esa-sceva/satcom-chunk-collection huggingface dataset:
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## 4 Experimental Results and Analysis
### Evaluation of Robustness
#### 4.2.1 Common Attacks  
In Figures 11-15, the extracting results under different image attacks are given, such as JPEG 100, salt and pepper noise with densities of 0.01 and 0.05, Gaussian noise with mean 0, and variances of 0.01 and 0.05, center cropping on the 256 \(\times\) 256 region, and median filter (3 \(\times\) 3).  
(1) JPEG (100).  
(2) Salt and pepper noise (0.01 and 0.05).  
(3)  
Figure 11: Watermarked images and extracted watermarks under JPEG (100): (**a**) watermarked [PERSON]; (**b**) extracted watermark \(w_{1}\); (**c**) watermarked [PERSON]; (**d**) extracted watermark \(w_{2}\).  
Figure 12: Watermarked images and extracted watermarks under salt and pepper noise: (**a**) watermarked [PERSON] under salt and pepper noise (0.01); (**b**) extracted watermark \(w_{1}\); (**c**) watermarked [PERSON] under salt and pepper noise (0.01); (**d**) extracted watermark \(w_{2}\); (**e**) watermarked [PERSON] under salt and pepper noise (0.05); (**f**) extracted watermark \(w_{1}\); (**g**) watermarked [PERSON] under salt and pepper noise (0.05); (**h**) extracted watermark \(w_{2}\).  
(3) Gaussian noise ((0, 0.01) and (0, 0.05)).  
(4) Center cropping (25%).  
Figure 14: Watermarked images and extracted watermarks under center cropping (25%): (**a**) watermarked [PERSON]; (**b**) extracted watermark \(w_{1}\); (**c**) watermarked [PERSON]; (**d**) extracted watermark \(w_{2}\).  
Figure 13: Watermarked images and extracted watermarks under Gaussian noise: (**a**) watermarked [PERSON] under Gaussian noise (0, 0.01); (**b**) extracted watermark \(w_{1}\); (**c**) watermarked Barbara under Gaussian noise (0, 0.01); (**d**) extracted watermark \(w_{2}\); (**e**) watermarked Lena under Gaussian noise (0, 0.05); (**f**) extracted watermark \(w_{1}\); (**g**) watermarked Barbara under Gaussian noise (0, 0.05); (**h**) extracted watermark \(w_{2}\).  
(5) Median filter (3 \(\times\) 3).
## 3 Results and Discussion  
### Extraction Yield and Biochemical Composition  
The extraction yield and the overall biochemical composition of JSP are detailed in Table 1.  
The extraction yield of JSP was around 4.55% (_u/v_) and close to those obtained by [PERSON] et al. [35], [PERSON] et al. [2], [PERSON] et al. [36] and [PERSON] et al. [37] for the extraction of galactans from the red seaweeds _Pterocladia capillaca_ (6.46%), _L. heterocladum_ (6.20%), _Gracilaria birdiae_ (6.50%) and _Sarconema filiforme_ (6.0%). This extraction yield was higher than those obtained from _J. rubens_ (\(<\)1.4%), _C. officialis_ (0.3%) and _B. orbigniana_ (0.25%) [3; 18], but remained much lower than those obtained by [PERSON] et al. [38], [PERSON] et al. [16] and [PERSON] et al. [39] for the red algae _Laurencia obtusa_ (12.80%), _Halymenia durvillei_ (-15-18%) and _Gratelupia indica_ (13.0%). Overall, the extraction yield of sulfated galactans from Corallinales are probably lower due to their strong calcareous covers.  
JSP was mainly composed of carbohydrates (70.34%), principally neutral (63.26%), with minor amounts of uronic acids (5.63%), polyphenols (0.74%), proteins (0.81%) and pyruvate (0.44%). These results were in accordance with the literature concerning sulfated galactans extracted from red seaweeds species, such as _C. officinalis_, _J. rubens_ and _H. durvillei_[16; 18; 40]. The presence of sulfate groups (10.82%) was in agreement with values reported for galactans isolated from _J. rubens_, _C. officinalis_ and _B. orbigniana_ (7-15%) [18]. As seen in Table 1, the proportion of 3,6-anhydroogalactose (3,6-AnGal) was close to 18.15%, confirming the great content of 3,6-AnGal residues obtained after an alkaline extraction [16]. Regarding the literature, this strong heterogeneity in extraction yields and global chemical compositions  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Extraction Yield** & **Total Sugar** & **Neutral Acid** & **Uronic Acid** & **Sulfate** & **Pyruvate** & **3.6-AnGal** & **Proteins** & **[NaCl]** & **Conductietry** \\
**(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **(\% _u/v_)** & **eq. (\%)** & **(\%/cm)** \\ \hline
4.55 & 70.34 \(\pm\) & 63.26 \(\pm\) & 5.63 \(\pm\) & 10.82 \(\pm\) & 0.44 \(\pm\) & 18.15 \(\pm\) & 0.81 \(\pm\) & \\  & 0.68 & 0.45 & 0.37 & 0.33 & 0.03 & 0.42 & 0.04 & 1.64 & 31.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global composition of JSP.  
of sulfated galactans is largely dependent on the algal species, environmental factors and extraction procedures [16; 38].
## 4 Results and Discussion
### Results Validation  
The projected results for the two ANN structure models were confirmed through the measured outcomes from the training and validation phases. Altogether, 69 datasets were allocated over the two stages--57 for the training stage and 12 for the validation stage. As seen in Figure 10, in the case of an ANN with single and double hidden layers, the convergence between the predicted and observed coefficient of friction is precise. It is evident that the greatest values for the absolute error between the measured and predicted values in the single and double hidden layer architectures were 0.145 and 0.151, respectively. Thus, this strongly suggests that optimal accuracy has been achieved for the two ANN structures. Nonetheless, it is important to note that the single hidden layer has a more accurate structure.  
Figure 8: Best validation performance for the (**a**) single (**b**) double hidden layers.  
Figure 10: Cont.  
Figure 9: ANN model performance at the four stages for (**a**) single (**b**) double hidden layer.
## 1 Introduction
### Motivation
In agriculture, IoT devices can be used to monitor crops and livestock in real-time, which can help to optimize production and reduce waste. 5G networks can support these applications by providing the necessary bandwidth, reliability, and low latency. In retail, IoT devices can be used to collect real-time data on customer behavior and inventory levels, which can be used to optimize store operations and improve the customer experience. 5G networks can provide the necessary bandwidth, reliability, and low latency to support these applications. Overall, the text shows how IoT and 5G technology can work together to enable a range of applications in different fields. By providing the necessary bandwidth, low latency, and reliability, 5G networks can support real-time data collection and analysis, which can help to improve efficiency, reduce costs, and enhance the quality of life in various sectors.
## 1 Introduction
* We evaluate the performance of the security model (fine-grained data access control and blockchain-based certificate) using AWS message broker metrics for a real-life scenario of the IoT-Fog-Cloud architecture. * We compare the performance of the IoT-Fog-Cloud architecture with and without our security model using AWS message broker metrics and present its efficiency and feasibility. The remainder of the paper is structured as follows: In Section 2, we provide an overview of the IoT-Fog-Cloud architecture. In Section 3, we explain the authentication model, a blockchain-based certificate model, and the authorization model, a fine-grained data access control model based on the ABE of the IoT-Fog-Cloud architecture. In Section 4, we detail the setup of the IoT-Fog-Cloud architecture experiments and describe the evaluation metrics that were used to evaluate the IoT-Fog-Cloud architecture performance with the two security models. In Section 5, we present the evaluation methods used to evaluate the IoT-Fog-Cloud architecture with the two security models. In Section 6, we evaluate the experiment results based on the evaluation methods of the IoT-Fog-Cloud architecture. In Section 7, we conclude the paper.
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 2 Reactive-Type Flame Retardants
### Incorporation of Nitrogen-Containing Groups  
The incorporation of nitrogen-containing groups is another effective approach for flame retardancy in polyurethane foam [59; 60; 61]. These compounds are usually incorporated into the polyol component during the production process, which then react with isocyanates to form polyurethane foam with improved flame retardancy [62]. During combustion, these compounds release nonflammable gases, such as nitrogen or ammonia, when exposed to high temperatures, diluting the flammable gases and reducing the combustibility of the foam [63; 64; 65]. Nevertheless, the flame-retardant effectiveness of nitrogen-based compounds is typically inferior to that of phosphorus-containing ones due to their single flame-retardant function.  
Melamine-based polyols are a type of reactive flame retardant used in the production of RPUF due to their high nitrogen content [66; 67]. These polyols are synthesized by reacting melamine with an excess of formaldehyde and an alcohol or polyol, resulting in a highly crosslinked and thermally stable polymer. [PERSON] et al. [62] developed a melamine-based polyol (MADP, Figure 2b) and incorporated it into the RPUF matrix, and the interactions between the PU-NCO groups and DOPO are shown in Figure 3. The findings from their study demonstrated a notable improvement in the LOI values (increasing from 19.0% to 28.5%) with the implementation of the flame-retardant system. This enhancement promotes the development of protective char layers and reduces the concentration of flammable gases in the gaseous phase. The synthesis of a Mannich base polyol derived from cardanol (MCMP, Figure 4) was conducted by [PERSON] et al. [68]. The incorporation of melamine into the molecular structure of MCMP led to improvements in the mechanical properties, thermal stability, and flame resistance of the resulting RPUF. [PERSON] et al. [69] developed an ecofriendly melamine-based polyether polyol referred to as GPP. They found that incorporating GPP in RPUF synthesis greatly enhances the flame retardancy of the resulting foam. The compressive strength of the RPUF samples increased by 106.0%, and an LOI value of 30.4% was achieved by fully incorporating GPP during the preparation process.  
Figure 4: The synthesis of MCMP.  
Figure 3: Schematic presentation of the interactions between PU—NCO groups and DOPO [62] (Copyright 2018). Reproduced with permission from Elsevier Science Ltd.
## 5 Proposed Hardware Architecture
### Control Unit  
The control unit design for the BEC model of ECC consists of FSMs. In the case of the pipelined architecture, the control unit requires 101 states to execute all its functionalities. Here is a detailed description of the 2-stage pipelined architecture.  
The first state (State 0) is the idle state. When the reset and start signals are activated, the execution process begins. As shown in Figure 3, the start signal triggers the transition from State 0 to State 1. State 1 to State 6 produces control signals for affine to omega conversions of Algorithm 1. State 7 to State 72 produces control signals for Quad block Itoh-Tusuji inversion operation. State 73 in the FSM counts the number of points on the provided BEC curve and verifies the examined key bit (k).  
If the value of \(k\) is 1, State 73 transitions to State 88; otherwise, it transitions to State 74. States 88 to 100 generate control signals for computing the \"if\" part of Algorithm 1, while States 74 to 87 produce control signals for the \"else\" part of Algorithm 1.  
States 87 and 100 are crucial since they check the number of points on the BEC curve (using m in Figure 3) for each value of \(k\) (either 0 or 1). The next state is State 101, which is reached when the value for m (initially set to 1) reaches 233. If m has not reached 233, the next state is State 73.
## 2 Materials and Methods
### Virtual Inertia Optimization Allocation Model
#### 2.2.3 Optimization Model  
The main objective of optimizing the virtual inertia allocation in this paper is to balance and minimize the frequency change rates among different regions, thereby reducing the oscillation of the system. This model takes the minimization of the frequency change rates and the absolute difference between the two regions as the objective function. Considering the above constraints, the following optimization model can be obtained:  
\[min\Big{[}RoCoF_{1}^{2}+RoCoF_{2}^{2}+(RoCoF_{1}-RoCoF_{2})^{2}\Big{]} \tag{18}\]  
\[-RoCoF_{max}\leq RoCoF_{1,2}\leq RoCoF_{max} \tag{19}\]  
where \(RoCoF_{1}\) and \(RoCoF_{2}\) represent the frequency change rates of autonomous regions 1 and 2, respectively; \(RoCoF_{\max}\) is the maximum limit of the frequency change rate, which is generally set at 0.5 Hz/s.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Coarsening Algorithm Based on Edge-Priority Matching
Next, other vertices are iteratively matched based on edge weights, resulting in a coarsened graph with 17 edges, which is a reduction of 3 edges compared to the maximum weight matching in Figure 3.  
The time complexity of Algorithm 1 is primarily determined by the sorting operation, matching operation, vertex merging, edge weight updating, and the outer loop. First, in each iteration, the algorithm sorts the edges of the graph by weight. The time complexity of this sorting operation is \(O(|E|\log|E|)\), where \(|E|\) is the number of edges in the current graph. Then, for each edge, the algorithm checks and attempts to match unmatched vertices. The time complexity of this operation is \(O(|E|)\). Vertex merging and edge weight updating also require traversing all related edges, so their time complexity is also \(O(|E|)\). The outer loop reduces the number of vertices by half with each coarsening operation, resulting in \(O(\log|V|)\) iterations, where \(|V|\) is the number of vertices in the initial graph. Therefore, considering all operations, the overall time complexity is \(O(\log|V|\cdot(|E|\log|E|+|E|))\).
# Free and Forced Vibration Modes of the Human Fingertip
## 4 Modal Analysis  
### Convergence Study  
We initially performed a convergence study to determine the minimal mesh resolution required to reach a precise solution. In this analysis, the number of nodes was exponentially increased (by a factor of approximately 1.5 at each step) until the natural frequencies of the relevant vibration modes changed by less than 1% compared to the previous values. For the converged mesh, there are seven modes whose frequencies are below or around 250 Hz. The converged frequency of the eighth mode was computed to be 295.1 Hz, which is relatively distant from the seventh natural frequency of 256.8 Hz. Thus, we considered only the first seven modes in the convergence analysis.  
Figure 2 shows the convergence of the first seven fingertip natural frequencies to their final values as we increased the number of nodes in the model. By examining these results, we selected a model with 11,637 nodes and 10,080 elements (see Figure 3), which satisfies our target tolerance. The volume of the deformable parts of the finger model is 4084.8 mm\({}^{3}\); hence, the discretization resolution was approximately 2.47 elements per mm\({}^{3}\), for an average element volume of about 0.405 mm\({}^{3}\). There are 1293 nodes on the fixed hypodermisbone boundary; therefore, the system's total number of degrees of freedom becomes \(N_{d}=3(11,\!637-1293)=31,\!032\) when the rear boundary is free. When the other 512 nodes on the rear boundary are also fixed, we have instead \(N_{d}=3(11,\!637-(1293+512))=29,\!496\).  
Figure 2: The convergence of the first seven fingertip natural frequencies to their final values as the number of nodes in the model was exponentially increased.
# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 4 Method Verification
### Verifying the Computational Speed of RPOS  
In this chapter, the controlled experiment was conducted to verify the computational speed of RPOS. The experiment measures the convergence times taken to identify a near-global solution by using the same site and constraining variables used in test case 4.1, but changing the grid size (\(gs\)). The experimental results are shown in Table 2. In experiment 1 with a grid size of 4 m, RPOS identified the same near-global optimal solution that requires operating time 4326.4 s in 75.1 s, as shown in Table 2. When the grid size is decreased to 2 m (Experiment 2), the size of solution search spaces is 6.53481 \(\times\) 10\({}^{31}\), which is much higher than for a grid size is 4 m (3.09878 \(\times\) 10\({}^{21}\)). RPOS identified a better solution that requires operating time 4280.1 s in 384.61 s. The convergence time was increased by 5.1 times compared to the grid size of 4 m. When the grid size is decreased to 1 m (Experiment 3), the size of solution search spaces, convergence time, and operating time are 1.54221 \(\times\) 10\({}^{42}\), 3738.91 s, and 4273.6, respectively. RPOS found a better solution, as the more the grid size decreases, the more the practicality of the mathematical models increases. On the other hand, the more the grid size decreases, the more the convergence time dramatically increases. As shown in the experiment, the user needs to consider the relationship between grid size, computational time, and total operation time, and determine an appropriate grid size according to the site conditions to be applied.  
If it is necessary to reduce the computation time, it is recommended to apply A\({}^{*}\) instead of DA for finding the shortest paths between points because A\({}^{*}\) is known as being faster than DA. In addition, considering that the demand points are sequential, it can be recommended for clustering the demand points before executing GA so as to reduce the convergence time of RPOS.\"
## 1 Introduction
Most MRI machines operate at a magnetic field strength of about 1.5 T, while there are commercial systems available with a field strength of 7 T [24]. The inductance of a magnetic field in the range of 7.0 T is not achievable in most tomographs, while inductance an order of magnitude lower is capable of influencing biophysical and biochemical processes in biological objects [25; 26]. It has been previously shown that under the influence of a static magnetic field with an induction of 0.5 T in stem cells, the expression of CD49d, CD54 and CD73 decreases, changes associated with differentiation and division occur, while there are no changes in DNA integrity [27]. A static magnetic field was shown to affect the proliferation, adhesion, and differentiation of various types of cells, making it a helpful tool for regenerative medicine [28]. With an increase in the inductance of the magnetic field, its effect on biological objects increases. Thus, with an inductance of 7 T, there is a 20-40% decrease in the viability of cells of the lines HTB 63 (melanoma), HTB 77 IP3 (ovarian carcinoma), and CCL 86 (lymphoma) [29]. Consequently, a large number of phenomena have been established to date that occur in aqueous solutions when exposed to a constant magnetic field. Moreover, all these phenomena are observed during direct action of a magnetic field on an object. The post-effects of magnetic field exposure are most pronounced in the presence of ions. In this case, the presence of cations and anions in the aqueous phase can have a significant effect on the measured values, both quantitatively and qualitatively. The nature of this effect is still not fully understood. In the current study we assessed a number of physicochemical properties of aqueous NaCl solutions using high-precision measuring instruments, namely: the concentration of hydrogen peroxide, the concentration of dissolved molecular oxygen, pH, and redox potential. The experimental conditions included varying NaCl concentrations and magnetic field induction. We demonstrated that both the concentration of NaCl and the magnetic field strength have a significant effect on the basic physicochemical properties of aqueous solutions. The impact of flow-mixing during sample preparation on the physicochemical properties of the solutions was also evaluated.
## 3 Research Methodology
### Fma  
FMEA is a risk-assessment tool that is a systematic approach used to detect errors in a product or a system and reduce the error effect caused by this situation [54]. FMEA is a specific methodology to evaluate a system, design, process, or service for possible ways in which failures (problems, errors, risks, and concerns) can occur [7]. The primary purpose of FMEA is to predict and evaluate risks to identify potential defects of a product/process and implement safe regulatory actions that can reduce its effects. Today, FMEA is applied in many fields, such as aviation, manufacturing, automotive chemistry, maritime, medicine, and nuclear [77].  
In classical FMEA, the identification and ranking of failure modes is determined by the risk priority number (RPN) value, which is found by multiplying the three determining risk components (severity (S), probability (P), and detectability (D)) [78]. The classical FMEA formula is given in Equation (5).  
\[\text{RPN}=\text{ P }\times\text{S}\times\text{D} \tag{5}\]  
According to many studies, classical FMEA has some areas for improvement in risk assessment. According to [PERSON] et al. [77], the shortcomings of FMEA are as follows:  
1. When different combinations of the three main components (severity (S), probability (P), and detectability (D)) give the same RPN value, risk values that are different but of the same importance emerge, which can make prioritization difficult.
2. Risks in a system may have different degrees of S, P, and D values. Therefore, giving the same importance weight to S, P, and D for different risks is not appropriate. The three risk components are assumed to have the same weight, but there is relative importance weight in real applications.  
Looking at the research results, using classical FMEA alone will not produce reliable results for risk assessment. For this reason, MCDM methods are used to improve the shortcomings of classical FMEA. In this study, the inadequacy of classical FMEA in measuring the value of the severity component will be eliminated by dividing the main severity component into five subcomponents, and the relative importance weights of the sub-risk components will be determined by using the fuzzy SWARA method, one of the MCDM methods.
## 1 Introduction
### The Value Proposition of 5G  
#### 1.2.1 Mobile Networks  
Mobile networks answer the need human beings have to communicate remotely while not being at home or anywhere close to a wireless local area network (LAN) access point (Wi-Fi). Since their first release in 1980 [4] until now, a series of consecutive generations has provided continuous technology improvements. Table 1 summarises data transmission (Tx) speed, technologies and some details of each generation [5] (refer to [5; 6; 7] on protocol acronyms used in Table 1):