esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 5 Conclusions  
In the case of target speckle images affected by noise, introducing high signal-to-noise autocorrelation as an intermediate optimization tool can optimize the network learning process, improve the learning efficiency, and enhance the robustness of noise. Particularly for speckle autocorrelation severely affected by noise, the experimental results reveal that high-quality autocorrelation information helps to improve the effect of reconstruction during phase recovery. Therefore, improving the signal-to-noise ratio of speckle autocorrelation is a crucial physical constraint in overcoming the noise terms of scattering imaging. By introducing a physical constraint, a method based on the construction of basic-element datasets using deep learning is introduced with the aim of enhancing speckle autocorrelation at low signal-to-noise ratios. This approach significantly elevated the average peak signal-to-noise ratio to 37.28 dB and achieved a structural similarity of 0.99. Importantly, the method achieves higher-quality physical-knowledge-based reconstruction than the end-to-end method. In addition, the high signal-to-noise autocorrelation helps to improve the reconstruction efficiency and solves the limitation of the target data domain. Moreover, extended imaging through frosted-glass plates with different scattering properties was achieved, providing a basis for further practical applications.  
Conceptualization, C.W. and J.Z.; methodology, C.W.; software, C.W.; validation, C.W., S.Y. and Y.Y.; investigation, C.W.; data curation, W.L. and H.Z.; writing--original draft preparation, C.W.; writing--review and editing, S.Y.; supervision, J.X.; project administration, J.X. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Technology Innovation 2025 Major Project (2020Z019, 2021Z063) of Ningbo and the Natural Science Foundation of Zhejiang (LQ23F050002). Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
We thank [PERSON] and [PERSON] for technical supports and experimental discussion. We thank the reviewers for useful suggestions.  
The authors declare no conflict of interest.
## 6 Evaluation of Simulation and Experimental Results

Table 6: RMSE between the rheological models and the experimental data at the time 0.26 s and 0.32 s.  
Figure 10: Temperature distribution of the simulated rheological models in the mid-plane at 0.26 s and 0.32 s.  
Figure 11: Shear rate distribution of the simulated rheological models in the mid-plane at 0.26 s and 0.32 s.  
Figure 12: Viscosity distribution of the simulated rheological models in the mid-plane at 0.26 s and 0.32 s.  
In order to illustrate the influence of shear rate and temperature on viscosity in different rheological models, a comparison is made between the specific characteristics of each model in Table 7. The relationship between the influence on viscosity is demonstrated by comparing Figures 10-12. The Power-Law model demonstrates a distinct correlation between viscosity and shear rate, with no temperature influence. This indicates that the viscosity in this model decreases with increasing shear rate, regardless of the temperature. In contrast, the Second-Order model demonstrates a sensitivity to changes in both shear rate and temperature. This model shows a decrease in viscosity with both increasing shear rate and increasing temperature. The Herschel-Bulkley model and the Carreau model appear similar to the Power-Law model in that they both demonstrate a strong dependence on the shear rate but no dependence on the temperature. The viscosity of these models decreases with increasing shear rate, but remains independent of temperature. The Carreau-WLF, Carreau-WLF Winter, and Cross-WLF models are notable for their dependence on both shear rate and temperature. In these models, it is observed that both an increase in the shear rate and an increase in temperature result in a decrease in viscosity. In contrast, the Carreau-Winter model demonstrates a dependence on the shear rate alone, with no temperature-related influence. Consequently, viscosity decreases as the shear rate increases, irrespective of the temperature. As with the other models, which are not temperature-dependent, the Bird-Carreau model and the Cross model also have a main reaction to the shear rate. In these models, viscosity is observed to decrease with increasing shear rate, independent of the temperature. It is evident that the viscosity of all rheological models depends on the shear rate. This implies that the viscosity of these models decreases with increasing shear rate. Only the Second-Order, Carreau-WLF, Carreau-WLF Winter, and Cross-WLF models in particular demonstrate a dependence on temperature, i.e., a decrease in viscosity with increasing temperature. These models offer a more differentiated adjustment of the viscosity under varying process conditions, while the other models, which only react to changes in the shear rate, remain less sensitive to temperature changes.
## 3 Results and Discussion
### Amino Acid Analysis  
According to previous studies, free amino acids provide flavor and color to foods by participating in complex reactions, such as Maillard reactions [33; 34]. Free amino acids in different douchis are shown in Figure 3. A total of sixteen free amino acids were detected and classified into four categories: umami amino acids (Asp and Glu), sweet amino acids (Thr, Ser, Gly, and Ala), bitter amino acids (Val, Ile, Leu, Met, Tyr, Phe, His, and Arg), and tasteless (Lys and Cys) [35]. The total contents of each douchi ranged from 14.99 to 20.03 g/100 g (Table S1); among them, bitter-amino-acid contents ranged from 5.98 to 8.30 g/100 g, while umami amino acids were 5.08-6.79 g/100 g (Figure 3a). As shown in Figure 3a, DC-4 had the most umami, sweet, and bitter amino acids, whereas DC-6 had the least. Figure 3b illustrates the relative abundances of amino acids, which were very similar in each douchi sample. Among them, Glu accounted for the highest proportion, reaching 20%, followed by Asp and Met, accounting for 13% and 10%, respectively. This finding is similar to previous research showing that soy proteins with plentiful Gul and Asp, such as in soybean fermentation products, like douchi, soy sauce, and dajiang, usually exhibit an excellent umami taste.  
The taste activity value (TAV) was used to evaluate the contributions of taste-active compounds, and a TAV of \(\geq\)1 was regarded as significant for the overall taste impact. In this study, Glu's TAV was the highest, as well as those of most umami and bitter amino acids. [PERSON] [2] found that umami amino acids (Glu and Asp) were one of the significant contributors to the taste of soybean fermentation products. A higher proportion of umami amino acids contributed to the higher umami score of the douchi. Nevertheless, excesses of bitter amino acids might have a negative influence on the umami-amino-acid taste stimulation, affecting its umami score. Another study claimed that high salt and acidity levels inhibited the perception of bitterness, despite the presence of bitter-causing amino acids [20].  
Figure 2: Correlation analysis among microorganisms. The color indicates the correlations between microorganisms, with higher values from negative (blue) to positive (red) and darker colors indicating a stronger correlation. Significant values were shown as * \(p\) < 0.1; ** \(p\) < 0.05.
# A Study on Generating Webtoons Using Multilingual Text-to-Image Models
## 1 Introduction
Treatment is a term used not only for webtoons but also for content production, such as movies and dramas. When producing movies, dramas, or webtoons, treatments are used as references to describe the key elements of a scene, such as the time, place, and characters involved. Thus, treatments contain rich information regarding the scenes of the final webtoon product, thus making them suitable input data for deep-learning-based text-to-image models. To overcome these limitations, we proposed a multilingual text-to-image model that can generate webtoon images from not only English but also other languages as inputs. To develop a multilingual text-to-image model, we constructed and trained a webtoon dataset consisting of English and Korean text and image data. The training process involves extracting features from multilingual text inputs using multilingual BERT and training a GAN-based text-to-image model with image data. Once the training is complete, the multilingual text-to-image model can generate webtoon images when given a multilingual text input. The main contributions of this study are as follows: (1) Unlike existing text-to-image technology that generates images from text written in English, the proposed multilingual text-to-image model uses multilingual BERT as a text encoder to generate images from multilingual input. (2) As the webtoon dataset is trained on a text-to-image model, it is expected to contribute to the creation of webtoon content by allowing the model to generate webtoon images from multilingual text inputs. The remainder of this paper is organized as follows: Section 2 describes the self-supervised pre-training models and deep learning-based text-to-image technology used in this study. Section 3 proposes a construction method for the multilingual treatment-webtoon dataset and the GAN-based text-to-image model. Section 4 trains the constructed multilingual text-to-image dataset using the text-to-image model and verifies the results. Finally, Section 5 concludes this study and discusses future research directions.
## 3 Results and Discussion
### Dominant Classes and Orders of Soil Bacteria Under Horse Beans Cultivated in Different Systems
On average, the shares of this order in these treatments were 1.5-2% greater than in the conventional tillage (ploughing) treatment and in the soil sample \"0\" collected from the plantless, fallow buffer zone of the experiment. According to the data provided in the reference publications, the presence of these bacteria in soil ecosystems is crucial for the decomposition of organic matter, as they decompose dead plant and animal material and, thus, release nutrients back into the soil. The decomposition process not only enriches the soil but also facilitates the formation of humus, a stable organic component that improves soil structure, water retention, and aeration. The activity of _Bacillus_ bacteria in the nutrient cycle helps to maintain plant growth by creating a dynamic balance in the soil ecosystem. Many species within the _Bacillus_ order interact with other soil organisms, which indicates the ecological importance of these bacteria. _Bacillus_ often colonize the rhizosphere and, thus, establish symbiosis with plants. They facilitate the uptake of nutrients in this microenvironment by converting them into forms available to plants. In consequence, the health and immunity of plants improve. This interrelation may increase agricultural productivity, thus indicating the integral role of _Bacillus_ in supporting sustainable agriculture [55]. Unlike _Bacillus_, there was an inverse relationship observed for the _Rhizobiales_ order. Its numbers were lower in the treatments with reduced tillage (Figure 6). This order of bacteria includes both plant and animal pathogens, such as the _Brucella_ and _Bartonella_ genera, the _Agrobacterium_ genus, as well as nitrogen-fixing bacteria, including the _Rhizobium_, _Sinorhizobium_, and _Bradyrhizobium_ genera [56]. The decrease in the number of OTUs of bacteria belonging to the _Rhizobiales_ order can be explained by the fact that the proliferation and activity of symbiotic diazotrophic bacteria are inhibited by the inflow of mineralized organic matter, especially the available forms of nitrogen [57]. The results of our study were in line with the aforementioned assumptions. The numbers of the _Rhizobiales_ order were most reduced in treatment 4 (strip-till cultivation) (Figure 6). The first two principal coordinates explained 95.07% of the variance in the cultivation systems based on the percentages of operational taxonomic units' dominant orders of bacteria (Figure 3C). The cultivation systems formed three groups. One of them was FA and CT, another was RT and NT, and the other was ST, which was clearly different from the others (Figure 3C).
## 2 Data and Methods
### Methods
#### 2.2.4 Correlation Analysis  
In this study, we utilize the method of time-lag intercorrelation to examine the lagged response of the upwelling phenomenon to the local wind field. The cross-correlation function (CCF) serves to represent the correlation degree between two time series, specifically the signals \(X_{[t]}\) and \(Y_{[t]}\), at two distinct moments, \(t_{1}\) and \(t_{2}\). Through this correlation function, we can identify the degree of \(Y_{[t]}\) lag when the sequence \(X_{[t]}\) may be correlated with the past lag of the sequence \(X_{[t]}\). The mutual correlation functions of the upward flow intensity, \(wui_{[t1]}\), and the wind field characteristics, \(wind_{[t2]}\), at moments \(t_{1}=t\) and \(t_{2}=t+n\), are defined as:  
\[CCF_{n}=f(wui_{t},wind_{t+n})=\frac{\sum\left(wui_{t}-\overline{wui_{t}} \right)\left(wind_{t+n}-\overline{wind_{t+n}}\right)}{\sqrt{\sum\left(wui_{t}- \overline{wui_{t}}\right)^{2}\left(wind_{t+n}-\overline{wind_{t+n}}\right)^{2}}} \tag{4}\]  
The time series of upwelling intensity and wind field characteristics are denoted as \(wui\) and \(wind\), respectively, and n represents the time lag distance [24].
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### RVE Variation Methods
#### 2.3.2 RVE Volume Fraction Variation  
Volume fraction can also be varied to create porous Functionally Graded Materials (FGMs). In the case of a strut-based unit cell, volume fraction refers to the relative thickness of the struts. Figure 8 shows the difference between a structure with a constant volume fraction and a gradient volume fraction. The second column of Table 1 is dedicated to highlighting examples of research where the volume fraction of the RVE is changed throughout the CS to result in non-homogeneous properties.  
Figure 7: RVE gradient in terms of morphology types and porosities. (**a**) Three control points. (**b**) The 3D structure integrates type Primitive of 30% porosity, type Diamond of 50% porosity, and type Gyroid of 70% porosity [55].  
Numerous papers have studied how to map the variation in the volume fraction (or conversely density) of CSs based on the gray scale gradient obtained from structural topology optimization algorithms. In such efforts, the typical goal is to ensure that the volume fraction of the RVE in the volume neighborhood maps to the topology optimization results in that region [11, 30, 51, 94, 95]. One such example is in the work by [PERSON] et al. [11], whereby they propose a method where the relative density is adjusted using the strut size in the structure. Similarly, [PERSON] and [PERSON] [29] have constructed multiple examples of CSs for which they have varied the density in a linear manner based on topologically optimized design spaces.
## 3 Results  
### Analysis of the Soil Tillage Operations and Remote Sensing Data  
The 73 farms comprised a total of 3268 agricultural parcels. On average, each farm consisted of 45 parcels. The farm with the fewest parcels had only 2, while the farm with the most parcels had 390. However, only a very small subset of these parcels, specifically 123, had soil tillage operations. Per farm, on average, there were nine soil tillage operations recorded. The highest number of operations per farm was 19, observed in a total of 27 parcels, accounting for 70% of the farm's parcels. Among the parcels with soil tillage operations, the average area was approximately 3.2 hectares. The minimum area was 0.01 hectares, while the maximum reached 43 hectares. Among all the used parcels, 70% were permanent crops. These parcels were categorized as follows: 37% were occupied by olive groves, 33% by fruit crops, 21% by temporary crops, 6% by vineyards, and the remaining 3% were distributed among permanent pastures, cork oaks, and mixed permanent crops.  
Most of the soil tillage operations occurred in April 2021, a month that accounted for approximately 50% of the total number of detected operations. About 20% of the operations took place in June 2021. No identified operations were reported in October and December 2020, as well as January and March 2021.  
When considering the total number of 15-day periods in the agricultural year of 2020-21 across all parcels where soil tillage operations were identified (a total of 2921 15-day periods), operations were identified in only 4% of the periods (123 operations).  
Figure 3 presents the time series for the Sentinel-1 VH, CR, and Sentinel-2 NDVI for a specific parcel (land cover: olive groves). In this parcel, the only soil tillage operation occurred on 21 April 2021. In this example, the highest discontinuity is observed in the NDVI time series between the image just before the operation and the image on the exact date of the operation. The operation did not produce a significant change in the VH and CR time series. There is also a significant discontinuity in the NDVI time series in January associated with olive tree pruning.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Network Infrastructure
#### 3.1.5 Data Flow Management  
The practice of capturing and analyzing network data traffic and routing it to the most appropriate resources based on priorities is known as data flow management.  
When considerable volumes of data are handled in the network, traffic-prioritization mechanisms allow for efficient bandwidth management and better distribution of node processing loads, thus preventing vital information from being lost in transit due to network saturation, either due to increased traffic or node loss [40].
## 2 Thermal and Strength Analysis of Compression and Tension Steel Elements in Fire
### Design Resistance of Tension and Compression Steel Members in Fire
#### 2.2.2 Compression Elements  
For compression members, with sections having Class 1, Class 2 or Class 3 proportions, with a uniform temperature across its cross-section, the design buckling resistance in the fire at time is expressed by formula [30]:  
\[N_{b,fi,0,Rd}=\chi_{fi}\;Ak_{y,\theta_{a}}\;\;f_{y}\;/\gamma_{M,fi} \tag{6}\]  
where:  
\(\gamma_{M,fi}\)--partial factor for material properties in a fire situation;  
\(k_{y,\theta_{a}}\)--reduction factor for the yield strength of steel at temperature \(\theta_{a}\) reached in the fire at time \(t_{fi}\), according to Table 1;  
\(A\)--cross-sectional area;  
\(f_{y}\)--yield strength;  
\(\chi_{fi}\)--flexural buckling coefficient in the design fire situation calculated according to the formula:  
\[\chi_{fi}=\frac{1}{\phi_{\theta_{a}}\;+\;\sqrt{\phi_{\theta_{a}}^{2}-\overline {\lambda_{\theta_{a}}}^{2}}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Steel Temperature \(\theta_{a}\)**} & \multicolumn{2}{c}{**Reduction Factors at \(\theta_{a}\) Temperature Relative to \(f_{y}\) or Y at 20 \({}^{\circ}\)C**} \\ \cline{2-3}  & \multicolumn{1}{c}{**Reduction Factor of the Effective Yield Strength (relative to \(f_{y}\))**} & \multicolumn{1}{c}{**Reduction Factor of the Modulus Of Linear Elasticity (relative to Y)**} \\  & \(k_{y,0_{a}}=f_{y,0_{a}}/f_{y}\) & \(K_{y,0_{a}}=Y_{0_{a}}/Y\) \\ \hline
20 \({}^{\circ}\)C & 1.000 & 1.000 \\
100 \({}^{\circ}\)C & 1.000 & 1.000 \\
200 \({}^{\circ}\)C & 1.000 & 0.900 \\
300 \({}^{\circ}\)C & 1.000 & 0.8\[\phi_{\theta_{a}}\ =\frac{1}{2}\Big{[}1+\overline{\alpha\lambda_{\theta_{a}}}+ \overline{\lambda_{\theta_{a}}}^{2}\Big{]} \tag{8}\]  
and  
\[\alpha=0.65\sqrt{235/f_{y}} \tag{9}\]  
Non-dimensional slenderness \(\overline{\lambda_{\theta}}\) at temperature \(\theta_{a}\) is given by the formula:  
\[\overline{\lambda_{\theta_{a}}}=\overline{\lambda}\Big{[}k_{y,\theta_{a}}/k_{ Y,\theta_{a}}\Big{]}^{0.5} \tag{10}\]  
where:  
\(\overline{\lambda}\)--non-dimensional slenderness in the basic design situation,  
\(k_{y,\theta_{a}},\ k_{Y,\theta_{a}}\)--respectively, the reduction factor of the yield point and the modulus of linear elasticity of steel at temperature \(\theta_{a}\), according to Table 1.
## 5 Performance Evaluation
### Experimental Setup  
We employed various Python 3.11.6 libraries for different tasks, including sklearn [53] for performance assessment, matplotlib [54] for data visualization, keras [55] for detector training, and pandas and numpy for data preparation. To train binary detectors, we merged the benign and malicious data for each consumer. For anomaly detectors, we only consider benign data. We then split the merged data into testing and training sets with a 1:2 ratio. Global detectors are built using the data from all consumers. Additionally, the selected data are divided into \(K\) clusters using the K-means algorithm, where \(K=5\) in the IRISH dataset and \(K=1\) in the PRECON dataset due to the limited number of consumers in the latter dataset. This division is used to build cluster-based detectors utilizing data from consumers within the same cluster.  
To ensure a fair comparison among all detectors, we tuned their hyper-parameters and utilized the optimal values. For OCSVM, the optimal values are as follows: kernel = 'rbf', gamma = 'auto', and nu = '0.03'. Table 1 summarize the optimal values for a DAE detector and the attacker's detectors (FFNN and CNN). The description values in these tables correspond to the layer type, number of neurons, and activation function. In terms of the hyper-parameters for adversarial evasion attacks, we explored a different range for each parameter and selected values that effectively bypass the ET detector while maximizing theft profit. For FGSM and BIM attacks, the optimal values are as follows: \(\alpha\) is a portion of optimal \(\epsilon\) for \(I=100\) and \(0.01<\epsilon<0.5\). For CW, ZOO, and DeepFool attacks, the optimal values are as follows: maximum iterations = 100, decrease factor = 0.6, learning rate = 0.01, and maximum perturbation = 0.5. Our experiments were conducted using the high-performance cluster of Tennessee Technological University.
## 4 The Cooperative Optimization Algorithm
### Solution Management Component  
The SMC stores and manages so far considered solutions and evaluations by the users. The set of solutions obtained from the OC in all major iterations is stored in a set we denote by \(\mathcal{X}\). For each solution \(X\in\mathcal{X}\), the SMC keeps track of its current surrogate objective value \(\hat{f}_{\Theta}(X)\). Hence, the surrogate objective values of the solutions in \(\mathcal{X}\) are updated in each major COA iteration whenever the EC updates the surrogate suitability function. The current best solution in \(\mathcal{X}\), i.e., the solution with the highest surrogate objective value, is denoted by \(\hat{X}^{*}\).  
All feedback obtained from the users via the presented location scenarios is collected and stored in the SMC in a hash map: Its set of keys, which we denote by \(K\), is the set of pairs \((r,v)\) with \(r\in R,\ v\in V\) for which suitability values \(w_{r,v}\) have been obtained from the users, and the respective values are the \(w_{r,v}\).  
Last but not least, through the FC, we are also able to obtain upper bounds on suitability values \(w_{r,v}\), with \(v\in V,\ r\in R\), as will be explained in Section 4.3. These upper bounds are stored in the SMC as \(w_{r,v}^{\text{UB}}\in[0,1]\).