esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Influence of Operating Parameters on Column Sorption
#### 3.3.2 The Effect of Inlet Concentration on Cu(II) Removal  
The effect of inlet Cu(II) concentration (0.5, 1.0 and 2.0 mg/L) on the penetration curve performance for a constant bed depth (20 cm) and a 10 min residence time is shown in Figure 8. The breakthrough time (\(t_{\rm b}\)) decreased with the increase of inlet Cu(II) concentration (\(t_{\rm b}\) was 7.35 h, 1.24 h and 0.18 h for inlet Cu(II) concentration of 0.5, 1.0 and 2.0 mg/L, respectively). The exhaustion time (\(t_{\rm e}\)) decreased from 154.92 h (0.5 mg/L) to 118.61 h (2.0 mg/L) with the inlet Cu(II) concentration increasing from 0.5 mg/L to 2.0 mg/L. The adsorption capacity increased from 87.75 mg/kg to 169.59 mg/kg with the increase of the Cu(II) concentration from 0.5 mg/L to 2.0 mg/L. The breakthrough point refers to the point where \(C/C_{0}\) is 0.1, and the exhaustion point refers to the point where \(C/C_{0}\) is 0.9. At a low inlet Cu(II) concentration, the breakthrough curves occurred later and the treated volume of the outlet was higher resulting in higher breakthrough time. This may be due to the fact that the lower concentration gradient resulted in slower transport due to the reduced diffusion coefficients [45]. whereas, at higher inlet Cu(II) concentrations, the adsorbent gets saturated early because the binding sites become more quickly saturated in the column, resulting in an earlier breakthrough and exhaustion time.  
Under the same concentration, the comparison of sorption capacity in the batch system and column system is shown in Table 6. The adsorption capacity obtained from the dynamic experiment  
Figure 7: The breakthrough curve for varying bed depth (cm) at a residence time of 10 min and 0.5 mg/L inlet Cu(II) concentration.  
is smaller than the adsorption capacity obtained from the static experiment. As the concentration increases, the ratio of the static adsorption capacity to the dynamic adsorption capacity gradually decreases. This may be due to the fact that the adsorption is particularly active in the dilute concentrations. [PERSON] et al. and [PERSON] et al. also observed a higher sorption capacity obtained from the batch experiment than from the column experiments [46; 47]. The fact was explained by the resistance to the film diffusion active in the low concentrations which are similar to the present study. In addition, the removal of Cu(II) could be improved by reducing the flow rate.
## 6 Results  
The results from previous works are reported for direct comparison. The methods applied are, by following the same nomenclature (see also Figure 2), (1) the log-scale CCs; (2) the linear CCs; (3) the classic definition of MFCCs; (4) the MFCCs with a cut-off frequency \(f_{\infty}=\frac{f_{s}}{4}\); and, (5) the MFCCs with a cut-off frequency \(f_{\infty}=\frac{f_{s}}{8}\) from [21]; the three proposed Mel-Modified Cepstral Coefficients (MMFCCs), i.e., the so-called (i) ECO-MMFCCs; (ii) MO-MMFCCs; and, (iii) LO-MMFCCs, from [22], plus the Bark-scale based (iv) BFCCs; finally, the novel features here presented, (I) the GTCCs and (II) the TECCs.  
For better readability, this Section is split between the numerical and the experimental data. All the results are expressed in terms of type 1 (false positives, i.e., false alarms) and type 2 (false negatives or false acceptance) errors. In the latter case, the system is declared healthy while not being so; the opposite happens for false alarms. Being life-safety the main aim of any SHM procedure, type 1 errors are generally overlooked respect to their type 2 counterparts. Nevertheless, economical, practical, and psychological concerns make these as valuable as the other ones. A simple yet effective reasoning is that an alarm system constantly affected by false alarm will most probably be ignored when actual damage is effectively spotted. This nullifies any possible gain from the deployment of the SHM apparatus.
## 4 Calculation Example  
As an example of application of our algorithm to the described system of equations Equations (7) to (9), we perform the simulations of 800 nm thick silicon target's response to ultrashort laser pulse irradiation. The parameters of the irradiation are 130 fs duration, 800 nm wavelength, and 0.26 J/cm\({}^{2}\) incident fluence. For these conditions, the experimental melting threshold fluence is 0.27 J/cm\({}^{2}\)[42], which is in agreement with the result of the \(n\)TTM model [43]. The value of fluence is chosen to be just below the melting threshold, providing the applicability of this simple model in the absence of phase transition processes. The sample was divided into 160 cells according to Figure 1. In Figure 2, we show the dynamics of electron-hole carrier density and electronic and atomic energy densitiesat the silicon surface. The shown energy density is scaled to the melting energy density, which is found to be \(3.86\times 10^{9}\,\mathrm{J/m^{3}}\), according to the simulations. Though Equations (8) and (9) are written in terms of temperature of carriers and atoms, we plotted the corresponding energy densities instead, because their dynamics represents the energy flow between the subsystems and allows plotting the same scale for electrons and atoms, whereas electronic temperature is much higher than the atomic one (see also Figure 2 in [25]). In addition, this choice provides a possibility to show the energy conservation with the total average energy density of the sample (shown with black solid line).  
The initial increase in the carrier density followed by the laser pulse is connected to the excitation of new carriers by one- and two-photon absorption processes. With time, the increase changes to the decay due to strong Auger recombination and diffusion processes. The strong peak in the electronic energy density is mostly connected to the free-carrier absorption. Finally, the thermal energy from electron-hole carriers is transferred to the atomic subsystem of the sample leading to gradual increase in the lattice energy density upon the electron-phonon equilibration.
## 3 Theory and Methodology
### The Optimal Segmentation Boundary
In fact, pixels on the edge possess different stability values, as Figure 3 shows. Therefore, we can conclude that pixels on the edge possess stronger stability. As the threshold varies continuously, a stable boundary remains consistent and is typically closer to the ground truth boundary. As previously mentioned, Tsallis entropy introduces an adjustable parameter \(q\), giving it an advantage when processing images with complex structures and noise. By leveraging the characteristics of Tsallis entropy, we can calculate the entropy values of the image at different thresholds and find the one with as many stable boundaries as possible corresponding to flat regions or special inflection points on the entropy curve. By adjusting the parameter \(q\), the sensitivity of Tsallis entropy to probability distributions can be modulated, thereby highlighting the features of stable boundaries. Consequently, exploring the representation of Tsallis entropy based on a certain probability distribution model will help to determine the optimum threshold for segmentation, therefore improving the accuracy and robustness of image segmentation.
# View Synthesis with Scene Recognition for Cross-View Image Localization
## 4 Performance Evaluation for View Synthesis
### Subjective Evaluation  
Existing similarity measures, such as self-similarity distance [33] and single-image FID [34], care about the similarity in deep feature spaces based on the features that are represented in each image. However, such methods lack judgment on how good the transformation is at keeping the structure features from the reference images while adding weather/season information.  
Figure 5 shows the style transfer results when we tried to patch images generated from Flickr to three Google Street View images with three pre-trained models, i.e., the mountain (first row), church (second row), and bedroom (third row) models.  
Apparently, the synthesized images produced by the pre-trained models have a combination of the structure from Google Street View and weather information from the patch image, i.e., snow. However, models achieved a different level of transformation in terms  
Figure 5: View synthesis performance with the same reference image (first column) on three pre-trained models, i.e., mountain (first row), church (second row), and bedroom (third row). Columns 2–5 represent transfer results from the same set of patch images filtered by keywords “Cloudy”, “Forest”, “Overcast”, and “Roads”, respectively.  
of the texture of objects in the original Google Street View image, such as roads, trees, and buildings. Specifically, the mountain and bedroom models accurately changed the color of trees and road surfaces from green to white but failed to keep the buildings on the roadsides in the transfer results. The church model keeps a building on the roadside but does not update the color of the trees. Therefore, we needed to find a numerical approach to effectively measure the quality of the view synthesis to determine which style transfer output can better reflect the changes from the inputs.  
Table 3 shows a subjective evaluation of the results in Figure 5, which takes a closer look at how the reference and patch images are combined to create new images. The mountain, forest, and bedroom models created similar images. The three models kept the road and the trees within the images. These three models also had trouble keeping the buildings behind the trees in the image. Upon closer inspection of the three model images, the texture of the road and sky is different from the original Google Street View image. Additionally, the forest model image adds extra branches in the top right corner. While the images are not perfect, it can be inferred that the original Google Street View image was changed to include snow on a cloudy day.
# Laser Induced Breakdown Spectroscopy of Er II for Transition Probability Measurements
## 2 Experiment  
Figure 1 shows a schematic of the experimental setup. A Q-switched Nd:YAG laser (Spectra Physics GCR-150) was used in the fundamental mode (1064 nm) for producing plasma. Laser pulses with a width of 8-9 ns and an energy of about 15 mJ were focused with a 150 mm focal length lens and introduced through a fused silica window. The pulse repetition frequency was 10 Hz.  
For the determination of transition probabilities, it is important to realize a thin plasma condition [21] as well as the LTE condition. If the plasma is so thick that it can absorb the emission occurring within the plasma (self-absorption effect), the observed intensity does not reflect the transition probability. An Al alloy with a low content (5 wt%\(\sim\)0.8 at%) of Er was thus used as a target in the present experiment for producing thin Er plasma. A rod-shaped target with a 6 mm diameter was installed in the chamber. The target rod was rotated while being moved up and down with a motion feedthrough system similar to that used in [22]. The target motion enabled each laser pulse to hit the unirradiated surface of the target. The target chamber was filled with 200 Pa of Ar after evacuating the chamber with a scroll pump.  
The emission from the plasma was observed through a fused silica window placed at 90\({}^{\circ}\) with respect to the laser direction. A set of synthetic silica plano-convex lenses was used to focus the emission onto the entrance of the fiber connected to an echelle spectrometer (LTB ARYELLE 200). A fast-gated image-intensified sCMOS (Andor iStar-sCMOS-18F-03) was used as a detector. The spectral resolution (specification value) is 9200 at 253 nm and 8000 at 404 nm. The data acquisition period was 1 \(\upmu\)s after a delay of 5 \(\upmu\)s from each laser pulse. The timing was controlled by a digital delay generator installed in the sCMOS detector. The spectrometer response function was obtained using a calibration light source (Ocean Optics DH-3_plus_-BAL-CAL).  
The emission line intensity \(I\) (photon number emitted in a unit of time) is determined by the product of the population of the upper level and the transition probability \(A\). It is thus represented as the following equation for the lines emitted from the local thermal equilibrium (LTE) plasma with a temperature \(T\):  
\[I=gA\cdot C\exp\left(-\frac{E}{kT}\right), \tag{1}\]  
where \(g\), \(E\), and \(k\) represent the statistical weight, the energy of the upper level, and the Boltzmann constant, respectively, and \(C\) is a constant depending on the ion species. By taking the natural logarithm, we obtain  
\[\ln\left(\frac{I}{gA}\right)=-\frac{E}{kT}+C^{\prime}, \tag{2}\]  
where \(C^{\prime}\) is a new constant. Thus the plot of the left side value as a function of \(E\) (Boltzmann plot) should indicate linearity when the LTE condition is established.
## 8 Unconsidered Chromosomal Mutations
### RNA Polymerase  
RNA polymerase subunits have also been involved in the development of low levels of quinolone resistance, with the _rpoA_ mutation N\({}_{294}\)Y being observed in 4 _S. enterica_ mutants [179]. While selected concomitantly with mutations in other genes, including well-established _acrAB-TolC_ regulator genes, the cloning of the mutant _rpoA_ results in final increases in the MICs of nalidixic acid (from 2 mg/L to 32 mg/L) and ciprofloxacin (from <0.015 mg/L to 0.015 mg/L) [179]. Regarding another subunit, the easy selection of _rpoB_ mutations after ciprofloxacin exposure has been highlighted, with 20-30% of selected mutants carrying an _rpoB_ alteration [180]. Thus, [PERSON] et al. observed the presence of the amino acid substitutions H\({}_{1244}\)L, E\({}_{1272}\)G, A\({}_{1277}\)V, E\({}_{1279}\)G, A\({}_{442-445}\) and duplication of the S\({}_{455}\) (DuS\({}_{445}\)) in _E. coli_ exposed to ciprofloxacin in vitro [180]. The authors cloned and analyzed three of the _rpoB_ mutations (those leading to the changes in the amino acids A\({}_{1277}\)V, A\({}_{442-445}\) and DuS\({}_{445}\)), observing increases from 0.015 mg/L to 0.023-0.045 mg/L in the MIC to ciprofloxacin [180]. Furthermore, the additive effect on the final MIC to ciprofloxacin was also observed when alterations in established quinolone resistance determinants (e.g., _gyrA_, _gyrB_, _soxR_, _marR_ or _acrR_) were also present [180]. In addition, a very modest effect (maximum MIC of 48 mg/L) of these three amino acid changes in the MIC of rifampicin was also generated [180]. When the impact on the expression of other genes of the six _rpoB_ mutations was determined, the authors observed that more than 100 genes were affected, including increased levels of _mdK_ expression, an efflux pump encoding gene, in all six cases, with further analysis suggesting a direct relationship between the presence of _rpoB_ mutations and overexpression of _mdtK_[180]. Meanwhile, using _qnrA1_-carrying _E. coli_ J53, [PERSON] et al. described that the _rpoB_ mutation R\({}_{1246}\)C increased the MIC levels of nalidixic and ciprofloxacin from 8 mg/L to 64 mg/L and from 0.016 mg/L to 1,5 mg/L, respectively [177]. The _rpoB_ mutant showed a significant decreased expression of _ompF_, thereby impacting quinolone intake within the bacterial cytoplasm [177]. Of note, while no effect on fitness was observed by [PERSON] et al., the opposite scenario was reported by [PERSON] et al., a finding that might be related to the final alteration induced [177; 180]. While not further explored, additional mutations in _rpoB_ and _rpoC_ have also been reported in ciprofloxacin-resistant \(E\). _coli_ mutants as well as in ciprofloxacin-resistant animal isolates [181; 185].
## 2 Materials and Methods  
This manuscript proposes an improved SAR-SIFT method to address the issue of low efficiency and high false feature points when processing large-scale SAR images. Firstly, the redundant calculation is reduced via the block processing of the overlapping region of the master and slave images. Secondly, in feature extraction, by replacing the SAR-Harris response function with SAR-Shi-Tomasi, the quantity of feature points is no longer affected by manually set thresholds, and the SUSAN operator is utilized to suppress multiplicative noise, while the Harris-Laplace operator and a method to determine multi-scale extrema are used to obtain accurate feature point locations. Additionally, a multi-scale and multi-orientation feature descriptor is constructed using GLOH. Thirdly, efficiency is enhanced by utilizing both CPU and GPU for the improved SAR-SIFT algorithm, and the nearest neighbor distance ratio (NNDR) matching approach based on Euclidean distance is employed, followed by the use of the Random Sample Consensus (RANSAC) algorithm to eliminate mismatches. Figure 1 displays the method's flowchart. Algorithm 1 shows the pseudo-code for the algorithm of this paper.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 3 MLE Discriminator
### Computation Cost  
In Figure 1, the basic frequency mixing and integration process can be implemented easily in the hardware circuit. However, the subsequent LM algorithm needs to be processed by the software. Compared with the existing methods [5; 6; 7; 8; 9; 10; 11], the proposed ML discriminator processes the coherent  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Discriminator Update Time (ms)** & 20 & 40 & 60 & 80 & 100 & 200 \\ \hline
**Dynamic Tolerance (m/s\({}^{2}\))** & 357 & 44.6 & 19.8 & 11.1 & 7.1 & 1.8 \\
**SNR Gain (dB)** & 76.1 & 72.9 & 74.7 & 75.9 & 76.9 & 79.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamic tolerance and signal-to-noise ratio (SNR) gain of the proposed algorithm.  
Figure 5: Cost function with respect to the frequency error and various phase errors.  
integration results instead of the sampling data. After the coherent integration process, the data rate is reduced from \(1/t_{s}\) to \(1/T\). Therefore, the amount of data is reduced by \(T/t_{s}\) times. For the sampling rate of 5.714 MHz and the integration time of 1 ms, the amount of data is reduced by 5714 times. Therefore, the proposed method has a great improvement in efficiency compared with the existing MLE-based methods.  
In the LM algorithm, the main computation is the gradient and Hessian matrices, i.e., (14). Removing repeated calculation, and with the \(n^{2}\) term stored in advance, the actual calculation cost in an iteration is \(N\) sine and cosine, \(6N\) multiplication and addition. Considering the maximum number of iterations is \(M\), the total computation cost in an observation interval is about \(MN\) sine and cosine, \(6 MN\) multiplication and addition operation. Therefore, for \(M=6\), \(N=20\) and \(T=1\) ms, the processing requirements are 120 sine and cosine, 840 multiplication and addition operation of 20 ms, which are easy to implement.
# Insight on Extraction and Preservation of Biological Activity of Cereal \(\beta\)-D-Glucans
## 2 The Molecular Structure of Cereal \(\beta\)-D-Glucans
The high content of consecutive cellotriosyl units along the chain can impose a certain conformational regularity and consequently a greater organization of the polymers in solution capable of decreasing their solubility [4]. The solubility/extractability in water (under comparable conditions of time, temperature, pH, and other extraction conditions) of oat \(\beta\)-D-glucans is generally higher than barley, which in turn is greater than wheat. Therefore, solubility appears to decrease with increasing DP3/DP4 ratio along \(\beta\)-D-glucan chains. Moreover, the coexistence of different biopolymers in the cell wall, their spatial organization, and interactions between wall components will probably affect mechanical strength and permeability, thus impairing the compounds' solubility [17]. In order to improve the applicability of \(\beta\)-D-glucans, some studies have investigated how environmental conditions can facilitate their solubilization in order to have a high extraction yield. As far as temperature is concerned, the best results were reported at 55.7 \({}^{\circ}\)C. In fact, at this temperature, there is a greater mobilization of \(\beta\)-D-glucans from cell walls. Higher temperatures are not indicated, as the gelatinization and partial solubilization of starch that would contaminate the extract would be favored [20]. Interestingly, the limited depolymerization of the polysaccharide increases its solubility in water and the stability of the solution. But depolymerization from a low MW (130,000 Da) leads to a reduction in solubility due to an aggregation of low MW units that give rise to aggregates that are no longer in solution [21]. Also, microwave treatment or germination (24 h) before the extraction of \(\beta\)-D-glucans from barley grains improves extraction capabilities, as well as nutraceutical properties. Methodologies are currently being studied to perform controlled depolymerization by acid, hydrogen peroxide, and enzymatic treatment in order to improve the solubilization of \(\beta\)-D-glucans in water [7; 8; 22]. The MW of \(\beta\)-D-glucans found in barley and oats can vary approximately between 50 and 2000 kDa [12]. These differences are mainly attributed to environmental factors but can also be influenced by extraction, purification, and depolymerization events and the analytical methodologies used to calculate MW [4].
## 5 Conclusions  
In order to reduce the number of retransmission signals for cooperative communication, an automatic retransmission strategy for cooperative communication based on the participation of listening nodes is proposed. In the second stage of cooperative communication, when the communication of a relay node fails, the listener node with better channel quality participates in the retransmission competition of the relay node. The probability of successful signal acceptance is improved, the system source node no longer participates in retransmission, and the system energy consumption is reduced. Experiments show that the LCARQ BER of the improved algorithm is reduced by a factor of 0.01. When the gain of the received signal is 0.99 times that of the transmitted signal, the energy consumed is saved by 0.1 J. Meanwhile, the configuration of four relay nodes and one listening node for collaborative communication is verified. In comparison with the existing literature [3; 35; 37; 40], the proposed method reduces the energy consumption in cooperative communication.  
Although the improved algorithm is able to meet the communication needs of scenarios such as large-scale UAVs, the proposed algorithm does not consider the impact after interference absorption. Therefore, in the future, the stability and robustness of the communication can be improved by conducting relevant research under policy-permitted conditions (Chinese policy prohibits the use of jamming sources by civilians). The existing experimental results show that the proposed algorithm can theoretically meet the requirements of industrial UAV cluster communication. The improved algorithm is highly innovative.  
G.C. ([PERSON]): Conceptualization (lead); writing--original draft (lead); formal analysis (lead); writing--review and editing (equal). G.C. ([PERSON]): software (lead); writing--review and editing (equal); methodology (lead); conceptualization (supporting); writing--original draft (supporting); writing--review and editing (equal). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Open Research Fund from National \"Thirteenth Five -Year Plan\" Advance Research Program of China under Grant No. 6141B012826, National Natural Science Foundation of China under Grant No. 61540022, and the Special Project on Industrial Technology Research and Development of Jilin Province, Research on Self-organizing Network System of Unmanned Platform for Optoelectronic Composite Communication, Grant No. 2022C047-8.  
The data used to support the findings of this research are included within the article.  
The authors declare no conflict of interest.