esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spatial Audio Reproduction Techniques for Soundscape Design
In 1984, binaural reproduction technique is introduced by Sony and since 1989, the transaural technique gained popularity [61]. In 1993, another technique called the wave field synthesis (WFS) was proposed with its first major commercial application demonstrated in 2001 by [PERSON]. In 2011, IOSONO came up with the world's first real-time spatial audio processor for WFS. In 2015, MPEG-H standard was announced, with the aim to ensure all types of sound formats are supported in any types of playback systems. The most recent attempts of spatial audio rendering were focused on VR/AR applications [27; 62]. The above mentioned spatial audio reproduction techniques are primarily divided into two categories. The first category uses the technique of physical reconstruction of sound, which aims to synthesize the entire sound field in the listening area as close to the desired signal as possible. The second category is the perceptual reconstruction of sound, which employs psychoacoustic techniques to create a perception of spatial characteristics of sound [59]. The evolution of reproduction techniques is shown in Figure 7 along with their classification into physical and perceptual reproduction methods. The primary aim of the section is to introduce the spatial audio techniques and to highlight the soundscape studies that have used these techniques. Moreover, since these techniques have different encoding and decoding formats, they require appropriate tools for rendering audio. Table 1 in Appendix A describes some of such tools that are available, where, many of these tools are open source and free to use. Usage of such tools in soundscape studies could enable the researchers to choose appropriate techniques according to the merits and demerits of each technique, which are described in the following Sections 3.1 and 3.2 and summarized in Table 2 in Section 3.3.
###### Abstract  
This paper is devoted to a theoretical investigation on the wave amplitude enhancement of surface sea water waves with under-sea periodic arrays of cylinders. A two-dimensional shallow water wave equation is derived and solved by using the plane-wave expansion method. The lattice types studied here include triangular, square and hexagonal lattices. These under-sea structures alter the sea bottom topography and induce constructive interference on the surface water waves. Given that the wave potential energy is dependent on the square of the wave amplitude, this mechanism can thus be used to increase the potential energy. It is shown that the enhancement factor depends on two geometric parameters and the maximum wave amplitude can be found by adjusting the two geometric parameters. Among the lattice types, the triangular and square lattice structures can induce more wave amplitude enhancement (and thus potential energy) than the hexagonal structures. Guided by numerical simulations, we have performed a reduced-scale water tank experiment to demonstrate the feasibility of the proposed idea. Preliminary experimental results show promising evidence of the predicted wave amplitude enhancement, suggesting perspective real-scale nearshore deployment and test.  
Keywords:wave propagation; surface water wave; shallow water wave equation; wave amplitude enhancement +
Footnote †: journal: Applied Mechanics and Mechanics of the Society of the Pacific Conference on Computational Physics
## 2 Materials and Methods
### Physicochemical Measurements  
For a comprehensive analysis of color changes throughout the study, the color parameters L* (lightness), a* (red-green chromaticity), b* (blue-yellow chromaticity), and hue (h) angle (position of the color on the color wheel, measured in degrees), of the cucumber samples were measured using a tristimulus chromatometer (CR-400, Minolta, Tokyo, Japan). Calibration was performed with a standard white plate (L*: 97.83, a*: \(-\)0.45, b*: +1.88). Measurements were taken from both the outer and inner surfaces of the cucumber samples.  
Figure 1: Packaging of cucumber samples.  
Utilizing an AquaLab dew point water activity meter (model 4 TE, METERGroup, Inc., Pullman, WA, USA), the water activity of the cucumber samples was measured. The moisture content of the cucumber samples was quantified by measuring the weight loss resulting from heating in a vacuum oven. The samples were initially weighed and then subjected to reduced pressure in the vacuum oven for a period of 24 h at a stable temperature of 70 \({}^{\circ}\)C. After drying, the mass of the samples was recorded in order to determine the moisture content.  
The texture of the cucumber flesh samples was assessed using a TA-XTplusC texture analyzer from Stable Micro Systems, Godalming, UK, in accordance with the methodology outlined by [PERSON] et al. [14]. The analysis was conducted on the peeled cucumber samples using a 6.0 mm diameter cylindrical plunger from the outside to the inside to evaluate the flesh while avoiding the placenta. The textural attributes evaluated were firmness, cohesiveness, adhesiveness, springiness, and chewiness.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Method Based on Model
#### 4.3.2 Markov Random Field Model  
One of the main uses of the Markov random field (MRF) model in image processing is image segmentation, which is the technology and process of dividing the image into several specific and unique regions and extracting the target of interest; hence, it is a key step between image processing and image analysis.  
In MRF, two random fields are often used to describe the image. One is the labeling field, which is often called the implicit random field. The prior distribution is used to describe the local correlation of the label field. The other is the grayscale field or feature field. The distribution function is often used to describe the distribution of observation data or feature vectors under the condition of the labeling field. The process of obtaining feature vectors is the process of detection.  
Based on Bayesian theory, MRF turns the image segmentation problem into a process of obtaining the maximum probability density. The formula is as follows:  
\[P(W|S)=\frac{P(S|W)P(W)}{P(S)} \tag{2}\]  
where \(P(W)\), \(P(S)\), and \(P(S|W)\) are the prior probability, fixed value based on the observed value, and conditional probability distribution based on the observation \(S\) (also called the likelihood function), respectively. Then, the problem is converted into finding the maximum value of \(P(S|W)P(W)\). The Markov random field was used as a texture analysis method, which was combined with a KNN classifier to achieve six kinds of steel surface defects detection, with classification rates of 79.36-91.36% Unsalan [80]. Table 4 shows the comparison of model-based detection methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{Fractal model (FM)} & Remain invariant to large & \multirow{3}{*}{Low characteristic dimensions} \\  & geometric transformations and & \\  & lighting variations. & \\ \hline \multirow{6}{*}{Markov random field model} & Can be used with statistical and & Not invariant to rotation \\  & spectral methods for & \\ \cline{1-1}  & segmentation applications. & \multirow{2}{*}{Cannot detect small defects.} \\ \cline{1-1}  & & Not suitable for global \\ \cline{1-1}  & & texture analysis. \\ \cline{1-1} \cline{2-2} \cline{3-3}  & Captures the local texture & \\ \cline{1-1}  & orientation information. & Strong spatial constraint. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Strengths and weaknesses of model-based detection methods for steel defects.
## 1 Introduction
The system has been validated through in-vehicle testing and incorporates a simulator to manually or randomly generate DTC codes, enhancing its utility for testing and real-world applications. By utilizing Mexico's existing voice communication infrastructure, this approach offers a practical and scalable solution to the challenges posed by high-cost data communication systems in developing regions. This proposed system is considered an advantage because it allows reporting unusual events such as accidents or mechanical failures in vehicles, since there is not enough coverage in some territories in Mexico. Currently, vehicles from different past generations circulate throughout Mexico and do not have current technology how the autonomous and electric vehicles. The rest of this article is organized as follows: Section 2 presents the evaluation of the methodology regarding the communication protocols; Section 3 provides the implementation of the communication protocol; Section 4 verifies the experimental results of the system proposed in this work; and Section 5 presents the summary and conclusions, as well as future works.
## 4 Methods and Results
### Neural Network Method
#### 4.2.2 Construction of Neural Network
Different scholars use different methods to calculate number of nodes in the hidden layer. There's no uniform formula to calculate the number of nodes in the hidden layer. The distribution range of the number of nodes in the hidden layer is usually calculated according to the empirical formula and then verifies and determines a specific number according to practical data. To optimize the neural network structure and save the learning time of characteristics, it is suggested to use the least nodes while assuring precision. Based on the above formula, the number of nodes in the hidden layer of the neural network shall be 3-13. The neural network was trained with data of practical working areas. When there were 9 nodes in the hidden layer, the training error was the lowest and the correlation among samples was the maximum (Table 2). Therefore, the number of nodes in the hidden layer of the constructed neural network was determined to be 9 in this study area. 4. Algorithm selection  
The Levenberg-Marquardt algorithm, Bayesian regularization algorithm and quantitative conjugate gradient algorithm are three common algorithms during data training [26]. The Levenberg-Marquardt algorithm is characteristic of quick calculation, but it incurs a operation machine requirement. The Bayesian regularization algorithm avoids overfitting of a characteristic variable by retaining all characteristic variable, which takes a longer operation time. The quantitative conjugate gradient algorithm doesn't need linear searching, has quick operation and requires a small computational memory, but is not applicable to all datasets. According to practical verification, the Levenberg-Marquardt algorithm can complete neural network training the most quickly and accurately; this algorithm was chosen for data training for the neural network in this study.
## Materials and Methods
### Validation of Method  
The method was validated according to International Conference of Harmonisation guidelines and validation included specificity, linearity, accuracy, precision, limit of detection (LOD), limit of quantification (LOQ), and robustness.  
Specificity was studied by injecting ASA and CB standard solutions, and solutions of polymers, in phosphate buffer pH 6.8 used in formulation studies. Specificity was also determined by injecting solution after disintegration of empty hard gelatinous capsules under the same RP-HPLC conditions to ensure that there was no interference. The chromatograms were examined at retention time of acetylsalicylic acid and clopidogrel.  
The linearity of the method was tested in the range 0.0275-0.1375 mg/mL for ASA, and 0.0200-0.1000 mg/mL for CB (Table 1).  
The accuracy was tested on three concentration levels: for ASA, 0.0550, 0.1100, and 0.1375 mg/mL; and for CB, 0.0400, 0.0800, and 0.1000 mg/mL (50%, 100% and 125%) of the target concentration (in this study, the target concentration (100%) was 0.1100 mg/mL for ASA and 0.0800 mg/mL for CB). Recovery (%) was calculated for each concentration and mean recovery value should be between 90% and 110% to be accepted.  
One level of method precision (as system precision) was tested by analysis of ASA and CB at three concentration levels--50%, 100%, and 125%. The intra-day precision of the method was determined from replicate analysis (n = 5) of ASA and CB test standards at concentrations within the linear range of the assay for each drug (0.0550, 0.1100, and 0.1375 mg/mL for ASA and 0.0400, 0.0800, and 0.1000 mg/mL for CB). The inter-day precision of the method was determined by replicate analysis (n = 5) of ASA and CB with three different concentrations on two different days. The results were acceptable if the relative standard deviations (RSD) is less than 2% [28].  
The values of LOD and LOQ were determined as described before by [PERSON] [29].  
Robustness was evaluated by accompanying small variations in chromatographic conditions, such as alteration in the pH of the mobile phase, percentage organic content, changes in temperature, and the impact of these changes on qualitative and quantitative system responses.
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
## 3 Comprehensive Sensitivity Analysis  
Based on the above analyses, a multi-objective optimization process for the LSPMM is proposed. The detailed multi-objective optimization flowchart is described in Figure 2, which contains the following steps.  
Step 1: comprehensive sensitivity analysis. In this step, first an analysis of the influence of single factors on the motor performance parameters is carried out. The single-factor optimization parameters are selected as the pole arc coefficient, PM thickness, stator outer diameter, core length, number of conductors per slot and air gap length, motor performance parameters are efficiency and thermal load. Then, a response-surface analysis of the multi-objective performance is performed. The multi-objective performance parameters are efficiency, no-load back EMF, power factor, core loss, copper loss and thermal load. Finally, based on the Pearson coefficient, the sensitivity of multiple factors to the motor performance parameters is calculated.  
Step 2: multi-objective optimization. This step realizes the multi-objective optimization, and generates Pareto front structures in 2D space based on objectives and constraints, and uses NSGA-II to solve the Pareto front and obtain candidate points. Then, the candidate points are compared and the final optimization scheme is determined.  
Step 3: performance evaluation. In this step, both the simulation and experiment evaluation are carried out to verify the electromagnetic performances and the temperature rise of the motor, while ensuring the feasibility of a multi-objective optimization scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of poles & 30 \\ Number of stator slots & 36 \\ Air gap length (mm) & 3 \\ Stator outer diameter (mm) & 863 \\ Core length (mm) & 930 \\ Pole arc coefficient & 0.7 \\ PM thickness (mm) & 11 \\ Number of conductors per slot & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the LSPMM.  
Figure 1: Structure of LSPMM.
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test showed that all the data were normally distributed (\(p>0.05\)). To elucidate the statistical differences in perceived lengths (i.e., perceived wrist length and perceived hand lengths of where five fingers locate) between groups, multivariate analysis of variance (MANOVA) with repeated measures was performed. The within-subject factor was time (i.e., before stimulation, right after stimulation, 10 min after stimulation and 20 min after stimulation). The between-subject factor was group (i.e., the real rPMS group and the shamrPMS group). MANOVA was also performed to determine whether there were significant differences in the percentage of perceived lengths compared to real lengths. Where [PERSON]'s test indicated a violation of the sphericity assumption, Greenhouse-Geisser correction was applied. Bonferroni correction was applied for multiple comparisons. If there was an interaction effect of time and stimulation identified through MANOVA, then a paired \(t\)-test was adopted for post hoc analysis to compare results for before stimulation with right after stimulation, 10 min after stimulation, and 20 min after stimulation, respectively, in both the real rPMS group and the sham rPMS group.  
Analyses were conducted using SPSS version 26.0 (IBM Inc., Chicago, IL, USA). Continuous variables are presented as the mean \(\pm\) standard deviation. A \(p\)-value of <0.05 (two-sided) was considered to indicate statistical significance.  
Figure 2: Six landmarks of both implicit body representation of the hand and actual hand on A4-sized paper with a given line and a given dot. Hollow circles represent the six perceived landmarks; diamonds represent the six actual landmarks.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Flocculating Activity of Purified Biofleculant
#### 3.7.3 Effect of pH on the Flocculating Activity  
The effect of pH on the flocculating efficiency of the purified bioflocculant was examined. The results show that the bioflocculant was active over a wide pH range and demonstrated excellent flocculating rate (above 80%) at either strong acidic or alkaline pH conditions (Figure 11).  
This may be attributed to the fact that the electric states of the bioflocculant differ at different pH conditions and this in turn affects the flocculation efficiency of the bioflocculant for the kaolin particles [67]. The highest flocculating activity of 95.9% was attained at pH 10. There was inconsistency in bioflocculant activity at acidic region noticed by a decline in flocculating activity at pH 5. This may be accounted for by the fact that the spatial arrangement of surface charge is not only pH dependent but is also contingent on temperature [68]. Therefore, it can be concluded that the spatial charge arrangements for bioflocculation were not ambient at pH 5 and at neutral pH. The pH tolerance of the bioflocculant at a wide pH range suggests that it could be applied in various industries to treat various waters or wastewaters without having to adjust the pH of the water thus rendering the bioflocculant cost-effective [69]. Similar findings were reported for MBF-5 bioflocculant, which showed high flocculating activity under both acidic and alkaline conditions [13]. The bioflocculant produced by _Halomonas_ sp. V3' had a wide pH range of 3-11 with a flocculating activity above 80% [27]. Bioflocculant NU-2 produced by _marine myxobacterium_ was tolerant under a wide pH range of 2-13 [70].
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 2 Methodology
### The Relative Permeability of the Flooded Regions by Low Salinity Brine  
The results of the published laboratory experiments have been collected and analysed to calculate a percentage of change in the parameters of the relative permeability. The estimated rate of change in each variable due to the impact of the low salinity brine is summarised in Table 2. The listed percentages of change were used to generate three sets of the relative permeability to address the uncertainty in the laboratory results using Corey Equations (1) and (2). The calculated flow curves of the regions flooded by the low salinity brine exhibited a shift to the right (i.e., towards a more water-wet state), as illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Normalised** & **Salinity,** & \begin{tabular}{c} **Salit** \\ **Concentration,** \\ **1b/bbl** \\ \end{tabular} & \begin{tabular}{c} **Formation** \\ **Volume Factor** \\ **(B\({}_{w}\)), rb/STB** \\ \end{tabular} & \
\begin{tabular}{c} **Compressibility** \\ **(C\({}_{w}\)), psi-1** \\ \end{tabular} &
\begin{tabular}{c} **Viscosity** \\ **(i\({}_{w}\)), cp** \\ \end{tabular} \\ \hline
0 & 3000 & 1.052 & 1.0153 & \(2.98\times 10^{-6}\) & 0.352 \\
0.2 & 22,400 & 7.851 & 1.0162 & \(2.84\times 10^{-6}\) & 0.391 \\
0.4 & 41,800 & 14.651 & 1.0169 & \(2.73\times 10^{-6}\) & 0.422 \\
0.6 & 61,200 & 21.451 & 1.0175 & \(2.65\times 10^{-6}\) & 0.444 \\
0.8 & 80,600 & 28.251 & 1.0181 & \(2.57\times 10^{-6}\) & 0.460 \\
1 & 100,000 & 35.051 & 1.0187 & \(2.49\times 10^{-6}\) & 0.478 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An illustration of the brine properties using an increment step of 20% in the normalised salinity. The table is based on a maximum salinity of 100,000 ppm and a minimum of 3000 ppm, using a linear formula and the conditions of the Qamchuqa reservoir. This table (except Columns 1 and 2) is utilised in the simulation model to calculate the brine properties in the mixing region.  
\[k_{ro\_altered}=\left(k_{ro}\right)_{S_{av}}\left[\frac{1-S_{w}-S_{or,a}}{1-S_{av}-S_ {or,a}}\right]^{n_{ro,a}} \tag{1}\]  
\[k_{rw\_altered}=\left(k_{rw,a}\right)_{S_{or}}\left[\frac{S_{w}-S_{av}}{1-S_{ av}-S_{or,a}}\right]^{n_{w,a}} \tag{2}\]  
where:  
\[k_{ro\_altered},k_{rw\_altered}\]  
Relative permeability of oil and water, respectively, for the region flooded by low salinity.  
\[k_{ro}at_{av}\]  
Oil relative permeability at connate water saturation.  
\[k_{rw\_a}at_{\mathcal{S}or}\]  
Water relative permeability at residual oil saturation after low salinity waterflooding.  
\[S_{w},S_{av}\]  
Water saturation and connate water saturation.  
\[S_{or\_a}\]  
Residual oil saturation of the flooded region by low salinity.  
\[n_{o\_a},n_{w\_a}\]  
Saturation exponents of oil and water.  
The units of the above parameters are fraction except for the saturation exponents which are dimensionless.
# Seasonal Variability of Volatilome from _Dictyota dichotoma_
## 2 Results and Discussion
### Variations of Volatilome of Fresh D. dichotomous Obtained by Hydrodistillation  
The composition of HD-VCCs (first-time report) was strikingly different in comparison to HS-VCCs from _D. dichotomous_ (Table 3). The major compounds in HD-FrDd were prenylated-guaiane diterpenes isopachydictylO A (up to 19.72%) followed by its isomer pachydictylO A (up to 5.54%). They were previously isolated from several species of _D. dichotomous_ such as _D. transtrualis_, _D. caribaea_, _D. dichotomous_, and _D. volubilis_[7; 23; 24; 25; 26; 27; 28]. IsopachydictylO A and pachydictylO A showed a potent antithrombotic effect through the inhibition of thrombin [27]. These compounds displayed moderate to strong cytotoxicity against hepatoma (HepG2), fibroblast (WI-38), African green monkey kidney (VERO), and MCF-7 cell lines [24]. PachydictylO A displayed significant antifouling activity against the mussel _Limnoperna fortunei_[25]. Another abundant diterpene alcohol was cembra-4,7,11,15-tetraen-3-ol up to 33.56% in September followed by cembrene A up to 0.61% and cembrene up to 0.12%. All these compounds were not found in HS-FrDd due to their lower volatility and higher molecular masses. Cembranoids were previously detected in the Xisha soft coral _Simularia_ sp. [29], including (1R,3S)-cembra-4,7,11,15-tetraen-3-ol. In nature, cembranoids may act as chemical defence compounds against fish predators and/or compete for reef organisms, bacteria, and parasites, to ensure their protection and survival [29; 30]. Figure 3: Score plot of the dominant compounds from headspace volatiles (two different fibres for HS-SPME/GC-MS: divinylbenzene/carboxen/polydimethylsiloxane (I) and polydimethylsiloxane/ divinylbenzene (II)) of fresh (Fr) and dried (Dr) _D. dichotomous_ samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**May**} & \multicolumn{2}{c}{**June**} & \multicolumn{2}{c}{**July**} & \multicolumn{2}{c}{**August**} & \multicolumn{2}{c}{**September**} \\ \cline{3-12}
**No.