esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Knowledge Generation Plane
#### 3.3.1 Generating Knowledge Using Heuristic Model-Based Methods
An information security management system and an intrusion detection system with a fuzzy logic-based decision-making module have been used in KDNs for making decisions regarding security [83]. A flow-based and packet-based intrusion detection system known as Kangaroo intrusion detection, which uses consecutive jumps, similar to a kangaroo, for announcing the attacks to the KDN controller, and other IDSs that use an attack detection algorithm to detect attacks from packets and flows, have been utilized in work [84]. A meta-heuristic approach to detecting DDoS attacks in KDNs using the Lion optimization technique, which is robust enough to detect the DDoS attack with the least magnitude of attack traffic, has been proposed [85]. An anomaly-based intrusion detection system using fuzzy logic combined with an InfoGain feature selection method has shown promising results for the detection of DDoS attacks [86]. An evolving fuzzy system to discriminate anomalies (normal and attack network situations) has been used to classify network traffic as normal or attack, by capturing time-series data, which are analyzed to establish a model of the normal network situation that evolves over time [87]. Furthermore, heuristic algorithms have been used for controller placement problems. Evolutionary algorithms have been utilized to solve a large-scale multi-objective controller placement problem, where capacities of the controllers and loads of switches are considered as constraints, and latency between nodes and controllers, the latency among controllers, and load balancing are considered as objectives [88]. A heuristic algorithm that computes the controller placements with at least the required reliability to yield a fault-tolerant placement that focuses on the number of controllers, number of nodes under the controller, and position of the controller has been studied in [89]. Some have considered energy consumption minimization in placing the controller, which uses a binary Integer Linear Programming optimization (BILP) problem, having constraints as the delay of the control path and the load of the controller. However, due to the computational complexity of BILP, a suboptimal solution using a genetic heuristic algorithm for energy-aware controller placement that yields results close to the BILP solution has been proposed for large networks [90]. Another controller placement approach uses particle swarm optimization and the Firefly algorithm to place the controllers, considering the effects of controller-to-switch latency, inter-controller latency, and multi-path connectivity between the switch and the controller [91].
## 3 Results and Discussion
### Anthocyanins Content  
Anthocyanins are among the compounds that determine the color of a product, as well as their health-promoting properties. The anthocyanin content of the final product depends on the type of raw material used and the method of processing and preservation. In our study, a significant effect of the production method on the content of the anthocyanins was shown (Table 3). Directly after production, the confitures obtained in the vacuum evaporator contained about 37% more anthocyanins than those obtained in the thermomix. Depending on the variety, the total concentration of anthocyanins in blue honeysuckle berry fruits ranges from 86 to 655 mg/100 g fresh weight [6; 32; 33]. Anthocyanins constitute on average 94% of the total content of polyphenic compounds present in these fruits [32].  
Four anthocyanin monomers were identified in the investigated confitures. The predominant anthocyanin was cyanidin-3-glucoside, accounting for 90% of all anthocyanins. Subsequently, cyanidin-3-rutinoside accounted for 6% of all anthocyanins, and cyanidin-3,5-diglucoside for nearly 3%. Also, peonidin-3-glucose was identified, which accounted for less than 1%. The shares of individual anthocyanins were within the limits normally reported in the literature [7; 34].  
A reduction in anthocyanin content was observed in both types of confitures during refrigerated storage (Table 3). After 180 days, 74% of the initial anthocyanin content remained in the confitures cooked in the thermomix, and 60% in the confitures concentrated in the vacuum evaporator. However, the final anthocyanin content of confitures from the vacuum evaporator was 10% higher than that of the corresponding confitures from the thermomix. The rate of transformations of anthocyanins depends on a number of factors such as initial concentration or chemical structure. During storage, anthocyanins primarily undergo polymerization and condensation processes with other polyphenols and phenolic acids [35].
# Metric for Estimating Congruity between Quantum Images +
## 3 A Measure for Fidelity between Quantum Images
### Shortcomings of the PSNR Image Quality Metric  
The peak-noise-to-signal (or simply PSNR) is an engineering term for the ratio between the maximum possible strength of a signal and the strength of corrupting noise that affects the fidelity of its representation [25; 26], which has found widespread applicability as a traditional measure of consistent quality. Its acceptability is mainly attributed to its simplicity, which has seen it widely used in digital image processing as a pixel difference-based measure [26] to assess the quality of reconstruction of lossy compression codecs (for example, in image compression) and in evaluating, for example, the extent of likeness (or congruity) between a watermarked image and is original (unmarked) version [26]. It has, however, been shown that for some applications, such as when dealing with medical images, the PSNR poorly correlates with subjective quality expectations [26]. Similarly, objective image quality metrics have been shown to outperform the PSNR metric in predicting subjective image quality [26].  
Motivated by these shortcomings of the PSNR image quality measure, various modifications to it have been suggested. Among them, the weighted PSNR (WPSNR) metric incorporating some parameters of the human visual system (HVS) into the computation was proposed in [26]. While it proves superior to the PSNR metric, the WPSNR method suffers from limited applicability because it fails to discriminate between the regions of interest and region of background of the image [26].  
To further stone for this shortcoming, [PERSON] et al. [25] proposed the modified WPSNR (or MWPSNR) metric, which they assert outperforms both the PSNR and WPSNR image quality metrics. In the nutshell, from the foregoing, we can deduce that even on the digital computing framework the PSNR often fails to effectively quantify the congruity between two (or more) images.  
Motivated by this argument and the established antinomy or contrariety that exists between data processing on the classical (digital) and quantum computing frameworks, we assert that the PSNR metric is unsuited for use as a quantum image quality metric. For lack of an alternative, however, all the available quantum image watermarking schemes [13; 21; 27] have relied on the classical interpretation of the PSNR to evaluate quantum image quality and/or fidelity [1; 17].  
In the sequel, we improve on our earlier attempt (in [1]) at formulating an efficient, wholly quantum-based approach to evaluate the fidelity between two quantum images on the quantum computing framework, such as the likeness between a watermarked quantum image and its unmarked version.
## 3 Results and Discussion
(**B**) The relationship between triclosan concentrations and the inhibition of bacterial adhesion for each treatment is represented as the coefficient of determination (R\({}^{2}\)). N = 9 for each result. Treat: Treatment. concentrations are used, this compound will have bactericidal properties [27; 70]. Triclosan has been incorporated into different polymers with wide degrees of success to achieve antimicrobial activity [25]. However, the incorporation method of adding triclosan to the packaging material formulation during the extrusion or injection molding, when the polymeric material is being manufactured, has given satisfactory results. As well, triclosan can be incorporated by mixing with thermoplastic polymers that are cooled in a solid [25]. In the case of the incorporation of triclosan by coating on polymeric surfaces, it consists of adding an antimicrobial agent to a coating solution, which is used to cover the surfaces of the packaging material [19]. In order to avoid the possible toxicity of triclosan in the environment, when this agent is added to a surface during its manufacture, studies have been directed towards triclosan grafting by covalent binding to the materials, limiting its release during its usage [63]. Due to current production needs, presently the working time to process food products is frequently increased, with a limited time for sanitation. The incorporation of bacteriostatic agents, such as triclosan, could preserve the hygienic conditions of some materials for a longer time. Thus, antibacterial surfaces can increase the hygienic safety of food industry environments, though their advantages and disadvantages must be measured to obtain a better use in real conditions. Moreover, antimicrobial resistance to antibiotics is becoming more common. As a frequent pathogen, _S. aureus_ is transmitted by materials and is resistant to a wide variety of antibiotics, increasing the risk of cross-contamination. The use of triclosan may control _S. aureus_ and other bacteria with a high capacity to survive on surfaces.
## 2 Sequential Modelling Strategy  
### Overview and Modelling Strategy  
#### 2.1.1 Overall Modelling Strategy and Assumptions  
In order to evaluate if secondary space debris generation can occur, three (to four) different models are required. The first model focuses on net deployment and contact dynamics, assuming the space debris object behaves as a rigid wall, implying no initial influence from net contact dynamics. This yields outputs which are contact forces, directly utilised in the subsequent space debris dynamic model. This latter model consists of two system dynamics components: a vibrational dynamic submodel yielding displacements at spacecraft connectors and a structural submodel estimating stress resulting from the simulation scenarios' loading configurations. Lastly, these outputs are then scrutinised by a simplified failure model, resulting in a binary outcome on failure occurrence. If failure is expected, the location and size of the newly formed secondary space debris are determined.  
Due to the intricacies and complexity of the physical problem and the associated interactions between the models, a decision has been made to decouple them, simplifying simulation implementation while still facilitating an extensive preliminary investigation.  
The sequential and decoupled nature of the strategy is represented in Figure A1. It is crucial to note that despite space debris not being directly caused by net contact dynamics, a post-processing simulation of its translational and rotational dynamics is incorporated. This preliminary analysis allows for an initial understanding of the effects and potential risks associated with net contact and wrapping.
## 3 Optimization Model
### MOGA Module
#### 3.3.2 Decision Variables  
Each solution comprises an N number of blocks, as displayed in Figure 6. Each block represents individual problem decision variables providing two different values. The firstvalue is the number of shifting times from the original plan, while the second value is a predecessor activity option.  
\[0\leq S_{i}\leq TF_{i} \tag{17}\]  
\[0\leq x_{i\text{{lg}}}\leq 1 \tag{18}\]  
where \(S_{i}\) is the shifting time of activity \(i\), \(TF_{i}\) is the total float of activity \(i\), and \(X_{i\text{{lg}}}\) is the predecessor option between \(h\) or \(g\) of activity \(i\).  
Initial decision variables are a dataset where:  
Set1: Defines shifting time and sets lower and upper bounds (\(S_{i}=0\) to total float of activity \(i\))  
Set2: Defines predecessor option (\(X_{i\text{{lg}}}\)) and sets lower and upper bounds (\(X_{i\text{{lg}}}=[0,1]\))  
The predecessor option is defined (\(X_{i\text{{lg}}}\)) and optimal scheduling is now processed by considering both Set1 and Set2 options. The Set2 option is proposed based on the renovation project characteristics, including additional levels of uncertainties and particular management issues. If the interactive planner's viewpoint is obtained, selecting the Set2 option can provide more efficient optimal scheduling. The shifting time and predecessor option are two sets of decision variables with 250 total number of activities for all decision variables. The population size is chosen as 500, as suggested in previous research for double or quadruple design variables [52].
## 2 Materials and Methods
### Methodological Description of Economic Viability
#### 2.3.2 Internal Rate of Return (IRR)  
The internal rate of return (IRR) or rate of return that is the type of update or discount that nullifies the NPV. Hence, it can also be called the marginal efficiency of capital. The rate is internal because it does not depend on factors exogenous to the investment.  
In this way, the IRR is calculated by obtaining the rate, r, which meets the following equation [60; 65]:  
\[\rm{NPV}(IRR,N)=-K+\sum_{t=1}^{N}\frac{Ct}{(1+IRR)^{t}}=0 \tag{3}\]  
Only those projects that meet the \"feasibility\" condition will be viable: r > i, where i is the interest rate that corresponds to the \"cost of capital\". The higher r, the better the investment.  
The calculation is usually laborious unless some computer program is used. Although it is not difficult to do it with the help of a calculator and following an iterative process of testing until you reach the solution.  
There has been a lot of controversy about which method is preferable: the NPV or the IRR. However, more than a preference problem, the interesting thing is to find the reconciliation of both methods, if possible, in the event that they can reach different results when we compare several projects.
## 2 Method
### Locally Improved A* Algorithm
If this distance is less than the preset safety distance \(d_{min}\), the neighbor node is abandoned and is not added to the pending \(Openlist\) for subsequent processing. Only when the distance of the \(Neighbor_{i}\) node meets the safety requirements will it be considered to be added to the \(Openlist\) for subsequent path search, as shown in Equation (6). The diagram illustrating safe distances is shown in Figure 4. This method ensures that nodes in dangerous areas are not selected as path points during the path planning process, thereby effectively ensuring the generated path's safety and feasibility. \[d(Neighbor_{i},Obstacle)=\min\biggl{(}\sqrt{(x_{n}-x_{i})^{2}+(y_{n}-y_{i})^ {2}}\biggr{)} \tag{5}\] \[d(Neighbor_{i},Obstacle)\geq d_{\min} \tag{6}\]  
where \(d(Neighbor_{i},Obstacle)\) is the distance from the \(Neighbor_{i}\) node to be expanded to the nearest \(Obstacle\) and \(d_{\min}\) is the minimum safety distance. connection is no longer possible or the path ends. Retain the starting point, endpoint, and all necessary turning points, removing redundant nodes to optimize the path. \[\vec{a}=(x_{b}-x_{a},y_{b}-y_{a}) \tag{7}\] \[\vec{b}=(x_{c}-x_{b},y_{c}-y_{b})\] (8) \[\frac{(x_{b}-x_{a})}{(y_{b}-y_{a})}=\frac{(x_{c}-x_{b})}{(y_{c}-y_ {b})} \tag{9}\]  
If Equation (10) is satisfied, indicating that the path consists of line segments with directional changes, node \(B\) serves as the turning point. Connecting nodes \(A\) and \(C\), where \(P(x_{p},y_{p})\) is any point on the straight line \(A\to C\), conduct a safety distance check for the nodes traversed by the segment \(A\to C\) and compare the path lengths. If there are impassable nodes, this indicates that the segment \(A\to C\) is not feasible and the path remains \(A\to B\to C\). If Equations (11) and (12) are met, where the length of path \(A\to C\) is shorter than \(A\to B\to C\) and meets the safety distance requirements, the redundant turning point \(B\) is removed, transforming the path from \(A\to B\to C\) to \(A\to C\), as shown in Figure 5b. \[\frac{(x_{b}-x_{a})}{(y_{b}-y_{a})}\
eq\frac{(x_{c}-x_{b})}{(y_{c}-y_{b})} \tag{10}\] \[h(n)=\sqrt{\left(x_{g}-x_{c}\right)^{2}+\left(y_{g}-y_{c}\right)^{2}} \tag{4}\]0 (11) \[h(n)=\sqrt{\left(x_{g}-x_{c}\right)^{2}+\left(y_{g}-y_{c}\right)^{2}} \tag{4}\]1  
In this way, check whether a straight line can directly connect the starting point and end point of the turning and oscillating path. If feasible, only these points are retained. After this processing, the path only contains the starting point, end point, and necessary turning points, significantly reducing the redundant nodes in the path and making the path more concise and efficient.
## 2 Antenna Array Structure  
### Reference Antenna Array  
The configuration of the reference antenna array shown in Figure 1a,b consists of a 2 \(\times\) 4 matrix of hexagonal patches that are interconnected to each other with a high impedance microstrip-line. The bottom side of the substrate is a ground-plane as shown in Figure 1b. The array is excited through a common central feedline. The patches in the array are based on a standard design and its performance was simulated and optimized using CST Microwave Studio. The array was fabricated on the FR-4 substrate with dielectric constant of 4.3, tan\(\delta\) of 0.025, and thickness of 0.8 mm. The thickness of the conductive layer made of copper is 0.035 mm. The total dimensions of the antenna are 20 mm \(\times\) 20 mm \(\times\) 0.87 mm. Geometrical parameters of the optimized array are listed in Table 1. The reflection-coefficient response of the reference antenna array in Figure 1c shows that it operates over 5.25 GHz to 10.1 GHz corresponded to 63.19% practical bandwidth, which falls short for UWB systems defined between 3.1 GHz to 10.6 GHz. Figure 1d show the average radiation gain of the array across its operating band is 1.5 dBi.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Dimensions (mm)** \\ \hline Radius of hexagonal patches & 2 \\ Width of connecting lines & 0.5 \\ Horizontal gap between patches & 7.5 \\ Vertical gap between patches & 1.55 \\ Length of the central feedline & 10 \\ Width of the feedline & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the reference antenna array.  
Figure 1: Geometry of the reference antenna: (**a**) top-side of substrate, (**b**) back-side of substrate (ground-lane), (**c**) simulated reflection-coefficient response, and (**d**) simulated radiation gain response.
## 5 Experimental Results
### Criterion to Determine the Severity of the Discharges  
Results presented in Section 5.2 show that \(INT_{normalized}\) can be used as a discharge severity indicator.  
Disruptive discharges were then performed to determine the upper limit of the \(INT_{normalized}\) indicator. The results obtained indicate that under the experimental conditions of this work, in case of disruptive discharges, the severity indicator \(INT_{normalized,upper}\)  
Figure 6: Experimental results. (**a**) Correlations between the normalized intensity of the digital images of the discharges and the applied voltage. (**b**) Correlations between the normalized intensity of the digital images of the discharges and the square root of the electric energy dissipated by the discharges.  
is always greater than 50, so an upper threshold value of this indicator can be established as \(INT_{normalized,upper}=50\).  
Finally, the lower limit of the \(INT_{normalized}\) indicator was tested. Without the presence of electric discharges, due to the noise of the CMOS sensor and the background, the value indicated by the \(INT_{normalized}\) indicator is not zero. Tests indicate that under our experimental conditions, this value is less than 0.1, so a threshold lower value \(INT_{normalized,lower}=0.1\) of this indicator can be established.  
Results obtained manifest that \(INT_{normalized}\) can be applied as a discharge severity indicator, as follows:  
1. When (\(INT_{normalized}\leq INT_{normalized,lower}\)), it corresponds to a healthy condition, without discharges.
2. When (\(INT_{normalized,lower}<INT_{normalized}<INT_{normalized,upper}\)), it corresponds to a fault condition, where discharges occur. In this case, a higher value of \(INT_{normalized}\) corresponds to a higher discharge severity.
3. When (\(INT_{normalized}\geq INT_{normalized,upper}\)), it corresponds to the worst fault condition, that is, a disruptive discharge.  
Therefore, a continuous monitoring of the discharges using a digital camera with additional processing to determine the \(INT_{normalized}\) discharge severity indicator allows detection of anomalous conditions and generating an alert, which can be very useful to apply predictive maintenance strategies.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis  
In the comparison tests, the procedures for data filling and processing were standardized in both transmission mechanisms, so the duration of these steps was not factored into the results. Each sender and receiver was allocated three memory blocks for data transmission and reception. This choice of three memory blocks was intended to simulate a realistic scenario where at least two blocks are necessary for continuous data transmission from the sender, with an additional block providing redundancy to address potential delays in data processing by the receiver. This configuration helps to balance sending and receiving activities. Furthermore, the data packet sizes varied from 64 B to 8 MB to cover a broad spectrum of transmission scenarios. In addition, the upper limit of 8 MB was chosen to ensure fairness in the tests. These considerations were made to thoroughly evaluate the proposed mechanism under practical conditions.