esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
## 1 Introduction  
The software contains a set of statements that provide instructions for a computer to perform a task. These statements are divided into sequence or control statements that are executed in order. Control statements must consider conditions under which the next statements will be executed. The next statements then perform different statements depending on conditions. Control statements act to implement different sets of statements depending on if the initial condition is true or false. Therefore, control statements affect software outputs and can be the source of error. Software testing processes help verify mistakes that may happen in control statements by determining a set of inputs for a test, called test case, which can then reveal the error or execute as many statements as possible. There are many software testing techniques, including genetic algorithms (GAs), particle swarm optimization, and ant colony optimization, all of which are applied to determine a set of inputs or generate test cases.  
GAs are utilized to solve optimization problems in many fields, such as network systems [1], image processing [2], or software testing [3; 4; 5]. GAs can search for an optimal solution to a complex system and locate the near global optimum [6]. However, traditional GAs may not be appropriate for solving some complex systems. Therefore, researchers have examined ways to improve GAs for applications to complex problems and have examined ways to enhance solution efficiencies. These efforts to improve GAs have relied in part on accurate configuration parameters, including the population size, crossover probability, and mutation probability.  
The multiple-searching genetic algorithm (MSGA) [1] has been proposed as an improved GA for routing between source and destination in network systems by configuring parameters to receive an optimal solution to the given problem. An MSGA can find optimal solutions more quickly than traditional GAs and can be applied in different fields. In this study, MSGA was utilized in software testing, with suitable parameter values defined to enable the generation of optimal test cases. Following the definition of the parameter values, MSGA was compared to a traditional GA and hybrid GA (HGA) in terms of the execution time, number of test cases, and percentage of coverage [7] using the software program Siemens Suite.
## 1 Introduction  
Today's world has faced numerous obstacles relating to chronic disease public health issues caused by hazardous viruses such as COVID-19 [1]. The increase in health-related issues coupled with rising healthcare expenditures has encouraged people, especially the elderly and disabled, to manage their health remotely. Many industries, such as remote and smart healthcare systems, have benefitted from the IoT in the past few years as a network of interconnected objects [2]. As part of the IoT, popular technologies like wireless body area networks (WBANs), wireless sensor networks (WSNs), and radio frequency identification (RFID) are used to transpose data to the cloud, which can then be analyzed and used for instantaneous decision-making [3, 4].  
It is possible to make health management systems more customized, provident, and cost-effective by using the IoT, including mental health services and the worldwide pandemic [5, 6, 7]. This strategy categorizes IoT implementation in healthcare into three categories: tracking people and other objects (staff, medical teams, and patients), person authentication and identity, and autonomous data sensing and gathering. IoT-based health monitoring, like WBAN, enables hospitals to prevent and manage hospital infections, manage emergencies, and dispense post-discharge care anywhere. As a result, the healthcare industry is completely redefined by the IoT in terms of its devices, applications, and users [8, 9]. Therefore, healthcare environments can be dramatically revolutionized by leveraging IoT technologies such as connected medical sensors or special medical devices [10; 11; 12].  
Through the use of HIoT technology in healthcare systems, medical devices are connected to the Internet so that appropriate therapeutic strategies can be developed for patients. Eldercare supervision, telemonitoring, teleconsultations, and computer-assisted rehabilitation are just some telehealth services offered by this technology [13]. Internet of medical things and HIoT are abbreviations that are frequently used interchangeably to refer to the integration of medical applications and equipment that can be linked to systems of health care information technology [14; 15]. Meanwhile, big data combined with IoT can be used to support sanitary systems that are more effective in managing healthcare operations. The analysis of healthcare practices can now be prescriptive, autonomous, and predictive thanks to big data analytics [16].  
Diabetes, obstructive lung disease, cancer, arthritis, and heart disease are some chronic diseases that can be managed remotely by customized healthcare [17; 18]. Rarely do systematic literature reviews (SLR) of HIoT research clarify the overall development and pinpoint particular research problems, patterns, and future directions [19; 20]. Investigating an HIoT research plan is essential given the increased interest in the IoT in healthcare. Discovering, classifying, and synthesizing a comparative technique of inquiry through a systematic review may lead to the transposition of knowledge within research organizations [21; 22].
# Estimating Hardness of Cosmetic Cream Using Electro-Mechanical Impedance Sensing Technique
## 5 Conclusions  
This study investigated the use of EMI sensing technology to estimate the hardness of cosmetic creams. The experimental program involved manufacturing cosmetic creams with different wax content levels and measuring their hardness using traditional methods. Simultaneously, the EMI signals of the piezoelectric sensor embedded in the creams were recorded. The results demonstrated a positive correlation between wax content and both hardness values and the magnitude of the EMI resonance peaks. This study established a strong correlation between traditional hardness values and EMI signals, affirming the effectiveness of the proposed EMI sensing technique in estimating cosmetic cream hardness. The logarithmic relationship between hardness and EMI signals highlighted the interaction between material hardness and electro-mechanical properties.  
This study contributes to the exploration of non-destructive testing methods in the cosmetic industry, offering a promising avenue for real-time monitoring of cosmetic properties. The findings provide valuable insights for cosmetic formulation optimization, quality control, and early detection of stability issues.  
Figure 8: Correlation between the hardness value and EMI resonance peak magnitude of cosmetic cream.  
Figure 7: EMI resonance peak magnitude according to wax content of cosmetic cream.  
**Author Contributions:** J.-C.L.: conceptualization, experimentation, data interpretation, and writing; D.-H.Y.: experimentation and data interpretation; I.-C.L.: conceptualization, data interpretation, and writing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Conclusions  
In conclusion, it is important to emphasize the basic regularities of local laser sintering by pulsed and CW radiation of silver NP arrays formed by dry aerosol printing. While sintering lines of silver NPs with initial dimensions about 15 nm on polished silicon substrates in the near-surface zone of the samples a pore-free conductive layer was formed. The thickness of the layer was about 0.5 \(\upmu\)m and about 1.0 \(\upmu\)m while sintering by pulsed laser radiation with pulse duration in the nanosecond range and wavelengths 527 and 1054 nm and CW laser radiation with wavelengths 527 and 980 nm, respectively. The highest values of the average specific conductivity, 7 and 2 times lower than the specific conductivity of bulk silver, were achieved in optimal sintering regimes by CW laser radiation with wavelengths 527 and 980 nm, respectively. Another important regularity is that if the conditions for radiation absorption induced by plasmon resonance are realized in the range of particle size growth during sintering, the efficiency of the NP sintering by laser radiation increases by of an order of magnitude. In our experiments, it is achieved by sintering silver NP arrays with pulsed and CW laser radiation with a wavelength 527 nm. In this case, samples with higher conductivity were characterized by a microstructure with well-formed contacts between silver crystallites and lower porosity.  
Additional experiments on laser sintering of silver NP lines on heat-sensitive polyamide substrates by pulsed laser radiation with wavelengths 527 and 1054 nm with sintering process parameters close to the optimal regimes showed three times higher values of average specific conductivity compared to lines sintered on silicon substrates. This makes the proposed method promising for the development of research on the use of laser sintering of silver NP arrays formed by dry aerosol printing on heat-sensitive substrates.  
Conceptualization, K.K. and V.I.; Data curation, D.K. and B.M.; Formal analysis, K.K.; Funding acquisition, D.S.; Investigation, D.K. and B.M.; Methodology, K.K.; Project administration, V.I.; Resources, D.T. and D.S.; Supervision, A.E. and V.I.; Validation, K.K., D.K. and D.T.; Visualization, K.K.; Writing--original draft, K.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Higher Education of the Russian Federation (project NO.: RFMEFI57517X0160).  
The authors declare no conflict of interest.
# Development of a Power and Communication Bus Using HIL and Computational Intelligence
## 2 New Communication Protocol  
The authors of this paper proposed a new interface, the Device Lightweight Network (DLN). DLN allows us to use only one pair of cables to transfer data and power. However, the goal is to implement an auto-addressing functionality. The first version of DLN was characterized by the following parameters: data link up to 2 Mbps, power link up to 48 V and 10 A. The data are transferred during small breaks in power delivery. To avoid electromagnetic interference (EMI), data are transferred on a low voltage (about 2 V) and power on a much higher voltage (9-48 V), as in the example in Figure 2 with a 24 V power supply. Power and data delivery time can be adjusted depending on whether a higher data rate or more power is expected. Figure 2 shows an example of transferring high power (1.2 ms for data, 12.8 ms for power in each period) due to the supply for high-power lamps, an electric heater and two electric motors (Figure 1).  
A proof of concept was built, where lamps and side mirrors are controlled. The side mirror was equipped with two electric motors and an electric heater. The network that was built operated perfectly while the devices were controlled at the same time, and the maximum current consumption was 6 A. The protocol was calibrated using engineering methods (Figure 2).  
Auto-addressing, due to the multiple parameters to adjust, and the multi-criteria assessment, required using artificial intelligence algorithms connected with the hardware-in-the-loop technique. Most scientists use particle swarm optimization (PSO) or genetic algorithm (GA). The authors decided to use both algorithms with different parameters in  
Figure 1: Proof of concept of DLN solution.  
Figure 2: Oscillograms from transmission line.  
order to compare the results of the optimization. To avoid unnecessary effort of simulations, the authors decided to find the optimal solutions using easier methods such as planning the experiment. For comparison purposes, the authors decided to find the best solution by applying an easier method based on a Latin hypercube sampling (LHS) approach to avoid unnecessary effort of simulations.
## 3 Proposed Work  
The \(MPSA\) scheme is devised carefully to ensure the same entropy measure of the encrypted image and the marked encrypted image. Here, at first, we encrypt the cover image using a stream cipher. The encrypted image is handled as blocks of identical size. Each block is processed to embed the secret data. For this, a special technique called local pixel swapping is introduced. Furthermore, two bits of data are embedded within each block using the proposed scheme. It is always important to maintain the correlation of the pixels in an image, which is exploited in the proposed technique for better cover image recovery. The scheme is evident enough to retain the entropy of the encrypted image even after data embedding. The proposed work is divided into two phases: the data-hiding phase and the image-restoration phase. The first phase explains how to embed certain information on a cover image through the proposed local pixel-swapping technique, and the second phase discloses how to retrieve the cover image and hidden information from the marked image. Initially, the encrypted image \(E\) is fed as an input to the RDH system. The system handles the input image as blocks of identical size without overlapping.
## 5 Results and Interpretation
### Random Walk-Based Clogging  
#### 5.2.1 Single-Phase Clogging  
We set the parameter setting for single-phase clogging as follows: \(K=100\),\(000\), \(v=1\), \(d=0.01\), \(N=1\),\(000\),\(000\), given a sequence of deposition probabilities \(q\in[10^{-5},10^{-4},10^{-3},10^{-2},10^{-1}]\). To access the robustness of the results, we performed  
Figure 6: Effect of velocity on particle invasion for four rock models. Two different values of velocity were chosen for comparison. For each parameter setting (\(v\), rock type), five independent simulations were performed, to assess the reliability of the results. The left panel shows the average number of particles that escaped from the outlet surfaces over the five simulations. The box plots in the right panel display the distribution of the escaped particles’ travel time across five simulations. There is no description for Estaillades in the right panel because no particle were able to escape during the allotted time in Estaillades.  
10 one-phase clogging processes for each value \(q\). The deposition process was based on the final passed array obtained at the end of the random walk simulation. We counted the number of clogged pore voxels after the deposition process and defined a new notation of porosity decrease, as follows.  
The _porosity decrease_ after the single-phase clogging was the difference between the porosity after the deposition process and that before the deposition, i.e.,  
\[\text{porosity decrease} =\text{porosity after deposition}-\text{porosity before deposition}\] \[=-\frac{\text{number of mineralized pore voxels}}{1000^{3}}\times 100\%. \tag{6}\]  
The porosity decrease defined in (6) was non-positive. The simulation result is shown in Figure 7. We can observe the fastest decreasing of the porosity when the deposition probability increased for Bentheimer; the decreases for Doddington and Ketton were almost at the same speed; the decrease was slowest in Estaillades. In addition, the standard derivatives of the porosity decreases were very small, ranging in interval \([2.2\times 10^{-11},\)\(1.1\times 10^{-8}]\).
## 3 Results and Discussion
### Effect of the Detected Sample  
When a sample is placed on the substrate under the THz near-field nanotip, the electric field enhancement effect is also investigated. The simulation parameters are set as follows: the sample is modeled as a dielectric hemisphere with a radius of 20 nm and the center of the hemisphere is on the substrate, the material of nanotip is Au, the incident THz wave frequency is 1.0 THz, the distance between centers of the apex and the hemisphere is 6 nm. Two different substrate materials of Si and Au are tested. The electric field distribution of apex-sample-substrate system is shown in Figure 5. The detected samples are modeled with different materials including air (\(\varepsilon=1.0\)), high-resistivity silicon (\(\varepsilon=11.7\)), Au (\(\varepsilon=-1.0\times 10^{5}+1.9\times 10^{5}i\) at 1.0 THz according the above parameters and the Drude model), perfect electrical conductor (PEC) (\(\varepsilon=-\infty\)) and InSb (\(\varepsilon=-37.9+2.6i\)). Table 1 presents the maximum electric field for different dielectric hemispheres on the Au or Si substrates under the Au nanotip. From the table, it is seen that the maximum electric field increases with the increase of dielectric constant from air to PEC materials, which is in agreement with theoretical derivation of the effective polarizability. However, for the InSb material, since there is a plasmon enhancement between the Au apex and the InSb sample as discussed above, the simulated maximum electric field is largest among the five materials. Furthermore, as shown in Table 1, the local electric field of an Au hemisphere with a certain radius \"pancaked\" on the Au substrate is larger than that of the flat Au substrate, indicating geometry-depend electric field coupling between the sample and the nanotip. In addition, the field enhancement values with Si substrate are lower than the counterparts with Au substrate, which also confirms that the difference of field enhancement can be caused by choosing different dielectric materials as the substrate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Substrate**} & \multicolumn{5}{c}{_E_ **(V/m)**} \\ \cline{2-5}  & **Air** & **Si** & **Au** & **PEC** & **InSb** \\ \hline Au & 275.8 & 632.2 & 740.9 & 759.6 & 800.7 \\ Si & 189.4 & 365.2 & 415.0 & 416.0 & 437.7 \\ \hline \end{tabular}
\end{table}
Table 1: Maximum electric field amplitude for different detected sample composed of different materials on the Au or Si substrate under the Au nanotip.  
Figure 5: The electric field distribution of apex (Au)-Sample (hemisphere of InSb)-substrate (Au) system.
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
## 3 BES Coupling with Other Technologies for Metal Recovery
### Catalyst-Free Biologically Driven ORR  
The oxygen reduction reaction is traditionally catalyzed by platinum-based materials on gas diffusion electrodes. However, biologically mediated ORR, driven by acidophilic iron-oxidizing bacteria (_Acidithiobacillus ferrooxidans_), presents a sustainable, catalyst-free alternative. These bacteria facilitate ORR by oxidizing ferrous iron (Fe\({}^{2+}\)) to ferric iron (Fe\({}^{3+}\)) under acidic conditions, utilizing dissolved O\({}_{2}\) as the electron acceptor. Fe\({}^{3+}\) is subsequently reduced at the cathode to provide the necessary redox potential for cathodic reactions. Unlike electroactive biofilms, the bacteria remain in suspension, acting as a biocatalyst, while the Fe\({}^{2+}\)/Fe\({}^{3+}\) redox couple serves as a mediator, and hence, the overall reaction is the oxygen reduction to water. The use of gas diffusion electrodes as a cathode in this configuration enhances oxygen mass transport from air, enabling efficient passive oxygen dissolution into the catholyte without requiring external aeration [117].  
While this approach slightly compromises cell voltage due to the higher redox potential of the O\({}_{2}\)/H\({}_{2}\)O couple (E\({}^{0}\) = +1.23 V vs. SHE) compared to Fe\({}^{3+}\)/Fe\({}^{2+}\) (E\({}^{0}\) = +0.77 V vs. SHE), the latter is kinetically more favored. Fe\({}^{3+}\) reduction involves a single-electron transfer, which is faster and more efficient than the sluggish, four-electron ORR. This kinetic advantage compensates for the voltage trade-off, improving reaction rates and overall system performance. Additionally, this approach removes the dependency onprecious metals, substantially lowering CAPEX and enhancing the system's economic viability at modest scales [118].  
One of the notable advantages of this system is the potential to utilize Fe-containing wastewater, such as acid mine drainage, as the catholyte. AMD, naturally enriched with _Acidithiobacillus ferrooxidans_, can serve as both the catholyte and the inoculum, simplifying system operation and further lowering costs. Moreover, coupling a bioanode with a bio-cathode in an MFC system introduces additional opportunities for sustainable applications. The bioanode can process organic waste or other pollutants, while the bioachode catalyzes ORR using Fe\({}^{2+}\) from wastewater, providing a second life to waste streams and decreasing the overall environmental impact.
## 5 Calculated and Measured Results at a Specific Site
### Measured and Calculated Transfer Functions of the Soil  
The transfer functions of the soil at this site were measured by hammer impacts and sensor distances between 3 and 30 m (Figure 9a). A soil model was fitted to these transfer functions, which consists of a layer of height \(h=11\text{ m}\), a shear wave velocity \(v_{S1}=270\text{ m/s}\) and an underlying half-space of \(v_{S2}=1000\text{ m/s}\). The theoretical transfer functions of this layered situation are presented in Figure 9b, which show all the same characteristics as the measured transfer functions. The low-frequency amplitudes are small because of the stiff half-space, whereas the high frequencies have the greater amplitudes of the softer layer. Between these two different amplitude ranges, a strong increase in amplitudes can be found, which is followed by a moderate resonance at 12.5 Hz.  
The transfer functions in Figure 9a,b hold for a point load. A number of point loads of a 1 kN amplitude per axle and one-third octave band were superposed stochastically to obtain the transfer function for a train load (the response to a standard train load, Figure 9c). The characteristics are the same as for the point load. The near field has almost the same amplitude, whereas the far field of the train load has higher amplitudes. This leads to a weaker attenuation with distance and a narrower band of transfer functions.  
Another representation is given for the results at a 30 m distance. Figure 9d shows the spectra for different train speeds \(v_{T}\), which are calculated by different dynamic loads of \(F=0.155\) to 1.0 kN per axle and one-third octave band. The curves are regularly shifted vertically upwards proportional to the dynamic axle load.