esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 3 Methodology and Data Acquisition
### Marking the Precise Timestamp  
To optimize the energy consumption of small code segments, knowledge regarding their execution times should be obtained. The execution time can be calculated using the timestamp related to when the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Debian 9 on** & **Debian 9 on** & **Debian 9 on** \\  & **Intel Xeon E5630** & **Intel Celeron G3930** & **Intel Xeon E3-1270v6** \\ \hline Average (\(\mu\)s) & 23.709 & 11.760 & 414.760 \\ Maximum (\(\mu\)s) & 60.081 & 40.054 & 1201.630 \\ Minimum (\(\mu\)s) & 10.014 & 6.676 & 00.272 \\ Standard deviation & 9.884 & 7.073 & 293.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of environment variable value change intervals.  
code segment starts and ends. In the previous section, we verified that real time cannot be guaranteed in a general-purpose operating system (GPOS). Thus, we introduced an intermediate device with a real-time operating system (RTOS) to help mark the timestamps and guarantee their accuracy. In this case, we required a method to notify the intermediate device of the runtime status of the code segment on the device being measured. Notification does not introduce much overhead, with a minimal effect on the execution process. Fortunately, most embedded systems possess GPIO interfaces; it only takes a few instructions to change the GPIO pin level within a few processor cycles, with the code execution being almost entirely unblocked and unaffected. Figure 1 shows the process of logging timestamps using GPIO.  
The toggle frequency of the GPIO interface and the time required to change the pin level are provided on the datasheet by chip vendors. For example, the STM32F4 series requires only four clock cycles to change its GPIO pin level. Function calls of gpio_high() and gpio_low() are placed just before and after the code segment to produce GPIO pin level changes. The measured embedded system connects the GPIO pin(s) to those of the intermediate device. When the intermediate device senses a GPIO pin rising edge, it logs the start timestamp, followed by it logging the end timestamp when the falling edge arrives.
## 3 Flexural Capacity Prediction Methods and Applicability Analysis
### Widely Used Methods for Predicting Flexural Capacity  
The strain and stress distribution at the failure cross-section for an un-bonded prestressed concrete beam can be plotted, as in Figure 11. Unlike in bonded prestressed beams, the un-bonded tendons mostly do not reach tensile strength \(f_{\mathrm{py}}\) but have a stress increment at the ultimate states. Generally, the tendon stress \(\sigma_{\mathrm{p}}\) can be denoted as the sum of effective stress \(\sigma_{\mathrm{pe}}\) and the stress increment \(\Delta\sigma_{\mathrm{p}}\), as shown in Equation (1). The ultimate tendon stress increment is the critical variable yet to be predicted for the flexural capacity calculation. Currently, there are three main kinds of widely used prediction methods: statistical-based methods [40], bond-reduction methods [39; 41; 42], and deformation-based methods [29; 35; 43; 44; 45].
## 3 ML Architectures and Results
### ANNs with Different Sets of Input Attributes  
In this section, we analyze the influence of the set of attributes considered on the accuracy rate obtained by the optimized ANN classifier.  
* Attributes \(M_{01}\), \(M_{10}\), and \(M_{11}\) vs. \(M_{01}\), \(M_{10}\), and \(M_{11}\) plus AoI. The results obtained with these attributes are shown in Figure 4. Considering only MMEs \(M_{01}\), \(M_{10}\), and \(M_{11}\), the overall classification accuracy obtained with the optimized ANN is 74.14%. It is easily seen that some classes are poorly identified, which means that the number of attributes is not enough. However, the results are better than when using RF. By adding AoI to the list of attributes, the accuracy obtained raises to 77.28 % (see the right panel of Figure 4). Still, the classes \"leaves\", \"rocks\", and \"wood\" present rather poor rates of success with these sets of input parameters.
* Attributes \(M_{01}\), \(M_{02}\), \(M_{10}\)\(M_{11}\), \(M_{12}\), \(M_{20}\), \(M_{21}\), \(M_{22}\) vs. \(M_{01}\), \(M_{02}\), \(M_{10}\)\(M_{11}\), \(M_{12}\), \(M_{20}\), \(M_{21}\), \(M_{22}\) plus AoI. By using as attributes just the MMEs \(M_{01}\), \(M_{02}\), \(M_{10}\)\(M_{11}\), \(M_{12}\), \(M_{20}\), \(M_{21}\), and \(M_{22}\), the accuracy obtained is now 89.3% (Figure 5, right panel). The results are better than when using RF. The results are much better than with the minimal set, but the classes  
Figure 3: Confusion table obtained using the RF algorithm with only \(M_{01}\), \(M_{10}\), and \(M_{11}\) as attributes (**left**) and all 15 MMEs (**right**). AoI was included as attribute in both cases.  
\"leaves\" and \"rocks\" are still not well distinguished. By adding the angle of incidence to the list of attributes, the accuracy obtained is now 91% (right panel). Once again, AoI appears to be an important input parameter.
* All MMEs plus AoI as attributes. With the full set of MMEs as attributes, the accuracy increases to 95.9% (see Figure 6), whereas with the angle of incidence added to the list it reaches 96.2%, a relatively small advantage in comparison with the other cases. We may understand it as being due to the fact that the compatibility of the not fully independent MME values already determines AoI implicitly. The numbers obtained here are very similar to those quoted in Ref. [9], also obtained with the full set of MMEs but with a slightly different database and definition of classes.  
* **Figure 6**: Same as in Figure 4 but with all 15 normalized MMEs, without and with AoI.
## 4 The Flow Field of the Model  
Figure 4 shows the leading edge's pressure contour from Plane A and Plane B. Since the fluid is blocked by the leading edge, we observed that the pressure was becoming larger and larger when the points were close to the leading edge. Therefore, a large adverse pressure gradient existed at the sail hull's leading edge.  
The pressure gradient of the leading edge was adverse, compared to the flow stream. Then, the lateral vortices were generated by the influence of the adverse pressure gradient and arranged into eddies, which propagated in the downstream under the impact of the incoming flow and were hindered by the sail hull. Meanwhile, the leading edge of the sail hull was just like a bow, where the eddies ran along the sail hull and were deflected in the longitudinal direction to generate the longitudinal vortices. When the longitudinal vortices flow through the transition zone, the speed of the flow increases, so that the longitudinal vortices are stretched further. Finally, the horseshoe vortex was formed around the sail hull. The horseshoe vortex promotes the ability of flow separation between the submarine body and the sail hull, the flow separation also further enhances the intensity of the horseshoe vortex. Therefore, the horseshoe vortex with high intensity and weak dissipation was one of the flow-induced noise sources.  
To show the three-dimensional structure of the horseshoe vortex in detail, the \(Q\) criterion was taken as the basis for the determination.  
\[Q=\frac{1}{2}\Big{(}||\Omega||^{2}-||S||^{2}\Big{)}, \tag{23}\]  
where \(\Omega\) is the tensor of the eddy, \(S\) is the tensor of strain rate.  
The region with \(Q>0\) in the flow field could be considered as the core of the vortices, which revealed that the movement of the fluid was dominated by the rotation. Figure 5 shows the horseshoe vortex at \(Q=500\). The horseshoe vortex along the sail hull has a 'U' shape. The horseshoe vortex was formed at the leading edge, developed in the downstream, and dissipated slowly. The surface of the horseshoe vortex remained stable, and the legs of the horseshoe vortex were sturdy.  
Figure 4: The pressure contour at the leading edge of Plane A and Plane B. The pressure is from 5000 Pa to 35,000 Pa, when the points are approaching to the leading edge. (**a**) Plane A; (**b**) Plane B.  
If the intensity of the horseshoe vortex can be effectively suppressed, the excitation force is weakened. Then, the flow-induced noise from the shell of the model excited by the horseshoe vortex is reduced. As we know, the mechanical VG is a device that changes the flow direction and brings the outside energy into the boundary layer. Hence, the separation of the boundary layer induced by the horseshoe vortex can be inhibited by the mechanical VG.
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
## 3 Result and Discussion  
### The Degradation of MTBE by Three Pure Strains  
Three MTBE-degrading strains were domesticated by gradually increasing the initial concentration of MTBE from 5 to 50 mg/L in the culture with MTBE as the sole carbon source. The biomass of three strains at different MTBE concentrations is shown in Figure 1a. It was found that a higher biomass was obtained in the culture with MTBE concentrations of 10-30 mg/L. The growth of strains was stimulated at MTBE concentrations of 5-20 mg/L, while was suppressed at more than 40 mg/L, suggesting that MTBE at concentrations higher than 40 mg/L was toxic to strains and inhibited the growth. In contrast, the M3 strain had a higher biomass at lower concentrations (5-10 mg/L), and the M10 strain preferred to grow in a medium supplemented with high concentrations (30-50 mg/L) of MTBE. Consequently, the concentration of MTBE had a significant effect on the biomass of strains (\(p<0.05\)).  
The degradation curves of three strains at different concentrations of MTBE are presented in Figure 1b. It can be shown that all three strains could degrade MTBE through direct metabolism when MTBE was added as the sole carbon source. At concentrations of 5-10 mg/L, the removal efficiency of MTBE was above 80% for all three strains, with the M3 strain being higher. However, the increase in MTBE concentration caused a significant decrease in MTBE removal, especially for the M3 strain. The degradation efficiency was reduced to 40-50% as the MTBE concentration further increased up to 40-50 mg/L. At concentrations of 30-50 mg/L, the M10 strain displayed a higher removal of MTBE, which could be attributed to the better growth of the M10 strain. ANOVA confirmed that there was a significant difference in MTBE degradation efficiency between the experimental groups with different MTBE concentrations (\(p<0.01\)). Although all three strains could degrade MTBE, exceeding the threshold concentration for the strains' metabolism caused a negative impact on both strain growth and MTBE removal. Therefore, it is important to explore methods to improve the degrading ability of MTBE by strains.
## 2 Principle and Signal Processing Method
### Short-Time Fractional Fourier Transform
Therefore, our proposed method overcomes the contradiction between the measurement distance and spatial resolution of the conventional OTDR.
## Materials and Methods
### Data Collection Procedure
#### 2.3.2 Echo Intensity and Vertical Velocity Data Acquisition  
Echo intensity value and vertical velocity profiles were obtained from ADCP data acquisition. ADCP instruments record acoustic backscatter signal as echo intensity on count scale [26]. The echo intensity was processed and converted to mean volume backscattering strength (MVBS) based on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Moored ADCP** & **Mobile ADCP** \\ \hline Frequency (kHz) & 750 & 307.2 \\ Sampling interval (s) & 60 & 1 \\ Range (m) & 15 & 50 \\ Bin size/Pulse length (m) & 1.5 & 1 \\ Transducer tilt angle (\({}^{\circ}\)) & 20 & 20 \\ Orientation & Upward-looking & Downward-looking \\ Number of transducers & 3 & 4 \\ Transducer depth (m) & 1\({}^{1}\) & 0.65\({}^{2}\) \\ \(C\), system constant (dB) & \(-\)160.5 & \(-\)151.64 \\ \(Tx\), temperature of transducer (\({}^{\circ}\)C) & 28.06 & 28.55 \\ \(P\), emitted wavelength (mm) & 2 & 5 \\ \(L_{DBM}\), logarithmic transmit pulse (dB) & \(-\)6.98 & \(-\)3.01 \\ \(P_{DBW}\), logarithmic transmit power (dB) & 7.95 & 17.5 \\ \(Kc\), beam specific scaling factor (dB count\({}^{-1}\)) & 0.72 & 0.43 \\ \(Er\), the minimum value of RSSI & 43 & 40 \\ Percent good threshold (\%) & 65 & 65 \\ \hline \hline \end{tabular} \({}^{1}\) Above seabed, \({}^{2}\) Below sea surface.  
\end{table}
Table 1: Instruments and parameter configuration of both ADCP instruments for data acquisition and processing.  
sonar equation [18; 19]. MVBS values in units of dB re 1 m\({}^{-1}\). A single ping in one ensemble recorded the echo intensity simultaneously with the velocity profile. The sampling point was designated after an initial dive survey. Additionally, the initial survey found that the observed area mainly consisted of small size zooplankton. Therefore, this research used moored 750 kHz ADCP [26].  
Vertical profiling of acoustic backscatter data from moored ADCP was recorded at a 20 m depth. Specific settings on the size of the pulse or the length of pulses emitted by ADCP in the data acquisition process were needed to observe the distribution and phenomenon of zooplankton migration based on the value of the echo intensity. On another acquisition method, mobile ADCP instrument was mounted on the ship at 0.65 m of depth below the water surface, connected with a D-GPS and motion sensor gyroscope for the position, heading, and time recording. The speed of the ship was set at 2 ms\({}^{-1}\) (4 knots), and the transect line was 2 km. The maximum detection range, bin size, and sampling intervals for each ADCP acquisition method were configured as shown in Table 1, and the data acquisition was visualized in Figure 2.
## 3 Results and Discussion
### Color
\begin{table}
\begin{tabular}{c c c c c} \hline
**Marinade Flavoring** & **Piperine (2014)** & **HHP Treatment** & **Storage Time** & **Mean of h\({}_{\text{ab}}\) [-]** & **Std. Deviation** \\
**Flavoring** & **Enrichment** & **[MPa]** & **[day]** & **Mean of h\({}_{\text{ab}}\) [-]** & **of h\({}_{\text{ab}}\) [-]** \\ \hline Raw & & 0 & 9.57 & 4.45 \\ \hline \multirow{8}{*}{BA\(-\)} & \multirow{8}{*}{0} & 0 & 88.84 & 1.68 \\ \cline{3-5}  & & 4 & 80.88 & 11.33 \\ \cline{3-5}  & & 7 & 76.38 & 19.62 \\ \cline{3-5}  & & 12 & 76.38 & 19.62 \\ \cline{3-5}  & & 14 & 74.52 & 8.64 \\ \cline{3-5}  & & 0 & 91.82 & 5.64 \\ \cline{3-5}  & & 4 & 84.03 & 12.87 \\ \cline{3-5}  & & 7 & 79.90 & 18.44 \\ \cline{3-5}  & & 12 & 79.90 & 18.44 \\ \cline{3-5}  & & 14 & 83.16 & 6.75 \\ \cline{3-5}  & & 0 & 87.27 & 6.25 \\ \cline{3-5}  & & 4 & 84.42 & 7.41 \\ \cline{3-5}  & & 7 & 81.39 & 5.50 \\ \cline{3-5}  & & 12 & 81.39 & 5.50 \\ \cline{3-5}  & & 14 & 80.55 & 1.87 \\ \cline{3-5}  & & 0 & 90.70 & 6.64 \\ \cline{3-5}  & & 4 & 89.72 & 6.70 \\ \cline{3-5}  & & 7 & 87.12 & 8.68 \\ \cline{3-5}  & & 12 & 87.12 & 8.68 \\ \cline{3-5}  & & 14 & 87.33 & 2.00 \\ \hline \multirow{8}{*}{300} & \multirow{8}{*}{450} & 0 & 89.59 & 3.00 \\ \cline{3-5}  & & 4 & 83.98 & 6.15 \\ \cline{3-5}  & & 7 & 82.94 & 1.67 \\ \cline{3-5}  & & 12 & 79.36 & 7.10 \\ \cline{3-5}  & & 14 & 79.58 & 2.18 \\ \cline{3-5}  & & 0 & 91.03 & 2.05 \\ \cline{3-5}  & & 4 & 84.73 & 2.12 \\ \cline{3-5}  & & 7 & 84.73 & 10.02 \\ \cline{3-5}  & & 12 & 83.81 & 11.73 \\ \cline{3-5}  & & 14 & 82.24 & 4.71 \\ \cline{3-5}  & & 0 & 89.61 & 1.77 \\ \cline{3-5}  & & 4 & 86.89 & 5.21 \\ \cline{3-5}  & & 7 & 89.56 & 7.84 \\ \cline{3-5}  & & 12 & 89.56 & 7.84 \\ \cline{3-5}  & & 14 & 91.38 & 2.74 \\ \hline \end{tabular}
\end{table} 
Table 8: Hue angle (hab) [-] results of meat sample groups made with different marinade flavoring (herbs or paprika), without or with piperine enrichment (BA\(-\) or BA+), with different HHP treatment (0 MPa, 300 MPa, 450 MPa or 600 MPa) and with different storage time (0 day, 4 days, 7 days, 12 days or 14 days).
## 4 Cryptographic Unit System  
Common symmetric key encryption standards like Advanced Encryption Standard [30] use long rounds of substitution-permutation network [31] for ciphered messages, along with long keys to enhance their security. For this reason, we design in this work a simple symmetric-key encryption scheme that is not time and memory intensive.
## 2 Materials and Methods
### Base Classifiers
#### 2.2.3 Decision Tree  
As depicted in Figure 1, decision tree (DT) [25] is a type of decision making, based on a tree structure, that classifies a dataset through multiple conditional discriminant processes and ultimately obtains the desired results. The CART decision tree is used in this study, using the Gini coefficient as the basis for partitioning. The advantage is that the nonparametric model does not require pre-assumptions about the samples and can handle complex samples. Its calculation speed is fast, the interpretability of the results is strong, and it is not sensitive to missing values. In the tree structure, each internal node represents a judgment on an attribute, each branch represents an output of a judgment result, and, finally, each leaf node represents a classification result. For a classification problem with \(K\) classes, where the probability of a sample belonging to the \(k\)-th class is \(p_{k}\), the Gini index for the probability distribution can be defined as:  
\[Gini(p)=\sum_{k=1}^{K}p_{k}(1-p_{k})=1-\sum_{k=1}^{K}p_{k}^{2} \tag{5}\]
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 3 Results
### Differential Stability Measurement  
Due to the constraints of the radios available and the SDR software in use, differential measurements between multiple cells were generally only possible when two or more radios were employed to allow for the simultaneous measurement of multiple downlink channels. Since this involved running multiple instances of the SDR and relying on the accuracy of the timestamps of the generated observables as inputs, it was considered essential to test the stability of this process as independent of the other variables.
## 2 Magnetic Compass (Analogue Instrument and Computer Simulation)
The instrument movement is calculated as a function of the aircraft path direction defined; then, the heading position of the aircraft is obtained from the Equation (1), while Equations (2) and (3) are solved to provide movement to the instrument indicators. \[\alpha^{t+\Delta t}=\alpha^{n-1}+\left(\alpha^{n}-\alpha^{n-1}\right)\left( \frac{t_{n}}{np}\right) \tag{1}\]  
\[\theta_{compass}\,1^{t+\Delta t}=\alpha^{t+\Delta t} \tag{2}\]  
\[\theta_{compass}\,2^{t+\Delta t}=-\alpha^{t+\Delta t} \tag{3}\]  
Here, (\(\alpha\)) and (\(\theta\)) are the yawing and heading angles, respectively; both are the equal. The super indexes (\(n\)) and (\(n-1\)) refer to the input data for the actual flight and the latest maneuvers. Figure 1a shows the segmentation of the dial by its movement areas and angles and Figure 1b shows the computational simulation of a magnetic compass. Here, (\(np\)) is the step number for performing the mathematical calculation and the graphical simulation. Thus, the term (\(t_{n}/np\)) is named step time (\(\Delta t\)). Figure 2 shows assumptions that are in concordance with the virtual terrain where the flight simulation is represented. Thus, the directions of the earth are the same as those represented in the compass [1; 2; 34; 35; 36; 37; 38; 39; 47; 24; 25; 34; 39; 48; 1]. The magnetic compass can move clockwise or against clockwise; its movement depends on the turning angle. Moreover, the yawing angles can adopt positive or negative values to represent the same heading angle; for the magnetic compass, the following facts are assumed, considering the aircraft maximum and minimum displacements along the horizontal axes. Here, (dx) and (dy) are the aircraft displacement along north-south and east-west directions, respectively. (a) If (dx\({}_{\text{max}}=0\)), it means that the aircraft travel towards the south. (b) If (dx\({}_{\text{min}}=0\)), it means that the aircraft travel towards the north. (c) If (dy\({}_{\text{max}}=0\)), it means that the aircraft travel towards the east. (d) If (dy\({}_{\text{min}}=0\)), it means that the aircraft travel towards the west. Figure 1: Magnetic compass representation: (**a**) segmentation of dial and movement areas; (**b**) instrument simulated computationally. Furthermore, the following facts are assumed for the magnetic compass, considering the heading angle (\(\alpha\)) along the horizontal axes. Consequently, symmetrical indications are considered for angles ranging from negative to larger than 360\({}^{\circ}\). 1. If (\(0<\alpha<90\)), it means that the aircraft flies between north and east. 2. If (\(90<\alpha<180\)), it means that the aircraft flies between east and south. 3. If (\(180<\alpha<270\)), it means that the aircraft flies between south and west. 4. If (\(270<\alpha<360\)), it means that the aircraft flies between west and north.
# Experimental Investigation into the Number of Phases in Debris Flows
## 5 Results
### Rheology of Experimental Debris Flows  
The results of the flow experiments on the 12 slurries (Table 1) are shown in Figure 9. The properties of the tested slurries (debris source, maximum grain diameter, \(d_{\rm max}\), and solid volume concentration, \(C_{\rm v}\)), rheological parameters, including yield stress (\(\tau_{\rm y}\)) and viscosity (\(\eta\)) determined by using the Bingham model to fit the rheometrical data, and their \(r^{2}\) are listed in the lower part of Figure 9.  
Figure 9: Rheological data points, optimal straight-line fits, and rheological parameters, including yield stress (\(\tau_{\rm y}\), Pa), viscosity (\(\eta\), Pa\(\cdot\)s) and their coefficients of determination (\(r^{2}\)), for 12 experimental slurries (Table 1), showing a linear relationship between shear stress (\(\tau\)) and rate of shear strain (\(\gamma\)), i.e., the similarity of the flow behavior of the slurry flows to the Bingham behavior.  
The shear stress (\(\tau\)) measured in all of the slurries was proportional to rate of shear strain (\(\gamma\)), showing that the slurries are Bingham fluid [1]. Twelve values of \(r^{2}\) are in the order of 0.94-1.00 (mean = 0.98, with a standard deviation = 0.02), and quantitatively demonstrate that the rheometrical data can be fitted reasonably well by the Bingham model.  
All six pairs of slurries of the same debris source and \(d_{\rm max}\) exhibited an overall trend of increasing \(\tau_{\rm y}\) and \(\eta\) with \(C_{\rm v}\). This was consistent with the results of previous studies [10; 54; 88]. The magnitude of \(\tau_{\rm y}\) and \(\eta\) of the slurries of the sediments from different sites and of different \(d_{\rm max}\), of course, are not strictly comparable because of their extreme sensitivity to debris composition, grain-size distribution, and solid concentration [28].
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Network Update Process  
In our design, the update of the network will consist of two parts: the core network controller and the MS controller. The former is mainly to update the current usage and congestion index of each slice and network in real time, while the latter is to update the location of MS in real time.