esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 3 Results  
### Effects of Noise Parameters  
Figure 4 shows the NASA-TLX scale scores, MATB task performance, and mean SampEn of HRV under different noise conditions. As shown in Figure 4a, the results of the NASA-TLX subjective scale showed that different sound pressure levels and sharpness had no significant effect on the subjective mental workload of participants. However, the overall mental workload score of NASA-TLX was relatively high under N75-S2 and relatively low under N80-S1.  
Although there was no significant difference in the overall performance of MATB under different noise conditions, the overall performance of MATB under N75-S2 was relatively lower (Figure 4b). Figure 4c-e shows the performance of the three subtasks, SYS, TRA, and RES of MATB, respectively. Among them, only TRA performance significantly changed with different noise conditions. TRA performance under N75-S2 was significantly lower than that under N85-S1 (\(p=0.007\)). When the noise sound pressure and sharpness changed, SYS and RES performance of the participants did not significantly change. As shown in Figure 4f, the mean SampEn of HRV under N75-S2 was significantly lower than that under N85-S1 (\(p=0.026\)) and N80-S1 (\(p=0.001\)). The mean SampEn of HRV was overall high under N80-S1.  
As shown in Figure 5, the best PVT performance was found under N80-S1, which was significantly better than that under N75-S2 (\(p=0.026\)). Moreover, both the response time and fastest \(10\%\) response time under N80-S1 were significantly lower than those under N75-S2. Figure 5d also shows that the slowest \(10\%\) response time under N80-S1 was relatively lower than that under N85-S1 (\(p=0.092\)).  
Figure 4: Boxplots of (**a**) NASA-TLX score, (**b**–**e**) weighted task performance, and (**f**) mean SampEn of HRV under the three noise exposure conditions (* \(p<0.05\); ** \(p<0.01\)).
## 3 Results and Discussion
### Functional Characteristics of BC\({}_{x}\)N\({}_{y}\) Films
#### 3.4.2 Wettability  
The wetting behavior of the films' surface was studied depending on the deposition parameters. The wettability of films was evaluated by measuring the contact angles (CA) of polar (water) and nonpolar (diethylene glycol, DEG) test liquids, followed by calculating the surface free energy. The contact angles of wetting were measured in the sessile drop mode. The profile of a sessile drop on a solid surface was created by numerically solving the Young-Laplace equation. The parameters derived from this theoretical droplet modeling method were compared with their respective counterparts from the ellipse fitting method. The results demonstrate that the ellipse fitting method is in excellent agreement with the Young-Laplace equation solution (Table 6). The repeatability of measurements is represented as errors in Table 6, which are less than 5%. It is known, the wetting behavior of a film's surface is dependent on both surface topography and surface chemistry. As shown above, the low-temperature PECVD and all LPCVD films have very low roughness.  
Figure 11: (a) Transmission spectra for a quartz substrate (1), for PECVD films of 210–250 nm thickness obtained at T\({}_{\text{dep}}\) = 373 (2), 423 (3), 473 (4), and 523 K (5); and for LPCVD films of 230–250 nm thickness obtained at T\({}_{\text{dep}}\) = 673 (6), 873 (7), and 973 K (8). (b) Temperature dependencies of optical band gap for the PECVD and LPCVD films. Black rhombs and red circles—spectrophotometric data. Red asterisks—spectral ellipsometry data.  
The angle values given in Table 6 indicate the moderate hydrophilicity of all deposited films. The water contact angles lie in the region from 59 \(\pm\) 3\({}^{\circ}\) to 63 \(\pm\) 2\({}^{\circ}\) for LPCVD BC\({}_{\rm x}\)-like films deposited at temperatures from 773 to 898 K, respectively. The contact angle is reduced to 53\({}^{\circ}\) for films produced at higher temperatures. It should be noted that, according to the Raman spectroscopy data, these films contain an impurity phase of amorphous graphite-like carbon. According to the results of the XPS data, the top layer of LPCVD films contains about 11-12 at.% oxygen. As is known, oxygen-containing groups can contribute to a decrease in the contact angles. The obtained values of the water contact angles are consistent with the literature data for BC\({}_{\rm x}\)N\({}_{\rm y}\) films deposited by magnetron sputtering where CA = 50-77\({}^{\circ}\)[95] and 20.7-54.0\({}^{\circ}\)[96].  
PECVD BC\({}_{\rm x}\)N\({}_{\rm y}\)O\({}_{\rm Z}\) films' wettability experiments showed that the films obtained at 373-423 K degraded upon interaction with the test liquid. Films synthesized at a higher temperature exhibited hydrophilic behavior (Table 6). The hydrophilicity of these films may be associated with the presence of NH and oxygen-containing surface functional groups.
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 3 Experimental Setup Design
### Multicomponent Gas Detection System  
The TDLAS gas detection system with wavelength modulation, as shown in Figure 8, consists of a laser, an optical path cell, a photodetector, and a lock-in amplifier. Increasing the number of lasers is a common strategy for this system to detect multiple types of gas. However, industrial applications need to consider environmental factors, such as integration, stability, and vibration.  
During system integration, the waveband and the number of lasers for detecting gas must be considered. Gas absorption lines in the mid-infrared region are intense, but the distance between the spectral lines is relatively large. In addition, the temperature requirement of a mid-infrared laser complicates the system. Therefore, the mid-infrared band is not conducive for system integration. In this paper, near-infrared spectra with relatively close spectral line spacings were selected to reduce the number of lasers. The detection sensitivity was improved by wavelength modulation and a long optical path cell (the customized optical path is 12 m). The absorption intensities of carbon monoxide, carbon dioxide, methane, and acetylene in the 5800-6600 cm\({}^{-1}\) spectral range are shown in Figure 9. The VPL spectral database [30] was used as a reference to select the near-infrared absorption spectra of ethylene and ethane.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Parameters** & **Specifications** \\ \hline Chamber volume & 1300 mL \\ Operating temperature & 50 \({}^{\circ}\)C \\ Operating pressure & 28 kPa \\ Stirring speed & 72 r/min \\ Oil sample volume & 650 mL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the degassing chamber.  
Figure 7: Structure of the degassing chamber.  
The 1532.83 nm wavelength was chosen as the central wavelength for the C\({}_{2}\)H\({}_{2}\) absorption spectra. The central wavelengths for CO and CO\({}_{2}\) were 1597.57 and 1579.74 nm, respectively. The wavelengths 1684.04, 1683.10, and 1683.35 nm were selected sequentially for CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), and C\({}_{2}\)H\({}_{4}\). The central wavelengths of the laser output are shown in Table 2.  
An optical coupler was used in the optical path. Using time-division multiplexing (TDM), different wavelengths of lasers enter the gas cell sequentially to form a common optical path. In this way, the complexity of the optical path is reduced, enhancing the structural strength of the system. The structure of the gas detection system is shown in Figure 10. The high-frequency modulated signal (20 kHz) and the laser scanning signal (10 Hz) were coloaded on DFB lasers, and the second harmonics of the characteristic gases were obtained by the lock-in amplifier.  
Figure 8: TDLAS measurement system of wavelength modulation.  
Figure 9: Absorption lines of four target gases (CO, CO\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\)) in 5800–6600 cm\({}^{-1}\).
## 3 Results and Discussion
### Machine Learning (ML) Model Development  
After an extensive data screening and assessment process, reliable data were generated for use in machine learning model development. This data set forms the foundation for building an accurate model, which was enhanced by incorporating new data points that reflect complex relationships between the pre-reformer outlet temperature and five relevant parameters. To capture these relationships, both quadratic and cubic terms were considered in the model development phase, ensuring that non-linear interactions were properly represented. A multi-regression mathematical model was selected due to its ability to account for categorical variables, such as gas composition. In particular, the actual oxygen content was incorporated with specified factor levels to reflect its critical influence on the component's behavior. This method ensures a more comprehensive understanding of how different factors interact within the process. Given the nature of the problem and the quantitative nature of the response variable (pre-reformer fuel outlet temperature), the regression approach was deemed the most suitable for modeling. It allows for accurate predictions while preserving interpretability, which is critical for practical applications in industrial settings.
## 2 Materials and Methods  
### Overview of YOLOv8  
YOLOv8 [14] is a new state-of-the-art object detection model offering cutting-edge performance in terms of accuracy and speed. YOLOv8 introduces new features and optimizations that build upon the advancements of previous YOLO versions, making it an ideal choice for various object detection tasks in a wide range of applications [16]. The YOLOv8 architecture builds upon the YOLO algorithm's earlier versions, and is shown in Figure 1.  
The backbone of YOLOv8 is a modified version of the CSPDarknet53 architecture, composed of 53 convolutional layers, and uses cross-stage partial connections to facilitate  
Figure 1: The architecture of YOLOv8 [17].  
information flow between the different layers. This backbone is responsible for extracting features from the input images, and the network's head is where the actual object detection takes place using extracted features to predict the class probabilities. Moreover, the bounding box coordinates each grid cell in the input image. In addition, the network's head includes a self-attention mechanism, allowing the model to focus on the most relevant features for object detection.  
Unlike previous YOLO versions, YOLOv8 is an anchor-free model that directly predicts an object's center instead of the offset from a known anchor box. This mechanism significantly improved previous versions of YOLO, as it eliminated the need for anchor boxes, which could struggle with objects of unusual shapes or sizes. YOLOv8 employs adaptive training to optimize the learning rate and balance the loss function, leading to better model performance. This model also uses advanced data augmentation techniques, such as MixUp [18] and CutMix [19], to enhance the model's robustness and generalization. Finally, YOLOv8's architecture is highly customizable, allowing users to easily modify the model's structure and parameters to suit their needs, making it a flexible tool for a wide range of object detection tasks.
## 4 Results
### Performance Evaluation Metrics
#### 4.1.3 Average Communication Cost (\(\tilde{\mathcal{C}}\))  
The mean of the total communication costs of all the nodes for all data collection cycles, as provided in Equation (39), can potentially be used to calculate the average total  
Figure 13: The process of routing in each routing cycle using the proposed machine learning-aided network contention-aware link stability- and delay-based routing framework in SDVN.  
communication cost per node per routing cycle (\(\bar{C}\)). A lower value of \(\bar{C}\) shows that the method used to acquire the data results in an average reduction in communication costs.  
\[\bar{C}\ =\ \frac{1}{\mathcal{K}N}\sum_{m=1}^{\mathcal{K}}\sum_{i=1}^{N}\sum_{ j=1}^{L}C_{j}P_{j} \tag{39}\]  
\(\mathcal{K}\) is the total number of data collection steps, \(N\) is the sum of nodes, and \(L\) is the sum of communication channels utilized by the \(i\)th node in Equation (39). Subscript \(i\) denotes the node index, \(j\) is the channel index utilized by the \(i\)th node, and subscript \(m\) denotes the \(m\)th data collection cycle. \(P_{ij}\) is the total packet size for sending a packet from the \(i\)th node using the \(j\)th communication channel, and \(C_{j}\) indicates the communication cost per byte for communicating using the \(j\)th communication channel.
## 4 Results
### Comparison of Path Planning Results  
The IGWO-IAPF algorithm was employed for path planning simulation experiments in the task environment designed in Figure 1. Additionally, the GWO algorithm, PSO algorithm, GA algorithm, and WPA algorithm were also included in the task environment for path planning simulation experiments, which were compared with the IGWO-IAPF algorithm. During the computation process, all four algorithms, except for the IGWO-IAPF algorithm, generated path effect maps after 20 iterations. Table 3 summarizes the data results obtained from running the different algorithms 30 times in the task environment scenario. Figure 12 illustrates the path effect generated by the IGWO algorithm in the task environment, while Figure 13 presents a comparison of the path planning results of the five algorithms.  
Figure 10: Algorithm fitness iteration curve.  
Figure 11: Initial path diagrams.  
Figure 12: IGWO-IAPF algorithm path planning rendering.
## 3 Case Studies and Analyses
### Case 1: Vibration of a SDOF System  
A SDOF vibration system driven by white noise is considered first.  
\[M\cdot a(t)+C\cdot v(t)+K\cdot x(t)=-F(t) \tag{13}\]  
where \(x\), \(v\) and \(a\) are respectively displacement, velocity and acceleration of system vibration. The assumed frequency of excitation \(F\) ranges from 0.1 Hz to 5.1 Hz and its power spectrum is constant, 1 N\({}^{2}\)s. Mean of \(F\) is zero. The SDOF system has a vibration mass \(M\) of 1 kg, natural frequency \(f_{n}\) of 0.45 Hz and damping ratio of 3%. The initial velocity and displacement are assumed as 0 cm/s and \(-\)3.2 cm.  
The dynamic responses including acceleration, velocity and displacement are solved by the Newmark-\(\beta\) method of \(\beta=\frac{1}{4}\)[38]. This method is stepwise and has been widely applied in civil engineering for years due to its accuracy, as long as a suitable interval \(\Delta t\) is chosen in computation. This study uses a small time interval, \(\Delta t=0.0586\) s. Figure 4 shows the time-domain numerical solution of excited acceleration for duration of 239.94 s.  
Figure 4: Simulated acceleration time history of single-degree-of-freedom (SDOF) system.  
Figure 5a shows the velocities reconstructed by the five methods against the reference velocity solved by the Newmark-\(\beta\) method. The comparison of displacements is given in Figure 6a. In these two figures, it can be clearly seen that after removal of trend by fittings, none of the reconstructed signals contains drift. Moreover, at the beginning and ending parts of the full duration, these two frequency domain methods fail to precisely reconstruct both velocity and displacement. By contrast, as illustrated in the enlarged details in Figures 5b and 6b, the velocity and displacement reconstructed by the WATEBI method match the reference time series with smallest deviations.  
The comparisons of average peak, maximum peak error and root-mean-square error for the underlying five methods are given in Tables 1 and 2, respectively for velocity and displacement. It can be shown from the three error indicators that for velocity the low-frequency cutoff method performs worst, while for displacement the low-frequency attenuation is of lowest accuracy. For both velocity and displacement, the best performance among all five methods is WATEBI. In addition, the good performances of Matlab Ode45 and trapezoidal integral are noticeable, because they are analogous to the Newmark-\(\beta\) algorithm for the adopted step-by-step integration scheme in the time domain and actually in this SDOF system no noise is contained in the acceleration signal. Nonetheless, in the following case studies, the limitation and drawback of time-domain methods will be demonstrated when random noise is mixed in the acceleration.  
Figure 5: Velocity of the SDOF system: (**a**) Full duration; (**b**) Enlarged detail at the tail end.
# Evaluation of Sub-Lethal Toxicity of Benzethonium Chloride in _Cyprimus carpio_ [PERSON]
## Materials and Methods
### Test Organisms and Experimental Procedure  
One-year-old _Cyprinus carpio_ individuals with health and origin certificate no. 54031 were purchased from a Romanian authorized fish farm. Fish with similar length, weight, and age were acclimatized in laboratory conditions at the Aquatic Biobase of the National Research and Development Institute for Industrial Ecology (ECOIND, Bucharest, Romania) accredited for acute toxicity studies. Fish maintenance and experimental tests were made in accordance with the Guide for the Use and Care of Laboratory Animals [26] and Organization for Economic Co-operation and Development (OECD) recommendation related to the animal suffering and to reduce the number of animals used in tests. This in vivo study was approved and supervised by the Commission of Ethics and Professional Deontology of ECOIND (Intern Regulation no. 18671).  
An acute semi-static toxicity test was performed, in accordance with OECD test no. 203. In 100 L aquariums, 40 fish (length 15 \(\pm\) 2 cm, weight 58 \(\pm\) 10 g) were exposed to 1 mg/L BEC solution (BEC was firstly prepared in distilled water and then diluted to 1 mg/L with free chlorine tap water in test aquariums) for a period of 24, 48, and 96 h. The test medium was 80% renewed every 24 h. The average analytical concentration of cationic surfactant in the test aquariums was 0.8 mg/L as measured using the Specord 205 spectrometer (Analytik Jena, Germany) according to Annex IIC of Detergent Regulation no. 648/2004.  
The BEC concentration used for exposure within the present research was chosen based on our previous studies concerning the assessment of lethal concentration 50 (LC50) for the same fish species [16]. To estimate the maximum acceptable toxicant concentration (MATC) we used the equation of Sprague:  
\[\text{MATC}=\text{LC50}\text{ value after 96 h (which was 4.57 mg/L)}\times 0.1 \tag{1}\]  
Due to the surfactant hydrophobic properties and to obtain a minim concentration of 0.5 mg/L in the test solution, a value of 1 mg/L BEC was used. Our environmental investigation in the local municipal wastewater and surface water showed the presence of this cationic surfactant in the range of 0.003 to 0.35 mg/L. Only the sum of anionic and non-ionic surfactant concentration is limited to 0.5 mg/L for the wastewater discharged in water bodies [16].  
Another lot of 40 fish not exposed to the surfactant was used as control. No feeding during the test period was done.  
After each time period of BEC exposure (24, 48, and 96 h), 5 fish were sacrificed and their liver was collected and frozen at -80 \({}^{\circ}\)C for biochemical analyses. Some parts of the liver were collected in formalin 10% to be used in histopathological investigations.
###### Abstract  
Commercially available 316L (1.4404) stainless steel is commonly used for industrial filtration due to its combination of good material properties, particularly its corrosion resistance, which is a critical factor for filters in corrosive (e.g., saltwater) environments. Recently, laser powder bed fusion (LPBF) has enabled new more complex and efficient filtration pieces to be manufactured from this material. However, it is critical to know how the corrosion resistance is affected by this manufacturing strategy. Here, the corrosion resistance of LPBF manufactured 316L stainless steel is compared with wrought 316L sheet. The corrosion of the samples in saltwater was assessed with asymmetric electrochemical noise, potentiodynamic polarisation curve, and electrochemical impedance spectroscopy. The samples before and after corrosion were examined with scanning electron microscopy and energy-dispersive spectroscopy. The LPBF samples had higher corrosion resistance than the sheet samples and were more noble. The corrosion resistance of the LPBF sample increased with time, while the wrought sample corrosion resistance reduced over time. The corrosion mechanism of both samples was stable with time, formed of a passive film process and a bared material process. This paper presents the first study about the temporal evolution of the LPBF 316L stainless steel corrosion mechanism.  
laser additive manufacturing; power bed fusion; 316L stainless steel; corrosion; asymmetric electrochemical noise; potentiodynamic polarisation curve; electrochemical impedance spectroscopy +
Footnote †: journal: Applied Sciences
## 2 Data and Methods
### Wave Spectra and Spreading Functions
\(\quad\) _Sech spreading function:_ \[f(K,\varphi)=0.5\beta\Omega(K,\varphi)\] (7) \[\Omega(K,\varphi)=\left[\text{sech}^{2}(\beta\varphi)+\text{sech}^{2}(\beta| \varphi\pm\pi|)\right]/2\] (8) \[\beta=\beta_{g}(1-a)+0.84a\] (9) \[a=0.165+0.835\text{tanh}\left[\left(K-5\sqrt{U_{10}}\right)/\left(25\sqrt{U_{ 10}}\right)\right]\] (10) \[\beta_{g}=\left\{ \begin{array}{ccc}1.22&,K/K_{p}\leq 0.31\\ 2.61(K/K_{p})^{0.65}&,0.31<K/K_{p}\leq 0.97\\ 2.28(K/K_{p})^{-0.65}&,0.97<K/K_{p}\leq 2.56\\ 10^{[-0.4+0.8393\exp(-0.56\ln(K/K_{p}))]}&,2.56<K/K_{p}\leq 30/K_{p}\end{array} \right.\] (11)  
_where \(K_{p}\) is the wave number at the peak spectral density. In (8), the \"\(-\)\" sign is used when \(0<\varphi\leq\pi\), while the \"\(+\)\" sign is used when \(-\pi<\varphi\leq 0\)._  
3. \(\quad\) _Gaussian spreading function:_ \[f(K,\varphi)=D(K,\varphi)/ID\] (12) \[ID=\int D(K,\varphi)\cdot d\varphi\] (13) \[D(K,\varphi)=\exp\left(-\frac{\varphi^{2}}{2\delta^{2}}\right)\] (14) \[\frac{1}{2\delta^{2}}=0.14+0.5\left[1-\exp\left(-\frac{Ku_{10}}{c_{1}}\right) \right]+5\exp\left[2.5-2.6\ln\left(\frac{u_{10}}{u_{n}}\right)-1.3\ln\left( \frac{K}{K_{n}}\right)\right]\] (15)  
_where \(c_{1}\) is equal to 400 rad/s and \(K_{n}\) is equal to 1 rad/m._  
Figure 2 shows the symmetrical forms of the Cosine, Sech, and Gaussian spreading functions at 10 m/s wind speed for wave numbers of 0.1, 1, 10, and 100 rad/m. As can be seen, the wave directional distributions of the Cosine spreading function are anisotropic over all wave numbers, with the most wave energy propagating in upwind and downwind directions and the least in the crosswind direction. However, the directionality at different wave numbers is different, which shows a trend of first weakening and then slightly strengthening as the wave number increases. The other two spreading functions generally exhibit similar behavior, except for a near-isotropic distribution at decimeter- to meter-scale waves and a stronger directionality at longer waves. Figure 2: The **(a)** Cosine, **(b)** Sech, and **(c)** Gaussian spreading functions at 10 m/s wind speed for wave numbers of 0.1, 1, 10, and 100 rad/m.