esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 4 Methodology of Model Validation: Simulation and Experiment
### Numerical Simulation  
In view of the hydraulic amplifier having a tiny structure, it is difficult to measure the flow information directly. Hence, a numerical model has been built as depicted in Figure 10. Additionally, the boundary conditions of the numerical simulation model are mainly comprised of a pressure inlet, pressure outlet and walls. Specifically, an inlet pressure of 21 MPa and an outlet pressure of 3.1 MPa are specified, which are the same as in actual scenarios. All the other surfaces are defined as non-slip wall boundaries, and it  
Figure 9: Solid impact surfaces on the shunt wedge.  
is assumed that the hydraulic oil is a Newtonian fluid with a density of 850 kg/m\({}^{3}\) and viscosity of 0.017336 Pa\(\cdot\)s. The Large Leddy Simulation (LES), which is supposed to be more accurate for reproducing time-varying turbulence, is applied in this case. Firstly, the RNG k-\(\epsilon\) is used to perform the time-average numerical calculation with time steps of 1000, and then, the time-average calculation results are used as initial conditions for LES with a time step size of 0.00001 s and time steps of 2000. With the hypotheses of incompressible flow assumed, the finite volume commercial code ANSYS/FLUENT is used to solve continuity and unsteady momentum equations. Additionally, a bounded central-differencing spatial discretization scheme is employed.  
In order to ensure the accuracy of the numerical simulation, a grid dependency test is necessary. Detailed information on five grids' generation schemes is shown in Table 3.  
The numerical simulation results obtained by using different grid schemes are depicted in Figure 11. The simulation results denote the outlet flow rate of different grid schemes at a given time. It can be seen that the mass flow rate error is acceptable between the results using grid 4 and grid 5. In order to save on numerical time and computing cost, grid 4 is used to complete further numerical simulation. Additionally, the simulation results in the paper are obtained by solving this numerical model.  
Figure 10: Numerical model of the hydraulic amplifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid Model** & \begin{tabular}{c} **Grid Size** \\ **/mm** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Cells** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Orthogonal Slope** \\ \end{tabular} &
\begin{tabular}{c} **Minimum** \\ **Orthogonal Mass** \\ \end{tabular} \\ \hline Grid 1 & (0.05, 0.1) & 361,319 & 0.57 & 0.14 \\ Grid 2 & (0.05, 0.08) & 626,406 & 0.64 & 0.19 \\ Grid 3 & (0.05, 0.06) & 989,735 & 0.50 & 0.21 \\ Grid 4 & (0.03, 0.06) & 1,208,667 & 0.58 & 0.21 \\ Grid 5 & (0.01, 0.06) & 1,596,869 & 0.62 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information on the grid schemes.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 3 Gamma Radiation Detection Strategy and Reference Detector
### Matched Filter for Signal Discrimination in Low Power Applications  
As previously mentioned, a critical aspect in operating standalone gamma-ray detectors in CubeSat missions is represented by the capability of extracting the meaningful waveform parameters (arrival time, charge, and amplitude) in very low power regime. In this section, the signal discrimination based on a matched filter circuit and its implementation is presented. Matched filters [22], widely used in many domains of science and technology such as radio communications, radar, and image processing, are linear filters that maximize the signal-to-noise (S/N) ratio in presence of random fluctuation that cannot be modeled (i.e., white noise). Matched filter algorithms for signal discrimination can be digitally implemented on FPGAs and run continuously to extract the topical parameters of a signal.  
As mentioned in the previous section, the amplitude signal (Figure 10A) generated by a photosensor is integrated and the charge waveform is sampled at 10 MHz by the ADC (Figure 10B). The ADC output is in turn passed to the matched filter stage and a digital output is produced, as reported in Figure 11.  
Figure 11: The photosensor integrated signal is sampled by a ADC (blue points). By combining the amplitude values of each sample S, the matched filter algorithm produces a new set of T values. The local maximum in T values is proportional to the energy released in the interaction.  
The digital output consists of a sequence of pairs of values generated through Equation (1) that defines the matched filter algorithm.  
\[\mathrm{T}_{k}=(\frac{\overrightarrow{W}\cdot\overrightarrow{S_{k}}}{\sum_{i}^{ d}\mathbf{S}_{k_{i}}},\mathrm{k}) \tag{1}\]  
where \(\overrightarrow{W}=(-1,-1,1,1)\) is the weight array, \(k\) is the sample number of the matched filter output, and \(\overrightarrow{S_{k}}\) is the array of the four \(S_{k}\) consecutive ADC samples (\(S_{k_{1}},S_{k_{2}},S_{k_{3}},S_{k_{4}}\)).  
An online peak finder algorithm running at the FPGA level extracts the position (in units of number of samples) and the value of the peak, respectively, corresponding to the event time stamp and to the energy released in the interaction.  
In order to evaluate the S/N ratio, a set of PMT waveforms generated by a release of energy of 20 keV has been simulated and a 3.3 keV \(\sigma\) white noise added (see Figure 12A). The same set of waveforms is then integrated (Figure 12B) and the matched filter applied (Figure 12C). The signal-to-noise ratio is finally inferred from the histogram reported in Figure 12D, where the distribution of the ratio between the maximum value and the standard deviation of each waveform in Figure 12C is reported. In the presented framework, the obtained S/N value is about 6.  
The proposed approach has been implemented in a GEANT4 simulation used to evaluate the detector performances and discussed in Section 5.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 6 Simulation Experiments and Performance Evaluation
### Node Deactivation  
A network traffic generator, a part of a simulator package, was used to analyze how the deactivation of switches affects energy consumption. The Poisson distribution was chosen for a flow start time generation, and the Pareto distribution with the exponent of 1.05 was used to determine the flow rate. The same probability of flows between any of the server pairs was assumed. About 80 flows of an average of 3 MB were generated during the simulation lasting 0.1 s. Two values (7000 ns or 70,000 ns) of the delay related to waking up or sleeping the device were used to test its impact on algorithm operation. In contrast, no delay case was provided as an idealized reference scenario. All switches were in the active state at the simulation beginning to allow the implemented heuristics to disable unnecessary nodes gradually. The obtained results are presented in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Topology**} \\ \cline{2-3}  & **Fat-Tree** & **Butterfly** \\ \hline Node activation cost (\(\mu\)J) & 139,996,472 & 223,990,136 \\ Data transfer cost (\(\mu\)J) & 17,612,800 & 21,135,360 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy cost of ECMP without energy-saving heuristic.  
Figure 5: Energy costs for varying port load threshold.  
Figure 6: Packet loss for varying port load threshold.  
Turning off redundant devices allowed for a significant energy cost reduction but also contributed to retransmitting part of the packets. The number of retransmitted packets was luckily small, thus avoiding losses and excessive increase in transmission part of energy consumption. In the case of fat-tree and longer device delay, the maximum increase amounted to about 1/1000 of primary energy consumption, which may be considered negligible.
## 3 Satellite Signal Measurements in Various Weather Conditions
### Measurements, Data Conversion Techniques
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time [s]** & **y [km]** & **x [km]** & **y2 [km]** & **x2 [km]** \\ \hline
0 & \(-2579\),463661 & 4104,031343 & \(-2579\),5000 & 4091,687906 \\
17 & \(-2579\),510050 & 4104,031565 & \(-2579\),5374 & 4091,687905 \\
47 & \(-2579\),576051 & 4104,028103 & \(-2579\),6034 & 4091,687916 \\
77 & \(-2579\),640251 & 4104,025706 & \(-2579\),6694 & 4091,687923 \\
107 & \(-2579\),706555 & 4104,021771 & \(-2579\),7354 & 4091,687935 \\
137 & \(-2579\),772036 & 4104,019176 & \(-2579\),8014 & 4091,687942 \\
155 & \(-2579\),808401 & 4104,013526 & \(-2579\),8410 & 4091,687959 \\ \hline Arithmetic mean [km] & \(-2579\),639572 & 4104,024456 & \(-2579\),669714 & 4091,687927 \\ Average deviation [km] & 0.092238622 & 0.00472354 & 0.092164286 & 0.0000141706205454284 \\
**Deviation difference** & **0.000074336** & **deviation** \\
**along Y axis [km]** & **0.000074336** & **difference along X axis [km]** & **0.00470937** \\  & & **axis [km]** & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation of average deviations.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 3 Experimental Results and Discussion  
### Experimental Results  
The simulation is carried out with the parameters of Table 3.  
The flight geometry and dot-matrix targets are shown in the Figure 4.  
Figure 4: Flight geometry and dot-matrix targets. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Center Frequency (\(f_{0}\)) & 8 GHz \\ Bandwidth (\(B_{r}\)) & 150 MHz \\ Sampling Frequency (\(f_{r}\)) & 180 MHz \\ Antenna Size (\(D_{r}\), \(D_{r}\)) & 2 \(\mathrm{m}\times 1.5\,\mathrm{m}\) \\ Some Size/km & 4 \(\mathrm{km}\times 4\) km \\ Targets Distribution (\(\mathrm{N}\times\mathrm{A}\)) & 9 \(\mathrm{\times}1\) \\ Sensor Velocity (\(V_{r}\)) & 100 \(\mathrm{m}\)/s \\ Pulse Repeat Frequency (\(PRF\)) & 300 Hz \\ Pulse Width (\(T_{r}\)) & 30 us \\ Center Slant Range (\(R_{0}\)) & 20 km \\ Squint Angle (\(\theta_{r}\)) & 45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of simulation. The gray dots in Figure 4 represent the dot-matrix targets. To make a clear and fair comparison, no weighting functions or methods of side-lobe controlling is applied in the algorithms. The results processed by different algorithms are shown in Figure 5.  
Figure 5: Simulation results processed by different algorithms. The sub-images in each column correspond to target 1, target 2, and target 3, respectively. (**a**,**c**) and (**b**,**d**) show the contours azimuth slice of the results processed by the extended nonlinear CS algorithm (ENLCSA) and generalized nonlinear chirp scaling (GNLCSA), respectively. The three targets focusing performances processed by different algorithms are shown in Table 4, where the focusing performances include resolution (Res), peak side-lobe ratio (PSLR), and integrated side-lobe ratio (ISLR). When comparing the results and performances in Figure 5 and Table 4, it is obvious that the results achieved by GNLCSA are better than the ones that are processed by ENLCSA. In Figure 5, the sub-images shown in (a) and (b) indicate that azimuth FM rates are mismatched during the processing of ENLCSA. The quadratic and cubic phases still exist in a large proportion, which causes the deterioration of azimuth compression. The focusing performance tends to be worse when the distance bias that is away from the referenced azimuth position becomes larger. The sub-images listed in Figure 5c,d are well focused. It is concluded that the azimuth variant FM rates and high orders are eliminated effectively in different azimuth position, and the azimuth focusing quality and the ADOF are greatly improved by GNLCSA. We selected eight points with different azimuth and range position in Figure 4, and signed with A, B, C, D, E, F, G, and H, respectively. Comparison of the focusing performances of GNLCSA and ENLCSA on points 1-3 and A-H is performed. The results are shown in Figure 6.  
The focusing performances of the 11 points marked in Figure 4 are shown in Figure 6.
## 5 Conclusions  
In this study, a new IMFL-IDSCS method was derived to detect intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS was deployed for every client, and an individual controller was utilized for data management. In addition, the IMFL-IDSCS technique used the ECOA-FS method for choosing the best features. This was followed by the ANFIS model, which was applied to recognize intrusions. Finally, the JSSO algorithm was used to optimize the MFs. A widespread simulation analysis was performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%. In the future, the performance of the IMFL-IDSCS technique can be improved by outlier removal approaches. In addition, the proposed model can be extended to the detection of malware, ransomware, and other kinds of attacks in the Android environment.  
Conceptualization, M.A.A. and M.E.; methodology, F.N.A.-W.; software, A.M.; validation, M.A.A., F.N.A.-W. and M.A.D.; formal analysis, A.M.H.; investigation, A.M.H.; resources, M.A.D.; data curation, A.M.; writing--original draft preparation, M.A.A., F.N.A.-W., M.A.D., M.E. and A.M.H.; writing--review and editing, M.A.A., A.M. and M.A.D.; visualization, M.A.D.; supervision, M.A.A.; project administration, A.M.H.; funding acquisition, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Research Groups Program Grant no. (RGP-1443-0051).  
Not applicable.  
The authors declare that they have no conflict of interest to report regarding the present study.  
Figure 11: \(Prec_{n}\) and \(reca_{l}\) analysis of IMFL-IDSCS system with other approaches.
## 6 Discussion
### Reflection Feature Analysis of Cavity Edge Profile  
Based on the forward simulation results, the seismic reflection characteristics of the salt cavern roof are evident. There is a noticeable amplification in the reflection wave width at the cavity top, which gradually diminishes as the salt cavity's diameter increases. This effect is mainly attributed to the limited longitudinal and transverse resolution of the seism imaging. As the salt cavity's diameter enlarges, the imaging accuracy on the seismic profile improves, leading to a significant enhancement in the three-dimensional carving accuracy of the salt cavity contour. Consequently, the amplification effect is greatly reduced. Importantly, the position of the cavity top surface aligns consistently with the location of the strong reflection trough and remains unaffected by other cavity shape factors. This alignment can serve as a reliable reference indicator for determining the depth of the actual old cavity roof in three-dimensional seismic responses. Additionally, the reflection characteristics of the connecting channel are greatly influenced by the cavity's morphology. When the horizontal length of the channel is small, the reflection characteristics become indistinct due to substantial waveform interference from other parts of the cavity. However, when the channel's waveform reflection is clear, the medium-strong peak and trough features presented by the top and bottom surfaces of the channel become apparent. These features are primarily influenced by the channel's height. In cases where the connecting channel is tall, the chaotic reflection characteristics of the residue in the channel contrast sharply with those on the top and bottom surfaces. Conversely, when the channel is low, the reflections from the top and bottom surfaces exhibit parallel and closely spaced waveform characteristics. These observable phenomena can be utilized as a basis for identifying and judging the height of the channel. Regarding the seismic response characteristics of the cavity bottom, the overall pattern is indistinct and challenging to discern, mainly due to waveform deviation and interference caused by media differences, such as brine and residue. This pattern differs from the one reported in literature [45], where there is no residue at the bottom of the horizontal cavity and a certain waveform pattern still exists in the reflection at the bottom of the cavity. Additionally, the circular feature observed in the forward simulation is actually composed of the reflection waveforms of the peaks and valleys from the top surfaces of both the cavity and the residue. When the circular features are clear, the waveforms of two surfaces display a symmetrical pattern with opposing peaks and valleys, which can be used as an indicator for cavity identification.
## 2 Results
### Map Validation with Berry Skin Color and Flower Type
Transferable marker pairs, one pistellate (rh_2_4497054 or rh_2_4599939) and one hermaphroditic (rh_2_4703733 or rh_2_4825658) could be used for pistellate (ff) and hermaphroditic (HH or Hf) identification with 100 and 99.7% overall accuracy, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trait** & **Chromosome** & **LOD** & \
\begin{tabular}{c} **Peak Position (Physical** \\ **Position (Mb))** \\ \end{tabular} &
\begin{tabular}{c} **R\({}^{2}\)** \\ **95\% Bayesian Interval** \\ \end{tabular} \\ \hline Berry color & 2 & 22.8 & 14.11 & NP & 11.00:17.48 \\ Flower type & 2 & 17.2 & 4.65 & NP & 4.50:4.70 \\ Total anthocyanin 2013 & 2 & 9.4 & 13.54 & NP & 6.97:17.85 \\ Total anthocyanin 2018 & 2 & 10.2 & 13.54 & NP & 6.97:17.85 \\ Total anthocyanin 2013 & 18 & 3.4 & 6.94 & NP & 6.23:10.21 \\ Total monoglucides 2013 & 2 & 12.2 & 9.13 & NP & 8.58:14.87 \\ Total monoglucides 2018 & 2 & 3.8 & 5.87 & NP & 2.99:16:74 \\ Total diglucosides 2013 & 2 & 5.7 & 8.11 & NP & 7.41:9.07 \\ Total diglucosides 2013 & 9 & 4.8 & 6.19 & NP & 0.89:6.99 \\ Total diglucosides 2018 & 9 & 3.2 & 6.52 & NP & 3.74:9.57 \\ Malic acid 2013 & 6 & 4.5 & 11.92 & 28.2 & 2.31:15.48 \\ Malic acid 2016 & 6 & 4.1 & 7.86 & 23.8 & 2.55:18.52 \\ Malic acid 2018 1 & 6 & 3.8 & 5.59 & 22.9 & 0.91:8.28 \\ Malic acid 2018 & 1 & 5.2 & 20.62 & 31.0 & 18.88:23.67 \\ Tittrah acidity 2016 1 1 & 3.8 & 6.29 & 22.5 & 0.97:8.38 \\ Tittrable acidity 2018 & 1 & 4.5 & 19.66 & 29.1 & 7.24:23.7 \\ Tittrable acidity 2013 & 6 & 4.8 & 15.25 & 29.7 & 2.85:16.63 \\ Tittrable acidity 2016 & 6 & 4.2 & 7.86 & 24.4 & 2.76:18.14 \\ Tittrable acidity 2018 & 6 & 4.1 & 5.59 & 28.1 & 0.28:18.52 \\ (E)-2-hexenal 2013 1 & 2 & 4.4 & 7.47 & 29.4 & 5.35:18.70 \\ (E)-2-hexenal 2018 1  & 2 & 4.0 & 4.83 & 27.8 & 0.18:17.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: QTL for berry color, flower type, tittrable acidity, malic acid, total anthocyanin, mono- and diglucoside anthocyanins and (E)-2-hexenal.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 3 Discussions and Outlook
### Better Electrode Filtment  
According to the progress of graphene ECG electrode mentioned above in recent years, much research has improved the contact between electrode and skin, such as making the electrode stick to skin more closely to reduce contact impedance, thus improving the quality of the collected signal, or using electrodes more consistent with skin surface characteristics to improve long-term comfort. In line with this trend, removing the substrate of traditional graphene electrodes and using the conductive graphene layer directly is a good choice to achieve conformable adhesion to the skin surface [26]. As shown in Figure 6e, through a sacrificial layer process, a layer of laser-scribed graphene was transferred to human skin directly with the aid of vapor. The substrate-free graphene electrode (at 2.3 mg--300 times lighter than the commercial gel electrode) is so thin and breathable that long-term wear does not lead to the accumulation of sweat, and the body feels almost nothing. Besides, the reduction in noise by removing the substrate makes the graphene electrode more sensitive to ECG signals. Apart from this, to ensure the durability of the electrode on parts of the human body with a wide range of motion, we use electrospinning fibers to strengthen the electrode. By spraying a graphene suspension, we achieved a custom electrode pattern on a breathable electrospinning fiber substrate (Figure 6f). In addition, the gecko-inspired electrode with high skin adhesion [36], the ultra-high skin-conforming graphene electrode [56], and the ultra-lightweight graphene sponge electrode [75] mentioned above are all typical methods to make the electrode better fit human skin.
## 2 Spectrum Sensing Problem
### RQA Benefits  
The RQA enjoys several specific properties:  
1. RQA is based on the chaos theory and is normally used to extract the hidden recurring states of a dynamic system. The various parts of a transmission chain, such as modulation, filtering, coding, multiplexing, etc, generate hidden recurring states in the communication signals. Therefore, RQA can help detect the presence of the PU's signal on a desired bandwidth.
2. In a previous work [20], we showed that RQA is a promising tool for the spectrum sensing task. Indeed, in a noncooperative context, we proposed the Recurrence-Rate-based detection model (RRD), and this previous algorithm was able to detect the presence of the PU's signals with \(\mathrm{SNR}\geq-5\,\mathrm{dB}\).
3. During the detection procedure, a spectrum sensing algorithm based on RQA does not require the estimation of the noise variance, as required by some spectrum sensing algorithms such as ED, which is a great advantage.
4. RQA can help detect a communication signal in a very low SNR; and contrary to ED, Recurrence Analysis can distinguish a noisy communication signal from a high energy noise.
5. RQA does not have a high computational cost like CFD. In noncooperative spectrum sensing, RQA is more robust compared with the widely used ED or CFD.