esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 3 Radiation-Hardened SRAM Cell Design
### Quadruple Cross-Coupled Latch-Based 10 T and 12 T SRAM Cell (QUCCE-10 T and QUCCE-12 T)
To make the RHBD-11 T robust to multiple node upset for the critical node pair, RHBD-13 T, shown in Figure 8b, is proposed. For example, when the datum \"0\" is stored so d = 0 V and db = a1 = a2 = VDD in RHBD-11 T, the upset can simultaneously occur as d = 0 \(\rightarrow\) 1 and a2 = 1 \(\rightarrow\) 0, which subsequently lowers db. Then, at the following refresh operation, a1 is pulled down by the lowered db; thus, the data state is changed to \"1\", which means that a data flip occurs. RHBD-13 T, shown in Figure 7b, applies the feedback structure of the Schmitt trigger for the pull-down path of the d to db inverter, through M12 and M13. With this revision, the inverter has a larger input threshold for the rising input, i.e., it becomes more difficult for d = 0 \(\rightarrow\) 1 upset to result in a fall in db. As a result, the memory cell becomes more resilient to the multiple node upset. Figure 9a,b show the schematics of the radiation-hardened dynamic 11 T and 13 T (RHD-11 T and RHD-13 T) [21]. As with RHBD-11 T and RHBD-13 T, the single-ended BL is used and the isolated nodes are used with refresh signals to enhance the error resilience. RHD-11 T has full immunity to single node upset through the use of the stacked inverter P3-P4-N3-N4. Unless X3 and X4 are simultaneously upset, X1 keeps the original value, so X2 is unaffected as well. As a result, the data status can be recovered by the following refresh operation. However, if X3 and X4 are both changed, X1 and X2 are changed subsequently, which results in the data flip. This problem is mitigated in RHBD-13 T through applying the Schmitt trigger inverter, to cause the inverter to trip more severely. RHBD-11 T, RHBD-13 T, RHD-11 T, and RHD-13 T all have full immunity to single node upsets and even have enhanced robustness to multiple node upsets. However, they  
Figure 9: Schematic of (**a**) RHD-11 T and (**b**) RHD-13 T SRAM cell. all suffer from the overhead of using additional control circuitry for the generation of a refresh signal periodically. In addition, these four structures suffer from larger write and read delays compared to the other designs. For example, when BL is lowered for the data \"0\" write, M10 in RHBD-11 T or -13 T (P3 and P4 for RHD-11 T or -13 T) should be turned off. However, due to the lengthened feedback path with the use of the pass gate to isolate the node, more time is required to turn off M10 (P3 and P4). For the read operation, the use of a single-ended BL requires a larger voltage drop for sensing, and the read current path inside the memory cell is composed of three-stacked nFETs. Thus, a significant increase in read delay is inevitable.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 2 Fundamentals of Accelerometer Data
### Calibration and Validation  
The calibration and recalibration of accelerometers are required to ensure that the device performs according to manufacturer specifications, that the measurements provide accurate results, and that the sensor is in a normal state of operation.  
One of the more common methods to calibrate accelerometer sensors is called back-to-back calibration [44]. In this setup, the accelerometer under investigation is fixed back-to-back with a known reference accelerometer, and the combination is mounted on a vibration source. Since both sensors experience the same accelerations, the ratio of their output sensitivity will be equal to the ratio of their output level.  
One downside of this method is that it can be time-consuming, as the sensors are excited at one frequency at a time.  
One way to reduce the calibration time is to excite the sensors with random noise values and analyze the outputs using the Fast Fourier Transform (FFT) to compute the frequency responses and the sensitivity.
## 3 The Proposed Approach
### Sequential Integration  
The proposed hybrid detection method is executed sequentially, beginning with anomaly detection to identify irregularities, followed by the application of data mining techniques to analyze and classify nodes, and culminating in cryptographic verification to authenticate and isolate malicious entities. This step-by-step process ensures comprehensive protection against black hole and gray hole attacks, enhancing the overall security and reliability of MANETs.
## 5 Optimal Sensor Placement and Validation Results Based on QR Pivoting
### Simulated Reconstruction Results of the Proposed Optimal Sensor Placement
The following conclusions can be drawn from these tables: (a) The reconstruction error and condition number can be significantly reduced when the number of sensors is greater than the number of basis vectors. This is because once the sensors exceed the number of basis vectors, the solution of the linear equation can be converted into an oversampling problem. (b) When the number of sensors is equal to the number of basis vectors, the error of random placement is even 20 times of the optimal placement, which illustrates that QR pivoting is a powerful optimization algorithm and can significantly improve the reconstruction results. (c) Compared with the optimization results under the two boundary conditions, although the reconstruction error is large under complex boundary conditions the QR pivoting can effectively reduce the reconstruction error, which proves the feasibility of the optimization method applied in real and complex wind fields. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Error Metric**} & \multicolumn{2}{c}{**6 Sensors**} & \multicolumn{2}{c}{**10 Sensors**} \\ \cline{2-5}  & **QR** & **Random** & **QR** & **Random** \\ \hline \(\log\{\Phi(\text{K}_{\lambda})\}\) & 1.47 & 3.15 & 1.38 & 1.62 \\ \(RE\) & 1.23 & 24.15 & 0.66 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Logarithm of condition number of \(\text{K}_{\lambda}\) and relative reconstruction errors (%) of random placement and QR pivoting in velocity field (Boundary Condition 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Error Metric**} & \multicolumn{2}{c}{**8 Sensors**} & \multicolumn{2}{c}{**15 Sensors**} \\ \cline{2-5}  & **QR** & **Random** & **QR** & **Random** \\ \hline \(\log\{\Phi(\text{K}_{\lambda})\}\) & 2.37 & 3.61 & 1.35 & 1.71 \\ \(RE\) & 12.06 & 101.46 & 7.25 & 10.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Logarithm of condition number of \(\text{K}_{\lambda}\) and relative reconstruction errors (%) of random placement and QR pivoting in pressure field (Boundary Condition 1). Figure 26: Reconstructed pressure nephogram (boundary condition 2) of the first plane with fifteen sensor positions (black points). In this section, we demonstrate that using the QR pivoting sensing on a tailored basis typically achieves more accurate wind field reconstruction than random measurements, facilitating a reduction in the number of sensors. Reducing the number of sensors may be critically enabling when an individual sensor is expensive or difficult to place.
## 2 Simulation-Driven Parametric Design of Auxetic Structures  
Based on the various existing auxetic patterns studied in the literature and their mechanical performance [43], a tetra-petal star-shaped unit cell (Figure 1) [44; 45; 46] was selected for the design and investigation in this study due to its symmetry and mechanical performance. The auxetic response of petal structures is attributed to a hinge and elastic support system similar to the response of star shape structures, with the advancement of avoiding sharp edges, which lead to stress concentrations and discontinuities during production under additive manufacturing technologies. Furthermore, tetra-petal structures exhibit an enhanced auxetic response compared to tri- and hexa-petal structures, and this behavior is strongly dependent on petal geometrical characteristics, form, and size.  
Following the selection of the unit cell form, a numerical parameter study was conducted by varying certain geometrical features of the unit cells, including the angles of petals and between petals, the distance of the base strut from center, and the petal radius and the strut thickness, as shown in Figure 2. Twelve (12) variations of the tetra-petal unit cell were used to explore the lowest Poisson's ratio that these unit cells could exhibit under compression loads, starting with two different polymers: polylactic acid (PLA) and thermoplastic polyurethane (TPU), which represent the high and low Young's moduli classes, respectively. Number (8) was the initial design configuration of the tetra-petal unit-cell and is used as a reference. All unit cells exhibited effective Poisson's ratios that are independent of the generated equivalent strains, i.e., they maintain their auxetic behavior independently from the material's strain. For the case of the elastomer material, \(\
u=-0.6\) was achieved, while for the case of a hard polymer, \(\
u=-0.4\) was observed (Figure 3). For both cases, a finite element analysis was conducted, in which symmetry conditions were applied on the bottom and left surfaces of the unit cell, roller boundary conditions on the rear face, and vertical displacement on the upper face. The hard polymer was modeled as von Mises plastic material and the soft polymer as hyperelastic Mooney-Rivlin material (Table 2).  
Figure 2: Twelve (12) geometrical configurations of tetra-petal unit cells with varying angles of petals and between petals, distance of the base strut from center, petal radius and strut thickness, with unit cell (8) used as reference.
## 2 Design and Method  
### Design  
A design scheme for the on-chip waveguide coupled single photon sources is illustrated in Figure 1. A GaAs QD embedded in a single mode AlGaAs waveguide emits single photons by off resonant excitation of a 532 nm continuous wave laser. Then, the single photons transmit into and propagate \(\sim\)2.5 \(\upmu\)m along the single-mode waveguide. Numerical simulation results show that the transmission efficiency from the QD single emitter to the waveguide with a width of 300 nm and height of 85 nm was about 76% (38% for each propagation direction). By adding grating output couplers consisting of \(\lambda\)/(2n) pitch grating [19; 25; 26] to both ends of the waveguide, emissions were scattered out of the on-chip plane into the detection apparatus. For a designed grating coupler with a period of 400 nm and ring width of 110 nm, simulated out coupling efficiency was about 40%, similar to other reports. The waveguide and out coupler structures are all directly fabricated from the QD membrane placed on a SiO\({}_{2}\) substrate.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 2 Concept
### Processing Strategies  
In this work, we utilize two processing strategies contrasted in Figure 3. The simulation and estimation of geodetic parameters from observations of geodetic sources performed in steps 1 and 2 follow the VGOS Processing. The simulation and estimation of the frame tie from observations of GENESIS performed in steps 3 and 4 follow the GENESIS Processing.  
Vgos Processing  
represents the standard procedure for geodetic VLBI simulations. Only observations of geodetic sources are considered. The observations are simulated including tropospheric turbulence, clock inaccuracies, and measurement noise. To solve the rank deficiency in the normal equations datum constraints are necessary. We apply a no-net-translation (NNT) and a no-net-rotation (NNR) constraint. The estimation of the parameters is performed in a least squares fit. The quality of the schedule is evaluated by the precision of the estimates of station positions and EOPs. Tropospheric delays and station clocks are estimated as a by-product. This procedure is well established and has been used in several previous studies, such as [PERSON] et al. (2018), [PERSON] et al. (2019), [PERSON] and [PERSON] (2020), and [PERSON] et al. (2021).  
GENESIS Processing represents a procedure for the evaluation of inter-technique frame ties. Observations of both geodetic sources and GENESIS are considered. Again, the observations are modeled with errors for tropospheric turbulence, clock inaccuracies, and measurement noise. Additionally, orbit errors are simulated for GENESIS. No NNT or NNR constraints are used. The VLBI station positions are estimated only from observations of GENESIS. Observations of geodetic sources are used to improve the estimation of the troposphere and the station clocks. EOPs and geodetic source positions are fixed because we assume that they can be derived with high accuracy following the VGOS Processing strategy. We do not perform an orbit determination for GENESIS. The concept of the GENESIS Processing strategy was applied to the simulation of VLBI observations of satellites in recent studies by [PERSON] and [PERSON] (2019) and [PERSON] et al. (2020).
## 3 Passive Thermal Control Optimization Design
### Optimization of the Convective Heat Transfer Coefficient Between the Optical Window and Lens  
The convective heat transfer coefficient between the optical window and the lens can be approximated as that between two parallel planes, and the calculation method can be derived as follows:  
\[h_{\text{pla}}=0.27\bigg{(}\frac{\text{Pr}gW^{3}}{\alpha v(0.2+\text{Pr})}\bigg{)} ^{0.28}\bigg{(}\frac{D}{W}\bigg{)}^{-1/4}\frac{\lambda_{f}}{W} \tag{9}\]  
where \(D\) is the diameter of the optical window, and \(W\) is the axial distance between the optical window and the lens.  
From Equation (9), it follows that \(h_{\text{pla}}\) is proportional to \(W^{0.09}\), and the axial distance between the window and the lens can be reduced to minimize the convective heat transfer between them, provided that the structure allows for this. The diameter of the window is 40 mm, and \(W\) is designed to be 36 mm. When the average temperature of the air changes within the range of 250 K to 300 K, the convective heat transfer coefficient between the optical window and the lens changes within the range of 7.77 W\(\cdot\)m\({}^{-2.\circ}\)C\({}^{-1}\) to 10.56 W\(\cdot\)m\({}^{-2.\circ}\)C\({}^{-1}\), which meets the requirements of the optimal range of values.
## 3 Verification
### Measurement of Coherent Length in Atmospheric Turbulence  
Based on the simulation parameters, we first verified the accuracy of atmospheric coherence length \(r_{0}\) measurement. If only one sub-aperture pair and one guide region are used, a \(r_{0}\) value can be obtained. Multiple \(r_{0}\) values can be obtained for multiple sub-aperture pairs and multiple guide regions on MD-WFS. Figure 6 shows the probability density function of a set of \(r_{0}\) measurement results.  
The above results indicate that if only one sub-aperture interval and one guide star are used to calculate the atmospheric coherence length, there may be significant errors, with a maximum value of 12.5 cm and a minimum value of about 8 cm. If the method proposed in this article is used to utilize all sub-apertures and guide regions on SHWFS, better measurement results can be achieved, and the measurement result (10.2 cm) is very close to the simulation design value (10 cm).
## 3 Results and Discussion
The surface roughness has a direct impact on the wear rate and COF, making it an essential parameter to be evaluated in tribological analyses. When compared to other surfaces, SCN's surface had the lowest R\({}_{\text{a}}\) values, showing that the carbon atoms were successfully introduced by the high-energy plasma, which led to the deposition of the rGO/CNT nanocomposite and the filling of the voids in the steel substrates. As a result, there was a significant reduction in the roughness, as confirmed by the SEM and AFM images. Similarly, the addition of N\({}_{2}\) to the plasma improved the adherence and homogeneity of the carbon-based coating, resulting in a smoother surface. The addition of nitrogen to the process reduced the reaction rate; however, it increased the sp\({}^{3}\)/sp\({}^{2}\) ratio, leading to  
Figure 7: AFM images of the coated surfaces: (**a–c**) SCN, and (**d–f**) SC. (As the color of the height scale shifts toward a lighter shade, the height of the atomic layer increases.) the formation of more crystalline structures on the surface. This, in turn, enhanced the hardness and wear resistance. Figure 8: Profilometer images of the coated surface of (**a**) SCN, (**b**) SC, and (**c**) a scratch on SCN. Figure 11 illustrates the relationship between the COF and the sliding distance during friction tests conducted on the US, SC, and SCN. Especially for US, the COF values displayed fluctuation until the sliding distance of 170 m. The average decrease in the COF was found to be roughly 40% for SCN compared to that of the US sample. Based on the absence of a sudden rise in the COF for SC and SCN, it may be inferred that none of the coatings were fully worn down to the substrate, even when subjected to dry-sliding circumstances. The decrease in the COF can be attributed to the reduction in surface roughness thanks to the polishing effect of the carbon-based deposition. The overall data related to the mechanical tests are tabulated in Figure 4:0 \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **US (Before N\({}_{2}\) Etching)** & \multicolumn{2}{c}{**SC**} & \multicolumn{2}{c}{**SCN**} \\ \hline
**Element** & **Weight \%** & **Atomic \%** & **Weight \%** & **Atomic \%** & **Weight \%** & **Atomic \%** \\ \hline C & 16.86 & 44.67 & 29.35 & 59.41 & 27.06 & 55.74 \\ N & - & - & 2.42 & 4.24 & 3.64 & 6.37 \\ O & 4.87 & 9.68 & 7.3 & 10.61 & 7.42 & 12.54 \\ Si & 1.89 & 2.14 & 1.52 & 1.44 & 1.41 & 1.16 \\ Fe & 76.37 & 43.5 & 59.41 & 24.3 & 60.45 & 24.2 \\ \hline \end{tabular}
\end{table} 
Table 6: Elemental composition of the samples (EDX). Figure 9: EDX spectrum and mapping of (**a**,**b**) SC, and (**c**,**d**) SCN. Figure 11: COF vs. sliding distance for all the samples. Figure 10: Optical images of the worn surfaces: (**a**) SCN, (**b**) AFM of SCN, (**c**) SC, and (**d**) US.
## 3 Results
### Volatile Organic Compound Analysis  
Figure 1 shows a typical chromatogram obtained from the SPME-GC/MS analysis of oleaster oil. A total of 23 volatile compounds were identified and quantified.  
As shown in Table 1, among the chemical classes identified there are, in decreasing order of abundance, alcohols, aldehydes, aromatic compounds and volatile phenols, terpen and esters. VOCs such as \(\alpha\)-, hexyl acetate, cis-3-hexenyl acetate, \(\beta\)-cis-ocimene, hexanal and trans-2-heptenal, etc., give the oil a herbaceous and fruity odor. Some of them may arise as a consequence of the breakdown of polyunsaturated fatty acids via a sequence of  
Figure 1: Chromatogram (TIC) of oleaster oil. Peaks: 1, ethanol; 2, hexanal; 3, isoamylacetate; 4, D-limonene; 5, 1-pentanol; 6, 2-pentylfuran; 7, \(\beta\)-cis-ocimene; 8, hexylacetate; 9, cis-3-hexenylacetate; 10, trans-2-heptenal; 11, nonanal; 12, acetic acid; 13, benzaldehyde; 14, trans-2-nonenal; 15, \(\beta\)-linalool; 16, 2-butylolactone; 17, \(\alpha\)-farmesene; 18, butanoic acid; 19, guaiacol; 20, 2-phenylethanol; 21, m-cresol; 22, nonanoic acid; 23, p-vinylguaiacol.  
enzymatic reactions known as the lipoxygenase pathway (LOX), which takes place during the extraction of oil. These compounds are also typical of high-quality virgin olive oils [43].  
Terpenes are responsible for floral and spicy notes. Monoterpenes and sesquiterpenes, such as \(\beta\)-cis-ocimene, \(\alpha\)-farnesene, and limonene, have been identified in virgin olive oils, and there appears to be a correlation between their presence/abundance and the cultivation area of olive groves [44].
## 2 Numerical Simulation Calculation Methods and Model Validation
### Model Accuracy Verification
#### 2.2.2 Sound Field Verification  
Referring to the noise test standard, \"GB/T 2888-2008 Methods of noise measurement for fans blowers compressors and roots blowers\" [28], noise receiving points were set up at 45\({}^{\circ}\) from the centre of the outlet surface, at a horizontal position of one times the diameter (1050 mm). Once the noise value at the adjusted operating point has stabilised, take a reading of the sound level meter and switch to the next operating point. Figure 6 shows a schematic diagram of the noise monitoring point setup, and Figure 7 shows a test setup diagram. The test device is a RINO NL-42 high-precision sound level meter. The device has a sampling frequency of 20-8000 Hz, a range of 25-130 Hz, and a sensitivity of 0.01 dB.  
Figure 5: Comparison of experiment and numerical simulation performance curve.  
Figure 6: Diagram of noise monitoring points.
# Hydrogel Tissue Expanders for Stomatology. Part II. Poly(styrene-maleic anhydride) Hydrogels
###### Abstract  
Self-inflating soft tissue expanders represent a valuable modality in reconstructive surgery. For this purpose, particularly synthetic hydrogels that increase their volume by swelling in aqueous environment are used. The current challenge in the field is to deliver a material with a suitable protracted swelling response, ideally with an induction period (for sutured wound healing) followed by a linear increase in volume lasting several days for required tissue reconstruction. Here, we report on synthesis, swelling, thermal, mechanical and biological properties of novel hydrogel tissue expanders based on poly(styrene-_alt_-maleic anhydride) copolymers covalently crosslinked with \(p\)-divinylbenzene. The hydrogels exerted hydrolysis-driven swelling response with induction period over the first two days with minimal volume change and gradual volume growth within 30 days in buffered saline solution. Their final swollen volume reached more than 14 times the dry volume with little dependence on the crosslink content. The mechanical coherence of samples during swelling and in their fully swollen state was excellent, the compression modulus of elasticity being between 750 and 850 kPa. In vitro cell culture experiments and in vivo evaluation in mice models showed excellent biocompatibility and suitable swelling responses meeting thus the application requirements as soft tissue expanders.  
hydrogel; self-inflating tissue expander; styrene-maleic anhydride copolymers; swelling; volume expansion; mechanical properties; biocompatibility; in vivo