esa-sceva/satcom-chunk-collection huggingface dataset:
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## 5 OCT in Brain Imaging  
OCT is a micrometer-scale imaging modality for cross-sectional imaging of tissue microstructure based on light backscattering properties of tissue. Since its invention in the 1990s, OCT is widely used in several branches of neuroscience such as neuroimaging and neurosurgery. It also allows to measure rapid intrinsic optical signal (IOS) reflection of neuronal activity rather than delayed hemodynamic responses [146]. However, penetration depth of OCT is only up to 2-3 mm. Therefore, noninvasive structural imaging of the human brain is not possible, but the technique can serve as a supportive tool in experimental studies when using small animal models. However, OCT has been used in intraoperative guidance for brain surgeries in neurosurgical interventions, for example in deep brain stimulation therapy and microscope-integrated OCT, enabling surgeons real time imaging by visualizing the instrument-tissue interaction [38].  
A basic OCT interferometer setup, shown in Figure 4, includes a broadband source, which is split by the beam splitter to the reference and to sample. The reference is a mirror. The backscattered light from the sample and reference are collected and allowed to interfere. The detector detects this interference pattern. The light source, sample, reference and detector are connected to the beam splitter using optical fibers [147].
## 3 Results
### Lean
#### 3.3.11. Lean and Industry 4.0: Conclusions  
Research on Lean tools for assembly operations is still an open topic. Firstly, it should be noted that since internal logistics are tightly associated with assembly, both should be analyzed together because changes to one will affect the other as well. Lean production systems typically employ assembly line or cell layouts to establish pull and create material flow. For certain contexts involving high-cost, high-variability, short product development cycles, _serva_ assembly systems are particularly competitive because they are focused on adaptability. KPIs and performance assessment frameworks are used to measure the effects of changes in Lean production systems. Establishing a set of KPIs needs to take into account multiple stakeholders and to align the strategic and operational goals of the organization. Simulations and case studies show the beneficial effects of Lean methods and allow to estimate the economic return of investment of Lean management decisions.  
The Toyota Production System (TPS) considers robots, machines and computers in the opposing side of _jidoka_ (\"automation with a human touch\"), but it should be noted that their lack of enthusiasm towards digital technologies could have been influenced by the current digital technologies of that era (1950-80's). Since the rate of changes in digital technology has been particularly remarkable in the past four decades, it seems bold to assume that TPS's views on computers in the second half of the 20 th century still apply. Currently, there is a wealth of evidence showing that Lean manufacturing is a valid approach to improve assembly operation in the context of mass customization and that Lean and Industry 4.0 can benefit from synergies because each one enhances the other. Some classic Lean tools--e.g., Value Stream Map--may need to change in order to remain useful for analyzing digital processes. In general terms, Industry 4.0 technologies are expected to support the ability of people to make Lean-oriented decisions. Management has a key role to play in the successful transition to Industry 4.0. From the Lean perspective, changes brought by Industry 4.0 could be used to free up manager time to be invested focusing on human relationships. Learning Factories could be a great tool to share the vision of Lean 4.0 assembly, but they need to mimic real-life scenarios to become useful for non-academic learners with industrial backgrounds, such as assembly operators. Industry 4.0 technologies could also be used to enhance the training environment of Learning Factories. Since both Lean and Industry 4.0 stress the importance of people, it seems only natural that supporting human capabilities becomes a priority in Lean 4.0 assembly
## 3 Results and Discussion
### Fluorescence Measurements with Doctor Vida Pocket Test  
The Doctor Vida Pocket test is a hand-held device for isothermal amplification for temperatures from 25 \({}^{\circ}\)C to 95 \({}^{\circ}\)C and enables simple and rapid measurement of fluorescence on-site. Compared to other portable or smartphone-based fluorescence detection devices, the Doctor Vida Pocket test offers temperature control and real-time monitoring of the fluorescence profile. Therefore, this technique is suitable for on-site monitoring of LAMP assays. Assays for COVID-19 and lactose intolerance have been published and highlight the ease of use [24; 39].  
The more affordable the developed test system is, the more tests can be performed at the various steps along the value chain, leading to greater integrity of the goods. Since the acquisition costs are spread over the number of assays performed, the cost per test is reduced with each test performed. With a list price of under USD 400, the Doctor Vida Pocket test device is very affordable [40].  
Despite the potential for a POC application, preliminary tests were carried out in a laboratory environment. The method suitable for POC was used to prepare the DNA extract, and the individually lyophilized reaction mixtures were directly reconditioned with the crude extract. The tests were performed for both primer sets with reaction mixtures stored for one week or 2 months. Figure 3 shows the real-time fluorescence curves for both assays.  
The results show amplification of the target DNA after a maximum of 15 min for the _C. tinctorius_ assay and a maximum of 20 min for the _P. platessa_ assay. The results for the safflower and sole assays are therefore consistent with the results of the long-term stability test. A linear baseline shift can be observed for all tested positive, negative and no template control samples. The LAMP reaction tends to cause a linear baseline shift due to accumulation of non-specific amplicons [9]. As the R&D software (Dr Vida Pocket PCR, Version 3.2.0) is currently not able to perform baseline correction, we performed it ourselves by subtraction of a linear function when necessary. The raw data was not  
Figure 2: Comparison of time-to-positive values of GFP and spin-filtered Bst 3.0 of individual reaction mixtures. Boxplots of time-to-positive values of _P. platessa_-specific assays (PP) are shown in green and those of _C. tinctorius_ (CT)-specific assays are shown in orange. \(\blacklozenge\) indicate outliers.
## 3 Energy Management Strategy
### Adaptive Equivalent Consumption Minimization Strategy with Target SOC
#### 3.2.1 Target SOC Decision with Road Information
As shown in Figure 12a, the main purpose is to determine the target SOC at the end of segment 1, which is the next road segment. In normal situations, \(SOC_{0}\) and \(\Delta SOC_{1}\) are added together when entering segment 1. The SOC range of the battery must be bounded by upper/lower limits considering its lifetime, efficiency, voltage, etc. This leads to the need to adjust the target SOC according to the conditions of the road to be traveled. In the situation in Figure 12b, when driving in EV mode in segment 2, it is expected that the SOC lower limit will be exceeded due to insufficient battery capacity. In response, the target SOC is adjusted in segment 1, which is HEV mode, to enable full EV driving in segment 2. In the case of Figure 12c, regenerative braking is expected to be limited to prevent overcharging when driving in region mode in segment 3. In response, the target SOC of segment 1 is adjusted to be lower. Therefore, all the regenerative braking energy can be obtained in segment 3. This adjustment can prevent situations where EV mode and regenerative braking are limited. The target SOC decision algorithm is shown in Figure 13.
# Preparation of Magnetic Polymers for the Elimination of 3-Isobutyl-2-methoxypyrazine from Wine
## 2 Results and Discussion
### Analysis of IBMP Adsorption in Spiked White Wine Samples  
Two commercial Sauvignon Blanc wines, one from Australia (Aus, 0.4 ng/L IBMP) and the other from New Zealand (NZ, 14.1 ng/L), were spiked with 30 ng/L of IBMP (yielding IBMP concentrations prior to treatment as shown in Table 3) and used for adsorption testing. Magnetic separation was realised by placing a permanent magnet beside the vial containing the magnetic polymer (Figure 7). There was a lack of significant difference (one-way ANOVA, Tukey (HSD) pairwise comparison, \(p<0.05\)) in the equilibrium adsorption amounts (\(Q\)) and percent adsorption of IBMP by MMIP and MNIP within the same wine. Interestingly, despite the wines being spiked with the same amount of IBMP, a higher amount of adsorption was observed for the NZ wine due to its higher initial IBMP concentration, which matches the adsorption isotherm data (Figure 4). The adsorption of IBMP on MMIP and MNIP in white wine was in line with that of adsorption in model wine solutions, including adsorption amount and binding properties. Overall, for a wine containing elevated IBMP levels (20 ng/L or above), an adsorption ability of up to 45% (using 1% \(w/v\) of polymer) and the reusability  
Figure 5: Kinetics for adsorption of 30 ng/L IBMP in model wine (mean \(\pm\) SD \(n=3\)) using thermally synthesised MMIP and MNIP.  
Figure 6: Adsorption on thermally synthesised magnetic polymers that were washed and cycled in model wine (mean \(\pm\) SD, \(n=3\)) for (**A**) 20 ng/L IBMP and (**B**) 50 ng/L IBMP.  
of the polymers described in Section 2.5 indicates they could decrease IBMP to a level below its sensory detection threshold (i.e., <10 ng/L) with perhaps single and certainly multiple treatments.
## 2 System Model
### System Overview  
We are considering a single-link SWIPT system consisting of one single antenna source node and one user capable of decoding information and harvesting RF power simultaneously, as shown in Figure 1.  
The source node transmits multisine signals \(s(t)\) to the user through a frequency-selective channel \(h(t)\). After reception by the antenna with adapted matching network, the signal is divided into two parts by the power splitter with power splitting ratio \(\rho\). The \(\rho\) portion of the received signal power is converted to DC power by the rectifier. The DC power is then accumulated in the energy storage device, i.e., battery. The stored energy is used to supply power to the mixer and the signal processor. The remaining \(1-\rho\) portion of the signal power is applied to the information decoder. In the information decoder, the received RF signal is first converted to baseband by the mixer. Then the baseband signal passes through the signal processor to recover the source information. After the ideal matched filtering in the processor, the information rate is bounded by the channel capacity. The power splitting factor \(\rho\) is considered to be a variable in this work.
## 3 Results and Discussion
### QCL Characterization
The tryptophan measurements were executed directly in a consecutive fashion; therefore, the different noise levels were taken into account. The optimum integration time was approximately 70 s for the noisier spectra, and 40 s for the less noisy spectra.
# Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis
## 2 The Proposed Method
### Wavelet-Nonlinear ICA Model
The mathematical model of RBFNN is [23]:  
\[y(t)=BK(t,p), \tag{3}\]  
where \(y(t)\) is an estimated source with input observation \(t\), \(B\) is the weight of the network output layer, \(p=(\mu,\sigma)\) is the hidden layer center parameters with center \(\mu\) and width \(\sigma\), \(K(t,p)\) is the kernel function vector. The Gaussian function is usually adopted as the kernel function:  
\[K(t,p)=[1,\exp(-(t-\mu_{1})^{\text{T}}(t-\mu_{1})/\sigma_{1}^{2}),\ldots,\exp (-(t-\mu_{t})^{\text{T}}(t-\mu_{t})/\sigma_{1}^{2})], \tag{4}\]  
where \(l=1,\,2,\,\ldots,\,L\) and \(L\) is the number of the Gaussian function. Assume that wavelet de-noising can eliminate additive noise and the nonlinear mixing function is reversible, so there is always a set of RBF parameters that make any estimated source close to the true one. Figure 2: Nonlinear blind separation of vibration signals of mining machines with additive noise. \[y(t)=\widehat{B}K(f(As),\widehat{p})\to s, \tag{5}\]  
where \(\widehat{B}\) and \(\widehat{p}\) are the optimized parameters by RBF. In order to get the optimal RBF parameters, the maximum mutual information criterion is employed in the RBF optimization:  
\[\left\{ \begin{array}{l}I(y)=\sum\limits_{i=1}^{n}H(y_{i})-H(y)\\ H(y)=-E[\log(p_{y}(y))]\\ H(y_{i})=-E[\log(p_{y_{i}}(y_{i}))]\end{array} \right., \tag{6}\]  
where, \(E[\cdot]\) is the expectation operator, \(p_{y}\) and \(p_{yi}\) are the probability distribution functions. By adjusting the RBF parameters to minimize the mutual information, the source components in the mixture can be separated. The gradient descent learning algorithm is used to update the RBF parameters to get the optimal \(B\), \(\mu\) and \(\sigma\):  
\[\left\{ \begin{array}{l}-\eta\frac{\partial I(y)}{\partial B}\rightarrow \Delta B\\ -\eta\frac{\partial I(y)}{\partial\mu}\rightarrow\Delta\mu\\ -\eta\frac{\partial I(y)}{\partial\sigma}\rightarrow\Delta\sigma\end{array} \right., \tag{7}\]  
where, \(\eta\) is the network learning rate, \(\Delta B\), \(\Delta\mu\) and \(\Delta\sigma\) are the adjustment increments of \(B\), \(\mu\) and \(\sigma\), respectively. By performing wavelet de-noising and RBF learning, the additive noise and the unwanted vibration source components can be eliminated from the mixture, and hence, the fault characteristic information can be obtained using the wavelet-nonlinear ICA.
# Stress Estimation Using the Acoustoelastic Effect of Surface Waves in Weak Anisotropic Materials
## 4 Specimens and Experiments
### Measurement of Acoustoelastic Coefficients  
The acoustoelastic coefficients were obtained prior to the stress estimation experiments. The measurement set-up is shown in Figure 9. Based on the definition of the acoustoelastic coefficient, the change rates of the surface wave velocity were measured with stress in the stress direction (\(K_{1}\)) and in the perpendicular direction (\(K_{2}\)) in the uniaxial stress state. A tensile tester (Zwick-Roell) was used to apply the stress. The loading force was varied between 2500 N-22,500 N with an interval of 2500 N. The loading interval of 2500 N corresponds to a stress of about 10 MPa. The velocity measurement set-up is identical to the set-up described in Figure 7.  
Figure 10 shows the change of the received pulsed surface wave signal according to the applied stress. Figure 10a corresponds to the \(K_{1}\) measurement and Figure 10b reflects \(K_{2}\). As shown in the zoomed-in box, the peak points of the pulse signal shift depend on the stress increment. That is, the surface wave velocity in the stress direction decreases with the stress increment, while it increases in the direction perpendicular to the stress.  
Figures 11 and 12 show the change rates of the time of flight (TOF) with respect to the applied stress in each direction for the SA.1 and SB.1 specimens, respectively. The results were linearly fitted and the slopes of the fitted lines are the acoustoelastic coefficients defined in Equations (11) and (12). The obtained acoustoelastic coefficients are listed in Table 4.  
Figure 8: Surface wave velocity distributions of the two specimens: (a) Hot rolled specimen (SA.1); (b) Extruded specimen (SB.1).  
Figure 11: Change rates of the TOF with respect to the applied stress in specimen SA.1 for measurements of (**a**) \(K_{1}\) and (**b**) \(K_{2}\).  
Figure 10: Received signals obtained in the acoustoelastic coefficient measurements: (**a**) Measurement of \(K_{1}\)—The peak moves to the right because the velocity of surface wave in the direction of tensile stress decreases with increasing stress; (**b**) Measurement of \(K_{2}\)—The peak moves to the left because the velocity of surface wave in the direction perpendicular to the tensile stress increases with increasing stress.  
Figure 9: Set-up for acoustoelastic coefficient measurement. The change in surface wave velocity under tensile stress loading is measured. For the measurement of \(KI\), transducers (blue colored) are placed in the loading direction, while for the measurement of \(K2\), transducers (orange colored) are placed in the direction perpendicular to the loading direction.
## 3 Interpretation of Laboratory Tests on Tournemire Shale
### Determination of the Strength Properties with WPM and HBm
compressive strength at \(\beta_{w}=0^{\circ}\) and \(\beta_{w}=90^{\circ}\) for the CD regression are quite lower than the measured data. The figure also shows the WPM functions obtained with the two regressions. Both functions do not properly match the experimental data. However, the lowest value of the uniaxial compressive strength which occurs at \(\beta_{w}=60^{\circ}\) is equal for both cases and is in agreement with the experimental data. This fact indicates that the cohesion \(c_{w}^{\prime}\) and the friction angle \(\phi_{w}^{\prime}\) of the weakness planes are very close in both cases. In the end, the strength parameters of WPM were calculated with the NB regression data. The cohesion \(c_{w}^{\prime}\) and the friction angle \(\phi_{w}^{\prime}\) of the weakness planes were calculated at \(\beta_{w}=60^{\circ}\) where the lowest value of the uniaxial compressive strength occurs. The plateau strength (\(c^{\prime}\) and \(\phi^{\prime}\)), which should correspond to the strength of the rock matrix, was calculated at \(\beta_{w}=90^{\circ}\), with the M-C criterion. Table 2 reports these parameters. The plot of WPM in the diagram (\(\beta_{w}\)-\(\sigma_{\xi\beta w}\)) was obtained by finding the intersection of the plateau of constant strength with Equation (2a). Figure 7b shows the comparison between the experimental uniaxial compressive strength \(\sigma_{\xi\beta w}\) and the interpolating function of the HBm and WPM. The figure indicates that the best data matching is obtained with HBm. Figure 6c shows the percentage change in uniaxial compressive strength between the functions and the experimental data. The figure indicates that WPM overestimates \(\sigma_{\xi\beta w}\) for all the inclinations of the weakness planes  
Figure 6: Interpretation of lab data with HBm (**a**) Comparison between the uniaxial compressive strength \(\sigma_{\xi\beta w}\) obtained with the regression (open symbol) and the experimental data of the Tournemire shale (solid symbol). The solid line is the interpolating function. (**b**) Values of \(m_{\beta\alpha}\) obtained with the regression and the interpolating function. (**c**) Percentage increase in uniaxial compressive strength \(\sigma_{\xi\beta w}\) from the minimum \(\beta_{w}=40^{\circ}\). (**d**) Percentage increase/decrease calculated between \(m_{\beta\alpha}=5.16\) and the function of \(m_{\beta\alpha}\). \(\beta_{w}\) with a maximum \(\Delta\sigma_{c\beta w}=+48.06\%\) at \(\beta_{w}=30^{\circ}\). HBM also overestimates \(\sigma_{c\beta w}\) for all the inclinations except at \(\beta_{w}=30^{\circ}\), with a maximum \(\Delta\sigma_{c\beta w}=+27.72\%\) at \(\beta_{w}=60^{\circ}\). The overestimation of the uniaxial compressive strength is undoubtedly higher for WPM.
## 2 Related Work
### Vehicular Communication and Driving Safety Applications Issue, Related Works and Methods
#### 2.1.1 Current Stages in DSRC, V2V, V2I, and V2X Communications
Critical applications in dynamic environments are most promising in the research studies because they highlight the limitations of a technology or protocol. Most communication models dedicated to the road sector and traffic safety, as well as non-safety (fluidization, prevention, and analysis), are based on radio technologies. Whether we are talking about direct C-V2X communications or the IEEE 802.11p standard [27], all these resources in an urban environment characterized by congestion and density of vehicles becomes outdated and problems arise. These issues are analyzed throughout the literature but do not focus on a mix of emerging technologies that function as a hybrid platform. That is why we still consider the approach of hybrid RF-VLC type communications as useful in road safety and traffic management systems [28; 29].
## 3 Materials and Methods
### XO Activity and Inhibition Test  
The activity test of XO was similar to other publications, with some modifications [32]. Firstly, 1 mL of water and 1 mL of xanthine (1.0 mg/mL) were mixed in the cuvette. Then, 20 \(\upmu\)L of XO solution (1.0 mg/mL) was added into the cuvette and the absorbance at 295 nm was continuously monitored by an UV-VIS Spectrophotometer. The increase in absorbance at 295 nm in 40 s was recorded as the control. For the inhibition test, 1 mL of test samples with different concentrations were firstly mixed with 20 \(\upmu\)L of XO solution (1 mg/mL) for one minute in the cuvette and then 1 mL of xanthine (1.0 mg/mL) was added. The increase in absorbance at 295 nm in 40 s was recorded by a UV-VIS Spectrophotometer. The inhibition of XO activity was calculated according to the following equation.  
\[\text{Inhibition }\%=(1-\Delta A/\Delta A_{0})\times 100\% \tag{1}\]  
where \(\Delta A\) and \(\Delta A_{0}\) are the absorbance increases of sample and control solution. The inhibition was expressed as the concentration of sample needed to inhibit 50% of the enzymatic activity (IC\({}_{50}\)). All the assays were performed in triplicate.