esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We showed,thanks to mRNA analysis, that the active ingredient was able to stimulate the expression of the main regulators of melanogenesis (MC1R, MITF, POMC) and related genes (AP3B1, CTNS, HPS5, MYO5A) in scalp skin explants exposed to oxidative stress, mimicking the canities model. Melanosome formation and autophagy were also reactivated. The induction of FST and KRT19 expressions, both involved in hair morphogenesis [31], suggests a renewal and protection of the stem cell reservoirs [13; 17]. Moreover, the expressions of antioxidant enzymes were up-regulated. In order to confirm these first observations, the active ingredient was then assessed in the premature canities-induced hair follicle model. Hair follicles are considered as an ex vivo model [17; 21], and a major route for substance penetration [32; 33]. Indeed, it is described that the hair follicle plays a subordinate role in percutaneous penetration as it allows a major proportion of global skin penetration [34]. Consequently, the active ingredient was tested at 1%, the same concentration as those for an in vivo application since it was clearly the most optimal concentration to use. As expected, the active ingredient demonstrated ROS scavenging activity by significantly reducing the ROS production. This clear reduction can also be correlated to the results observed with the mRNA analysis that showed an increase in gene expressions encoding for antioxidant enzymes. Moreover, the number of positive cells stained by NKI/beteb, specific marker of active melanocytes and melanoblasts, was significantly increased in the presence of the active ingredient. This result suggests that the melanocyte reservoirs were thus protected, which is also important for hair follicle renewal [17]. Therefore, we can confirm that, thanks to the scavenging property of the active ingredient, the pool of melanocyte precursors was preserved. In this way, one of main causes of canities development was controlled. Finally, we conducted experiments to evaluate the effect of our active ingredient on grey hair follicles, which is the final proof of the control of canities development. We demonstrated with Fontana-Masson staining that the melanin content was significantly increased in natural grey hair follicles, proving the global reactivation of canities-impaired mechanisms, including oxidative stress control, stem cell protection and melanogenesis. After this validation, we carried out a clinical study to finally evaluate the active ingredient and confirmed its activity on male volunteers presenting 30 to 95% grey hair. We used two complementary studies: the evaluation of grey hair proportion as a semi-qualitative method and the grey hair density per cm\({}^{2}\) measurement as a quantitative one. The results presented a clear and significant reduction of the proportion of grey hair and the effect was significantly higher than the placebo. A second method of measurement demonstrated that the density of grey hair per cm\({}^{2}\) was significantly reduced. Overall, these clinical data confirmed the efficacy of our active ingredient against canities by reactivating hair pigmentation.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 2 Related Work
### Contribution Target Analysis of Decision Variables
If the values of \(co_{ij}\) are equal, it indicates that the contribution target of the decision variable \(x_{i}\) is the same, so the target is selected as \(co_{i}\) of \(x_{i}\); if the values of \(co_{ij}\) are different, the target with the highest frequency in \(co_{i}\) is recorded as the alternative contribution objective of \(x_{i}\) (lines 8-10). ```
0:\(D\) (Number of decision variables), \(M\) (Number of targets), \(P\) (Current population), \(W\) (Weight vector)
0:\(FO\) (Contribution targets of decision variables)
1:for\(i=1\) to \(D\)do
2:for\(k=1\) to \(|W|\)do
3:\(NS\leftarrow\) Selecting \(n\) solutions randomly from population \(P\)
4:for\(j=1\) to \(ns\)do
5:\(SP\leftarrow\) Perturb the \(i\)-th variable \(x_{i}\) of the \(j\)-th individual in \(NS\) randomly \(np\) times to generate \(np\) new individuals
6:\(od_{im}=|\lambda_{m}|\leftarrow\) Compute the optimization value of \(x_{i}\) on each target
7:\(co_{i}\leftarrow\) Compute the contribution goal \(co_{i}\) of the decision variable \(x_{i}\)
8:if\(i\in CV\)then
9:\(co_{ik}\leftarrow\) If the value of \(co_{ij}\) (\(j\in 1\) to \(ns\)) are consistent, the value is recorded as the contribution objective; otherwise, the objective appears with the highest frequency in \(co_{ijk}\) is recorded as the alternative contribution objectives of variable \(x_{ik}\)
10:endif
11:endfor
12:endfor
13:endfor
```  
**Algorithm 3** Contribution Target Analysis (\(D,M,P,W\))
## 4 Proposed Scheme
### Cluster-Head Rotation
```
1:Policy for varying roles
2:Data rate transmitted by each normal node in the network
3:round = round + 1
4:Obtain policy for the varying roles of the node over the network lifetime from BS
5:if node.role = Normal Node
6:Perform sensing operation and detect data
7:if change in the parameter value, then
8: Wake up the communication module
9: send the data to the Cluster Head (CH)
10:endif
11:elseif node.role = Cluster Head
12: keep the communication module awake
13: receive data from the member nodes
14: send the aggregated data towards Base Station (BS)
15:endif
```  
**Algorithm 1**Sensing nodes  
```
1:Number of cluster member nodes '\(n\)', their initial locations \((x_{i},y_{i},z_{i})\), heterogeneous fixed data rate \(T_{i}\), and initial energies \(\mathbb{E}_{i}\) of each sensor node. 2:Role of each node over the network lifetime, ensuring balanced energy network operation. 3:for\(i\gets 1\) to \(n\)do
4:for\(j\gets 1\) to \(n\)do
5:\(D(i,j)\leftarrow\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2 }+\left(z_{i}-z_{j}\right)^{2}}\)
6:\(E(i,j)\leftarrow\left\{ \begin{array}{ll}T_{i}\cdot E_{ELEC}+T_{i}\varepsilon_{ f}s_{d}j_{i}^{2},&d_{ij}<d_{o}\\ T_{i}\cdot E_{ELEC}+T_{i}\varepsilon_{up}\cdot d_{ij}^{4},&d_{ij}\geq d_{o} \end{array} \right\}\)
7:endfor
8:endfor
9:for\(j\gets 1\) to \(n\)do
10:\(E_{T}(j)\leftarrow\sum_{i=1}^{n}e_{ij}\)
11:\(E_{\text{avg}}(j)=\frac{E_{T}(j)}{n}\)
12:endfor
13:\(\textbf{c}\gets sort(E_{T})\)
14:\(\textbf{z}\gets index(\epsilon_{o},E_{T})\)
15:for\(rnd\gets 1\) to \(rnd_{max}\)
16:\(\textbf{i}\gets index_{\min}(\mathbb{E})\)
17:for\(i\gets 1\) to \(n\)do
18:\(A\gets J(i)\)
19:while\(E_{(i,A)}\leq E_{\text{avg}}(A)\)&&\(E(A)\geq\alpha*\max(\mathbb{E})\)do
20:for\(j\gets 1\) to \(n\)do
21:if\(j=A\)then
22:\(e_{ij}\leftarrow\left\{ \begin{array}{ll}T_{i}\cdot n\cdot E_{ELEC}+T_{i} \varepsilon_{f_{i}}\left(d_{ij}\right)^{2},d_{ij}<d_{o}\\ T_{i}\cdot n\cdot E_{ELEC}+T_{i}\varepsilon_{up}\left(d_{ij}\right)^{4},d_{ij} \geq d_{o}\end{array} \right\}\)
23:else
24:\(e_{ij}\leftarrow\left\{ \begin{array}{ll}T_{i}\cdot E_{ELEC}+T_{i}\varepsilon_{ f_{i}}\left(d_{ij}\right)^{2},d_{ij}<d_{o}\\ T_{i}\cdot E_{ELEC}+T_{i}\varepsilon_{up}\left(d_{ij}\right)^{4},d_{ij}\geq d_{o }\end{array} \right\}\)
25:\(\mathbb{E}(i)\leftarrow\mathbb{E}(i)-e_{ij}\)
26:endif
27:endfor
28:endfor
```  
**Algorithm 2**Cluster-Head Rotation (BS)  
The role rotation policy for the nodes is established by the base station (BS) through the operations outlined in Algorithm 2. The base station establishes this policy during the network initialisation phase and communicates it to all participating nodes throughout the network's lifetime to ensure balanced energy consumption and extended network operation.
# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 5 Experimental Evaluation
### Privacy Disclosure  
In this subsection, we present the experimental privacy disclosure results for the aggregated datasets from different sources. \(QI_{1}\) and \(QI_{2}\) are two \(QI\) attribute sets from two source. Assume that the attacker can acquire background knowledge of the original \(QI_{1}\) or \(QI_{2}\). The multi-source linkability attack algorithm (Algorithm 2) can get accurate original values of another \(QI\) set. The experiment results are obtained via executing Algorithm 2. We test privacy disclosure on the Adult dataset and the Census dataset.  
Tables 4 and 5 present the privacy disclosures of k-anonymity and l-diversity, respectively, on the Adult dataset. Our techniques are multi-source (_k,d_)-anonymity and multi-source (_k,l,d_)-diversity. As described above, for the Adult dataset, the {a1, a2, a3} attributes are {age, workclass, marital}, and for {a4, a5, a6}, the attributes are {occupation, relationship, native-country}. The values in {a1,..., a6} represent the number of distinct original values in an equivalence class. For example, if an equivalence class has {[40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59], Private,...} in {a1,..., a6}, a2 has a distinct original value. However, a1 has a generalized value that cannot leak accurate age information. The rows of {a1,..., a6} store distinct original values. Top-down has the most equivalence classes, and its privacy disclosure is significant. The sum of \(QI_{1}\) and \(QI_{2}\) is 6085. Mondrian has fewer privacy disclosures. For the Incognito and Flash algorithms, the results are interesting and different. These two algorithms generate only 10-200 equivalence classes. Hence, the number of distinct original values is small. Incognito has zero, in that all values in an equivalence class are range values instead of accurate values. Our algorithm performs well, with zero distinct original values. Table 5 presents the l-diversity algorithm. Incognito and Flash can execute 1-diversity. These results are similar to those shown in Table 5.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Offline (Data-Collection) Phase  
For this phase, two distinct data-collection methods were developed: hardware setup and mobile application. While hardware setup was used to collect large numbers of Wi-Fi signal strengths, the mobile application was used to obtain room-to-room transition time when the user was walking between rooms. The data obtained were combined for further processing in the online phases. The data-collection process is shown in Figure 2. The design and implementation details of each data-collection method are discussed below.
## 3 Case Study
### Implemented Methods
#### 3.2.1 Database and Web Application Optimizations  
For optimal user experience, web applications have to be quick in their responses to user actions and demands. There are several ways of improving responses from a database, and in general for the web application.  
* Database optimizations include the usage of indexes and query optimization. Coordinates are stored as proper spatial fields for easy integration with spatial services and web GIS;
* Balanced server-client processing. Querying of data subsets is performed ad server side because of better efficiency and a lower transfer rate of data. Visualization, statistical processing and average calculations for multi-depth data are performed client side;
* Usage of automatic or manual (depending on database features) table partition for huge datasets;
* Asynchronous data loading (in the background with proper events handling).
## 4 Mathematical Model of Symbol Error Rate
Thus, the equation can be written as  
\[\overline{P_{M}}=\frac{2A}{\sqrt{\pi}}\int\limits_{0}^{\infty}\left(\int\limits_ {0}^{\infty}\frac{1}{r_{r}}\exp\left[-\frac{1}{2}\left(\frac{\ln r_{r}-m_{r}}{ \sigma_{r}}\right)^{2}-\left(y+C\right)^{2}\right]dr_{r}dy\right). \tag{15}\]  
Integration over \(y\) gives  
\[\overline{P_{M}}=2A\int\limits_{0}^{\infty}\frac{1}{r_{r}}erfc[C]\exp\left[- \frac{1}{2}\left(\frac{\ln r_{r}-m_{r}}{\sigma_{r}}\right)^{2}\right]dr_{r}. \tag{16}\]  
Further, plugging constants back in, the average probability becomes  
\[\overline{P_{M}}=\frac{4}{\sqrt{2\pi}\sigma_{r}}\int\limits_{0}^{\infty}\frac {erfc[C]}{r_{r}}\exp\left[-\frac{1}{2}\left(\frac{\ln r_{r}-m_{r}}{\sigma_{r} }\right)^{2}\right]dr_{r} \tag{17}\]  
\[C=\sqrt{\frac{3s}{2(M-1)}}\exp\left[-\left(\frac{k\ln[10]}{10}\right)r_{r}^{ \kappa}\right] \tag{18}\]  
In order to write the constant \(C\), we have used  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]0  
Therefore, the complete term for the probability of symbol error is  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]1  
This can be solved numerically, but there is also a closed form in terms of Hermite polynomials. We substitute  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]2  
and insert this into (20). We obtain  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]3  
This takes the general form of  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]4  
which can be expressed as  
\[F(s(r_{r}),M)=\sqrt{\frac{3}{2(M-1)}ns(r_{r})} \tag{6}\]5where \(t_{i}\) is the \(i\)th zero of the Hermite polynomial \(H_{n}(t)\), and \(w\) is the weight:  
\[ \begin{array}{l}w_{i}=\frac{2^{n-1}n!\sqrt{\pi}}{n^{2}(H_{n-1}[t_{i}])^{2}}\\ R_{n}=\frac{n!\sqrt{\pi}}{2^{n}(2n)!}f^{(2n)}[\xi],\hskip 28.452756 pt(-\infty<\xi<\infty) \end{array}  \tag{25}\]  
Both the weights and the zeros, \(w_{i}\), and \(t_{i}\) may be obtained from tables in [41].
## 4 Simulation Experiment and Performance Analysis
### Simulation Results of Placement Optimization Based on Genetic Algorithm
In addition, the improvement effect of TOA station on positioning accuracy is better than that of TDOA station, and the performance of hybrid source localization of TOA plus TDOA is better than that of pure TDOA positioning, for instance, the positioning accuracy of 1 TOA station plus 4 TDOA stations is better than that of 5 TDOA stations or 7 TDOA stations, and the hybrid source localization accuracy of TOA and TDOA stations does not deteriorate much with the increase in height, while the pure TDOA station positioning accuracy deteriorates more significantly with the increase in altitude. Compared with the above-mentioned station placement schemes, the positioning accuracy of 1 TOA station plus 4 TDOA station can obtain higher positioning accuracy with fewer stations, and the performance of positioning accuracy is more stable, and the performance deterioration is not apparent. Figure 7 shows the contour distribution surfaces of the GDOP values in the YOZ plane for \(x\) values equal to \(-1\times 10^{3}\), 0, and \(1\times 10^{3}\), respectively, for this station placement optimization scheme. From the figure, it can be seen that the GDOP distribution curves in three different YOZ profiles do not change much in both the Y-axis and \(Z\)-axis direction, which shows that the station placement optimization also has good stability in the \(X\)-axis direction. When the target trajectory to be located is in other shapes, such as when the target trajectory is a point or a region, the integral region of the objective function needs to be adjusted to obtain the corresponding optimal placement of stations. Figure 6: Target trajectory positioning accuracy of different station layout schemes.
## 3 Proposed Testbed for Cybersecurity Analysis of Nuclear Power Plants
### Gns3 Topology  
The open-source program GNS3 (Graphical Network Simulator-3) [21] was used to create a simple TCP/IP network topology needed to set up the testbed and carry out the planned insider cyber-attack. This program allows emulation and simulation of various network equipment, such as routers, switches, and firewalls; besides OS VMs. It supports several free hypervisors such as VirtualBox and VMware Workstation Player. The elements used to build the topologies are called appliances. The main elements used in our implementation, all free, were the following: VyOS Router; 2 Ubuntu 20.04 VMs (ScadaBR and OpenPLC), and; Kali Linux. Another facility provided by GNS3 is its simple integration with the well-known communication protocol analyzer software Wireshark [28], which in turn is able to analyze Modbus traffic. Several such units can be inserted into the topology segments at the same time.  
In essence, our testbed was assembled to study and manipulate the Modbus/TCP communication in an industrial subnet between the PLCs of a nuclear power plant and its  
Figure 6: Mapped holding registers area of ModRSsim2.  
Figure 7: ANS Modbus communications interface.  
supervisory system. As intervening elements, inserted in the system by the Insider, we have: (a) a computer with the OS Kali Linux distribution installed, a well-known platform used for pen test (penetration testing) and equipped with several libraries for cyberattacks; and (b) a \"Rogue\" PLC whose function is to substitute an internal control of the plant, previously neutralized by the malicious agent. All mentioned elements were installed in VMs whose IP and MAC addresses were fixed, and are in turn interconnected through Ethernet via a simple switch.  
This configuration is sufficient to carry out insider attacks between the ANS and the HMI, since it is assumed that the subnet is segregated from the Internet in critical infrastructures such as NPP (Air Gap). However, the VyOS router [22] was also added and configured in the topology to allow access to the internet of the test environment and thus facilitate the installation and configuration of the programs and also allow for HIL testing (Arduino Wi-Fi). The resulting GNS3 topology is shown in Figure 8 and its IP scheme in Table 4 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Roles**} & **Main** & \multirow{2}{*}{**OS**} & **IP** & **MAC** \\  & **Applications** & & & \\ \hline SCADA, & ScadaBR, MSQL & Ubuntu 20.04 & 10.0.0.4/8 & 0 cfd:eded:11:0b:00 \\ Historian & Workbench & Ubuntu 20.04 & 10.0.0.4/8 & 0 cfd:ed:11:0b:00 \\ NPP Simulator, & ANS, & & & \\ Modbus Server & ModRSsim2 & Windows 10 & 10.0.0.2/8 & 08:00:27:44:d6:ef \\ Cyberattack & Kali Linux & Debian & 10.0.0.5/8 & 08:00:27:5c:65:26 \\ Plataform & OpenPLC & Ubuntu 20.04 & 10.0.0.3/8 & 0 cfd:ed:28:84:00 \\ Router & VyOS & GNS3 Appliance & 10.0.0.1/8 (eth1), & 0 cfd:ed:2f:d8:01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nuclear testbed IP addressing.  
Figure 8: GNS3 nuclear testbed topology.
## 4 Application Examples
### QRS Peaks in ECG Signals  
Electrocardiography (ECG) time series contain information about the direction and magnitude of the electrical activity caused by electrophysiological effects (depolarisation and repolarisation) of the atria and ventricles. The time series comprises a recurrent sequence of characteristic signal waveforms (P, QRS, T) where the QRS complex is the most striking element. The maximum of the QRS complex is the R-peak. The sequence of RR intervals is the heart rate. The variability of the RR interval lengths is called hart rate variability (HRV), associated with the state of the autonomic nervous system [52].  
To demonstrate the usefulness of the proposed AMPD algorithm, we used a self-recorded ECG signal (sampling frequency: 128 Hz; device: 1-channel ECG, MK3-ETA, TOM-Medical, Graz, Austria). The analyzed time series had a duration of 200 s (~3.3 min). The results of the analysis are given in Figure 8. All 221 R-peaks were correctly detected (no false negative errors) and no false detection occurred (no false positive errors).  
Figure 7: Results of applying the AMPD algorithm to a CO\({}_{2}\) time series.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 7 Conclusions  
This article shows that a lot of data collected by aircraft maintenance support IT systems can be used in a very interesting way. By building a simple mobile application for recording services, you can collect data to analyze the times of their implementation, which will allow you to reduce organizational problems by creating a credible and reliable schedule.  
The designed application was successfully verified in a test environment, operating inside its own separate wireless network. Data from the execution of the considered periodic service were collected via the application. This information will be used to study the maintainability of the M-346 aircraft. Thus, the usefulness of the application was confirmed.  
The proposed methodology for planning periodic maintenance inspections, based on the determined probability characteristics of individual task execution, is expected to be implemented in Polish military aviation. The method can be implemented as one of the modules of the IT system supporting the operation of aircraft, e.g., \"Samanta\". The introduction of the developed method in the aircraft operation would result in faster and more effective performance of periodic maintenance, increasing the level of technical readiness of training aircraft at the same time.  
Further work in this field focuses on creating mechanisms for the automatic scheduling of maintenance services and real-time response to any disruptions in the operation process. Unexpected failures, issues of the availability of spare parts, and staff shortages are another challenge in modeling the serviceability of aircraft.  
In summary, this research presents a forward-looking and practical approach to leveraging IT tools for improving aircraft maintenance processes. While the results are highly promising, continued development and testing in broader operational contexts will further enhance the value and versatility of the proposed solution. These efforts can pave the way for more reliable scheduling and improved maintainability, benefiting both operators and maintenance teams alike.  
Conceptualization: A.Z.; methodology: M.Z.; software: K.K.; validation: B.G.; writing--original draft preparation: K.K.; writing--review and editing: B.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 5 Antenna Design via HFSS Optimization Tool
Resonance frequencies are obtained as 2.33 GHz, 4.54 GHz, and 5.38 GHz by HFSS simulation. The maximum realized antenna gains are 5.6 dB, 2.8 dB, and 6.5 dB, respectively, for the resonance frequencies. Simulation results obtained by the SVR model approach given in Sections 3 and 4 were compared with the results obtained with HFSS optimizations; the comparison is provided in Table 5. Computation time for the SVR model is the required time for obtaining training and testing patterns via HFSS parametric simulation given in Section 3, and computation time for HFSS optimization is the total optimization time required by the HFSS optimization tool, as presented in Table 5. Computed resonance frequencies for the SVR model are given in Figure 3 of Section 4. Computed maximum realized gains at the resonance frequencies are given in Figure 4 of Section 4. In Table 5, _TAE_ denotes the total absolute error, and it is used to evaluate the accuracy of the methods used in this paper. _TAE_ for resonance frequencies can be calculated by:  
\[TAE_{f}=\left|f_{r1_{d}}-f_{r1_{c}}\right|+\left|f_{r2_{d}}-f_{r2_{c}}\right|+ \left|f_{r3_{d}}-f_{r3_{c}}\right| \tag{24}\]  
where \(f_{r1_{d}},f_{r2_{d}}\), and \(f_{r3_{d}}\) are the desired resonance frequencies; and \(f_{r1_{c}},f_{r2_{c}}\), and \(f_{r3_{c}}\) are the computed resonance frequencies.