esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### ANN Parameter Setting  
An ANN is a method for performing supervised machine learning in which the number of neurons in each layer determines the efficiency and accuracy of model computation. Therefore, understanding the operation and principles of each network layer and variable is conducive for establishing a suitable deep neural network to obtain the desired results. The ANN constructed in this study comprised one input layer, two hidden layers, and one output layer. (1) Input layer: For the convenience of network training, the text data used for training and validation were transformed into numerical data, and the transformed data were used to represent the input features. For example, the values of each major risk factor were used as weights (eigenvalue vectors) in the AHP. The goal values were set as the auditing score ranges of the four auditing grades, and the data dimensions were set as 19 \(\times\) 4 \(\times\) 948 in accordance with the collected 948 auditing projects. (2) Hidden layers: The two hidden layers of the constructed network contained 1500 neurons each. A sufficient number of neurons and layers enables an ANN to approximate complicated continuous functions. ReLU was used as the activation function in both hidden layers because of its unlimited output range. Moreover, because of the high gradient descent speed, the training duration for these layers was short. (3) Output layer: The Softmax function was used as the activation function of the output layer. The construction auditing records were classified into different grades according to the corresponding auditing scores and were used to predict the auditing grade (construction project quality) for each input data point.  
The learning rate affects the results of ANN learning and determines the number of parameters input per update. An excessively high or low learning rate might have adverse effects on model prediction results. If the learning rate is excessively high, the developed model might exhibit excessive deviation. Moreover, if the learning rate is excessively low, the developed model has a low convergence speed. A suitable learning rate can result in a high model training speed. The learning rate is determined in the parameter adjustment process, during which stochastic gradient descent is used to determine the optimal weight and bias combination. These parameters and functions are repeatedly tested to achieve the optimal prediction accuracy. The optimal learning rate enables the accurate updating of the derivative of stochastic gradient descent, which allows the minimal loss function to be achieved in each epoch. In this study, the Adam optimizer was used to optimize the learning parameters of the constructed ANN. Table 3 presents the structural layers and parameters of the constructed ANN.
## 3 Theoretical Model of Slope Seepage and Stability under Rainfall Conditions  
### Seepage Theory  
The calculation of rainfall infiltration considering the unsaturated zone can better reflect the actual situation. The differential equation for saturated-unsaturated seepage in a homogeneous isotropic medium expressed by the improved Darcy's law is as follows:  
\[\frac{\partial}{\partial x}\left(k_{\text{u}}\frac{\partial h_{p}}{\partial x} \right)+\frac{\partial}{\partial y}\left(k_{\text{u}}\frac{\partial h_{p}}{ \partial y}\right)+\frac{\partial}{\partial z}\left(k_{\text{u}}\frac{ \partial h_{p}}{\partial z}\right)+\frac{\partial k_{\text{u}}}{\partial z}= \left[C\left(h_{p}\right)+\beta S_{\text{s}}\right]\frac{\partial h_{p}}{ \partial t} \tag{1}\]  
\[k_{\text{u}}=k_{\text{s}}k_{\text{r}}\left(h_{p}\right) \tag{2}\]  
\[S_{\text{s}}=\rho_{\text{w}}g(\alpha_{\text{r}}+\phi\alpha_{\text{w}}) \tag{3}\]  
where \(k_{\text{u}}\) is the unsaturated hydraulic conductivity (m/s); \(k_{\text{s}}\) is the saturated hydraulic conductivity (m/s); \(k_{\text{r}}\) is the relative permeability; \(h_{\text{p}}\) is the pressure head (m); \(C\) is the water capacity (\(-\)); \(S_{\text{s}}\) is the specific storage (m\({}^{-1}\)), for unsaturated media, \(S_{\text{s}}=0\); \(\beta\) is the selection parameter (\(-\)) with saturated region \(\beta=1\) and unsaturated region \(\beta=0\); \(\alpha_{\text{r}}\) is the compressibility of rock (\(-\)); \(\alpha_{\text{w}}\) is the compressibility of water (\(-\)); \(\phi\) is the porosity (\(-\)); and \(\rho_{\text{w}}\) is the density of water (kg/m\({}^{3}\)).  
The water content curve (moisture characteristic curve) of the material expresses the relationship between the volumetric water content \(\theta\) and the matrix suction \(\psi\). Numerous experiments and numerical simulations [17; 18] showed that the classical Van Genuchten model with appropriate parameters is suitable for representing the water content relationship in unsaturated fractured rocks and soils, and accordingly, accurate seepage field results are beneficial for slope stability analysis, expressed as  
\[S_{\text{e}}=\frac{\theta-\theta_{\text{r}}}{\theta_{\text{s}}-\theta_{\text {r}}}=\left[\frac{1}{1+\left(\alpha\psi\right)^{n}}\right]^{m} \tag{4}\]  
where \(S_{\text{e}}\) is the effective saturation (\(-\)); \(\theta_{\text{s}}\) is the saturated volumetric water content (\(-\)); \(\theta_{\text{r}}\) is the residual volumetric water content (\(-\)); and \(\alpha\), \(n\) and \(m\) are empirical parameters indicating the shape of the soil moisture characteristic curve with \(m=1-1/n\).  
The permeability function (\(k_{\text{u}}-\psi\) equation) of the material is related to the relationship of \(\theta-k_{r}\). The moisture characteristic curve is brought into the [PERSON] [19] conceptual model to calculate \(k_{\text{r}}\):  
\[k_{\text{r}}=S_{\text{e}}^{1/2}\left[1-\left(1-S_{\text{e}}^{1/m}\right)^{m} \right]^{2} \tag{5}\]  
Combining Equations (4) and (5), the permeability function of the material can be obtained.
## 4 Numerical Examples
### Case in Which the Height \(H\) Is Fixed
Figure 5: Bending moment (**a**) and vertical deflection comparative (**b**) for linear (**solid line**) and nonlinear (**dashed line**) in the case in which the height \(H\) is fixed. Regarding the computational performance of the CRO-SL in this problem, Figure 6a,b shows the number of larvae (potential solutions to the problem) getting into the reef per substrate, over the iterations. Note that a new individual can enter in the population on a random place, if its associated fitness value is better than the occupied one, or if the place is empty. Due to the initial hollows in the reef, in the first iterations of the algorithm's run there is a higher probability to enter, independently of the quality of solutions, so this number is usually greater at the beginning of the algorithm's run. In Figure 6c,d, the accumulative ratio of times that each substrate is producing the best larva is shown. It is remarkable that the best larva might not be better than the fittest coral of the reef. Regarding both figures, it can be pointed out that DE and NaFa substrates are good in search space exploration and exploitation. On the one side, they get into the reef a higher amount of larvae, which means, that they usually produce fitter individuals that the existed ones in the reef. It can be observed that GM does not show a good performance, as the solutions proposed by this method never fit in the reef. In addition, note that around 50% and 30% of the iterations these substrates, respectively, are producing the best larva in the linear model. Furthermore, in the nonlinear model DE substrate reaches the 80%. Other substrates such as HS, GM, MPx, or 2 Px, are not producing fitted individuals because they are collaborating in a further exploration of the search space, instead of its exploitation. Figure 6: Number of larvae entering to the reef per substrate for the linear (**a**) and nonlinear (**b**) cases, and ratio of times that each substrate generates the best larva in the linear (**c**) and nonlinear (**d**) cases when the height \(H\) is fixed. Figure 7 shows the fitness evolution for the linear and nonlinear cases. Note that the linear solution produces the lowest value of the functional, though it needs 37 iterations to be close of the final functional value.
## 2 Experimental
### Preparation of Thermal Conductivity Enhanced SSPCMs  
EGP (1-12 wt %), MWCNT (1-10 wt %), or EGP\({}_{\mathrm{x}}\)-MWCNT\({}_{\mathrm{10-x}}\) (10 wt %), was mixed with SBS, HDPE, and paraffin (with a mass ratio of 30/10/60). Next, the mixture was melt-blended at 140 \({}^{\circ}\)C for 10 min in a torque rheometer (RM-200A Rheometer, Harbin Hapro Electrical Technology Co., Harbin, China) at 60 rpm. Then, it was hot-pressed at 140 \({}^{\circ}\)C into disks for thermal conductivity determination. The compositions of the SSPCMs (shape-stabilized phase change material) are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Density** & **Thermal Conductivity** & **Specific Heat** & **Melting Point** \\  & **(g/cm\({}^{3}\))** & **(W/mK)** & **(J/gK)** & \({}^{\circ}\)C** \\ \hline HDPE & 0.94–0.96 & 0.44 & 2.3 & 130 \\ SBS & 0.92–0.95 & 0.16 & 1.9 & - \\ Paraffin & 0.915 & 0.14 & 2.5 & 20–25 \\ EGP [20] & 1.8 & 100 & 0.71 & - \\ WMCNT [36; 37] \({}^{\star}\) & 1.8 & 200/3000 & 0.71 & - \\ \hline \hline \end{tabular} \({}^{\star}\)The thermal conductivity of a single MWCNT is over 3000 W/mK [37], while the value of a MWCNT film in the nanotube axis direction is about 200 W/mK [36].  
\end{table}
Table 1: The thermo-physical properties of the materials.
## 4 Discussion  
After obtaining the prediction results of each method from the three regions, for further analysis and discussion, we use Equations (10) and (12) to calculate the prediction accuracy indicators of each method separately. The results are shown in Table 4.  
It can be observed from Table 4 that in the three regions, the PICP values obtained by each method are all greater than 0.90, indicating that the prediction effects of these methods are excellent. However, the PICP of the interval prediction method (M1) proposed in this paper is the largest, and at the same time, the corresponding PIAW is smallest. This observation illustrates that the proposed method in this paper is superior to the commonly used interval prediction methods of which these three are representative.  
The comparison shows that the prediction interval coverage of M2 is better than that of M3 and M4, but its interval width is too large. The interval average width of M4 is narrower, but the success rate of interval prediction is not high. In general, each of these three methods has advantages and disadvantages, and it is impossible to choose an optimal method. However, the optimal parameters selected in this paper are based on the same importance of PICP and PIAW, but at times it is necessary to emphasize security in the scheduling process, and thus the requirements for PICP are higher in the two evaluation indicators. Applying the method in this paper endows the PICP with a higher weight, but because of the need to consider high security, the prediction process tends to be conservative, resulting in an increase in the interval width. In contrast, at times we require a narrower prediction interval in the scheduling process. In this case, the requirements for PIAW are higher. The smaller the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **PICP(R1)** & **PIAW(R1)** & **PICP(R2)** & **PIAW(R2)** & ** PICP(R3)** & **PIAW(R3)** \\ \hline M1 & 0.979 & 219.238 & 0.979 & 228.249 & 0.969 & 253.741 \\ M2 & 0.969 & 240.773 & 0.969 & 243.602 & 0.958 & 263.314 \\ M3 & 0.958 & 235.523 & 0.958 & 237.006 & 0.958 & 260.336 \\ M4 & 0.948 & 220.913 & 0.948 & 231.248 & 0.948 & 258.471 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison between the four methods.  
Figure 6: Prediction results of applying four methods in region 3: (**a**) Prediction results based on the optimal parameter \(K\) and \(t\); (**b**) prediction results of ANN algorithm; (**c**) prediction results based on ELM; (**d**) prediction results of GPR algorithm.  
PIAW, the easier it is to meet the requirements such that the PIAW is endowed with a higher weight when applying this method, but this increases the probability of prediction failure and decreases PICP. It can be observed that the selection of the optimal parameters for the prediction model actually changes with different situations, and the actual scheduling must be considered.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 3 Regulation of Related Product Biosynthesis by PAS-LuxR Family Regulators
### Regulation of Polyene Macrolide Biosynthesis by PAS-LuxR Family Regulators
#### 3.1.1 Vertical Regulatory System Formed with SARP-LAL and PAS-LuxR Family Regulators
The expression of _pteB_, _pteC_, and _pteD_ is also reduced, and the downregulated expression of _pteB_ and _pteC_ was consistent with those of _pteA3_, _pteA4_, and _pteA5_. Therefore, they were cotranscribed as _pteA3_-_A4_-_A5_-_B_-_C_, and the promoter of _pteA3_ was regulated by PteF. By contrast, _pteR_, _pteH_, and _pteG_ expression levels were upregulated in _ApteF_ compared with those in wild-type strain, suggesting that PteF may be a negative regulator or that the disruption of PteF may activate the negative regulator of the genes. For the two other filipin producers, FilF- FilR and PteF-PteR correspond to the regulator combinations of PimM-PimR. SARP-LAL family regulator PimR can bind to heptameric repeats separated by four nucleotides, TGGCAAAGAAGCGGCAGGTGTCGCAAG, at least two protein monomers are required  
Figure 6: Phylogenetic tree of PAS-LuxR family regulators. for effective binding, and the bound _pimM_ region does not overlap with the -35 box [31]. Considering the potential site of TGGCAAGAAGCGGAGTGTTCGGCAAG in the promoters of _petF_ and _filF_, the vertical regulatory system formed with SARP-LAL and PAS-LuxR family regulators in the filipin pathway.
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 4 Discussion
### The Potential of Service Engineering  
While the energy industry strives to implement DTs as soon as possible to leverage its potential benefits, one of the biggest current obstacles remains to be that in-depth knowledge from various domains is required. Within the energy sector, most technical contributors have a background in electrical, chemical, mechanical, thermal or operations engineering, due to the fact that IES to date have been very complex systems requiring profound expertise in these fields to manage and develop. Industry 4.0, and with it concepts such as the DT, set a paradigm shift in motion, introducing more and more novel technology from information and communications technology to this sector. While energy domain experts struggle to understand modern IoT methods and what the implications for their work are, computer scientists typically do not fully comprehend the intricacies of IES and the challenges of operating them. The DT platform provides a common understanding of operational DTs, and it also defines clear interfaces to separate the work of the engineersand the computer scientists. In this way, everyone can focus on their strengths while still working efficiently on the big picture: the DT.  
The engineering of complex IES-related models and services will still rely on expertise and profound experience with the respective assets in the years to come. However, these services can be encapsulated in standardized form in the DT platform. Deployment is facilitated by providing an appropriate IT infrastructure, making information from both physical and virtual entities easily accessible. Thus, on one hand, IES experts who develop new services and models have clear interfaces within the DT platform. Therefore, they can concentrate on application-related domain problems instead of deployment and connection within the DT.  
On the other hand, computer scientists can focus on the infrastructure to provide an open, scalable, reliable, and secure DT platform solution by converging the operation technology and IT world. In this context, interoperability is key for integrating third-party systems and providing openness to enable new business opportunities. Therefore, interoperability must be established on a technical, syntactical, semantical, and operational level.  
We presume that on the technical side, interdisciplinary work in DT development between computer scientists and energy system experts will have some distinct focus points. We see such a major overlap in the area of knowledge representation, i.e., the development of ontologies or knowledge graphs for IES. Methodologies such as METHONTOLOGY [117] structure and guide the work to build ontologies, but they also rely on the knowledge of domain experts.
## 3 Preliminary Methodology
### Verification and Validation of the Digital Twin Through Simulation  
Simulation and functional testing represent a crucial phase in validating the digital twin. This stage involves simulating various operating scenarios to identify design flaws, such as collision zones or improperly implemented control logic. The digital twin is linked to the PLC control logic, allowing the verification of code implementation under simulated conditions.  
Additionally, the model's interoperability is assessed by testing its functionality across different software environments and ensuring compatibility with other simulation tools. This phase is essential for detecting and resolving potential issues before the system's physical implementation, ensuring a more reliable and optimized deployment.
## 4 Discussion
### Limitations and Future Directions  
Several limitations should be acknowledged. First, the study included only young, healthy adults, which limits the generalizability of the findings to older individuals or populations with musculoskeletal disorders. Second, although the use of global sagittal joint angles provides a standardized and objective measure of posture, it does not capture underlying muscle activation or pressure distribution, which are essential for understanding the physiological basis of postural control. Third, potential confounding factors such as time of day, participant fatigue, and diurnal variation were not controlled or recorded, even though these variables may influence postural behavior. Additionally, the experimental protocol did not incorporate functional tasks typically performed while seated--such as computer work, reading, or interpersonal interactions--which would likely influence postural adjustments and biomechanical demands in real-world contexts. Although the controlled laboratory setting allowed for precise measurement of joint angles, it reduced ecological validity by overlooking how individuals naturally adjust their posture in response to environmental and task demands. For instance, postural strategies used during active computer work or customer-facing roles would likely differ from the static postures measured in this study.  
To improve ecological validity and better reflect real-world scenarios, future research should incorporate task-based seated activities and extended observation periods to capture natural postural adaptations over time. In addition, integrating complementary tools such as surface electromyography or infrared thermography could provide deeper insights into the neuromuscular and thermal correlates of postural variability. These modalities have been successfully applied in posture-related contexts, including the evaluation of idiopathic scoliosis [57] and combined posture-thermography assessments in healthy individuals [58]. Furthermore, sensor-based motion tracking systems have shown promise for ergonomic risk evaluation during task engagement [59]. Incorporating these technologies could help elucidate the dynamic and individualized nature of seated posture in occupational and clinical applications.  
Future research should examine postural variability across diverse age groups, occupational settings, and clinical populations. Longitudinal studies are needed to determine whether postural patterns change with prolonged exposure to specific chair types or posture training. Experimental designs should also incorporate longer observation periods with continuous monitoring to track postural evolution during realistic seated tasks. Moreover, integrating surface electromyography, pressure mapping, or infrared thermography with kinematic analysis may yield deeper insights into the neuromuscular and biomechanical mechanisms underlying posture regulation. Task-based evaluations--such as typing, reading, or interacting with clients--would further enhance ecological validity and support the development of targeted ergonomic interventions.
# Resveratrol: A Review on the Biological Activity and Applications
## 3 Antitumor Properties
### Epidermoid Carcinoma and Melanoma  
Nonmelanoma skin cancer is the most frequent malignancy caused by solar UV radiation, principally by its UVB segment (290-320 nm). RSV is a powerful antioxidant known as a \"photochemopreventive agent\" that can improve the damages provoked by UVB exposure to the epidermis. In a study on hairless mice, the topical application of RSV suppressed the UVB-induced effects, such as an increase in bifold skin thickness, skin edema, induction of COX and ornithine decarboxylase (ODC) enzyme activities, and protein expression of ODC, which are important factors in tumor development [113; 114].  
Regarding epidermoid carcinoma, the effect of RSV on transformed keratinocytes exhibiting mutant p53 was analyzed. It induced G1 cell cycle arrest by significantly suppressing G1 cell cycle regulatory proteins, including cyclins A and D1, CDK6, and p53-independent induction of p21 WAF1. RSV also down-regulated activating protein 1 DNA-binding and promoter activity, repressed MAPK/ERK1 or ERK1/2 signaling, and inhibited c-Jun [115].  
With reference to melanoma treatment, the aim of various surveys was to investigate the aptitude of RSV to complement chemotherapy. In vitro RSV substantially reduces melanoma cell viability, targeting only the cancer cells and exempting fibroblast cell lines.  
In comparison to the administration of temozolomide alone, the treatment of malignant cells with 50 \(\upmu\)M RSV and temozolomide for 72 h strongly augments cytotoxicity [116]. Western blot analysis confirmed that RSV generates the apoptosis of human melanoma cells by increasing the expression of Bcl-2-associated X protein and Bcl-2, presumably through the p53 pathway and stimulation of caspase-9 and caspase-3 [117].  
In a study intended to evaluate the chemopreventive and antimutagenic action of RSV against skin neoplasm in vivo, croton oil-induced increase in ODC effects of dorsal epidermis cells in mouse and mouse ear edema model was used to highlight the anti-promotion activity of RSV. Also, 7,12-dimenthylbenz[\(a\)]anthracene (DMBA)/croton oil was utilized to cause mouse skin cancer. Administration of RSV reduced ODC activity in croton oil-induced dorsal epidermis. It also was demonstrated that RSV could inhibit mouse skin papilloma produced by DMBA/croton oil, which involves the expansion of cancer occurrence duration, reducing the prevalence of papilloma and decreasing tumor number per mouse [118].