esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Justification and Objectives  
It is worth mentioning that most of the studies concerning deep columns have been focused on individual beam-column elements, or on subassemblies of beams and columns representing a part of the building, subjected to the action of static and cyclic loads. In addition, as stated earlier, it seems there is an implicit assumption in many of these studies in the sense that the structural demands, namely maximum interstory drifts or top displacements, are the same for the buildings with both column sizes. The response of steel buildings with deep columns under the action of different strong motions with different frequency contents has not been studied; it is known that the dynamic response of a structure depends not only on the structural properties, but also on the loading properties, such as those of earthquake loading.  
It is emphasized that it is not an objective of this paper to compare the seismic responses of steel buildings with medium and deep columns for the same drift demand, but rather to compare their dynamic responses numerically obtained considering models and seismic records with different dynamic characteristics. The comparison is made in terms of displacements and resultant stresses (member forces), which includes overall (global) and local as well as single and combined response parameters. The equivalence of the two column sizes is expressed in terms of weight (equal cost). It is assumed in this study that drifts of up to 3.5% can be reached without the occurrence of lateral buckling of beams or columns twisting.  
It is important to mention that a small part of a big issue is addressed in this research; the specific objectives are as follows:  
(1) Calculate the nonlinear seismic responses of the building models with medium columns in terms of interstory drifts and roof displacements, which represent overall response parameters. These parameters are compared with those of the same buildings, but with equivalent deep columns in terms of cost (W27 and larger).  
(2) Compare the seismic responses of the buildings with medium and deep columns for the case of single global resultant stresses (interstory shears) and local resultant stresses (axial load and bending moments) at the exterior and interior columns located on the ground level.  
(3) Compare the responses, as in Objective (2), but now for normalized single global and normalized combined local resultant stresses.
## 2 MCR Frequency Modulation Method and Laser Processing Platform
### Functional and Precision Requirements of Laser Processing Platform for MCR Frequency Modulation  
The laser processing platform for MCR frequency modulation mainly completes two functions: positioning and laser etching. The positioning function is divided into reclaim, fixing, transfer, alignment, and discharge. The overall function of the laser processing platform for MCR frequency modulation is shown in Figure 6.  
In the manufacturing and assembly processes of the laser processing platform for MCR frequency modulation, the machining error and assembly error of the parts will cause the error between the actual and ideal position of the platform components, affecting the position of the MCR to be processed. The error diagram is shown in Figure 7.  
According to the process requirements, the positioning accuracy of the laser processing platform for MCR frequency modulation in the X, Y, and Z axis is 300 \(\upmu\)m, 100 \(\upmu\)m, and 100 \(\upmu\)m, respectively.  
Figure 5: Qualified and unqualified products of MCRs. (**a**) Normal products, (**b**) unclean processing, (**c**) insufficient processing, and (**d**) silver layer damage.  
Figure 6: Subdivision function of the laser processing platform.
## 1 Introduction
When the 16S rDNA sequence diversity is insufficient for accurate identification, species-specific PCR methods are applied, targeting highly specific genes [32; 33; 34]. Unequivocally, genome sequencing and its analysis shows the specific identification of strains. Molecular methods are also versatile in analyzing the biodiversity of lactobacilli in different environments and food. Strain diversity among _Lactobacillus_ isolates is often analyzed with the Random Amplification of Polymorphic DNA (RAPD) method. RAPD involves the random amplification of fragments from the target bacterial genome using single primers under less stringent conditions. In this way, a complex profile is created out of the large number of amplification products from random loci scattered throughout the genome [25; 35; 36]. The spontaneous fermentation of a flour-water mixture to produce sourdough as a leavening agent in bread and bakery product preparation precedes the widespread application of baker's yeast. With the introduction of yeast, the sourdough application was limited to artisan production practices. However, in recent decades, the interest in sourdough as an element in bread production has been renewed due to the metabolic activities of lactobacilli, such as organic acid production, the synthesis of aroma-related volatile components, proteolysis, and the production of metabolites with antifungal activity [36]. Fermentation by lactobacilli contributes to the improved shelf life, sensory, and nutritional quality of a product [37; 38]. The microbial composition of sourdoughs is dominated by lactobacilli, yeast, and acetic acid bacteria. [PERSON] et al. (2017) [39] reported more than 90 different species of lactic acid bacteria in sourdoughs. The most prevalent LAB species are _Fructilactobacillus sanfranciscensis, Levilactobacillus brevis, Lactiplantiobacillus plantarum, Companilactobacillus paralimentarius_, and _Limosilactobacillus fermentatum_[40; 41]. The microbial consortium found in sourdough depends on the production method and flour composition. Flours may differ vastly in their composition, all the more so because cereals (wheat, rye, barley, and maize) and non-cereals (buckwheat, quinoa, and alternative seeds) can be used for flour production [42; 43]. The production method divides sourdoughs into two main groups: Type I, with a dough yield (DY) of less than 200 and incubation at temperatures up to 30 \({}^{\circ}\)C for 24 h with regular back-sipping, and Type II, with a DY above 200, fermentation at an elevated temperature, and a prolonged fermentation time [44]. The aim of the present study was to isolate and identify the species and examine the strain diversity of the lactic acid bacteria isolated from spontaneously fermented sourdoughs prepared from different flour types, i.e., Khorosan wheat, wheat, barley, buckwheat, spelled, spelt, and corn.
# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 6 Simulation Results
However, the CI-based techniques result in robust DOA estimation performances even when signal sources are quite close to each other in terms of directions. The proposed simultaneous CI-based DOA estimation technique can be applied to other antenna array structures, including ULA. Figures 11-13 show the RMSE of DOA estimation performance of the proposed simultaneous CI-based DOA estimation technique with ULA structure at the receiver for varying SNR when the number of signal sources is equal to 1, 2, and 3, respectively. In the ULA structure at the receiver, the steering vector of the received signal is provided by  
\[a_{m}(\phi_{k})=e^{-j\frac{2\pi}{\lambda}d(m-1)cos(\phi_{k}))}, \tag{11}\]  
where \(d\) denotes antenna spacing. Note that the ULA structure can estimate only one-dimensional DOA due to its fundamental physical constraint. Figure 11 shows the RMSE of DOA estimation performance of the proposed simultaneous CI-based DOA estimation technique with ULA structure at the receiver according to SNR when a single source exists. In this figure, the source is assumed to be located with the azimuth angle of \(-40^{\circ}\), i.e., \(\phi_{1}=-40^{\circ}\). In this figure, the CI-based techniques perform much better than the MUSIC algorithm, especially in a low-SNR regime, and the proposed simultaneous CI-based technique results in a very similar DOA estimation performance with the conventional CI-based technique. Figures 12 and 13 show the RMSE values of DOA estimation performance of the proposed simultaneous CI-based DOA estimation technique when two and three signal sources exist, respectively. In Figure 12, we consider the following azimuth angles for two signal sources: \(\phi=\{-30^{\circ},10^{\circ}\}\). In Figure 13, we consider the following azimuth angles for three signal sources: \(\phi=\{-30^{\circ},10^{\circ},50^{\circ}\}\). We show that the proposed simultaneous CI  
Figure 9: RMSE of DOA estimation performance for varying SNR when \(K=2\) and \(M=20\) at UCA receiver. based DOA estimation technique outperforms the conventional CI-based and MUSIC-based algorithms regardless of SNR values and the number of time samples. Interestingly, the performance gaps between the proposed simultaneous CI-based DOA estimation technique and the conventional techniques becomes large as the number of signal sources increases. Figure 11: RMSE of DOA estimation performance with ULA structure according to SNR when a single signal source exists (\(K=1\)). Figure 4:0 RMSE of DOA estimation performance for varying SNR when two signal sources are located in a similar direction at UCA receiver.
###### Abstract  
The direct sowing of rapeseed requires shallow tillage for stubble removal, which can cause significant vibrations that impact the seed metering device, thereby affecting the quality of seeding. This study focuses on a double-row hole-wheel-type seed metering device on the 2 BYG-220 type combined rapeseed planter. Initially, vibrations experienced by the seed drill during field operations were measured and analyzed, revealing that the vibration frequencies during field operations predominantly ranged between 0 and 25 Hz. Consequently, an eccentric wheel-return-spring-type vibration seed metering test rig was designed, manufactured, and set up. By swapping out the eccentric wheel on the test rig, the amplitude was mainly concentrated within 3 mm. The test results indicate that amplitude had a minimal impact on the performance of the double-row hole-wheel-type rapeseed seed metering device, whereas vibration frequency had a more significant effect. When the vibration frequency was between 0 and 10 Hz, the seed metering device maintained a stable output between 7.6 and 8.2 g/min, with minimal impact from vibrations: the coefficient of variation for seeding uniformity ranged between 35.81% and 44.58%, indicating stability and good uniformity. However, when the vibration frequency ranged from 10 to 24 Hz, the output of the seed metering device decreased rapidly and exhibited a linear relationship with frequency changes, with a determination coefficient (R\({}^{2}\)) of 0.92376. The coefficient of variation for seeding uniformity increased rapidly and also showed a linear relationship with frequency changes, with a determination coefficient (R\({}^{2}\)) of 0.87973. Vibrations with frequencies greater than 10 Hz had a considerable impact on the performance of the seed metering device.  
rapeseed; seeder drill; vibration frequency; amplitude distribution; main frequency range; determination coefficient +
Footnote †: journal: Applied sciences
## 4 Concepts of Integrating the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)  
In addressing the challenges and improving both supply chain efficiency and integration partnership, machine learning-based forecasting could be used. The predictive analytics processes make use of a machine-learning approach that includes algorithms to analyze diverse datasets [39]. By learning from data from past and current performances, the approach continuously refines and converges to the final acceptable solution [40]. However, most machine-learning models require initial data training, and reply on the classification of learning models in terms of supervised or non supervised mode. In addition, they are difficult to tune the model parameters by themselves [41]. Compared with regular machine-learning techniques, heuristic algorithms need not to have initial data training procedure and determine the model mode in advance. ACO and PSO are two major algorithms of swarm intelligence in this field. Nevertheless, the main challenge using heuristic algorithms is finding a suitable initial point and updating mechanism to avoid local premature convergence [19]. When the two algorithms are applied separately, due to the inherent defects and deficiencies of the individual algorithm itself, the solving capability is relatively limited. Although the ant colony algorithm has a strong ability to search for a better solution in solving the supply chain partnership integration problem, it suffers from the blindness and long convergence time caused by lack of information in the initial stage. For the particle swarm optimization algorithm, this has a good global search ability, but due to the lack of use of feedback information in the system, its performance in local optimization is poor, and the convergence speed is slow and easy to stagnate in the later stage. To overcome these drawbacks from each of the individual algorithms, a hybrid algorithm based on PSO and ACO to achieve complementary advantages is considered. The proposed PSACO algorithm combines the stronger capability of global search of PSO with positive feedback of ACO to avoid the premature convergence to a local optimal solution and improve the efficiency [42].
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 4 Analysis and Description of the Proposed Palm Clipping Method  
In this section, we present the Palm Clipping method and its basic principles. The Palm Clipping block is added to the OFDM System (PC-OFDM) before transmission over the channel, as shown in Figure 6. The main target of this work was to find a good compromise between PAPR values and a good value of BER.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.5 ConvNeXt Model  
In the realm of wetland remote sensing image segmentation, this study introduces the application of the ConvNeXt model. Recognized for its expertise in processing high-resolution imagery and conducting multi-scale feature extraction, ConvNeXt proves particularly effective for the analysis of the intricate spectral-spatial characteristics inherent to wetland landscapes [36]. The advanced interpretative abilities of this model not only propose an innovative methodology for wetland segmentation but also establish a pioneering standard for subsequent studies utilizing deep learning within complex ecosystems. These qualities distinctly position ConvNeXt as an exemplary selection for augmenting the accuracy and comprehensiveness of wetland image analysis [37].  
In this research, the ConvNeXt model is deployed for complex wetland segmentation, starting with an initial convolutional layer for spectral-spatial feature extraction. Following this are specialized ConvNeXt blocks containing convolutional and MaxPooling2D layers for spatial reduction. The architecture utilizes 64-filter initial layers with a \(7\times 7\) kernel and'relu' activation. Group convolutions in subsequent blocks enhance capacity without added complexity [38]. Fully connected layers with 256 and 128'relu'-activated units lead to a three-unit'softmax' output layer for class categorization. Optimized with'sparse_categorical_crossentropy' and 'RMSprop', this efficient architecture underscores ConvNeXt's suitability for high-resolution wetland segmentation. The model underwent preliminary runs, setting the epoch number at 120, a parameter established through meticulous consideration of cross-validation results to assess model complexity, learning rate, and potential scenarios of overfitting or underfitting. To circumvent overfitting, a strategic amalgamation of CE_Loss and FocalLoss was implemented [28,29]. Figure 7 delineates the ConvNeXt model's architecture as applied in this investigation.  
Figure 6: The architecture of the EfficientNet B7 model.  
Figure 7: The architecture of the ConvNeXt model.
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 3 Integrated POD Method and Strategy
### Processing Strategy
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Description** \\ \hline \hline
**Observation model** & \\ \hline Observation & Undiferenced ionosphere-free code \& phase \\ \hline Sampling Interval & 300s \\ \hline Arc length & 24 h \\ \hline Elevation Mask & \(\mathcal{T}^{z}\) for ground station and \(1^{z}\) for LEO \\ \hline GNSS PCOMCPV & Values used in simulation from 19s14.txt \\ \hline Station PCOMCPV & Values used in simulation from 19s14.txt \\ \hline LEO PCOMCPV & None \\ \hline Force model & \\ \hline Earth gravity & EIGEN\&C (12\times\)12) for GNSS and EIGEN\&C (120\times\)120) for LEO [33] \\ \hline N-body & JPL DE405 \\ \hline Ocean tide & FES 2004 [34] \\ \hline Relativity & IERS 2010 [35] \\ \hline Solid tide \& Pole tide & IERS 2010 \\ \hline Solar radiation pressure & ECOM model for GNSS [36] and macro-model for LEO \\ \hline Atmospheric density & DTM94 [37], only considered for LEO \\ \hline Empirical accelerations & Only considered for LEO, one set of period parameters per revolution \\ \hline GNSS estimated parameters & \\ \hline Initial state & Position and velocity at initial epoch \\ \hline Solar radiation pressure & Five ECOM SRP parameters (three constant terms and two periodical terms) \\ \hline Satellite clock offset & Each epoch as white noise \\ \hline \multicolumn{3}{c}{LEO estimated parameters} \\ \hline Initial state & Position and velocity at initial epoch \\ \hline Atmospheric drag & One constant scale parameter per 360 min \\ \hline Empirical accelerations & Piecewise periodical terms in the along-track, cross-track and radial components per revolution \\ \hline Receiver clock offset & Each epoch as white noise \\ \hline Ambiguities & Estimated as float constant values for each continuous arc \\ \hline Inter-system bias & Estimated as constant \\ \hline Inter-frequency bias & Estimated as constant \\ \hline \multicolumn{3}{c}{Ground station estimated parameters} \\ \hline Station coordinate & Constraint based on PPP weekly solution \\ \hline Zenith wet delay & One constant set per 120 min \\ \hline Receiver clock offset & Each epoch as white noise \\ \hline Ambiguities & Each continuous arc. Double-difference ambiguity resolution for the ground network. \\ \hline Inter-system bias & Estimated as constant \\ \hline Inter-frequency bias & Estimated as constant \\ \hline Earth rotation parameters & One set per arc \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed processing strategy for the integrated multi-GNSS POD. Figure 2: Distribution of global stations from MGEX and iGMAS.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 2 Methods for Observing and Capturing Geomagnetic Anomalies
### Research on Rules of Earthquake Prediction  
The above algorithm is extended into daily magnetic field monitoring, and the cumulative geomagnetic anomaly energy and geomagnetic anomaly gradient are calculated once a day. The multi-day gradient \(G\) forms a time sequence \(A\). If a threshold is set and an extraction algorithm is applied to sequence \(A\), a prediction time sequence \(B\) can be extracted according to the following rules which we propose:  
1. The daily geomagnetic anomaly gradient exceeding the threshold value is retained to form sequence \(A^{\prime}\) (only obtain data sequences of geomagnetic anomalies).
2. The gradient of the geomagnetic anomalies that exceed the threshold on the day of an earthquake and 1-3 days after the earthquake will not be considered for prediction and will be removed.
3. Only one gradient value of the geomagnetic anomalies continuously exceeding the threshold is retained (only one continuous multiple geomagnetic anomalies is retained).
4. If one gradient value is selected, the corresponding value in sequence \(B\) is taken 1; otherwise, 0 (1 indicates a possible earthquake, 0 indicates no earthquake).  
In addition, according to the seismic data released by the China Earthquake Administration Network Center, the time of earthquake occurrence within 500 km around the monitoring area is selected. If there is an earthquake, 1 is taken, and if there is no earthquake, 0 is taken, forming the seismic time series \(C\), with one datum per day.  
Based on the experimental data collected in this project, it can be proven that the correlation between the predicted time sequence \(B\) and the earthquake time sequence \(C\) can reach over 85.7%. This proves that the sequences \(B\) and \(C\) has a strong correlation, which means that the probability of the geomagnetic anomaly before each earthquake is 85.7%. That is to say, it is reasonable and feasible to use geomagnetic anomaly data for earthquake prediction.
## 2 Results and Discussion  
### Extract Composition and Characteristics  
The concentrated _Sargassum muticum_ extract evaluated in this study was produced according to the scheme shown in Figure 1. The hydrothermal extract produced by hydrothermal processing in hot pressurized water media was further concentrated in membranes during successive washing cycles to selectively remove low molecular weight components. The salt content in the concentrated product (SmE) obtained after the ultrafiltration stage operating in the diafiltration mode was reduced by almost six times, to less than 2% of the dry weight. The arsenic content in the product was lowered to a safe-to-use concentration (0.4 ug g\({}^{-1}\)) from the original value of the autohydrolysis crude extract, which was 29.4 ug g\({}^{-1}\). The fucoidan content accounted for more than 50% of the product dry weight, being fucose the most abundant constituent, followed by galactose, glucose, xylose and mannose; with an average sulfation of 7%. Along the diafiltration process the phenolic content of the concentrated product could be increased up to 10-15%. The SmE product showed antiradical properties equivalent to 20% of those presented by Trolox [17]. The fractions with molecular weight in the range of 50-100 kDa and that with more than 100 kDa contained higher phenolic and sulfate content. Both could be responsible for the biological properties, in particular for cytotoxicity against tumoral cells and antioxidant capacity. However, those fractions under 5 kDa contribute in lower extent to these properties.  
The Fourier transform infrared spectroscopy (FT-IR) spectra of the concentrated product (Figure 2a) shows characteristic absorption bands in the following regions: 2923 cm\({}^{-1}\), corresponding to C-H stretching; at 1629 cm\({}^{-1}\), corresponding to carbohydrate O-C-O asymmetric stretching vibrations; 1421 cm\({}^{-1}\), corresponding to C-OH deformation vibration; and 1384 cm\({}^{-1}\), corresponding to C-C-H and O-C-H deformation. Shoulders at 1260 cm\({}^{-1}\) correspond to S=O stretching vibration of the sulfate. The bands in the 1064 cm\({}^{-1}\) region correspond to C-O and C-C stretching vibrations, and those in the 820 cm\({}^{-1}\) region correspond to the C-O-S vibration of sulfate groups.  
The GPC chromatogram (Figure 2b) shows that the concentrated product is distributed in fractions higher than 50 kDa and a smaller fraction between 5 and 12 kDa, whereas the original hydrolysate included a wider distribution of different molecular weight fractions, also corresponding to low polymerization degrees [16].  
Figure 1: Flowchart of the extraction and concentration of the _Sargassum muticum_ concentrated extract (SmE).  
Figure 2: (**a**) The Fourier transform infrared spectroscopy (FT-IR) spectra of the concentrated product and (**b**) gel permeation chromatography (GPC) chromatogram of hydrolysate after autohydrolysis (blue line) and concentrated product (green line).