esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Bland-Altman Plots
The mean differences in tumors were \(-11.4\) Gy (\(-68.4\); \(45.7\)) for HCC and \(-8.23\) Gy (\(-68.9\); \(52.5\)) for metastatic patients, while in NTL they were \(0.22\) Gy (\(-6.02\); \(6.47\)) for HCC and \(-1.41\) Gy (\(-11.9\); \(9.06\)) for metastatic patients. Again, these results were obtained in a smaller patient cohort (\(19\) HCC and \(20\) liver metastases), with tumor volumes delineated on the three-phase injected CT and subsequently transposed on the \({}^{99m}\)Tc-MAA SPECT/CT and \({}^{90}\)Y PET/CT studies with a slight visual adaptation to compensate for the small imperfections of co-registration and volume variations. However, this readjustment operation might have positively affected the results reported in [17] when compared to ours. In addition, the volume of the injected liver was determined on functional images using thresholding of \(1\%\) of the maximum activity, while our propagation was based on the morphological images (i.e., CT images). Of note, the search of the actual liver volume through a threshold operation on functionalimages could be distorted by blurring artifacts related to respiratory motion in both PET and SPECT images [9] and by different image resolutions. The Bland-Altman plots in [PERSON] et al. [18] also demonstrated that in 95% of cases, NTL doses calculated with \({}^{90}\)Y imaging were 21.2% below and 20.6% above whole normal liver doses calculated with MAA imaging (0.788-1.206; 95% LOA of logarithmic differences), while tumor-absorbed doses calculated with \({}^{90}\)Y imaging were 74.6% below and up to 4.5 times above tumor-absorbed doses evaluated with MAA imaging (0.254-4.485; 95% LOA of logarithmic differences).
# Probiotics in the Sourdough Bread Fermentation: Current Status
## 4 Probiotics and Postbiotics in Sourdough and the Impact on Human Health
A meta-analysis with 583 sourdough-related literature articles in the period 1999-2017 was done by [PERSON] et al. 2017 [34] based on the study of species diversity (LAB and yeasts) and process condition (type I and II). The authors concluded that the species number of yeasts is lower than LABs and the microbial diversity of type I sourdoughs is lower than type II. Through the principal component (PC) analysis, three clusters were detected. The first group had a high relative abundance of _Pichia kudriavzevii_ and/or _Torulaspora delbrueckii_. In the second group, a high relative abundance of _Wickerhamomyces anomalus_ and/or _Candida glabrata_ was established, and the third _Saccharomyces cerevisiae_ and/or _C. humilis_ species were detected. A systematic analysis from 1990 to 2020 using \"sourdough\" as a keyword showed the following number of articles by geographic location: North America (13), South America (6), Africa (31), Europe (175), Asia (54), China (20), and Oceania (1--New Zealand). In Latin America and America, very few studies have been published. _Lactobacillus__santarium_ and _Saccharomyces cerevisiae_ appeared in several sourdough microbiota characterizations [1]. It also showed that some bacteria not belonging to the LAB group could significantly function in sourdough and need to be explored more. Some species are listed in Table 1. On the other hand, in a study that characterized 500 samples from sourdough microbiota, almost all from the United States of America (429 samples) showed evidence of different bacterial groups of the Rhodospirillales order despite the Lactobacillus being the dominant order. _Lactobacillus__santranciscensis_ was the prevalent species. It was demonstrated that the acetic acid bacteria group substantially impacted aroma and leavening properties in sourdough fermentation [53]. A sourdough starter is in essence, a culture of probiotic yeast and lactic bacteria to be added to the fermentation process, besides the native microbiome of the cereals and external contaminations. Starters also can be used to propagate the sourdough as a live fermentation. In this context, it is interesting to explore the technological and biological properties of the selected starter associations of bacteria/yeast to improve the quality of fermentation and bread properties. Acidification and proteolysis analysis (including free amino acids), and volatile compound profiles are examples. In a recent review of [PERSON] et al. [53], working with 500 sourdough starters from four continents, it was suggested that geographical location has little influence on the microbial diversity of bacteria and yeast species. This result contrasts sharply with widespread assumptions relative to the biogeographic distribution of starters from sourdough [34]. In Brazil, for instance, _Lactobacillus fraciminis_ was the prevalent species rather than _Lactobacillus surfranciscensis_. Table 2 lists some starters used in sourdough and produced compounds.
## 3 Model Validation
### Time Evolution of System State
#### 3.3.1 Synthetic (Vehicular) Mobility Model  
Let us consider a scenario with \(N=100\), \(T_{c}=5\) s, and \(T_{s}=20\) s. In Figure 10a,b, we plot the numerical solutions for \(N+1-\mathcal{M}(t)\) (number of content requests), \(\Omega_{o}(t)\) (number of content items delivered through opportunistic contacts), and \(\Omega_{c}(t)\) (number of content items delivered using the cellular network), for \(\lambda_{0}=0.2\) req/s and \(\lambda_{0}=0.8\) req/s, respectively. In Figure 10, we also show the same system states obtained from simulations. We observe that the model accurately predicts the system state, effectively capturing its temporal evolution.  
Figure 10: Vehicular mobility model: time evolution of the number of content items for \(T_{c}=5\) s, \(T_{s}=20\) s, and \(N=100\). (**a**) \(\lambda_{0}=0.2\) req/s; (**b**) \(\lambda_{0}=0.8\) req/s.  
Figure 9: Real-world mobility model: system performance vs. content request rate for \(T_{c}=2\) min. (**a**) Offloading efficiency (**c**); (**b**) Content latency (min); (**c**) Number of copies.  
Figure 11a,b compare model predictions and simulation results for \(\mathcal{I}(t)\), the number of content copies at time \(t\), with the same settings as in Figure 10. Again, the plots confirm that the ODE model is accurate in predicting the number of infected nodes in the system, which also justifies the model's ability to precisely determine the offloading efficiency. Interestingly, the plots indicate that opportunistic dissemination efficiently distributes the initial content received by users via the cellular network. The initial transient phase is brief, and the number of content copies rapidly increases, then stabilises around a constant value. This confirms that the system exhibits a quasi-stationary regime. Additionally, in the same figures, we have plotted the \(C_{s}\) value as computed in Section 2.4. The results show a remarkable accuracy of the ODE model in estimating the performance of the stationary regime. However, it is worth noting that ODE-based models may not provide complete distributions of system performance metrics [9].
## 3 Preliminaries and System Model  
We modeled a buffer-aided relay network with virtual duplexing capabilities. In our model, sources communicate with their corresponding destinations through relays. The channel sate information (CSI) is initially exchanged between the relays and the source-destination pair. When this phase is completed, a centralized/distributed algorithm selects the best relay to achieve a successful communication between the source and destination.  
Figure 3: Over view of full-duplex relaying.
# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 3 Materials and Methods
### Determination of the Reference Pattern of the Signal and the Residue  
In this section, the reference pattern that the signal of each turbine follows is determined. This signal will be used as a reference to see how the signal of each turbine deviates from this standard by calculating the residual, i.e., the result of subtracting this reference from the signal of each turbine.  
By calculating the pattern signal, it is assumed that most of the time the turbines work correctly and that it is highly improbable that more than half of the turbines fail simultaneously. Under this assumption, and for each \(k\) point of the sequence, the pattern signal will be the median of the values of the 5 turbines at the point \(k\). More precisely, given the same signal of all WTs in the time instant \(k\), named \(s_{WT8x}(k)\), the pattern \(p(k)\) in \(k\) is defined as \(p(k)=median([s_{WT80}(k)\ s_{WT81}(k)\cdots s_{WT84}(k)])\). Therefore, from now on, the pattern signal will be named as the median. In Figure 4, in the upper plot, the shape of the median over-imposed on the set of example signals is represented, and in the lower plot the residuals are represented. Note that the residuals of a well-functioning turbine stay or oscillate around zero or show sporadic peaks that return to the origin, but in the case of WT82 this residual diverges very perceptibly at the moment when it was stopped.  
To improve the interpretation of the residuals, the signals can be smoothed by applying a moving average or a low-pass filter.  
Figure 3: Graphical result of the proposed outlier filtering algorithm, consisting of determining that a value will be an outlier if it is more than 3.2 local standard deviations from the local mean calculated on a moving average window of 25 samples. Unlike the traditional method, this variant takes into account the local characteristics of the signal and does not remove the correct high amplitude values. The values of the outliers are indicated with a cross.
## 4 Research Methodology
### Selection of Simulation Parameters
#### 4.2.6 Clear Water Flow Process Line  
The clear water hydrograph represents the real-time rainfall runoff during a mudslide simulation. Multiplying the clear water hydrograph by a magnification factor generates the mudslide hydrograph, which accounts for the additional volume and altered flow characteristics due to the presence of sediment.  
This study employed a rainfall-runoff method to estimate mudslide discharge, assuming that mudslides and rainstorms occur concurrently and with the same frequency. Under this assumption, the design flood discharge at the catchment outlet, including all designated inflows, is considered to be transformed into mudslide discharge. The calculation process begins by using hydrological methods to determine the design flood discharge for various rainfall frequencies at the sub-basin outlet (refer to hydrology manuals for specificcalculation methods). Subsequently, appropriate blockage coefficients are selected, and Equation (10) is used to compute the peak mudslide flow discharge.  
\[Qc=(1+\varphi)Qp\cdot Dc \tag{10}\]  
where \(Qc\) represents the peak debris flow discharge (m\({}^{3}\)/s) for a given rainfall frequency P; \(Qp\) denotes the peak flood discharge (m\({}^{3}\)/s) for the same rainfall frequency P; \((1+\varphi)\) indicates the mudslide sediment correction factor, taken as 1.50 according to Table G.2 [30] in the \"Specifications for Engineering Investigations of Mudslide Disaster Prevention and Control\"; \(\varphi\) represents the mudslide sediment correction factor; and Dc denotes the mudslide blockage coefficient, determined based on Table I.1 [30] in the same \"Specifications for Engineering Investigations of Mudslide Disaster Prevention and Control\", with a value of 2.0 adopted in this study.  
For catchment areas (F) less than 3 km\({}^{2}\), Equation (11) is used to calculate the peak flood discharge.  
\[Qp=\psi\cdot F\cdot S \tag{11}\]  
where \(\psi\) represents the stormwater runoff coefficient; \(F\) denotes the catchment area (km\({}^{2}\)); and \(S\) indicates the hourly rainfall intensity (mm/h).  
Using these equations, the peak flood discharge (\(Qp\)) and peak mudslide discharge (\(Qc\)) were calculated for each section, as presented in Table 1.  
The amplification factor \(BF\), used to convert the peak flood discharge to peak debris flow discharge, is calculated based on the volume concentration (\(Cv\)) using Equation (12).  
\[BF=\frac{1}{1-C_{V}} \tag{12}\]
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 5 Experiments
### Results and Analysis
#### 5.4.4 Parameter Experiment and Result Analysis  
To further analyze the effectiveness of the Inference Module in the Understand Unit under different numbers of settings, we tested the impact of the Inference Module on model performance for different numbers of settings. The experimental results are shown in Table 6.  
Through our investigations, we discovered that the model attained its optimal overall performance when the number of settings in the Inference Module was set to 2. Notably, as the number of settings in the Inference Module increased, we observed a decrease in the model's overall performance, which was particularly evident in the Distinct-1 and Distinct-2 metrics. The observed decline in these metrics indicates a higher occurrence of text repetition and redundancy within the generated output, which may suggest a reduced generalization ability of the model. We attributed this phenomenon to the fact that a limited number of Inference Modules failed to fully extract the intrinsic logical information embedded within the context. Conversely, an excessive number of Inference Modules can lead to the loss of contextual information or the acquisition of irrelevant details during the reasoning and retrieval process. This behavior resembles the human tendency to forget earlier information during dialogues. Therefore, by setting the number of settings for the Inference Module to 2, the DialogCIN model effectively extracted the inherent logical information within the context without suffering from information loss or learning superfluous details, thereby yielding an optimal model performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Part**} & \multicolumn{3}{c}{**Representation**} & \multicolumn{3}{c}{**Understand**} & \multirow{2}{*}{**PPL**} & \multirow{2}{*}{**BLEU**} & \multirow{2}{*}{**D-1**} & \multirow{2}{*}{**D-2**} \\ \cline{2-3} \cline{5-8}  & **Context** & **Speaker** & **Context** & & & & & \\ \hline \multirow{4}{*}{3} & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 94.93 & 0.0091 & 0.0363 & 0.1972 \\  & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 99.91 & 0.0087 & 0.0349 & 0.1926 \\  & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 98.79 & 0.0089 & 0.0344 & 0.1898 \\  & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 103.15 & 0.0079 & 0.0310 & 0.1644 \\ \hline \end{tabular}
\end{table}
Table 5: Cont.  
the response, the responses generated by DSHRED and ReCoSa did not contain any emotional bias, while only DialogCIN's generated responses aligned with the semantic and emotional context of the conversation. This observation substantiates the notion that our approach possesses a profound comprehension of the conversational context, facilitating the generation of emotionally nuanced responses.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 3 Experimental Verification
### Dual-Channel Background Radiation Mapping Experiment
(**a**) Temperature–gray level value calibration curve of reference spectral channel; (**b**) gray level–temperature calibration curve of target spectral channel; (**c**) dual-channel gray value mapping calibration curve. Figure 3: Dual-channel background radiation mapping experiment. We analyzed the error of the dual-channel background radiation mapping, set the blackbody temperature to 55 \({}^{\circ}\)C and collected the blackbody gray images under the target spectral channel and the reference spectral channel, respectively. Through the mapping relationship, the blackbody temperature distribution image and the blackbody gray image under the target spectral channel could be obtained. We calculated the difference between the mapped blackbody grayscale image and the blackbody grayscale image actually captured through the target spectral channel, and the smaller the difference, the higher the mapping accuracy. The mapping results are shown in Figure 5 and Table 1. Figure 5 is the blackbody grayscale image captured through the reference spectrum channel, Figure 5 is the mapped temperature distribution image, Figure 5 is the mapped blackbody grayscale image and Figure 5 is the gray value difference image. The values of the temperature distribution image and the gray value difference image are evenly distributed, the error of the temperature distribution image is about 0.3%, the average of the temperature distribution image is 55.138 \({}^{\circ}\)C, and the difference error of the gray image is about 1.9%, indicating that the mapping has high precision.
## 3 Results
### Solution Availability
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & **GNSS** & **Number of Used Satellites** & **Availability [\%]** \\ \hline \multirow{3}{*}{BRDM} & G & 8.9 & 100.0 \\  & E & 6.8 & 99.8 \\  & GE & 15.6 & 100.0 \\ \hline \multirow{3}{*}{HAS} & G & 5.0 & 74.0 \\  & E & 6.9 & 99.9 \\  & GE & 10.6 & 99.1 \\ \hline \multirow{3}{*}{CNES} & G & 9.0 & 100.0 \\  & E & 6.5 & 99.7 \\ \cline{1-1}  & GE & 15.5 & 100.0 \\ \hline \multirow{3}{*}{CODE} & G & 9.3 & - \\ \cline{1-1}  & E & 8.1 & - \\ \cline{1-1}  & GE & 17.4 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Availability of obtained solutions and used satellites. Averaged across all analyzed stations.
_Appl. Sci._**2015**, \(5\), 1980-1991; doi:10.3390/app5041980  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Image Hiding Scheme Based on the Atrial Fibrillation Model**  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Research Group for Mathematical and Numerical Analysis of Dynamical Systems,  
Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania;  
E-Mails: [EMAIL_ADDRESS] (M.V.); [EMAIL_ADDRESS] (J.R.);  
[EMAIL_ADDRESS] (P.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.:+370-698-22456.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 29 October 2015 / Accepted: 14 December 2015 / Published: 21 December 2015_  
**Abstract:** An image communication scheme based on the atrial fibrillation (AF) model is presented in this paper. Self-organizing patterns produced by the AF model are used to hide and transmit secret visual information. A secret image is encoded into the random matrix of initial cell excitation states in the form of a dot-skeleton representation. Self-organized patterns produced by such initial cell states ensure a secure and efficient transmission of secret visual images. Procedures for digital encoding and decoding of secret images, as well as the sensitivity of the communication scheme to the perturbation of the AF model's parameters are discussed in the paper.  
**Keywords:** pattern formation; atrial fibrillation; communication scheme
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## 3 Results
### Characterization of NPs and MNPs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degree of Freedom** & **Mean Square** & \(F\)**Value** & \(P\)**Value** & **Remarks** \\ \hline Regression & 2976.04 & 9 & 330.67 & 144.94 & 0 & Significant \\ Linear & 2546.38 & 3 & 848.79 & 372.05 & 0 & Significant \\ \(X_{1}\) & 423.48 & 1 & 423.48 & 185.63 & 0 & Significant \\ \(X_{2}\) & 1739.86 & 1 & 1739.86 & 762.64 & 0 & Significant \\ \(X_{3}\) & 383.03 & 1 & 383.03 & 167.9 & 0 & Significant \\ Square & 320.03 & 3 & 106.68 & 46.76 & 0 & Significant \\ \(X_{1}\)\({}^{2}\) & 36.28 & 1 & 21.38 & 9.37 & 0.012 & \\ \(X_{2}\)\({}^{2}\) & 281.11 & 1 & 272.98 & 119.66 & 0 & Significant \\ \(X_{3}\)\({}^{2}\) & 2.64 & 1 & 2.64 & 1.16 & 0.308 & Insignificant \\ Interaction & 109.63 & 3 & 36.54 & 16.02 & 0 & Significant \\ \(X_{1}\)\(\times\)\(X_{2}\) & 17.64 & 1 & 17.64 & 7.73 & 0.019 & Significant \\ \(X_{1}\)\(\times\)\(X_{3}\) & 23.19 & 1 & 23.19 & 10.16 & 0.01 & \\ \(X_{2}\)\(\times\)\(X_{3}\) & 68.8 & 1 & 68.8 & 30.16 & 0 & \\ Residual Error & 22.81 & 10 & 2.28 & – & – & \\ Lack of fit & 17.18 & 5 & 3.44 & 3.05 & 0.123 & Insignificant \\ Pure Error & 5.64 & 5 & 1.13 & – & – & \\ Total & 2998.85 & 19 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA of chromium removal. Figure 3: Response surface plots showing: (**a**) the effects of pH (X\({}_{1}\)), adsorbent dose (X\({}_{2}\)), and their interaction on chromium RE, with constant initial chromium concentration (X\({}_{3}\)); (**b**) the effects of X\({}_{1}\), X\({}_{3}\), and their interaction on chromium RE (%); (**c**) the effects of X\({}_{2}\), X\({}_{4}\), and their interaction on chromium RE (%). Figure 4: Response contour plots showing: (**a**) the effects of pH (X\({}_{1}\)), adsorbent dose (X\({}_{2}\)), and their interaction on chromium RE, with constant initial chromium concentration (X\({}_{3}\)); (**b**) the effects of X\({}_{1}\), X\({}_{3}\), and their interaction on chromium RE (%); (**c**) the effects of X\({}_{2}\), X\({}_{3}\), and their interaction on chromium RE (%). The surface plots shown in Figure 3a-c reveal information on the combined effects of pH (\(X_{1}\)), adsorbent dose (\(X_{2}\)), and initial chromium concentration (\(X_{3}\)) on the chromium RE. For instance, Figure 3a shows that chromium RE decreases with increasing \(X_{1}\) and increases with increase in \(X_{2}\). As can be observed from Figure 3a, there is a decrease in the chromium RE with an increase in the pH from 3.0 to 9.0. This is because of the interaction between the chromium ions and OH' ions. As can be observed from Figure 3b, there is a decrease in the chromium RE with an increase in the initial chromium concentration. Under the optimum conditions of pH = 2.6, adsorbent dose = 5 g/L, and initial chromium concentration = 20 mg/L, chromium RE of 95.8% was obtained.