esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
Following the literature, we mainly wanted to establish whether:  
* participants react differently (in terms of SCR, HRV, blinking rate, and de Boer ratings) to glare by different light source combinations;
* glare by specific light source combinations leads to short-term changes in visual acuity and contrast sensitivity;
* preference for a specific light source (as measured by the tabletop experiment) plays a role in the reaction to glare (in terms of SCR, HRV, blinking rate, and de Boer ratings); and
* whether there is a link between the subjective de Boer rating and the psychophysiological variables.  
Therefore, the following dependent variables were examined:  
* skin conductance response (SCR);
* eye-blinking rate, computed from the vertical electrooculography (vEOG) signal;
* number of post-glare correctly identified characters from the Snellen, Tumbling E, and the Pelli-Robson chart, respectively; and
* the de Boer ratings for each light source combination.  
Originally, we aimed to study heart rate variability (HRV), computed from the ECG signal; however, due to the insufficient length of the post-glare interval, the LF band could not be analyzed, and therefore HRV analysis and results are not presented in this paper. Breathing frequency was also intended to be analyzed together with HRV, as these two measurements are known to correlate [7], but due to significant missing data (despite adjusting the respiration belt multiple times, the participants' breathing signal was very weak overall), this variable was not included in the analyses.
## 2 Results  
The trans-IDS Gene Family in P. trichocarpa Possesses a Single FPPS Gene and Four Putative GPPS Genes  
A BLAST analysis showed that _P. trichocarpa_ possesses 16 genes with high sequence similarity to _trans-IDS_ genes from other plants (Figure 1). According to their position in a phylogenetic tree of plant _trans-IDS_ genes, two of these candidates (Potri.006g003400 and Petri.016g004100) were considered as putative _FPPS_ genes. However, since the protein encoded by Petri.016g004100 lacked about 160 amino acids including the catalytically important FARM motif (Figure S1), the gene was assumed to be a pseudogene. The remaining putative _FPPS_ gene Petri.006g003400 was named _PtFPPS1_. Two other genes, Petri.007g031100 and Petri.005g127100, showed similarity to a GPPS large subunit from hops and were designated as _PtGPPS1.LSUI_ and _PtGPPS2.LSUI_, respectively. Two putative GPPS small subunits Petri.015g043400 and Petri.009g139600 grouped together with the catalytically inactive small subunits of GPPSs from hops and rice and so were named _PtGPPS.SSUI1_ and _PtGPPS.SSU2_, respectively. The remaining ten _IDS_ candidates were classified as _GGPPS_ genes, medium-chain _IDS_ genes, or long-chain _IDS_ genes based on their sequence similarity to respective _IDS_ genes from other plants (Figure 1). Because they are not involved in volatile terpene formation, they were not considered further in this study.  
Figure 1: Rooted tree of putative _IDS_ genes of _Populus trichocarpa_ together with a selection of characterized _IDS_ genes from other plant species. Bootstrap values (\(n=1000\) replicates) are shown next to the branches. The tree is drawn to scale, with branch lengths in units of base substitutions per site. Genes characterized in this study are shown in bold and red. The _P. trichocarpa_ terpene synthase gene _PtTPPS1_ was used as outgroup. Accession numbers (NCBI) or gene identifiers are given in parentheses.  
In general, the monoterpene precursor GPP is produced in plastids and the sesquiterpene precursor GPP is produced in the cytosol [26]. A signal peptide prediction showed that the putative GPPS proteins PtGPPS1.LSU, PtGPPS2.LSU, PtGPPS.SSU1, and PtGPPS.SSU2 indeed contained N-terminal signal peptides that likely target the proteins to plastids (Figure S2). PtFPPS1, however, had no signal peptide and is therefore presumably located in the cytosol. The characteristic FARM and SARM motifs required for catalytic activity could be identified in PtFPPS1, PtGPPS1.LSU, PtGPPS2.LSU, and PtGPPS.SSU2 (Figure S1). Both motifs were absent in the small subunit PtGPPS.SSU1. The CxxxC motif was present in all small and large GPPS subunits but not in PtFPPS1. Notably, PtGPPS.SSU2 contained a second CxxxC motif located upstream of the SARM motif (Figure S1).
## 2 Results
### Analysis with e-Tongue Using PCA and CA  
All milk samples W30, W40, W50, W60, T10, T20, T30, T40, T50, the control group (CK) and raw milk (RM) were analyzed by the e-tongue. In order to diagnose and characterize the correlation among the taste of samples produced by processing different treatments, PCA and CA were mapped using the relative voltage response of the e-tongue sensors to milk samples. The subsequent PCA of samples processing with different preheating temperature and time have 96%, 93% of the variations represented by the first two principal components. The variations seem to provide enough grouping information on the samples [36].  
The PCA score plot of samples produced by preheating at different temperature is shown in Figure 3a and the dendrogram of the samples is shown in Figure 3b. The PCA and CA classifications coincide. RM and W50 are classified as the same cluster, which indicated that the taste of W50 was the most similar to RM. There are also great similarities between W30, W40 and W60. Meanwhile, the results of PCA (Figure 3a), CA (Figure 3b) and sensory analysis (Figure 1) were consistent and fully demonstrate taste of sample preheated at 50 \({}^{\circ}\)C was preferred.  
The classifications of samples produced by preheating for different time are shown in Figure 4. From Figure 4a, seven different samples were clearly dispersed. T30 was classified as a same cluster with RM. In addition, parts of CK and T20 was classified as the same group with RM and T30. This result indicated that the similarity of taste between T30 and RM was highest and they held slight similarity with T20 and CK. Other groups held distinct differences on the taste.  
Figure 4: Classification of all skim milk samples preheated for different time: (**a**) Score plot after PCA of the samples. (**b**) Dendrogram from CA of the samples.  
Figure 3: Classification of all skim milk samples preheated at different temperature: (**a**) Score plot after PCA of the samples. (**b**) Dendrogram from CA of the samples.  
Meanwhile, combined with sensory evaluation, the taste of preheating for 30 min was perfected. Therefore, combining the results of the sensory evaluation with PCA and CA, preheating at 50 \({}^{\circ}\)C for 30 min before the defatting processing was significantly (\(p<0.05\)) conducive to improving the taste of skim milk relative to other processing conditions.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 5 Risk Evaluation of Compound Dynamic Disaster  
### Verification of the Accuracy of Evaluation Methods  
The fourth mining area of mine A in the Pingdingshan mining area is selected as the evaluation object to verify the accuracy of the model. The mining depth of this mining area is 880-1039 m, the average thickness of the coal seam is 3.0 m, the geological structure of the mine is complex and the occurrence of the coal seam is unstable. See Table 4 for specific basic data. In the history of this mining area, there were 48 times of compound dynamic disasters, all of which were near the fault. The coal seam thickness changed greatly, and the working face was a goaf working face. The basic data of three working faces with dynamic disasters in this mining area were extracted for model verification.  
The compound dynamic disaster risk evaluation matrix is based on the AHP-EWM is \(B=Q\times R=\) [0.5475, 0.8459, 0.6668, 0.9399], according to the principle of maximum membership degree. The evaluation result is \(B=\) 0.9399 and the risk level is IV (strong risk).  
The residual gas pressure in the fourth mining area is 0.6\(\sim\)1.5 MPa, and the initial speed of gas is 12.3-13.22 mL/s. According to the limit value provisions in the Detailed Rules for the _Detailed Rules for the Prevention and Control of Coal and Gas Outburst_, the coal seams in the fourth mining area have the risk of coal and gas outburst. According to the requirements of outburst prevention measures in the _Detailed Rules_, drilling pressure relief and other measures were taken to reduce the outburst risk during the mining in this mining area. However, three working faces in the fourth mining area suffered from dynamic disasters.  
The comprehensive index method is used to evaluate the rock burst risk in the fourth mining area. The evaluation result is \(W_{t}\) = 0.38, and the mining area is of weak rock burst risk. However, 48 dynamic disasters occurred in this mining area during the mining process, so the evaluation result is low and inconsistent with the site's conditions.  
It can be seen that the compound dynamic disaster cannot be evaluated and treated separately as a single dynamic disaster, and the integrated evaluation must be adopted. The compound dynamic risk evaluation method based on the AHP-EWM comprehensively considers geological factors, coal property factors (coal impact tendency and gas conditions) and mining technology factors, and uses subjective and objective weights for comprehensive weighting. The weights determined are scientific and reasonable, and the assessment results are highly consistent with the actual situation.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 5 Experiments and Results  
The proposed watermarking method was run on a laptop computer with an Intel Core i7 @ 1.6 GHz, 16 GB of RAM, and without a GPU card. The watermarking algorithm had a time consumption of 2.971 s for the insertion stage and 2.640 s for the extraction process. The method was implemented in a non-optimized and non-parallelized script in MATLAB using images of \(512\times 512\) px. The parameters used were \(s_{1}=s_{2}=100\) for the JST, \(\mathcal{N}=2\), maximum order decomposition \(D=4\) and \(T=1\) for the HT, \(N_{lite}=275\) and \(k=17\) for the ECA, \(b\times b=4\times 4\) for the HVS analysis, and \(\alpha=0.00001\) for the SVD-based insertion.  
We evaluated our algorithm in different experiments: the insertion and extraction processes, robustness against attacks, and a comparison with other methods.  
All the experiments were carried out using the 49 publicly available grayscale images of \(512\times 512\) px (see Section 3.1).
## 4 Electronic Sensing Design for MLE-Based Position Estimation
### RSSI Attenuation and Digital Filtering Analysis  
The effectiveness of the logarithmic attenuation model and the algorithm implementation for target localization is demonstrated at different distances. As the first step, the model parameters \(\beta=-2.466\) and \(A=-60.214\) were computed from RSSI measurements by considering a 1 m distance between the transmitter and receivers. Likewise, a fifth-order Butterworth digital filter was implemented with a cutoff frequency of \(W_{n}=100\) Hz in order to smooth the RSSI signal.  
Figure 9 illustrates the raw RSSI data from the rover to each module A, B, C, and D at three different distances: \(1,3\) and \(5\) m. In this test, each one of the sensing modules individually acquires its respective RSSI data.  
Figure 8: GLS integrated with electronic RSSI-BLE receivers: (a) UAV approaching to the GLS, (b) UAV with battery replaced.  
Figure 9: \(Cont.\)Once the RSSI data were measured, the logarithmic attenuation model is computed, and the distance from the rover to each sensing node of the PSOC-BLE network is also verified. Figure 10 shows the resulting estimated position of each sensing module A, B, C, and D, which are located on each corner of the GLS.  
As can be seen in the data, if the distance is closer between the UAV and the landing platform, the error is reduced. Therefore, better results are achieved when the UAV is close to the GLS system.  
Figure 10: Results of the distance estimation analysis: (**a**) from three different distances of the UAV to sensing module A, (**b**) the same distances of the UAV to sensing module B, (**c**) results of the distance estimation from UAV to sensing module C, and (**d**) UAV to sensing module D.  
Figure 9: RSSI measurements of three UAV positions for each sensing module: (**a**) Raw data device A, (**b**) Raw data device B, (**c**) Raw data device C, and (**d**) Raw data device D.
## 3 Results and Discussions
### Prediction of Oil Spill Transport Behavior Based on Machine Learning
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Flow Rate** & **Wind Speed** & **Wind** & **Leakage** & **Leakage** & **Timing (h)** & \(\mathbf{D}\) & \(\mathbf{A}\) & \(\mathbf{L}\) \\  & **(m\({}^{3}\)/s)** & **(m/s)** & **Direction (\({}^{\circ}\))** & **Mode** & **Amount (t)** & **Minning (h)** & **(km)** & **(10\({}^{4}\) km\({}^{2}\))** & **(km)** \\ \hline Mean & 4380 & 2.1 & - & - & 1721 & 13.5 & 9.97 & 170.08 & 6.21 \\ Min & 500 & 0 & 0 & 0 & 180 & 0.5 & 0.81 & 4.29 & 0.81 \\
25\% & 1500 & 1 & 135 & 0 & 720 & 4.5 &  
The selection of hyperparameters in the above models all use the traditional Random Search algorithm, and the model accuracy is relatively low. To further improve the prediction performance of the learner, this paper adopts an intelligent optimization algorithm for hyperparameter preference. Considering the problems of local optimum and insufficient search capability, three intelligent optimization algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE), are introduced [48; 49; 50], which form a combination of six classes with BPNN and SVR respectively algorithms. Table 3 shows the computation of the three optimization algorithms. The prediction effect of the combined model is shown in Figure 14.  
As can be seen from the figure, the prediction performance of the model is significantly improved after the hyperparameters are preferred by the intelligent optimization algorithm. For label **D**, PSO-SVR has the highest \(R^{2}\) and _1-MAPE_, 0.980 and 0.977, respectively. For label **A**, GA-BPNN has the highest \(R^{2}\), and DE-SVR has the highest _1-MAPE_. The \(R^{2}\) and _1-MAPE_ of GA-BPNN are 0.982 and 0.971, respectively, and those of DE-SVR are 0.929 and 0.979, respectively. Combining the two model evaluation metrics of \(R^{2}\) and _1-MAPE_, the GA-BPNN has a predictive performance is better. For label **L**, PSO-SVR has the highest \(R^{2}\), and PSO-BPNN has the highest _1-MAPE_. The \(R^{2}\) and _1-MAPE_ of PSO-SVR are 0.978 and 0.940, respectively, and those of PSO-BPNN are 0.972 and 0.975, respectively, so that PSO-BPNN has a better prediction performance. In summary, the PSO-SVR, GA-BPNN, and PSO-BPNN combination models were finally selected as the prediction models for the oil spill transport state in the Lancang River Crossing Pipeline Section Basin in this study, respectively. The differences between the predicted and true values of **D**, **A**, and **L** by the three combined models are shown in Figure 15.
## 5 User Preference Modeling
### User Preference Modeling for Deep Features
#### 5.1.1 Normal Assumption  
User preference means that the user likes to see the kind of mobile video, in which the user's viewing log is a concrete embodiment of the user preference. If the user preference modeling is regarded as a probabilistic model, then the user may prefer mobile videos that have a higher probability to be sampled from this probabilistic model.  
If \(H=(v_{1},\ v_{2},\ \ldots,\ v_{M})\) denotes the video set containing \(M\) videos watched by a user, the representation of each mobile video in a feature space can be regarded as sampling in the user preference modeling for deep features. For the \(i\)-th user \(u^{(i)}\), assume that each dimension in a feature space obeys a normal distribution and the deep features are linearly independent in each dimension  
\[u_{j}^{(j)}\sim N\Big{(}a_{j},\sigma_{j}^{2}\Big{)} \tag{3}\]  
where \(a_{j}\) and \(\sigma_{j}^{2}\) denote the mean and variance of the \(j\)-th dimension of the user preference modeling for deep features, respectively, and \(u_{j}^{(i)}\) denotes the values of the user preference modeling of the \(j\)-th dimension of the \(i\)-th user.
# A Wideband High-Voltage Power Amplifier Post-Linearizer for Medical Ultrasound Transducers
## 4 Conclusions  
In ultrasound techniques such as harmonic techniques and the coded excitation method, low-harmonic distortion components of the echo signal from ultrasound transducers are desired to obtain high-quality echo signal in ultrasound systems. A linear HVPA is preferable because it can suppress non-linear harmonic distortion components of ultrasound echo signals. To the best of our knowledge, this paper presents the first post-linearizer circuit to linearize the HVPA gain over a broad bandwidth. Results demonstrated that the capacitances in the post-linearizer nullified the parasitic input capacitances of the transistor, and inductances of the post-linearizer were able to resonate with the input and output and capacitances of the transistor, resulting in a reduction of the harmonic distortion components of the echo signal that lead to the development of an efficient HVPA that yielded adequate wide-bandwidth operation. The post-linearizer technique introduced here may be effective in precisely suppressing the non-linearity of ultrasound transducer.  
Lower second-, third-, fourth- and fifth-harmonic distortion components of the echo signal (\(-8.94\) dB, \(-10.01\) dB, \(-11.95\) dB, and \(-19.34\) dB) were attained by the HVPA with post-linearizer circuit in UBM systems as compared to those of the HVPA without post-linearizer (\(-4.32\) dB, \(-8.19\) dB, \(-8.94\) dB, and \(-15.31\) dB). Considering these results, the proposed post-linearizer circuit demonstrated potential to be used as a method to achieve gain performance linearization of the HVPA over a wide range of input powers and frequencies.  
This work has been supported by National Research Foundation of Korea (NRF-2016M2B2 A9A02945226) and by the MSIP (Ministry of Science, ICT and Future Planning). Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2718-16-0015) supervised by the IITP (National It Industry Promotion Agency).  
[PERSON] and [PERSON] conceived the idea, [PERSON] and [PERSON] carried out the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper together.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Factors Affecting
#### 3.2.2 Effect of Number of Atoms  
The obtained results show that, at temperature (T), T = 300 K, P = 0 GPa the Fe\({}_{63}\)Ni\({}_{32}\)Co\({}_{5}\) alloy with 2916 atoms has the structure shape (Figure 3a1), the lengths of the links rFe-Ni = rNi-Co = 2.475 A, rFe-Co = 2.525 A and the g(r) of RDF is g(r)Fe-Ni = 8.28, g(r)Fe-Co = 7.20, g(r)Ni-Co = 8.25 (Figure 3b1). The number of the structural unit of FCC, HCP, BCC, Amor (Figure 3c1), and the size of the alloy (I) corresponds to I = 3.20 nm, the total energy of the system (Etot), Etot = -12,657 eV. When it is increased the number of atoms from N = 2916 atoms to N = 5324, 8788, 13,500 atoms, the shape of the structure changes greatly, corresponding to an increase in the density of structural atoms (Figure 3a1-a4). At the same time, rFe-Ni = rNi-Co = 2.475 A, especially for rFe-Co when decreases from rFe-Co = 2.525 A to rFe-Co = 2.475 A; also, g(r) of RDF changed, the g(r)Fe-Ni changed down in the range from g(r)Fe-Ni = 8.28 to g(r)Fe-Ni = 7.01; g(r)Fe-Co changes up from g(r)Fe-Co = 6.40 to g(r)Fe-Co = 8.20; g(r)Ni-Co varies from g(r)Ni-Co = 8.25 to g(r)Ni-Co = 5.97 (Figure 3b1-b4) and number of structural unit of FCC increased from 1741 FCC to 7290 FCC, HCP increased from 1063 HCP to 4860 HCP, BCC increased from 18 BCC to 364 BCC, Amor increased from 94 Amor to 986 Amor (Figure 3c1-c4), and the size of the alloy (I) corresponds to an increase from I = 3.20 nm to I = 5.34 nm; the total energy of the system (Etot) decreases from Etot = -12,657 eV to Etot = -58,263 eV. The obtained results show that when increased the number of atoms leads to structural units FCC, HCP, BCC increase, and Amor decreases (the cause of increased crystallization is by the size effect cause). We choose Fe\({}_{63}\)Ni\({}_{32}\)Co\({}_{5}\) alloy with 13,500 atoms as the standard material to investigate influencing factors in the next section.  
Figure 3: Structure shape (**a1–a4**), RDF (**b1–b4**), number of structural unit (**c1–c4**) of Fe\({}_{63}\)Ni\({}_{32}\)Co\({}_{5}\) alloy at T = 300 K, P = 0 GPa with different numbers of atoms.
###### Abstract  
A novel process model simulating methanol production through pyrolysis oil gasification was developed, validated, then used to predict the effect of operating conditions on methanol production yield. The model comprised gasification, syngas post-treatment, and methanol synthesis units. The model was validated using experimental data from the literature, and the results obtained by the model were consistent with reference data. The simulation results revealed that gasification temperature has a significant impact on syngas composition. Indeed, rising temperature from 400 \({}^{\circ}\)C to 600 \({}^{\circ}\)C leads to higher syngas stoichiometric number (SN) value. Conversely, SN value decreases when the gasifier temperature is above 1000 \({}^{\circ}\)C. Moisture content in pyrolysis oil also affects both syngas composition and SN value; an increase in the first (from 10 to 30%) leads to an increase in SN value. The Rectisol unit deeply influences the syngas SN value and methanol yield, the best results being obtained with operating conditions of \(-\)20 \({}^{\circ}\)C and 40 bar. Increasing the operating temperature of the methanol synthesis unit from 150 \({}^{\circ}\)C to 250 \({}^{\circ}\)C leads to an increase in the yield of methanol production; the yield decreases beyond 250 \({}^{\circ}\)C. Although high pressures favor the methanol production yield, the operating pressure in the synthesis unit is limited at 50 bar for practical considerations (e.g., equipment price, equipment requirements, or operational risks).  
**Keywords:** renewable resources; Aspen simulation; methanol synthesis; Rectisol; sensitivity analysis; beechwood chips +
Footnote †: journal: Applied Physics Letters