esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Healthcare Knowledge Management Systems
### High Blood Pressure Diagnosis Support  
Blood pressure is the blood force exerted against vessel walls as it moves through the vessels [25]. Blood pressure is expressed as two numbers: systolic pressure and diastolic pressure. Systolic is the higher number, which corresponds to the period when the heart beats to push the blood in the arteries. Diastolic is the lower number, which corresponds to the rest period between two consecutive heartbeats. Typically, high blood pressure is when the blood pressure measured in medical facilities is greater than or equal to 140/90 mmHg. According to the seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) [26], the classification of blood pressure for adults aged 18 and older is presented in Table 1.  
In this study, we build a big-data-driven healthcare knowledge management system supporting high blood pressure diagnosis and classification. Besides the previous diagnosis results from doctors, we rely on Table 1 to be able to label the levels of high blood pressure  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Systolic** & **Diastolic** \\ \hline Normal & \(<\)120 & and \(<\)80 \\ \hline Prehypertension & 120–139 & or 80–89 \\ \hline Stage 1 hypertension & 140–159 & or 90–99 \\ \hline Stage 2 hypertension & \(\geq\)160 & or \(\geq\)100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of blood pressure for adults.  
disease based on systolic and diastolic blood pressure. The machine-learning algorithm to be used in the knowledge layer for this type of disease is decision trees.  
Feature extraction: We choose VectorAssembler1 to transform the information in the dataset into feature vectors. The string data in the input dataset will be converted into input requirements of VectorAssembler. This conversion is performed by indexing string data with StringIndexer2 and representing the indexes as binary vectors through OneHotEncoder3. We use VectorAssembler on the input dataset and generate a feature extraction model. This feature extraction model will be stored in the database for reuse in the testing phase.  
Model training: We build decision trees with the extracted feature dataset. One of the important variables to keep in mind when building a decision tree is maxDepth. It allows us to decide the depth of the tree, and we can tweak this parameter to improve the accuracy of the model. However, the deeper the tree, the higher the likelihood of over-fitting problem occurring. Over-fitting occurs when the models make rules that fit exactly against the training data but will make wrong predictions on testing data.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models
### Inversion and Data Weighting Through the Covariance Matrix
We defined the reference value as the value computed from a null model \(\chi_{ref}^{2}\) as  
\[\chi_{ref}^{2}=\mathbf{d_{o}}^{T}\mathbf{C_{d}}^{-1}\mathbf{d_{o}} \tag{6}\]  
This defined the misfit relative to this reference value \(\chi^{2}(\%)\) as  
\[\chi^{2}(\%)=\frac{\chi^{2}}{\chi_{ref}^{2}}\times 100 \tag{7}\]  
This value being independent of the total amplitude of displacements, it is useful for comparing inversion results at different time steps of the temporal inversion. We conducted inversions according to a weighted misfit scaling each dataset by its own reference misfit:  
\[\chi_{w}^{2}=\left(\sum_{i=1}^{N}\frac{\chi^{2i}}{\chi_{ref}^{i}}+\frac{\chi ^{2 GNSS}}{\chi_{ref}^{2 GNSS}}\right)\times\frac{\chi_{ref}^{2}}{N+1} \tag{8}\]  
where \(i\) refers to the i\({}^{th}\) interferogram and \(N\) is the total number of interferograms. This scaling is equivalent to applying different scaling factors of \(\chi_{ref}^{2i}/\chi_{ref}^{2}\) with \(i=1,..,N\) and \(\chi_{ref}^{2 GNSS}/\chi_{ref}^{2}\) to the covariance matrices of the InSAR and the GNSS data, respectively. In terms of Bayesian inference, this scaling gives the same likelihood to each of the InSAR and GNSS datasets and each of these datasets has the same contribution to the posterior probability density distribution.
# Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins
## 2 Materials and Methods
### Digital Twin
#### 2.3.1 Model Architecture
The reconstruction is conducted through 7 consecutive deconvolutional layers with (256, 128, 64, 32, 16, 8, 1) channels, respectively. input to the GP-based controller. The fan settings vector comprises one hundred repetition of the duty cycle values for each fan. This extension is necessary since the original vector contains only three entries, one for each fan. By appending the initial vector to itself, its relevance to the output of the neural network is increased. This enables the model to learn the impact of the ventilators on the plate's temperature distribution. The size of the second dense layer is predetermined as \(m\times 5120\), where \(m\) represents the length of the predicted sequence. This ensures that the output is rescaled to \(m\times 4\times 5\times 256\) to initiate the upscaling of the prediction. To accurately capture the influence of the fans, dropout is disabled in this layer. The activation and initializer used are the same as in the first dense layer. The final component of the model is the decoder, which mirrors the structure of the encoder. It consists of seven \"deconvolutional\" layers with a decreasing number of channels. Unlike the encoder, the deconvolutional blocks in the decoder upscale their inputs. Therefore, non-strided convolution and an upsampling layer, which doubles the height and width of the input, are utilized. The structure of the block includes (i) ConvLSTM cell (same as the encoder cell but with a stride of 1), (ii) batch normalization and (iii) upsampling layer. The decoder output has the shape \(m\times 512\times 640\times 1\) and represents the prediction for the next \"\(m\)\" frames in the sequence. It is pertinent to highlight that the digital twin model operates as a functional approximator. In essence, this model facilitates the mapping of the temperature distribution across a defined spatial region, over a specific time interval. This mapping takes the form of predicting the temperature field for the upcoming minute based on the temperature distribution observed in the preceding minute--a configuration often referred to as a sequence-to-sequence prediction. It is crucial to emphasize that this mapping encompasses not only the intricate temporal relationships but also the intricate spatial correlations within the field. These predictions are executed on a grid whose spatial resolution mirrors the input dimensions (512 \(\times\) 640), preserving the structured nature of the grid and facilitating a seamless translation of insights between the physical domain and the digital representation. This framework, driven by the principles of neural networks, extends the familiar principles of function approximation to the realm of dynamic systems, such as the multi-mode heat transfer setup developed in this work.
## 4 A Survey on FPGA-Based Sensor Systems
### Industrial Control  
[PERSON] _et al_. proposed a sensor node based on an Actel FPGA and an 8-bit AVR microcontroller to be used in two WSNs for infrastructure measurement and analysis [79]. The first one consisted of a ground vibration monitoring application. The FPGA processes the signals and the AVR transfers the data and puts the FPGA into sleep mode when it is not required. The radio is based on 868 MHz and the sensor board is equipped with a 16-bit ADC and an analog signal processor. In order to detect ground motions, two MEMS accelerometers were used.  
The second application is based on the wheel deformation measurement of trains via axial and radial forces estimation using strain gauges. The measurements are filtered on the FPGA, which consumes 60 mA during measurements and 10 \(\mu\)A in standby.
###### Abstract  
Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the \"ground truth\" experimental values to within 3% for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.  
patient-specific modeling; silicone vocal fold models; Bayesian inverse analysis; finite element analysis Article Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model
# Independent System Calibration of Sentinel-1B
## 9 Interferometric Verification
### Interferometric Verification of Long Data Takes  
In this section, one of the first interferograms computed during the S1B commissioning phase is shown. The main goal is to evaluate whether a proper combination of S1A and S1B images is possible without observing artifacts, especially in the phase, which is more sensitive to errors in the calibration of certain aspects, for example, the phase of the complex elevation antenna pattern, which are individual for S-1A and S-1B.  
Figure 30 shows the reflectivity image (top), coherence (middle) and interferometric phase (bottom) of a Sentinel-1A/B IW cross-interferogram acquired from an ascending orbit over east Europe as part of a long data take containing eight data slices with a total of 72 bursts while covering a 1400 km long strip on the ground. The data take duration was 3 min and 20 s. The images were acquired on 13 June (S1A) and 19 June 2016 (S1B). No phase discontinuities between sub-swaths have been detected, indicating that the correction of the individual complex elevation antenna pattern was correctly applied to the data during operational SAR processing. Moreover, an analysis of the residual azimuth coregistration error is provided in Figure 31, where the measurements for each sub-swath of the IW mode are color-coded. This plot provides an accurate measurement of the azimuth mis-registration between both acquisitions by exploiting the differential phase of the overlapping areas between bursts [22] of the same sub-swath using the Enhanced Spectral Diversity (ESD) method. The weighted mean of the measured azimuth image coregistration is \(-7.8\) cm, which is mainly related to orbital/timing errors of the satellites. The standard deviation of the measurements is 1.5 cm, which is larger than the one reported in [23] (a few millimeters) for S-1A InSAR image pairs acquired from a descending orbit geometry. The larger dispersion of the measurements is very likely related to the higher activity of the atmosphere (ionosphere and troposphere) and not to the system, since the current analyzed data take has been acquired in ascending geometry, which occurs in the evening, hence being prone to more turbulent atmospheric perturbations.  
Figure 30: _Cont._
## 5 Extended Kalman Filter Tracking  
In this section, we formulate an extended Kalman filter to track the propagation delay \(\tau\) and clock offset \(T\) between two users \(A\) and \(B\). The terminology introduced in this section is summarized in Table 2.
# Machine Learning for Predicting Neutron Effective Dose
###### Abstract  
The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R\({}^{2}\) metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.  
machine learning; Monte Carlo; neutron dosimetry; effective dose +
Footnote †: journal: Medical Science
## 3 Test Results Analysis
### Stress-Strain Analysis under Different Hole Spacing  
From the reduction ratio of the peak strength and strain value in Table 4, it can be seen that compared with the non-porous sample, the peak strength and strain value of the test block decrease to varying degrees with the decrease in the borehole spacing.  
According to the strain diagram at the peak stress in Figure 4a, it can be seen that the peak strength is similar to the strain image at the peak stress as the borehole spacing decreases and gradually decreases. When the drilling spacing is reduced from 4 cm or 5 cm to 3 cm, the peak intensity is greatly reduced. However, when the hole spacing is reduced to 1 cm, the rock mass strength and the strain at the peak stress increase instead. This shows that the hole spacing is not the smaller the better; the hole spacing is too small to approximate single-hole pressure relief.  
According to the influence of the borehole diameter, borehole depth, and double holes on the rock mass strength, it can be seen that the pressure relief effect of double holes is more obvious than that of a single hole, and the pressure relief effect of expanding aperture is more obvious than that of increasing hole depth. Therefore, the reasonable arrangement of drilling parameters can not only improve the efficiency of pressure relief but also maintain the stability of roadways.
## 1 Introduction
Then, large-scale separated flow is induced, and a suspended shock structure typically appears near the leading edge of the lip [36; 37]. [PERSON] and [PERSON] conducted systematic research into overrated unstart and clarified the flow characteristics and variation in the regularity of overrated unstart. Furthermore, these researchers also found that the phenomenon of hysteresis also occurs during overrated unstart [38; 39; 40]. The use of CFD methods can quickly and effectively solve hypersonic flow problems. In contrast to low-speed flow problems, it is necessary to accurately capture the wave structure of the flow field and truly reflect the shock boundary layer interference phenomenon when solving hypersonic flow problems [41]. However, hypersonic flow is often influenced by internal and external uncertainties, which increases the difficulty of CFD calculations. In order to overcome this difficulty, the Uncertainty Quantification method was developed in recent years. The use of Uncertainty Quantification can support our understanding of physical problems and improve the accuracy and reliability of numerical simulations [42]. Thanks to recent improvements in both soft computing algorithms and hardware performance, the application of Uncertainty Quantification is becoming increasingly widespread [43; 44], and great progress has been made in the field of hypersonic flow and shock boundary-layer interaction [45; 46; 47]. To sum up, fruitful research has been achieved regarding the start problem of hypersonic inlets, which helps lay a strong theoretical foundation for inlet start regularity, factors influencing inlet start, the process of self-start, simulation methods of the start problem, and so on. However, no research on self-start characteristics has been documented in cases when multiple unstart modes exist in a specific hypersonic inlet, let alone a discussion about the diversity of self-start abilities among these different unstart modes. Because of complex flow in hypersonic inlets where intense shock boundary-layer interference and significant three-dimensional effects exist, multiple unstart modes inevitably occur. For example, in a recent study, three distinct types of unstart modes which are not related to overrating were discovered in a hypersonic inlet [48]: the flow mode of large separation on body (LSB), small separation on body (SSB), and dual separations on both body and lip (DSBL). These are described in detail in Section 3.1. Based on this reality, this paper describes profound research into the self-start characteristics of the hypersonic inlet when LSB, SSB, and DSBL unsstart modes exist. As for the most inferior unstart mode, DSBL, this paper also presents the Flow Field Reconstruction Method to improve self-start performance, and shows why self-start is difficult through flow mechanisms.
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Silver Nanoparticles and Silver Ions
#### 5.6.1 Cell Membrane Damage  
Two main mechanisms have been proposed for the silver nanomaterials-induced cell membrane damage. According to the hard and soft Lewis acids and bases theory, silver, a soft acid, has a high tendency to form strong covalent bonds with soft bases such as phosphorus and sulfur compounds. Since the bacterial cell membrane is rich in sulfur-containing proteins, these ones are likely to be the preferential sites for silver nanoparticles binding. Denaturation of cell membrane proteins affects membrane integrity resulting in increased permeability and eventual disruption of the cell membrane [86]. [PERSON] and [PERSON] [87] envisaged another mechanism for the cell membrane damage by silver nanoparticles. Using SEM and transmission electron microscopy (TEM) these authors revealed the formation of irregular-shaped pits in the outer membrane of _E. coli_ cells treated with nanosized silver particles as well as accumulation of the silver nanoparticles in the bacterial membrane and significant increase in membrane permeability due to progressive disruption of the tight packing of lipopolysaccharide-LPS molecules in the cell wall. A third mechanism relies on the electrostatic attraction between positively charged silver nanoparticles and negatively charged cell  
Figure 7: Inhibitors of the synthesis of the (p)ppGpp alarmone.  
membranes, but such a mechanism fails to explain the adhesion and uptake of negatively charged silver nanoparticles. In a recent paper, [PERSON] et al. [88] reported the strong antibiofilm activity of silver nanoparticles capped by hydrolyzed casein peptides (8.3 nm in diameter) against _E. coli_ AB1157, _P. aeruginosa_ PAO1, and _Serratia proteamaculans_ 94. Studying the effect of several factors such as mutations in genes involved in DNA repair, mutations in porin genes, as well as Luxl/LuxR QS systems on the bacterial sensitivity towards silver nanoparticles, the authors concluded that the OmpF and OmpC porins-mediated transport of silver ions into the cell is mainly responsible for the antibacterial activity of silver nanoparticles. This study brings direct evidence of the major effect bacterial cell wall penetration by silver nanoparticles has on bacterial cell viability. However, this mechanism of toxicity requires partial (surface) oxidation of silver nanoparticles followed by the release of the chemisorbed Ag\({}^{+}\) ions formed on the particle's surface [89]. Release of Ag\({}^{+}\) ions from silver nanoparticles is induced by molecular oxygen [90; 91] according to Equations (3) and (4) below:  
\[4\text{Ag}+\text{O}_{2}\to 2\text{Ag}_{2}\text{O} \tag{3}\]  
\[2\text{Ag}_{2}\text{O}+4\text{H}^{+}\to 4\text{ Ag}^{+}+2\text{H}_{2}\text{O} \tag{4}\]
## 3 Guiding Technologies
### Trusted Platform Module
#### 3.1.2 Remote Attestation  
The remote attestation of a TPM allows the evaluation of a remote environment's trustworthiness through two fundamental processes: TPM authentication and platform integrity verification. TPM authentication is based on a cryptographic challenge involving its Endorsement Key (EK), whose public portion is validated by a certification authority (CA). After this validation, an encrypted secret is sent to the TPM, which must decrypt it and return it in plaintext, demonstrating its legitimacy.  
Integrity verification consists of analyzing the measurement logs stored in the PCRs. In particular, PCR 10 summarizes the integrity of the applications loaded from the firmware initialization to the operating system execution. The attestation includes a report (quote) signed by an Attestation Key (AK), derived from the EK, ensuring that the reported values come from a genuine and unaltered TPM.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### System Evaluation
#### 4.7.2 Comparison of Sinusoidal Vibration Tests  
After the mirror component has been subjected to sinusoidal vibration tests in X, Y, and Z directions, the values of the two test points of the mirror component are not enlarged between the frequencies of 0 and 100 Hz, as shown in the Figure 10, which indicates that the mirror component can withstand sinusoidal vibration of this magnitude.