esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SWellEx-96 Experimental Data Analysis
### Data Processing Results and Analysis
#### 4.2.1 Narrowband Source Range Localization  
For the narrowband case, \(p(t)\) in Equation (2) is calculated and the 127 Hz line is extracted from the spectrogram of the time series that was acquired continuously during the 75 min of the experiment, which is shown in Figure 9. The size of the window for the FFT is 1 s and the number of FFT points is 1500.  
The time step is set to be \(T=1\) s, and Equation (3) is calculated using the 128-point FFT. The horizontal wavenumber spectrum of the first-time step is obtained and shown in Figure 10. The maximum value of the spectrum appears at \(k_{r}=1.325\). We set the average phase velocity to be \(\overline{v}_{p}=1490\) m/s and obtain \(\overline{k}=0.5355\) m\({}^{-1}\). Then an estimated value of the radial velocity \(\overline{v}_{s}=2.4741\) m/s can be obtained  
Figure 8: Time–frequency diagram of measured signal.  
Figure 9: Variation of the real part of 127 Hz sound pressure during the 75 min of the experiment.  
according to Equation (7). Following this method, the radial velocity in the 75 min duration of the experiment is obtained as shown in Figure 11, wherein the black solid line is the maximum point. It can be seen that the estimation of the radial velocity is good for most of the time. Around the 10 th minute, some points with large deviations appear, caused by the strong interference mentioned above.  
The best fit between the model of radial velocity given in Equation (1) and the estimated source velocity can be obtained using the least squares criterion, as shown in Figure 12. This provides a range and time of CPA of \(r_{0}=1058\,\mathrm{m}\) and \(t_{0}=3520\,\mathrm{s}\), respectively, and a linear source velocity along the track of \(v_{0}=2.48\,\mathrm{m}/\mathrm{s}\). These estimated values are in good agreement with the GPS data, for which the range and time of CPA are \(r_{0,\mathrm{GPS}}=902\,\mathrm{m}\) and \(t_{0,\mathrm{GPS}}=3540\,\mathrm{s}\), respectively, and the linear source velocity is \(v_{0,\mathrm{GPS}}=2.55\,\mathrm{m}/\mathrm{s}\). This also indicates that the outliers of the radial velocity estimate have little impact on the final range parameter estimates.  
Figure 11: Estimated source velocities versus time.  
Figure 10: The horizontal wavenumber spectrum of the first-time step. The maximum value is at \(k_{r}=1.325\).  
The source range is estimated and shown in Figure 13. It can be seen that the estimated source ranges are in good agreement with the ranges from GPS data, and exhibit a bias of 6.23%.
## 3 Results and Discussions
### Environmental Factor (E-factor)  
The _E-factor_ was calculated assuming glycerol was a waste; however, a lower _E-factor_ can be obtained if both biodiesel and glycerol are taken as products. Hence, all the calculations for each metric were done making the same assumption and taking biodiesel as the only main product. The _E-factor_ was calculated for each condition when producing biodiesel using waste cooking oil and methanol or ethanol as the reactant. For each condition, it was noticed (Figure 2) that methanol has a lower _E-factor_, which means that less waste was produced when using methanol as a reactant. However, it is known that ethanol is an \"environmental friendly\" alcohol because it can be produced from renewable material. However, it was noticed that higher amounts of soap were present in the reaction, which was  
Figure 1: Reaction scheme for transesterification of triglycerides, modified from [26].  
higher when compared to the use of methanol. This implies that the environmental factor is able to calculate or to predict which process (or material) will create more waste. Additionally, it can be observed that the _E-factor_ is almost zero for a higher methanol to oil molar ratio, but it is higher when increasing the catalyst concentration. This can be attributed to a high content of free fatty acids present in the waste cooking oil, which increases the soap formation as the catalyst concentration increases. The _E-factor_ for different oil volumes was influenced by the process intensification effect of microwave and ultrasound at a fixed power supply which is defined as power density. Under this condition, the _E-factor_ for ethanol reactions was lower because ethanol was able to act as a solvent in the reaction [18]. Methanol does not have a similar ability to act as solvent and therefore has lower reaction yields and higher waste production. This suggests that the diffusive process can be used to reduce the energy requirements for biodiesel production but excess solvents will be required.
## 4 Applications to Biomedical Devices
### Laryngoscope  
Laryngoscope is used as a diagnostic device for throat inspection or as an aid to intubation. Laryngoscope's blades can be made of metal (stainless steel) or plastic (high impact grade ABS) material. Plastic blades represent a cost-effective alternative to reusable and single-use metal blades. These advantages, along with design flexibility of AM techniques, make the analysis of suitable materials for plastic laryngoscop application much more important.  
In particular, its blade must be geometrically compatible with patient's anatomy to provide a good view to doctors with minimal discomfort to patients. For this reason, when laryngoscope blades are designed in the parametric way and are produced with materials that may be pleasant to the touch, the advantage is remarkable. The customizable blade model was developed by following a feature-based approach with eight morphological parameters (Figure 7a). The thickness of such a blade was determined through numerical simulations of ISO (International Organization for Standardization) certification tests. The model was built from the chosen parameters. The blade was tested in silico. Lastly, the blade was produced with Hemp bio-plastic compounds.  
Figure 6: 3D-printed neck orthosis with Hemp filament: (**a**) full model and (**b**) (**c**) model with a pattern of voids.  
In this case, as in neck orthosis, the best orientation of the object, in order to obtain the best accuracy, was parallel to the printing bed (Figure 7b). The authors employed a layer height of 0.25 mm, which varied the filling density and speed of deposition by discovering that the surface roughness ranged between 2.46 \(\upmu\)m and 22.48 \(\upmu\)m. It is necessary to use infill ratios higher than 50% to guarantee the necessary stiffness and breathability.
# Caroenoid Biosynthesis in _Fusarium_
## 2 _Fusarium_ Carotenoids  
Early reports mentioned the occurrence of carotenoids in _Fusarium_ species, e.g., _F. oxysporum_[3], but the first detailed biochemical analyses were described in _Fusarium aquaeductuum_[15]. Such analyses showed the occurrence of non-polar carotenoids and an acidic carotenoid fraction, which reminded the one assigned as the xanthophyll neurosporaxanthin (hereafter NX) in _Neurospora crassa_[16]. NX is a carboxylic C\({}_{35}\) apocarotenoid, chemically defined as \(\beta\)-apo-4\({}^{\prime}\)-caroteneoic acid [17]. NX is not a frequent carotenoid in fungi, and to date, besides the genera _Neurospora_ and _Fusarium_, it has been only found in _Verticillium_[18; 19] and _Podospora_[20].  
Carotenoid analyses were refined in _F. aquaeductuum_ in subsequent years, and confirmed the occurrence of NX together with precursor neutral carotenes, that included \(\zeta\)-carotene, neurosporene, lycopene, \(\gamma\)-carotene, and torulene [21]. Later studies in _Fusarium fujikuroi_, and data presented in Section 5, corroborated the carotenoid composition of _F. aquaeductuum_ with the exception of the detection of \(\beta\)-zeacarotene instead of lycopene. This indicates that the cyclization may be achieved either on neurosporene or on lycopene, depending on the species. Taken together, the chemical analyses are consistent with the biosynthetic pathway depicted in Figure 1. From phytoene, five desaturations and the cyclization of the non-saturated end yield the reddish torulene. The steps from torulene to NX were deduced from the enzymatic activities of the responsible enzymes, discussed below. In _F. fujikuroi_ there are minor amounts of \(\beta\)-carotene [22], showing that the CarRA cyclase is also able to recognize \(\gamma\)-carotene as substrate. Because of its carboxylic group, NX may be a subject of esterification. In a marine _Fusarium_ species, a fraction of the produced NX is accumulated as a glycosyl ester [23].
## 5 Conclusions  
In summary, NR supplementation resulted in lower fasting blood glucose concentrations in ApoE/- mice fed with HFD. Although NR supplementation did not change plasma lipid and lipoprotein profiles, it may have the potential to promote cholesterol uptake, efflux and conversion to bile acids via upregulating the mRNA expressions of intestinal ABCA1 and hepatic CYP7A1 and downregulating the mRNA expression of hepatic PCSK9 in ApoE/- mice fed with HFD. Of note, concurrent with the changes in gene expression involved in cholesterol metabolism and glucose concentrations, the relative abundance of the phylum _Actinobacteria_ and genus _Coriobacteriaceae_UCG-002_ were lower in HFD-fed mice supplemented with NR, and the relative abundance of the genus _Bacteroides_ and _Lactococcus_ were lower in both the CD- and HFD-fed mice supplemented with NR. In agreement with these findings, the relative abundance of _Coriobacteriaceae_UCG-002_ was positively correlated with the hepatic mRNA expression of PCSK9 and inversely correlated with the intestinal mRNA expression of ABCA1 and the relative abundance of _Lactococcus_ was inversely correlated with the hepatic mRNA expression of CYP7A1. Collectively, NR supplementation may influence gene expressions in cholesterol metabolism via changes in the gut microbiota in ApoE/- mice fed with HFD, which requires further investigation.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/nu15040973/s1](https://www.mdpi.com/article/10.3390/nu15040973/s1)]([https://www.mdpi.com/article/10.3390/nu15040973/s1](https://www.mdpi.com/article/10.3390/nu15040973/s1)), Supplementary Table S1: Sequences of primers; Supplementary Table S2: Relative abundance of gut microbiota at class, order, family and species levels in ApoE/- mice fed with a CD or HFD, without or with NR at the end of the study; Supplementary Table S3: Fecal concentrations of short-chain fatty acids in ApoE/- mice fed with a CD or HFD, without or with NR at the end of the study; Supplementary Table S4: Correlations between relative abundance of gut microbiota and blood biochemical parameters, Oil Red O staining of aorta and gene expression in cholesterol metabolism; Supplementary Table S5: Correlations between the relative abundance of gut microbiota and concentrations of short-chain fatty acids in feces.  
Conceptualization and methodology, H.M. and H.Z.; experiments, H.Z., W.L., Y.L., K.L., Y.W., T.K., S.M. and Y.B.; data analysis, H.Z., W.L. and H.M.; writing--original draft preparation, H.Z., W.L. and H.M.; writing--review and editing, H.M. and H.Z.; supervision, H.M.; project administration, H.M. and H.Z.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Science and Technology Program (grant number GXWD20201231165807008, 20200830143250001).  
Not applicable.  
The authors would like to thank [PERSON], [PERSON] and [PERSON] for their technical support in the animal experiments.  
The authors declare that they have no conflict of interest.
###### Abstract  
The accuracy of current measurements can be increased by appropriate amplification of the signal to within the measurement range. Accurate current measurement is important for energy monitoring and in power converter control systems. Resistance and inductive current transducers are used to measure the major current in AC/DC power converters. The output value of the current transducer depends on the load motor, and changes across the whole measurement range. Modern current measurement circuits are equipped with operational amplifiers with constant or programmable gain. These circuits are not able to measure small input currents with high resolution. This article proposes a precise loop gain system that can be implemented with various algorithms. Computer analysis of various automatic gain control (AGC) systems proved the effectiveness of the Mamdani controller, which was implemented in an MCU (microprocessor). The proposed fuzzy controller continuously determines the value of the conversion factor. The system also enables high resolution measurements of the current emitted from small electric loads (\(\geq\)1 A) when the electric motor is stationary.  
automatic gain control; current measurement; power converter; fuzzy logic +
Footnote †: journal: Applied Sci. 2021, 11, 5793. [[https://doi.org/10.3390/app11135793](https://doi.org/10.3390/app11135793)]([https://doi.org/10.3390/app11135793](https://doi.org/10.3390/app11135793))
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 3 The Novel Anti-Jamming Technique
### BBVI Filter Based on Gaussian Distribution
\(\mathbf{P}_{k[k-1]}=\mathbf{\Phi}_{[k-1]}P_{k-1}\mathbf{\Phi}_{[k-1]}^{\mathrm{T}}+\mathbf{Q_{ k-1}}\) Measurement update (BBVI method)
3. Draw S samples: \(\mathbf{R}_{k,S}\sim q\big{(}\mathbf{R}_{k,S}\big{)}\), \(q\big{(}\mathbf{R}_{k,S}\big{)}\sim\mathrm{N}\Big{(}\mathbf{R}_{k,S};\hat{\mathbf{\mu}}_{k [k-1]},\hat{\mathbf{\sigma}}_{k[k-1]}\Big{)}\)
4. for\(S=1\) to \(S\)do
5. Update\(f_{s}(\mathbf{\mu}_{k})\) and \(\mathbf{h}_{s}(\mathbf{\mu}_{k})\) as in (28) and (29). 6. Update \(F_{s}(\mathbf{\sigma}_{k})\) and \(\mathbf{H}_{s}(\mathbf{\sigma}_{k})\) as in (30) and (31). endfor
7. Calculate \(\mathbf{a_{\mu_{k}}}\) and \(\mathbf{A_{\bar{\
u}_{k}}}\) as in (26) and (27). 8. Estimate the gradients \(\hat{\mathbf{\
abla}}_{\mu_{k}}\mathscr{L}_{c}(\mathbf{\mu}_{k},\mathbf{\sigma}_{k})\) and \(\hat{\mathbf{\
abla}}_{\mathbf{\sigma}_{k}}\mathscr{L}_{c}(\mathbf{\mu}_{k},\mathbf{\sigma}_{k})\) as in (32) and (33). 9. Update \(\hat{\mathbf{g}}_{k}\) and \(\hat{\mathbf{G}}_{k}\) as in (24) and (25). 10. Update \(\hat{\mathbf{\mu}}_{k\bar{k}}\) and \(\hat{\mathbf{\sigma}}_{k\bar{k}}\) as in (22) and (23). 11. Calculate \(\mathbf{R}_{k}\) as in (35). 12. \(\hat{\mathbf{X}}_{k}=\mathbf{S}_{k[k-1]}+\mathbf{H}_{k}^{\mathrm{T}}\Big{(}\mathbf{H}_{k}\mathbf{ P}_{k[k-1]}H_{k}^{\mathrm{T}}+\mathbf{R}_{k}\Big{)}^{-1}\)
13. \(\hat{\mathbf{X}}_{k}=\hat{\mathbf{X}}_{[k-1]}+\mathbf{K}_{k}\big{(}\mathbf{Z}_{k}-\mathbf{H}_{k} \hat{\mathbf{X}}_{[k-1]}\big{)}\)
13. \(\mathbf{P}_{k}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\mathbf{P}_{k[k-1]}\) Output:\(\hat{\mathbf{X}}_{k},\mathbf{P}_{k}\). ```  
**Algorithm 1** Summary of the novel anti-jamming method for INS/GNSS integration based on BBVI. To clearly demonstrate the difference between the VBAKF algorithm based on the Inv-Gamma or Inv-Wishart distribution and the proposed algorithm, the two algorithms are shown in \[=\log p(\mathbf{Z}_{k,S}\bigl{|}\mathbf{R}_{k,S},\mathbf{Z}_{1:k-1,S})+\log p( \mathbf{R}_{k,S}\bigl{|}\mathbf{Z}_{1:k-1,S})\]4  
\[=\log p(\mathbf{Z}_{k,S}\bigl{|}\mathbf{R}_{k,S},\mathbf{Z}_{1:k-1,S})+\log p( \mathbf{R}_{k,S}\bigl{|}\mathbf{Z}_{1:k-1,S})\]5 The algorithm flowchart of VBAKF and the proposed algorithm where \(I\) is the degrees of freedom parameter, and \(\mathbf{U}\) is the inverse scale matrix. As can be seen in Figure 6, in order to describe the prior distribution of the MNCM more accurately, the Gaussian distribution was selected as the approximate distribution of MNCM in this paper. Furthermore, in order to solve the problem that the prior distribution and likelihood distribution are non-conjugate after selecting the Gaussian distribution, and the problem that VBAKF cannot be applied, we proposed estimating the MNCM by the stochastic optimization of the BBVI algorithm.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 8 Implementation Setup and Results  
### Multi-Media Modem  
Figure 12 shows the modem design and tests done using the multi-media approach. In Figure 11, 'a' represents the setup of modem and 'b' represents the operation performed using multi-media communication. The conceptual modem was developed using a combination of Raspberry Pi 3+ and Beagle-bone Black. The conceptual model setup was done with PC-to-PC link using acoustic and visible light communication. In this approach, an acoustic modem developed by [61] and the visible light (VL) scheme developed by [62] were combined. The experimental result shows that this modem can send 30 bytes of data at 1000 ms, and that it can send 1000 times each to acoustic and VL. Table 4 shows the conceptual modem's specifications.  
Figure 11: Source to sink node routing using the medium selection mechanism.  
Figure 12: Multi-media modem setup and operation.
# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 5 Experiment Implementation
### Preprocessing  
Preprocessing techniques were applied to SEED to improve the quality of the EEG signals. Electromyography (EMG) and electrooculographic (EOG) artifacts were removed manually.  
The signals were downsampled to 200 Hz, and a bandpass frequency filter was applied between 0.5 Hz and 75 Hz, which contains the EEG information and discards the noise from the signals.  
In addition, we applied more preprocessing steps, which are discussed below. Figure 2 represents some of the EEG data before and after preprocessing.  
1. Since frontal brain areas are correlated with an individual's choice state [37], we picked 12 related channels for detecting induced choices. The 12 channels were selected based on the guidelines provided in reference [37], which recommends the selection of channels that have been shown to be reliable and informative in previous studies. Specifically, these channels were chosen based on their relevance to measuring emotional responses and have been used in previous studies on emotion recognition using EEG signals. The selected channels include FP1, FPZ, FP2, AF3, AF4, FZ, F3, F4, F8, F7, F5, and F6.
2. Because emotions may not be triggered at the beginning of each trial, we discarded the first 60 s and cropped the time to obtain 1 min long EEG data.
3. We further applied a bandpass filter between 1 Hz and 50 Hz.  
Figure 2: Data representation before (**a**) and after preprocessing (**b**).
## 3 Experimental Results and Analysis
### Inertial/Magnetic Sensor and Processing Platform  
A wireless commercial off-the-shelf inertial/magnetic sensor (sample frequency: 100 Hz) was utilized to collect data, and a portable computer was used for real-time processing. The sensor, Mtw Awinda (manufactured by Xsens), was small (47 mm \(\times\) 30 mm \(\times\) 13 mm) and included a three-axis accelerometer (range: \(\pm\)16 g), a three-axis gyroscope (range: \(\pm\)2000 \({}^{\circ}\)/s), and a three-axis magnetometer (range: \(\pm\)1.9 Gauss). The detailed specifications of the sensor are given in [55]. Before the experiments, the magnetic sensor had to be calibrated in an environment without magnetic disturbances.  
To implement our proposed hybrid framework, we designed the two-layer software framework shown in Figure 4. The system can be configured to a raw data mode or a navigation mode by the software layer. In the real-world experiments, the two-layer software framework was deployed to a portable computer (name: GPD Pocket; size: 18 cm \(\times\) 11 cm; CPU: Intel Atom X7-Z8750 @ 1.6 GHz; RAM: 8 G). The software framework was implemented using MATLAB R2013a. First, the MATLAB firmware provided by the official sensor software development kit was leveraged to obtain the inertial and magnetic data. Second, the collected base-level data were sent to the software layer for real-time processing. The software framework was validated in the indoor positioning and indoor navigation (IPIN) competition Track 2 (real-time) [56; 57] in 2017 and was proven to be practical.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Framework**} & \multicolumn{2}{c}{**State of Heading Error Correction Algorithms**} \\ \cline{2-3}  & **EC** & **HDR** \\ \hline ZUPT & 0 & 0 \\ ZUPT–MED–EC & auto & 0 \\ ZUPT–AFM & auto & auto \\ ZUPT–HDR & 0 & auto \\ ZUPT–EC & 2 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Generating different frameworks using our proposed framework.  
Figure 4: Wearable PNS: The proposed two-layer software framework was deployed to a portable computer. The framework included firmware and software layers. The wireless inertial measurement unit (IMU) was driven by the firmware layer and connected with the computer via a wireless USB dongle. The system could be configured to output raw sensor data (raw data mode) or output the position, velocity, and attitude (navigation mode) by the software layer.
## 4 System Implementation
### MRSMC System Implementation  
Figure 6 shows the schematic diagram of the proposed MRSMC strategy for a DFIG system. The dc-link voltage was controlled by a PI controller in GSC, which produces the command of the GSC average active power \(P_{\mathrm{gcom}}\). As shown, the active and reactive powers in both RSC and GSC were regulated by the developed MRSMC in the \(\alpha\beta\) frame. It should be noted that the control strategy does not require any synchronous coordinate transformations as well as angular information of grid voltage.  
However, it can be seen from (23) that the fundamental grid voltage is necessitated. This paper is to delay the input signal by a quarter of the fundamental frequency period \(T_{1}\)[8] as follows  
\[\mathbf{u}_{\mathrm{s}\alpha\beta}^{+}(t)=0.5[\mathbf{u}_{\mathrm{s}\alpha\beta}(t)+ j\mathbf{u}_{\mathrm{s}\alpha\beta}(t-0.25T_{1})] \tag{26}\]  
Figure 7 shows experimental results of the \(T_{1/4}\) delay technique, in which the grid voltage was ideal before \(t_{0}\) and contained 6.13% unbalanced, 3.5% \(5-\) and 2.6% 7+ harmonic voltage. From the results, the fundamental voltage was separated from the distorted grid voltage within 0.005 s, which validated the effectiveness of the \(T_{1/4}\) delay technique.