esa-sceva/satcom-chunk-collection huggingface dataset:
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
## 4 Discussion  
_K. lactis_ is well known for its distinguished activity for metabolizing lactose as a sole carbon source. In this report, we further analyzed its lactose-metabolizing activity and utilized it to remove lactose from the oligosaccharidesadric mixtures, HMOs and GOSs.  
We have shown that _K. lactis_ could assimilate lactose as efficiently as glucose and maintain cells' viability even without the addition of a nitrogen source or other media components such as peptone, yeast extract, or salts, which enable the biopurification to be simple. With the immobilization, the lactose removal efficiency was increased 3.4 fold compared to that of suspension culture. For the repeated use of immobilized cells, at least 4 h of pre-activation was necessary for the recovery of lactose consumption efficiency. Finally, lactose was effectively removed from HMOs or GOSs without altering the overall distribution of oligosaccharidesadries by utilizing immobilized cells. As a result, we developed a biopurification system for oligosaccharidesadric mixtures using immobilized _K. lactis_ as biocatalysts.  
Lactose removal from oligosaccharidesadricures like HMOs and GOSs is a critical step for maximizing the efficacy of oligosaccharidesadries. Hence, pilot-scale filtration or chromatographic processes have been studied for their industrial application [10; 11; 12]. Small-scale separation methods were also studied for the identification of oligosaccharidesadries [13; 14]. However, these methods are expensive and require complicated procedures to operate. Biological removal with \(\beta\)-galactosidase [15] is an alternative to these, but the enzymatic degradation results in glucose and galactose that could reduce the benefits. By utilizing our novel approach, it is possible to readily purify oligosaccharidesad mixtures from lactose.  
Data Curation, I.-S.Y., Y.-J.Y., N.S., H.J.A., and J.-H.K.; Investigation, Y.-J.Y. and N.S.; Writing - Review & Editing, I.-S.Y., H.J.A., and J.-H.K.  
This research was supported by the research grant from Chungnam National University.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.2.5 Contribution Analysis  
The partial first-order derivative of each regression predictor can be termed a \"sensitivity parameter\". Therefore, the sensitivity parameter equated to the ridge regression coefficient, and this parameter indicated changes to the response variable resulting from variations in the corresponding driver [38]. Simultaneously, the linear trend evident in the driving variable is representative of the average variation per unit of time. Therefore, changes to the dependent variable due to the unit time driving variable are enumerated as the product of the linear trend and sensitivity parameter, referred to as contribution of the driver to the dependent variable [7].  
The impacts of drivers on WUE were enumerated using the ridge regression coefficient and the trends in the drivers:  
\[\eta_{c1}=\alpha_{1}*X_{1s\_trend} \tag{5}\]  
where \(\eta_{c1}\) is the contribution of the normalized WUE variation, \(\alpha_{1}\) is the ridge regression coefficient, and \(X_{1s\_trend}\) is the trend evident in the independent normalized factor.  
The present study confirmed the actual and relative contributions of different drivers to WUE:  
\[\eta_{rc1}=\frac{\eta_{c1}}{|\eta_{c1}|+|\eta_{c1}|+|\eta_{c1}|+\cdots} \tag{6}\]  
\[\eta_{ac1}=\frac{\eta_{c1}}{WUE_{s\_trend}}*WUE_{trend}=\frac{\alpha_{1}*X_{1s \_trend}}{WUE_{s\_trend}}*WUE_{trend} \tag{7}\]where \(\eta_{rc1}\) represents the proportional contribution of \(X_{1}\) to the WUE trend, \(\eta_{ac1}\) represents the contribution to variation in WUE, \(WUE_{s\_trend}\) is the normalized WUE trend, and \(WUE_{trend}\) is the trend of WUE [8].  
To facilitate the comparative analysis, during the identification of dominant factors, this study considered the combined effects of the MAT, MAP, Rg, SWC, and VPD as the climate change (Cli) effects and that of LAI variations and elevated Ca as separate factors to characterize vegetation growth and environmental change, including Cli, LAI variations, and elevated Ca. In addition, if a factor (e.g., Cli, LAI, or Ca) had a relative contribution rate of more than 50% to the change in WUE in the corresponding pixel, this factor could be used as the dominant factor; moreover, if the sum of the relative contribution rates of the two factors exceeded 80%, then the combination of these factors was considered a dominant factor. The others were balanced by three factors.
# Two-State Lasing in Microdisk Laser Diodes with Quantum Dot Active Region
## 1 Introduction
Two-state lasing can only be observed in a certain range of optical loss [18]. On the one hand, if the loss is too low, the onset of two-state lasing requires unpractically high current density. On the other hand, if the loss is too high, lasing starts immediately through the ES1 optical transition, while the GS spectral component is not observed. Since optical loss can be easily changed in stripe lasers by varying the cavity length, to date, two-state lasing has been investigated in detail, both experimentally and theoretically, for the stripe QD lasers. Dynamic characteristics associated with the switching of lasing between different states of quantum dots in such lasers were investigated in [19] using a reverse biased absorber section. Temperature evolution of two-state lasing conditions were studied in [13; 20]. An analytical description of two-state lasing and its characteristics, in particular, quenching of the GS emission at high injection levels, were investigated in [21; 22; 23]. An influence of modulation \(p\)-doping of QDs, which results in a change of lasing threshold currents, was studied in [18]. In a QD microdisk with a diameter of 1 \(\upmu\)m, lasing under optical pumping was observed through the ES1 optical transition [24]. However, the two-state lasing phenomenon has not yet been studied in QD-based microlasers. The present work is aimed at eliminating this gap and is focused on an experimental study of the emission characteristics of microlasers with disk cavities of various diameters comprising InAs/InGaAs/GaAs QDs. In the case of MDs, optical loss is varied by changing the cavity diameter [3] similar to changing the resonator length in stripe lasers, which, as we will show, leads to an appearance of a dependence of the threshold current densities for GS- and ES-lasing on the microdisk diameter.
## 4 Experimental Results
### Evaluation of Log-DRSN on Public Datasets  
With the method proposed in our study, the classical DRSN method was also tested on public datasets such as MNIST, CIFAR-10, and Mini-ImageNet. As demonstrated by the experimental findings in Table 6, the Log-DRSN technique outperformed the regular DRSN in the literature, showing the highest accuracy on the Dataset_Fabric with 99.17% compared to 96.86%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Computational Time (s)** \\ \hline Classical DRSN in the literature & 765.0 \\ Leaky activation-DRSN in this study & 773.0 \\ Proposed Log-DRSN in this study & 781.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational time of methods in this study.  
Figure 12: Comparing the Log-DRSN and classical DRSN’ learning rates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & \multicolumn{2}{c}{**Accuracy Ratio (\%)**} \\ \cline{2-3}  & **Classical DRSN** & **Log-DRSN** \\ \hline
5 & 0.6345 & 0.8740 \\
10 & 0.7901 & 0.9283 \\
15 & 0.8345 & 0.9604 \\
20 & 0.8592 & 0.9654 \\
25 & 0.9037 & 0.9777 \\
30 & 0.9135 & 0.9802 \\
35 & 0.9530 & 0.9753 \\
40 & 0.9626 & 0.9827 \\
45 & 0.9703 & 0.9901 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Learning rates in different iterations of the methods in this study.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 2 Antenna Geometry Design
### Parametric Analysis of the Suggested Antenna  
The broadband characteristics of the suggested antenna are achieved with the help of parametric study. This study is used to obtain the optimal values of the antenna dimensions and the best performance by modifying one parameter and fixing the other parameters.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 2 Nonparametric Adaptive Partitioning and Stitching
As such, it appears the approach of equally partitioning a sample first, applying the one-shot NMEM on each region independently, and then stitching the solutions back together will provide a more accurate and much faster algorithm. However, practical considerations make this approach too idealistic. When dividing data into partitions, it may seem natural to use partitions with an equal number of sample points. However, this approach will generally not work in practice because of complications related to the form of the distribution that is being estimated. For example, if the first partition contains complicated features while the other partitions consist of relatively featureless, smooth density, running the NMEM process on the first partition may take as long as running the entire calculation in one-shot, while the other partitions finish quickly. This can cause a bottleneck, making the total time needed using this parallel computing strategy approximately twice as long as the one-shot NMEM because of the relatively slow processing time required just of the most difficult block in layer 1 and the most difficult block in layer 2. Clearly, indiscriminate data partitioning based solely on the number of data points is insufficient due to the potentially heterogeneity of difficult features within a PDF. The above example illustrates that the efficiency gains of partitioning data depend on the underlying distribution, which is not known beforehand. To achieve a load balance, we need a data-driven approach that partitions the blocks in a way that ensures the NMEM method takes approximately the same amount of time for each block. In other words, we need to find a way to guarantee approximate load balance, where the distribution of load over all blocks is sharply peaked around a mean with relatively small variance. This can be achieved by using a divide and conquer strategy that balances the non-uniformity of the density in all blocks with the number of units within a block. Our approach requires sorting the data as well as sorting the differences of this sorted data in each partition, which results in a complexity of \(O(N\log N)\). The last step is to stitch the PDF estimates of neighboring partitions together.
## 2 Image Data-Driven Monitoring System Framework  
A schematic diagram of the proposed image data-driven monitoring system framework is presented in Figure 1.  
The monitoring system framework is composed of three parts, including the image acquisition system, the vertical shaft movable sensor network [28] and the upper computer. The image acquisition system includes a light source, CCD (charge coupled device) cameras, an acquisition card and memory, and can realize the real-time collection of the BTRs image data. The movable sensor network transfers the collected image data to the upper computer. The upper computer is made up of one or more high-performance deep learning workstations, allowing it to achieve the deep mining of big image data features, analyze the data, and give BTR fault warnings. If the tail rope is found to be twisted, broken, or unevenly distributed, the diagnosis information will be sent out immediately so as to avoid the enlargement of the fault. Our work mainly focuses on the study of health monitoring methods. Other aspects of the system, such as the design of the hardware and software of the image acquisition system and the design of the movable sensor network, are not discussed in this paper.  
As shown in Figure 1, the proposed image data-driven framework for monitoring BTRs is developed by the following steps:  
**Step 1.** Generate the training and testing dataset: collect the BTR image data, clean the data, and divide the processed BTR image data into training and testing datasets [29].  
**Step 2.** Develop the model: based on the dataset, apply data-driven algorithms to develop models for predicting the BTRs' condition [29]. To adjust and optimize the parameter settings of algorithms, the trial-and-error method [20; 30] is employed.  
**Step 3.** Model selection: compute the prediction accuracy based on the developed models, and select the most accurate one for monitoring the BTRs' condition.  
**Step 4.** Online monitoring: design the hardware and software of the monitoring system, and apply them to online monitoring.
# Examination of Intermolecular Forces Influencing Headspace Analysis of Biological Samples
## 3 Results  
### Effect of Analyte Solubility on Headspace Concentration  
The sample matrix effect on volatiles' headspace concentration was evaluated in experiment 1. Three volatile compounds ranging in octanol-water partition coefficients were added to one of three sample matrices, differing in polarity: 1-hexanol (Kow 1.80), octanal (Kow 2.55), and 2-nonanone (Kow 3.16) in water, serum, or 1% intralipid. There was a highly significant media*volatile interaction (\(p<0.0001\)) for the raw responses reflective of compounds that not only differ in media solubility but also differences in response factors arising from electron impact mass spectrometry. In general, the headspace concentrations of volatiles were reduced in lipid-rich serum and intralipid versus polar water (Figure 1). A reduction in HS concentration was correlated with Kow value. 2-Nonanone, being the most lipophilic (as evidenced by having the largest Kow value), exhibited the greatest reduction in peak response among the three odorants in the lipid emulsion relative to water. The HS concentration of 1-hexanol, which is relatively hydrophilic, exhibited the least variation among the three sample matrices, whereas octanal showed the most significant decrease in headspace when analyzed from the serum matrix.  
Normalized responses were also subject to media*volatile effects (\(p<0.0001\)) (Supplementary Table S2). It was evident that acetophenone-2\({}^{\prime}\),3\({}^{\prime}\),4\({}^{\prime}\),5\({}^{\prime}\),6\({}^{\prime}\)-d5 was an appropriate internal standard to account for matrix effects for octanal which showed the greatest variation in the sample matrices. Solubility differences in the three sample matrices became less evident after normalization, as intended. 2-Nonanone, however, failed to be normalized with the assistance of the internal standard due to its high solubility in the lipid medium.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Measurement Length
#### 4.4.1 Reconstruction Success Rates  
Figure 8a,b illustrate reconstruction success rates versus the measurement length \(M\) for synthetic and real signals. The horizontal axis represents different \(M\), and the vertical axis shows the corresponding success rates (in percentages). For better comparison, the measurement lengths for synthetic sparse signals were drawn from \([75,\ 120]\), while those were assigned to \([90,\ 140]\) for real sparse signals.  
Figure 7: Reconstruction success rates vs. variable step sizes. (**a**) Synthetic signals; (**b**) Real signals.  
From Figure 8, with the increase of the measurement length, success rates obtained grow considerably. Meanwhile, as \(M\) becomes larger, the difference between reconstruction success rates calculated by different algorithms becomes more and more obvious. For both synthetic and real sparse signals, FRSVssMP outperforms all the greedy algorithms in the first category obviously, especially with a large \(M\). FRSVssMP makes a more accurate estimation of the sparse signals by the initial sparsity level estimation approach as a result of the increasing information acquired from the measurement vectors. FRSVssMP performs better for almost all measurement lengths compared to AStMP and CBMP because of the accurate initial sparsity level estimation along with the filtering mechanism. As the nearest rival to FRSVssMP, CBMP shows superiority over other algorithms with two kinds of composite strategies, followed by AStMP, SAMP, CoSaMP, and StOMP.
## 3 One-Dimensional Degradation Models Based on Stochastic Processes
### Degradation Models Based on Univariate Gamma Process with Random Effects  
Different from the Wiener process, whose degradation path is not necessarily monotone, the Gamma process [22] not only imitates the monotonically increasing degradation process but also incorporates meaningful model parameters, random effects, and uncertainties. In this paper, we assume that the degradation path of a product's parameter satisfies the Gamma process, and the degradation data of this parameter are \(G(t)\) at a moment, \(t\), with a shape parameter, \(\alpha\), and a scale parameter, \(\beta\), which is a stochastic process satisfying:  
1. \(G(0)=0\)
2. \(\{G(t),t\geq 0\}\) is stable and has independent increments
3. The increment at any time follows a Gamma distribution, namely, \(\Delta G(t)=G(t+\Delta t)\)\(-\)\(G(t)\sim Ga(\alpha\Delta t,\beta)\)  
where \(\alpha\) and \(\beta\) are unknown parameters, and the shape parameter, \(\alpha\), describes the influence of the external environment and other factors on the product performance, which is used to describe the common attributes of all samples, while the scale parameter \(\beta\) describes the random effects, such as the influence of manufacturing techniques and raw materials, which is used to describe the individual differences between samples. According to the definition of the Gamma process, its PDF is:  
\[f(g|\alpha,\beta)=\frac{(\beta)^{at}}{\Gamma(\alpha t)}g^{at-1}\cdot e^{-\beta g },g>0 \tag{8}\]  
where \(\Gamma(\alpha)=\int_{0}^{\infty}g^{\alpha-1}e^{-g}dg\) is a Gamma distribution. Considering the individual differences between the samples, random effects are introduced into the scale parameter \(\beta\). To simplify the analysis, we usually assume that \(\beta\) follows a Gamma distribution [23], that is, \(\beta\sim Gamma(\kappa,r)\), whose PDF is:  
\[g(\beta)=\frac{r^{\kappa}}{\Gamma(\kappa)}\beta^{\kappa-1}\cdot e^{-r\beta}, \beta>0 \tag{9}\]  
Then, Equation (9) can be deduced by applying the total probability formula of continuous random variables as follows:  
\[ \begin{array}{ll}f(g|\alpha)&=\frac{\Gamma(at+\kappa)}{\Gamma(at)\Gamma( \kappa)}\cdot\frac{(r)^{\kappa}g^{at-1}}{(r+g)^{at+\kappa}}\\ &=\frac{\Gamma\left(\frac{2 at+2\kappa}{2}\right)}{\Gamma\left(\frac{2 at}{2} \right)\Gamma\left(\frac{2\kappa}{2}\right)}\cdot\frac{g^{\frac{2 at}{2}-1}}{( r)^{\frac{2 at}{2}}\left(1+\frac{\kappa}{2}\right)^{\frac{2 at+2\kappa}{2}}}\end{array}
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Robustness in Dynamic and Harsh Environments  
Indoor localization systems must adapt to constantly changing conditions, such as moving machinery in industrial plants, crowded hospital corridors, and combat environments. Research should focus on developing adaptive algorithms capable of real-time calibration and recalibration to maintain accuracy despite environmental changes [65]. Robust hardware designs, such as RFID-sensitive tags that detect metal corrosion or UWB modules resilient to interference, will further enhance reliability in harsh conditions [23; 26].
## 4 Proposed Scheme
### Election of CH
#### 4.3.2 Fuzzy Algorithms  
The use of fuzzy algorithms [22] is able to better solve fuzzy, difficult-to-quantify problems by mimicking human experience and decision-making behavior to arrive at a simple and effective overall evaluation of the object or physical object, so in this paper study we used a fuzzy algorithm to evaluate three indicators for each node in the cluster to determine the node that is more suitable to take on the role of CH. As shown in Figure 2, the algorithm is divided into three parts: first of all, we have to input three fuzzy variables into the fuzzy controller; in the fuzzy controller the fuzzy variables will be fuzzy, through the fuzzy algorithm as well as the fuzzy rules to obtain the fuzzy evaluation of the things; and then the fuzzy evaluation is de-fuzzified to obtain an accurate value.  
The fuzzification process of fuzzy algorithms requires the conversion of clear values of fuzzy parameters into fuzzy values. The membership functions of the fuzzy linguistic variables of the input fuzzy parameters obey the trapezoidal affiliation function. The fuzzy inference module needs to be utilized to make the conversion, and these conversion rules are called fuzzy rules. The fuzzy rules defined by the proposed scheme are based on If-Then statements and the fuzzy AND operation and OR operation.  
For example: IF (distance 0.75 to 1) and (energy 0.8 to 1) and (node density 0.75 to 1) THEN (total trust is high).  
We make use of the characteristics of the fuzzy algorithm and the clear input values to determine the fuzzy rules and thresholds in Table 1 according to the experience of other scholars [23].  
The rules for the proposed scheme using the fuzzy rules are specifically shown in Table 1.  
Figure 2: Flowchart of the fuzzy algorithm.  
The membership function [24] is essentially a curve that represents the way membership is assigned to a specified clear multi-attribute trust value. There are different types of membership functions, and the trapezoidal function [25] is less computationally complex if it is accurate enough.
## 3 Results and Discussion  
### X-ray Diffraction Characterization of Single Ferroelectric Films  
The X-ray diffraction spectra of two ferroelectric materials, HDA-BiI\({}_{5}\) and [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]SbI\({}_{5}\), formed as thin films on glass/ITO substrates are given here for a structural analysis and comparison with the literature. According to reports in the literature [16; 17], [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]BiI\({}_{5}\) has a bandgap of 1.89 eV, which is claimed to be the narrowest bandgap among the known ferroelectrics, implying an absorption range between 380 nm and 660 nm, capable of absorbing most of the UV and visible light. [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]SbI\({}_{5}\), a molecular ferroelectric electrode with a similar structure to [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]BiI\({}_{5}\), is being explored by researchers at the initial stage [13]. The blue curve in Figure 1 shows the X-ray diffraction spectrum of HDA-BiI\({}_{5}\), and the crystallographic indices (110), (111), (120), (320), (322), (330), (113), and (440) of the ferroelectric material can be observed, which almost exactly match the XRD of the HDA-BiI\({}_{5}\) film mentioned in the literature, indicating a good reduction of the ferroelectric materials in the literature. The red curve shows the X-ray diffraction spectrum of [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]SbI\({}_{5}\), and its peak position and peak intensity are in general agreement with the X-ray diffraction spectrum (PXRD) of polycrystalline powder in the literature (the XRD spectrum of this material is not given in the literature). Through observation and comparison, it can be found that both [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]SbI\({}_{5}\) and [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]BiI\({}_{5}\) had the highest peak intensities at a crystal orientation index of (110), i.e., the main peaks were in agreement.