esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 3 Interferometric Phase Calibration Method
### Out-Referenced Method  
The out-referenced interferometric method uses the reference wave coming from outside the SLM to realize the interference with the measured modulated beam. It is flexible to adjust the foregone reference wave to make a variety of setup transformations, such as the four-step phase-shifting alignment with piezo-mirror which could measure global LUT and the spatial uniformity [28; 29].  
Figure 4: (**a**) Configuration of the self-referenced phase calibration method able to measure spatial nonuniformity. PS: monochromatic point light source; L: collimating lens; P: polarizer; SLM: spatial light modulator; CAM: camera. (**b**) Grayscale pattern uploaded on the SLM for the nonuniformity measurement and image captured by the camera [21].  
Another method is using a reference mirror to compose a Twyman-Green interferometer calibration system [30; 31]. With all kinds of interferometric setups, the phase nonuniformity of SLM can be measured precisely. Considerations regarding the avoidance of environmental turbulence should be made, such as air fluctuation.  
Digital holography technology plays a role in phase recording and reconstruction [32], and it is reasonable to draw lessons from it. One of the out-referenced SLM calibration methods inspired by digital holographic microscopy (DHM) is shown in Figure 5[33]. The illumination wavelength is 532 nm, and the modulated wavefronts of SLM (Holoeye Pluto, resolution \(1920\times 1080\), pixel pitch 8 microns) are gathered and magnified by a microscope objective (\(\times 10\), NA = 0.25). As one microscope objective is used for pixel magnification, the other microscope objective is used for compensation of the quadratic phase term [34]. A camera is used for capturing the hologram of the SLM. There is an offset angle between the reference beam and object beam so that the twin image can be filtered in the spectrum domain [35]. Theoretically, this DHM setup is able to reach the diffraction resolution of approximately 1.6 microns according to the Rayleigh criterion for coherent illumination [36]. Apparently, the single pixel sized phase calibration can be realized using this setup. However, the size of the SLM active area is usually larger than 10 mm \(\times\) 20 mm, and the FOV(field of view) of the DHM setup is roughly smaller than 10 mm \(\times\) 10 mm due to a limited space bandwidth [37]. Therefore, the displacements of CCD are needed for capturing the complete SLM active area.  
The methods based on DHM [14] and phase shifting [29] are the two calibration methods able to reach single pixel spatial resolution. However, not all the proposed methods using sophisticated setups to realize the quantitative phase imaging could be used directly for phase calibration, such as Fourier Ptychography [38]. The active area of SLM is usually larger than the FOV of the accurate quantitative phase imaging system with a good horizontal resolution. In addition, there is also the limitation of illumination.
## 2 Materials and Methods
### Experimental Observation
#### 2.2.4 Statistical Analysis  
In order to use soil temperature and soil water content to explain the variance in the carbon fluxes (NEE, GPP, R\({}_{\rm e}\), CH\({}_{4}\)), the generalized additive mixed-effects models (GAMMs) were performed to test their interactive effects on the carbon fluxes. GAMMs enable capturing non-linear asymmetric or bimodal biological responses and the mixed models allow treatment of autocorrelation and repeated measure situations [42]. The model allowed for flexible specification of the dependence of the response on the covariates by defining regression splines (smoothing functions) and estimating their optimal degree of smoothness, rather than calculating parametric relationships [43]. The choice to include a mixed (random) effect in the models was a way to avoid the potential influence of block that could induce spatial bias in this study. Because both soil temperature and soil water content fluctuate nonlinearly, GAMMs was run with soil temperature and soil water content as smooth terms, allowing flexible specifications of relationships without restricting relationships to be linear, quadratic or cubic. The model structure of the GAMM in the analysis was: carbon fluxes (soil temperature) \(\times\) s (soil water content) + (11 Block), where's' indicates smooth function and '(11 Block)' indicates random effect because of the completed randomized block experimental design. GAMMs were conductedusing gamm4 package and the contour plot that visualizes the partial interaction of soil temperature and soil water content was performed using pvisgam function in itsadug package in R version 3.5.0.
## 4 Results
### Visual Graphical Analyses  
To understand whether the delay frequencies increase or decrease, we plot the proportion of dwell time delays for different passenger volumes for both types of stations under consideration. The results of this are presented in Figure 4. Figure 4a shows the frequency of delays for stations with a scheduled dwell time of 42 s. Two trends can be identified here. When the volume of boarding and alighting passengers exceeds 10 passengers, the frequency of delays starts to increase and differs from the unconditional frequency distribution. The second trend is visible for volumes exceeding 21 boarding and alighting passengers. When this happens, delays larger than 15 s become more frequent. An increase in the volume of boarding and alighting passengers thus leads to an increase in both the frequency and size of dwell time delays at these stations.  
A different pattern is visible for stations with a scheduled dwell time of 120 s, as shown in Figure 4b. Stops with 10 or fewer passengers result in a far greater proportion of non-delayed stops compared to the expected proportion of delays and the other groups  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scheduled Dwell Time 42 s**} & \multicolumn{2}{c}{**Scheduled Dwell Time 120 s**} \\ \hline  & **Chi-Square** & \(p\) **Value** & **Chi-Square** & \(p\) **Value** \\ \hline
0 to 10 passengers & 2515 & 0.000 * & 221 & 0.000 * \\ \hline
11 to 20 passengers & 2963 & 0.000 * & 27 & 0.001 * \\ \hline
21 to 30 passengers & 4272 & 0.000 * & 69 & 0.000 * \\ \hline
31 to 40 passengers & 3122 & 0.000 * & 33 & 0.000 * \\ \hline
41 to 50 passengers & & & 34 & 0.000 * \\ \hline
51 to 60 passengers & & & 12 & 0.134 \\ \hline
61 to 70 passengers & & & 23 & 0.003 * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chi-square goodness of fit test results. Significant results indicated with an *, significance level \(p<0.05\).  
Figure 4: Proportion of dwell time delays for different passenger volumes for stations with a scheduled dwell time of 42 s (**a**) and 120 s (**b**).  
of passenger volumes. Once passenger volumes start to exceed 10 boarding and alighting passengers, delays of up to 15 s become more prominent. Larger delays, however, follow the expected proportion of delays more closely compared to stops with a scheduled dwell time of 42 s. Although an increase in the volume of boarding and alighting passengers leads to an increase in the frequency of dwell time delays, it does not lead to an increase in the size of delays at stations with a longer scheduled dwell time.
###### Abstract  
In this paper, we propose a new frequency-dependent amplitude panning method for stereophonic image enhancement applied to a sound source recorded using two closely spaced omni-directional microphones. The ability to detect the direction of such a sound source is limited due to weak spatial information, such as the inter-channel time difference (ICTD) and inter-channel level difference (ICLD). Moreover, when sound sources are recorded in a convolutive or a real room environment, the detection of sources is affected by reverberation effects. Thus, the proposed method first tries to estimate the source direction depending on the frequency using azimuth-frequency analysis. Then, a frequency-dependent amplitude panning technique is proposed to enhance the stereophonic image by modifying the stereophonic law of sines. To demonstrate the effectiveness of the proposed method, we compare its performance with that of a conventional method based on the beamforming technique in terms of directivity pattern, perceived direction, and quality degradation under three different recording conditions (anechoic, convolutive, and real reverberant). The comparison shows that the proposed method gives us better stereophonic images in a stereo loudspeaker reproduction than the conventional method without any annoying effects.  
stereophonic image for stereo loudspeakers; frequency-dependent amplitude panning; azimuth-frequency analysis; two closely spaced omni-directional microphones +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Cleaning Test Methods
#### 2.3.2 Gel Systems  
Normally, gelling materials for the cleaning of painted surfaces should be chosen taking into account several features, including the following:  
* Transparency;
* Capacity of loading polar solvents;
* Adaptability to uneven and porous surfaces;
* Ease of removal after application.  
Finding a compromise among the above-mentioned criteria, the availability of the products and the specific conservation needs of the artwork, we selected the following gel systems:  
* Rigid agar gels: By exploiting the ability of water to create emulsions with solvents in part or completely immiscible, it is possible to make two phases, physically separated, coexist in the presence of a gelling agent such as agar [14]. A small amount of benzyl alcohol (10% \(w/v\)) was added to the 3% gelled aqueous solution with agar and then applied when the gel became cold and rigid.
* PVA-borax gel: High viscosity polymer dispersion that can be charged up to 15-30% with some organic solvents. The gel was prepared by mixing PVA (4% \(w/w\) in water) and borax (8% \(w/w\) in water) in a ratio of PVA: B = 5:1. The ability of PVA-borax to adapt to the surface and to be easily removed makes it particularly suitable for paintings with uneven reliefs and thicknesses [15; 16]. In this case, some gels were prepared with the addition of ethanol or benzyl alcohol (up to 20% \(v/v\)).
* Nanorestore Gels(r): They are water-based chemical gels, which have highly retentive networks that reduce their action on the surface. They were developed by the Center for Colloid and Surface Science of Florence under the UE-funded research project named NANOFORART [17]. Hydrogels, Dry or Peggy, can be loaded with polar solvents or water-based nanostructured fluids of the Nanorestore Cleaning(r) series and they can be safely used on particularly water-sensitive substrates. In our research, we tested Dry gel Medium Water Retention MWR, Dry gel High Water Retention HWR, Peggy 5 gel and Peggy 6 gel. First, they were loaded with blend solutions of either water and ethanol or water and methyl ethyl ketone. Secondly, among the Nanorestore Cleaning(r) series, Polar Coating B (a nanostructured mixture of methyl lectone and 2-butanol) and G (a nanostructured mixture of methyl ethyl ketone, 2-butanol, ethyl acetate and propylene carbonate) showed to be more effective and were tested loaded in different Nanorestore Gel(r). Based on the observation of limited results achieved with Polar Coating G, it was tested only with MWR gel and Peggy 6 gel. Instead, Polar Coating B was loaded into all the above-mentioned gels.  
According to the technical sheet information, the two nanostructured fluids do not have a quantifiable polarity value, but they are designed for the removal of aged synthetic varnishes. The gels were carried out by immersing them in the chosen cleaning fluid for at least 12 h, and it can be used up to 4-5 times. All instructions for use and technical information were found in the technical sheets of the products [18].
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 2 Traffic Data Collection and Status Prediction Modeling Methods
### Greenshields Theory and Models  
The Greenshields model has been widely used to study highway traffic conditions by examining the relationships among traffic flow, density, and vehicle speed. Owing to its simplicity, this model has been extensively applied in traffic flow prediction, road design, and traffic management [17]. Generally, the highway traffic status can be classified into two modes: congested mode and non-congested mode. In the congested mode, traffic has reached saturation, where an excessively high vehicle density causes significant congestion and a sharp reduction in travel speed. In contrast, the non-congested mode refers to conditions where vehicles move at a relatively low density and high speed, resulting in  
Figure 1: Illustration of various highway detectors.  
smooth and unobstructed flow. In the Greenshields model, the relationship between traffic flow \(q(k)\) and vehicle density \(k\) primarily describes how the traffic flow varies as the vehicle density changes across a road segment. This relationship can be expressed as [17]  
\[q(k)=k\cdot v_{f}-\left[\frac{v_{f}}{k_{j}}\right]k^{2}, \tag{1}\]  
where \(q(k)\) denotes the traffic flow, \(k\) is the vehicle density, \(v_{f}\) is the free-flow speed, and \(k_{j}\) is the jam density parameter. The flow-density relationship curve is illustrated in Figure 2.  
Based on measurements of uninterrupted traffic flow on highways, the Greenshields model expresses the relationship between vehicle speed and density as [17]  
\[v(k)=v_{f}-\left[\frac{v_{f}}{k_{j}}\right]\cdot k, \tag{2}\]  
where \(v(k)\) is the average vehicle speed, \(k\) is the average vehicle density, \(v_{f}\) is the free-flow speed, and \(k_{j}\) represents the density parameter. The speed-density curve corresponding to this model is shown in Figure 3.  
Figure 3: Vehicle speed vs. traffic density curve of the Greenshields model.  
Figure 2: Traffic flow vs. traffic density curve of the Greenshields model.  
The Greenshields speed-flow relationship curve illustrates how the average vehicle speed varies with an increasing traffic volume on highways. This relationship can be expressed as [17]  
\[q(v)=v\cdot k_{j}-\left[\frac{k_{j}}{v_{f}}\right]v^{2}, \tag{3}\]  
where \(q(v)\) is the traffic flow and \(v\) is the average vehicle speed, \(v_{f}\) is the free-flow speed, and \(k_{j}\) is the density parameter. The corresponding speed-flow curve is shown in Figure 4.  
Based on the traffic relationships from Greenshields models, one can evaluate the designs of different highly efficient traffic control and management schemes that can alleviate congestion. It has also been extensively used and validated in subsequent traffic engineering research.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 3 Experiments
### Quantitative Evaluations
#### 3.2.3 Aluminum Alloy Surface Defect  
The segmentation accuracy of networks trained on the aluminum alloy surface defect dataset with different loss functions is presented in Figure 11. The balanced loss still shows a major promotion of accuracy. Other aspects of the results are similar to those of Severstad.  
The class-wise IoU in Table 5 demonstrates the effectiveness of the balanced loss on solving pixel-level label imbalance. As we report in Section 3.1, corrosion spots are very tiny defects (3% image area on average), while usually, chemical denuduation contains 89% of an image area on average. This label imbalance makes misprediction on corrosion spots very impactful. The results of the balanced loss on corrosion spots, corrosion pits, and corrosion products are improved by 12%, 18% and 7%. For chemical denuduation, which is very salient, the performance is close to other loss functions. We also experiment on the sensitivity of hyper-parameters on the aluminum alloy surface defect dataset. The conclusion is consistent with that of [PERSON] except that the gap between \(\beta=0.5\) and the others is not as huge as the gap in Table 4, which suggests this task is less threatened by easy-hard example imbalance. The sensitivity of the hyper-parameters of the balanced loss function is tested on this dataset, by the same approach as the experiment with [PERSON]. The result is presented in Table 6.  
Figure 11: Mean IoU comparison of the balanced loss function and other loss functions on various networks trained on aluminum alloy surface defect dataset.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 5 Instance Validation
### Feature Extraction
#### 5.1.2 Generalized Multiscale Sample Entropy  
Entropy is a measure of information uncertainty, so entropy features are often used as a class of effective features for feature extraction in regression models. To measure the uncertainty and complexity of the signal distribution, the entropy of different frequency bands can be used to quantify the information contained in the signal. The mean value feature of entropy can reflect the complexity of the information within the system as well as the error orientation.  
The most commonly used entropy functions include scatter entropy, fuzzy entropy, and sample entropy, as well as the derived multiscale entropy. Among them, the generalized multiscale sample entropy, which is improved on the basis of multiscale sample entropy, has the advantages of robustness, simple calculation, and fast operation, which can overcome the shortage brought by the coarse-grained method of using homogenized data in the calculation process on the sample.  
In this paper, the generalized multiscale sample entropy is selected as the entropy feature, and the optimal scale factor is selected by analyzing the change trend of the GMSE mean value under different scales \(s\). Figure 8 shows the GMSE mean values of the discharge voltage attributes under different cycles when different scale factors are selected, and the most suitable scale is selected based on the scale-mean decay relationship, i.e., the dimensionality of the entropy feature vector is determined.  
As can be seen from Figure 8, with the increase in the scale factor, the mean GMSE values of the discharge voltage signals of each cycle show a continuous increasing trend, and at the scale factor of 5, the voltage curves of different cycles appear to overlap and  
Figure 8: Mean values of GMSE at different scale factors.  
cross. Considering that the dimensionality of the feature set is closely related to the aging information, a smaller scale factor cannot fully extract the feature information and is prone to information overlap; a larger scale factor will cause redundancy of feature information and affect the model prediction effect. Therefore, the GMSE values of the first six scale factors constitute the entropy feature sample set, which is used as the second type of entropy feature input.
## 3 Results
### Production of Ectoine by the Recombinant E. coli  
To evaluate the expression level of the _ectABC_S. salarius_ genes in _Escherichia coli_, the predicted gene cluster was synthesized artificially and subsequently introduced into _E. coli_ BL21 strain using pDK6 as a heterologous expression vector. After an induced incubation, the supernatant and precipitation of cell extracts from recombinant strain _E. coli_ BL21(DE3)-pDK6-_ectABC_S. salarius_ (referred to BL-SsEct) were evaluated by SDS-PAGE analysis. The results showed three clear bands of EtAc\({}_{S. salarius}\), EtB\({}_{S. salarius}\) and EtC\({}_{S. salarius}\) with molecular masses of about 25 kDa, 48 kDa and 18 kDa as revealed by Figure 5.  
Figure 4: Crystal modelling of the EtC\({}_{S. salarius}\) protein from _Salinicola salarius_ 1A01339. The overall fold of the EtC\({}_{S. salarius}\) shown as a head-to-tail dimer was rendered by using the (PI)EtC:ADABA crystal structure from _Paeinobacillus lautus_ (PDB code 5 ONO). Two monomers were depicted in surface and cartoon representation marked in blue and green, respectively. The substrate ADABA was represented as blue sticks located in the center of the monomer. The figure was generated by SWISS-MODEL and PyMOL.  
In the following studies, ectoine was successfully synthesized by the recombinant strain BL-SsEct using a whole-cell biocatalytic method. In the reaction mixture, aspartate provided the substrate to synthesize L-aspartate-\(\beta\)-semialdehyde (ASA) and glucose could offer energy and the acetyl group for ectoine synthesis. KCl was added to the mixture to improve the activity and stability of EttB\({}_{S.\ salarins}\). The culture samples were analyzed by HPLC and LC-MS, and the chromatogram showed a new peak possessing the same retention time as the authentic ectoine (Figure 6). After 24 h, the concentration of extracellular ectoine reached 3.28 g/L in the flask-level conversion. Furthermore, the molecular weight was confirmed as 142 (C6H10N2O2) utilizing HRESIMS (\(m/z\)[141.1]-) (Figure 7).  
Figure 5: SDS-PAGE analysis of EtABC\({}_{S.\ salarins}\) expressed in _E. coli_ BL21(DE3). Lane M, protein molecular markers. Lane 1, the supernatant of cell extracts from induced _E. coli_ BL21(DE3)-pDK6; Lane 2, the precipitation of cell extracts from induced _E. coli_ BL21(DE3)-pDK6; Lane 3, the supernatant of cell extracts from the uninduced recombinant strain _E. coli_ BL21(DE3)-pDK6-_ectABC\({}_{S.\ salarins}\); Lane 4, the precipitation of cell extracts from the uninduced recombinant strain _E. coli_ BL21(DE3)-pDK6-_ectABC\({}_{S.\ salarins}\); Lane 5, the supernatant of cell extracts from the induced recombinant strain _E. coli_ BL21(DE3)-pDK6-_ectABC\({}_{S.\ salarins}\); Lane 5, the supernatant of cell extracts from the induced recombinant strain _E. coli_ BL21(DE3)-pDK6-_ectABC\({}_{S.\ salarins}\).  
Figure 6: HPLC analysis of biosynthetic ectoine from whole-cell catalysis. HPLC chromatograms obtained from authentic ectoine (**A**) and extracts (**B**) from conversion by engineered strain BL-SsEct.