esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 ANFIS Model  
The ANFIS algorithm uses fuzzy treatment based on the traditional neuro network algorithm. Thus the adaptive neuro-fuzzy inference machine is generated. The machine learning system is utilized to perform adjustments and realize supplementation between the neuro network and fuzzy inference [22]. The ANFIS algorithm can also significantly increase prediction accuracy and effectively decrease errors.  
Figure 1: Korla fragrant pear damage test platform based on negative pressure sucker. 1: Air compressing machine; 2: machine frame; 3: vacuum generator; 4: guide rail; 5: cantilever; 6: sucker; 7: guide screw; 8: pneumatic motor.  
Figure 3: Measuring points of Korla pear quality index (\(\blacksquare\)).  
Figure 2: Measurement of the damaged parts of the fragrant pear.  
In the present study, 70% of data were randomly chosen as the training set, while the remaining 30% were used as the prediction set. The models were constructed using the ANFIS ToolBox in MATLAB. The Matlab software version is R2017a (MathWorks, Natick, MA, USA). Both the damaged area and storage time of fragrant pears were input into the system. The system output the L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\) values. The training set data were loaded in Grid Partition, which was chosen to generate the initial fuzzy inference system. Eight membership functions were input, including trimf (triangle), trapmf (trapezoidal), gbellmf (generalized bell), gaussmf (Gaussian), gauss2 mf (two-sided Gaussian), pimf (Pi curve), psgmf (product of two sigmoidal functions), and dsigmf (difference between two sigmoidal functions). The different membership functions could directly determine the output results of models. The optimal membership function could also accurately predict the colors of the damaged fragrant pears during the storage period to the maximum extent. The grid partition method was used to generate the fuzzy inference system, and the \"hybrid\" optimization method was used in training. The adaptive fuzzy neural network model was established in the MATLAB environment. Eight different membership functions could realize the fuzzy processing of input data. The error-tolerant rate and times of iteration were set to 0 and 100, respectively. The initial values of the parameters are presented in Table 1.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 3 Optimization Analysis of SSPSWs
### Modeling and Validation of the FE Model  
The FE model in ABAQUS, as shown in Figure 4, incorporates SS400 steel material properties, defined as follows: elastic modulus, \(E_{0}=2.06\times 10^{5}\) MPa; yield stiffness, \(E_{1}=0.01E_{0}\); [PERSON]'s ratio, \(\mu=0.3\), for steel SS400; yield strength \(\sigma_{y}=392\) MPa; and ultimate strength, \(\sigma_{u}=441\) MPa. A tri-linear constitutive model is used to accurately simulate the steel's material behavior, as shown in Figure 5. [PERSON] yield criterion is adopted. An isotropic hardening model is used under monotonic loading conditions, and a kinematic hardening model considering the Bauschinger effect is applied for low-cycle cyclic loading. Four-node reduced-integration shell elements (S4 Rs) employing hourglass control and hyperelastic formulation are used. A mesh size of 50 mm is adopted in the FE model. Boundary conditions enforce full fixity at column/beam bases and out-of-plane translational constraints at tops, while permitting free out-of-plane rotations at the frame apex. The assembly integrates beam-column nodal connections. For the bilateral-joined SSPSW, the slotted steel plates are tied to upper/lower beams. For the quadrilateral-joined SSPSW, all four edges of the slotted steel plate are tied to the beams and columns. Cyclic loading analysis executes 40 displacement-controlled steps, initiating at 0.25% inter-story  
Figure 3: SF specimen and test setup.  
drift with 0.5% incremental increases per cycle until 5% drift attainment, following the protocol in Figure 6.  
Figure 4: FE model in ABAQUS. (**a**) Bilateral-joined SSPSW and (**b**) quadrilateral-joined SSPSW.  
Figure 5: Steel constitutive model.  
Figure 6: Loading history of frame fatigue test.  
Figure 7 compares experimental and finite FE-simulated hysteresis curves with deformation plots, demonstrating consistent cyclic behavior trends. The FE model predicts an ultimate load of 249 kN versus experimental measurements of 236 kN (5.2% deviation), while deformation patterns of slotted steel plates and frame components show close agreement with physical tests. These results validate the modeling approach's capability to accurately replicate steel frame-SSPSW mechanical responses within acceptable error margins (\(\leq\)5%), confirming its reliability for subsequent parametric optimization studies.
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Accuracy** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & \(-1.023/(0.333)\) & **6.305/(0.000)** & \(-1.468/(0.176)\) & \(-0.475/(0.646)\) & \(-\)**3.222/(0.010)** & \(-\)**2.284/(0.048)** \\ \hline ANN & \(1.023/(0.333)\) & — & **4.851/(0.001)** & \(0.379/(0.714)\) & \(0.720/(0.490)\) & \(-\)**2.552/(0.031)** & \(-1.650/(0.133)\) \\ \hline LR & \(\mathbf{-6.305/(0.000)}\) & \(\mathbf{-4.851/(0.001)}\) & — & \(\mathbf{-6.820/(0.000)}\) & \(\mathbf{-5.484/(0.000)}\) & \(\mathbf{-7.323/(0.000)}\) & \(\mathbf{-6.737/(0.000)}\) \\ \hline SVM & \(1.468/(0.176)\) & \(-0.379/(0.714)\) & **6.820/(0.000)** & — & \(0.536/(0.605)\) & \(\mathbf{-2.482/(0.035)}\) & \(-1.872/(0.094)\) \\ \hline LGBM & \(0.475/(0.646)\) & \(-0.720/(0.490)\) & **5.484/(0.000)** & \(-0.536/(0.605)\) & — & \(\mathbf{-3.054/(0.014)}\) & \(-1.958/(0.082)\) \\ \hline CB & \(\mathbf{3.222/(0.010)}\) & \(\mathbf{2.552/(0.031)}\) & \(\mathbf{7.323/(0.000)}\) & \(\mathbf{2.482/(0.035)}\) & \(\mathbf{3.054/(0.014)}\) & — & \(1.707/(0.122)\) \\ \hline XGB & \(\mathbf{2.284/(0.048)}\) & \(1.650/(0.133)\) & \(\mathbf{6.737/(0.000)}\) & \(1.872/(0.094)\) & \(1.958/(0.082)\) & \(-1.707/(0.122)\) & — \\ \hline
**Precision** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & \(\mathbf{-3.905/(0.004)}\) & \(1.700/(0.123)\) & \(-1.013/(0.338)\) & \(-0.852/(0.416)\) & \(\mathbf{-4.622/(0.001)}\) & \(\mathbf{-4.172/(0.002)}\) \\ \hline ANN & \(\mathbf{3.905/(0.004)}\) & — & \(\mathbf{3.176/(0.011)}\) & \(\mathbf{3.034/(0.014)}\) & \(\mathbf{3.071/(0.013)}\) & \(-0.518/(0.617)\) & \(-0.083/(0.936)\) \\ \hline LR & \(\mathbf{-1.700/(0.123)}\) & \(\mathbf{-3.176/(0.011)}\) & — & \(-2.028/(0.073)\) & \(-1.830/(0.100)\) & \(\mathbf{-4.092/(0.003)}\) & \(\mathbf{-3.917/(0.004)}\) \\ \hline SVM & \(1.013/(0.338)\) & \(\mathbf{-3.034/(0.014)}\) & \(2.028/(0.073)\) & — & \(-0.270/(0.794)\) & \(\mathbf{-3.855/(0.004)}\) & \(\mathbf{-3.913/(0.004)}\) \\ \hline LGBM & \(0.852/(0.416)\) & \(\mathbf{-3.071/(0.013)}\) & \(1.830/(0.100)\) & \(0.270/(0.794)\) & — & \(\mathbf{-4.396/(0.002)}\) & \(\mathbf{-3.793/(0.004)}\) \\ \hline CB & \(\mathbf{4.622/(0.001)}\) & \(0.518/(0.617)\) & \(\mathbf{4.092/(0.003)}\) & \(\mathbf{3.855/(0.004)}\) & \(\mathbf{4.396/(0.002)}\) & — & \(1.018/(0.335)\) \\ \hline XGB & \(\mathbf{4.172/(0.002)}\) & \(0.083/(0.936)\) & \(\mathbf{3.917/(0.004)}\) & \(\mathbf{3.913/(0.004)}\) & \(\mathbf{3.793/(0.004)}\) & \(\mathbf{-1.018/(0.335)}\) & — \\ \hline
**Recall** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & \(\mathbf{2.949/(0.016)}\) & \(\mathbf{14.933/(0.000)}\) & \(-1.318/(0.220)\) & \(0.766/(0.463)\) & \(1.217/(0.254)\) & \(1.943/(0.084)\) \\ \hline ANN & \(\mathbf{-2.949/(0.016)}\) & — & \(\mathbf{6.914/(0.000)}\) & \(-5.473/(0.000)\) & \(\mathbf{-3.251/(0.010)}\) & \(\mathbf{-3.778/(0.004)}\) & \(\mathbf{-2.411/(0.039)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._
## 2 Materials and Methods
### Principal Component Analysis (PCA)  
The principal component analysis is one of the statistical techniques that simplify datasets. This was proposed by [PERSON] in 1901 as a problem in geometric optimization to determine a plane that best fits n-dimensional space in the concept of least squares [23]; it was subsequently proposed by [PERSON] in 1936. In the analysis of the correlation between two sets of variables, the lower independent variables that determine the variation of the original n variables are called components.  
The most modern PCA theory was established as follows. PCA is a linear transformation that converts data for a new coordinate system. The new variable set, which is the main variable, is a linear function of the original variable and has no correlation; the largest variance because of data projection appears in that direction. The first vector has the first largest variance, and the second vector has the second largest variance [25]. This can be achieved by obtaining a covariance matrix for the entire dataset and calculating the eigenvectors and eigenvalues of the covariance matrix; thereafter, these are sorted according to decreasing eigenvalues. The procedure is illustrated in Figure 1[26]. However, PCA bias is not always appropriate; actually, a vector with a low variance may be highly predictive.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Cristobalite** & **Alumina** & **Sum** \\ \hline
**Total** & 47 & 20 & 67 \\ \hline
**Intact** & 37 & 20 & 57 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Damage \\ type \\ \end{tabular} } & Sub Total & 10 & - & 10 \\ \cline{2-4}  & Porcelain Defect & 4 & - & 4 \\ \cline{2-4}  & Cap Defect & 3 & - & 3 \\ \cline{2-4}  & Internal defect & 3 & - & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of specimens according to material and defect.  
PCA using the covariance matrix calculation by extracted features, as shown in Figure 1, is performed as follows [26].  
Given that a feature data matrix (\(X=\left[x_{1},\,x_{2},\,\ldots,\,x_{N}\right]\)) has a total number of \(N\) samples, and \(x_{i}\) represents the '\(i\)'th sample, calculate the mean of all samples using the following:  
\[\mu=\frac{1}{N}\sum\
olimits_{i=1}^{N}x_{i}. \tag{2}\]  
The deviation (D) is calculated by subtracting the mean (\(\mu\)) from all samples as follows:  
\[\mathrm{D}=\left\{d_{1},d_{2},\ldots,d_{N}\right\}=\sum\limits_{i=1}^{N}x_{i} -\mu. \tag{3}\]  
The covariance matrix (\(\Sigma\)) is calculated by the following:  
\[\sum\
olimits=\frac{1}{N-1}D*D^{T}. \tag{4}\]  
Calculate the eigenvectors (V) and eigenvalues (\(\lambda\)) of the covariance matrix (\(\sum\)). Thereafter, sort the eigenvectors according to their corresponding eigenvalues.  
Figure 1: Visualized steps in calculating principal component analysis (PCA) space using covariance matrix.  
Select the eigenvectors that have the largest eigenvalues, W = {v1,..., vk}; the use of selected eigenvectors (W) could represent the projection space of PCA. All samples could be projected on the lower dimensional space of PCA (V) by Y = W\({}^{\text{T}}\)\(*\) D.
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 2 Overview of ASK-OFDM  
The PSK and QAM schemes are the most common modulation techniques used with OFDM. More specifically, QPSK and 16-QAM schemes have been widely used. Both methods use \(1/T\) as the separation distance between the subcarriers to maintain orthogonality among them. On the other hand, ASK requires only \(1/2T\) as the minimum frequency separation, since the subcarriers differ only in amplitudes and frequencies, while their phases are the same (\(0,\pi/2\) or \(\pi\)) [4; 5; 6]. Therefore, ASK-OFDM attains better bandwidth efficiency. Furthermore, the ASK-OFDM system is not as sophisticated as conventional OFDM systems that use QAM or PSK. \(M\)-ary ASK-OFDM signal as introduced in [5]:  
\[s(t)=\sum_{k=0}^{N-1}A_{k}cos\left(2\pi f_{k}t\right) \tag{1}\]  
where, \(A_{k}\) is the amplitude of the \(M\)-ary ASK-OFDM signal, \(M\) is the modulation order, \(N\) is the number of subcarriers and \(f_{k}=k/2T\) is the subcarrier frequency. The subcarrier frequency separation is \(1/2T\).  
Figures 1 and 2 show the spectra of OFDM subcarriers with \(1/T\) spacing for PSK and QAM, and \(1/2T\) for ASK, respectively. It is obvious from the figures that the bandwidth saving ratio of ASK-OFDM compared to PSK and QAM-OFDM as given by [5] is:  
\[\text{BandwidthSaving}=\frac{\left(2(N+1)\right)}{(N+3)} \tag{2}\]  
ASK on the other hand has lower power efficiency when compared to PSK or QAM for the same modulation order \(M\). The increase in PAPR compared to PSK-OFDM is expressed as [5]:  
\[PAPR_{\text{degr}}=\frac{3(M-1)}{M+1} \tag{3}\]  
However, ASK is still favorable for implementation with the OFDM system since there are techniques in the literature which reduce PAPR. One dynamic solution to solve the high PAPR problem is through coding. There are some preferable coding approaches dedicated to the PAPR issue discussed in [7].
# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 5 Matrix Factorization-Based Methods
### Independent Component Analysis-Based Approaches
Therefore, we cannot verify the robustness of their approach. [PERSON] et al. developed an open-source MATLAB package for calcium data processing and behavioural data analysis in mice [27]. The package is intended to be used for calcium data acquired from head-mounted imaging setup and behavioural data acquired from multiple cameras. The author call the package \"Calcium Activity Explorer\" or CAVE. The package has sub-routines for motion correction, \(\Delta F/F\) calculation, cell (neurons) detection (segmentation) and trace analysis. For cell detection, the ICA framework proposed by [PERSON] et al. [56] is used to generate initial ROI estimates. Then thresholds are applied to minimum and maximum sizes of the ROI to delete ROIs smaller or larger than an expected size range (30 to 300 pixels). Those ROIs which overlap by more than 30% are merged together. Moreover, those ROIs whose fluorescence is lower than a threshold are deleted and those ROIs whose temporal correlation is greater than 0.8 are merged. Finally, the users can themselves add or delete ROIs manually. CAVE has sub-routines for calcium activity deconvolution and behavioural analysis as well but they are out of the scope of this survey. The authors have tested their package on data acquired from both a cortical region (primary somatosensory cortex, S1) and a hippocampal region (DG). However, they have not provided any quantitative performance figures for their package on cell/neuron detection. [PERSON] et al. [52] developed an open-source tool for automatic extraction, counting and localization of calcium signals from calcium imaging stacks based on continuous wavelet transform-guided peak detection in calcium signals. The tool needs three parameters: grid window size (WS) which is used to locate calcium activity events, signal to noise ratio (SNR) which defines what part of the ROI is signal and what is the background and signal average threshold (SAT) which is used as a threshold for background removal. The average signal threshold is either entered manually or calculated from a cell-free region/low activity region of the stack. These three parameters are used to extract an automated pdf report about time and location of calcium activity in the imaging stack. This tool was developed in ImajeJ and R. The authors have not provided a comparative or quantitative performance for this tool. This tool is simple to understand, easier to use and according to the authors and performs activity detection satisfactorily. However, this tool is non-robust as it cannot deal with varying levels of background activity. It cannot isolate neurons on its own and relies solely on a fixed background model.
## 6 Extraction Methodology of Bioactive Compounds from _A. leptopus_
### Non-Conventional Extraction Techniques (Green Technology)
#### 6.2.2 Supercritical-Carbon-Dioxide (S-CO\({}_{2}\)) Extraction  
Supercritical carbon dioxide (S-CO\({}_{2}\)) extraction is regarded as green technology, that utilizes CO\({}_{2}\) as a solvent at a temperature above 31 \({}^{\circ}\)C and pressure above 7.3 MPa to extract bioactive compounds from plant materials. Supercritical fluid extraction (SFE) is currently gaining attention due to its many advantages and sustainable applications over traditional extraction methods. The supercritical fluid properties, moderate temperature, and low pressure required for extraction allows for the selective and efficient separation of bioactive compounds while preserving its functional properties. Furthermore, the utilization of CO\({}_{2}\) with SFE technology to eliminate hazardous waste materials is currently being considered as a method to reduce CO\({}_{2}\) to reduce pollution and overall environmental impacts [56]. Specific studies on the application of S-CO\({}_{2}\) to _A. leptopus_ are currently lacking; however, the technique has been successfully employed in extracting bioactive compounds from various other plant species [59; 60]. However, the main drawback of SFE is its costly equipment, operating expenditures, and the requirement for specialized knowledge operate the equipment. These factors make this extraction method not as accessible as other methods. Additionally, SFE is not suitable for all extractions as some compounds may be difficult to extract while using supercritical fluids such as non-polar compounds [61].
## Results and Discussion
### Effect of Rock Permeability  
There are three cement-rock models in which we embedded microchannels to investigate the effect of rock permeability on the volume flow rate. In each case study, we used the same microchannel shape and size: that is, a square shape and a 64 pixel2 (0.2273 mm2) cross-sectional area. Figure 19 shows the velocity streamlines demonstrating the direction of fluid flow through the cement-rock sample. The color of the velocity streamlines represents the magnitude of the local velocity value at a specific position. In Figure 19a, the rock had lower permeability than the interface and, as a result, higher outlet velocity streamlines were observed in the interface region. When the rock permeability was equal to interface permeability, which was the case in Figure 19b, higher outlet velocity streamlines were observed both in the rock and in the interface sections. As the rock permeability became higher than the interface permeability, which was the case in Figure 19c, the outlet velocity streamlines through the rock section were higher than those of the interface section.  
Figure 19: The velocity streamlines for cases with different rock permeability. (**a–c**): k1 (krock < kineteface), k2 (krock = kineteface), k3 (krock > kineteface).  
Figure 18: (**a**) Contact area vs. inclination angle for slanted microchannel models with a 16 pixel2 (0.1279 mm2) circular microchannel and a 16 pixel2 (0.1279 mm2) square microchannel, respectively; (**b**) volume flow rate vs. contact area for slanted microchannel models with a 16 pixel2 (0.1279 mm2) circular microchannel and a 16 pixel2 (0.1279 mm2) square microchannel, respectively.  
The volume flow rates through individual sections are summarized in Table 13. According to the results shown in Table 13, as rock permeability becomes higher, the volume flow rate through each section increases. This means that a higher rock permeability enables rock to transmit more fluids. Such fluids flow through the rock section and then enter the interface, microchannel, and cement sections.  
Table 14 summarizes the average outflow velocities in each section obtained by dividing the volume flow rate by the corresponding cross-sectional area. The results show that, for all cases, the highest average outflow velocity occurs in the microchannels. When the rock permeability is lower than the interface permeability, a relatively higher average outflow velocity occurs in the interface region. When the rock permeability is equal to or higher than the interface permeability, the rock has a higher average outflow velocity than the interface.
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
###### Abstract
Therefore, this paper will propose an optimization model and design an efficient method to solve this challenge. The main contributions of this paper can be concluded as follows. 1. This paper considers the location of multi-type sensors when discussing the sensor-deployment problem along highways. 2. This paper takes the problem of computing server placement into consideration to meet the demand of realizing the construction of intelligent transportation systems. 3. This paper studies the hybrid traffic sensor deployment problem with communication consideration by formulating a binary optimization model and designing a heuristic algorithm. The remainder of this paper is organized as follows: Section 2 illustrates the sensor deployment problem and formulates it into a mathematical model. Section 3 introduces the solution algorithm for the hybrid sensor deployment problem. Case studies and conclusions are presented in Sections 4 and 5, respectively.
## 4 Design Consideration and Simulation
### Output Capacitor Value Calculation  
From Table 4, substitution of \(I_{o}\) = 1.2 A, \(V_{in(max)}\) = 16 V, \(\alpha\) = 1%, and \(f_{s}\) = 400 kHz into (7) can obtain \(C_{obk}\) = 9.4 \(\upmu\)F; in addition, substitution of the aforementioned parameters and \(\beta\) = 20% into (8) can obtain \(ESR_{bk}\)\(\leq\) 109 m\(\Omega\). Moreover, substitution of \(I_{o}\) = 1.2 A, \(V_{in(min)}\) = 9 V, \(V_{o(max)}\) = 16 V, \(\alpha\) = 1%, and \(f_{s}\) = 400 kHz into (9) can obtain \(C_{obt}\) = 4.1 \(\upmu\)F. Substitution of the aforementioned parameters and \(\beta\) = 20% into (10) can obtain \(ESR_{bt}\)\(\leq\) 62 m\(\Omega\). These calculating values, including \(L_{bk}\), \(C_{obk}\), \(ESR_{bk}\), \(L_{bt}\), \(C_{obt}\), and \(ESR_{bt}\), are listed in Table 5. Circuit element specifications in Figures 2 and 9 are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Specification** & **Parameter** & **Specification** \\ \hline \(V_{in(min)}\) & 9 V & \(V_{in(max)}\) = 16 V \\ \(V_{o(min)}\) & 13 V & \(V_{o(max)}\) = 16 V \\ \(I_{o}\) & 1.2 A & \(f_{s}\) = 400 kHz \\ \(\alpha\) & 1\% & \(\beta\) = 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: High-power laser diode driving controller (HPLDDC) parameters and specifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Depiction** & **Specification** \\ \hline Operating forward voltage (\(V_{LD1}\)) & 4.7 V \\ at the operating forward current \(I_{LD1}\) = 0.9 A & \\ \hline Operating forward voltage (\(V_{LD2}\)) & 4.8 V \\ at the operating forward current \(I_{LD2}\) = 1.2 A & \\ \hline Optical output power & 1.6 W \\ Maximum operating current & 1.5 A \\ Laser emission wavelength & 450 nm \\ Operating temperature range & \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C \\ Package & TO56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of high-power laser diode (HPLD) at 25 \({}^{\circ}\)C.