esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
## 2 Methods and Materials
### Hardware and Software Performance  
In previous UAV work, the AUV laboratory developed a task assignment GCS in which the C# language program code trajectory optimization algorithm was embedded and ran on this PC-based GCS. Due to a different system architecture, a UAV with native computation ability through the use of Jetson Xavier NX is required for rerouting purposes by onboard real-time edge computing. Before the real flight experiment, the algorithm programs were tested on both the PC and the Jetson Xavier NX platform. The computation capabilities of a PC with an intel 8 th general CPU far exceed those of the embedded Jetson  
Figure 11: Monitoring station.  
Figure 12: Prototype UAV.  
Xavier NX computer, but Jetson Xavier NX still provides acceptable computation speed and can be carried on a small size UAV.  
The calculation time of the trajectory algorithm was used to compare the performance of the PC and Jetson Xavier NX. Two programming languages, Python and Cython, were separately used to program the algorithm, which was then run on two different computers for performance comparison by programming language. Algorithm parameters for both groups had 5 to 15 targets and 1 to 2 no-fly zones.  
Cython is a programming language that combines the syntax of C and Python. It can be simply thought of as adding static syntax to Python, allowing developers to maintain most of the Python syntax without major adjustments. Cython can directly compile a program into a binary, thus providing significant performance improvement over Python.  
According to the system flow chart in Figure 1, the dynamic path programming using edge computing can be sorted into two kinds of sub-functions: (A) pre-flight optimization--that is, multiple vehicle path optimizations, including known no-fly zone avoidance; and (B) in-flight rerouting, a real-time unknown obstacle avoidance based on its dispatched route.  
For pre-flight optimization, Figure 13 shows the performance of using Python on both Xavier and PC. For a no-fly zone and 15 target cases, the Xavier took 1.25 s, while the PC took 0.5 s. Figure 14 shows that running Cython instead of Python can save time in path programming. In the same condition as in Figure 13, Xavier took 0.7 s to calculate, and the PC took 0.21 s. The results showed that Cython was much faster than Python, which is more suitable for edge computing. Figure 15 shows the comparison of running Cython instead of Python on the Jetson Xavier NX, which can save at least \(1/3\) the time. The comparison results clearly showed that Cython outperformed Python, and the performance gap increased with workload. Thus, Cython was selected to run on the edge computing system in this study. Cython can reach the goal of path re-planning to avoid the known no-fly zones in just a short time of a few seconds.  
Figure 13: Calculation time of Python on PC and Jetson Xavier NX.  
Figure 14: Calculation time of Cython on PC and Jetson Xavier NX.  
Figure 15: Comparison of Python and Cython on Jetson Xavier NX.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 2 System Model of Diffusive Molecular Communications
### CIR for Diffusive Molecular Communication  
\(P_{AC}(r,t|r_{0})\) is the probability density function that molecule \(A\) emitted from the transmitting molecule at position \(r_{0}\) at time \(t=0\) received as molecule C by the receptor on the surface of the receiver molecule. Then, the probability density function that the released molecule \(A\) exists at position \(r\) at time \(t\) is \(P_{A}(r,t|r_{0})\). Hence, the diffusion equation in  
Figure 1: Block diagram of diffusive molecular communication.  
Figure 2: System model of diffusive molecular communications.  
diffusive molecular communication is expressed by the following equation as the diffusion reaction equation, including the decomposition reaction.  
\[\frac{\partial P_{A}(r,t|r_{0})}{\partial t}=D_{A}\
abla^{2}P_{A}(r,t|r_{0})-k_{ \text{d}}P_{A}(r,t|r_{0}) \tag{3}\]  
Here, \(\
abla^{2}\) is the Laplacian in spherical coordinates. Furthermore, (4) is applied as the initial condition.  
\[P_{A}(r,t\to 0|r_{0})=\frac{1}{4\pi r_{0}^{2}}\delta(r-r_{0}) \tag{4}\]  
In the above equation, \(\delta\) denotes the delta function. Number (4) indicates that the molecule is released in impulses from the transmitting molecule. Furthermore, applying the boundary conditions, we obtain (5) and (6) as  
\[\lim_{r\to\infty}P_{A}(r,t|r_{0})=0 \tag{5}\]  
\[\begin{split}& 4\pi a^{2}D_{A}\left.\frac{\partial P_{A}(r,t|r_{0})}{ \partial r}\right|_{r=a}\\ &=k_{\text{f}}^{\star}P_{A}(a,t|r_{0})-k_{\text{b}}\int_{0}^{t}4 \pi a^{2}D_{A}\left.\frac{\partial P_{A}(r,\tau|r_{0})}{\partial r}\right|_{r =a}d\tau\end{split} \tag{6}\]  
where \(k_{\text{f}}^{\star}\) is the corrected reaction rate coefficient in the positive direction. the reception probability on the surface of the receiving molecule is expressed as  
\[\left.-J(\textbf{r},t|\textbf{r}_{0})\right|_{r\in\Omega}=D_{A}\
abla\left.P _{A}(\textbf{r},t|\textbf{r}_{0})\right|_{r\in\Omega} \tag{7}\]  
where \(\Omega\) denotes the receptor region on the surface of the receiving molecule. Molecules \(A\), which are instantaneously released from the transmitting molecule, are observed as molecules \(C\) after reacting with molecules \(B\) on the surface of the receiving molecules. The number of molecules observed by the receiving molecule is given by  
\[N_{C}(t|r_{0})=N_{A}P_{AC}(t|r_{0}) \tag{8}\]  
where \(N_{A}\) is the number of molecules \(A\) released.
###### Abstract  
The use of nanofluids in energy systems allows for increasing efficiency and developing more economic systems. Alumina-water is one of the most common nanofluids used but little information is available about the aqueous theta-phase aluminum oxide. Given the lack of thermal conductivity data for this nanofluid, in this research, this property is experimentally evaluated. Nanofluid is prepared using the two-step method, employing a magnetic stirrer and a sonication bath. A high-precision sensor is employed for measuring thermal conductivity, using the method of transient hot wire. The thermal conductivity measurements for the base fluid (water) are compared with data provided by NIST. Nanoparticle mass fraction in the nanofluid is increased from 1 to 10% and the temperature from 22.1 to 59.3 \({}^{\circ}\)C. Three sonication times (1.5, 4 and 16.5 h) are used. A strong dependence between the thermal conductivity and the temperature and nanoparticles concentration has been found, while the sonication time has a negligible influence on the thermal conductivity in the range of times tested. A correlation to obtain the thermal conductivity of the water-based nanofluid using theta-phase aluminum oxide has been developed, including nanoparticle volume concentration and temperature. An excellent agreement is obtained between predicted and experimental data.  
thermal conductivity; aqueous theta-phase aluminum oxide; nanofluids; volume fraction; sonication time; temperature
## 2 Model and Method
### Probability Density Evolution Method for Nonlinear Stochastic Analysis  
#### 2.3.1 Multi-Distribution Stochastic Sample Selection Method  
The stochasticity of the vehicle parameters also needs to be considered in a stochastic analysis because there are differences between vehicles, and the loading condition and service status of the same vehicle can vary [44]. In this paper, the stochasticity of the vehicle parameters acted together with the stochastic track irregularities, and we assumed that the vehicle parameters were normally distributed.  
The stochastic parameters in the above-mentioned track irregularity simulation methods has a uniform distribution, but the probability distribution of many stochastic parameters in actual engineering is not unique; distributions like normal distribution are also commonly used. When multiple stochastic factors have to be considered, the selection of stochastic samples and their probabilities given under multiple distributions become the key to the stochastic analysis based on the generalized probability density evolution method [41]. During calculation, the stochastic variable dimension S and the probability distribution of each stochastic variable should be determined, and the set of stochastic variables \(\mathbf{\Theta}=(\Theta_{1},\,\Theta_{2},\,\ldots,\,\Theta_{s})\) should be defined. In this paper, the number of stochastic variables required for the stochastic irregularity simulation was 2, and each variable obeyed a uniform distribution on \([0,2\pi]\); considering the stochasticity of the vehicle body mass, bogie mass, wheel pair mass, stiffness, and damping of both single-stage suspension and secondary suspension, each parameter obeyed a normal distribution, with a mean E\({}_{i}\) and a variation coefficient \(\mu_{i}\) (\(i=1,\,2,\,\ldots 7\)). After determining the stochastic variables, the number theoretic point selection method was used to select stochastic sample points. The number theoretic point selection, compared with the traditional Monte Carlo method, can generate highly uniformly distributed low-deviation sequences. In this paper, the GP split-circle domain method suggested by [PERSON] et al. was used [57], and the uniformity distribution profile of the low-deviation sequences generated by the nine-dimensional GP split-circle domain method is shown in Figure 2. It can be seen that the selected stochastic points in the nine-dimensional GP split-circle domain are distributed uniformly without any cluster phenomenon occurring, so that the calculation accuracy is be influenced by such GP split-circle domain method.  
The Voronoi region divide method is used to divide the resulting sample points into representative regions, and the initial probability of each point is equal to the area of its representative region.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 3 System Model and Problem Description
### Design of Burst Structure  
The definition of the short in short bursts varies from a few tens to hundreds of bits in different literature [31]. The literature [32] designed a short convolutional code for machine-type communications, where the information word length was 64 bits and the codeword length was 128 bits. In [31], the authors considered a scenario with only a few tens of bits over the duration of a burst. The number of transmitted CPM symbols in a single burst is was symbols with a burst duration of 1 ms and a very high-frequency baud rate in [33]. The burst structure is shown in Figure 2, with the training sequence using only a small number of bits to quickly and reliably lockthe receiver, rather than for channel estimation in the conventional one. It is particularly important to note that the BER results in the simulation are a function of the average (information) bit-energy-to-noise-power density spectral ratio \(E_{b}/N_{0}\). The following relationship holds  
\[\frac{E_{s}}{N_{0}}=\frac{E_{b}}{N_{0}}\cdot log_{2}(M)\cdot R_{c}\cdot\eta_{ tr}. \tag{13}\]  
where \(E_{s}\) is the average symbol energy, M is the modulation base number of CPM, and \(R_{c}\) is the code rate. A data-aided approach is used as a benchmark scheme in the subsequent simulations, with training bits for channel estimation and \(\eta_{tr}=N_{tr}/N_{all}\) representing the power efficiency loss caused by the training bits.  
Figure 1: Communications system model, including the transmitter, channel, and turbo receiver.  
Figure 2: Block diagram of the transmitted CPM burst signal. Labels Train. and Tail. refer to the training and transition symbols, respectively.
## 2 Methods
### System Evaluation  
The subject-independent paradigms were evaluated as follows: in subject independent I, classifiers were trained on only able-bodied data and were tested on all the PD patient data. The results were then averaged across the subjects. In subject independent II, leave-one-subject-out cross validation was performed across PD patients' data. Each time, data from one patient was left out, and the model was trained on the remaining patients' data. The one patient data not included in the training step was used to test the performance of the model. The results were reported as the average across the patients. In subject -dependent paradigm, data from the same patient was used in the training and test sets. Within patient leave-one-trial-out cross validation was performed to evaluate the models. At each evaluation step, one trial of a given task was excluded from the training set and was used to test the model.  
To evaluate the models, we used F1 score which is the harmonic mean of precision and recall--Equation (1). The F1 score is typically employed for imbalanced datasets where some classes have larger number of samples compared to others. In such scenarios using accuracy as an evaluation metric can be misleading since the majority class could be classified with high accuracy while the minority class is highly misclassified.  
\[\text{F1 score}=2\times\frac{\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}} \tag{1}\]  
In Equation (1), precision is the number of correctly classified samples out of total number of samples classified as the target class. Recall presents correctly classified samples out of the total samples of the target class. Confusion matrices were also computed to quantify the classification results of the proposed scenarios. They provide information about the number of correctly classified as well as misclassified windows across the subjects during each locomotor task. We performed analysis of variance (ANOVA) with the factors being classification algorithms, training paradigms, and signal sources. Post-hoc tests were performed where statistically significant effects were reported (\(\alpha\) = 0.05). [PERSON]'s HSD was used to find out which specific group's means (compared with each other) are different.
## 7 Conclusions  
The impedance mismatches between the FSC-VSPSU and the power grid can induce oscillation phenomena, posing risks to the secure and stable operation of the power system. To address this, this article studies the oscillation mechanism and the suppression strategy of the grid-connected FSC-VSPSU. By using the SISO impedance measurement method and the derived oscillation analysis criterion, the oscillation mechanism and the corresponding parameter influence rules of the grid-connected FSC-VSPSU are obtained, which indicates that the power grid network and the grid-side converter of FSC-VSPSU have a greater impact on the grid-connected wideband oscillations compared to the machine-side converter, the excitation system, and the governor. Based on this, the corresponding oscillation suppression strategies are further formulated. Notably, this work introduces for the first time the concept of oscillation suppression through power grid network resistance compensation, which provides guidance for the design of converters capable of mitigating oscillations.  
Conceptualization, P.P.; Data curation, X.Z.; Methodology, P.P.; Resources, Y.T.; Software, G.C.; Validation, H.S.; Writing--original draft, P.P.; Writing--review and editing, L.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Project of State Grid Sichuan Electric Power Company under Grant 521997230002 and the Sichuan Science and Technology Program under Grant 2024 NSFSC0121.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by State Grid Sichuan Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Conclusions  
In this paper, the finite element model of the GS15006 seal ring was established by using ABAQUS finite element simulation software, and the working state of the hydro-pneumatic spring under impact load was simulated by using a pressure penetration module. The influence of different compression rates of the GS seal ring under reciprocating impact pressure on the stress and contact pressure of the seal ring was analyzed.  
(1) For the GS seal ring used with the YQHF-CC180/150*200B hydro-pneumatic spring, the contact pressure on the sealing surface increases with the increase in the compression of the seal ring. The contact width of the sealing surface also increases with the increase in the compression of the seal ring. The maximum contact stress on the seal ring occurs at the lip of the step seal ring, and the maximum sealing pressure of the seal ring is determined by the contact pressure on sealing surfaces I and III.  
(2) When the compression ratio of the seal ring is 10%, the seal ring shifts relative to its original position after being subjected to pressure impact, which can lead to oil leakage during use, as confirmed in experiments.  
(3) By analyzing the contact pressure curves on sealing surfaces I and III under different compression ratios and pressure impacts, it was found that the contact pressure on the sealing surface is jointly determined by the compression and the impact pressure.  
(4) Static and dynamic loading tests were conducted in experiments to verify the sealing performance of the hydro-pneumatic spring seal rings under two compression ratios. This validation confirmed the reliability of the simulation, which provides a reference for subsequent structural design and selection of seal parts for different types of hydro-pneumatic springs.  
It should be pointed out that in the test verification to ensure the tightness of the hydro-pneumatic spring, we are currently unable to detect the contact pressure and contact width of the sealing surface of the seal when the hydro-pneumatic spring is working, which will continue to be studied in the future.  
Conceptualization, G.L. and Z.D.; methodology, S.W.; software, P.L.; validation, P.L.; formal analysis, Z.D. and G.L.; investigation, P.L. and Z.D.; data curation, D.L.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and G.L.; supervision, D.L.; project administration, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Nature Science Foundation of China (Grant-No.52105054) and key scientific and technological projects in Henan Province (Grant-No.222102220012); all support is gratefully acknowledged.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Satellite and Ground Station Commissioning  
During the AlfaCrux satellite and ground station commissioning, the SDR start/stop procedures were constantly tested and verified before the passage, leading to new scripts to improve the efficiency of the operation. The elevation and azimuth rotors' start/stop procedures and the remote control interface were also tested and checked constantly, leading to adjustments in the antenna mounting, pointing calibration, soft start/stop ramp, and creation of preventive maintenance routines.  
For the first contact, the main actions were to receive beacons; verify communications (Ping command to the satellite); check preliminary TLE by taking notes of time/frequency of Doppler shifts, especially at the moment in which the Doppler shift equals 0 Hz; and check the overall health of the satellite. The critical housekeeping information for those days were battery level, solar panels' voltages and currents, battery charge current, EPS output currents, and internal and external temperatures.  
For the next contacts, the procedure includes receiving beacons, verifying communications (Ping/Pong), checking if operational mode was set to Survival, establishing a reliable  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Access** & **Start Time (UTC)** & **Stop Time (UTC)** & **Duration (s)** \\ \hline
1 & 2 April 2022 & 2 April 2022 & \\  & 01:14:35.727 & 01:19:56.946 & 321.218 \\ \hline
2 & 2 April 2022 & 2 April 2022 & \\  & 02:46:49.656 & 02:55:04.360 & 494.704 \\  
communications channel, obtaining telemetry errors (if any), and obtaining telemetry since the first hours, storing it, and analyzing the critical parameters.  
Recently, the clock was synchronized with the ground station, and a new procedure was included in the daily passage, that is, a time reference update. As of 27 July 2022, 01:56 UTC, the AlfaCrux changes to Nominal mode.
## 3 GNSS Data and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Period** & \(\mathbf{V_{East}}\) & \(\mathbf{V_{North}}\) & \(\mathbf{V_{Up}}\) \\  & **(°)** & **(°)** & & **(mm/Year)** & **(mm/Year)** & **(mm/Year)** \\ \hline  & & & 1 January 2009–16 & 20.86 \(\pm\) 0.02 & 3.83 \(\pm\) 0.02 & \(-\)0.50 \(\pm\) 0.05 \\ SPAN & 38.7813 & 20.6736 & 6.88 years & & & \\  & & 18 November 2015–31 & 18.97 \(\pm\) 0.04 & 1.37 \(\pm\) 0.03 & \(-\)0.95 \(\pm\) 0.07 \\  & & & October 2021 & 5.94 years & & \\ \hline EXAN & 38.7540 & 20.6186 & 25 November 2015–16 May 2021 & 19.46 \(\pm\) 0.07 & 4.06 \(\pm\) 0.12 & \(-\)5.90 \(\pm\) 0.51 \\  & & & 5.48 years & & & \\ \hline  & & & 24 November 2015–17 June 2018 & 18.10 \(\pm\) 0.13 & 4.59 \(\pm\) 0.11 & \(-\)5.06 \(\pm\) 0.36 \\ DRAN & 38.6838 & 20.5746 & 2018 & 2.56 years & & \\ \hline  & & & 1 January 2009–16 & 20.45 \(\pm\) 0.03 & 6.81 \(\pm\) 0.02 & \(-\)0.71 \(\pm\) 0.08 \\  & & & 6.88 years & & & \\  & & & 18 November 2015–6 & 5.28 \(\pm\) 0.81 & \(-\)17.31 \(\pm\) 0.65 & 18.52 \(\pm\) 2.06 \\ PONT & 38.6190 & 20.5852 & August 2016 & 0.72 years & & \\  & & & 6 August 2016–31 October 2021 & 18.27 \(\pm\) 0.05 & 4.61 \(\pm\) 0.04 & \(-\)0.63 \(\pm\) 0.13 \\  & & & 5.24 years & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Velocity components for the Lefkada GNSS stations (ITRF 2014).
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Secure and Resilient Communication Channels
#### 4.1.2 Communication Reliability and Scalability  
After ensuring information security, communication reliability and also scalability become the next stage of consideration. On the one hand, communication reliability refers to the situation when communication failures, re-transmitting packages, or discarding packages occurred [24]. On the other hand, smart communication scalability is also important because many renewable energy resources, smart IoT devices, smart meters, and smart vehicles are joining the smart grid and also the communication network [12].  
[PERSON] et al. provided SDN-based self-healing network management applications, which not only solve transmission failure and hardware resource limits but also deal with cyber attacks such as isolating compromised PMUs and establishing the best path for disconnecting non-compromised PMUs [38]. The SDN self-healing application is built into the ONOS platform and works in between the network control center and energy storage assets. The restoration connection is run by [PERSON]'s shortest path algorithm based on the dynamic topology in ONOS, which also supports multi-path forwarding [38].  
[PERSON] et al. discussed the communication technologies in smart grids, listing the existing technologies for AMI communications by comparing their coverage and cost [39]: (1) special-purpose wired network connection, (2) cellular connection, (3) fixed broadband, (4) TV white space, (5) power line communications, (6) 802.15.4 mesh radio, and (7) Wi-Fi mesh radio. The auto-configure and reconfigure solutions also need to be studied at the same time. In the meantime, communication protocols resilience [40; 41; 42] and communication algorithms resilience need to be studied as well [43].