireless charging operations. The rear portion predominantly accommodates battery storage, ensuring ample energy capacity for train propulsion. This configuration also includes the connection between the onboard power electronics and the receiver coils that facilitate the energy transfer from the power transmitter embedded between the railroad tracks.  
Figure 1: Wireless power charging of electric locomotives showing side view and front view.
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
## 4 Multi-Objective Optimization of Concave I-Shaped Honeycomb Structure
### Multi-Objective Optimization Results and Discussion  
The 401 sets of data are optimized, yielding 146 sets of workable optimum solutions. Figure 22 depicts the Pareto front distribution of SEA and PCF. SEA and PCF have a significant positive correlation, and as SEA steadily rises, so does PCF.  
The design objectives SEA, PCF, and mass are 22.196 kJ/kg, 7.433 kN, and 7.777 g, respectively. The values are \(S\) = 6.026 mm, \(h\) = 2.0 mm, \(b\) = 2.039 mm, and \(t\) = 0.501 mm for each optimization parameter.  
Figure 21: The optimization process of NSGA-II.  
Figure 22: Pareto fronts from the surrogate model.  
with decreases in the vertical short cell wall height \(h\) and the horizontal short cell wall \(b\). The structure has negative Poisson's ratio characteristics at certain dimensions.
* The cellular length \(S\), the vertical short cellular length \(h\), the horizontal short cellular length \(b\), and the cellular thickness \(t\) are defined in the cellular structure, and the effects of these four parameters on the specific absorption energy and the peak crushing force of the impact resistance index of the cellular structure are investigated. The results show that the values of specific absorption energy and peak crushing force decrease with increases in the variables \(S\) and \(h\), and they increase with increases in the variables \(b\) and \(t\), which are all factors affecting the impact resistance of the cellular structure.
* The specific absorption energy and peak crushing force are found to be positively correlated by optimizing the design. The geometric parameters of the optimal structure are \(S\) = 6.026 mm, \(h\) = 2.0 mm, \(b\) = 2.039 mm, and \(t\) = 0.501 mm. The values of specific absorbed energy, peak force, and mass are 18.959 kJ/kg, 7.452 kN, and 4.710 g. Compared with the original design, the SEA increased by 44.175%, and the PCF increased by 25.857%. Meanwhile, the mass decreased by 31.140%. Hence, the optimal structure has better crashworthiness.  
Methodology, M.L.; software, M.L.; validation, T.W. and M.L.; formal analysis, D.Q.; writing--original draft preparation, M.L.; writing--review and editing, T.W.; visualization, T.W.; supervision, D.Q.; project administration, J.C. and H.W.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nature Science Foundation of China, funding number 51705468 and the Key Technologies Research and Development Program, funding number 2018 YFB0106204.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 1 Introduction
This paper and all presented results are the result of doctoral research. The methodology and part of the results were taken from the doctoral dissertation [2]. The main contributions of this paper are:  
* The application of different types of wavelets including db4 and db8 and one-level decomposition of images obtained by using the discrete wavelet transform on Oak Ridge Spoofing and Interference Test Battery (OAKBAT) GPS and Galileo datasets in static scenarios for GNSS spoofing detection. * The detection of fake signals and classification of signal type using an integrated approach based on the discrete wavelet transform and machine learning models. * The comparison of the performance of machine learning models using different types of input classification data. * Reducing the computational complexity of the proposed approach by using pre-extracted classification input data. This paper is organized as follows. The Section 1 gives an introduction, motivation, and the aim of the research. The Section 2 provides an overview of the methods for detecting spoofing attacks that have been explored so far. In Section 3, the proposed methodology used in our research is presented. The Section 4 presents the results obtained by using the proposed approach, while Section 5 brings conclusions.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
###### Abstract  
This study reports the application of an optoacoustic method for evaluating the bonding strength of ultrasonically bonded joints in a non-destructive and non-contact fashion. It is proposed that the bonding strength is correlated with the resonant frequency of bonded joints. The bonding strength measured with a destructive tensile test roughly increased with the vibration time, however, it varied, causing the transitional and dispersed formation of micro-bonds at the bonding interface. Scanning Electron Microscopic observation of the fractured surface suggested that the bonding strength depends on the total bonded area of micro-bonds. Frequency response of the bonded joint was examined with a non-destructive method using a piezo-electric vibrator. The experiment revealed that the resonant frequency exponentially increased with the bonding strength. In addition, this vibration behavior was dynamically visualized with electronic speckle pattern interferometry (ESPI). The correlation between the bonded area and the resonant frequency is discussed based on finite element analysis. The results indicate the possibility for in-situ evaluation of the ultrasonic bonding strength.  
ultrasonic bonding; bonding strength; nondestructive testing; resonant frequency; electronic speckle pattern interferometry (ESPI) +
Footnote †: journal: Applied Physics Letters
# Signal Consensus in TSP of the Same Grid in Road Network
## 2 Prior Knowledge and Consensus Algorithm
### Description of Consensus Algorithm  
A topology network \(\mathbf{G}=\{\mathbf{V},\mathbf{E}\}\) is composed of \(n\) agents, each node represents an agent \(\dot{x}_{i}=u_{i}\). If the state of all agents eventually converges, that is \(\|x_{i}-x_{j}\|\to 0\), the system tends to be consistent.  
The continuous time consensus algorithm of basic fixed topology [26] is described as:  
\[\dot{x}_{i}(t)=\sum\limits_{j\in N_{i}}a_{ij}(x_{j}(t)-x_{i}(t)). \tag{3}\]  
**Lemma 1**.: _Let \(G\) be a connected undirected graph. Then, the algorithm in (3) asymptotically solves an average-consensus problem for all initial states [22]._  
**Lemma 2**.: _Let \(G\) be a strongly connected direct graph on \(n\) nodes. Then \(rank(\mathbf{L})=n-1\) and all nontrivial eigenvalues of \(L\) have positive real parts. Furthermore, suppose \(G\) has \(c\geq 1\) strongly connected components, then \(rank(\mathbf{L})=n-c\)[27]._  
**Definition 1**.: _A direct graph \(G\) is called balanced if \(\sum_{j\
eq i}a_{ij}=\sum_{j\
eq i}a_{ij}\) for all \(i\in V\)[26]._  
**Definition 2**.: _A spanning tree \(T\) of an undirected graph \(G\) is a subgraph that is a tree that includes all of the vertices of \(G\), with minimum possible number of edges [28]._  
**Theorem 1**.: _A topology network \(\mathbf{G}=\{\mathbf{V},\mathbf{E}\}\) is composed of \(n\) agents. Each agent applies the following consensus algorithm (a). If the network \(G\) is a strongly connected direct graph, then:_  
1. _A consensus is asymptotically reached for all initial states;_
2. _The group decision value is_ \(\alpha=\sum_{i}\omega_{i}x_{i}(0)\) _with_ \(\sum_{i}\omega_{i}=1\)_;_
3. _If the direct graph is balanced, an average-consensus is asymptotically reached and_ \(\alpha=(\sum_{i}x_{i}(0))/n\)[22].
## 3 Method and Data
### Data Collection  
Considering data availability and reliability, this study selected panel data from 30 Chinese provinces (excluding Tibet, Hong Kong, Macau, and Taiwan) spanning the years 2011 to 2021 as the research sample. The data used in this study were primarily obtained from the Chinese Research Data Services Platform (CNRDS), the China Construction Industry Statistical Yearbook, the China Energy Statistical Yearbook, the Digital Finance Research Center at Peking University, and various provincial statistical yearbooks, among other official and authoritative data sources. Furthermore, to ensure data completeness and scientific rigor, linear interpolation was employed to estimate missing values.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 3 Design and Performance Analysis of Sparse SAR Imaging Mode
### Mixed-Norm Regularization-Based Sparse SAR Imaging
The \(L_{2,q}\)-norm regularization-based method has also been applied to other sparse modes, such as TOPS [40]. The convergence analysis of this method is detailed in [42].
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results
### Optimization of \(w_{i}\) and \(\alpha_{i}\)  
We now consider an optimization problem to find the optimal power-splitting ratios, \(w_{i}\) (\(w_{2}=1-w_{1}\)) and \(\alpha_{i}\), to minimize the worst PER among both users when using D-NOMA-HARQ. For a given SNR and rate \(R=k_{j}/n\), the optimization problem is summarized below:  
\[\min_{\{w_{1},\alpha_{1},\alpha_{2}\}}\max_{i}\ \xi_{i} \tag{12}\] \[\text{s.t.}\ \ C_{1}.0\leq\alpha_{2}\leq\alpha_{1}\leq 1,\] \[\ C_{2}.0.5<w_{1}\leq 1,\]  
Figure 14: Effect of \(w_{i}\), \(\alpha_{1}\) and \(\alpha_{2}\) on the PER performance of D-NOMA-HARQ with CC-HARQ at \(R=0.5\) and SNR = 4 dB.  
Figure 13: Effect of \(w_{i}\) over delay performance of S-NOMA-HARQ with CC-HARQ at \(R=0.5\) and SNR = 4 dB.  
condition C\({}_{1}\) is to limit the search space for practical optimization, and C\({}_{2}\) is to allocate a higher power to user 1.  
We numerically solved (12) by fixing \(w_{i}\) and finding \(\alpha_{1}\) and \(\alpha_{2}\). Table 1 shows optimal values of \(\alpha_{1}\) and \(\alpha_{2}\) when \(R=0.5\) at different SNRs and \(w_{i}\). As can be seen in the Table, IR-HARQ performance is slightly better than CC-HARQ. Furthermore, as SNR increases, higher values of \(\alpha_{i}\) can be chosen to improve the performance. Also, with a higher value of \(w_{1}\), PER performance improves due to better SIC, with proper choice of \(\alpha_{i}\). The parameter \(w_{i}\) refers to user pairing. As BS is assumed to know the CSI of each user, it can choose \(w_{i}\) by pairing user with specific power difference to meet the target PER requirements of each user. As shown in Table 1, when \(w_{1}=0.55\), the PER of \(10^{-6}\) can be achieved for both users by choosing \(\alpha_{1}=\alpha_{2}=0.6\). The advantage of D-NOMA-HARQ is that this level of reliability can be achieved with much less latency compared with S-NOMA-HARQ.  
Compared to standard NOMA, D-NOMA-HARQ has a higher SIC decoding complexity, which also incurs some delay in the decoding. This is because the S-NOMA-HARQ receiver performs SIC to separate the signals of different users, whereas, in D-NOMA-HARQ, retransmissions and regular packets of a user are also separated using SIC. However, the additional decoding delay is much less than the delay caused due to packet retransmissions, feedback, processing, and queuing delays. Fortunately, the SIC complexity can be significantly reduced using efficient parallel decoding techniques. However, the exact analysis is beyond the scope of this work and would be more relevant when actual encoder and decoder such as low-density parity-check codes (LDPC) are being used. The exact complexity analysis can be performed by adding associating SIC cost to the retransmission states of the MM in Figure 2. The analysis can be extended to incorporate the decoding delay simply by adding delay penalty at packet retransmission state \(J=1\) of the MM. Furthermore, D-NOMA-HARQ requires multi-bits feedback signaling due to more received signals in a time slot during retransmission than S-NOMA-HARQ.