esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### Acquisition Measurement and Investigation Protocol  
The intracranial and systemic pressure signals from the in vitro model were digitised by a data acquisition system (NI cDNA9172, National Instruments, Austin, TX, USA),  
Figure 2: Representation of the intracranial chamber environment. The brain phantom is enclosed in the chamber, and its vascular bed is connected to the chamber’s inlet and outlet, connected to the pulsatile pump and reservoir, respectively. The brain phantom is surrounded by pressurised extracerebral fluid (deionised water). The intracranial chamber is then sealed with an acrylic cover and an opaque skull layer diffusing light from the nICP sensor.  
whereas the optical signals measured from the brain phantom were digitised by a second data acquisition system (USB6212, National Instruments, Austin, TX, USA) directly connected to the instrumentation unit. The digitised signals from both data acquisition cards were read by a software application developed in LabVIEW (National Instruments, Austin, TX, USA), and all measurements were digitised and acquired simultaneously at a sampling frequency of 1 kHz and saved in a text file for post hoc analysis.  
The investigation protocol involved the acquisition of continuous optical measurements from the brain phantom during stepwise increase/decrease in ICP. By varying the height of the column of water in the burette, the ICP was changed in steps of approximately 2 mmHg from an initial pressure of 10 mmHg (i.e., physiologically normal ICP in adults). A 2-way valve between the burette output and the intracranial chamber allowed for the circuit to be closed once the chamber was pressurised. Figure 3 shows an example of the variations in ICP measured inside the intracranial chamber during incremental increases in pressure.  
Three distinct and independent measurement protocols were performed in which the ICP was either incrementally increased (Dataset 1 and 2) or gradually decreased (Dataset 3). The protocol timing comprised 60 s of continuous acquisition of signals at each pressure-step, whereas a transition period of 30 s between steps was allocated to vary the pressure and to allow the signals to stabilise. The heart rate and stroke volume for all three measurement protocols were set at 80 ppm and 60 mL, respectively, whereas the systemic mean arterial pressure (MAP) in the circuit was regulated between 105-115 mmHg.
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexible Dielectric Materials
#### 3.1.4 Further Discussion of the Material Systems  
As shown in Figure 9, through the comparison of several typical inorganic and organic materials, it can be found that the inherent brittleness of inorganic storage media makes the device only work under relatively low bending conditions, usually the bending radius is not less than 5 mm and the number of bending cycles is generally less than 1000 times. Compared with flexible inorganic materials, flexible organic materials are slightly inferior in electrical properties but exhibit better mechanical flexibility: higher bending cycles (>1000) and smaller bending radii (<4 mm).  
Figure 8: Storage performances of Au/Cu3(BTC)2/Au/PET device based on MOF: (**a**) current-voltage characteristics; (**b**) evolution of the HRS and LRS resistances; (**c**) threshold voltages (V\({}_{\text{SET}}\) and V\({}_{\text{RESET}}\)) as a function of the bending radius or strain level [70].  
Figure 9: The reported bending radii and bending cycles with different material systems: inorganic and organic.  
Therefore, researchers began to look for a suitable material system. Organic-inorganic composite materials have the excellent flexibility of organic materials and at the same time have the good storage performance and stability of inorganic materials, and thus they have been developed.
## 4 Conclusions  
This paper incorporates the hybrid operational strategy into the multi-objective optimization model to optimize the equipment capacity of a DES. An office building was taken as an example with which to compare and analyze the comprehensive performance of the DES under different operational strategies and load structures. The conclusions are as follows:  
1. Compared with the FEL and FTL operational strategies, the HOS reduced the energy waste of the DES by 19.7% and 15.5% and improved the comprehensive performance of the DES by 5.2% and 7.1%, respectively, through the cooperation between the annual hourly load characteristics and the equipment efficiency.
2. According to the performance analysis of the DES optimized based on multiple sets of discrete load with different fluctuations, it was found that the comprehensive performance of the DES-HOS decreased by 1.8% with the increase in the load fluctuation by 15%.
3. Compared with using a typical daily load, using the annual hourly load for DES-HOS optimization improved the accuracy of load forecasting and the comprehensive performance of the DES by about 5.2% and lowered the adverse impact derived from load fluctuations.  
Conceptualization, X.Q. and H.X.; methodology, X.Q.; software, X.Q., J.W. and M.L.; validation, X.Q. and H.X.; formal analysis, X.Q. and J.Z.; investigation, X.Q. and Z.Z.; resources, X.Q. and Z.Z.; data curation, X.Q. and H.X.; writing--original draft preparation, X.Q.; writing--review and editing, X.Q., H.X. and X.W.; visualization, X.Q.; supervision, X.Q.; project administration, X.Q. and H.X.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (51806110).  
Not applicable.  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Prediction Method of Swirling Flame Lean Blowout Based on Flame Image Morphological Features
## 3 Prediction Method Based on Flame Image Morphological Features
### Blowout State Description Method  
It is difficult to judge whether the near-LBO swirling flame extinguishes or not because only the two states of the complete blowout and near-stable combustion can be clearly judged, while the flame state between them is difficult to judge.  
In this paper, a flame area threshold is used as the criterion for whether the flame is in a blowout state or not. Then the moments when the blowout state changes are used as \"zero-nodes\". The frame spacing from the current frame to the closest \"zero-nodes\" is set to the value of the blowout state, and if the current state is the blowout state, a negative sign is added to the original value as the final blowout state value. This method transforms the original binary judgment problem into a numerical prediction problem so that the description of the blowout state has higher stability and statistical distribution uniformity.  
This type of blowout state value also has a corresponding physical meaning, that is, when the value is positively larger, it means that the current state is farther away from the blowout state; when the value is close to zero, it means that the current state is close to blowout; and when the value is negative, a smaller value means that the current state is closer to complete extinguishment.  
Figures 6 and 7 exhibit the result of describing the blowout state of Cases 4 and 9 using the blowout state description method described above, in which the red dashed line is the threshold of LBO in the flame area (set to 300,000 here). The intersections of the solid red line and the dashed red line are used for estimating the value of the blowout state. It can be observed that, in this way, the originally complicated and irregular flame area change process is transformed into a blowout state change process with statistical distribution uniformity, which reduces the difficulty of modeling and improves the modeling accuracy.  
Figure 6: Temporal evolution of flame area \(S\) (red line with the left scale) and corresponding blowout state value (black line with the right scale) in Case 4 mentioned in Table 1.
## 4 Results  
This section presents the results obtained by the application of the smart irrigation system, considering the proposed optimal energy management with predictive control. For the irrigation modeling, the meteorological data forecasts relevant weather for crops on daily basis, the acquired data being temperature, solar radiation, wind speed, relative soil moisture and precipitation. These records are obtained from the 'POWER Single Point Data Access' ([[https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)]([https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/))) accessed on 11 August 2022, which provides solar and meteorological datasets from NASA research to support renewable energy, building energy efficiency and agricultural needs. The obtained climatological data were recorded in an hourly computational database from July 1 through August 29, totaling 60 days, which corresponds to a full growing period of the alfalfa crop.  
To determine the reference evapotranspiration, a database was programmed and by means of the FAO Penman-Monteith equation (Equation (7)) the daily reference evapotranspiration \((ET_{o})\) was determined in the Excel add-in HF Irrigation.  
The solar irradiance data were obtained from 'POWER Single Point Data Access' records, the irradiance data are used for the EMS with a daily prediction horizon totaling 24 samples per hour, which allows the optimization of energy management using the FICO(r) Xpress Workbench software, as it has the ability to solve optimization problems.
## 3 Distributed Mission Scheduling Model
### ATSA for Dynamic Scheduling  
Dynamic scheduling occurs when emergency missions arrive or change of initial plan is required. This section presents an ATSA to solve this problem. Before introducing the algorithm, we firstly analyze what will happen to the solution when an emergency comes up.
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 1 Introduction
### Scaling and Issues of SiO\({}_{2}\)/poly-Si Gate Stacks
For a dielectric without defects, \(\Phi_{B}\) could be determined by the energy band alignment as shown in Figure 3. For electron transportation, the barrier is conduction band offset given as \(\Delta E_{\text{C}}\). For hole transportation, it is valence band offset given as \(\Delta E_{V}\). It is easy to see that \(J_{S}\) strongly depends on \(\Phi_{B}\). In a real dielectric material, the barrier height is also relative to the defect density in SiO\({}_{2}\). The defects could form energy levels in the SiO\({}_{2}\) band gap and trap the charges. Such a trap-assisted mechanism of charge transportation can be described as Frenkel-Poole emission or hopping conduction [4]. The gate leakage depends strongly on \(T_{ox}\), as shown in Equation (1) and Figure 4. For the layers thinner than 1 nm, the extremely high tunneling leakage will cause intolerable power consumption and joule heat. Reliability degradation is an equally important issue attributed to the dielectric down-scaling. [PERSON] gave a fundamental mechanism of dielectric breakdown in a thin SiO\({}_{2}\) film based on percolation model [5]. The breakdown occurs when the accumulation of different defects in the SiO\({}_{2}\) network reaches the threshold and builds a conductive filament. Such a failure mechanism explains well the sensitivity of breakdown to the film thickness. Another issue of the conventional poly-Si/SiO\({}_{2}\) gate stack worsen by the dimension scaling is the boron penetration [6]. In a poly-Si gate, the carriers derive from the doped III or V valence elements. For a p-type poly-Si, boron is widely used as dopants. The boron ions in the heavily doped poly-Si could diffuse through the gate dielectric. For a thick dielectric layer, the diffusion could be very small. As the SiO\({}_{2}\) is scaled to nanometer thick, the out-diffused B dopants accumulating in the SiO\({}_{2}\) and  
Figure 4: Gate leakage current density as a function of SiO\({}_{2}\) physical thickness. Figure 3: Band diagram of the poly-Si/SiO\({}_{2}\)/Si gate stack. Si substrate would change the threshold voltage, increase the defect density and further worsen the leakage and reliability issues.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 5 Conclusions
### Future Research Directions  
1. The formalization of time-dependent UCAs.  
It can be observed from Section 3.3 that \"Type 3: Too early or too late\" and \"Type 4: Stopped too soon or applied too long\" have not been formalized, which brings ambiguity to the identification of UCAs. [PERSON] et al. added timing property to the definition of UCA [2] (p. 153), which is beneficial to solve the problem, but further empirical evidence is still in need.
2. Improving the automatic conversion between the SysML model and the NuSMV model.  
Although our prototype tool is capable of converting the SysML model into the NuSMV model, the technique is not general and is still far from maturity.
3. The variants of STPA-SN could be developed.  
Similar to STPA-SN, AltaRica or AADL could replace SysML, and other model checkers, such as SPIN and PAT, could replace NuSMV.
4. The STPA that can automatically identify the loss scenario for hybrid system.  
Construct the analytical model of a hybrid system and analyze the model through simulation. For example, discrete dynamic behaviors can be modeled with Stateflow and continuous dynamic behaviors can be modeled with the differential/difference equations in Simulink (Stateflow and Simulink are two tools of MATLAB). After modeling, simulation can be used to identify the loss scenario.  
Conceptualization, D.Z. and R.S.; methodology, D.Z. and R.S.; Software, H.G.; validation H.G.; formal analysis, R.S. and H.G.; investigation, D.Z. and H.G.; resources, D.Z. and R.S.; data curation, H.G.; writing--original draft preparation, D.Z. and H.G.; writing--review and editing, D.Z. and T.W.; visualization, H.G.; supervision, D.Z.; project administration, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by Civil Aviation Joint Funds under National Natural Science Foundation of China and Civil Aviation Administration of China, grant number U1533201, and by Science and Technology on Reliability and Environmental Engineering Laboratory, grant number 6142004200405.  
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
###### Abstract  
In this study, it was demonstrated that ice-templating via freeze drying with custom-made moulds, in combination with air brushing, allows for the fabrication of poly(glycerol sebacate urethane) (PGSU) scaffolds with hierarchical multilayer microstructures to replicate various native soft tissues. The PGSU scaffolds were either monolayered but exhibited an anisotropic microstructure, or bilayered and trilayered, with each layer showing different microstructures. By using freeze drying with custom-made moulds, the ice crystals of the solvent were grown unidirectionally, and after freeze-drying, the scaffolds had an anisotropic microstructure, mimicking tissues such as tendon and skeletal muscle. The anisotropic PGSU scaffolds were also examined for their tensile strength, and a range of mechanical properties were obtained by altering the reactants' molar ratio and polymer concentration. This is of importance, since soft tissues exhibit different mechanical properties depending on their native location and functionality. By combining freeze drying with airbrushing, scaffolds were fabricated with a thin, non-porous layer on top of the porous layers to allow three-dimensional cell co-culture for tissues such as skin and oral mucosa. These results show that fabrication techniques can be combined to produce PGSU scaffolds with tailored hierarchical microstructures and mechanical properties for multiple tissue engineering applications.  
poly(glycerol sebacate urethane); tissue engineering; multilayer scaffold; anisotropic scaffold; soft tissue +
Footnote †: journal: Applied Sciences
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### Envelope Extraction by Demodulation Algorithm  
One critical point of the accuracy of the object localization is the precision of the start point time of the reflected ultrasonic signal, and the TOF can be calculated by the start point time. In general, the reflected ultrasonic signal is a set of 40 KHz signals, whose envelope must be captured first for the subsequent algorithm to calculate the start point time. To extract an envelope of the 40 KHz ultrasonic signal, the demodulation algorithm is proposed in this study. The concept of the demodulation algorithm is to extract the envelope signal by separating the high and low frequency of the original signal and retain the low-frequency part of the original signal through a low pass filter. The echo signal of the ultrasonic signal is modeled as:  
\[r_{dm}(k)=a(k)sin(\omega k+\phi) \tag{1}\]  
where \(k\) is the time step, \(a(k)\) is the amplitude; that is, envelope, \(\omega\) is the center frequency of ultrasonic, and \(\phi\) is the phase shift.  
Let (1) be multiplied by a sinusoidal signal and a cosine signal whose frequency is the same as that of the ultrasonic signal to generate \(x_{1}(k)\) and \(x_{2}(k)\), the results can be obtained as follows:  
\[x_{1}(k) =a(k)sin(\omega k+\phi)*sin(\omega k)\] \[=\frac{1}{2}a(k)cos(\phi)-\frac{1}{2}a(k)cos(2\omega k+\phi) \tag{2}\] \[x_{2}(k) =a(k)sin(\omega k+\phi)*cos(\omega k)\] \[=\frac{1}{2}a(k)sin(\phi)+\frac{1}{2}a(k)sin(2\omega k+\phi) \tag{3}\]  
In (2) and (3), the original signal is divided into two parts, the low-frequency part with \(\phi\), and the high-frequency part with \(\omega\). Therefore, a low-pass filter with a cut-off frequency around \(\phi\) can be designed to filter out the high-frequency part, and the following results can be obtained:  
\[z_{1}(k) =\frac{1}{2}a(k)cos(\phi) \tag{4}\] \[z_{2}(k) =\frac{1}{2}a(k)sin(\phi) \tag{5}\]  
Figure 3: Illustration of blind area. (**Top**): Interference signal 1 and reflected signal 2; (**Bottom-left**): Paths of interference signal (A) and reflected signal (B); (**Bottom-right**): Path of interference signal (C).  
where \(z_{1}(k)\) and \(z_{2}(k)\) are the sample signals after passing through a low pass filter. As a consequence, the envelope \(e_{dm}(k)\) can be obtained as follows:  
\[e_{dm}(k)=a(k)=2\sqrt{z_{1}(k)^{2}+z_{2}(k)^{2}} \tag{6}\]
## 3 Multiobjective Optimization Problem
### Visualisation of the n-Dimensional Pareto Fronts  
In case of problems with two objectives, the Pareto front is a curve; in case of three objectives--a surface; and in case of higher orders of solution space--a hypersurface (Figure 8). A large number of objectives may happen to cause problems with representation of results in the graphic form, as the traditional methods may become unclear. Traditional methods of representation of result in the case of four objectives coming down to display the data as a three-dimensional plot, with three objectives shown on the axes of a coordinate system and the fourth objective displayed as a color on data points. For four objectives or more it is even harder to find an efficient way of data visualization. One way of dealing with high-dimensional results' visualization is utilizing a scatter plot matrix, where objectives are displayed pairwise, resulting in a matrix of \((n^{2}-n)/2\) scatter plots, where \(n\) is the number of objectives. In case of six objectives, as investigated in this paper, this requires the data to be displayed on 15 scatter plots. Such an approach means that the visualization may not carried out in a clear, simple and precise manner. Utilizing self-organizing maps (SOMs) as a visualization tool helps one to overcome some of the flaws of the aforementioned methods.  
SOM is an artificial neural network, which has been used, among others, in the visualization of multidimensional data sets. This method was proposed by [PERSON] in [40]. In the case of multi-criteria optimization problems with a large number of objectives, which result in multidimensional Pareto fronts, SOM can help to present the results in a graphical form but an alternative way. Multidimensional solution space is transformed into the form of \(n\) colorful maps, where \(n\) is the number of dimensions of solution space, and the colors of units (nodes) on the map correspond to the approximate values of the objective function. Each unit of SOM is described by a codebook vector of size \(n\). Values of  
Figure 8: Visualization of the Pareto front for the different number of objectives.  
codebook vector elements are determined in an iterative learning process based on input data (Pareto front) presented to the network. The network learning process preserves the topological order of data, so solutions located close to each other on the front of Pareto remain close to each other on the SOM. Such an approach to visualization of results allows for easy determination of tradeoff relationships between objectives of the optimization problem. An effective method of visualizing a set of solutions is a key factor at the stage of information exchange between the person processing the results and the person deciding on the choice of a particular solution. In addition, SOM can be used to group similar solutions into clusters.