esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a modification of the modal-based ground motion selection (MGMS) method for improving the reliability of the nonlinear response time history analysis (NLRHA) of reinforced concrete (RC) shear wall structures. The original MGMS procedure quantified the impact of frequency content combinations in the time domain (FCCTD) of input ground motions (IGMs) on the seismic response of building structures using the level of interaction of the first three modes induced by IGMs. However, previous research found that the first two modes have far larger modal mass coefficients than those of higher modes and dominate the vibration of the RC shear wall structures with a symmetric plan. Therefore, the MGMS procedure should be modified by employing the interaction of the first two modes induced by IGMs to properly account for the effect of the FCCTD of IGMs on the seismic response of structures. In the MGMS procedure for RC shear wall structures, seven IGMs that caused the most significant interactions of the first two modes were selected from a suite of twenty seed IGMs, which were chosen with a conventional spectra-matching-based IGMs selection procedure for the NLRHA of the structure. A comprehensive case study involving three RC shear walls with different heights was conducted to investigate the capability of the MGMS in selecting suitable IGMs for the NLRHA of RC shear wall structures. Sets of seed IGMs were selected, adopting conditional mean spectra and design spectra as the target spectra. It was found that the seismic demands computed using MGMS selected IGMs can ensure a more reliable and reasonable computation of seismic demands compared with conventional spectra-matching-based IGMs selection methods.  
nonlinear response time history analysis; reinforced concrete shear wall structure; modal-based ground motion selection; frequency contents combination in the time domain; dominated mode +
Footnote †: journal: Applied Sciences  
11 August 2021  
1
## 3 Results and Discussion
### Influence of Excitation Wavelength, Resolution and ROS Technology  
For the automated determination of the ripeness of the fruit, a Raman spectrometer with suitable features should be used; it needs an appropriate excitation wavelength and sufficient resolution paired with the highest possible robustness. Three different Raman spectrometers were used to determine the degree of ripeness of the fruit (for spectral comparison, see Figure 2).  
The three characteristic carotenoid signals at 1001, 1156 and 1515 cm\({}^{-1}\) were detected using the three spectrometers with laser wavelengths at 638, 785 and 1064 nm. The spectrum with the laser wavelength at 638 nm suffered from strong fluorescence, so that an extended spectral editing was required. While the fruit surface contains a variety of compounds, the interference stemmed presumably from chlorophylls showing broad absorbance around 662 nm. However, the intensities after background correction remained significantly lower compared to the results of the other two spectrometers. The resolution of the instrument having the laser at 1064 nm amounted to 6 cm\({}^{-1}\) and was much better than that of the handheld instrument. However, it was a benchtop device for laboratory use and was therefore not suitable for field or on-site use. The spectrum recorded with the handheld device having a laser wavelength of 785 nm displayed more intense signals and an enhanced signal-to-noise ratio as would be expected due to the wavelength dependence of the Raman intensity (\(\lambda^{4}\)). The excitation wavelength was sufficiently red-shifted not to fall within the steeply declining carotenoid absorption in the visible range. The most commonly available 785 nm Raman laser proved indeed the best choice due to the lack of interference and the most available sensitivity. In addition, the raster orbital scanning technology contributed to improved sensitivity due to averaging over a wider area of the fruit surface [38]. The narrow, focused laser beam followed a circular motion over a larger sample area to yield a better resolution and intensity since an alternating color  
Figure 1: Raman spectra of hot peppers during the maturation process obtained with handheld Raman spectrometer at 785 nm laser wavelength [13; 23; 31]. Unripe pepper (green), ripening pepper (yellow) and fully ripe fruit (red) are each represented by four deliberately selected spectra.  
was observed over the fruit surface due to the maturing process, which the raster orbital scanning (ROS) technology took into account better than the sampling technique of the other Raman spectrometers. Thus, the handheld spectrometer was judged more suitable for field use to determine the degree of ripeness. In addition, the spectrometer was coupled directly with a laptop for spectrometer control and evaluation software.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### EA in Spray Dried and lyophilized Powders
The best microdispersed powder, obtained with 1:4.5 EA:pectin ratio, showed a mean particle diameter of about 10 \(\upmu\)m and a DL equal to 21%, allowing solubilizing 63 \(\upmu\)g/mL of EA in water. Moreover, the drug loading of EA in the powder remained stable for one year of storage in a desiccator at 25 \({}^{\circ}\)C.  
Recently, the search for promising systems suitable for dietary supplements enlarged the research field to other natural polymers, such as alginic acid (ALG) [60]. EA-ALG nanoparticles (NPs) were prepared by dissolving both EA and ALG at the concentration of 250 \(\upmu\)g/mL in an aqueous medium at pH 8.5. The solution was spray dried and the obtained powder was added to a CaCl\({}_{2}\) solution in order to induce reticulation by ionic gelation. The resulting NPs had a mean diameter of 670 nm with zeta potential around \(-\)27 mV. Thanks to the high degree of EA solubilization in the basic medium, the encapsulation efficiency (EE) was 50%, much higher than the EE of NPs prepared from a neutral medium. As rational remark to this procedure performed in basic environment, it may be highlighted that the possible degradation of EA in alkaline medium during preparation was not taken into consideration. This EA-ALG system showed a biphasic release profile with a fast phase during the initial 3 h, justified by EA adsorbed on NPs surface, followed by a sustained, complete release until 8 h. A mice model of epilepsy was used to assess in vivo EA-ALG NPs efficacy as anticonvulsant and neuroprotective agent by oral treatment every other day for 33 days. Results indicated that EA contained in this dosage form prevented seizures during the experimental period, and had a more pronounced effect, compared to free EA, by reducing oxidative stress damage, inhibiting apoptosis and down-regulating cytokine levels.
## 3 Results
### Cross-Validation Analysis  
Table 6 presents the cross-validation results for both SMOTE and SMOTEENN across different sampling strategies (100 to 300 instances). The table includes the mean accuracy and standard deviation for each method at each sampling level. SMOTEENN consistently achieves higher mean accuracy compared to SMOTE across all sampling strategies, with the exception of the 250-instance level, where both methods have the same mean accuracy of 0.97. The highest mean accuracy for SMOTEENN is observed at the 150-instance level (0.99), while for SMOTE, it is at the 250 and 300-instance levels (0.97). SMOTE shows a steady increase in mean accuracy as the number of instances increases, from 0.94 at 100 instances to 0.97 at 300 instances. SMOTEENN's mean accuracy peaks at 150 instances and then slightly decreases at higher instance levels, suggesting an optimal point for this method.  
The standard deviation for SMOTE ranges from 0.010 to 0.030 across different sampling strategies, indicating relatively consistent performance. SMOTEENN's standard deviation ranges from 0.008 to 0.029, with the lowest value observed at the 200-instance level (0.008). Both methods show a decrease in standard deviation as the number of instances increases, suggesting improved stability in performance with larger sample sizes. The peak mean accuracy of SMOTEENN at the 150-instance level indicates that this sampling strategy may be optimal for achieving the best predictive performance. The slight decrease in mean accuracy at higher instance levels for SMOTEENN suggests that there may be a trade-off between sample size and model performance, as also observed in the learning curves discussed earlier.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sample Generation Method** & **Sampling Number** & **Mean** & **Standard Deviation** \\ \hline \multirow{5}{*}{SMOTE} & 100 & 0.94 & 0.020 \\  & 150 & 0.95 & 0.030 \\  & 200 & 0.96 & 0.020 \\  & 250 & 0.97 & 0.010 \\  & 300 & 0.97 & 0.010 \\ \hline \multirow{5}{*}{SMOTEENN} & 100 & 0.97 & 0.029 \\  & 150 & 0.99 & 0.009 \\ \cline{1-1}  & 200 & 0.977 & 0.008 \\ \cline{1-1}  & 250 & 0.97 & 0.029 \\ \cline{1-1}  & 300 & 0.98 & 0.009 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross Validation Results.
# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 4 Discussion
### Comparing Simulated and Experimentally Acquired Brain Cortical Map for Speech Motor Control  
The cortical activation maps in DIVA_EEG, instead of being represented as the set of nodes obtained from DIVA, were constructed by implementing an EEG generative model to simulate EEG scalp topographies, from which current density maps in the brain were estimated. This allowed for a fair comparison between the model-based brain activity maps and those estimated from experimentally acquired EEG. An appropriate match between the predicted and the EEG-driven cortical maps was obtained, at the level of both cortical regions and voxels (Figure 6E). Differences between these cortical representations may be due to different factors, including the use of point sources for generating the synthetic EEG. Therefore, tunning the size and shape of the brain areas used as seeds for the EEG generation shall be considered for further developments of DIVA_EEG. Noteworthy, all the cortical regions selected as seeds in DIVA_EEG were present in the cortical activation maps estimated from real EEG (Table S5, Supplementary Materials). The fact that brain activation maps estimated from both synthetic and experimentally acquired EEG extends beyond the seed regions of DIVA_EEG primarily relies on the following aspects. First, the spatial resolution and precision of the EEG source estimation methods in lower than that of the fMRI. In the case of LORETA, the cortical activity is represented in a grid of 6239 voxels, each of 5 \(\times\) 5 \(\times\) 5 mm [50], which is much larger than the typical 1 \(\times\) 1 \(\times\) 1 mm voxel size of the fMRI data. Second, one of the assumptions made for solving the EEG inverse problem using LORETA is that the electrical activity of neighboring voxels has maximal similarity [100], which leads to smooth cortical activations. Third, different statistical approaches have been used for estimating speech-related cortical activation maps from fMRI [3; 18; 86] and EEG [95]. Finally, fMRI and EEG reflect the hemodynamic and electrical activity of the brain, respectively. In other words, these neuroimaging modalities are different in nature and have largely different dynamics. Therefore, complementary but different results are expected when assessing brain activity from EEG and fMRI. A less restricted set of cortical regions resulted from the EEG feedback perturbation paradigm (Table S4, Supplementary Materials) when compared with its analogue fMRI paradigm [3; 18; 86]. This indicates that speech production, rather than relying on a discrete and reduced set of brain areas, is controlled by a broadly distributed network in which information is interchanged between primary nodes (seeds in DIVA_EEG) and between them and occipital, frontal and limbic areas.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 4 Simulation Results  
### Simulation Setup  
To evaluate the performance of the proposed DRCC approach, we used a simulation environment consisting of three different software packages to generate the traffic model and simulate the wireless communication between vehicles. The traffic simulator used was Simulation of Urban Mobility (SUMO) [63]. SUMO is a free and open source microscopic simulation software implemented in C++ that can be used to simulate vehicles, pedestrians, public transport, etc. The network simulator used was OMNeT++ [64], which is a modular component-based C++ simulation library and framework. OMNeT++ was used for simulating the wireless transmission protocols. Tying these two software packages together is VEINS, which is a framework that includes models for making traffic simulations realistic and providing communication between SUMO and OMNeT++ by means of the traffic control interface (TraCI). VEINS contains a basic implementation of the IEEE 802.11p and IEEE 1609 protocols that facilitates the testing of VANET protocols, and provides communication between SUMO and OMNeT++ by means of a TCP socket connection.  
We considered a 1000 m long four-lane highway composed of two lanes in either direction, with 80 stationary vehicles placed 50 m apart along the highway. To generate different levels of channel load, we used different combinations of BSM packet sizes and beacon intervals, as indicated below:  
* LOW load: BSM packets of 256 bytes broadcast at intervals of 0.1 s and 0.05 s.
* MEDIUM load: BSM packets of 1024 bytes and 256 bytes broadcast at intervals of 0.1 s and 0.02 s, respectively.
* HIGH load: BSM packets of 1024 bytes broadcast at intervals of 0.05 s.  
The parameters for our simulations are summarized in Table 1.
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 2 Materials and Methods  
### Overall Framework  
The geo-location system introduced in this article is composed of a ground control station, a digital and image transmission link and an UAV with an airborne optoelectronic platform, as shown in the Figure 1. The UAV was developed by Changchun Institute of Optics Fine Mechanics and Physics, Chinese Academy of Science for civilian applications such as rescue of the wounded and forest fire prevention. The ground control system is responsible for the control and status display of the UAV and the airborne optoelectronic platform. The transmission link is responsible for real-time downloading of video data. The airborne optoelectronic platform is composed of visible camera, Thermal imaging camera, laser rangefinder, stabilized platforms, image trackers, inertial measurement unit (IMU), etc. The stabilized platforms include the azimuth axis and the pitch axis, and each axis has an encoder which can output the current azimuth and pitch angle in real time.  
The geo-location process is shown in the Figure 2. First, an operator selects the target in the real-time video in the ground control station. Then when the airborne optoelectronic platform is tracking the selected target and the operator turns on the laser rangefinder, the airborne optoelectronic platform starts the geo-location process. The geo-location results of the target are sent to the ground control station for real-time display.  
Figure 1: The geo-location system introduced in this article (The top left figure shows the ground control station and the figure on the bottom left shows the inside of the station; the top right figure shows the UAV with the airborne optoelectronic platform, and the bottom right figure shows the detail of the airborne optoelectronic platform).  
In fact, the geo-location process can realize the geo-location at any position in sight of the camera. In this article, in order to realize the correction of the systematic error, only the target at the center of the field of view is considered. Since the axis of the laser ranging is parallel to the axis of the cameras during the design and installation of the airborne optoelectronic platform, the laser rangefinders' value is the distance between the target and the photoelectric payload.
## 3 Methods
### Measuring Sea Ice Motion and Deformation
#### 3.1.1 Preprocessing of Satellite Image  
Band saturation in optical remote sensing images (caused by snow and ice covers and showing very high reflectance properties) has been reported in high-latitude regions [33]. To prevent omitting sea ice surface texture details from slight brightness differences among snow, exposed ice from ablation of snow and snow covered or frozen melt pond, pixel value distribution in every MS band was examined, before a common band showing none or a small number of saturated pixels in the images from both satellites was selected for further analyses.  
The images obtained from two satellite sensors were basically quantized to 10 bit and 14 bit, respectively (Table 1). To compare pixel value between them (i.e., digital number, DN), ranges for the selected band were equalized by simply multiplying 2\({}^{4}\) by the 10 bit KOMPSAT-2 image.  
Two datasets of high- and mid-resolution were prepared to investigate the feasibility of satellite images of different resolutions for measuring sea ice motion and deformation. In the case of the high-resolution, the KOMPSAT-3 MS bands were resampled to the coarser resolution of 4 m to be identical to the pixel size of the MS bands of the KOMPSAT-2 so a direct comparison between them could be made. For the mid-resolution dataset, the spatial resolutions of the MS bands from both satellites were resampled to 15 m, similar to the images from major mid-resolution optical satellite sensors such as Landsat-7 ETM+ (Enhanced Thematic Mapper Plus), Landsat-8 OLI (Operational Land Imager) and Sentinel-2 MS bands, to investigate the applicability of these mid-resolution satellite images covering wider areas to sea ice motion and deformation measurements. The resampling was conducted by weighted averaging of the input pixel values within the extent of output pixel boundary.  
Figure 3: Working processes for sea ice motion and deformation measurements.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 2 Related Work
The approach incorporates machine learning-based PV failure detection, reducing supercapacitor operation and computation time compared to traditional methods, as validated in experiments. In [19], the application of remote sensing (RS) technology in various stages of solar PV system development is explored, including potential estimation, array detection, fault monitoring, and cross-cutting areas. The comprehensive review highlights RS as a crucial tool for effective resource assessment, data analysis, and health monitoring, offering valuable support for PV system planning, management, and decision-making. Finally, in [20], a real-time fault detection and performance monitoring system for PV systems is presented. Using various sensors, the system identifies issues such as encapsulation failure and module corrosion. It employs a one-diode model, Python software, and a database generated from field testing to detect faults, providing users with easy-to-understand error messages and remote access for efficient PV system management and maintenance. Based on all the above, it may be observed that, while significant progress has been made in modeling and monitoring single-diode PV systems, there remains a notable research gap concerning the integration of accurate parameter estimation, real-time error detection, and efficient MPPT mechanisms. The existing literature has predominantly focused on individual aspects, such as parameter estimation techniques, environmental parameter correlations, and fault detection systems. However, there is a need for a holistic approach that combines these elements to enhance the robustness and reliability of PV models. This paper aims to bridge this research gap by proposing a comprehensive methodology. The objectives include the development of a novel genetic algorithm for accurate MPPT optimization, the integration of the Lambert function to ensure adherence to PV constraints, and the implementation of a real-time error detection system. The proposed approach seeks to address the challenges associated with accurate modeling and error handling in single-diode PV systems, offering a more complete and effective solution for continuous and fault-free PV operation.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 3 Decolorization Methods
### Basic Methods
Resulting grayscale image has a very wide range of tones and a high level of detail, especially in scenes with bright colors and high contrast. The CIE XYZ color system from 1931 was transformed in 1976 into the CIE L*a*b* color space. In this system, L* represents relative lightness, ranging from 0 (black) to 100 (white), while a* and b* correspond to the red-green and blue-yellow axes, respectively. Samples with a* = b* = 0 are achromatic. The L*, a*, and b* values can be calculated from the tristimulus values XYZ using the following equations [20]:  
Figure 2: Basic decolorization method. \[f(t)= \begin{cases}t^{1/3}&\text{if }t>(6/29)^{3}=0.008856\dots\\ \left[(29/3)^{3}t+16\right]/116&\text{otherwise}\end{cases}  \tag{12}\] \[L^{*}=116f(Y/Y_{\text{W}})-16\] \[a^{*}=500[f(X/X_{\text{W}})-f(Y/Y_{\text{W}})]\] \[b^{*}=200[f(Y/Y_{\text{W}})-f(Z/Z_{\text{W}})]\]  
The basic methods commonly used in image decolorization are favored for their simplicity and high execution speed, typically yielding acceptable results. However, these approaches can be ineffective in specific cases, such as when dealing with images that contain areas of isoluminant colors.
## 2 Mathematical Model
### Generalized Model Description
#### 2.2.4 Resource Allocations Constraints and Objective Function  
In this set of constraints, we involve only \(d_{jt}\) from scheduling decision variables. We denote as \(c_{jt}\in[0,p_{max,jt}d]\) the volume of work related to job \(j\in J\) in period \(t\in T\) done by resource \(r\in R\)It has upper and lower limit defined by the minimal and maximal amount of assigned resources and job duration:  
\[p_{min,jr}d_{jt}\leq c_{jrt}\leq p_{max,jr}d_{jt},\ \forall j\in J,\ \forall r\in R,\ \forall t\in T. \tag{21}\]  
All resource types must implement given total amount required to each job:  
\[\sum_{t\in T}c_{jrt}=W_{jr},\ \forall j\in J,\ \forall r\in R. \tag{22}\]  
In the objective function, we use the amount of extra usage of each resource \(o_{rt}\), defined by the following constraints:  
\[o_{rt}\geq\sum_{j\in J}c_{jrt}-L_{rt},\ \forall t\in T,\ \forall r\in R. \tag{23}\]  
The objective function is the minimization of the extra resource allocation cost. We define the extra capacity of resource \(r\in R\) needed in period \(t\in T\) as \(o_{rt}\). Therefore, the objective function is  
\[\textit{Mininize}\ \sum_{r\in R}\sum_{t\in T}e_{r}o_{rt}. \tag{24}\]