esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parameters Study
### Influence of Prestress of Tendon  
This section discusses the influence of prestress variations on the acceleration spectrum. Similar to the shear stiffness of connections, the prestress of the tendon is also a structural parameter of the composite beam itself. The composite beam was the same as the previous Jiubao northern approach bridge, with the only change in prestress magnitude \(F_{P}\) for the composite beam, taking \(F_{P}\) = 10\({}^{2}\) N, \(F_{P}\) = 10\({}^{4}\) kN, \(F_{P}\) = 10\({}^{6}\) kN, \(F_{P}\) = 1.6 \(\times\) 10\({}^{6}\) kN for analysis. The following vehicle parameters remained the same: vehicle mass \(m_{v}\) = 1200 kg,  
Figure 16: The influence of shear stiffness on the acceleration spectrum: (**a**) \(k_{s}\) = 10 MPa; (**b**) \(k_{s}\) = 100 MPa; (**c**) \(k_{s}\) = 1000 MPa; (**d**) \(k_{s}\) = 10,000 MPa.  
stiffness \(k_{v}=500\) kN/m, vehicle damping ratio \(\xi_{v}=0.2\), and vehicle velocity \(v=5\) m/s. The acceleration spectrum under the four prestress levels are shown in Figure 17. With the increase of prestress, the overall acceleration spectrum of the composite beam shifted to the left, indicating a decrease in frequency with increasing prestress. This observation is consistent with the results of the free vibration analysis, as illustrated in Figure 18. The amplitude of the spectrum slightly increased with the increase of prestress, but the change was not significant.  
Figure 17: The influence of the prestress of the tendon on the acceleration spectrum: (a) \(f=100\) kN; (b) \(f=10\),000 kN; (c) \(f=1\),000,000 kN; (d) \(f=1\),600,000 kN.  
Figure 18: The prestress–frequency curve obtained from free vibration analysis.  
In practical applications, the identified bridge frequency from the indirect method can be compared with the prestress-frequency curve obtained from free vibration analysis to calculate the loss of prestress. For example, if the bridge is designed with a prestress of \(10^{6}\) kN, and the identified change in the bridge's fundamental frequency is 1.11 Hz, which corresponds to \(0.8\times 10^{6}\) kN on the prestress-frequency curve, the predicted prestress loss would be about 20%. However, it is important to note that this example provides only one application scenario considering the variation in prestress. In actual practice, the frequency change of the bridge should take into account other factors that can cause stiffness variations, and the accuracy of the analysis should meet the required specifications.
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 2 ALD of the Metal Gates
### ALD of P-Type Metal Gate
The PEALD was carried out using TDMAT and H\({}_{2}\)/N\({}_{2}\) under well controlled plasma condition for much lower ion dose and energy compared to PVD. The MOSCAP with PVD TiN clearly showed larger trap density and higher interfacial defect density than the PEALD. The n- and p-MOSFETs with PVD and PEALD TiN had similar electrostatic performance, with very good short channel performance, say small subthreshold swing, DIBL and \(V_{t}\) roll-off. The gate dielectric quality of them, however, are remarkably different. The gate leakage of those with PVD TiN was much higher than PEALD, consistent with the high density of defects in the bulk SiO\({}_{2}\) leading to trap-assisted tunneling and significantly higher gate dielectric failure probability. Taken together, the electrical results suggested that during PVD of TiN, the gate dielectric was damaged by energetic ions and ultraviolet (UV) photons which break Si-O bands and leave defects states, inducing increased gate leakage and reliability issues. In summary, most of the research works on TiN ALD were carried out using TiCl\({}_{4}\) and NH\({}_{3}\) or TDMAT and NH\({}_{3}\), with and without plasma enhancement. In most cases, the chloride based ALD has a p-type work function [117; 123], while the TDMAT-based process gave a n-type work function [121; 125], compared to the midgap one of the TiN by PVD [126]. It was attributed to the different chemical composition of the interfacial layer. The TiCl\({}_{4}\)-based process could generate a Ti-rich interfacial layer, and thus had a Ti-like work function. The PVD TiN had a Ti:N ratio near 1:1, and as a result, had a work function at midgap. The TDMAT process generated TiN with high oxygen and carbon content, inducing n-type metal behavior. Real mechanism of the difference might be more complex. Many works revealed that the _EWF_ of the TiN in the gate stacks shifted with the thickness [97; 127], post deposition anneal [97; 128], microstructure of the TiN [129; 130] and different doped ions such as Al [44]. As [131], P/BF\({}_{2}\)[132]. Careful engineering of the ALD TiN is always needed for the application in real gate stacks.
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 7 Conclusions  
In this paper, we reveal the reasons for false targets in USF and develop a solution to the issue. Specifically, we illustrate that false targets are generated by periodically appearing CP signals. We unveil that false targets have the same Doppler bin but different range bins compared with the true target. Moreover, the differences between the range bins of false and true targets are derived. Other features, such as strength, of false targets are also provided. In addition, we propose to remove false targets by nullifying CP signals of a coefficient matrix used for processing echo signals. We also propose a second method performing sequential target detection and removal to counteract the impact of false targets. Extensive simulations are performed, validating the effectiveness of the proposed methods. The proposed algorithm can reduce the false alarm rate by more than 50% compared with the previous work [32]. Meanwhile, the detecting probability can be maintained at one for a large SNR region.  
Methodology, K.W. and J.A.Z.; resources, X.H. and Y.J.G.; writing--original draft preparation, K.W. and J.A.Z.; writing--review and editing, X.H. and Y.J.G.; funding acquisition, J.A.Z and Y.J.G. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Long-Range Sensing** \\ **(Figure 6)** \\ \end{tabular} & \begin{tabular}{c} **False Targets** \\ **(Figure 5)** \\ \end{tabular} & \
\begin{tabular}{c} **Detection Probability** \\ **(–10 dB, Figure 9)** \\ \end{tabular} &
\begin{tabular}{c} **False Alarm Rate** \\ **(–10 dB, Figure 9)** \\ \end{tabular} \\ \hline
1 & yes & yes & 0.54 & \(4.8\times 10^{-4}\) \\ \hline
2 & no & no & 0.42 & \(6.3\times 10^{-4}\) \\ \hline
3 & yes & no & 0.44 & \(2.8\times 10^{-4}\) \\ \hline
4 & yes & no & 1 & \(2.05\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons among different algorithms.  
Figure 9: Detecting performance versus SNR (with respect to **y**), where four targets are set with randomly generated parameters over \(10^{4}\) independent trials. The detecting probability is given in (**a**) and the false alarm rate is given in (**b**). Please note that the symbol “-” before all numbers, as automatically generated by MATLAB during plotting the figure, denotes the negative sign.  
**Funding:** This research is partially funded by the Australian Research Council under the Discovery Project Grant DP210101411.  
**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.
# Survey of Finite Element Method-Based Real-Time Simulations
## 5 FEM-Based Training of Neural-Networks  
A rather specific direction of the development that could be interpreted as an \"indirect\" model order reduction technique relies on the use of FEM results to train the neural networks for fast computations. Additionally, in this case, the idea is to perform rather cumbersome and time-consuming computations prior to simulation. The fact that the approach allows to tackle the problem of nonlinear structural deformations, whereby both geometrical and material nonlinearities can be addressed, makes it particularly interesting. Hence, the (linear or nonlinear) FEM computations provide a set of results used in the training phase of neural networks.  
The approach was applied by [PERSON] et al. [37] for real-time simulation of tennis racket and ball deformation. As input data for the artificial neural network, they used a set of randomly generated parameters, such as the impact angle, ball impact velocity and impact zone, while the deformation of the ball and strings together with the impact force made the output data set. This development aimed at a virtual reality simulator including a haptic device with force-feedback. [PERSON] et al. [38] used a multi-layer feed-forward neural network to produce a reduced model for geometrically nonlinear deformations of a cantilever beam. [PERSON] et al. [39] reported on a similar work for shell structures. [PERSON] et al. [40] used fuzzy logic, neural networks and three-dimensional (3D) finite element calculations to develop a numerical model that allows fast predictions of the response of longwall coal mining installation to changing operation conditions. The results were further represented to the user by means of virtual reality modelling language (VRML) tools. [PERSON] et al. [41] presented a method for simulating the deformation of organ models that relies on usage of neural networks to find a relation between external forced and model deformations. They confirmed the possibility of real-time simulations whereby the results were comparable with those yielded by the nonlinear FEM. [PERSON] et al. [42] used Ansys for FE computations and Matlab to perform neural training. Neural networks memorized only surface displacements of the models and the parametric deformation matrices were converted into \"behavioral modules\" for the virtual reality engine.  
Systems for real-time control can particularly benefit from strongly reduced models that offer adequate accuracy in simulating the system to be controlled. Such models can be used to improve the predictor part of the controller as [PERSON] et al. [43] demonstrated by implementing the FEM-based neural-network approach for nonlinear longitudinal dynamics of an experimental lorry. [PERSON] et al. [44] used neural networks to capture the finest nonlinearities based on FE and needed for simulation and control of modular soft robots.
## 2 Digital Image Correlation Technique  
The DIC technique represents an optical approach for reliably measuring the two-dimensional or three-dimensional image alterations to quantify deformations and strains via advanced image registration and target tracking algorithms [17]. Compared to other approaches, such as mechanical deflectometers and speckle shearing interferometry, for deformation and strain measurement, DIC is an easy to operate and cost-effective technique. For example, in many cases, it does not need any special working environment and can even work for extreme conditions, e.g., at the temperature higher than 1000 \({}^{\circ}\)C. Meanwhile, it is more accurate than manual measurements. DIC can be used to estimate strains in a wide range (e.g., from the lower order of 0.001 to the higher order of 1) with its measurement sensitivity on deformations reaching 1/100,000 of the view field in-plane. With clean cameras and patterned speckles, the strain resolution can be around 20 microstrains. Due to these advantages, along with advances in computer technology, DIC has been extensively adopted for monitoring deformation patterns, failure modes of materials, and structural health without damaging the objects under investigation.
## 5 Extracellular Components of the CRC Microenvironment
### Extracellular Matrix (ECM)
#### 5.1.2 Targeting Therapy at the ECM  
A detailed characterization of the ECM in CRC, especially metastatic stroma, is a challenge due to the shortage of biological samples. On the other hand, in vitro and in vivo studies do not fully capture the real disease mechanisms. This leads to difficulties in designing therapies targeted at the ECM [248].  
Somewhat distant in vitro studies focusing on serine proteases (such as uPA) in tumor invasion drew attention to inhibitors of these factors as potential therapeutics [249]. However, other studies questioned their effectiveness, as continued cancer development was observed [250]. Since a variety of cellular components and particles are involved in ECM remodeling, [PERSON] et al. proposed a model/suggestion for therapy that takes into account the ECM characteristics for different stages of cancer:  
Early stage: maintaining the proper elasticity of the ECM by limiting fiber deposition; targeting MMPs to restrict early metastasis.  
Intermediate stages: reversing ECM modifications induced by tumor progression and inhibiting further fibrosis; improving chemotherapy effectiveness by facilitating drug transport.  
Advanced stages: regulating ECM degradation, limiting neo-vascularization and metastasis, enabling drug transport, improving quality of life.  
To normalize the state of the extracellular matrix, the researchers proposed the use of natural substances, such as flavonoids, phenols, polysaccharides, saponins, terpenoids, alkaloids, and quinones. Their action has been demonstrated in reducing the deposition of ECM components, reversing potential modifications, inhibiting ECM degradation, and targeting cancer-associated fibroblasts (CAFs) [251].
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Pathogens
28.9%, and _Bacillus cereus_ 8.1% [42]. Fortunately, sensor technologies play a key role in monitoring and tracing these pathogens, and thus nowadays, controlling such outbreaks is much easier than before. In contrast, the number of reported deaths due to foodborne has decreased over the years from thousands [43] in the period between 1998-2008 in USA to hundreds in the period between 2009-2015 in USA [44]. The decrease in mortality rate is due to having better tracking and prevention procedures of food. Studies done in 2016 [45], and in 2014 [46], suggested that there is a potential growth of micro-organisms inside plastic bags during transportation, distribution, and retail storage. This is an important discovery, as it emphasizes the need to have precise monitoring systems to track and control the variations due to various environmental changes during the stages of supply chain, and to warn when the number of these pathogens exceeds the allowed range. The reason for microorganisms' growth is related to the temperature fluctuations along the supply chain since temperature variations has a great effect on their growth rate inside the package. It was found that for _E. coli O157:H7_, the growth rate can be inhibited if storage temperature is kept \(<\)5 \({}^{\circ}\)C [34; 47]. It was noticed that a more significant growth rate occurs from 8 \({}^{\circ}\)C to 12 \({}^{\circ}\)C while the visual freshness of the product is maintained. The count of this strain of _E. coli_ increases with temperature [48; 49; 50]. For _Salmonella_, a significant increase in the population is noticed for packaged arguula over 6 days when stored at 15 \({}^{\circ}\)C (4.05 log\({}_{10}\) CFU/g ) compared to when stored at 7 \({}^{\circ}\)C (2.11 log\({}_{10}\) CFU/g ) [51]. Another study done in 2001 [34], showed that _L. monocytogenes_ growth rate is increased by approximately 1.5 log cycles as the temperature rises from 4 \({}^{\circ}\)C to 8 \({}^{\circ}\)C over a period of 12 days for packaged lettuce. _Shigella somei_ showed a rapid growth of approximately 2.72 log\({}_{10}\) CFU/g on chopped parsley at 21 \({}^{\circ}\)C within two days while an inhibited growth is noticed when chopped parsley is kept at 4 \({}^{\circ}\)C [52]. The growth rate of _Bacillus cereus_, often found in cooked chilled food and pasteurized vegetables such as cowgette broth, is inhibited when kept \(<\)10 \({}^{\circ}\)C [53]. Therefore, understanding how pathogens react to temperature variation is essential to check safety of packaged food. Hence, monitoring the storage and the display units is very crucial to ensure that pathogens' growth is inhibited or controlled for compliance with food safety.
## 2 Material and Methods  
### Site Description  
The field research site was located in Sigou village of Menyuan County on the southern slope of Qilian Mountains, the Tibetan Plateau (37.13\({}^{\circ}\) N, 102.37\({}^{\circ}\) E, 2520 m). The tower-based eddy covariance of CO\({}_{2}\) exchange was erected in a spruce forest (_Picea crassifolia_) with a height of 35 m on 10 th August 2020 (Figure 1). It is characterized by plateau continental climate with average precipitation and air temperature of 442.0 mm and 498.3 mm and 3.78 \({}^{\circ}\)C and 4.41 \({}^{\circ}\)C in 2021 and 2022 (Table 1). Average relative humidity, wind speed, and wind direction were 27.49%, 1.32 m/s, and 162.58\({}^{\circ}\) (south). Soil organic carbon and pH were 17.56 g/kg and 7.5 while total nitrogen, total phosphorus, and total potassium were 7.1, 1.82, and 20.47 g/kg, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Years & Air Temperature \({}^{\circ}\)C & \begin{tabular}{c} **Precipitation** \\ **mm** \\ \end{tabular} & \begin{tabular}{c} **Relative** \\ **Humidity \%** \\ \end{tabular} & \
\begin{tabular}{c} **Wind Speed m/s** \\ \end{tabular} &
\begin{tabular}{c} **Wind Direction \({}^{\circ}\)** \\ \end{tabular} \\ \hline
2021 & 3.78 & 442.0 & 32.21 & 1.29 & 163.15 (south) \\
2022 & 4.41 & 498.3 & 22.77 & 1.34 & 162.01 (south) \\ Average & 4.10 & 470.2 & 27.49 & 1.32 & 162.58 (south) \\ \hline \end{tabular}
\end{table}
Table 1: Annual climate meteorologic parameter of Qilian Mountain forests in 2021 and 2022.  
The age of this secondary forest is approximately 70 years. Dominant species included _Picea crassifolia_, _Betula plattyphylla_, _Sabina przewalskii_, _Picea wilsonii_, _Pinus tabuliformis_, _Tamarix ramossissima_, _Larix principis-rupprechtii_, _Salix cuularis_, and _Populus davidiana_.
## 4 Hardware Architecture
### External Sensors Interfaces  
Three external digital sensors were selected for implementation of the MSP: a strain gauge sensor to monitor mechanical stress imposed on a workpiece, an accelerometer to measure the vibration of the device, and a temperature sensor for the deformations due to temperature. The external sensors are mounted on the MSP board through interfaces. The connection is realized through the use of six coupled connectors. The electrical components of the interface are standardized and equal for all types of sensors. The main components are an analog-to-digital converter, an instrumentation amplifier, a voltage regulator, and EEPROM memory. These interfaces comprise a power supply and data interfaces, such as SPI and I2C, in a 18 (2 \(\times\) 9)-pin configuration, which is needed for the sensor modules.
## 3 Results
### Relationship of GPP with VIs and SIF, 2001-2020
#### 3.3.1 Correlation of GPP with VIs  
The kNDVI-GPP relationship (meanR\({}^{2}\) = 0.74) was the lowest among the three VIs. Specifically, the kNDVI-GPP relationship was significantly lower in grassland, forest-shrub-grass vegetation mosaic belt, deciduous forest, and evergreen forest than in NDVI and NIR\({}_{\rm V}\). Compared to sparse vegetation and cropland, the kNDVI-GPP relationship for grassland, forest-shrub-grass vegetation mosaic belt, deciduous forest, and evergreen forest declined the most in spring, summer, and autumn. In particular, the R\({}^{2}\) of kNDVI-GPP for forest-shrub-grass vegetation mosaic belt, deciduous forest, and evergreen forest in summer was only 0.34, 0.23, and 0.14. It is noteworthy that kNDVI performed best in sparse vegetation (meanR\({}^{2}\) = 0.85). Although the NDVI-GPP relationship was lowest during the growing season, removal of winter, and entire study period (excluding the growing season in deciduous forests) in each of the land classes, the NDVI-GPP relationship (meanR\({}^{2}\) = 0.76) remained slightly higher than kNDVI.  
The NIR\({}_{\rm V}\)-GPP relationship (meanR\({}^{2}\) = 0.84) was much improved across vegetation types compared to NDVI and kNDVI. The correlation with GPP was much higher than that of NDVI and kNDVI in all land types except in sparse vegetation and cropland, where it was slightly lower than kNDVI. NIR\({}_{\rm V}\) performed best in the non-growing season (meanR\({}^{2}\) = 0.61) and even outperformed SIF in sparse vegetation, grassland, and forest-shrub-grass vegetation mosaic belt.
## 3 Proposed Algorithm
### SBL Algorithm  
This section introduces the SBL algorithm based on expectation maximization (EM) iteration to solve the channel model in Equation (23). Assuming that the elements in the noise vector \(\widetilde{\mathbf{w}}\) are independent of each other and obey the complex Gaussian distribution \(\widetilde{\mathbf{w}}\sim\mathcal{N}(0,\sigma^{2}\mathbf{I})\), the Gaussian likelihood model is obtained as follows:  
\[p(\mathbf{y}|\mathbf{c},\widetilde{\mathbf{w}})=\left(2\pi\sigma^{2}\right)^{- MN/2}\exp\left\{-\frac{1}{2\sigma^{2}}\|\mathbf{y}-\mathbf{\Phi}\mathbf{c} \|_{2}^{2}\right\} \tag{24}\]  
Making a prior assumption about \(\mathbf{c}\) is a typical operation to avoid overfitting and is equivalent to adding a regular term from the Bayesian perspective [24]. Assuming that each component of the basis coefficients \(\mathbf{c}\) obeys Gaussian distribution and that each variable \(c_{i}\) corresponds to an A hyperparameter \(\alpha_{i}\), the Gaussian prior distribution of \(\mathbf{c}\) can then be calculated as follows:  
\[p(\mathbf{c}\,\mathbf{l}\,\boldsymbol{\alpha})=\prod_{i=0}^{MN}\mathcal{CN} \left(  
\[\sigma^{2}=\frac{\left|\left|\mathbf{y}-\mathbf{\Phi}\mathbf{\mu}^{c}\right|\right|_ {2}^{2}}{MN-\phi_{n}^{c}(1-\alpha_{n}\Sigma_{n,n}^{c})} \tag{31}\]  
where \(\Sigma_{n,n}^{c}\) denotes the \((i,i)\)-th diagonal element of the covariance matrix \(\mathbf{\Sigma}^{c}\). The algorithm makes use of the convergence of EM and realizes the global optimization. However, the matrix inversion in Equations (23) and (24) require a lot of computation since the SBL algorithm cannot be applied to large-scale problems. Next, we will develop a low-complexity sparse Bayesian learning algorithm based on GAMP.