esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Data Analysis  
Statistical analysis will be carried out by a blinded statistician with the SPSS software package (version 29.00; SPSS Inc., Chicago, IL, USA) [76]. Summary statistics of continuous demographic and clinical variables will be reported as mean and standard deviation, median, and inter-quartile range. Categorial variables will be reported as relative frequencies (n and percentage).  
For hypothesis testing, the primary end-point will be the differences between the groups at post-intervention, obtained as the average treatment effect (ATE). For this comparison, mean differences will be obtained by analysis of covariance (ANCOVA), adjusting for baseline values of each continuous outcome measure. All models will be of type y-Group + baseline. This allows us to increase the statistical precision by shrinking the standard error and is currently recommended for obtaining unbiased ATEs in randomised controlled trials. Statistical significance of between-group differences will be obtained via non-parametric tests (Mann-Whitney U) if the outcome measure does not follow a normal distribution, or via independent samples \(t\)-tests if it does. Before applying any statistical modelling, the distribution of all outcome measures will be screened via density plots and QQ plots, and if needed, kurtosis and asymmetry will be obtained. In addition, after modelling, normality of residuals will be screened by QQ plots. Corrections to the normality assumption being violated will include transformation of the outcome measure or full non-parametric ANCOVAs that do not make any assumptions on the distribution of the data. Other corrections include the use of beta regression for the outcome measures that have clear upper and lower bounds and/or where evidence of floor/ceiling effects is present. Should the trial yield inconclusive results, we will also consider the use of Bayesian analyses to evaluate the evidence in favour of the null hypothesis. As we acknowledge that multiple comparisons will be made given that numerous outcome measures will be analysed, type I error rates will be corrected by the Holm method at each time point. Standardised effect sizes will be calculated by Hedges' g, given that this is recommended for small sample sizes.  
Intention-to-treat analysis will be conducted if there is any drop-outs from the study. This will be performed by multiple imputation with m = 10 imputed samples and predictive mean matching. Sensitivity analyses will be carried out with other imputation methods to ensure the robustness of the results.  
In all analyses, the confidence level will be 0.95 (i.e., alpha will be set at 0.05), and 95% confidence intervals will be obtained for parameter uncertainty.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### A Character-Level Extension and Scrambling Implementation of a NOMA System  
Figure 7 illustrates that the process for scrambling can also be used at the symbol level. This is shown in the diagram. In this particular variation of NOMA, the so-called hybrid symbol processing is carried out at the output modulator. This involves expansion as well as scrambling. When utilizing the OFDMA technology, following the display of the cycles, a discrete Fourier transform block is required. Bit-level processing may also be applied as an additional processing option [35].  
In the RSMA technique introduced by Qualcomm, each modulated symbol has its own brief expanding sequence that is employed during symbol expansion. And in the RSMA plan for creating group signals, both short and long code sequences are utilized sequences (scrambling at the character level). It is possible to use Zadoff Chu sequences as a basis to develop new scrambling sequences.
## 3 Results and Discussion
### Crop Water Stress Index and Degrees above Non-Stress
However, when trees were submitted to moderate water stress, CWSI, AT and DANS were robust water indicators able to assess young and mature sweet cherry tree water statuses. Figure 8: Relationship between midday stem water potential (\(\Psi\)stem) and crop water stress index (CWSI). Each point corresponds to the mean \(\pm\) standard error of the mean of six measurements per treatment. CTL and S correspond to control and deficit irrigation treatment for young (\(\Upsilon\)) and mature (M) sweet cherry trees, respectively.
# Corpsia--A Feasible Second Best Solution?
## 2 Institutional and Technical Background
### Challenges for CORSIA's Climate and Regulatory Integrity
#### 2.3.2 Adverse Effects  
Adverse effects can challenge carbon offsetting. From the perspective of policy makers, carbon offsetting is complex. It brings together subjects from very different jurisdictions, usually highly regulated carbon emitters and carbon savers that operate in less developed and regulated markets. Frequently, it is not only carbon that is traded, it is regulation of economic activity, which increases the risk of adverse effects such as social oppression or damage to biodiversity. In the case of adverse effects, risk factors include the location, project type, and project size. Furthermore, there is substantial variation in co-benefits and adverse effects across different project types [23; 24; 25; 26; 27; 28; 29]. Biomass, land-use, and wind projects contribute more co-benefits and fewer adverse effects compared to projects targeted on industrial emissions or large dams [14; 23; 30]. However, variation can be high even within the same project type. For example, the probability of social oppression or negative effects on biodiversity is higher for large-scale projects independent of the project type [31; 32; 33; 34; 35].  
Currently, small-scale energy efficiency projects targeted to local populations, such as cook stove projects which replace fireplaces with efficient stoves, tend to be the best offsetting practice, especially due to the context of high financial need. From an economic point of view, it appears counterintuitive to invest in inefficient projects. However, this is a simple mechanism to guarantee additionally, and is often used in carbon offset standards. The less profitable a project is, the less likely it is that a private investor would have invested in it anyway. However, the corresponding literature does not analyze how such inefficient investments affect the local economy in the longer term. In theory, they could crowd out more efficient and longer-lasting investments that would have been installed by the local government due to political pressure or by local businesses, thus reducing economic growth [36].  
For this reason, the current best practices in offsetting potentially induce a trade-off between additionality and adverse effects. While the importance of this might be limited today due to the small share of such projects, it could became sizable if offsetting increases in importance. More importantly, if projects have a negative impact on the local population they are less likely to be maintained as carbon stores in the long term [37; 38].
_applied_  
_sciences_  
_Article_  
Separable Reversible Data Hiding in Encrypted AMBTC  
Images Using Hamming Code  
[PERSON]  
Department of Computer Engineering, Sejong University, Seoul 05006, Korea; [EMAIL_ADDRESS]  
Received: 23 July 2022  
Received: 12 August 2022  
Published: 17 August 2022  
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**Copyright** © 2022 by the author. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))  
4.0/).  
**Abstract:** Data hiding is a field widely used in copyright, annotation, and secret communication for digital content, and has been continuously studied for more than 20 years. In general, data hiding uses the original image as a cover image to hide data, but recently, the research area has expanded to research on improving the security and privacy protection of image contents by encrypting the image. This research is called separable reversible data hiding in an encrypted image (SRDH-EI). In this paper, we proposed a more efficient SRDH-EI method based on AMBTC. AMBTC can guarantee very good network transmission efficiency for applications that do not require a particularly high image quality because the compression time is short and calculation is simple compared to other existing compression methods. The SRDH-EI method presented here divides AMBTC into non-overlapping 4 \(\times\) 4 blocks and then performs image encryption on them. Thereafter, data can be hidden using a Hamming code for each block. The proposed method has an advantage in that the quality of the cover image and the hiding capacity can be adjusted by appropriately using the value \(T\) of the difference between the two quantization levels. The experimental results proved the efficiency and superiority of our proposed model.  
**Keywords:** reversible data hiding (RDH); absolute moment block truncation coding (AMBTC); hamming code; separable RDH in an encrypted image (SRDH-EI)
### Calculation of the Green's Function  
A high accuracy Green's function is the basis for a successful waveform inversion (see Section 2.1 for more details). Due to limited computing resources in the early years, the frequency wavenumber (FK) method that adopts a one-dimensional (1D) horizontal layered velocity model was usually used to model Green's function. Moreover, [PERSON] et al. [12] utilized different 1D velocity models from the source to each station to reduce the influence of velocity models errors. However, in complex structural areas including mines, there is strong three-dimensional (3D) heterogeneity, thus, a 1D velocity model usually cannot accurately model broadband seismic waveforms in such regions. In particular, it performs unsatisfactorily in characterizing the high frequency waveform information.  
In recent years, with the improvement of parallel computing capabilities, the Green's function numerical computation based on 3D velocity model (3D Green's function) has been efficiently developed. [PERSON] et al. [13], [PERSON] and [PERSON] [14], [PERSON] and [PERSON] [15], [PERSON] et al. [16] and [PERSON] et al. [17] separately modeled 3D Green's functions by utilizing finite element method (FEM), finite difference method (FDM), boundary element method (BEM) and pseudo spectral method (PSM). However, the FEM is usually based on low-order polynomials that have poor performance in modeling high-frequency signals. The FDM is easy for implementation and has high calculation efficiency, but the FDM is hard to adapt to complex geological structures and irregular boundary conditions for preset numerical accuracy. The BEM can effectively deal with complex boundary problems, but it is often limited to simulate linear wavefield response in relatively simple media. The PSM has a very high calculation precision, but it is also difficult to handle complex regions and interfaces [18]. The spectral element method (SEM), combining the high accuracy of spectral methods and flexibility in grid generation of FEM, is becoming more and more popular in simulating broadband seismic wavefields in 3D complex structures. [PERSON] and [PERSON] [19] and [PERSON] et al. [20] developed open source parallel programs (1D\(\sim\)3D SEMs) for modeling seismic waveforms. On this basis, [PERSON] et al. [21] and [PERSON] et al. [22] conducted the 3D SEM for imaging large-scale 3 Dvelocity structures. While [PERSON] et al. [23], [PERSON] et al. [24] and [PERSON] and [PERSON] [25] obtained focal mechanisms and locations of natural earthquakes in large-scale regions through the 3D SEM and full waveform inversion (FWI). In conclusion, 3D SEM-based waveform inversions have been successfully applied in imaging large-scale velocity structures, and inversions of focal mechanisms and source location for natural earthquakes. Inspired by this, we attempted to introduce the 3D SEM into broadband waveform modeling and source location in a mine, which has a smaller spatial scale but with more complex structures.
## 3 Results and Discussion
### Qualitative and Quantitative In Vitro Evaluation of the Antifungal Activity of the LAB  
The antifungal activity of the LAB was evaluated using the overlay method against fungus of the genera _Aspergillus_ and _Penicillium_. It is worth noting that the strain with the highest antifungal effect was _P. pentosacous_ TI6; the antifungal activity results are plotted in Table 2.  
Moreover, the MIC-MFC concentrations were evaluated in order to quantify the antifungal capacity of the CFS of isolated LAB strains, as summarized in Table 3. The _P. pentosacous_ TF8 strain gave rise to the compounds with the most significant antifungal effect at its lowest CFS concentrations. In addition, we could highlight the activity of the microorganisms _L. plantarum_ TF2 and _P. pentosacous_ TI6. The TF2, TF8, and TI6 strains give the best MIC-MFC values, reaching minimum fungicidal concentrations of 25 g/L versus _P. verrucosum_ and about 100 g/L against _A. flavus_.  
As expected, _P. digitatum_ is the fungus that presents more resistance, for removing their spores requires the highest dose of fermentations produced by the strains. However, visually it is more complex to see their growth since the MFC of all bacteria does not exceed 50 g/L.  
The quantitative evaluation of the antifungal activity of the selected strains evidenced the antifungal potential of these isolated LAB as sourdough ferment. Previous studies have evaluated the antifungal capacity of isolated LAB from sourdough. For instance, [PERSON] et al. [35] showed that a _P. pentosacous_ strain prevented mycelial growth of _Aspergillus_ fungi. Knowing the mode of action of the LAB strains can be a biocontrol procedure in its application, taking into account that the growth stage of conidia is the most susceptible stage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Fungi**} & \multicolumn{5}{c}{**Strains *} \\ \cline{2-6}  & **TN10** & **TF2** & **TF8** & **TE4** & **TI6** \\ \hline _P. expansum_ & + & + & +++ & + & +++ \\ _P. digitatum_ & - & - & - & - & + \\ _A. flavus_ & - & ++ & + & - & ++ \\ _P. commune_ & + & +++ & + & - & +++ \\ _P. verrucosum_ & +++ & +++ & +++ & +++ & +++ \\ \hline \end{tabular}
* Strains: Lactiplantiobacillus plantarum TN10, Lactiplantiobacillus plantarum TE2, Pedicococcus pentosacous TF8, Pedicococcus acidilactic TE4, and Pedicococcus pentosacous T6.  
\end{table}
Table 2: Overlay assay results of the five lactic acid bacteria selected against fungi _Penicillium expansum_, _Penicillium digitatum_, _Penicillium commune_, _Penicillium verrucosum_, and _Aspergillus flavus_. Results are expressed as ‘+’ if the inhibition is of 1–2 mm, ‘++’ 2–5 mm, ‘++’ 5–10 mm.  
Contemplating the fungal inhibition, this study investigated the antifungal compounds generated during sourdough fermentation and after it was dried.
## 3 Results and Discussion
### Minerals in Carrot Powder (CP) and Pumpkin Powder (PP)
#### 3.1.2 Microelements  
Figure 3 presents the microelements content of the vegetable powders tested. The present study identified Cu and Mn in CP at 0.35 \(\pm\) 0.03 mg/100 g and 0.39 \(\pm\) 0.04 mg/100 g; these values were much lower than those reported by [PERSON] et al. [48] (0.98 mg/100 g of Cu, 4.03 mg/100 g of Mn). The Fe (0.474 mg/100 g) and Zn (0.347 mg/100 g) contents determined by [PERSON] et al. [51] in carrot pomace were lower than those in our study (2.21 \(\pm\) 0.02 and 1.85 \(\pm\) 0.07 mg/100 g). Also, [PERSON] et al. [48] showed a lower Fe content in CP (0.653 mg/100 g). While the Fe content of both powders was the same, the Zn content was higher in CP (_p_ < 0.05). In the study from [PERSON] et al. [47] on pumpkin pulp meal, the Fe and Zn contents were 3.55 mg/100 g and 2.40 mg/100 g, respectively, and were higher than those obtained in our study. On the other hand, in the study of [PERSON] et al. [52], the Fe content of PP was also high (3.74 \(\pm\) 0.00 mg/100 g), while the Zn content (1.24 \(\pm\) 0.00 mg/100 g) was lower than that determined by us. The Fe and Zn content of PP may vary depending on the drying method used. [PERSON] and [PERSON] [53] indicate that Zn content was highest in oven-dried powder and lowest in enhanced solar. In contrast, the highest Fe content was identified in open sun-dried powder. In the production of PP, the use of appropriate pretreatment plays an important role in improving product quality and inhibiting enzymatic browning. Depending on the pretreatment carried out, which included hot water blanching, steam blanching, or dipping in 0.1% citric acid, the Fe  
Figure 2: The content of individual macro minerals in vegetable powders. Abbreviations: Na—sodium, K—potassium, Ca—calcium, Mg—magnesium, CP—carrot powder, PP—pumpkin powder. Means with \({}^{\star}\) is significantly different at \(p\) < 0.05.  
content of the pumpkin flours ranged from 5.078 \(\pm\) 0.00 to 22.54 \(\pm\) 0.05 mg/100 g [53]. PP contained twice as much Cu and Mn compared to CP. It should be noted that the content of heavy metals, including copper, manganese, or zinc, in vegetables may depend on the type of pesticides used, soil and weather conditions, and the location of the growing areas [54].
## 3 Methodology
### Soft-SloUNMS  
A loss function is a method to measure the accuracy of model prediction results. The original YOLOv5s network uses CIoU Loss as a loss function of the bounding box. Many IoU loss functions, such as GIoU, DIoU, and CIoU, do not take into account the direction between the real frame and the predicted frame. The predicted frame may have a large position deviation in the training process and eventually produce a worse model, resulting in slow convergence speed. To solve this problem, SIoU Loss [37] was used in this paper to replace CIoU Loss. The SIoU redefines the correlation loss function by introducing the vector angle between the real box and the prediction box. Considering that rail surface defects are characterized by a small target scale and high density, the YOLOv5s network may cause missed detection, thus reducing the recall rate of the model. In this paper, Soft-NMS [38] was combined to further improve the YOLOv5s network. Furthermore, Soft-SIoUNMS was combined with SIoU Loss and Soft-NMS, used as the loss function of border regression. The SIoU calculation method is shown in Equation (3) and the Soft-NMS calculation method is shown in Equation (4).  
\[\left\{\begin{array}{l}SloU=1-IoU+\frac{\Delta+\Omega}{\Omega}\\ \Omega=\sum\limits_{t=w,h}{(1-e^{-w_{i}})}^{\Omega}\\ \Delta=\sum\limits_{t=x,y}{(1-e^{-\gamma p_{t}})}\\ \end{array}\right. \tag{3}\]  
\[S_{i}=S_{i}e^{-\frac{iw(M_{i})^{2}}{\sigma}},\forall b_{i}\
otin D \tag{4}\]  
where \(\Omega\) represents the shape cost, \(\Delta\) represents the redefined distance cost after considering the angle cost, \(b_{i}\) represents the \(i\)-th anchor box, and \(S_{i}\) represents the score of the \(i\)-th anchor box.
## 2 Materials and Methods
By monitoring the total diameter of the fibre during the latter drawing, it is possible to estimate quite accurately the core size of the resulting fiber. Typically, core diameters varying between 3 and 6 \(\upmu\)m are obtained when the outer fiber diameter is roughly between 120 and 240 \(\upmu\)m.  
The wavelength-dependent refractive index curve for each glass can be determined with different interpolation schemes based on two-pole Sellmeier equations fitting near-IR measurements on bulk materials (more details are given in References [25; 27]). The resulting refractive index curves are shown in Figure 4a. The core-cladding index difference is \(\sim\)0.1 at 2 \(\upmu\)m. Based on this simple step-index profile, our fibers do not exhibit a strictly single-mode behavior in the mid-IR spectral range, but high numerical apertures and a rather strong confinement of the fundamental guided mode. Properties of the fundamental guided mode are investigated by numerically solving the dispersion equation (also called the eigenvalue equation) for cylindrical step-index waveguides. Figure 4b-c report the calculated dispersion curves and effective mode area variations as a function of wavelength. We observe that our fiber design provides distinct dispersive regimes by simply varying the core diameter from 3 to 4 \(\upmu\)m. An all-normal dispersion profile and even a multi-ZDW dispersion profile are obtained in Figure 4b. We find that anomalous dispersion only exists for core diameters \(\phi\geq\) 3.25 \(\upmu\)m. The first ZDW can be  
Figure 3: Cross-section of the small core step-index fibre under optical binocular with (**a**) a \(\times\) 20 objective and (**b**) a \(\times\) 100 objective. Figure 2: Large-core (TZNF/TZNG) fiber losses measured by means of the cutback method. easily tuned between 1.92 and 2.35 \(\upmu\)m, while a second ZDW is present for \(\phi<\) 3.8 \(\upmu\)m, and shifted from 2.4 to 4.3 \(\upmu\)m. The flexibility of GVD properties beyond 2 \(\upmu\)m combined with strong optical confinement allow us to investigate an overview of relevant nonlinear dynamics of SC generation, as already addressed for the picosecond pumping regime in Reference [25]. In contrast, we focus this study on the femtosecond pumping regime with moderate peak power in order to preserve SC coherence properties.
## 1 Introduction
### Key Research Contributions  
This research represents a continuation of our previous work, where a complex fuzzy logic-based model for VQA was developed (as published in [6]). In the current study, that model is enhanced by incorporating neural network techniques into the inference system development. Hence, the following contributions of the current work can be identified:  
1. The integration of neural network techniques within the fuzzy inference system allows the model to simultaneously learn membership function parameters and rules, resulting in a more adaptive and robust system.
2. By employing subtractive clustering with optimized parameters such as range of influence, squash factor, and accept/reject ratios, the model generates a lightweight, compact rule base that minimizes computational demands without sacrificing accuracy.
3. The new model achieves lower RMSE (Root Mean Squared Error) and higher PCC (Pearson Correlation Coefficient) and SROCC (Spearman Rank-Order CorrelationCoefficient) compared to traditional, as well as recently developed, VQA objective models, enhancing the accuracy of MOS prediction.