esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Network Architecture
#### 3.2.3 Chaos Mapping Enhanced Image Steganography Network (CHASE)
In our setup, CHASE serves as the generator to generate the reconstructed secret image and the original secret image as the real image. The generator aims to produce a reconstructed image indistinguishable from the real image, while the discriminator is trained to differentiate between the generated and real images. The discriminator network structure is detailed in Table 2. Through iterative adversarial training, the distribution difference between the reconstructed secret image generated by the backward reconstruction process using auxiliary variable z and the distribution of the original secret image diminishes. The introduction of the GAN aims to enhance the quality of the reconstructed image, bringing it closer to the original image. Continuous adversarial training within the backward reconstruction process enables the reconstruction of higher-quality secret images using the auxiliary variable z as input. The primary objective of this deep learning framework is to achieve superior visual quality and fidelity while preserving secrecy.
# Evaluation Methodology for Circular and Resilient Information Systems
## 2 Background Literature
### Circularity and Resiliency in Information Systems' Evaluation
[30] followed in their research, based on the pre-evaluation and post-evaluation stages (including evaluations through prototype demonstration, surveys, and expert interviews), appears to align with our objectives. Using quantitative and qualitative evaluation criteria is strongly suggested by [PERSON] et al. [31] in their selection process for the most applicable Security Information and Event Management system. Specifically, they divide the evaluation methodology into two phases: (i) quantifying each requirement of the information system and (ii) \"measuring the applicability of the solution using a qualitative-based method after defining a list of indicators that enables the evaluation of this applicability\". However, as they mention, an installation-testing phase should be conducted at the end to further confirm the IS's applicability. Lastly, for a higher-level CRIS system evaluation, addressing issues such as functionality, reliability, performance, scalability, security, interoperability, maintainability, compliance, and standards is described in the ISO/IEC/IEEE 29119-1:2022(en) software testing, from the International Organisation for Standardisation and the International Electrotechnical Commission [32]. Regarding the evaluation of the information system's overall user experience, [PERSON] et al. 's [33] seminal research can be used to record the overall experience of the factory employees during the use of the developed system. This questionnaire measures the attractiveness and the perspicuity of the IS, examines its efficiency and dependability, and examines its stimulation and novelty. Lastly, a qualitative approach is considered relevant to the evaluation methodologies for information systems because it provides in-depth information, mainly through a series of interviews or Delphi studies [34]. There is a huge body of literature concerning using qualitative methods in IS research [4]. But, to a better degree, in the case of Plooto project evaluation and the CRIS system, qualitative data analysis via semi-structured interviews will shed light on unknown phenomena and previously unidentified variables.
# Overview of Active Ingredients Used in Deodorants and Antiperspirants Available on EU Market
## 2 Results
### Composition of Stick Deodorants (n = 20)  
The prevalence of active ingredients in stick deodorants was rather poor. The largest share was demonstrated by Triethyl Citrate (20% of preparations), followed by Ethylhexylglycerin, Caprrylyl Glycol, and Potassium Alum, each present in 10% of deodorants, and then Farnesol, Zinc Ricinolate, Zinc Gluconate, C12-15 Alkyl Benzoate, and Sodium Usnate, each present in 5% of the considered products (Figure 5).  
Complexity of Composition of the Stick Deodorants  
Most preparations in the stick deodorants were of a \"one-component\" complexity (n = 11), as follows: Triethyl Citrate (three products), Potassium Alum (two products), Caprrylyl Glycol (one product), Ethylhexylglycerin (one product), Zinc Gluconate (one product), Farnesol (one product), C12-15 Alkyl Benzoate (one product), and Sodium Usnate (one product). In total, 7 out of 20 deodorants did not contain any of the active ingredients shown in Figure 5. In turn, two of the \"two-component\" formulations contained the following pairs of components: (Triethyl Citrate + Zinc Ricinolate) and (Ethylhexylglycerin + Caprrylyl Glycol) (Figure 6).  
The number of occurrences of active substances according to the form of the deodorant (viz. spray, roll-on, and stick) and their total quantities (incl. %) are summarized in Table 1. The most frequently used component was the scent-giving \"parfum\", found in 81.7% of products. Many deodorants also contained natural fragrance-giving ingredients of the essential oils specified in their composition, e.g., Limonene, Linalool, Citronello, Citral, and Geraniol.  
The most popular substance was Triethyl Citrate, present in 51/120 (42.5%) formulations, followed by Alcohol, in 31 (25.8%), Ethylhexylglycerin in 30 (25.0%), Caprrylyl Glycol in 15 (12.5%), and Potassium Alum in 12 (10.0%). The remaining ingredients were only present in 10% or less of products, e.g., Zinc Ricinolate (9.2%), Aluminum Chlorohydrate (8.3%), and C12-15 Alkyl Benzoate (6.7%).  
Figure 5: Frequency of use of active ingredients in stick deodorants (n = 20).
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 3 Introduction to Fuzzy Metric Space
The three-tuple \((\mathcal{F}(\mathcal{X}),M,\ast)\) is said to be a fuzzy metricspace for fuzzy sets if \(M^{{}^{\prime}}\) is a fuzzy set (fuzzy metric for fuzzy sets) on \(\mathcal{F}(\mathcal{X})\times\mathcal{F}(\mathcal{X})\times(0,\infty)\) satisfying the following conditions for all \(u,\,v,\,z\,\in\mathcal{F}(\mathcal{X})\) and \(t,s>0\):_  
* \(M^{{}^{\prime}}(u,v,0)>0\)_;_
* \(M^{{}^{\prime}}(u,v,t)=1\) _for all_ \(t\,>\,0\) _if and only if_ \(u\,=\,v\)_;_
* \(M^{{}^{\prime}}(u,v,t)=M^{{}^{\prime}}(v,u,t)\)_;_
* \(M^{{}^{\prime}}(u,v,t)*M^{{}^{\prime}}(v,z,s)\leq M^{{}^{\prime}}(u,z,t+s)\)_;_
* \(M^{{}^{\prime}}(u,v,. ):(0,\infty)\rightarrow[0,\,1]\) is continuous. **Example 2**. : _Suppose that \((\mathcal{F}(\mathcal{X}),M^{{}^{\prime}},\star)\) is a fuzzy metric space for fuzzy sets, and we define \(a*b=\min\{a,b\}\) or \(a*b=ab\) and \(\forall u,v\in\mathcal{F}(\mathcal{X})\):_  
\[M^{{}^{\prime}}(u,v,t)=\frac{kt^{n}}{kt^{n}+md(u,v)},\,\,\,k,m\in\mathbb{R}^{ +},n\geq 1.\]  
_It is easy to show that \((\mathcal{F}(\mathcal{X}),M^{{}^{\prime}},\star)\) is a fuzzy metric space for fuzzy sets. Let us say now that \(M^{{}^{\prime}}(u,v,t)\) is the standard fuzzy metric induced by the \(d\) metric, whenever \(k=n=m=1\):_  
\[M^{{}^{\prime}}(u,v,t)=\frac{t}{t+d(u,v)}, \tag{2}\]
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 3 Results
### Effect of CAP on Multispecies Biofilms  
The effect of CAP on multispecies biofilms formed by MRSA, _P. aeruginosa_ and _E. faecalis_ can be observed in Figure 2. The median value (CFU/mL) of the non-treated control for _E. faecalis_ was \(2.3\times 10^{9}\); for treatment groups the median values (CFU/mL) were \(5.0\times 10^{8}\) (reduction of 0.66 log, \(p\) < 0.01), \(8.0\times 10^{8}\) (reduction of 0.5 log, \(p\) < 0.05), \(3.0\times 10^{8}\) (reduction of 0.9 log, \(p\) < 0.001) and 7.0 \(\times 10^{8}\) (reduction of 0.5 log, \(p\) < 0.05) after 1, 3, 5 and 7 min of CAP exposure, respectively. The median value (CFU/mL) of the non-treated control for MRSA was \(1.7\times 10^{8}\); for treatment groups the median values (CFU/mL) were \(1.1\times 10^{8}\) (reduction of 0.2 log, \(p\) > 0.05), \(3.7\times 10^{7}\) (reduction of 0.66 log, \(p\) > 0.05), \(2.0\times 10^{7}\) (reduction of 0.9 log, \(p\) < 0.05) and \(4.0\times 10^{7}\) (reduction of 0.6 log, \(p\) < 0.05) after 1, 3, 5 and 7 min of CAP exposure, respectively. The median value (CFU/mL) of the non-treated control for _P. aeruginosa_ was \(3.0\times 10^{7}\); for treatment group the median values (CFU/mL) were \(6.0\times 10^{6}\) (reduction of 0.7 log, \(p\) > 0.05), \(5.0\times 10^{6}\) (reduction of 0.8 log, \(p\) > 0.05), \(1.0\times 10^{6}\) (reduction of 1.5 log, \(p\) < 0.001) and \(3.0\times 10^{6}\) (reduction of 1.0 log, \(p\) > 0.05) after 1, 3, 5 and 7 min of CAP exposure, respectively.
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
###### Abstract  
Amazon, Uber Eats, and United Parcel Service (UPS) are planning to launch drone delivery services in the near future. Indeed, recently, Google has received Federal Aviation Administration (FAA) approval for its Wings delivery platform. Amazon claims that a drone logistics network is more cost-efficient and quicker than a motor vehicle delivery network. In this paper, we propose a data delivery service by the drone network in addition to parcel delivery. We propose Heuristic Flight Path Planning (HFPP) that plans a drone's flight path based on parcel delivery destination as well as data delivery destinations (waypoints). We further extend the solution to include drone charging stations for range extension. Our simulation studies show that our proposed method has delivered the data and consignments such that HFPP delivers up to 33% more data packets compared with Encounter-Based Routing (EBR), Epidemic, and a similar path planning method. Also, HFPP reduces the data delivery delays by up to 72% while the overhead ratio is low.  
intelligent transportation systems; vehicular networks; forwarding strategy; dynamic mobility models; unmanned aerial vehicles; optimization algorithms; smart city; encounter-based routing; delay tolerant network; Ad Hoc +
Footnote †: journal: Accepted: 5 June 2019; Published: 11 June 2019
# Influence of Quality of Mounting Process of RF Transistors on Their Thermal Parameters and Lifetime
## 3 Solder Process
Due to the fact that it is not known how the power devices behave when the above-described limits of non-soldered surfaces are exceeded, and it is not known how the operating temperature of these devices is distributed with the release of the set power, it is necessary to measure thermal parameters. They will allow estimating the operating conditions of the system in the natural working environment. It is necessary to determine whether the emerging voids under the soldered power devices have an influence on the service life of the component and therefore the whole module. This knowledge will help determine the risk of complaints on the manufactured products and also determine the acceptable ranges of the dimensions of void spaces of the soldered elements also for new products. Based on the Quality House analysis [39], it was found out that the values of thermal parameters of the transistors included in the analyzed system are significantly important. The next section presents the results of the measurements of these parameters for selected test circuits containing the investigated transistors. Figure 4: Values of the quality index in selected months of production. For testing, 4 PCBs were selected, on which the tested transistors were mounted in the manner shown in Figure 5. On each PCB there were 2 transistors of the BLC9G22 LS-160 VT type marked with the symbols TR2_B and TR4_B and 2 transistors of the BLC10G22 LS-240 PVT type marked with the symbols TR1_A and TR3_A. For each of the transistors, the level of voids was measured using the X-ray Dage [38]. The selection of the obtained results of such measurements is shown in Figure 6.  
As can be seen in Figure 6, the measured total void area in the soldering area is 15.3% and the largest individual void is 8.7%. It is marked red in Figure 6. This is an example, where the customer's imposed maximum 8% of the individual void size in the soldering area was exceeded. The measured values of the total voids and the largest individual voids for each transistor are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**PCB** & **Transistor** & **Total Void [\%]** & **Maximum Individual Void [\%]** \\ \hline
1 & TR1\_A & 18 & 12.7 \\
1 & TR3\_A & 8.7 & 2.7 \\
2 & TR1\_A & 13.5 & 2.9 \\
2 & TR3\_A & 15.2 & 8.7 \\
3 & TR3\_A & 30.6 & 9.6 \\ \hline \end{tabular}
\end{table} 
Table 1: Voids sizes for the investigated RF power transistors. Figure 5: Arrangement of the measured transistors on the tested PCBs (**a**) and the zoom of this PCB (**b**). Figure 6: Example of the image of solder voids in the source electrode area of the transistor. As can be seen in Table 1, the total void values range from 8.7% to 30.6% for the BLC10G22 LS-240 PVT transistor and from 9.9% to 21.1% for the BLC9G22 LS-160 VT transistor. The largest individual void ranges from 2.4% to 12.7% for the BLC10G22 LS-240 PVT transistor and from 2.9% to 11.7% for the BLC9G22 LS-160 VT transistor, respectively.
## 3 LEO-SMC-ICN-IoT Caching Framework  
### Mobility Prediction  
With the high movement of satellite nodes in the LEO-SMC-ICN-IoT, the topology of the LEO satellite mega-constellation is high dynamics. However, the topology changes periodically as the satellite nodes move around periodically. Therefore, the mobility of the satellite nodes is predictable. Each satellite node follows an established orbit, returning to its initial position after one orbital period. The predictability of satellite nodes' mobility can solve the problem of content caching due to satellite nodes' movement.  
According to [PERSON]'s form of [PERSON]'s third law, the satellite orbit period \(P\) can be calculated using the following equation:  
\[P=2\pi\sqrt{\frac{\alpha^{3}}{G(M_{e}+M_{s})}}, \tag{7}\]  
where \(G\) represents the gravitational constant, \(M_{e}\) is the earth mass, and \(M_{s}\) is the satellite mass.  
The handover management of satellite nodes is important because the satellite nodes' high-speed movement, despite the handover management of satellite nodes, is not analyzed in this study. Even if the mobile terminal remains stationary on the ground, the mobile terminal must frequently hand over between satellites and beams, which makes the connection relationship between the terminal, the satellite nodes, and the GS relatively complex [45].
# Control of Corner Separation with Plasma Actuation in a High-Speed Compressor Cascade
## 2 Numerical Methods and Plasma Actuation Layouts
On the blade suction surface, as indicated in Figure 3a, the first actuator SA1 is located 15% chord lengths downstream of the blade's leading edge, and the other five actuators, i.e. SA1-SA6, are placed at the downstream blade surface continuously, with an interval of 15% chord length in the chordwise direction. On the endwall surface, as depicted in Figure 3b, the six actuators, i.e. EA1-EA6, have the same layout as the blade suction surface actuators. Figure 3: Plasma actuation layout. (**a**) Suction surface plasma actuation; (**b**) Endwall plasma actuation. Figure 2: Sketch of the dielectric barrier discharge (DBD) plasma actuator. In this paper, a high voltage is imposed on the upper electrode, and the voltage of the lower electrode is set to zero. With these boundary conditions, the distribution of the electrostatic potential around the plasma actuator in the air is solved with [PERSON]'s Equation:  
\[\
abla(\varepsilon_{\mathrm{r}}\
abla\Phi)=0 \tag{1}\]  
On the other hand, the spatial charge density is estimated on the basis of [PERSON]'s equation:  
\[\
abla(\varepsilon_{\mathrm{r}}\
abla\rho_{c})=\rho_{c}\Big{/}{\lambda_{d}}^{2} \tag{2}\]  
where \(\varepsilon_{\mathrm{r}}\) is the relative permittivity of the air and \(\lambda_{\mathrm{d}}\) is the Debye length. The detailed introduction of the boundary condition to solve [PERSON]'s and [PERSON]'s equations as well as the values of the parameters can be found in reference [25]. With the results of Equations (1) and (2), the body force generated by the plasma actuator can be obtained:  
\[F=\rho_{c}\cdot\overset{\rightarrow}{E}=\rho_{c}\cdot(-\
abla\Phi) \tag{3}\]  
During the simulations, Equations (1) and (2) are solved firstly in quiescent air with user defined functions in Ansys CFX. Then the body force of the plasma actuator is calculated with Equation (3) and is applied as a boundary condition where plasma actuator of Figure 3 is located to control the high-speed compressor cascade flow separations. Figure 4 shows the distribution of the induced velocity by the six plasma actuators in quiescent air with different body forces of plasma actuators. In this paper, the body forces of plasma actuators are changed through altering the high voltage imposed on the upper electrode. It can be found that, when the body force produced by a single plasma actuator reaches 837 mN/m, the maximum induced velocity of the six plasma actuators in quiescent air reaches around 30 m/s, and this is consistent with the multiple plasma actuators used by [PERSON] et al. [23]. This indicates that the overall plasma actuation strength can be realized with carefully designed multiple plasma actuators.
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 3 Control Algorithm  
### Conventional Model Predictive Current Control  
According to (1), the differential equation of \(dq\)-axes current can be obtained as  
\[\frac{\mathrm{d}}{\mathrm{d}t}\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}=\begin{bmatrix}-R/L_{f}&\omega_{e}\\ -\omega_{e}&-R/L_{f}\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}+\begin{bmatrix}(u_{d}-e_{d})/L_{f}\\ (u_{q}-e_{q})/L_{f}\end{bmatrix} \tag{2}\]  
Then, via the forward Euler discretization equation, the currents at \((k+1)\)th, namely \([i_{d}(k+1)\) and \(i_{q}(k+1)]\) can be predicted as  
\[\begin{bmatrix}i_{d}(k+1)\\ i_{q}(k+1)\end{bmatrix}=\begin{bmatrix}1-RT_{s}/L_{f}&\omega_{e}T_{s}\\ 1-RT_{s}/L_{f}&-\omega_{e}T_{s}\end{bmatrix}\begin{bmatrix}i_{d}(k)\\ i_{q}(k)\end{bmatrix}+\begin{bmatrix}T_{s}/L_{f}&0\\ 0&T_{s}/L_{f}\end{bmatrix}\begin{bmatrix}u_{d}(k)-e_{d}(k)\\ u_{q}(k)-e_{q}(k)\end{bmatrix} \tag{3}\]  
where \(T_{s}\) denotes the sampling period; \((k)\) and \((k+1)\) represent the components measured at \((k)\)th and predicted at \((k+1)\)th, respectively. The stator voltages at \((k)\)th used in (3) can be obtained as  
\[\begin{bmatrix}u_{d}(k)\\ u_{q}(k)\end{bmatrix}=\begin{bmatrix}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{bmatrix}\begin{bmatrix}u_{\alpha}(k)\\ u_{\beta}(k)\end{bmatrix} \tag{4}\]  
with  
\[\begin{bmatrix}u_{\alpha}(k)\\ u_{\beta}(k)\end{bmatrix}=\begin{bmatrix}\begin{array}{cc}2/3&-1/2&-1/2\\ 0&\sqrt{3}/3&-\sqrt{3}/3\end{array}\end{bmatrix}\begin{bmatrix}u_{\alpha}(k) \\ u_{b}(k)\\ u_{c}(k)\end{bmatrix} \tag{5}\]  
where the subscripts \(\alpha\) and \(\beta\) mean the components in the \(\alpha\) and \(\beta\) axes, respectively. \(\theta\) is the power grid phase. \(u_{x}(k)=0.5U_{dc}(S_{x}+1)\), \(x\)\(\in\)\(\{a,b,c\}\), \(S_{x}\)\(\in\)\(\{0,1\}\) is the switch states of GCI as shown in Figure 3.  
Figure 3: Space vectors distribution of 2L-VSI.  
At last, all VVs will be evaluated by (6) after which the optimal VV can be chosen.  
\[g=\left|i_{d}^{ref}(k+1)-i_{d}(k+1)\right|+\left|i_{q}^{ref}(k+1)-i_{q}(k+1)\right| \tag{6}\]  
where the superscript _ref_ means the ideal component.  
Obviously, the prediction of future current is highly dependent on system parameters which fail to hold constant in the PV applications. So, it is necessary to develop an MPCC with better robustness against the parameter variation.
## 3 Proposed Model and its Phases  
In this section, we present the proposed network topology where the network is distributed between two physical levels called the _first level_ and _second level_, as shown in Figure 5. In the first level, homogeneous nodes containing the same energy levels are deployed randomly within the base station's target-oriented distance. At the time of network initialization, every node shares its location with BS to further realize its criteria of cluster formulation. It is mentioned in the flowchart that if the node is within the specified range, it will be marked as the first level, otherwise, it will be a second-level node. In the second level, located outside the inner zone, heterogeneous nodes in terms of initial normal and advanced energy levels are placed randomly. The BS node is located in the center of the network.  
The first-level region contains \(m\) homogeneous nodes deployed randomly within a specific range around the BS, and a centralized clustering technique is used to select the CHs. To improve network stability, \(n\) heterogeneous nodes with advanced initial energy levels are deployed in the second level, and a distributed cluster formation technique is applied in this region. Thus the whole network comprises \(N=m+n\) nodes. The clustering of nodes in different zones using this hybrid cluster formation technique is shown in Figure 6.  
We have the following motivation for using this topology:  
1. We placed BS in the center of WSN to assume that the center is often the most critical area of connectivity in the domains related to environmental, biological, and nuclear sciences.
2. Homogeneous nodes are easy to program, and their deployment is planned within a suitable area to conserve energy and to have easy access to the sink.  
Figure 5: Energy-efficient routing protocols for WSNs.  
Figure 6: Clustering in energy-efficient routing protocols for WSNs.  
Heterogeneous nodes are a mixture of normal and advanced initial energy nodes. Thus, they are deployed far away from the BS. The advanced energy nodes have a higher probability of being selected as CHs, while the normal nodes will often be selected as the cluster members. This technique will help to improve network stability.  
The proposed algorithm's operating mechanism is divided into time-based slots (rounds), which are required for the cumulative network settling time and network transmission time in the cluster-based routing protocols. Each round contains multiple sub-phases called the cluster-head selection phase (CHSP), member association phase (MAP), and network transmission phase (NTP). An epoch is the execution time required to perform some specific algorithmic tasks in each round.