esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Prediction of Cluster Member Movement  
#### 4.5.1 Problem Description  
Because UAVs are not always relatively static, cluster heads need to adjust their transmitting power in real-time according to the distance between the UAVs and cluster members. This can avoid resource waste caused by excessive transmitting power or the failure of cluster members to receive information due to the CH's insufficient transmitting  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Grid** & **P/mW** \\ \hline Purple & \(P_{1}\) \\ Dark blue & \(P_{2}\) \\ Light blue & \(P_{3}\) \\ Green & \(P_{4}\) \\ Yellow & \(P_{5}\) \\ Orange & \(P_{6}\) \\ Red & \(P_{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transmitting power corresponding to different grids.  
Figure 3: Each color grid represents the power required by the cluster head to transmit the signal. The transmitting power corresponding to different grids is shown in Table 2. (Note that this figure is a demonstration diagram for easy narration, the specific range is adjusted according to the calculation).  
power, as shown in Figure 4. A movement prediction mechanism is set up in this paper, which is used to predict the grid position of its members in the next transmission cycle and adjust the transmission power in time.
## 3 System Design
### Micro-Diphragm Pump Drive  
The micro-diaphragm pump was controlled by the PWM technology. When the spray controller received the prescription value information, the high-level time was used as the percentage of the whole cycle of change, thus obtaining the PWM square wave signals with different duty cycles. Since the PWM signal generated by the STM32 series microcomputer (MCU) was 5 V and the rated voltage of the micro-diaphragm pump was 12 V, the PWM square wave output from the I/O port of the MCU cannot directly drive the micro-diaphragm pump. As a result, the driving amplification effect of the metal-oxide-semiconductor (MOS) transistor was used. Moreover, the rotation of the micro-diaphragm pump was drive by the PWM square wave signal.  
When the UAV was in flight operation, the variable spray system adjusted the rotational speed of the diaphragm pump by changing the duty ratio of the PWM square wave signal according to the prescription value. As a result, the flow rate of the system was adjusted. The period of the PWM square wave signal was set to 200 ms. When the duty ratio was lower than 40%, the micro-diaphragm pump could not be started. To ensure normal operation of micro-diaphragm pump, the duty ratio changed within the range of 40% to 100% during the test. In order to measure the change of flow clearly, the duty cycle of the PWM square wave signal was increased by 5% each time.  
The Hall flow sensor was used to measure the single nozzle flow rate of the micro-diaphragm pump under different duty ratios. Additionally, the relationship between the flow rate of the micro-diaphragm pump and the duty ratio of PWM square wave signal was obtained. The result is shown in Figure 8. By using the cubic polynomial to fit the actual flow rate and duty cycle curve [15; 16], the relationship between the flow rate of the variable system and the duty cycle of the PWM square-wave signal is:  
\[duty=(20.776v^{3}-21.452v^{2}+8.242v-0.431)\times 100\% \tag{1}\]  
where \(v\) is the flow rate of a nozzle in L/min, and \(duty\) is the duty cycle of the PWM square wave signal in %.
## 3 The Proposed EFD Framework Based on Convolutional GAN and History-State Ensemble
### Dimension Reduction of the Raw AE signals  
The high sampling frequency of AE signals results in a large amount of acquired data, which increases the computational burden of the model. Therefore, a Moving Variance Window (MVW) is applied to the raw AE signals for dimension reduction, as illustrated in Figure 6. With the step-wise shifting of the window, the variance of the covered signal is calculated, i.e., each windowed data segment is transformed into a single value of the variance. The output is a dimensionless number, which measures the dispersion of the data; thereby, the sub-signal is de-dimensionalized. The function of MVW is to capture the transient events and highlight some essential detailed features of the data. Moreover, since the signal dimension is vastly reduced, processing by the neural network is faster and easier. The MVW is mathematically described as follows:  
\[\hat{\textbf{X}}=\frac{1}{l}\sum_{\textbf{x}\in min_{k,l,s}^{\textbf{X}}}| \textbf{x}-\mu|^{2} \tag{19}\]  
\[\textbf{win}_{k,l,s}^{\textbf{X}}=\textbf{X}\Big{[}x_{start}^{k,s}:x_{start}^{ k,s}+l-1\Big{]} \tag{20}\]  
\[x_{start}^{k,s}=(k-1)\times s+1,\;\;k=1,2,\ldots n_{k} \tag{21}\]  
where **X** denotes the raw AE signal, \(\textbf{win}_{k,l,s}^{\textbf{X}}\) represents the area of signal **X** covered by the moving window, and \(k\), \(l\) and \(s\) are integers specifying the moving step, window length and moving stride, as illustrated in Figure 6. \(x_{start}^{k,s}\) is the start point of the window on signal **X**. The total number of moving steps is computed as \(n_{k}=[(N-l)/s]+1\), where \(N\) is the length of the recorded AE signal; \(\mu\) in definition (19) denotes the mean of \(\textbf{win}_{k,l,s}^{\textbf{X}}\). The MVW applies a moving window slide over the original AE signal to extract the variance; thus, the signal dimension can be largely reduced, which makes it easier to be processed by the neural network. Additionally, the MVW helps to capture the transient events and highlight some important detailed information in the data.  
Figure 6: Illustration of the moving window \(\textbf{win}_{k,l,s}^{\textbf{X}}\).
# A Quantum Detectable Byzantine Agreement Protocol Using Only EPR Pairs
## 3 The 3 Player EPRQDBA Protocol  
### Entanglement Distribution Phase  
The EPRQDBA protocol can be conceptually organized into 3 distinct phases. The first is the entanglement distribution phase, which refers to the generation and distribution of qubits and entangled EPR pairs. As we have briefly explained in assumption (\(\mathbf{A_{1}}\)), we assume the existence of a trusted quantum source that undertakes this role. It is a relatively easy task, in view of the capabilities of modern quantum apparatus. Hence, the quantum source will have no difficulty in producing  
* \(2m\) qubits in the \(|+\rangle\) state by simply applying the Hadamard transform on \(|0\rangle\), and
* \(2m\) EPR pairs in the \(|\Psi^{+}\rangle\) state, which can be easily generated by contemporary quantum computers.  
In the above scheme, the parameter \(m\) is a properly chosen positive integer. Further suggestions regarding appropriate values of \(m\) are provided in the mathematical analysis of the protocol.  
Afterwards, the source transmits the qubits to the intended recipients through the quantum channels following the pattern outlined below.  
* The \(2m\) EPR pairs are numbered from \(0\) to \(2m-1\).
* From every EPR pair \(k\), where \(0\leq k\leq 2m-1\), the first qubit is sent to [PERSON].
* The source sends to [PERSON] \(2m\) qubits: \(q^{\prime}_{0^{\prime}}\), \(q^{\prime}_{1}\),..., \(q^{\prime}_{2m-2}\), \(q^{\prime}_{2m-1}\). The transmission alternates qubits in the \(|+\rangle\) state (in the even positions of this sequence) with the second qubit of the odd-numbered EPR pairs (in the odd positions of this sequence).
* Symmetrically, the source sends to [PERSON] \(2m\) qubits: \(q^{\prime\prime}_{0}\), \(q^{\prime\prime}_{1}\),..., \(q^{\prime\prime}_{2m-2}\), \(q^{\prime\prime}_{2m-1}\). In the even positions of this sequence, the source inserts the second qubit of the even-numbered EPR pairs and in the odd positions qubits in the \(|+\rangle\) state.  
The end result is that the quantum registers of [PERSON], [PERSON] and [PERSON] are populated as shown in Figure 1. Qubits of the same EPR pair are shown in the same color. Red is used to indicate the EPR pairs shared between [PERSON] and [PERSON], which occupy the odd-numbered positions in their quantum registers. Analogously, green is used for the EPR pairs shared between [PERSON] and [PERSON] that occupy the even-numbered positions in their quantum registers. The gray qubits represent those in the \(|+\rangle\) state that occupy the remaining positions in [PERSON] and [PERSON]'s registers.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 2 System Composition  
In this paper, the center and attitude positioning method for cross laser-pattern has been successfully applied to the position and attitude measurement system in the real-time measuring device of lever-arm vector in shipboard. Given the good directivity and energy concentration of a laser, the attitude measurement system takes the cross-laser beam fixed near the main inertial navigation system (INS) as the measurement datum, and measures the displacement and attitude of the measured position relative to the main inertial navigation system in real time. The system consists of two parts: laser emission system and image acquisition system, as shown in Figure 2.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 5 Conclusions and Future Works  
This article proposes a novel joint optimization algorithm for the radar detection-communication transmission of DAR. Compared with current research on the resource allocation of DAR, the JRCO algorithm is mainly of the following two prominent features: firstly, it synergistically utilizes the degrees of freedom in both the radar system and communication system. It overcomes the limitation of traditional radar resource allocation that only focuses on the optimization variables of radar systems. The proposed JRCO algorithm could achieve a better radar detection performance while ensuring communication performance constraints. The second is that the JRCO algorithm was completely distributed, which was in stark contrast to the current centralized algorithms for resource allocation. Distributed algorithms can enable the system to be of a higher task execution efficiency, lower communication transmission delay, and stronger anti-damage ability.  
In order to implement the JRCO algorithm, this article first took the estimation of the target location in the passive AOA measurement mode as an example; this comprehensively modeled the system from various aspects, such as the location error and communication coverage of the UAV and provided a formal representation of the joint optimization problem. Then, the JRCO algorithm was elaborated into eight subsections. Finally, the effectiveness of the JRCO algorithm was verified by a numerical simulation in formal verification that is, the JRCO algorithm could achieve a better target localization performance.  
However, the main drawback of the JRCO algorithm was that the iteration and update process was relatively slow. From Figure 4e, it could be seen that as the number of the UAV swarm increased, the iteration efficiency of the JRCO algorithm significantly decreased. In future work, we intend to focus on improving this issue. In addition, more complex radar systems and communication system topologies that are suitable for airborne radar will be considered. For radar systems, it is also possible to optimize the CRLB for target position estimation under active detection background. For communication systems, the presence of relays should be considered.  
Methodology, G.D. and Q.W.; Supervision, Y.H. and J.Y.; Writing--original draft, G.D.; Writing--review and editing, J.Y. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 91948303.  
Not applicable.  
The authors declare no conflict of interest.
# Advances in High-Precision NO2 Measurement by Quantum Cascade Laser Absorption Spectroscopy
## 2 Materials and Methods
### Field Inter-Comparison Setup and Instrumentation  
An inter-comparison between our custom-made QCLAS and three different commercial instruments took place in winter 2019/2020 at a suburban air quality monitoring station (47\({}^{\circ}\)24\({}^{\prime\prime}\)09.2\({}^{\prime\prime}\) N, 8\({}^{\circ}\)36\({}^{\prime\prime}\)51.9\({}^{\prime\prime}\) E) of the Swiss National Air Pollution Monitoring Network (NABEL) in Dubendorf, located 10 km southeast of the city of Zurich. The station consists of two rooms, main and annex, each having its own independent inlet of similar design and an air intake located 4 m above the ground and 1.5 m above the rooftop. The air intakes are horizontally separated by 5 m and therefore considered collocated. The gas flow through the inlet is 300 L/min and the corresponding residence time is about 1.2 s. Prior to the ambient air measurements, all NO\({}_{2}\) instruments were calibrated with three different calibration methods (see Section 2.2.2). Ancillary measurements (temperature, humidity and O\({}_{3}\)) were provided by the NABEL.
## 4 Discussion  
### Satellite Geometries  
In the MDPO demonstration experiment, the normal matrix \(\mathbf{G}^{\mathrm{T}}\mathbf{G}\) in Equation (18) sometimes approaches nearly singular and the corresponding HDOP value becomes extremely large. When a pair of GPS satellites are chosen as shown in Figure 3, they are usually allocated in different orbital planes. Since the shapes of these two GPS orbits are nearly identical except for the right ascension of the ascending node, the baseline vector between the two satellites may move parallel in two consecutive times as shown in Figure 5. The resulting differential LOS vectors in Equation (12) become virtually identical, and it makes the corresponding normal matrix nearly singular.  
Fortunately, it will not happen for a pair of satellites in the same orbital plane since the baseline vector between them is continuously rotating at the rate of their orbital frequency. That is one of the reasons why we proposed a dual-satellite lunar navigation system sharing the same orbital plane with some phase separation.  
In addition to that, there are many other satellite geometry design parameters that need to be considered to satisfy the mission requirements. For example, long-term stability of the orbital phase difference is one of the key requirements to minimize the total delta-V. More details of the multi-objective optimization work for the satellite geometry of the dual-satellite lunar navigation system will be discussed elsewhere.
# Crack Propagation Behavior of Fused Silica during Cyclic Indentation under Incremental Loads
###### Abstract  
Fused silica is an important optical material with important applications, where the surface must be precisely machined without subsurface damage. In this study, multi-cyclic indentations under incremental loads were performed on fused silica using two kinds of indenters to clarify the mechanisms of crack generation and propagation induced by precision grinding. It was found that incremental loading cyclic nanoindentation induced various patterns of subsurface cracking and surface spalling. Four kinds of surface spalling were identified at different locations around an indent, the temporal formation mechanisms of which were clarified by microscopic observation and topographical measurement. Load-displacement curve analysis demonstrated that incremental propagation of lateral cracks during early indentation cycles caused large-scale brittle fractures during later cycles. Compared with a Berkovich indenter, a cube-corner indenter caused more significant brittle fractures and surface spalling. The findings in this study will deepen the understanding of subsurface damaging mechanism of fused silica and other brittle solids caused by cyclic tool-workpiece interactions in grinding and other mechanical machining processes.  
fused silica; micro/nano-indentation; cyclic load; hard brittle materials; subsurface damage; crack
## 2 Three-Phase Single-Stage AC-DC Converter in Inductive-Power-Transfer-Based Wind Power Systems
### Operation Modes of Inductive-Power-Transfer-Based Three-Phase Single-Stage AC-DC Converter
This transfer of energy establishes each link voltage (V\({}_{\text{Link\_x1}}\), V\({}_{\text{Link\_x2}}\)) based on the areas (Y\({}_{\text{A}}\), Y\({}_{\text{A}}\) + Z\({}_{\text{A}}\)) corresponding to the slopes of the two and three reset current segments of the boost inductor currents (I\({}_{\text{BI}}\), I\({}_{\text{B2}}\), I\({}_{\text{B3}}\)). At this time, the instantaneous average reset currents (<I\({}_{\text{Link\_x1}}\)> ; <I\({}_{\text{Link\_x2}}\)>) are defined as the average values of the reset currents of the boost inductors for each phase according to the critical angle \(\Phi_{\text{cr\_x}}\) over one switching period (T), as specified in Equations (17) and (18). Here, \(\Phi_{\text{x}}\) is 0 for the a-phase, \(-2\pi/3\) for the b-phase, and \(+2\pi/3\) for the c-phase. \[\langle\text{I}_{\text{Link\_x1}}\rangle=\frac{1}{\text{T}}\int_{\text{D}_{1} \text{T}}^{\text{D}_{2}\text{T}}\text{i}_{\text{Link\_x1}}\text{dt}=\frac{ \text{D}_{1}^{2}\Big{(}\text{V}_{\text{pk}}\sin(\omega\text{t}+\text{\theta}_{ \text{x}})\Big{)}^{2}}{\text{f}_{\text{s}}\text{L}_{\text{BI}}\Big{(}\text{V} _{\text{Link\_x2}}\!-\!2\text{V}_{\text{pk}}\sin(\omega\text{t}+\text{\theta}_{ \text{x}})\Big{)}}=\frac{\text{V}_{\text{Link\_x1}}}{\text{R}_{\text{Link \_x2}}} \tag{17}\]  
\[\langle\text{I}_{\text{Link\_x2}}\rangle=\frac{1}{\text{T}}\int_{\text{D}_{1} \text{T}}^{\text{D}_{2}\text{T}+\text{D}_{3}\text{T}}\text{I}_{\text{Link\_ x2}}\text{dt}=\frac{\text{16D}_{1}^{2}\big{(}\text{V}_{\text{pk}}\sin(\omega\text{t}+ \text{\theta}_{\text{x}})\big{)}^{2}\!+\!\big{(}2-\text{s}\text{D}_{1}^{2}\big{)} \text{V}_{\text{Link\_x1}}\!V_{\text{pk}}\sin(\omega\text{t}+\text{\theta}_{ \text{x}})-\big{[}(2\text{D}_{1}-1)\text{V}_{\text{Link\_x2}}\big{]}^{2}}{3 \text{f}_{\text{L}}\text{L}_{\text{BI}}\big{(}\text{V}_{\text{Link\_x}}\!-\! \text{V}_{\text{pk}}\sin(\omega\text{t}+\text{\theta}_{\text{x}})\big{)}}= \frac{\text{V}_{\text{Link\_x2}}}{\text{R}_{\text{Link}}} \tag{18}\]  
To determine the instantaneous link voltages V\({}_{\text{Link\_x1}}\) and V\({}_{\text{Link\_x2}}\) using the instantaneous average reset currents <I\({}_{\text{Link\_x1}}\)> and <I\({}_{\text{Link\_x2}}\)>2, the secondary side load is reflected to the primary side using the link equivalent load resistance R\({}_{\text{Link}}\) of the PFC AC-DC stage. This is expressed in Equation (19). Here, N represents the turn ratio of the contactless coupler between the primary and secondary coils (N\({}_{1}\)/N\({}_{2}\)). \[\text{R}_{\text{Link}}=(4\text{N})^{2}\text{R}_{\text{DC}}=\frac{(4\text{N} \text{V}_{\text{o}})^{2}}{\text{P}_{\text{o}}/\eta}, \tag{19}\]  
where \(\eta\) is the efficiency of the single-stage AC-DC converter. By examining the relationships among Equations (17)-(19), the instantaneous link voltages V\({}_{\text{Link\_x1}}\) and V\({}_{\text{Link\_x2}}\) can be expressed using Equations (20) and (21).
## 3 Coverage Prediction Tool
### Validation  
For the validation of the ray-launching algorithm, we compare simulation results for a simple environment to results from FEKO, which is an analytic electromagnetic (EM) wave solver based on the Method of Moments integral formulation of [PERSON]'s equations. As the room dimension is electrically large considering the high carrier frequency, we use an engine based on the uniform theory of diffraction (UTD), which is a ray-based asymptotic high-frequency numerical method similar to our ray-launching algorithm. We use a room measuring 3 m by 2 m with an L-shaped wall in the middle and define 1 TX location and 4 RX locations, all at the same height. Three RX locations have an NLOS link for which reflected and diffracted paths exist. One RX location has a LOS link. We compare the received power of the EM solver to the ray-launching result. For all RX locations, the estimated power of the ray-launching tool is below the calculated power using the EM solver, with an average offset of 1.4 dB. Not taking into account diffracted paths in the ray-launching algorithm explains the underestimation of the ray-launching tool compared to the EM solver. We also performed simulations with a physical optics (PO) engine, which uses a current-based asymptotic high-frequency numerical method. EM fields are approximated by optical fields using ray optics, and scatterer surfaces are illuminated by an incident current source, which is computed via MoM. To limit the required computational resources, all room dimensions are lowered by a factor of 5, i.e., the outer walls measure 60 cm by 40 cm. The simulations with the PO engine result in lower received powers compared to our ray-launching tool, with an average offset of 6 dB. In [61], we also compare the simulation results using the UTD solver for a larger room, measuring 10 by 6 m, and come to the same conclusions.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline MCS & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} PJS (Allen) \\ \end{tabular} } & -78 & -60 & -66 & -64 & -64 & -64 & -62 & -63 & -62 & -61 & -59 & -55 & -54 & -53 \\  & DENSE & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R & \(\pi/249\)S/R \\  & DIN duplex & \(27.5\) & \(386\) & \(770\) & \(462.5\) & \(1155\) & \(1251\) & \(1546\) & \(1923\) & \(2316\) & \(2302\) & \(3800\) & \(3800\) & \(4620\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Receiver sensitivity (\(P_{RS}\)), modulation, and data rate (DR) used in IEEE 802.11 ad for the different modulation and coding schemes (MCS) [1].