esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Platform and Benchmarks  
We use a Digilent Genesys development board to build the embedded SoC on a Xilinx Virtex 5 FPGA, and implement the hardware-based integrity-monitoring module inside the OR1200 processor. The SoC is implemented in a fully synchronous manner, and its configuration is shown in Table 3. To evaluate the performance overhead, a timer is embedded in the SoC to record the program execution time and the extra time consumed by the integrity protection method proposed in this paper.  
Since the security architecture mentioned is oriented to application scenarios based on security industrial control, we selected nine groups of programs or algorithms frequently used in the industrial control field in Mibench Benchmark. In addition, in order to evaluate the functions and costs of the proposed architecture when executing larger-scale programs, a set of open-source OpenECC is selected as a benchmark [34]. We use the OR1200-based GNU cross-compilation tool chain to compile and link these programs on a completely safe host computer, extract BBs, and generate HI\({}_{\text{REF}}\) and bitmaps.
# Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain
## 1 Introduction
This work aims to present a joint genetic algorithm (GA) and optimal control method for the suppression of oscillations in EV drivetrain. The genetic algorithm searches for the optimum selection of weighting matrices off-line, and this process regards root mean square (RMS) of motor rotational oscillation, drive-shaft rotational oscillation, and drive-shaft twist angle as the multi-objective optimization cost function, so that a reduced jerking feel and improved shift quality can be realized. This active anti-jerking control system is tested on AMEsim(r) (Version 15, Siemens, Beersel, Belgium, 2016). The results demonstrate that it can realize rapid unloading of the motor output torque to the transmission to zero before opening the synchronizer mechanism for shifting. At the same time, oscillations of EV driveline are actively damped to a satisfactory extent in comparison to open-loop and a reference control strategy.
## 3 Results and Discussion
### Phytochemical Characterization and Bioactivity  
As shown in Table 5, from all the halophyte plants, _S. ramossissima_ and _S. fruticosa_ stood out for their highest total phenolic content (TPC), 0.41 mg GAE/g FW and 0.33 mg GAE/g FW, respectively. Nevertheless, the value obtained in the studied _S. ramossissima_ plants was clearly below the described value for the same species in previous works with plants cropped in 2019 (1.02 \(\pm\) 0.04 mg GAE/g FW) [11]. The difference could be attributed to the impact of the cultivation system and environmental conditions on the plants' phenolic composition. Regarding the _S. fruticosa_ plant, the TPC value of 0.33 mg GAE/g FW (2.47 mg GAE/g DW), was slightly lower than the values described for the _Sarcocornia_ plants collected in the southwestern Iberian Peninsula (3.231-3.892 mg GAE/g DW) [65]. By opposition, _M. nodiforum_, _M. crystallinityum_, and _D. crassifolium_ were the species with the lowest TPC values (0.098 to 0.11 mg GAE/g FW). For _M. nodiforum_ and _M. crystallinityum_, the obtained TPC results (1.533 and 2.871 mg GAE/g DW, respectively) were higher than the ones described for the same species collected in south Tunisia (1.72 mg/g DW and 1.43 mg/g DW, respectively) [66]. Factors such as the variability in the halophyte species, the cultivation conditions, the cropping year, and the extraction conditions contributed to impairing the comparison of the results obtained and the ones described in the literature.  
Following the same trend of the total phenolic content, the in vitro antioxidant activity, measured by ORAC and HOSC assays, expressed by fresh matter, reaches the highest values in _S. ramossissima_ and _S. fruticosa_. However, the lowest values in _M. nodiforum_, _M. crystallinum_, and _D. crassifolium_, suggesting the relevance of the halophytes' total phenolic content for the peroxyl and hydroxyl radicals' inhibition (R\({}^{2}\) = 0.829 and R\({}^{2}\) = 0.937; Figure S1--Supplementary Materials) and consequent prevention of the oxidative damage in cellular biomolecules.  
Regarding the ACE-inhibitory activity, among the halophyte species evaluated, _M. nodiforum_, _M. crystallinum_, _I. crithmoides_, _S. fruticosa_, and _S. ramossissima_ showed the highest antihypertensive activity, requiring a lower extract concentration to inhibit an angiotensin-converting enzyme (ACE), and _C. maritium_ showed the lowest antihypertensive activity, showing a higher extract concentration to inhibit ACE. The weak correlation between the total phenolic content and the antihypertensive activity (R\({}^{2}\) = 0.0186) indicated that in the phenolic extracts obtained from the halophytes' specific phenolic compounds had a low impact on this inhibition [67]. However, the presence of other components in phenolic extracts, such as the ACE-inhibitory peptides (low molecular weight peptides), could contribute to the plant's antihypertensive activity [68; 69].
## 2 Experimental Methods  
Our experiment was carried out with a home-build VMI apparatus with the same approach as described in our previous works [16; 17]. Briefly, the CF\({}_{2}\)Br\({}_{2}\) sample was seeded by argon gas and ejected into the source chamber. After adiabatic expansion of the supersonic molecular beam, the pressure inside the source chamber and the reaction chamber increased from 2.2 \(\times\) 10\({}^{-7}\) and 4.0 \(\times\) 10\({}^{-9}\) mbar to 2.2 \(\times\) 10\({}^{-6}\) and 7.4 \(\times\) 10\({}^{-8}\) mbar, respectively. Here we set the operating frequency of the pulse valve (general valve, Parker) to 100 Hz and the opening time to 170 ns. The 800 nm (Spectra-Physics) and 400 nm (Generated from BBO I) laser pulses were focused into the reaction chamber and interact with sample molecules. The pulses duration in full width at half maximum (FWHM) for 800 nm and 400 nm were about 70 fs and 100 fs, respectively. The intensity of 800 nm laser pulses in the focal volume were estimated between 5 \(\times\) 10\({}^{13}\) W/cm\({}^{2}\) and 2.5 \(\times\) 10\({}^{14}\) W/cm\({}^{2}\) by calibration with the Ar\({}^{2+}\)/Ar\({}^{+}\) yields ratio (See Supplementary Materials, Figure S1). The intensity of 400 nm laser pulses were estimated to be between 5 \(\times\) 10\({}^{12}\) W/cm\({}^{2}\) and 4.0 \(\times\) 10\({}^{13}\) W/cm\({}^{2}\). As the ions passed through the velocity mapping lens, they were stretched to meet the slice condition. Finally, the ion population was detected by a pair of microchannel plates coupled with a phosphor screen. The two-dimensional momentum images of each fragment correspond to the central slice of the corresponding three-dimensional momentum distribution and were collected by a charge coupled device camera (PI-MAXII, Princeton Instrument) with 5 ns time resolution. In our experimental condition, the laser pulse polarization vector was perpendicular to the flight axis.  
The quantum chemical calculations were performed with the Gaussian 16 software to illustrate the dissociation and ionization processes [18]. Single point energies of the reaction products were calculated in Gaussian-4 theory (G4 theory) [19]. The potential energy surface (PES) was obtained by the equation of motion coupled cluster singles-and-doubles (EOM-CCSD) method with the correlation consistent polarized Valence Triple-zeta (cc-pVTZ) basis set [20; 21]. The vibrational frequency was calculated at the B3 LYP/6-31G\({}^{\star}\) level [22] and relaxed force constant was obtained by the compliance software [23],which can calculate the individual bond strength. The intrinsic reaction coordinate (IRC) calculation of transition state was calculated at the same level. The ionization energy of molecule CF\({}_{2}\)Br\({}_{2}\) was calculated as 10.75 eV, which was in agreement with the previous experimental values 11.18 eV [24].
## 5 Results and Discussion
### Axial Velocity Profiles  
Compared to the experiments, the mean axial velocity profiles shown in Figure 3 are all reasonably predicted using 20 combinations of \(C_{\rm D1}\) and \(C_{\rm D2}\). Unfortunately, no result completed overlaps with the experiments. One of the possible reasons might be the results from the turbulent model. Large differences among the cases occur in the zones where the mixtures burn intensely. From Figure 3a, it can be found that the predicted mean axial velocity along the axis of case 16 is the closest to the experiments, while the results of cases 17 and 18 deviate the most from the experiments. Therefore, these two cases are not recommended. Results of other cases stay between those of cases 8-16. Large deviations for the radial mean axial velocity profiles at \(x/d=30\) near the axis are found in Figure 3d. The predictions of the radial mean axial velocity for 20 cases are very close to each other, indicating that the EDC model has a negligible effect on the flow when the reactions are not very intense, as shown in Figure 7b,c,f.
## 3 System Model and Problem Formulations  
### Network Architecture  
As is shown in Figure 2, the network architecture of S-IoT-N can be divided into space segments and ground segments. In the space segment, there are multi-satellites \(S=\left\{s_{11},s_{12},s_{13},\ldots,s_{ij},\ldots,s_{nm}\right\}\), which are composed of a satellite constellation in orbit of height; \(h\). \(s_{ij}\) denotes a satellite, where \(i\) and \(j\) are the \(i\)-th satellite in \(j\)-th orbit in the constellation. Next, the number of satellites is expressed as \(m*n\). There are intra-links \(l_{s_{ia_{ij}}*s_{ijb}}\) between one satellite \(s_{ia_{ij}}\) and another satellite \(s_{ijb}\). The satellites provide users with communication coverage on the ground, which exploits the PAA to achieve dynamic beam-hopping. The coverage areas are further planned and configured according to the satellite constellation and the surface terrain of the earth. These consist of sets of F-Sat-Beams \(B=\left\{B_{1},B_{2},B_{3},\ldots,B_{N}\right\}\), where \(B_{i}\) corresponds to the coverage area. All F-Sat-Beams cover the globe; the sum of coverages is \(\sum_{0}^{N}S_{e}(B_{i})\). In the system using Ser-BH, the coverage of F-Sat-Beam \(B_{i}\) is a virtual boundary for the serving satellite on the ground. There are several Ser-Beams \(b_{ij}\) in each F-Sat-Beam \(B_{i}\). F-Ser-beams correspond to the actual position of the physical beams serving users. The number of F-Ser-Beams in each F-Sat-Beam can be different. The sum of the coverage area of F-Ser-Beam \(\sum_{0}^{M}s_{e}\big{(}b_{ij}\big{)}\) should be larger than that of the F-Sat-Beams. For example, the OneWeb constellation is a near-polar-orbit satellite constellation comprising 720 satellites in 18 circular orbital planes at an altitude of 1200 km [15; 16]. The satellite can be set as \(S=\Big{\{}s_{11},s_{12},s_{13},\ldots,s_{ij},\ldots,s_{(40)(18)}\Big{\}}\). Each satellite has about 6 adjacent satellites and 16 identical, non-steerable, highly-elliptical F-Ser-Beams. The F-Ser-Beams in each F-Sat-Beam \(B_{i}\) can be set as \(b_{ij}\,\ j=1\cdots 16\). If the users in the coverage area are served, the situation is assumed as \(b_{ij}(\text{on})\); otherwise, the situation is \(b_{ij}(\text{off})\). In the ground segment, there are several earth stations (ES) and users (U). The ESs are the nodes that receive or transmit information to or from the satellites, whose functions are similar to the functions of a base station (BS). The users are the IoT terminals, the number of them in the world is assumed as N. The users are distributed in spatio-temporal non-uniform by geography. We will further demonstrate the user model and distributions in the next chapter.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Decryption Process  
The decryption scheme is the reverse process. Specific steps are as follows:  
1. Perform key processing to obtain chaotic sequences \(K_{1}\), \(K_{2}\), \(L_{1}\), and \(L_{2}\).
2. Perform the reverse process of the DNA dynamic encoding operation. First perform steps (1) and (2) of Section 2.4.3 to obtain the mask sequence \(I_{1}\) and four rule sequences \(rule_{1}\), \(rule_{2}\), \(rule_{3}\), and \(rule_{4}\). The cipher image is rearranged into a one-dimensional sequence in the order of columns first and then rows, and the sequence \(Q_{1}\) is obtained by encoding with the coding rule of \(rule_{4}(i)\), and the sequence \(I_{2}\) is obtained by encoding the mask sequence \(I_{1}\) with the coding rule of \(rule_{2}(i)\). The DNA inverse operation is performed between the sequences \(Q_{1}\) and \(I_{2}\) to obtain the coding sequence \(P_{2}\). If \(rule_{3}(i)=2\), the addition operation is performed. If \(rule_{3}(i)=1\), the subtraction  
Figure 5: Schematic diagram of the way of bit recombination.  
operation is performed. Otherwise, the XOR operation is performed. Traverse the coding sequence \(P_{2}\), and decode according to the decoding rule of \(rule_{1}(i)\) to obtain the scrambled sequence \(P\).
3. Perform the inverse process of stage-merging bit scrambling. Convert the sequence \(P\) to a binary sequence, then select the three pixels, the selection process is the reverse step of step (2) in Section 2.4.2. Then, the reverse process of right circular shift and bit sequence recombination is carried out. Finally, the selected pixels are revalued.
4. When all the scrambled pixels are revalued, convert the sequence back to decimal and reassemble into an image \(I\) of size \(M\times N\), which is the final decrypted image.  
Figure 6: Schematic diagram of the stage-merging bit-scrambling process.
## 3 Control Method and Control Characteristics of the Switching Process of the DFVSPS Unit
### Generating Mode
#### 3.1.4 Orderly Shutdown Stage  
Disconnection: Disconnection means disconnecting the generator from the system on the electrical circuit, and the instantaneous current is required to be zero. Since the DFIM is subjected to constant power control before disconnection, the decoupling control requires changing the guide vane opening to reduce the mechanical output of the pump turbine, and, on the other hand, the rotor current is controlled by the rotor excitation system and gradually reduced to zero, so that the stator current of DFIM is gradually changed to 0, and finally disconnected from the grid in the zero current state.  
Shutdown: After the unit is disconnected from the grid, close the guide vane opening to 0. There are two ways to shut down the unit: one is mechanical braking: the speed of the unit is reduced to 0 by the friction existing in the unit itself, but, due to the large inertia of the rotating part, a long braking time is required. Another is electric braking. When the voltage of the generator terminal is reduced to the residual voltage and the unit speed is lower than 50% of the rated speed, the stator windings are short-circuited and the excitation system re-injected to achieve the purpose of accelerating the unit shutdown [30].  
In the shutdown stage of this paper, the guide vane opening of the pump turbine is closed to 0. First, the deceleration is performed for a period of time under the action of the DFIM's own friction. Then, the stator side of the DFIM is short-circuited, and the rotor-side converter is connected to the excitation system, which accelerates the unit shutdown. The simulation waveforms are shown in Figure 12.  
Figure 12: Waveform of shutdown stage in generating mode. (**a**) \(T_{m}\) and \(T_{e}\); (**b**) speed.  
According to the speed waveform diagram, it can be found that the first 15 s rotor-side converter is not excited. The speed of the unit is slowly reduced under the action of its own friction factor. After the excitation control is applied after 15 s, the speed decreases rapidly until the speed drops to zero.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Hierarchical Classification Trees Construction by Decision Tree Algorithms
#### 3.6.1 Training Sample Sets  
A total of 22,115 field samples collected by field investigations (detailed description shown in Section 3.4) were conducted to build the classification trees. For each mapping unit, because the assigned field samples are usually limited and unevenly distributed in space, many supplementary training samples were appended. These samples came from the visual interpretation of selected satellite images by the experienced mappers. Meanwhile, the existing field samples and high spatial resolution Google Earth images also provided references for the visual interpretation.
## 2 Materials and Methods
### Data Collection  
Following the methodology of [PERSON] et al., this research uses their dataset, derived from a high-fidelity medical mannequin simulating common movements of bedridden patients. [PERSON] et al. adapted a mannequin, typically used in medical training simulations, to replicate inpatient movements and positions. Movement data were captured using strategically placed Movella DOT sensors, wearable platforms known for their signal processing and sensor fusion capabilities. Each sensor, comprising an accelerometer, gyroscope, and magnetometer, recorded velocities, accelerations, and Euler angles, transmitting data wirelessly via Bluetooth. Sensor fusion algorithms combined these data to precisely determine the mannequin's orientation, generating a robust dataset of patient movements. These movements were categorized as \"Roll right\", \"Roll left\", \"Drop right\", \"Drop left\", \"Breathing\", and \"Seizure\" [14], while [PERSON] et al. employed SMOTE to address class imbalance within this dataset, our study investigates whether SMOTEENN, a hybrid resampling method, offers superior performance for predictive model accuracy in fall risk assessment, potentially contributing to more effective real-time monitoring systems and the reduced need for constant patient supervision.  
The dataset included velocities, accelerations, and Euler angles at specific time intervals from a high-fidelity mannequin equipped with Movella DOT sensors [15]. [PERSON] et al. used sensor fusion algorithms to combine these components in order to accurately calculate the mannequin's orientation [16]. Both adult-sized and infant-sized mannequins were used to simulate various patient demographics. Each movement type was repeated around 100 times for substantial data collection. Breathing data were collected by having the mannequin perform one full tidal volume inhalation-exhalation cycle for 3 min straight, while seizure data were gathered from a 10 min continuous seizure simulation. For rolling and falling off the bed movements, the infant mannequin was used, starting in the supine position and rolling approximately 90 degrees to either side before returning to the original position (repeated 100 times per side). Drop data were collected by rolling the mannequin beyond its left side until it fell off the bed, also repeated 100 times. These movements were categorized by [PERSON] et al. into six key labels in the used dataset as fikkiws: \"Roll right\" (0), \"Roll left\" (1), \"Drop right\" (2), \"Drop left\" (3), \"Breathing\" (4), and \"Seizure\" (5). Each label captures a common patient action or position, offering a comprehensive view of patient behavior in a bed setting.
# Synthesis and Properties of Octet NiCr Alloy Lattices Obtained by the Pack Cementation Process
## 3 Results and Discussion
### Comparison with Other Porous Materials  
The Ashby Material Properties Chart allowed us to compare multiple properties of different materials. Figure 10 shows the difference between the mechanical properties of Ni-based alloy lattice materials and other porous materials. It can be seen that the strength and modulus of homogenized NiCr lattice materials were much lower than those of solid metals and ceramics, while those foam materials had medium values.  
Figure 10: Comparison of the mechanical properties of different porous materials: (**a**) strength; (**b**) elastic modulus; (**c**) energy absorption; (**d**) energy absorption efficiency.  
For a wide range of density values (between 300-700 kg/m\({}^{3}\)), the mechanical properties of Ni-based alloy lattices were basically equivalent to those of the octahedral electrodeposited Ni lattice prepared by [PERSON], because of the presence of fine grains strengthening the nanocrystals in the electrodeposited octahedral Ni lattice [7]. These properties were better than those of the hollow octet NiP lattice reported by [PERSON] and of the ZrNiAl metallic glass lattice reported by [PERSON] [9; 40]. In addition, the compressive strength and elastic modulus exceeded those measured for the NiCr, NiCrAl and NiCrFe alloy foams prepared by [PERSON] and [PERSON] [10; 11; 20; 21].  
The octet structure Ni-based alloy lattice showed an excellent static energy absorption capacity. The energy absorbed per unit volume was higher than those of octahedral and layered NiP microlattice, Al microlattice [38], NiB-polymer lattice [41], Al\({}_{2}\)O\({}_{3}\)-polymer [42] lattice and NiCrFe alloy foam [17]. The energy absorption efficiency was basically equivalent to those of Al Foam Duocel\({}^{\circledR}\), EPS foam [38] and octet resin lattices [9; 35].
# A Spectral-Aware Convolutional Neural Network for Pansharpening
###### Abstract  
Pansharpening aims at fusing a low-resolution multiband optical (MBO) image, such as a multispectral or a hyperspectral image, with the associated high-resolution panchromatic (PAN) image to yield a high spatial resolution MBO image. Though having achieved superior performances to traditional methods, existing convolutional neural network (CNN)-based pansharpening approaches are still faced with two challenges: alleviating the phenomenon of spectral distortion and improving the interpretation abilities of pansharpening CNNs. In this work, we develop a novel spectral-aware pansharpening neural network (SA-PNN). On the one hand, SA-PNN employs a network structure composed of a detail branch and an approximation branch, which is consistent with the detail injection framework; on the other hand, SA-PNN strengthens processing along the spectral dimension by using a spectral-aware strategy, which involves spatial feature transforms (SFTs) coupling the approximation branch with the detail branch as well as 3D convolution operations in the approximation branch. Our method is evaluated with experiments on real-world multispectral and hyperspectral datasets, verifying its excellent pansharpening performance.  
pansharpening; multispectral/hyperspectral image; spectral-aware; convolutional neural network; spatial feature transform; 3D convolution +
Footnote †: journal: Applied Sciences