esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 2 Materials and Methods
### Catalytic Performance Tests  
The AAOSR reactions (P= 1 atm, T = 400-700 \({}^{\circ}\)C) were carried out on a MICROACTIVITY-PRO unit (PID Eng&Tech. S.L., Alcobendas, Madrid, Spain), whose schematic diagram can be found elsewhere [25]. Co/\(x\)CeO\({}_{2}\)-SBA-15 catalysts (300 mg) were loaded inside the reactor and with an in situ reduction by pure hydrogen flow (30 mL/min) for 30 min at 600 \({}^{\circ}\)C before the reaction. After the catalyst activation, a pure nitrogen stream was fed into the reactor until the reaction conditions were reached. A Gilson 307 piston pump (Gilson, Middleton, WI, USA) was used to feed the acetic acid aqueous solution to the reactor (steam-to-carbon molar ratio = 2) at a WHSV of 30.2 h\({}^{-1}\) (WHSV was defined as the mass flow rate of reactants--acetic acid, water, oxygen, and nitrogen--divided by the mass of the loaded catalyst in the reactor). The O\({}_{2}\) supply was set to operate with an O\({}_{2}\)/C molar ratio of 0.0375, maintaining the nitrogen/oxygen mixture's total flow at 60 mL/min. Condensable reaction vapors were condensed at 4 \({}^{\circ}\)C, collected, and subsequently analyzed on an Agilent 7820A GC System (Agilent, Santa Clara, CA, USA) while the non-condensable gaseous stream was analyzed in line on an Agilent MicroGC 490 (Agilent, Santa Clara, CA, USA). The mean value of three measurements of each gas sample was used. Figure 1 shows a simplified schematic of the Microactivity--Pro unit, focusing on the feed section, reaction section, and product analyses.  
The reaction performance was evaluated through acetic acid conversion (X), H\({}_{2}\) yield (Y\({}_{\mathrm{H2}}\)), and carbon-containing product selectivities (S\({}_{\mathrm{CO}}\), SCH\({}_{4}\), SCO\({}_{2}\), SC\({}_{3}\)H\({}_{6}\)O), calculated as shown in Equations (11)-(13):  
\[\mathrm{X_{reactant}(\%)=\frac{F_{reactant,in}-F_{reactant,out}}{F_{reactant,in}} \cdot 100 \tag{12}\]  
\[\mathrm{Y_{H_{2}}(\%)=\frac{F_{H_{2},out}}{n_{i}\cdot F_{reactant,in}}\cdot 100 \tag{13}\]  
\[\mathrm{S_{icarbon--containing\ products}(\%)=\frac{F_{i,carbon--containing \ products}}{a_{i}\cdot(F_{i,in}-F_{i,out})}\cdot 100 \tag{14}\]where \"F\" refers to the molar flow rates of \"i\" species at either the inlet (in) or the outlet (out) of the reactor, \"n\" represents the stoichiometric coefficient for maximum hydrogen generation, and \"a\" is the stoichiometric factor for carbon between the acetic acid and each compound.
## 4 Results and Discussion
### Performance of Models for Dynamic Conditions  
In this section, we examined the performance of the proposed intelligent models to forecast SF of RWs under dynamic conditions. As noted earlier, various parameters were used to design the RWs with different ranges. The impact of these parameters was investigated on intelligent models, and then the best models were used to forecast SF values. Figure 12 illustrates 100 data (out of the database used in this study) which was randomly selected as real SF values together with the predicted ones by ICA-ANN and GA-ANN techniques. As can be seen, the results obtained by GA-ANN (black line) are closer to real values (blue line) in most of cases compared to another developed hybrid intelligent system. Therefore, the GA-ANN model can implement more effectively for SF values of RWs. Results of GA-ANN models for both training and testing datasets are shown in Figure 13. Based on these results, a high accuracy level can be obtained by the developed GA-ANN and this model can be suggested as an accurate one in the field of RW design.  
Figure 11: PIs changes for testing section of GA-ANN models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Population** \\ \end{tabular} } & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Training Rate**} & \multicolumn{2}{c}{**Testing Rate**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-10}  & & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** & **RMSE** & **Rank** \\ \hline
1 & 25 & 0.7881 & 0.0994 & 0.7764 & 0.1014 & 1 & 1 & 1 & 4 \\
2 & 50 & 0.8979 & 0.0805 & 0.8977 & 0.0846 & 3 & 2 & 2 & 2 & 9 \\
3 & 75 & 0.9206 & 0.0655 & 0.9207 & 0.0586 & 5 & 5 & 6 & 7 & 23 \\
4 & 100 & 0.9218 & 0.0636 & 0.9275 & 0.0605 & 6 & 6 & 7 & 6 & 25 \\
5 & 150 & 0.8966 & 0.0747 & 0.9015 & 0.0721 & 2 & 3 & 3 & 4 & 12 \\
6 & 200 & 0.9242 & 0.0604 & 0.9344 & 0.0557 & 7 & 7 & 8 & 9 & 31 \\
7 & 250 & 0.9356 & 0.055 & 0.9351 & 0.0574 & 9 & 9 & 9 & 8 & 35 \\
8 & 300 & 0.9117 & 0.0699 & 0.9035 & 0.076 & 4 & 4 & 4 & 3 & 15 \\
9 & 350 & 0.9268 & 0.0595 & 0.9157 & 0.0677 & 8 & 8 & 5 & 5 & 26 \\
10 & 400 & 0.9418 & 0.0507 & 0.9415 & 0.0536 & 10 & 10 & 10 & 10 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranking and CIR systems to select the best GA-ANN model.  
Figure 12: Comparison of real and predicted 100 number of data which was selected randomly in design of SF of the RWs.  
Figure 13: Results of the best predictive model (GA-ANN) in estimating SF values of RWs (**a**) Train, (**b**) Test.
## 2 Proposed Method
The encoder includes several known memory protection technologies, like 1-bit parity and 7-bits ECC. Before encoding data word, filter module generates encoder-selecting signals by analyzing the zero counts in the data word. A few filter rules could be applied for the filter, such as the number of zero count, the consecutive zero bits, and so on. These threshold values of rule, such as the number of \"0\" bits, could be adjusted according to the demands of reliability in the system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Injected** & **Undetected Error of Error Checking** & **Undetected Error of** \\
**Error** & **and Correction (ECC)** & **2D Parity** \\ \hline
1 & 0 & 0 \\
2 & 0 & 0 \\
3 & 0 & 0 \\
4 & 0.003\% & 0.007\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fault injection simulation of ROM size 8 \(\times\) 32. Figure 4: Length of redundant bits depends on the content of data word. The same as those in the 2D-parity, the generated redundant bits are stored in the signature area. Figure 6 shows that the redundant bits are stored at the addresses from No. 21 to No. 22 as marked in the red box. Address 20 stores the \(Y\)-axis parity bits. For example, the bit-0 of data word in the address 20 is equal to XOR all bit-0 from address 0 to address 16 as presented in the green box. That is even parity check for all bit-0 from address 0 to address 16. Before CPU boots up, Boot ROM checker module first performs the boot ROM checking. Boot ROM checker module reads data word, sends data word to filter module to perform encoding process again and finally compares the redundant bit in the NVM with re-calculated ones. If the results of the identical comparison are true, CPU will fetch instructions normally. If the results of the comparison are false, Boot ROM checker module will alarm an error signal to external interface.
## 2 Materials and Methods
### Power Saving Strategies  
A possible power-saving strategy involves utilizing the built-in power-saving schemes integrated into the MCU platform's software. These schemes are firmware components responsible for activating specific power-saving modes with distinct characteristics [43; 44; 45], such as sleep mode, deep sleep mode, power-down mode, and power-save mode. They also dictate the conditions under which the MCU should wake up. Typically, these schemes are synchronized with an internal timing mechanism, which allows the MCU system to enter sleep mode for a predefined duration. Wake-up conditions are often triggered by internal or external interrupts.
## 2 Principle
### Generation of ASK Signal  
A binary coding signal is sent to the Y-DPMZM, the DC biases of MZM3, MZM4, and the main MZM are set at MATP. Then, the output signal \(Ey(t)\) of the Y-DPMZM can be expressed as:  
\[\begin{array}{ll}E_{y}(t)&=\frac{\sqrt{2}}{2}E_{0}e^{j\omega_{0}t}\left[e^{ j\beta_{3}s_{4}(t)}+e^{-j\beta_{3}s_{4}(t)}+e^{j\beta_{3}s_{4}(t)}+e^{-j\beta_{3}s_{4}(t)} \right]\\ &=\sqrt{2}\ E_{0}e^{j\omega_{0}t}\cos(\beta_{3}s_{4}(t))\end{array} \tag{5}\]  
### Generation of ASK Signal  
Regarding the generation of ASK signal, an electrical baseband coding signal \(s_{a}(t)\) with a bit rate of 2.5 Gbit/s and a duration of 8 ns is sent to the Y-DPMZM. After the electro-optic modulation, the modulated optical signal is combined by a PBC. Then, the combined signal is amplified by the 20 dBm EDFA which introduces a noise of 4 dB. The following PBS is employed to split the optical signal at two orthogonal polarization directions. Through square law detection by the BPD, multiple wavebands ASK signal is obtained. The spectrum of the obtained signal is presented in Figure 2. It is obvious that the signal has eight wavebands at 10 GHz, 20 GHz, 30 GHz, 40 GHz, 50 GHz, 60 GHz, 70 GHz, and 80 GHz, and the simulation results are consistent with the derivation of Equation (6).  
The waveform of \(\cos[\beta_{a}s_{a}(t)]\) is shown in Figure 3a. As can be seen, the coding signal \(\cos[\beta_{a}s_{a}(t)]\) is \"00010010011011101000\". For investigating the performance of generated signal at a single frequency, Figure 3b displays the filtered ASK signal centered at 10 GHz. The envelope of signal has been extracted and shown in Figure 3c. It can be seen that the envelope matches well with the binary coding signal, which shows good performance of recovery. What is more, Figure 3d-f shows the \(\cos[\beta_{4}s_{a}(t)]\) with bits of \"11101011000101101111\", the signal at 80 GHz, and corresponding envelope. The result demonstrates the generated ASK signals present good performance.  
Figure 3: (**a**,**d**) The coding signal \(\cos[\beta_{4}s_{p}(t)]\), and waveform, envelope of ASK signals centered at 10 GHz (**b**,**c**), and 80 GHz (**e**,**f**).  
Figure 2: The spectrum of ASK signal at eight wavebands.
## 2 Research Method  
### Availability Indicator  
The availability of the vehicle is defined as the probability that the vehicle is capable of performing the required function under given conditions at a given moment in time. The state of unavailability of the object means that the vehicle is unable to work due to technical malfunctions or due to organizational errors, e.g., related to the provision of necessary external resources, including spare parts and warranty services [23]. System availability is defined similarly in relation to the fleet of vehicles cooperating in the system.  
The model of the vehicle operation process can be treated as a sequence of successive states of availability and unavailability [7]. A simple two-state model of the vehicle operation was chosen. The formula for \(AT\) availability indicator [30; 31] can be written as  
\[AT=\frac{\sum_{i=1}^{n}AT_{i}}{\sum_{i=1}^{n}AT_{i}+\sum_{i=1}^{n}UT_{i}}*100\% \tag{2}\]  
where \(AT_{i}\)--availability time of the tested sample of objects (vehicles) in the \(i\)-th observation interval (Availability Time); \(i=1\), \(2\), \(3\),..., \(n\)--number of the considered observation interval (month).  
In this study, \(AT_{i}\) availability time was measured by the number of active vehicle-days defined as the product of the number of working days in the considered month of observation and the total number of vehicles conducting transport activities and operational vehicles remaining in reserve.  
\(UT_{i}\)--unavailability time of the tested sample of objects (vehicles) in the \(i\)-th observation interval.  
The sum written in the denominator, found in Formula (2), is the total inventory time \(T_{c}\) of the tested vehicle sample expressed in vehicle-days.  
\[T_{c}=\sum_{i=1}^{n}AT_{i}+\sum_{i=1}^{n}UT_{i} \tag{3}\]  
Adequately alongside Formula (2), the \(UT\) unavailability indicator can be represented by the following formula:  
\[UT=\frac{\sum_{i=1}^{n}UT_{i}}{\sum_{i=1}^{n}AT_{i}+\sum_{i=1}^{n}UT_{i}}*100\% \tag{4}\]
## 4 Numerical Simulation and Results Analysis  
### Numerical Model  
To further reveal the mechanical mechanism of the failure pattern in a jointed rock under blasting, a finite element method (FEM) was used to investigate the propagation of a blast wave. Among the pieces of finite element software, ABAQUS has been commonly used to simulate the stress evolution under impact, blast and other dynamic loadings [29; 30; 31]. Therefore, the ABAQUS software was used to carry out the numerical simulation in our study. The size of the numerical model employed in our study is the same as the physical experiment (Figure 3). The dynamic mechanical properties of PMMA is applied in the numerical model. The model of the specimen was developed in ABAUQS/CAE using the 3D part option and eight node linear brick element (C3D8R) with reduced integration and hourglass control. Figure 13 shows the typical mesh of the model around the borehole and the unfilled joint. The sweep technique with a medial axis algorithm is adopted to generate the mesh element around the borehole, and the structure technique is applied elsewhere. The mesh convergence investigation has been performed and a higher mesh density is used around the borehole and the unfilled joint for achieving higher accuracy. The minimum size of the element around the borehole and the joint is 0.5 mm, and the boundary elements are set as 1.5 mm in size. Similar to the physical model, the free  
Figure 11: Dynamic stress intensity factors of wing crack versus with time.  
Figure 12: Wing crack velocity versus with time.  
boundary condition is applied. And the dynamic explicit solution procedure is used to calculate the stress evolution in the medium with an unfilled joint under blast loading.  
For the blast loading simulation, the Conwep interaction, which has been built in ABAQUS, is applied in our numerical model. The Conwep function is a conventional empirical blast loading function established by the US army engineer. The Conwep interaction is defined as an incident wave interaction with the blast area of test specimen. The properties of the Conwep interaction were defined as an air blast with the equivalent mass of Trinitrotoluene (TNT). In our numerical model, the source point is defined at the center of the borehole. The specific equivalent mass of TNT in our study is 100 mg.
## 4 Discussion  
FTIR analysis is seeing increased use in engine exhaust measurements. Since the first prototype instruments in the 1980s, many laboratories use FTIRs as a standard technique for engine development (see introduction). This review summarized the differences between FTIR and other methods for various gaseous components of engine exhaust. The differences were in most cases at acceptable levels (5-10%), but in some cases and for some compounds, higher differences were noticed. The main question is whether FTIR can be used for on-road regulatory purposes. To answer this question, the following topics need some analysis:  
* Can FTIR measure undiluted exhaust accurately enough?
* Can FTIR be used on-board?
* How the FTIR accuracy can be ensured for regulatory purposes?
###### Abstract  
The primary objective of this study is to evaluate and compare the classification performance of feed-forward neural networks (FFNNs) and one-dimensional convolutional neural networks (1D-CNNs) on magnetoencephalography (MEG) signals from epileptic patients. MEG signals were recorded using the NEUROMAG-122 whole-brain superconducting quantum interference device (SQUID), installed, and operated in our laboratory. The dataset comprised over 5000 MEG segments, each one with a duration of 1 s and sampled at a frequency of 256 Hz. Each segment was classified by expert neurologists as either epileptic or non-epileptic. The FFNN with five hidden layers demonstrated promising results, achieving a classification accuracy of approximately 92%. The 1D-CNN, utilizing four layers, achieved an accuracy of 90.4%, with a significantly reduced training time. Building on these findings, the study's secondary objective was to enhance the artificial neural network (ANN) model by incorporating transfer learning-stacked generalization for FFNN in various configurations. These enhancements successfully improved the performance of the pretrained network by approximately 1%.  
Keywords:magnetoencephalography; epilepsy; deep learning signal classification; artificial neural networks; feed-forward neural networks; convolutional neural networks; transfer learning; stacked generalization +
Footnote †: journal: Applied sciences
# Wireless Covert Communication with Polarization Dirty Constellation
## 2 Background
### Wireless Covert Channels with Dirty Constellation  
To reduce the probability of covert information being detected by potential eavesdroppers, we can convert the covert messages to constellation error of common signal, which is also called wireless covert communication with dirty constellation (WCC-DC).  
The framework of WCC-DC is illustrated in Figure 1(a). The premise of WCC-DC is wireless communication physical layer with OFDM structure. In order to reduce the statistical correlation, the transmitter can divide all OFDM subcarriers into secret subcarriers or normal subcarriers. The corresponding mapping sequence is also generated. The secret information will be modulated with QPSK to obtain covert signals. Covert signal and random noise signal are superposed on the subcarrier signal. To ameliorate the undetectability of covert communication, the covert constellation will be rotated at a certain angle, as shown in Figure 1(b). The transmitter and receiver should share the mapping sequence before the covert communication starts. The receiver should demodulate the carrier message as normal first. Then, the carrier message should be modulated again to obtain the ideal carrier signal. The receiver subtracts the ideal carrier signal from the received signal to get residual signals, which is the covert signal with noise. The covert signal will be rotated in the opposite direction at a certain angle and demodulated with QPSK. The covert message will be extracted from modulated signal according to the mapping sequence. However, the wireless covert channel with dirty constellation has a high BLER (Block Error Rate) when the \(SNR\) is small. When we increase the \(SNR\), the undetectability will deteriorate. Therefore, we plan to ameliorate the reliability of covert messages at low \(SNR\) with polar codes.
## 3 Results  
### Numerical Validations  
#### 3.1.1 Pressure-Temperature Curve  
Since the subsequent simulations of elastic-plastic ejection from grooved aluminum surfaces under double supported shocks involve the loading and unloading of shock waves and melting under strong impacts, we conducted tests on the Hugoniot curve, melting curve, and isentropic unloading curves for aluminum with shock pressure ranging from 1 GPa to 150 GPa in the SPH program, and compared them with the experimental results, as shown in Figure 1. The Hugoniot curve and melting curve derived from SPH simulations demonstrated good agreement with the experimental data [55; 56]. In addition, the unloading melting pressure of aluminum was 64.0 GPa, which is consistent with the experimental results of 62-65 GPa [57]. The shock loading and unloading process was categorized into the following three distinct regions based on the variation in shock pressures: Region I (0.0-64.0 GPa, loading solid, unloading solid), Region II (64.0-123.0 GPa, loading solid, unloading liquid), and Region III (greater than 123.0 GPa, loading liquid, unloading liquid). Based on this characterization, we chose the first shock pressure of 43.0 GPa to be at the state of loading solid and unloading solid.  
Figure 1: Hugoniot curve, melting curve, and isentropic unloading curves for aluminum with shock pressure ranging from 1 GPa to 150 GPa. The Hugoniot curve and melting curve derived from SPH simulations were compared with the experimental data [55; 56].
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
## 3 Numerical Analysis
### The Modeling and Verification
#### 3.1.3 Numerical Verification  
Based on the theoretical model presented above, numerical verification was performed. Figure 4 shows that the interior ballistics pressure-time curve calculated by the theoretical model is in good agreement with the experimental results. The mean error is less than \(5.6\%\), which verifies the rationality of the theoretical model.
## 5 Comparison and Future Works Related to the Methods for Optimizing Power Flow in EV Powertrains
### Key Areas for Future Research Works
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**From 2018 to Early 2022**} & \multicolumn{1}{c}{**2022 Onwards**} \\ \hline \multirow{3}{*}{- Optimizing the gear ratios for powertrain efficiency and performance using advanced optimization techniques. Highlighted the importance of determining the appropriate gears’ size for optimum efficiency and driving performance. - Optimizing gearshifting strategy by taking into account the road conditions and driver’s input for the powertrain efficiency and performance. - Preliminary works on the gearshifting mechanism and controls. Here, only evaluating the mechanism in terms of jerking, without being integrated into the multispeed transmission. - Optimizing and discretizing the CVT ratios for optimum power consumption without too frequent shifting based on hydraulic actuation system. - Optimizing the distribution of traction on the wheels (four-motor configuration) and the shifting of the speed and torque couplings (two-motor configuration) for efficiency and driving performance. - Implement Eco-driving strategy for optimum efficiency (power consumption) and driving performance by taking into account motor’s efficiency map, transmission losses, drivers’ behaviors and battery’s SOC. & - Research on the control of the gearshifting mechanism are expected to be intensified, evaluating the jerking and efficiency of the powertrain completely. - Research in evaluating the performance and practicality of electro-mechanical actuation system in CVT for EV powertrain. - Research on the mechanism for mode shifting in the multi-motor configuration. - Research on the steering assists in the four-motor configuration for driving dynamics and safety. Also, durability and cost analysis of the configuration. - Research on the lubricant and cooling system for optimizing the powertrain performance and lifespan of the battery. Focus on the integrated lubrication and cooling system for multi-components in the powertrain. - Research on the detail maintenance cost. The objective is to determine the cost threshold acceptable to wider consumer demography. - Research on the possibility of applying single type of powertrain for wider vehicle segments for cost and sustainability. - Research on the implementation of holistic eco-driving strategy for not only optimum power consumption and driving performance, but also for driving safety and battery state of health. This can be achieved by considering the motor’s efficiency, transmission losses, drivers’ behaviors, battery’s SOC and temperature characteristics and surrounding traffic. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Expected key research areas on optimizing power flow in EV powertrain.