esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Embedding Capacity  
To evaluate the embedding capacity, the embedding rate \(E_{r}\) was adopted:  
\[E_{r}=\frac{N_{e}}{N_{t}} \tag{20}\]  
where \(N_{e}\) and \(N_{t}\) are the number of embedded bits and the total bits of the original halftone image, respectively. We divided the six test images into blocks of the same size of \(1\times 8\), and we embedded 4 bits into each block. The embedding capacity \(E_{c}\) of these images was the same:  
\[E_{c}=w\times 4\times 3\times 3=\lfloor\frac{512}{1}\rfloor\times\lfloor\frac{51 2}{8}\rfloor\times 4\times 3\times 3=1,179,648\ (\mathrm{bits}) \tag{21}\]  
where \(w\) is the total amount of blocks for each channel image, we embedded three times on each channel image and there were three channel images. Table 3 shows various embedding capacity when several different sizes of blocks were employed. Lower embedding capacity may lead to a higher visual quality of the marked image. If we want to know the content of the image and we directly decrypt the image, the higher is the image quality, the clearer is the image content.
# Experimental Evaluation of Load Distribution between Piles in Case of Block Failure
## 1 Introduction
### Importance of the Study  
When the studies in the literature are examined, it has been seen that the behavior of the piles that settle within the limits of the allowed settlements has been examined in almost all of the studies. However, studies on the load distribution of collapsed pile groups are limited.  
Within the scope of this study, the load distribution of the pile group which excessively settled was examined. In this study, the interaction between pile material and soil was not investigated. In this paper, settlements in case of failure were referred to as excessive settlements. The relationship between the amount of settlement in pile raft foundations and the load distribution (\(\mathrm{q_{cor}}/\mathrm{q_{cen}}\)) between corner and center piles has been determined. Thus, this study will contribute to the determination of the behavior of structures which suffered from excessive settlement.  
There are very few articles on the causes and mechanism of block formation in pile groups. Additionally, in these articles, the cause of block formation has not been investigated in detail. In this study, the relationship of block formation with soil strength parameters and distance between piles was researched. In addition, the effect of this situation on the load distribution between piles was investigated.  
The evaluation of the condition of pile foundations after over-settlement (post-failure), the bearing capacity of their current condition, etc., is important in terms of determining the reinforcing methods to be applied to the system. Due to incorrect subsoil exploration, design defects or unpredictable conditions, excessive settled pile foundations may be encountered in the field. In this study, firstly, the load distribution mechanism between the piles of pile foundations sitting within the permissible limits was investigated.  
Then, under the same conditions, the load distribution between piles in case of excessive settlement and a change of load distribution between settlement and piles were investigated. There was also determine the load-bearing mechanisms of pile foundations in the case of excessive settlement. So, it is thought that this study will be an example for the reinforcement of pile foundations or for the evaluation of bearing capacity after failure in similar conditions.  
In most of the model experimental studies in the literature, cohesionless soils with a grain size smaller than 1 mm were used [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. However, in nature, the soils are mostly composed of clay and sand mixtures. For this reason, experiments were carried out on sand and clay mixtures which were consolidated with three different consolidation pressures in this study.
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 4 Model Evaluation and Comparison  
The mean square error (MSE) [36] and the coefficient of determination (R\({}^{2}\)) [36] were adopted as prediction error metrics to compare the two models quantitatively.  
\[MSE=\frac{\sum_{i=1}^{n}\left(\widetilde{Y_{i}}-Y_{i}\right)^{2}}{n} \tag{11}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(\widetilde{Y_{i}}-Y_{i}\right)^{2}}{\sum_{i= 1}^{n}\left(\overline{Y_{i}}-Y_{i}\right)^{2}} \tag{12}\]  
where \(\widetilde{Y_{i}}\) is the predicted life, \(Y_{i}\) is the actual life, and \(\overline{Y_{i}}\) denotes the average of the actual life. The smaller the \(MSE\) value or the larger the \(R^{2}\) value, the better the prediction performance. In addition, the generalization capabilities of the models were analyzed by predicting the fatigue life of GCr15 bearing steel.
# The Effect of Slow-Release Fertilizer on the Growth of Garlic Sports and the Soil Environment
## 3 Results and Analysis
### The Effects of Different Treatment Groups on Garlic Sprout Growth  
As shown in Figure 3, the application of fertilizer in the T1, T2, and T3 treatment groups significantly increased plant height and stem thickness compared with those in the control group (\(p<0.05\)). The plant heights in the T1, T2, and T3 groups increased by 14.85%, 17.81%, and 27.75%, respectively. No significant difference in plant height was observed between the T1 and T2 groups (\(p>0.05\)). Similarly, stem thickness increased by 9.36%, 8.83%, and 13.96% in the T1, T2, and T3 groups, respectively, relative to that in the control. A significant difference (\(p<0.05\)) in stem thickness was detected between the T3 group  
Figure 2: The effect of different treatment groups on the total yield per unit area of garlic sprouts. The different letters represent a significant difference between different treatments (\(p<0.05\)). Error bars represent \(\pm\)SD.  
and both the T1 and T2 groups, whereas no significant difference was found between the T1 and T2 groups. These results indicate that the application of slow-release fertilizer can promote garlic sprout growth while reducing the overall fertilizer input. Under conditions of equal fertility, the slow-release potassium magnesium sulfate fertilizer demonstrated a more pronounced effect on garlic sprout growth. Although the fertility level in the T2 group was lower than that in the T1 group, there was no significant difference in the plant height and stem diameter of the garlic seedlings between the T2 and T1 groups (\(p>0.05\)) under the application of slow-release fertilizer. However, both parameters in the T2 group were significantly different compared to those in the T3 group (\(p<0.05\)). This is likely due to the rapid nutrient loss in the T1 group during crop growth, which limits timely nutrient uptake by the plants. In contrast, the slow-release fertilizers applied in the T2 and T3 groups provided a more sustained and balanced nutrient supply, thereby promoting the growth in the garlic seedlings' height and stem diameter. These results are consistent with the experimental findings reported by [PERSON] [51] and [PERSON] [52].
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.9 Immunomodulatory Properties  
In a randomized, placebo-controlled, double-blinded clinical trial authored by [PERSON] et al. [123], a group of healthy young adults (aged 18 to 25 years) underwent a four-week intervention involving the administration of 500 mg, 1 g, or 2 g of powdered black seed in capsule form. The findings revealed that a 1 g dose of _N. sativa_ led to a significant increase in lymphocyte absolute count, T-lymphocyte (CD3*), and T-helper cell (CD4+) levels within the subjects. However, it was observed that elevating the dosage to 2 g resulted in the disappearance of this effect [123]. In vitro studies using nigella hydroethanolic seed extract showed that these extracts increase the phagocytic and killing activity of three types of macrophages [124]. Research conducted by [PERSON] et al. [125] proved that _N. sativa_ oil exhibits immunomodulatory activity. It effectively supported the immune response in _S. typhimurium-_infected rats by maintaining normal leukocyte, neutrophil, eosinophil, basophil, lymphocyte, and monocyte levels and activating macrophages [125]. The effect of oil on peripheral blood lymphocytes and interleukin-2 (IL-2) expression in healthy smokers was studied. Although there was a tendency for an increase in IL-2 expression compared to the placebo, these differences were not statistically significant [126].
## 4 Shaft Lining Model Machining and Test Methods
### Shift Lining Model Preparation and Test Method  
Specimens of the shaft lining model were prepared by pouring concrete into special molds to ensure that the boundary conditions of the two ends of each specimen strictly followed those of the full-scale shaft lining. After curing, the two ends of each specimen were finely machined on a lathe and a grinding machine in a workshop to achieve the smooth finish required for the test. Figure 3 shows a specimen after the machining process. During installation, two rubber seal rings were placed at each end of each specimen model to ensure that the model could slide freely in the radial direction and that its two ends were sealed. To subject each specimen to planar stress during the loading process, rigid bolts were used to constrain the cover plates at its two ends. Figure 4 shows the loading system used in the test. The lateral pressure on the shaft lining was simulated by applying high-pressure oil using an oil pump.  
The pre-established loading scheme was implemented after the loading test on each specimen. First, preloading was performed three times. Then, the specimen was subjected to multistage constant loading. At each stage, constant load was maintained for 10-20 min. The strain was measured after the deformation of the shaft lining tended to stabilize. Finally, the ultimate bearing capacity of the specimen was determined by loading until failure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Strength Grade** & **Cement (kg)** & **Slag (kg)** & **Fly Ash (kg)** & **Water–Binder Ratio** & **Sand Content (\%)** & **Additive Dosage (\%)** \\ \hline C60 & 400 & 0 & 100 & 0.30 & 38 & 1.20 \\ C65 & 400 & 0 & 130 & 0.29 & 37 & 1.23 \\ C70 & 400 & 60 & 100 & 0.28 & 37 & 1.23 \\ \hline \end{tabular}
\end{table}
Table 2: Mix ratios for concrete to be pumped into the shaft lining.  
Figure 3: Well-processed shaft lining model.  
To determine the stress-strain characteristics during the loading process, each specimen of the shaft lining model was divided vertically into two layers, and four measuring zones were set equidistantly along the circumferential direction in each layer. In each measuring zone, a measuring point was set on each of the surface of the inner steel plate, the surface of the outer steel plate, and the inner side of the concrete. At each measuring point, a strain gauge was placed in each of the transverse and longitudinal directions. A strain brick was embedded in the concrete on the inner side of the concrete layer. In addition, a hydraulic sensor installed in the loading system for the high-performance shaft lining test was also used to measure the load applied to the specimen and its ultimate bearing capacity.
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 4 Heuristic-Based Solution
However, this gap should be less than the remaining time of the second line (the reason will be explained in Step 2.4). **Step 2.4:** Remove the smaller block of the chosen combination from the second line and stack the larger block of the chosen combination on the second line instead. If the gap between two blocks exceeds the remaining space of the second line, the larger block of the combination cannot be inserted into the second line. Repeat Step 1.2 for the modified second line. If the length of the modified second line exceeds the operating hours, return to Step 2.3 and choose another combination that has the next largest gap of block time between two elements. Otherwise, (if the length of the modified second line does not exceed the operating hours), remove the larger block of the combination from the first line and then stack the smaller block of the combination to the first line. At this moment, the modified second line becomes fixed, while the modified first line can generate a space for a new insertion, as shown in Figure 3b. **Step 2.5:** If there is no combination to consider between the first and second lines, return to Step 2.2 but choose the first line and the next line (e.g., third, fourth, fifth, etc.) with the next longest remaining times. **Step 2.6:** If there is enough space in the modified first line, stack a new block and check the feasibility of the line using Step 1.2. Repeat Steps 2.1-2.6 until the sum of the remaining time of all the lines is less than the length of any remaining block. If there is no appropriate combination to try further, it is terminated. After obtaining the final lines from the proposed heuristic, we can convert them to the final schedule for drone delivery. Figure 4 shows an example of how the generated line can be converted to the final schedule. Since any blocks in the line can be processed (delivered) regardless of the current sequence in the line, various schedules that generate the same objective value (i.e., the number of parcels) can be obtained. In other words, because the blocks in the line are already set to not exceed the operating hours, the sequence of the blocks can be changed inside the line except for the final group of blocks, which are intentionally allocated at the end of the line. The overall flow of the proposed heuristic can be seen in Figure 5. Additionally, the pseudocodes of the proposed heuristic are attached in the Appendix A.  
Figure 2: Building a solution by stacking blocks. (**a**) Initial block stacking; (**b**) grouping and relocation. Figure 4: Final schedule generation. Figure 3: Improving the solution by making space and inserting blocks. Figure 5: Overall flow of the proposed heuristic.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 3 Proposed Methodology
### Loss Function  
The decision network is trained using the cross-entropy loss function, as shown in Equation (5):  
\[L_{dec}=-\frac{1}{N}{\sum_{i=1}^{M}}\begin{array}{c}\left(y^{(n)}.log\left( \widetilde{y}^{(n)}\right)+\right.\\ \left.\left(1-y^{(n)}\right)\cdot log\left(1-\widetilde{y}^{(n)}\right)\right) \end{array} \tag{5}\]  
where \(y\) is the class label, and \(\widetilde{y}\) is the predicted output of the defect network. The learning is performed separately for the segmentation and decision networks. First, the segmentation network is independently trained, and then the weights of the segmentation network are frozen while training only the decision network layer. Training exclusively the decision layer mitigates overfitting concerns arising from the numerous weights in the segmentation network.
## 3 System Framework Overview
### Middleware Layer
#### 3.2.4 Self-Adaptation  
The ability of self-adaptation is a strong requirement in the context-aware middleware cloud so that context is being provided as input to produce the proper services and control actions [46; 47; 102]. Moreover, self-adaptation is responsible for diagnosing, locating and recovering any possible failures in the workflows as in [102]. The self-adaptation module of the proposed context-aware middleware cloud is distinguished in the following two main mechanisms as illustrated in Figure 13:  
* The Evolution Management mechanism, which checks whether the context-aware middleware cloud is properly deployed to the proposed system through surveying the middleware and maintaining its coherency.
* The Adaptation Management mechanism which receives and evaluates the surveillance results collected by the evolution manager to find new aspects of the context-aware middleware cloud deployment and allow any new services to be established on demand.  
Specifically, the context-aware middleware of the proposed system can identify action sets and store this information into the cloud repository, enhancing the knowledge base. In this way the development  
Figure 11: Indicative entity database.  
Figure 12: Indicative entity (**a**) properties, (**b**) table and (**c**) workflow.  
Figure 13: Mechanisms of self-adaptation module.  
cycle of the system is reduced when a similar pattern is detected in the context. Furthermore, it is considered that according to the proposed solution, the model can meet the requirements of processing and obtaining the most possible accurate results while maintaining on the analysis the real-time constraint.
## 2 Materials and Methods
### Methods for Calculating the Quality Factor (Q)
#### 2.3.2 The ICRU Report 40 Recommendation  
The International Commission on Radiation Units and Measurements (ICRU) Report 40 was the first to officially link the quality factor \(Q\) with the microdosimetric quantity lineal energy (\(y\)) using the following analytic expression:  
\[Q_{ICRU40}=\frac{5510}{y}\Big{[}1-Exp\Big{(}-5\times 10^{-5}y^{2}-2\times 10^{- 7}y^{3}\Big{)}\Big{]}. \tag{29}\]  
As a result, for a specific proton energy, the mean quality factor \(\overline{Q}\) can be calculated from:  
\[\overline{Q}=\frac{\int Q_{ICRU40}(y_{test})d(y_{test})dy}{\int Q_{ICRU40}(y_{ ref})d(y_{ref})dy}, \tag{30}\]  
where \(d(y)\) is the dose-weighted distribution of lineal energy defined by \(d(y)=(y/y_{F})f(y)\). The difficulties in understanding the effects of high-LET radiation, and thus in the calculation of \(\overline{Q}\), were compensated by the choice of a quantity that can be measured and at the same time be physically appropriate. Therefore, lineal energy was to be determined in a tissue-equivalent sphere with a diameter equal to 1 \(\upmu\)m, which is experimentally feasible using TEPCs.
## 1 Introduction  
The advantage of employing polymer semiconductors in the organic transistor fabrication process is not only the improvement of the film-forming property due to the high viscosity of the semiconductor ink, but also the reduction of variation in the electrical characteristics [1] due to the formation of a uniform film. In addition, since polymer semiconductors generally have high heat resistance, they can withstand the heating process after the formation of organic semiconductors. Recently, very high performance, exceeding 10 cm\({}^{2}\)/Vs, has been reported due to annealing treatment and orientation control after film formation [2]. A printing fabrication process using organic materials that can be made into ink facilitates continuous large area device and mass production, and roll to roll (R2R) becomes possible by using a flexible substrate created in a low-temperature process [3; 4; 5; 6]. In particular, device fabrication using an on-demand printing method such as ink-jet printing is the ultimate resource-saving process, and has the potential to be low cost [6; 7; 8; 9; 10; 11; 12]. An advantage of the complementary organic integrated circuit composed of p-type and n-type organic thin film transistors is that it has low power consumption and a high noise margin [13; 14]. Therefore, it has the potential to be applied to a wide range of devices, including low power consumption devices driven by small batteries. On the other hand, since p-type and n-type semiconductors are fabricated through deposition methods, the number of fabrication steps is greater than those required for positive channel metal oxide semiconductor (p-MOS) and negative channel metal oxide semiconductor (n-MOS) configurations. In addition, to improve the transistor characteristics, it is necessary to match the work function of the source and drain electrodes with the highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO-LUMO) level of the semiconductor, and selection of the source and drain electrode material or electrode treatment with the self-assembled monolayer (SAM) is required. For this reason, in order to achieve both the performance of p-type and n-type organic thin-film transistors (OTFTs) in a complementary integrated circuit, two types of electrode materials or SAM materials are required, and the development of a simple fabrication process is an important task.  
In this paper, complementary organic integrated circuits were demonstrated using p-type semiconductors, which are not significantly affected by the work function of the source and drain electrodes. The fabrication process used in this study was reduced in steps to about half compared with the complementary circuit using a stacked structure [15; 16; 17; 18; 19]. Further, the dynamic characteristics of the inverter circuit were evaluated using a five-stage ring oscillator.
## Preliminaries
### ACO Applied to BNs
```
/* Initialization */
1 Set the iteration counter \(t=0\);
2 Generate \(M_{\text{ant}}\) ants;
3 Initialize the pheromone matrix \(\tau(0)\): for all arcs \(a_{ij}\), set \(\tau_{\bar{q}}(0)=\tau_{0}\);
4 Set \(G^{+}\) be an empty graph;
5 /* Iterative search */
6 while termination criteria are not satisfied do
7 iteration counter \(t=t+1\)
8 for\(k=1\):\(M_{\text{ant}}\)do
9 Generate an empty network \(G_{k}\): for\(i=1\) to \(n\), do\(\pi_{i}=\phi\);
10 Calculate the heuristic information: for\(i,j=1\) to \(n\) (\(l\
eq j\)) do\(\eta_{ij}=f_{\text{BIC}}(X_{i},X_{j})-f_{\text{BIC}}(X_{i},\phi)\);
11 while\(\exists\eta_{ij}>0\)do /* Add an arc */
12 Select an arc \(a_{ij}\) from the feasible domain allowed according to (9) and (10);
13 if\(\eta_{ij}>0\)then\(\pi_{i}=\tau_{i}\cup[X_{j}]\) and construct the network \(G_{k}=G_{k}\cup_{k\
eq j}\);
14 Set \(\eta_{ij}=-\infty\);
15\(\tau^{\prime}\)Avoiding directed cycles */
16 for\(u,v=1\) to \(n\)do
17 if\(G_{k}\cup_{k\
eq n}\) includes a directed cycle, then\(\eta_{uv}=-\infty\);
18 endfor /* Calculation the heuristic information */
19 for\(u=1\) to \(n\)do
20 if\(\eta_{iu}>-\infty\)then\(\eta_{iu}=f_{\text{BIC}}(X_{i},\pi_{i}\cup[X_{i}])-f_{\text{BIC}}(X_{i},\pi_{i})\);
21 endfor /* Local updating */
22 Update the pheromone: \(\tau_{ij}=(1-\psi)\cdot\tau_{\bar{q}}+\psi\cdot\tau_{0}\);
23\(\prime\)* Local updating */
24 Update the pheromone: \(\tau_{ij}=(1-\psi)\cdot\tau_{\bar{q}}+\psi\cdot\tau_{0}\);
25 endwhile
26
27 endfor
28
29 /* Chromosome update */
30 Select \(G_{i}=\text{arg max}f_{BIC}(G_{i},D)\)
31 if\(f_{BIC}(G_{i},D)\geq f_{BIC}(G^{+},D)\), then\(G^{+}=G_{i}\)
32 Update the pheromone matrix according to (8) and (12) using\(f_{BIC}(G^{+},D)\)
33 endwhile
34 Return the best BN structure \(G^{+}\). ```  
**Algorithm 2**Basic ACO based BN learning. Figure 1: The construction process of a BN.