esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Removal of Phenolic Compounds-Decolorization  
_Morchella_ species reduced satisfactory phenolic compounds of molasses. The overall maximum reduction ranged between 22.6-56.6% for M-20 (C/N = 20; 53.41 g/L molasses) and between 26.4-49.6% for M-25 (C/N = 25; 89.02 g/L molasses; Figure 2). The most capable species were _M. conica_ followed by _M. rotunda_ and at last _M. vulgaris_. In general, agitation enhanced the phenolic compounds' reduction. Regarding the ability of _Morchella_ species in decolorization of molasses content, only _M. conica_ in M-20 achieved it by 26.7% in static culture and 20.3% in the corresponding agitated culture, while in M-25, which contained a larger amount of waste, decolorization was not achieved. This lower (or no) percentage of color removal suggests the occurrence of another type of compound, different from the phenols that are not eliminated by the fungi assayed [62]. Compared to other studies, basidiomycete _Flavodon flavus_ removed 70% of the color from raw molasses spent wash, used at 10% concentration and when its concentration increased to 50%, w/w, about 60% decolorization was achieved [63]. [PERSON] et al. [62] carried out aerobic degradation of beet molasses (wastewater diluted to 50%) in shaking-flasks using _Penicillium_ sp., _Penicillium_ _Ignorum_ and _Aspergillus niger_. Phenolic compounds' removal was similar for the four microorganisms, _P. decumbens_ showing a maximum value of 74% after three days of treatment. The highest decolorization was achieved 4 th-5 th day of treatment, with _P. decumbens_ achieving the best results, 41% after four days of treatment. The ability of _Saccharomyces cerevisiae_ MAK-1 to detoxify blends of molasses and olive mill wastewaters under aerated and non-aerated conditions was studied by [PERSON] et al. [64]. Noticeable decolorization, 60% and moderate removal of phenolic compounds up to 28%was observed. The results of this study are very interesting as there are no studies in which _Mordechla_ species were used to examine the reduction of wastes' pollutant load (reduction of phenolic compounds and decolorization) and in particular in nutrient media with molasses. Also, most of the studies used molasses after great dilution, but in our experiments, they were used in high concentrations.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 2 Principles of Power Flow in Power Systems
### MT-HVDC Systems Power Flow
#### 2.2.2 AC Side of the Converter Station  
The PF equations of at the PCC bus of the \(i\)th converter station for the asymmetric bipolar MT-HVDC systems is as follows:  
\[P_{gi}-P_{gi}^{p}-P_{gi}^{n}=0 \tag{8}\]  
\[Q_{gi}-Q_{gi}^{p}-Q_{gi}^{n}=0 \tag{9}\]  
where \(P_{gi^{\prime}}^{p}\), \(P_{gi^{\prime}}^{n}\), \(Q_{gi}^{p}\), and \(Q_{gi}^{n}\) are the active and reactive power of the positive and negative poles of the \(i\)th converter station, respectively.  
Therefore, the apparent power at the PCC bus of the \(i\)th converter station by the positive and negative poles can be written as follows:  
\[S_{gi}^{p}=\overline{V}_{gi}^{p^{\prime}}S_{gi}^{p^{\prime}}=P_{gi}^{p}+jQ_{gi }^{p} \tag{10}\]  
\[S_{gi}^{n}=\overline{V}_{gi}^{p^{\prime}}S_{gi}^{n}=P_{gi}^{n}+jQ_{gi}^{n} \tag{11}\]Applying KCL at the PCC bus of the \(i\)th converter station leads to deriving the following equation.  
\[\overline{I}_{gi}=\overline{I}_{gi}^{p}+\overline{I}_{gi}^{p} \tag{12}\]  
Using KVL between the PCC bus and the converter terminal of the \(i\)th converter station leads to deriving the following equations.  
\[\overline{I}_{gi}^{p}=\frac{\overline{V}_{ti}^{p}-\overline{V}_{gi}}{Z_{i}^{p}} =\big{(}\overline{V}_{ti}^{p}-\overline{V}_{gi}\big{)}\big{(}G_{i}^{p}+jB_{i}^{ p}\big{)} \tag{13}\]  
\[\overline{I}_{gi}^{n}=\frac{\overline{V}_{ti}^{n}-\overline{V}_{gi}}{Z_{i}^{n}} =\big{(}\overline{V}_{ti}^{n}-\overline{V}_{gi}\big{)}\big{(}G_{i}^{n}+jB_{i}^{ n}\big{)} \tag{14}\]  
where \(Z_{i}^{p}=R_{i}^{p}+jX_{i}^{p}\) and \(Z_{i}^{n}=R_{i}^{n}+jX_{i}^{n}\). Also, \(\overline{V}_{ti}^{p}=V_{ti}^{p}e^{i\theta_{ti}^{p}}\) and \(\overline{V}_{ti}^{n}=V_{ti}^{n}e^{i\theta_{ti}^{n}}\) are the voltage of the positive and negative pole converter terminal, respectively.  
Substituting Equations (13) and (14) into Equations (10) and (11), and also separating the active and reactive power, the following equations are obtained.  
\[P_{gi}^{p}=V_{gi}\big{[}-V_{gi}G_{i}^{p}+V_{ti}^{p}\big{[}G_{i}^{p}\cos\big{(} \theta_{gi}-\theta_{ti}^{p}\big{)}+B_{i}^{p}\sin\big{(}\theta_{gi}-\theta_{ti} ^{p}\big{)}\big{]}\big{]} \tag{15}\]  
\[Q_{gi}^{p}=V_{gi}\big{[}V_{gi}B_{i}^{p}+V_{ti}^{p}\big{[}G_{i}^{p}\sin\big{(} \theta_{gi}-\theta_{ti}^{p}\big{)}-B_{i}^{p}\cos\big{(}\theta_{gi}-\theta_{ti} ^{p}\big{)}\big{]}\big{]} \tag{16}\]  
\[P_{gi}^{n}=V_{gi}\big{[}-V_{gi}G_{i}^{n}+V_{ti}^{n}\big{[}G_{i}^{n}\cos\big{(} \theta_{gi}-\theta_{ti}^{n}\big{)}+B_{i}^{n}\sin\big{(}\theta_{gi}-\theta_{ti} ^{n}\big{)}\big{]}\big{]} \tag{17}\]  
\[Q_{gi}^{n}=V_{gi}\big{[}V_{gi}B_{i}^{n}+V_{ti}^{n}\big{[}G_{i}^{n}\sin\big{(} \theta_{gi}-\theta_{ti}^{n}\big{)}-B_{i}^{n}\cos\big{(}\theta_{gi}-\theta_{ti} ^{n}\big{)}\big{]}\big{]} \tag{18}\]
## 4 Qualitative Findings and Discussion
### Individualizing Needs and Balance of Design
#### 4.3.1 Aesthetics  
The perception of LSAD aesthetics among participants was polarized, with some individuals believing it had no impact, while others contending that it affected their outdoor activities. Some young participants expressed the view that, after wearing it, they were unwilling to engage in, or intentionally avoided, socializing and activities outdoors. This is mainly because they believed it impacted their image and caused awkwardness due to the \"attention received\".  
\"I can't stand it when people always share at me when I'm walking down the street. That feeling makes me very uncomfortable. [...] Now, I'll put it on for you to see. [...] It really affects one's image, doesn't it? That's why I always tuck it inside my clothes every time.\" (Participant 4)  
Another young participant further supported this viewpoint, stating that if these LSAD could be designed in a stylish or trendy manner, in line with fashion trends, they might not feel pressured when going out.  
\"In my opining, this kind of thing is mainly used by the elderly and the sick. It's indeed awkward for young people when they go out. [...] I feel like people look at me with the impression of'so young and already with back problems'. The most embarrassing thing was when someone offered me a seat on the bus, making me feel particularly awkward. [...] Have you seen the Teenage Mutant Ninja Turtles? It's a similar feeling, not wanting others to instantly think you're a patient.\" (Participant 13)  
In contrast, the majority of older participants expressed the view that they did not become the \"center of attention\" when out in public. They were more concerned with the practicality of LSAD, and aesthetics are optional for them.  
\"I'm almost halfway into the coffin [...] The most important thing is that it's effective; I don't really care much about how others see it. Sometimes we may be too concerned about other people's opinions, but everyone has their own problems. Who can truly be perfect?\" (Participant 8)
## 2 Materials and Methods
### System Modeling and Membership Functions
#### 2.1.4 Inference and Calculation of \(\alpha(i)\)  
The inference process determines the value of \(\alpha(i)\) by applying a set of fuzzy rules based on the membership functions of the input variables defined above. The centroid method is used for defuzzification, providing a crisp output value for \(\alpha(i)\):  
\[\alpha(i)=\frac{\sum\alpha_{j}\mu_{j}}{\sum\mu_{j}} \tag{16}\]  
where \(\alpha_{j}\) is the \(\alpha\) value for a specific rule, and \(\mu_{j}\) is the membership degree associated with that rule.  
The output power \(P_{PVC}(i)\), which will be injected into the distribution network, is calculated using discrete the low-pass filter formula:  
\[P_{PVC}(i+1)=\alpha(i)\times P_{PVC}(i)+(1+\alpha(i))\times P_{PV}(i) \tag{17}\]  
Therefore, to achieve such power, it is necessary for the storage system to deliver the following power:  
\[P_{LI}(i)=P_{PVC}(i+1)-P_{PV}(i) \tag{18}\]  
The ranges used in the membership functions \(SoC_{LI}(i)\), \(P_{pv}(i)\), and \(\alpha\) were defined based on the specific capacities of the isolated microgrid evaluated at the Microgrid Laboratory of the University of Cuenca. For example, the output \(\alpha\), which represents the smoothing factor, was modeled using triangular membership functions in a range between 0 and 10, with five linguistic labels: Very Low, Low, Medium, High, and Very High. These categories allow power fluctuations to be adjusted according to system conditions.  
The minimum and maximum limits of \(SoC_{LI}(i)\) and \(P_{pv}(i)\) were established according to the nominal capacities of the solar panels (15 kWp), the storage system (44 kWh), and controlled tests, such as a constant three-phase load of 20 kW and intermittent solar generation. The ranges were validated through MATLAB (R2024b) simulations and real-time testing, ensuring that the fuzzy logic controller responds appropriately to operating conditions.
## 3 Results and Discussion
### The Effect of Different Treatments on Soil Property and Soil Crystal Structure
#### 3.2.2 The Effect of Different Treatments on Soil CEC  
As shown in Figure 4, soil CEC of the treatments with stabilizer were significantly higher than that of CK (CEC: 10.15 cmol-kg\({}^{-1}\)). The content of soil CEC after the four treatments (BC, BC + FeCl\({}_{3}\), BC + FeSO\({}_{4}\) and BC + Fe) significantly increased by 2.13, 2.16, 2.01, and 2.58 cmol-kg\({}^{-1}\) respectively compared with that of the control sample at the end of incubation. CEC refers to the amount of cationic ions in the soil that can be absorbed by the soil colloid [52]. Its amount can reflect the soil system buffering ability, which is also an important basis for evaluating synthetical fertility and soil improvement. The soil CEC got increased after the use of stabilizer, which means that the fertility of the soil also got increased. The difference of soil CEC by the treatment of (BC) and iron-based materials is not significant, but there are significant differences with the treatment of blank control. This indicates that the soil CEC can be significantly increased by adding the silk-worm excrement biochar. Due to the micropore structure of biochar, it can adsorb a lot of mineral elements, so causing the application of biochar to increase the soil CEC [53]. In addition, the increase of soil CEC may be  
Figure 3: The effect of different treatments on soil pH. Note: different letters for the Figure 3 indicates that the differences are statistically significant (\(P<0.05\)).  
related to relatively high content of ash of silk-worm excrement biochar in this study. [PERSON] et al. [PERSON] et al. (2018) reported that because insect feces tend to have high ash content, biochar made from them has higher CEC than charcoal or straw, which leads to a significant increase in soil CEC by the application of silk-worm excrement biochar compared with CK.
## 2 Methods
### Response Surface Method  
There exist three fundamental experimental design methodologies for investigating multifactorial and multilevel phenomena: orthogonal experimentation, uniform design, and response surface method. The orthogonal experimental methodology employs an orthogonal table to select a limited number of experiments that exhibit high representativeness, thereby facilitating optimal or improved conditions. This approach is characterized by uniform dispersion and clear comparability among the selected tests. The uniform design method produces complex statistics, fewer trials, and higher sampling error, and it is not appropriate in the presence of considerable experimental error. The response surface methodology (RSM) is a systematic approach employed to identify optimal parameter values utilizing experimental design and data acquisition. This process is subsequently followed by the formulation of regression equations that establish relationships between the optimized variables and the desired response [29; 30]. It offers the advantages of intuition, comprehensiveness, and resource efficiency, as it can visually communicate the input-output relationship, comprehensively reflect the relationship and interaction of components, minimize the number of experiments, and enhance efficiency. This study utilized nozzle jet pressure X, target distance X\({}_{2}\), and impact angle X\({}_{3}\) as independent variables. In the context of water jet cleaning and decontamination in deep-sea environments, the pressure of the water jet impacting the track tooth directly influences the de-attachment effect. Hence, the pressure of the jet hitting the track tooth was considered as the response variable, denoted as Y. The experimental ranges and levels of the independent variables are detailed in Table 2. Based on the coded values in Table 2, 17 groups of numerical experiments with 5 center points were designed, with results presented in Table 3. Following the synthesis and analysis of the experimental results utilizing Design Expert software (v.13), a response surface model was developed employing the least-squares method [31].  
\[Y=\beta_{0}+\sum\
olimits_{i=1}^{k}\beta_{i}X_{i}+\sum\
olimits_{i=1}^{k} \beta_{ii}X_{i}^{2}+\sum\limits_{\begin{array}{c}i,j\\ i<j\end{array}}^{k}\beta_{i,j}X_{i}X_{j} \tag{4}\]  
where \(Y\) is the response variable, \(x\) is the design variable, \(\beta_{0}\) is the linear term, \(\beta_{i}\) is the slope of the variable \(x_{i}\), \(\beta_{ii}\) is the quadratic term of the variable \(x_{i}\), and \(\beta_{i,j}\) is the coefficient of the cross-product term of \(x_{i}\) and \(x_{j}\).  
Figure 4: Verification of grid independence.
## 4 Discussion
### Expression of EGFR and HER-2 in Ascitic Fluid-Derived Ovarian Cancer Cells  
We have also demonstrated that ascitic fluid-derived cells have measurable levels of EGFR and HER-2 protein expression. Of these samples, 45% are EGFR positive cells, 40% are EGFR/HER-2 positive and 15% have low EGFR/HER-2. However, we did not detect any cells that had HER-2 protein expression alone. Even though EGFR and HER-2 protein expression has been studied in ovarian cancer, those previous studies only identified the proteins in primary and secondary tumours, and not in cells from ascitic fluid [13; 14; 32]. However, a recent study has shown that both ascitic fluid-derived cells and cells from solid ovarian tumour sites have broadly expressed HER-2 protein [33]. There is a notable discrepancy of the expression levels of these proteins in these previous studies; this is perhaps due to the different technical platforms utilised to assess the protein expression. EGFR protein immuno-expression was seen in 28% of primary and 33% of corresponding recurrent ovarian serous carcinomas evaluated in a cohort of 80 patients [34]. Another study showed that primary serous ovarian cancer has varied levels of immunostained EGFR (7%), pEGFR (12%), HER-2 (5%), PAKT (8%) and pERK (37%). This study also showed a marginal increase of similar proteins in a small number of patients with recurrent disease [32]. HER-2 protein and its gene expression level are significantly higher in ovarian cancer patients, compared to patients with benign ovarian tumours and normal ovaries [14]. In a cohort of 52 patients with invasive epithelial ovarian cancer, EGFR is expressed in 59% of the cases, and HER-2 expression is found in 35%, without any mutations of the tyrosinekinase domains of EGFR and HER-2 [35]. The study showed that of 50 patients with high-grade serous ovarian carcinomas, HER-2 was only expressed by 29%. More importantly, this study also showed that cells derived from astic fluid over-express HER-2 when compared to normal ovarian epithelial cells. However, the study did not use alternative protein markers to confirm whether cells derived from astic fluids were actually malignant ovarian cells. Another study conducted in a large cohort of ovarian cancer patients with 67% having the serous ovarian cancer subtype, showed that only about 7% of HER-2 genes were over-expressed and amplified [36]. Yet, another study delineated that the overall expression of HER-2 in ovarian cancer was about 33%, and that patients with HER-2 positive ovarian tumours had an increased risk of mortality [37].
## 2 The Influence of Different Factors on Oral Microbiome
### Diseases  
#### 2.2.1 Chronic Systemic Diseases  
Family history and chronic diseases such as endocarditis, diabetes, Crohn's disease, ulcerative colitis, and gastrointestinal malignancies significantly impact the oral microbiome [55]. These conditions alter systemic inflammation and immune responses, thereby influencing oral microbial communities [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68]. For instance, diabetes is well known for its effects on the oral microbiome, leading to increased levels of pathogenic bacteria and a higher prevalence of periodontal disease. The hypergly environment in diabetic patients promotes microbial growth and inflammation, resulting in a dysbiotic oral microbiome, characterized by increased levels of pathogenic bacteria such as _Porphyromonas gingivalis_ and _Fusobacterium nucleation_[56]. These bacteria are known to promote chronic inflammation and have been implicated in the pathogenesis of OSCC. The persistent inflammatory state and impaired immune response in diabetes may facilitate the malignant transformation of OPMD to OSCC [69]. Similarly, chronic inflammatory diseases like [PERSON]'s disease and ulcerative colitis can affect oral microbial populations due to the systemic inflammatory burden they impose. Patients with these conditions often exhibit altered oral microbiomes, with increased presence of pro-inflammatory bacteria, which may contribute to the development of OPMD and its progression to OSCC [57; 58; 59; 60; 61; 62; 70]. Gastrointestinal malignancies, such as colorectal cancer, can also influence the oral microbiome. Studies have shown that patients with gastrointestinal cancers often have distinct oral microbial profiles, which may reflect the systemic changes in the immune system and the inflammatory milieu associated with OSCC [63]. These alterations in the oral microbiome could potentially serve as non-invasive biomarkers for early detection and monitoring of these malignancies [64; 65]. Endocarditis, an infection of the inner lining of the heart, is another condition that significantly impacts the oral microbiome. The presence of specific oral pathogens, such as _Streptococcus mitis_ and _Fusobacterium nucleatum_, has been linked to an increased risk of this chronic disease. These bacteria can enter the bloodstream through periodontal lesions, leading to systemic infections. The chronic inflammatory state induced by these pathogens may create a pro-carcinogenic environment in the oral cavity, possibly facilitating the development and progression of OPMD to OSCC [66; 67; 68; 71].
# Digital Processing of Seismic Data from Open-Pit Mining Blasts
## 4 Results and Discussion
This becomes possible since all the other parameters (substance mass, rock nature) influencing the blast wave propagation used in all five experiments were identical. The assessment of the results is presented in the form of a graph in Figure 8.  
Figure 4:0 Distance and energy correlation estimation for the wavelet analysis on each frequency range (blue line—the correlation coefficient for different frequency ranges, red lines—0.9 limits of correlation coefficient). Figure 4:1 The signal wavelet analysis. As illustrated, the correlation coefficient is more than \(\pm\) 0.9, namely, \(-\)0.96 is estimated on the 12 th frequency range. It can be assumed that, in order to predict the distance values, it is necessary to analyze the value of the wavelet energy transform at the 12 th frequency range. Additionally, a close correlation is observed on the 2, 4-8, and 11-16 ranges. This evidence proves that, for distance estimation, it is necessary to analyze several frequency ranges at once. However, to verify this assumption, we need additional experimental data. The fractal analysis provided additional information on the seismograms [39]. Primarily, it showed the chaotic system delay time at different distances, as well as the embedding dimension determined from these data. It was also possible to determine a correlation dimension, entropy and predictability time for each signal. The results are presented in Figure 4:2  
The final values of the Entropy exponent show the chaotic nature of the system under consideration and exclude its randomness (since the exponent is a final number, which does not tend to infinity, and entropy greater than zero indicates the existence of chaos). According to the data in Table 3, entropy and correlation dimension values decrease as the distance from the blast center increases. This proves the decreasing influence of various parameters on the blast wave propagation. The entropy value decreases by a factor of 8.5, and the correlation dimension decreases by a factor of 14.8. Thus, chaos decreases 1.74 times slower than the number of parameters affecting the system. Both characteristics tend to zero, which means that the forecast horizon for all parameters is sufficient and the signal under investigation is not a random value at the entire experimental area. Predictability time was determined for each time series. The low-level forecast (for minimal distance to the blast) is possible for 5 points (values), and the max-level one is possible (for max distance) for 50 points. Predictability time shows that a chaotic time series can be continued for a limited number of steps when the system itself is unclear.