esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Quantitative Evaluation
#### 4.3.2 Structural Similarity Index (SSIM)  
SSIM is a metric that evaluates the structural similarity between two images. SSIM imitates the human visual system (HVS) to implement the relevant theory of structural similarity and is more sensitive to the perception of local structural changes in the image. It quantifies the quality of an image from three aspects: brightness, contrast, and structure. The mean is used to estimate brightness, the variance is used to estimate contrast, and the covariance is used to estimate structural similarity. SSIM values range from 0 to 1. The larger the SSIM value between two images, the closer they are. When its value is the maximum value 1, the two images are the same. Given two pictures of \(X\) and \(Y\), the illumination \(L(X,Y)\), contrast, and structure between the two are as follows:  
\[L(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+c_{1}^{\prime}} \tag{16}\]  
\[C(x,y)=\frac{2\sigma_{x}\sigma_{y}+c_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}^ {\prime}} \tag{17}\]  
\[S(x,y)=\frac{\sigma_{xy}+c_{3}}{\sigma_{x}\sigma_{y}+c_{3}}. \tag{18}\]  
SSIM is calculated as in Equation (19).  
\[SSIM(x,y)=[L(x,y)^{\alpha}\cdot C(x,y)^{\beta}\cdot S(x,y)^{\gamma}]. \tag{19}\]  
Let \(\alpha=\beta=\gamma=1\), and then the SSIM can be calculated as in Equation (20):  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{20}\]  
where \(\mu_{x}\) and \(\mu_{y}\) represent the pixel gray average values of images \(X\) and \(Y\), respectively; \(\sigma_{x}\) and \(\sigma_{y}\) represent the pixel gray standard deviation of image \(X\) and image \(Y\), respectively; \(\sigma_{xy}\) represents the covariance of the grayscale of the image \(X\) and the image \(Y\) pixels; \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are two constants to avoid division by zero; and \(L\) represents the maximum range of pixel values. Generally, the value of 8-bit integer data is 255, and the value of floating point data is 1. In general, \(k1=0.01\) and \(k2=0.03\).
# Effect of Impeller Trimming on the Energy Efficiency of the Counter-Rotating Pumping Stage
## 1 Introduction
They also clarified the role of turbulence-induced blade configuration in improving rotary engines [17]. The implementation of various bowl designs in an HPDI natural gas engine, focused on performance and pollutant emissions, was presented in [18]. After considering all the above, the following problems can be formulated. The effect of impeller trimming on the counter-rotating stage should be investigated. There are still no analytical formulae for calculations. However, such an energy transfer requires thorough study. This is because it can clarify the pump's operation principle for counter-rotating stages. This study aimed to analyze the energy efficiency of a counter-rotating stage of centrifugal pumps by impeller trimming. To achieve this goal, the following objectives were formulated. Firstly, hydrodynamic processes in the counter-rotating stage of a centrifugal pump were studied. Secondly, various types of impellers for the counter-rotating stage under impeller trimming were designed. A numerical study of the flow parts using the proposed changes was carried out by means of numerical simulation. Finally, an approximation of the obtained pressure and energy characteristics for the trimming range up to 25 mm were realized. In this regard, a couple of case studies were considered. The first impeller type had a blade disk that lengthened when the trimming increased. The overall size of this blade disk remained unchanged. However, the second type had a constant height. In this case, the overall size decreased as the trimming increased.
# Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
## 1 Introduction
The output regulation approach has been applied to solve the consensus problem for heterogeneous agents in [16; 17; 18]. Then, by solving the linear matrix equations, called the regulation equations, they developed the consensus algorithms, which consist of three parts: the first part is the state observer, the second part is the reference generator, and the third part is the regulator. Then, by choosing control gain matrices such that the error systems are Hurwitz, the consensus is analyzed. However, in the presence of output saturations, the analysis techniques of [16; 17; 18] cannot be applied, since the observer contains saturation nonlinearity. Therefore, inspired by the works [32; 33], this paper proposes the nonlinear observer and analyzes the consensus based on the Lasalle's Invariance Principle and the input-to-state stability. The rest of this paper is organized as follows. In Section 2, the mathematical background and problem formulation are presented. In Section 3, the observer-based consensus algorithms for the leaderless and leader-following cases are constructed. In Section 4, numerical examples are provided, and conclusions are made in Section 5.
## 2 Numerical Analysis  
### Modeling and Material Properties  
This study presents a three-dimensional numerical analysis model to simulate a tunnel excavation adjacent to a piled raft and to evaluate the settlement of the raft and the axial stress of the pile. The numerical analysis model encompasses a range of variables, including the modulus of elasticity of the ground, pile diameter, pile length, foundation width, number of piles, pile spacing, pile-to-tunnel horizontal and vertical separation distances, and tunnel face-to-foundation horizontal distance, which are outlined in Table 1 along with the scope of their application. The elastic modulus of the ground is classified based on the relative density of the soil, as presented by [PERSON] and [PERSON] [18]. Furthermore, the pile diameter and pile length are categorized as either long piles or short piles, as per the guidelines outlined in the Basic Design Criteria for Structures [19]. The pile spacing is considered both as a group pile and a single pile by referring to the Basic Design Criteria for Structures [19] and is utilized to compute the range of each variable. Numerous numerical investigations have been carried out employing various constitutive models to analyze the behavior of piled raft systems, as reported by [PERSON] et al. [20]. Additionally, [PERSON] and [PERSON] [21] conducted a settlement analysis of Neogene clay utilizing a modified hypoplastic-model-based finite element modeling. The hypoplastic model can provide greater accuracy than a simple Mohr-Coulomb or Cam-clay model for clayey soils. However, this study assumes a general sandy soil condition. In the numerical analysis, the applied ground is assumed to be sandy soil, and the Mohr-Coulomb model is employed, with the pile and raft modeled using the linear elastic model. The constitutive model and material properties utilized in the numerical analysis are outlined in Table 2.  
[PERSON] [22] proposed the dilatancy angle as Equation (1), which is applied in this study, as shown in Table 2.  
\[\psi=\varphi-30^{\circ} \tag{1}\]  
Figure 1 shows a conceptual view of the numerical analysis, and Figure 2 summarizes the mesh generation of the numerical analysis. The numerical modeling approach employed in this study encompasses a mesh consisting of over 68,500 individual elements. The model is subject to boundary conditions, where the \(\mathrm{x_{min}}\), \(\mathrm{x_{max}}\), \(\mathrm{y_{min}}\), and \(\mathrm{y_{max}}\) values are fixed, while full fixity is imposed at \(\mathrm{z_{min}}\), and \(\mathrm{z_{max}}\) is left unconstrained. Furthermore, the excavation of the tunnel is assumed to produce a \(-2\%\) volume loss, while the groundwater conditions are disregarded in the model. To safeguard against any potential displacement of the piled raft affecting the boundary, the ground mass was secured at a distance of no less than \(5\mathrm{B_{R}}\) the width of the foundation.
## 4 Numerical Results, Evaluations, and Discussion
The BER curves in Figure 8 illustrate the performance of the UOWC system with adaptive optics for different lens diameters cascaded with our proposed \((16\times 16)\) OPA antenna in [8] in a clean ocean scenario. The BER curves show that with adaptive optics, we can increase the communication reach by almost three times compared to the uncompensated  
Figure 6: Probability of fade for varying lens sizes using the aperture averaging method, \(w=-0.2\), and \(F_{th}=6\,\text{dB}\) in the clean ocean (\(c=0.151\)\(1/\text{m}\)). Figure 7: Average SNR vs. \(\text{SNR}_{0}\). scenario. A soft-decision forward error correction (SD-FEC) code with an overhead of 15.8% can reach an FEC threshold of \(1.25\times 10^{-2}\). The compensation methods demonstrate that this improvement in BER allows for a greater operational range while staying within the FEC threshold, thereby enhancing the overall system effectiveness. However, the benefits of increasing the lens size diminish beyond a certain point, so a trade-off between performance enhancement and the receiver size and weight should be found for practical implementations. By comparing (a) and (b) in Figure 8, we can see that it is possible to use either a larger lens without an LC-SLM or a smaller lens with an LC-SLM.
## 4 Additional CT-Measurable Parameters for Cardiovascular Risk Stratification  
### Aortic Calcium  
The assessment of extra coronary calcifications, such as aortic valve calcification, mitral annular calcification, and thoracic aortic calcium (TAC), can improve cardiovascular risk stratification beyond the CAC score. Notably, non-contrast cardiac CT estimates coronary and extra coronary calcifications with the same scans without additional scanning or radiation.  
Different methods for evaluating TAC have been reported. The simplest one is just identifying the presence or absence of aortic calcium, recording TAC in a binary fashion. Alternatively, TAC can be expressed with the Agatston method, similar to CAC [135]. Several studies demonstrated a correlation between TAC and cardiovascular events. In the Multi-Ethnic Study of Atherosclerosis (MESA), [PERSON] et al. demonstrated that TAC predicted future cardiovascular events over risk factors and CAC, but only in women [136]. Another study by [PERSON] et al. showed that the presence of TAC was associated with all-cause mortality. This relationship was independent of conventional cardiovascular risk factors and the presence of CAC. Accordingly, [PERSON] et al. showed that multisite calcifications, including thoracic aortic calcifications, predict total mortality [137].  
Furthermore, previous studies have reported a close relationship between TAC and CAC. [PERSON] et al. analyzed a cohort from the MESA and demonstrated that TAC is significantly associated with CAC incidence and progression [138]. Previously, [PERSON] and colleagues showed that the presence of TAC and carotid plaques in postmenopausal women is significantly related to CAC [139].
## 2 Materials and Methods
### Image Pre-Processing  
Before using the images to train the CNN, they all had to be pre-processed to alleviate their heterogeneity. First, their intensity was normalized by applying Equation (1) to every image \(I\), such that 98% of the voxels in \(I_{new}\) fall within the range \([0,1]\).  
\[I_{new}=\frac{I-percentile(I,1)}{percentile(I,99)-percentile(I,1)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **N** & \begin{tabular}{c} **Scanner** \\ **Manufacturer (\%)** \\ \end{tabular} & \begin{tabular}{c} **Endorectal** \\ **Coil** \\ \end{tabular} & \begin{tabular}{c} **Pixel** \\ **Spacing (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Slice** \\ **Spacing (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Field** \\ **Strength (T)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Girona} & \multirow{2}{*}{34} & G. Elec. (59\%) & No & 0.27–0.55 & 1.00 & 1.5 \\  & Siemens (41\%) & No & 0.68–0.79 & 1.00 & 3.0 \\ \hline \multirow{3}{*}{Promise12} & \multirow{3}{*}{48} & Siemens (25\%) & Yes & 0.63 & 3.6 & 1.5 \\  & G. Elec. (25\%) & Yes & 0.25 & 2.20–3.00 & 3.0 \\  & Siemens (25\%) & No & 0.33–0.63 & 3.00–3.60 & 1.5 \& 3.0 \\  & Siemens (25\%) & No & 0.50–0.75 & 3.60–4.00 & 3.0 \\ \hline \multirow{2}{*}{Promise12\_test} & \multirow{2}{*}{30} & Unknown & Yes \& No & 0.27–0.63 & 2.2–3.6 & 1.5 \& 3.0 \\ \hline \multirow{2}{*}{Prostate-3T} & \multirow{2}{*}{12} & Siemens & No & 0.60–0.62 & 3.60–4.00 & 3.0 \\ \hline \multirow{3}{*}{IVO} & \multirow{3}{*}{280} & G. Elec. (96\%) & No & 0.35–0.74 & 0.60–7.00 & 1.5 \\  & & Philips (3\%) & No & 0.28–0.49 & 3.00 & 1.5 \& 3.0 \\  & & Siemens (1\%) & No & 0.62–0.69 & 3.00–3.50 & 1.5 \\ \hline \multirow{3}{*}{Private} & \multirow{3}{*}{90} & Philips (81\%) & No & 0.30–0.62 & 2.91–5.00 & 1.5 \& 3.0 \\  & & Siemens (11\%) & No & 0.52–0.69 & 3.00–3.60 & 1.5 \& 3.0 \\ \cline{1-1}  & & G. Elec. (8\%) & No & 0.37–0.43 & 3.40–6.00 & 1.5 \& 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the MR datasets.  
Figure 1: Center slice of a sample from MR and US datasets (from left to right): “Girona”, “Promise12”, “Prostate-3T”, “IVO (MR)”, “Private (MR)”, “IVO (US)” and “Private (US)”.  
Then, the center crop of each image (and its respective segmentation mask) was taken, using a size of \(112\times 112\times 32\) and a spacing of (1, 1, 3) mm for the MR images, and a size of \(160\times 112\times 80\) and a spacing of (0.75, 0.75, 0.75) mm for the US images. B-Spline interpolation of third order was employed for all image interpolation tasks, while Gaussian label interpolation was used for the masks.
## 3 Results and Discussion
### Wind Direction Analysis  
The wind directions were assessed for the entire study period and across the four seasons, as illustrated by the wind rose diagrams in Figures 9 and 10, respectively. Figure 9 highlights that the dominant wind direction for the 2021-2023 period was from the southeast, with the highest relative frequency of 16.35%. In contrast, winds from the northeast showed the lowest relative frequency, at just 0.4%. Identifying the prevailing wind direction is crucial for correctly orienting a wind turbine [36,42], which in this case should face southeast at Fort Hare University.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distribution** & **WPD for Distribution (W/m\({}^{2}\))** & **Wind Power Class** \\ \hline GAM & 30.19 & 1 (Poor) \\ GEV & 32.29 & 1 (Poor) \\ GUM & 19.95 & 1 (Poor) \\ IGA & 33.78 & 1 (Poor) \\ RAY & 31.43 & 1 (Poor) \\ WEI & 32.20 & 1 (Poor) \\ \hline \hline \end{tabular}
\end{table}
Table 14: Estimated wind power densities for the six distributions and their classification.  
The seasonal wind patterns shown in Figure 10 reveal that winds predominantly blow from the southeast during summer, spring, and autumn, aligning with the overall trend. However, winter winds primarily come from the northwest. A similar seasonal variation in  
Figure 10: Wind rose diagram for seasonal wind direction variations for the 2021–2023 period.  
Figure 9: Wind rose diagram for overall wind direction for the 2021–2023 period.  
wind direction during winter was also observed by [34], where the winter wind direction diverged from the general pattern.
# Digital Twins' Application for Geotechnical Engineering:
## 2 The Current Situation of Digital Twins in Geotechnical Engineering
This precise process control framework reduces unplanned operational interruptions while ensuring project continuity through proactive risk mitigation protocols. During the operations and maintenance (O&M) phase of geotechnical projects, digital twin technology enables comprehensive lifecycle management through continuous data assimilation and model recalibration. This framework facilitates predictive maintenance scheduling, structural integrity diagnostics, and emergency response protocols. By implementing condition-based maintenance strategies, the system can reduce maintenance expenditure, enhance operational efficiency, and extend infrastructure service life through proactive degradation mitigation. Crucially, it ensures the long-term stability and safety of geotechnical projects. Post-Disaster Recovery and Safety Assessment  
When geotechnical engineering projects confront disaster scenarios, digital twin technology executes high-resolution simulations of post-event geomechanical alterations and structural integrity degradation. This capability provides quantitative safety evaluations and real-time damage quantification, guides rapid damage assessment and rehabilitation planning, identifies latent hazards through scenario-based failure simulations, and develops high-efficiency emergency response and rehabilitation plans. Multi-Stakeholder Collaboration  
Digital twin technology enables unified integration of multi-stakeholder data (including designers, construction teams, and operators) to facilitate collaborative work. This approach significantly reduces communication overheads and minimizes information transfer errors through real-time data synchronization. By implementing a centralized digital thread architecture, the technology enhances cross-disciplinary coordination efficiency,effectively eliminating information silos and communication bottlenecks. The resulting collaborative environment ensures on-schedule, high-quality project delivery throughout the project lifecycle. The key examples, DT benefits, and remaining gaps in each field of digital twin application in geotechnical engineering are summarized in Table 1. In conclusion, digital twin technology can provide more precise, real-time, and predictable tools for geotechnical engineering, thereby effectively helping to solve problems such as inaccurate design, construction risks, difficult operation and maintenance, and delayed disaster response in geotechnical engineering. The safety, economy, and sustainability of geotechnical engineering have been significantly improved by digital twin technology, making it an important development direction in the field of geotechnical engineering in the future.
## 2 Experimental Setup
### Isolation and Identification of Fungi Associated with Dates  
One hundred grams of each sample was physically examined for fungi [23] in order to separate the obviously infected dates. In addition, 10 dates from each sample were surface-sterilized with a 2% sodium hypochlorite solution for 2 min, washed twice with sterile double-distilled water, and then cut into tiny pieces (1 cm\({}^{2}\)/piece) under aseptic conditions. Each fragment was individually cultivated on Potato Dextrose Agar (PDA) plates and incubated at 26 to 28 \({}^{\circ}\)C \(\pm\) 1; RH 72-75 \(\pm\) 1 for 5 days. Single hyphen tips of each different shape or color were sub-cultured under the same previous conditions until pure fungal growth was observed. _Aspergillus niger_, _Fusarium_, and _Penicillium_ are the fungal species that have been isolated from all varieties of palm dates. The pure isolates were identified manually in accordance with [24; 25] and then stored at 4 \({}^{\circ}\)C until use. A. niger represented the highest percentage of isolated fungus in our study. In this experiment, A. niger niger was chosen due to its high resistance to non-thermal plasma treatment [26]. A glass slide was sterilized with ethanol and then cleaned with a towel sprayed with 70% ethanol. In every run, a 5 mm _A. niger_ disc was treated. The fungi disc has been put onto the glass slide. The SDBD plasma system was used to inhibit _A. niger_. The samples of fungi were placed on the sample holder at a distance of 0.5 cm from the electrode. In addition, the plasma dose supplied by the SDBD plasma system was 1, 1.5, 2, 2.5, and 3 min.
## 4 Discussion  
Based on the review of previous research work, a summary based on building energy consumption forecasting methods is presented in Table 4.  
Figure 4: Accuracy metrics to validate forecasting models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Building Type** & **Forecasting Category** & **Forecasting Method** & **Model** & **Input Variables** & **Prediction Horizon** \\ \hline
[40] & Nonessential & Whole-building energy & Physical & Econometrics, end-use account model & Historical + Calendar & Long-term \\ \hline
[41] & Nonessential & Other & Physical & SARIMA & Historical + Calendar & Very short-term \\ \hline
[42] & Both & Cooling energy & Physical & Regression model & Historical + Calendar & Long-term \\ \hline
[43] & Nonessential & Cooling Energy & Physical & System identification method & Historical + Calendar & Short-term \\ \hline
[44] & Both & Other & Physical & Bayesian analysis & Historical + Calendar & Long-term \\ \hline
[45] & Nonessential & Whole-building energy & Physical & Regression & Historical + Calendar & Very short-term \\ \hline
[46] & Residential & whole-building Energy & Physical & Bottom-Up approach & Historical + Calendar & Long-term \\ \hline
[68] & Residential & Whole-building Energy & Physical & Gaussian Processes & Historical + Calendar & Very short-term \\ \hline
[47] & Nonessential & Whole-building energy & Physical & Linear, Seasonal Linear, Quadratic model & Historical + Weather & Short-term \\ \hline
[48] & Nonessential &
\begin{tabular}{c} Cooling and heating energy \\ \end{tabular} & Physical & Bottom-up approach & Historical + Calendar & Short-term \\ \hline
[49] & Residential & Heating energy & Physical & Rule set theory & Historical + Calendar & Long-term \\ \hline
[52] & Nonessential & Whole-building Energy & Data-driven & SVR & Historical + Calendar & Short-term \\ \hline
[53] & Nonessential & Whole-building energy & Data-driven & ANN & Historical + Weather & Short-term \\ \hline
[54] & Nonessential & Whole-building energy & Data-driven & RNN & Historical + Weather & Short-term \\ \hline
[55] & Nonessential & Whole-building energy & Data-driven & ANN & Historical + Calendar & Short-term \\ \hline
[56] & Nonessential & Whole-building energy & Data-driven & RNN & Historical + Calendar & Very short-term \\ \hline
[57] & Nonessential & Whole-building energy & Data-driven & Multiple regression & Historical + Weather & Short-term \\ \hline
[58] & Residential & Whole-building energy & Data-driven & \
\begin{tabular}{c} Deep residual neural \\ network \\ \end{tabular} &
\begin{tabular}{c} Historical + Weather \\ + Calendar \\ \end{tabular} & Short-term \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of previous research papers and their contributions to hybrid methods.