esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Invasive Graphene ECG Electrodes
#### 2.7.1 Microprobe Graphene ECG Electrode  
For higher-quality ECG signal monitoring, ECG electrodes cannot only be deployed on the body's surface, but can be used for invasive monitoring or even monitoring ECG signals from various bio-objects. [PERSON] et al. proposed a flexible and hydrophilic graphene ECG microprobe to realize high-resolution ECG signal monitoring for various creatures [81]. Through microelectromechanical (MEMS) technology, graphene grown by chemical vapor deposition (CVD) was transferred to a SU-8 substrate. After being encapsulated by PDMS and treated with steam plasma, the graphene microprobe showed flexibility, biocompatibility, and hydrophily, which allow it to form a low-impedance electrical contact with the electrolytes of living organisms (Figure 5b). When used in ECG monitoring of zebrafish and crayfish, the electrode shows a low noise level (4.2 \(\mu\)V\({}_{\text{rms}}\)) and high signal-to-noise ratio (27.8 \(\pm\) 4.0 dB), which shows potential in long-term, in vivo ECG monitoring.
## 3 Results
### The 2D Sparse Array Simulation Results  
Figure 6 below compares the simulated images of the scattering phantom imaged using a 2D sparse array where active element positions are determined randomly. Images shown are 2D slices in the x-z plane of a 3D volume. Figure 6a shows the image from a synthetic aperture imaging scheme that takes 128 transmits, and Figure 6b shows the image from the encoded HAPI synthetic aperture transmit scheme that takes one extended transmit. We can visually see how HAPI-SA produces the best image in terms of signal-to-noise and contrast-to-noise in much less transmits than synthetic aperture imaging in the context of noise.
## 2 Materials and Methods
### Microcosm Tests  
The microcosm-scale experimental campaign was carried out following a fully randomized strategy with the following characteristics:  
* Four soil types: A, B, C and D (as described in Table 1), with 500 g of contaminated soil per pot;
* Three plant species: _Medicago sativa_ (alfalfa)--0.8 g seeds, _Zea mays_ (corn)--5 seeds, _Lupinus albus_ (lupine)--6 seeds;
* Three different tests per plant and soil type: a control (CT) using neighboring uncontaminated agricultural land with similar characteristics, a \"base\" test using contaminated soil, and an additional test by adding PGPB inoculum to the contaminated soil.  
Control tests were run in duplicate, while 5 replicates were conducted for the other tests, for a total of 144 microcosms. The tests occurred inside a climatic chamber (CCL300 BH-AS S.p.A., Perugia, Italy) to allow full control of the environment. The night and day cycle was set as follows: 24 \({}^{\circ}\)C with a light on (photon flux density of 130 umol m\({}^{-2}\) s\({}^{-1}\)) for 14 h, followed by a 10 h period with no light at 19 \({}^{\circ}\)C. For irrigation purposes, tap water was administered daily with no addition of fertilizer due to the good concentration of nutrients (mainly N, P, and K) already present in the soil. Inocula were added at a concentration of 10\({}^{8}\) CFU (colony forming units). After 30 days of experimentation, plants were collected and, after the separation of roots and shoots, the fresh biomass (FW) was measured through gravimetrical methods [47]. A GC-MS analysis was conducted following the protocol described in EPA 3545A:2007 and EPA 8270E:2017 to determine the presence and concentration of PAHs in both soil and plants. The PAH concentration in soils was measured at the beginning of experimentation and after plant harvest.
## 3 Results  
### Pathogen Identification in Monocultures  
Our first approach was to investigate the capability of Raman spectroscopy to detect a discriminatory signal between species to identify them as early as possible within 24 h of  
Figure 1: Raman spectra for each monoculture: _L. monocytogenes_ (L), _S. aureus_ (AU), _E. coli_ (E), and _S. enterica_ (S); lines are displayed for better observation and represent the spectra acquired at different time points from top to bottom: 24 (blue), 18 (orange), 8 (green), 6 (red), 4 (purple), 2 (brown), and 0 h (pink).  
bacterial growth in BHI broth. We used this media as it is rich in nutrients and allows the growth of various fastidious microorganisms; therefore, it can be used as standard media. Figure 1 shows that signals increased in intensity after 18 h and 24 h of growth compared to spectra found at 8 h or earlier. This indicates that more distinctive and intense signals were achieved after 18 h of growth at 37 \({}^{\circ}\)C, offering a better signal-to-noise ratio. The same was observed for all the strains tested, significantly _E. coli_ and _S. aureus_.  
Noticeably, signals for _L. monocytogenes_ were weaker than the other strains. This correlates to _Listeria's_ lower growth rate than the other monocultures (Table 1). Indeed, _Listeria's_ Log CFU mL\({}^{-1}\) was significantly lower when cocultured with _S. aureus_ and _S. enterica_ (Figure 2).  
Results from the 5-fold validation show that SVM can identify the difference between the monoculture samples. Confusion matrices presented in Figure 3 show the actual and predicted classes for the 5-fold cross-validation. Correct predictions follow the diagonal, and incorrect predictions can then be seen in the other cells of the matrix. The output at 24 h gave the best response to discriminate between the bacterial spectra with the highest accuracy. In Figure 3, SMV results for the 5-fold cross-validation after 24 h showed an  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & \\ \hline \hline \multicolumn{1}{c}{**Minoculures**} & \multicolumn{1}{c}{**Coulture (s)**} & \multicolumn{1}{c}{**Coulture (y)**} &accuracy of 0.93 (\(\pm\)0.20) predicting the right strain, whereas 18 h and 8 h showed accuracies of 0.88 (\(\pm\)0.27) and 0.46 (\(\pm\)0.10), respectively. This positive correlation among accuracy, time, and cell growth is related to the increase in the intensity of Raman signals and the rise of metabolites concentration achieved at the stationary growth phase. Moreover, at 24 h, SVM predicted _E. coli_ and _L. monocytogenes_ with high accuracy (\(\approx\)1) but predicted, with a marginal error, _S. enterica_ with 0.81 and _S. aureus_ with 0.89 accuracies. In addition, the error increased 0.33 times for _L. monocytogenes_ prediction, misleading prediction to _S. enterica_ at 18 h of growth.
## 3 The Proposed Method  
### Overview  
The proposed algorithm is shown in detail in Figure 2. To begin, each geometric object (polyline/polygon) is extracted from the GIS vector map to compute a backbone object. The backbone object is then selectively simplified by the multi-scale simplification algorithm with differential scale factors in order to obtain new geometric objects. If the backbone object cannot be simplified, it is then encrypted by a key value and randomized by a set of random Gaussian numbers to generate an encrypted object. The key value is generated by a hashing function with a user's key input. New geometric objects, which are simplified from the backbone object, are then used to compute the feature vertices of the backbone object. Here, the backbone object can be divided into two parts: feature vertices and the remaining vertices. The feature vertices of the backbone object will be continuously encrypted by a key value in order to generate the encrypted feature vertices. Finally, the remaining vertices and encrypted feature vertices of the backbone object will be randomly encrypted by a set of random Gaussian numbers in order to obtain the encrypted object. The encrypted GIS vector map is a set of the encrypted geometric objects.  
A GIS vector map contains a number of data layers. Each layer \(\mathbf{L}\) contains a number of objects (polyline/polygon) \(\mathbf{L}=\left\{\mathbf{P}_{i}|i\in[1,\,|\mathbf{L}|]\right\}\) and each object contains a number of vertices \(\mathbf{P}_{i}=\left\{v_{i,i}|j\in[1,\,|\mathbf{P}_{i}|]\right\}\). We briefly define the main notations as the following: \(\mathbf{P}_{i}\) indicates a polyline/polygon object, and \(|\mathbf{L}|\) and \(|\mathbf{P}_{i}|\) are the cardinalities of a layer \(\mathbf{L}\) and an object \(\mathbf{P}_{i}\) respectively. Thus, \(v_{i,j}\) indicates the \(j^{th}\) vertex in the \(i^{th}\) object of a layer \(\mathbf{L}\) and is defined by two coordinates \(v_{i,j}=\left(x_{i,j},y_{i,j}\right)\). Next, \(\mathbf{B}_{i}\) is the backbone object of \(\mathbf{P}_{i}\) and \(\mathbf{B}_{i}^{s}\) is the simplified object of \(\mathbf{B}_{i}\) after the multi-scale simplification process with the differential scales. \(\mathbf{B}_{i}\) is the changed backbone object after the feature vertices encryption process or the backbone object encryption process. \(\mathbf{F}_{i}\) represents the feature vertices of the backbone object \(\mathbf{B}_{i}\), \(\mathbf{G}\mathbf{n}_{i}\) is a set of random Gaussian numbers, \(\mathbf{K}\) is the secret key, and \(\mathbf{E}_{i}\) is the encrypted object of \(\mathbf{P}_{i}\). Finally, \(C_{P}(.),\,\,G_{P}(.)\), and \(R_{P}(.)\) are the cipher function, the random Gaussian number function, and the randomization function, respectively.  
Figure 2: The proposed algorithm.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 1 Introduction
Compared with the eddy viscosity model (EVM), the RSM model can accurately predict anisotropic turbulence [15], and thus is more suitable for simulating wind flow over complex terrain. [PERSON] et al. [15] also found that the RSM model gives better results in simulating wind flow over complex terrain, especially in turbulence generation on the leeward side, which is quite improved, but there is still some error between the numerical and experimental results. In summary, there are many research results on the numerical simulation of wind flow over complex terrain, but they are inconclusive. This is because (1) most of the studies use limited validation cases, and the differences in atmospheric boundary layer characteristics in the field and wind tunnel are not fully considered; (2) not enough consideration is given to the reasonableness of the inlet boundary conditions (including the consistency of the inlet boundary profile and the influence of the inlet boundary on the accuracy of the flow field of the downstream numerical simulation object). Due to the above factors, the assessment of the turbulence model performance may be biased. Therefore, in this study, (1) two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST \(k\)-\(\omega\) model and the linear Reynolds stress model (RSM) with optimized closure constants, as this methodology is more convenient and efficient for engineers compared to the other modifications mentioned above. (2) A comprehensive comparison was performed to evaluate the capabilities of the standard SST \(k\)-\(\omega\) model, the standard linear pressure-strain RSM model, and innovative variants of these two models featuring optimized closure constants. (3) Several typical wind tunnel and field experiments were selected as validation cases. (4) The LES model was used to estimate the performance of the wind flow behavior, and also as a validation of the RANS turbulence model. (5) The self-sustainability of the inlet parameters is investigated to ensure the appropriate inlet boundary conditions. The objective of this paper is to select the most appropriate model to simulate wind flow over complex terrain, one that minimizes the error in wind speed and turbulent kinetic energy. The rest of the paper is organized as follows: Section 2 describes the flow governing equations and several RANS turbulence models; Section 3 provides the computing settings, including validation cases, computational grids, and boundary conditions; Section 4 offers an analysis of the results of the validation cases; the last section presents the conclusion and perspectives.
## 3 Results  
The radiation dose exposure of the medical staff in head scans is shown in Figure 2(a). \(H_{\text{p}}\)(3) without Pb glasses, \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) outside apron in head scans were 0.44 \(\pm\) 0.05, 0.51 \(\pm\) 0.06 and 0.53 \(\pm\) 0.06 mSv per examination, respectively. \(H_{\text{p}}\)(3) with the Pb glasses, \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) inside the apron per examination were 0.14 \(\pm\) 0.02, 0.035 \(\pm\) 0.004 and 0.035 \(\pm\) 0.004 mSv.  
Figure 2(b) shows the results in chest scan. \(H_{\text{p}}\)(3) without Pb glasses and \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) outside apron per examination were 0.13 \(\pm\) 0.01, 0.12 \(\pm\) 0.01 and 0.12 \(\pm\) 0.01 mSv, respectively. \(H_{\text{p}}\)(3) with Pb glasses and \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) inside apron per examination were 0.047 \(\pm\) 0.005, 0.015 \(\pm\) 0.002 and 0.015 \(\pm\) 0.002 mSv, respectively.  
Figure 2(c) depicts the personal dose equivalent in upper abdominal scan, \(H_{\text{p}}\)(3) without Pb glasses and \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) outside apron per examination were 0.12 \(\pm\) 0.01, 0.095 \(\pm\) 0.011 and 0.095 \(\pm\) 0.011 mSv, respectively. \(H_{\text{p}}\)(3) with Pb glasses and \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) inside the apron per examination were 0.041 \(\pm\) 0.005, 0.0050 \(\pm\) 0.0006 and 0.0050 \(\pm\) 0.0006 mSv, respectively.  
Figure 3: Personal dose equivalent in (**a**) head scan, (**b**) chest scan and (**c**) upper abdomen scan. Pb glasses (\(-\)) and Pb glasses (\(+\)) denote without Pb glasses and with Pb glasses, respectively. The errors bars indicate the uncertainty of the measured value.
## 5 Conclusions  
In this paper, the average BER and average capacity performance of an FSO communication system were investigated, taking into consideration the thermal, quantum and background noise. For the reason that lognormal and Gamma-Gamma distribution models have been verified to perform well for weak and moderate-to-strong turbulence  
Figure 8: **(a)** Average BER for different symbol transmission rates under the influence of thermal noise; **(b)** average capacity for different symbol transmission rates under the influence of thermal noise (\(C_{n}^{2}=0.75\times 10^{-14}\) m\({}^{-2/3}\) for moderate turbulence, \(\lambda=850\) nm, \(T_{e}=300\) K).  
Figure 9: Average BER versus average electrical SNR under different modulation schemes (\(C_{n}^{2}=0.75\times 10^{-14}\) m\({}^{-2/3}\) for moderate turbulence, \(\lambda=850\) nm, \(T_{e}=300\) K, \(R_{b}=0.155\) Gb/s).  
in recent research, both these two models of atmospheric turbulence were employed and the system performance was compared under these models with different noises. It was found that both the system parameters (such as the transmission rate, wavelength, and link distance) and the channel parameters (such as the structure constant and temperature) play crucial roles in the scintillation index, further affecting the average BER and the average capacity performance of the FSO communication system. Simulation results also reveal that thermal noise has a more obvious influence on the system performance than the other types of noise. In addition, it was shown that OOK and DPSK modulation performed best in the lower and higher SNR regimes, respectively. Above all, the FSO system will perform better through adjusting parameters to restrain noises or improve turbulence and choosing appropriate modulations.  
Conceptualization, G.X.; methodology, G.X. and Z.X.; software, Z.X.; validation, Z.X.; formal analysis, Z.X. and G.X.; investigation, Z.X. and G.X.; resources, Z.X. and G.X.; data curation, Z.X. and G.X.; writing--original draft preparation, Z.X., G.X. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (under Grant Nos. 61801181, 61831008, 62027802), the Verification Platform of Multi-tier Coverage Communication Network for Oceans (LZC0020), Shanghai Space Innovation Fund (under Grant No. SAST2020-054), the Young Elite Scientist Sponsorship Program by CAST, the Open Research Fund of Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University (under Grant No. EMW201902), and in part by the Science and Technology Commission of Shanghai Municipality (under Grant No. 18 DZ2270800).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods
### Construction of the Pulse Simulator  
This section describes the design and fabrication of a portable pressure waveform simulator system that utilizes the 3D cam. It is intended to generate age-specific pulse pressure waveforms and adjust the diastolic pressure with a simple press of control buttons. Figure 3a shows a conceptual diagram of the pulse pressure waveform simulator. As shown in the figure, this system consists of three main components that include stepping motors, a built-in pressure sensor, and a control unit with an LCD display. A high-speed stepping motor (PKP546N28A2-TS20) is used to rotate the 3D cam in order to produce a precise pulse waveform and the pulse rate (see 1 in Figure 3b). To measure the pulse rate, a hall sensor is used, and it can count the rotation of the cam by detecting the magnetic field from the small magnet embedded in the side of the cam. The rotational speed of the cam is then used to determine the pulse rate. To adjust the pulse pressure waveform for a desired age group, another stepping motor (2 in Figure 3b) is employed. It moves the cam-follower assembly, which is installed on linear motion guides, along the length of the cam to the specific location of the 3D cam, where it generates the desired pulse profile. 3 in Figure 3b shows the cam-follower assembly that incorporates a miniature ultra-soft bellows module. The roller tip of the cam-follower is in contact with the 3D cam surface, and the piston of the cam-follower compresses or extends the bellows according to the contour line of the 3D cam as it rotates. The compression and extension of the piston, in turn, produce the pulse waveform. The bellows compensate for the leakage and friction between the piston and cylinder, improving the accuracy of the output pulse pressure waveform [28]. To measure the pulse pressure waveform generated in the cam-follower and bellows module in real-time, a small air pressure sensor (Honeywell, 40 PC006G) was attached to the bellows by a tube. The pressure sensor readings are processed by the microprocessor unit. As shown in 4 in Figure 3b, the system includes a third stepping motor to regulate the diastolic pressure. A small ultra-soft bellows module is augmented to this motor in order to regulate the diastolic pressure level by adjusting the amount of air in the bellows. Built into the housing of the system, the microcontroller unit calculates the pulse pressure, diastolic pressure, and mean pressure from the measured pulse pressure waveform value in real-time. These values are controlled by buttons on the control panel (see 5 in Figure 3b). The LCD module displays the state of the control inputs and the output pulse pressure waveform in real-time (see 5 in Figure 3b).  
Figure 2: Design and fabrication of the proposed 3D cam.
# The Link Between Physical Function, \(\beta\)-Amyloid, and Cognitive Aging in Women
## 2 Materials and Methods  
### Participants  
The study was approved by the University of Melbourne Human Research Ethics Committee and conducted in accordance with the National Health and Medical Research Council's ethical guidelines (HREC 931149X, 1034765, 110525, 1339373, 010411, 1647448, 1750632) and the Declaration of Helsinki. Written informed consent was obtained from each participant.  
Participants were drawn from the Women's Healthy Ageing Project (WHAP), a longitudinal, prospective cohort study of Australian women, with the detailed methodology described previously [13]. WHAP is an extension of the Melbourne Women's Midlife Health Project, a cross-sectional study that initially recruited 2001 women through random population sampling. Women were eligible for participation if they were aged 45-55 years, had a uterus and at least one ovary, were menstruating, and had not used hormone replacement therapy in the three months prior to recruitment. Of those eligible, 438 women consented to participate in the longitudinal follow-up study.  
Since its inception in 1992, the WHAP has conducted multiple waves of data collection. In 2012, 125 participants consented to undergo cerebral magnetic resonance imaging (MRI) and positron emission tomography (PET) using F-18 Florbetaben (Bundoora, Australia), a radioligand that enables the in vivo quantification of amyloid-\(\beta\) (A\(\beta\)) pathology. Participants were subsequently reassessed in 2014 and 2019. Figure 1 shows the participant flow chart and Table 1 summarizes the variables collected across each wave, along with their corresponding time points of interest.  
For the present analysis, participants were included if they (i) were cognitively normal at baseline, (ii) had available PET-A\(\beta\) imaging data, and (iii) completed at least one follow up assessment of FC and cognitive performance after 2012. A total of 89 women met these criteria (see Figure 1).
## 3 System Identification
In addition, since the identified delay time \(\tau\) (0.003 s) is far less than 1 s, it is reasonable that the pure delay element is approximated by a first-order inertia element in Equation (3). After system model has been identified, it is necessary to validate whether the model is precise enough to represent the real system. The validation experiment is very required since a good match on  
Figure 6: Locations of the zeros and poles. Figure 7: Comparison of system responses with the chirp signal excitation: (a) time responses; and (b) frequency responses. one signal alone does not necessarily mean it is well matched on another. For this test, the plant was subjected to a summation excitation of several sinusoidal signals of 1.6 V; their frequencies were at 0.5, 1.5, 6 and 9 Hz, respectively. The time responses for the validation signal are plotted in Figure 8a,b. A comparison between the actual structure and the identified transfer function when subjected to the validation signal is shown in Figure 9a,b. It can be seen that the output signals are very close, thus confirming that the system identification has performed properly. The fitness percentage (FP) between the real output and estimated output was calculated for quantitative comparison, which is defined as Equation (9). \[FP=1-\sqrt{\frac{\sum\limits_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}}{ \sum\limits_{i=1}^{N}y_{i}^{2}}} \tag{9}\]  
where \(N\) is the number of the acquisition data, \(y_{i}\) is the measured system response, and \(\hat{y}\) is the estimated response of the identified model. The calculation results are summarized in Table 6.  
It is clearly observed from the validation test that the identified model considered in this study performs well. Comparing the FP in Table 5, it is noted that the proposed model \(G\)(s) performs better than the widely used model \(G_{c}\)(s). Consequently, the identified model \(G\)(s) with the delay element is chosen to represent the real plant. \begin{table}
\begin{tabular}{c c c} \hline
**Identified Model** & **Chirp Excitation** & **Validation Excitation** \\ \hline \(G_{c}\)(s) & 77.9\% & 81.4\% \\ \(G\)(s) & 97.2\% & 90.5\% \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of fitness percentage (FP) of identified models. Figure 8: Time response of the validation excitation and measured output signal: (**a**) input signal; and (**b**) sensor output signal.