esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dynamic Allan Variance  
### Computation Process of Dynamic Allan Variance  
The dynamic Allan variance (DAVAR) is an extension of the Allan variance. Firstly, truncate the data with a rectangular window centered at a given time point. Secondly, calculate the Allan variance of the truncated data. Then repeat the above two steps at every time epoch. In the end, by collecting all the variances obtained at every epoch and plotting the results in a signal 3D graph [8], we can obtain the DAVAR. The detailed computation process can be found in [9]. For continuous-time signals, the DAVAR could be obtained using Equation (10):  
\[\sigma_{\omega}^{2}(t,\tau)=\frac{1}{2\tau^{2}(N_{w}-2\tau)}\int_{t-\frac{N_{ w}}{2}+\tau}^{t+\frac{N_{w}}{2}-\tau}(\varpi(u+\tau)-\varpi(u))^{2}du, \tag{10}\]  
where \(N_{w}\) is the length of the truncation window, \(t\) is the analysis time point, \(\tau\) is the observation interval, and \(\sigma_{\omega}^{2}(t,\tau)\) is the DAVAR.  
The DAVAR given in Equation (10) is continuous both in \(t\) and \(\tau\). For discrete time signals, by sampling the integral variable \(u\) and the observation interval \(\tau\) as follows,  
\[u=m\tau_{0},(m=\frac{N_{W}}{2},2,\cdots N-\frac{N_{W}}{2}),\ \tau=k\tau_{0},(k=1,2, \cdots\frac{N_{W}}{2}), \tag{11}\]  
we can obtain the standard notation of discrete-time signals.  
\[\sigma_{\omega}^{2}[n,k]=\frac{1}{2k^{2}\tau_{0}^{2}}\frac{1}{N_{w}-2k}\times \sum_{m=n-N_{w}/2}^{n+N_{w}/2-2k-1}(\varpi[m+k]-\varpi[k])^{2}, \tag{12}\]  
where \(\tau_{0}\) is the sampling interval, \(k\tau_{0}\) is the observation interval of the discrete time, and \(N\) is the available number of \(\omega(n)\). \(N_{w}\) is the length of the analysis window. \(N_{w}\) is assumed to be even.
## 2 Materials and Methods
### Estimation of Body Composition  
An anthropometric methodology was used to estimate the participants' body composition. All measurements were taken in the same laboratory, at ambient temperature (\(22\pm 1\)\({}^{\circ}\)C) and by the same researcher, who was a level-2 anthropometrist of the International Society for the Advancement of Kianthropometry (ISAK). Anthropometric measurements followed the protocols of [PERSON] and [PERSON] [17] and were taken three times for each participant. The equipment used included a Holtain skinfold caliper (Holtain Ltd., Crumych, UK), a Holtain bone breadth caliper (Holtain Ltd., UK), scales, a stadiometer and an anthropometric tape (SECA LTD., Hamburg, Germany). Data on physical characteristics were collected in the following order: age, body mass and stature. The following measurements were also taken: biepicondylar humerus, bi-styloid and biepicondylar femur breadths, arm relaxed, arm flexed and tense, mid-thigh and calf girths, subcapular, biceps, triceps, suprailiac, supraspinale, front thigh, medial calf and abdominal skinfolds. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Age** & **BMI1** & **VO2 Max2** & **PA 3** \\ \hline BJ (\(n\) = 14) & 21.14 \(\pm\) 4.2 & 22.39 \(\pm\) 2.2 & 36.74 \(\pm\) 2.3 & 7.43 \(\pm\) 2.6 \\ CG (\(n\) = 13) & 22.00 \(\pm\) 3.6 & 22.23 \(\pm\) 2.2 & 37.43 \(\pm\) 2.9 & 7.31 \(\pm\) 2.2 \\ \hline \hline \end{tabular} \({}^{\ddagger}\)BMI = body max index, 2 VO2 Max = maximum oxygen uptake, 3 PA = weekly physical activity hours.  
\end{table}
Table 1: General data of the participants.  
intra-observer technical error of the measurement (TEM) specified by the ISAK (5% for skinkfolds and 1% for girths and breadths) was always considered for the measurements.  
Muscle mass was calculated using [PERSON] et al.'s equation [18]. Body density (BD) was calculated by the equation of Durnin and Womersley [19]. Once BD was obtained, the Siri equation [20] was applied to obtain the percentage of body fat (% body fat = (4.95/BD \(-\) 450). Bone mass was evaluated with the Dobeln equation, modified by [PERSON] [21]. A nutritionist (JMM) held a personal interview with each of the participants during the previous week to the tests. This interview aimed to ensure that they had a diet adequate to the requirements of the physical activity practice they performed and to verify that none of them suffered from an eating disorder. After this interview, it was decided that the participants did not change their eating habits during the study.
## 3 Methodology
### Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism
The depiction of the temporal dual branch-gated convolution with a self-attention mechanism structure is illustrated in Figure 5.  
Figure 4: Structure diagram of the self-attention mechanism. Moreover, the 1-D Conv layer divides the matrix into two segments on average, namely \(S_{1}\) and \(S_{2}\). Each segment then separately undergoes processing through two GLUs (gated linear units), after which their respective outputs are combined. The output of the two GLUs is fused to obtain \(S^{\prime\prime}\in\mathbb{R}^{K\times(L+1)}\). This approach offers an advantage by enabling different GLUs to concentrate on learning distinctive feature representations of various components, thereby augmenting the model's ability to adapt to disparate data models. In the context of predicting cellular network traffic, the traffic patterns may potentially vary due to temporal, geographical, or other contextual factors. Therefore, the adaptability of the model becomes pivotal. The related equations are shown in Equations (4)-(8). Firstly, the gating ratios \(g_{1}\) and \(g_{2}\) were calculated, and the sigmoid activation function was used:  
\[g_{1}=\sigma\big{(}W_{g1}*S_{1}+b_{g1}\big{)} \tag{4}\]  
\[g_{2}=\sigma\big{(}W_{g2}*S_{2}+b_{g2}\big{)} \tag{5}\]  
where \(\sigma\) represents the sigmoid function, and \(W\) and \(b\) represent the weight and bias, respectively. Then, we calculated the inputs \(h_{1}\) and \(h_{2}\) of the activation function. \[h_{1}=W_{h1}*S_{1}+b_{h1} \tag{6}\]  
\[h_{2}=W_{h2}*S_{2}+b_{h2} \tag{7}\]  
Finally, the two outputs were fused to obtain \(P^{\prime}\in\mathbb{R}^{N\times L}\). \[S^{\prime\prime}=g_{1}\bigodot h_{1}+g_{2}\bigodot h_{2} \tag{8}\]  
where \(\bigodot\) represents the Hadamard product and \(*\) denotes the convolution operation.
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
###### Abstract  
Today, Moon exploration is driven by the desire to expand the human presence beyond Earth and to use its resources. This requires the development of reliable navigation systems that can provide positioning information accurately and continuously on the lunar surface and orbits. Initiatives such as Moonlight (by ESA) and the Cislunar Autonomous Positioning System project (by NASA) are underway to address this challenge. The aim is to use ranging signals transmitted by satellites, similar to Earth's GNSS, for lunar user positioning. This paper proposes a solution that involves local sensors deployed on the Moon surface to enhance the performance of the satellite system. These sensors can serve as differential reference stations, correcting satellite pseudorange measurements obtained by lunar surface receivers. The local sensor can also be used as a pseudolite, transmitting satellite-like signals to improve system availability and accuracy in obstructed areas. Additionally, the local sensor can act as an independent beacon that provides range and angle measurements. Higher navigation performance can be achieved by increasing the complexity of the system, depending on the implemented solution. This paper proposes and shows the concept, the intial design, and a preliminary definition of the protocol for the third solution. The three different solutions are compared in terms of position accuracy by exploiting the Cramer-Rao Lower-Bound formulation and Monte Carlo simulations. Finally, possible implementations for future use on the Moon are discussed.  
Keywords:lunar navigation; lunar beacon; differential positioning; pseudolite; ELO +
Footnote †: journal: Sensors
# Wedge-Splitting Test on Carbon-Containing Refracories at High Temperatures
## 5 Conclusions  
A new testing device to perform the WST was introduced in this paper. This new device allows fracture mechanical characterization of carbon-containing refractories up to 1500 \({}^{\circ}\)C under reducing conditions. Tests on resin bonded magnesia carbon material were successfully carried out. The oxidation of the carbon in the sample during the high-temperature test can be avoided by inert gas purging during the whole test. The displacement measurement directly on the sample shows a resolution below 3 \(\upmu\)m and works stably over the whole temperature range. Based on the data obtained from laboratory experiments, fracture parameters can be determined and implemented in finite element simulations with an inverse estimation procedure. Besides, simulations including the tensile creep behavior were performed and the fracture parameters under pure mode I failure were determined. The influence of creep on the results obtained from the inverse evaluation for the specific fracture energy is between 15% (1100 \({}^{\circ}\)C) and 47% (1370 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **1470 \({}^{\circ}\)C** & **1370 \({}^{\circ}\)C** & **1100 \({}^{\circ}\)C** \\ \hline Specific fracture energy \(G_{f}\), creep considered [N/m] & 417 & 238 & 137 \\ \(\Delta G_{f}\) [N/m] & 146 (26\%) & 215 (47\%) & 25 (15\%) \\ Tensile strength \(f_{i}\), creep considered [N/mm\({}^{2}\)] & 2.47 & 2.99 & 1.71 \\ \(G_{f}\)/\(t_{i}\), creep considered [mm] & 0.17 & 0.08 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fracture mechanical parameters at different temperatures calculated considering creep and influence on \(G_{f}\).  
**Author Contributions:** Conceptualization, M.S. and D.G.; Methodology, M.S.; Software, S.J.; Validation, D.G., S.J. and H.H.; Formal Analysis, M.S. and. S.J.; Investigation, M.St.; Resources, M.S. and D.G.; Data Curation, D.G.; Writing--Original Draft Preparation, M.S.; Writing--Review & Editing, D.G.; Visualization, M.S.; Supervision, H.H.; Project Administration, D.G.; Funding Acquisition, H.H.  
**Acknowledgments:** This research was funded by K1-MET GmbH, metallurgical competence center. The research program of the competence center K1-MET is supported by COMET (Competence Centre for Excellent Technologies), the Austrian program for competence centers. COMET is funded by the Federal Ministry for Transport, Innovation and Technology, the Federal Ministry for Science, Research and Economy, the province of Upper Austria, Tyrol, and Styria, the Styrian Business Promotion Agency. The authors gratefully thank the company ZwickRoell Testing Systems GmbH (former Messphysik Materials Testing GmbH) for the good cooperation and manufacturing of the newly developed device.  
**Conflicts of Interest:** The authors declare no conflict of interest
# Realisation of a Novel Functionally Redundant Actuation System for a Railway Track-Switch
## 5 Experimental Results and Redundancy Actuation of the Track Switch
### Testing Normal Actuation with Three Actuators  
In normal operating conditions, all the three-actuator bearers will work to perform the switching task. Figure 13 shows the performance of the system operating with all the actuator bearers. The cam angle plots ensure that the cams are rotating synchronously. The cam position signal, which is the reference input signal to the control system (see Figure 8), changes from 0\({}^{\circ}\) to 180\({}^{\circ}\) (a move from Normal position to Reverse position). It is clear that the resulting closed-loop system, shown in Figure 13, meets the step response requirements (Table 2); with a settling time of 5.1 s, zero overshoot and no steady-state error. The maximum input current for each motor does not exceed 1A, which also meets the required value stated in Table 2. Thus, the system achieves the required rail switching. Since the current controller is a PI, during the transient response the current of each motor changes its direction (see current curves in Figure 13) before the switch reaches its steady-state which is the new position of the track switch. It can be seen from these current curves that the performance across each pair of motors is not similar. For example, the third bearer (B3) experiences high torque (load) in side-A and the second bearer (B2) in side-B. This is almost certainly caused minor mechanical alignment issues that manifest as uncertainty in the loading on the motors. To calculate mechanical power for each motor in the switch the equation \(P_{M}=\omega_{M}i_{a}K_{T}\) is used. The torque constant of the servo motor (BMH100 ZT02A1A from Schneider Electric), is 0.72 Nm/A (Table 1). The mechanical power plots from experiments are presented in Figure 14 and show that there is no significant difference between the total mechanical power for the first bearer (B1) and the second (B2), which indicates that B1 and B2 take a similar load in this case. However, the power curves show that the third bearer (B3) does most of the useful work (with a peak mechanical power of 0.85 kW provided jointly by the two motors of B3). Dealing with this was beyond the scope of this current study (which permitted only limited time for on-track testing); however, this might require a further mechanical adjustment of the three bearers to improve the load balanced between all three bearers.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 1 Introduction
Among the available magnetostrictive materials, the FeNi features very small variation of coercive field and narrow and steep magnetic hysteresis, and additionally, it has excellent rust resistance and better process ability over the FeCo film, which makes it very promising for magnetic/current sensing, and was employed as the sensitive materials in this contribution. So, the first aim in this work is to perform the simulation on the sensor response by solving the coupled electromechanical field equation in layered structure considering the magnetostrictive effect and approach of effective constant [12], and then to determine the optimal design parameters for FeNi coating preparation by analyzing the effect on sensor response from the film thickness and aspect ratio. Then, a FeNi-coated SAW based current sensor was constructed, which was composed of a differential SAW delay line oscillator configuration, and magnetostrictive FeNi thin film deposited along the SAW propagation path of the sensing device. The scheme and working principle of the proposed sensor are depicted in Figure 1. YZ-LiNbO\({}_{3}\) was utilized as the piezoelectric substrate, which provides larger electromechanical coupling coefficient (4.1%) to reduce the insertion loss of the SAW chip, and linear temperature coefficient of frequency (TCF) contributing to the temperature compensation by using the differential structure mentioned in Figure 1. Additionally, the single phase unidirectional transducers (SPUDTs) with Al electrodes were used to structure the SAW device to reduce the insertion loss [18,19]. The FeNi thin film was then coated on the top surface of the sensing device by RF magnetron sputtering and overlay technique. Prior to FeNi coating, a 50 nm SiO\({}_{2}\) film was deposited on the piezoelectric substrate to provide a well protection on interdigital transducers (IDTs) in procedure of FeNi coating. The FeNi film produces magnetostrictive strain stimulated by the electromagnetic field, where the thickness, density and young's modulus of the FeNi film are changed accordingly, and then the SAW propagation characteristic are modulated in the form of shift in SAWvelocity. The applied electromagnetic field can be evaluated by using the corresponding differential oscillation frequency signal. Utilizing the Helmholtz coil system, the developed SAW sensor was characterized, and in comparison to FeCo coated sensor.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 3 Fast SAR Image Simulation Based on Spatial Segmentation  
The specific workflow of fast SAR image simulation based on spatial segmentation flow is shown in Figure 5. Firstly, the basic data preprocessing mainly includes the 2D sampling time axis, mode facet set, and target latticed radiation area at the host side CPU, and the basic data are imported into the GPU device side. Then, the spatial segmentation processing is completed by dividing the latticed radiation area into a number of latticed blocks, and each latticed block is denoted by \((P_{z},z<Z)\). The echo generation between the latticed blocks has parallelizability. The azimuth and range positions of the target echo matrix correspond to the thread index \((X,Y)\) in the planar dimension of the 3D Grid, and the position of the segmented latticed blocks corresponds to the thread index \((Z)\) along the longitudinal dimension (\(Z\)-axis) of the 3D Grid, respectively.  
On this basis, the CUDA kernel function design is completed by integrating the EM wave transmission simulation and backscatter field calculation in Section 2. Each thread executes the same CUDA kernel function, which contains the two modules of GPU I (EM wave transmission simulation) and GPU II (backscatter field calculation) in Figure 5. Each thread deals with each latticed block \((P_{z},z<Z)\) using the echo matrix cell method to set the target echo matrix cell as a subfield of the target backscatter field. We use the echo matrix cell \((x,y)\) position to determine the two-dimensional sampling moments, and then construct a two-dimensional envelope window to select the coherent lattices in the current latticed block \((P_{z},z<Z)\). Then, we can transmit the discrete EM waves for the coherent lattices and calculate the multiple backscatter field of each discrete EM wave using the method in Section 2.2. All the multiple backscatter fields of EM waves for the coherent lattices are superimposed as the backscatter field of the current lat  
Figure 5: The workflow of fast SAR image simulation based on spatial segmentation.  
The whole target SAR echo simulation task is packaged into the CUDA platform support mode, and all the threads execute the CUDA kernel function. After all the threads have finished executing the task, the backscattered field of all the latticed blocks along the longitudinal dimension (\(Z\)-axis) of the 3D Grid is vectorially superimposed to obtain the raw echo matrix. Finally, the RD algorithm is used to focus the target simulation echo signal to obtain the target SAR simulated image.
## 4 Experimental and Analysis  
### Precise Dataset  
This paper focuses on the SNR evaluation of UAV signals in complex electromagnetic environments. Since no open-source datasets are available for UAV signals on the Internet or in the literature, the datasets used in this study are generated by self-simulation based on the characteristics of UAV signals. The classification interval for the different categories of data is 1 dB, and the data are classified into 21 categories. However, in the objective conditions of SNR evaluation, such an interval is still very large, so it is expected to generate signals with smaller intervals (greater accuracy) to increase the classification results. The number of classification results is increased by generating smaller intervals (greater accuracy) to further validate and optimize the neural network model. Practically, the dataset can be collected and generated with an acceptable accuracy range.  
Following the dataset generation process, the signals were regenerated in the range of \(-\)10 dB to 10 dB, with signal intervals of roughly 0.2 dB, for signal-to-noise ratios of \(-\)8.4, \(-\)8.2, \(-\)5.8, \(-\)5.6, \(-\)5.4, \(-\)5.2, \(-\)2.8, \(-\)2.6, \(-\)2.4, \(-\)2.2, 2.2, 2.4, 2.6, 2.8, 5.2, 5.4, 5.6, 5.8, 8.2, and 8.4 dB. Similarly, the signal is pre-processed in the same way as in the previous statement. For example, the data in the range of 2 dB to 3 dB are shown in Figure 6. As seen from the above figure, the data in the updated dataset has increased significantly. For the classification model, the more categories that need to be distinguished, the lower the accuracy. The increased number of classes adds more uncertainty to the performance of the classification.  
Figure 4: The work process of our proposed model.  
Figure 5: Schematic diagram of edge filling.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 5 Problem Formulation
### Effect of Initial Credit  
The choice of initial credit is a rather significant factor in our system. The initial credit is an indication of how much helper work can be received before a requester has to help others or perform mining to get system credits. If the initial credit is set too low, users are discouraged from performing D2D cooperation. In the extreme case in Figure 5, where initial credit is set to zero, the users cannot perform any D2D cooperation, resulting in a self-computing performance with a high average task execution time and no selfishness.  
As initial credit increases, users are encouraged to perform D2D cooperation, leading to a fast drop in average task execution time in the two cases with our credit system. Particularly, the average task execution time of the heuristic case with our credit system drops to <5% more than that without our credit system, while remaining 20% less selfish. This is an indication that with a proper selection of initial credit, a little sacrifice on average task execution time can be exchanged for a much higher level of fairness. The greedy-credit case has much lower \(LoS\) than that without the credit system, but its performance is much worse than the heuristic-credit case, implying the importance of our heuristic algorithm.  
\begin{table}
\begin{tabular}{l l} \hline Number of users \(u\) & 10 \\ Mean number of requester \(\bar{p}\) & \(0.4u\) \\ Mean number of miners & \(0.1u\) \\ Tasks period \(T\) & \(60\) s \\ Size of time slots \(\Delta t\) & \(5\) s \\ Total number of periods \(\chi\) & \(30\) \\ Initial credit \(\omega\) & \(5000\) \\ Block reward credit per task period \(\eta\) & \(50\) \\ \(\alpha\) & \(1\) \\ \(\beta\) & \(1\) \\ Maximum computation capacity \(c^{\max}\) & \(100\) \\ Minimum computation capacity \(c^{\min}\) & \(40\) \\ Computation capacity \(c^{p}_{i}\) or \(c^{h}_{j}\) & \(U[c^{\min},c^{\max}]\) \\ Mean task size per second \(\sigma\) & \(30\) (\(U[15,45]\)) \\ Mean maximum wait time & \(40\) s (\(U[20\) s,\(60\) s)) \\ \(\lambda_{i,j}\) & \(U[10^{-5},10^{-3}]\) \\ \(\mu_{i,j}\) & \(U[10^{-3},10^{-2}]\) \\ Initial connection probability at \(\tau=0\) & \(50\)\% \\ \hline \end{tabular}
\end{table}
Table 1: Default simulation parameters.  
Figure 5: Effect of initial credit. (**a**) Effect on average task execution time; (**b**) effect on the level of selfishness.