esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Nanoemulsion Characterization--Particle Size (MDD), Particle Size Distribution (PDI), Viscosity, and Color (Whiteness)  
Nanoemulsion technology enables the development of functional beverages by masking undesirable flavors, enhancing bioaccessibility of hydrophobic bioactives through increased solubility and absorption, and ensuring better availability of active ingredients. The small droplet size and narrow distribution improve stability, reduce oxidation, and extend shelf life, preserving ingredient efficacy while enhancing taste, texture, and consumer acceptance. This makes o/w nanoemulsions an ideal system for incorporating bioactive compounds into stable and palatable beverages. The physicochemical properties of nanoemulsions significantly depend on the type of lipid phase, the ratio of oil/emulsifier and water in the mixture, and the addition of other soluble compounds. The non-ionic emulsifier Tween 40 (HLB value, 15.6) was selected, as it is generally recognized as safe (GRAS) and is widely used in the food industry, as well being less affected by ionic strength and pH changes than other emulsifiers. The effect of oil type and the addition of antioxidant compounds on the droplet size and distribution, viscosity, and whiteness index were examined to select the most appropriate o/w nanoemulsion as a delivery system for lycopene. Various oils were examined as the lipid phase determines the droplet size, the viscosity, the stability, and the bioaccessibility of the prepared nanoemulsions (Table 1). According to size-distribution graphs based on intensity, the selected two-step homogenization condition and emulsion composition (Tween 40 and various vegetables oil) resulted in o/w nanoemulsions with uniform droplets (MDD \(<\) 300 nm, PDI \(<\) 0.3), with no significant differences among them. Previous studies used more intense conditions (higher pressure and temperature values and emulsifier concentration) to create o/w emulsions in the nano-range with narrow droplet distribution [24; 42; 43]. Olive pomace oil resulted in a larger droplet size, namely 286.6 \(\pm\) 10.0 nm, and fish oil in the smallest droplet size, at 259.3 \(\pm\) 9.1. Furthermore, the incorporation of ascorbic acid did not significantly impact the colloidal stability of the system. A minor average increase of 5% in droplet size was observed, with a more substantial increase of 8% in the case of fish oil nanoemulsions. Although ascorbic acid did not significantly affect droplet size, its presence facilitated homogenization by leading to a narrower droplet size distribution. Due to its high surface activity, ascorbic acid interacts with emulsifying agents, improving their
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 6 Summary  
The main intention of the research described in this paper is the development of a DT for a detailed analysis of manufacturing layouts and their parameters. Consequently, the paper explained the concept of the DT and described analysis possibilities throughout the life-cycle of a product and its production system. As one specific analysis approach, a bottleneck detection method was presented in more detail. More concretely, the Active Period Method was used to detect bottleneck processes in manufacturing systems. This approach proved its ability to detect bottlenecks in a robust way, independent of physical layouts and for different process sets and time frames. Despite having some boundaries in the implementation, particularly the need for well maintained shop-floor data, the tests in real world scenarios show that this method is useful and bottleneck machines become tangible. The proposed process visualization allows one to detect how stable a certain machine is in terms of becoming a bottleneck. This constitutes a meaningful input to the optimization of single machines as well as complete production processes.  
The bottleneck analysis presented in this paper can cope with flexible process layouts but the behavior of each machine is described in the form of a static model. As a next research step, the DT concept will be extended into a self-learning and adaptable DT, enabling one to automatically learn the system behavior based on execution and performance data and to dynamically simulate system behavior and performance under different assumptions and conditions. It is important to note that this paper concentrated on the conceptual aspects of the self-learning DT. Further research is planned in order to enable a holistic implementation.  
**Author Contributions:** Conceptualization, B.A., J.H., J.T., C.B., W.H. and R.S.; methodology, B.A., J.H. and C.B.; compilation of the state of the art, B.A., J.H., C.B. and R.S.; simulation, B.A. and J.H.; integration, W.H.; writing--original draft preparation, B.A., J.H., C.B., W.H. and R.S.; writing--review and editing, W.H., J.T. and R.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Parts of the described research were funded by the Carl Zeiss Foundation in the scope of the project AI-based digital twin (KI-basierter digitaler Zwilling--KIDZ).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 4 Problem Solution
### MDQN-Based Trajectory Design and Power Control
#### 4.2.2 MDQN Algorithm
```
1:Step 1: Model Training
2: Initialize the evaluation network parameter \(\omega_{e}\) and the target network parameter \(\omega_{t}\) and make \(\omega_{t}=\omega_{e}\)
3: Initialize the experience replay pool \(e\)
4:for each episode do
5: Initialize UAV, cellular users, and D2D cluster locations
6:for each step \(t\)do
7: Update the action strategy parameters \(\delta(t)\) according to Equation (37)
8:for each UAV do
9: Generate the state space \(S\) according to Equation (34)
10: Determine the action \(A\) according to Equations (37) and (38)

Figure 5: Diagram of MDQN-based trajectory design and power control algorithm. ```
11: Perform action \(A\), observe reward \(R\) and next state \(S^{\prime}\)
12: Store experience \(e=(S,A,R,S^{\prime})\) to experience pool \(\mathcal{E}_{total}\) according to Equations (34) and (35)
13: Random sampling from the experience pool \(\mathcal{E}_{total}\)
14: Calculation of target \(q_{target}\) according to Equation (39)
15: Perform the optimization step according to Equation (40)
16:if update \(\omega_{e}\)do
17:\(\omega_{t}\leftarrow\omega_{e}\)
18:endif
19:\(S\gets S^{\prime}\)
20:endfor
21:endfor
22:endfor
23:Step 2: Model Run
24: Load the trained network parameters \(\omega_{e}\)
25:for each episode do
26:for each UAV do
27: Generate the state space \(S\) according to Equation (34)
28: Determine action \(A\) from equation \(A=\max_{A}Q(S,A,\omega_{e})\)
29: Executing action \(A\), the UAV and the user move and interact with the environment, observing state \(S^{\prime}\) according to Equation (34)
30:\(S\gets S^{\prime}\)
31:endfor
32:endfor
```
**Algorithm 2**_Cont._

### Joint Algorithm Design

Based on the above analysis and solution, the optimization algorithm for joint channel assignment, trajectory design, and power control is designed in this section to solve the problem (25a) in an iterative manner. The NOMA-based rate optimization algorithm for multi-UAV-assisted D2D communication networks can be summarized as Algorithm 3. ```
1: Initialize the maximum tolerance error \(\xi\), the maximum number of iterations \(J\), the subchannel assignment vector \(I_{i,DT_{j}}^{u}(0)\), the UAV position vector \(\left(x_{u}^{(0)},y_{u}^{(0)},h_{u}^{(0)}\right)\), and the power allocation factor \(a_{i,DR_{j,\mu}}^{u}(0)\)
2:
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 2 Miner's Rule and Fatigue Damage Accumulation Under Variable Amplitude Cyclic Load  
### Miner's Rule  
In fatigue damage accumulation theory, Miner's rule, representing linear damage accumulation, is widely used in engineering for its computational simplicity. The mathematical expression of Miner's rule [21]:  
\[D=\sum_{i=1}^{k}\frac{n_{i}}{N_{i}} \tag{1}\]  
where \(n_{i}\) denotes the number of loading cycles at stress level \(\sigma_{i}\), \(N_{i}\) represents the failure cycles at stress level \(\sigma_{i}\), and \(k\) indicates the total number of loading stress cycles. Fatigue failure typically occurs when the accumulated damage \(D\) reaches 1.  
Figure 1a illustrates that the load spectrum under CCF loading circumstances is made up of several identical load blocks, each of which has one LCF and \(n\) HCF cycles. A figure in Figure 1b shows indicating the number of failed load blocks \(N_{\text{B}}\), which can be calculated according to the following Equation (2) [29]:  
\[N_{\text{B}}=\frac{1}{\frac{1}{N_{L}}+\frac{n}{N_{H}}} \tag{2}\]  
where \(N_{\text{B}}\) denotes the failure load block determined by [PERSON]'s rule, and \(N_{H}\) and \(N_{L}\) denote the number of cycles to failure at high cyclic stress \(\sigma_{H}\) and low cyclic stress \(\sigma_{L}\), respectively.  
Figure 1: Load spectrum of CCF. (**a**) CCF block loads; (**b**) number of failed load blocks.  
Therefore, the cumulative damage under CCF loading according to [PERSON]'s rule may be expressed as follows [30]:  
\[D=\frac{N_{\text{B}}}{N_{L}}+\frac{nN_{\text{B}}}{N_{H}} \tag{3}\]  
Since [PERSON]'s rule is independently applied at stress levels, but CCF involves complex damage accumulation, it struggles to accurately predict fatigue life in CCF.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.7 Plant Ontology  
Unlike the already discussed ontologies, the plant ontology depicts an application-specific ontology because it represents the concrete structure of plant or manufacturing line. To this end, the existing field devices and components are represented as instances (imported from the device ontologies, compare Section 3.2.8), which have properties to represent the actual location of the devices in the  
Figure 3: Part of the function ontology.  
plant and the relations (e.g., structural, process-related, physical, electrical) between the devices. Existing knowledge sources such as circuit diagrams or CAD models have been used as a basis for the modeling process.
## 3 Results  
### Hydrological Simulations  
#### 3.1.1 Sensitivity Analysis  
Sensitivity analysis was carried out to test the sensitivity of runoff simulations to the soil data input parameters. Following the proposed methodology, the sensitivity analysis was performed using the \"Ihoat()\" function incorporated in the HydroPSO package that uses the Nash-Sutcliffe efficiency (NSE) as measure of model performance. The sensitivity analysis was carried out using observed discharge time series. The results are reported in the Table 2, where only the ranking of sensitive parameters are presented. This step allowed to reduce the number of parameters to be calibrated using the HydroPSO where only sensitive parameters were considered for the runoff simulations.
## 6 RF Feature Classifiers
### ML-Based Classification
#### 6.2.1 kNN Classifier  
The kNN classifier is the most used classifier from the class of unsupervised ML approaches. The principals behind it are simple: for every sample, it will look at the k nearest neighbours, and the class of this sample will be determined by the class of the majority in the nearest neighbours. Figure 13 presents an example when the nearestneighbours are three: the three nearest neighbours of a testing yellow dot are two red dots and one blue dot, and as a result, the yellow dot is determined as a red class.  
Figure 13(a),b demonstrate the impact of a different number of nearest neighbours on the boundary of two classes (a spoofer and a Galileo E1 signal). A large number of nearest neighbours may lead to an over-fitting problem while an insufficient number of nearest neighbours degrades the classification performance.
## 2 Thin Film Deposition and Characterization by X-ray Diffraction
### TDBS Experiments at Two Angles of Probe Incidence
However, the maximum measured value of \(n\) is larger only by about 1.5% and 0.8% than the values for the measured neighbor Pr substitution concentrations of x = 0.10 and 0.15, respectively (Figure 8). Thus, our measurements do not provide any evidence of strong optical anomaly in BPFMO thin films deposited on LAO substrate. The measured acoustic velocity in the BPFMO films with different concentrations of Pr substitution deposited on LAO substrate (Table 3) demonstrates highly non-monotonic variation in the vicinity of the rhombohedral-tetragonal structural phase transition at around 12.5% of Pr substitution (Figure 8), similar to the one revealed earlier in films deposited on Si substrate. Although there are no published data for the direct comparison with our measurement, our observations in BPFMO films (Figure 8) follow the tendency revealed for BFO films in Ref. [33], i.e., increase in the sound velocity in the transition from rhombohedral to tetragonal phase (see Introduction). More importantly, the measurements in Table 3 reveal a very sharp minimum in the longitudinal sound velocity at the structural phase transition for x = 0.125 Pr substitution concentration. The minimum measured value of \(v_{S}\) is smaller by 19%, 14%, and 21% than the values for the measured neighbor Pr substitution concentrations of x = 0.10, x = 0.15, and x = 0.20, respectively (Figure 8). Correspondingly, the longitudinal elastic modulus could be softer in the phase transition point by 34%, 26%, and 37% than for the neighbor measured Pr substitution concentrations of x = 0.10, x = 0.15, and x = 0.20, respectively. Figure 8: Dependencies of the optical refractive index and the longitudinal sound velocity on the Praseodymium substitution concentration in (Bi\({}_{1-x}\)Pr\({}_{x}\))(Fe\({}_{0.95}\)Mn\({}_{0.05}\))O\({}_{3}\) films deposited on LAO substrate.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 3 TLLFD for NR IQA
### Local Normalization  
In applications of image processing, given a distorted color image, the color image is transformed to gray scale first.  
\[I(i,j)=0.2989\times R(i,j)+0.5870\times G(i,j)+0.1140\times B(i,j) \tag{1}\]  
where \(R(i,j)\), \(G(i,j)\), \(B(i,j)\) represent the three color components of the color image respectively. Then, using the same processing model as [21; 22], \(I(i,j)\) is normalized locally to obtain the mean subtracted contrast normalized (MSCN) coefficient of the image brightness \(\hat{I}(i,j)\).  
\[\hat{I}(i,j)=\frac{I(i,j)-\mu(i,j)}{\sigma(i,j)+C} \tag{2}\]  
where \(i\) and \(j\) represent the spatial index of the image respectively, \(i=1,2,\ldots,M\), \(j=1,2,\ldots,N\),\(M\times N\) represents the size of the image.  
\[\mu(i,j)=\sum\limits_{k=-K}^{K}\sum\limits_{l=-L}^{L}\omega_{k,l}I_{k,j}(i,j) \tag{3}\] \[\sigma(i,j)=\sqrt{\sum\limits_{k=-K}^{K}\sum\limits_{l=-L}^{L} \omega_{k,l}(I_{k,l}(i,j)-\mu(i,j))^{2}}\]where \(\omega=\left\{\omega_{k,l}|k=-K,\ldots,K,l=-L,\ldots,L\right\},\)\((2K+1)\times(2L+1)\) is the size of Gaussian window. Take \(K=L=3\), \(\mu\) and \(\sigma\) are the mean and standard deviation of the local block of the image. \(C\) is a normal number to avoid the denominator to take 0 which selected \(C=\left(\alpha L\right)^{2}\), \(L=255\). \(\alpha\) is a small constant.  
Taking the above local normalization method as the image preprocessing step, the normalized result has a good statistical feature change analysis for the distorted image and test image. At the same time, quantifying these changes will make it possible to predict the distortion type affecting the image and its perceived quality.
## 4 Results and Discussion
### Energy Efficiency Improvement Strategies and Implementation Challenges
In conclusion, energy efficiency improvement strategies have great potential to reduce the carbon footprint and develop sustainable production processes. However, various challenges, primarily financial and technological, stand in the way of implementing these strategies. To overcome these challenges, policy support, technological innovations, and employee training are critical. The strategies for improving energy efficiency and the challenges encountered in their implementation are summarized in the following Table 8.  
Table 8 highlights the contributions of various energy efficiency strategies and the challenges encountered during their implementation. Among the strategies, Energy Monitoring and Management Systems (EMS) contribute the highest percentage (30%), reflecting their critical role in real-time energy tracking and management. Renewable Energy Integration (20%), Energy-Saving Technologies (25%), and Waste Heat Recovery (15%) also make significant contributions, demonstrating their potential to reduce energy consumption and carbon footprints. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Value** & \
\begin{tabular}{c} **Energy Effi-** \\ **ciency Strategies** \\ \end{tabular} &
\begin{tabular}{c} **Implementation** \\ **Challenges** \\ \end{tabular} \\ \hline Energy Monitoring and Management Systems (EMS) & 30 & \\ Integration of Renewable Energy Sources & 20 & \\ Use of Energy Saving Technologies & 25 & 90 \\ Waste Heat Recovery & 15 & \\ High Initial Costs & 30 & \\ Technological Adaptation Issues & 20 & 180 \\ Data Deficiency and Indeateate Analysis & 15 & \\ Lack of Personnel Training and Awareness & 15 & \\ Policy and Regulatory Deficiencies & 10 & \\ Energy Efficiency Strategies & 90 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Challenges and Contributions of Energy Efficiency Strategies: A Focus on Implementation Barriers. The table also outlines the major challenges, with High Initial Costs (30%) identified as the most significant barrier to implementing energy efficiency strategies. This is followed by Technological Integration Issues (20%), which arise from incompatibility between older industrial machines and modern energy-efficient technologies. Additional barriers include Data Deficiency and Insufficient Analysis (15%), Lack of Personnel Training and Awareness (15%), and Policy and Regulatory Deficiencies (10%), emphasizing the need for adequate infrastructure, employee education, and supportive government policies. Overall, while energy efficiency strategies hold immense potential for sustainable production, overcoming these barriers requires coordinated efforts, including financial investments, technological upgrades, and regulatory support.
## 4 Experimental Results and Discussion
### Comparison Between Shapley Values for Different Datasets  
The global relevance by sectors has also been evaluated for the other datasets considered, DRISHTI-GS1 and REFUGE. Figure 11 shows the results obtained.  
Figure 11: Global relevance by sectors according to the Shapley values for the different network architectures and the different data sets considered, separating the data by the class predicted by the networks. Given an input image, the graph indicates each sector’s probability of being the highest contributor according to the Shapley values for that class. The relevance by sector is shown for our own set (1 st and 4 th rows), for DRISHTI-GS1 (2 nd and 5 th rows), and REFUGE (3 rd and 6 th rows).  
It can be seen that the greatest similarity in terms of the distribution of probability values by sector is between our set and that of DRISHTI-GS1. The correlation in this case was 0.80679 (\(p\)-value \(<\) 0.001). Among some differences that we can appreciate, it stands out the rise of the background probability in ResNet50 for the normal class and its decrease for the glaucoma class. Additionally, the probability decreases in the T, TI, and N sectors in the normal class, but increases in the glaucoma class, whose TI sector is now the most probable. A decrease in the probability of the background sector in InceptionV3 is also noticeable for the glaucoma class, while the probability of the TI, NI, and TS sectors increases. In the normal class for this network architecture, there is an exchange of importance between the background and the nasal sector, which now appears as the major contributor in 29% of the cases. In VGG19 the probability of B also decreases in both classes. For the glaucoma class, the probability of the T sector increases, and for the normal class, the probability of the N sector increases, and that of TI decreases. In Xception, the probability of the N sector also increases for the normal class, which would now be the most probable, followed by TI, which also increases. For glaucomas, as with InceptionV3, there is a reduction in the percentage of the B sector and an increase in the TI, NI, and TS sectors.  
The changes in probabilities are most notable between our set and REFUGE, where the Background stands out mainly for the normal class in all architectures. However, for the glaucoma class, the N and TI sectors are the most relevant except for the Xception architecture where N and B are the most important contributors overall. The correlation between the Shapley values for our set and REFUGE was 0.53530 (\(p\)-value = 0.003).
## 3 Dataset Construction
### Constructing the SysML Model  
Using the MagicDraw tool, RFLP (requirement, functional, logical and physical) model [39] is applied to perform rough SysML manual modeling of a generic spacecraft system based on the SysML language. The study focuses on domain modeling; therefore, the model content is based on structural models.  
As shown in Figure 2, the model references spacecraft system design and uses the block definition diagram (BDD) as a bearer for mission environment analysis, with each block describing one environmental influencing factor. Mission environment analysis is a comprehensive study of the environment in which a particular mission or project is located. It focuses on external and internal factors that may have a direct impact on the execution of the task. The primary purpose of mission environment analysis is to identify, assess and manage the risks and opportunities that may affect the successful completion of the mission. For the mission environment of a spacecraft, the main influencing factors are the ground, the spacecraft itself, space, launchers, users, ground stations and GPS. The ground influences include the natural environment, and the space influences include electromagnetic waves, satellites, the sun and other planets. The * in the diagram indicates that there can be more than one, e.g. the Mission Environment can contain one or more users. The word next to the relationship link is the name of the instance, which can be modified according to the actual situation, and the diagram uses the same name as block by default.  
When analyzing the overall mission environment, in addition to the internal and external environment analysis, it is also necessary to identify the constraints that may be encountered during mission execution (e.g., time, budget, resource limitations, etc.) as well as the assumptions and prerequisites that may affect mission execution. As an example, the representative constraints of the spacecraft mission environment are mass constraints, cost constraints and energy analysis constraints.  
The rest of the model instances are represented in the block definition diagram (BDD), which is based on the block representation of entities or components in the system. The block can represent physical entities (e.g., hardware devices, sensors, etc.) or logical entities (e.g., software modules, algorithms, etc.). Each block has its own attributes, operations and behaviors, as well as relationships with other blocks. The relationships between the blocks  
Figure 2: SysML model example.  
can be represented by different lines and arrows, such as associations, generalizations and dependencies. The simple semantics based on BDD facilitate the subsequent extraction of entities, attributes and relationships to support the subsequent work of analyzing the data.