esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
In practice, sensors attached to a human body are subjected to various environmental conditions. Thus, a high stability is required under various conditions of temperature and humidity. Figure 5c shows a resistance change of 1.3% with the increase in temperature from 20 to 120 \({}^{\circ}\)C. In addition, a resistance change of 0.057% for the humidity of 70% is shown in Figure 5d. These results are mainly attributed to the intrinsic property of CB and its passivation layer [34]. However, an unstable performance was observed above the humidity of 70%, which may be attributed to the high possibility of water penetration through the passive layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Strain** & **Gauge Factor** & **Year** \\ \hline CB/AgNPs composite & 0.61\%\(-\)1.4\% & 192.77\(-\)444.6 & this work \\ Carbon black/natural & 0.1\% & 43.2 & 2016 [27] \\ thermoplastic elastomer & 10\% & 31 & 2007 [30] \\ (Evoprene)/carbon black & 10\% & 15.75 & 2016 [31] \\ CE/CB nanocomposites & 10\% & 11.2 & 2015 [28] \\ ChNCs-CB/NR & 70\% & 5 & 2018 [33] \\ CB/elastomer & 60\% & 3 & 2015 [32] \\ SWCNTs/CB & 120\% & 1.25 & 2017 [29] \\ PVC/CB & 0.14\% & 741 & 2020 [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of gauge factor (GF) of previous reported strain sensors based on carbon black. Figure 4: (a) Dynamic bending–release cycle response of the sensors for various strains 0.61–1.44% with the screen-printed CB/AgNP lines at different lengths and various line widths. (b) Schematic diagram of sensing mechanism for CB/AgNP paste and SEM image of surface for the screen-printed CB/AgNP paste. As our CB/AgNP strain sensors are highly flexible, scalable, and sensitive, they can be employed in wearable and flexible human motion detection platforms. Figure 6a shows the response of the sensor attached to a finger at a bending angle of 0\({}^{\circ}\)-90\({}^{\circ}\). In addition, the CB/AgNP strain sensor was attached to a human neck to detect head rotation. It demonstrated a high sensitivity to reflect head rotation accurately, as shown in Figure 6b. Figure 5: (**a**) Continuous progressive input of different strain. (**b**) Durability of composites under 1000 cycles with low tensile deformation. \(\Delta R/R_{0}\) as a function of multiple bending and releasing cycles with 0.275% strain for CB/Ag composites. \(\Delta R\) refers to the resistance differences under bending and release, \(R\) is the initial resistance in the relaxed state. Effect of temperature (**c**) and humidity (**d**) on the conductivity of the CB/Ag (CB-H) strain sensor. Figure 6: Human motion detection by the CB/AgNP nanocomposite strain sensors. (**a**) Response of the CB/AgNP strain sensor to the bending angles from 0\({}^{\circ}\) to 90\({}^{\circ}\). (**b**) Motion detection of head turning left to right.
## 2 Materials and Methods
### Total Phenolic Content (TPC) and Total Flavonoid Content (TFC) in CSC  
Total phenolic content (TPC) was determined by a spectrophotometric method using Folin\(-\)Ciocalteu (FC) reagent [25]. Briefly, 200 \(\upmu\)L (1 mg/mL) of a CSC sample was mixed with 200 \(\upmu\)L FC reagent. After 5 min, 600 \(\upmu\)L of sodium carbonate solution (20%) was added and the mixture was left at room temperature for 1 h. The absorbance was read at 765 nm using an ultraviolet-visible spectrophotometer (UV-2120 Optizen, Mecasys, Korea). TPC was calculated as mg gallic acid equivalent (GAE) based on a gallic acid calibration curve.  
CuO NP treated and non-treated CSC samples (100 \(\upmu\)L, 100 mg/mL) received FC reagent followed by 15% sodium carbonate. The absorbance was read at 755 nm. Total flavonoid content (TFC) of CuO NP treated and non-treated CSC samples (100 \(\upmu\)L, 100 mg/mL) were measured using an established calorimetric method [31]. TFC was calculated as mg quercetin equivalent (QE) based on a quercetin calibration curve.
## 4 Experimental Simulation
### System Bit Error Rate Analysis  
The following further validates the conclusions drawn above through the system's bit error rate (BER). BER curves for OFDM signals demodulated by the FUBSR system and the SUUBSR system under different subcarrier modulation schemes were plotted within  
Figure 14: The influence of damping coefficient on the demodulation effect of OFDM signal. (**a**) The demodulation effect of the system when \(\gamma=0.5\) is met. (**b**) The demodulation effect of the system when \(\gamma=0.6\) is met. (**c**) The demodulation effect of the system when \(\gamma=0.7\) is met. (**d**) The demodulation effect of the system when \(\gamma=1\) is met.  
an SNR range of \(-4\) to \(6\) dB, as shown in Figure 15. From Figure 15, it can be observed that as the SNR increases, both systems can improve the demodulation performance of OFDM signals with different modulation schemes, and the demodulation effect is better for OFDM signals modulated with QPSK than for those modulated with \(16\) QAM and \(64\) QAM. During the demodulation process of OFDM signals with different subcarrier modulation schemes, the system proposed in this paper demonstrates significantly superior demodulation performance compared to the FUBSR system, especially for the \(64\) QAM modulation scheme, which requires a higher noise environment, where the proposed system exhibits stronger noise resistance and demodulation effectiveness. In summary, the system proposed in this paper can achieve the enhancement and demodulation of OFDM signals with different subcarrier modulation schemes, demonstrating universality. Furthermore, under the same SNR conditions, it has a lower BER compared to the FUBSR system, providing advantages in terms of demodulation performance for OFDM signals.
## 2 Multi-Arm Mach-Zehnder Interferometer  
A MAMZI is an optical configuration consisting of two MBSs. The first one splits an optical input and guides the resulting beams towards the arms. The optical beams then pass through the arms and are recombined in the second MBS. In this recombination, the optical interference will be affected by the phase information of each arm (\(\phi_{k}^{*}\)). A schematic of a MAMZI is shown in Figure 1.  
The \(MBS\) can be modeled as a \(N\times N\) unitary matrix, with elements \(U_{k,l}\) (with \(k=[1,..N]\) and \(l=[1..N]\)) dependent of a phase \(\theta_{k,l}\) induced at each input \(l\) related to the \(k\) th output, and on the reflection and optical transmission probabilities of both pairs. A widely used example of an ideal MBS is the Fourier matrices described by [28]:  
\[F_{N}=\left[\begin{array}{cccc}U_{11}&U_{12}.&...&U_{1N}\\ U_{21}&U_{22}&...&U_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ U_{N1}&U_{N2}&...&U_{NN}\end{array}\right],\quad where:\quad U_{k,l}=\tfrac{1} {\sqrt{N}}e^{\frac{2\pi i(k-1)(l-1)}{N}}. \tag{1}\]  
In this case, the induced phases are given by \(\theta_{k,l}=\frac{2\pi(k-1)(l-1)}{N}\), while the transmission and reflection information is in \(\frac{1}{\sqrt{N}}\). On the other hand, considering a MAMZI with attenuations (\(\tau_{k}\)) and phases (\(\phi_{k}^{*}\)) induced in each arm of the MAMZI, its internal matrix is expressed by  
\[M_{N}=\left[\begin{array}{cccc}\tau_{1}e^{i\phi_{1}^{*}}&0&0&0\\ 0&\tau_{2}e^{i\phi_{2}^{*}}&0&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\tau_{N}e^{i\phi_{N}^{*}}\end{array}\right]. \tag{2}\]  
In our model, \(\tau_{k}\) is a complex variable representing the optical loss and polarization changes induced in each arm. Finally, for the MZI architecture, the output optical field (\(y\)) can be described as a function of the input optical field (\(x\)) using the following [29]:  
\[y=MBS^{T}\cdot M_{N}\cdot MBS\cdot x, \tag{3}\]  
where the input and output intensities vectors are given by \(I^{in}=x^{*}x\) and \(I^{out}=y^{*}y\), respectively.
# Analysis of the Delamination Process with Nitric Acid in Multilayer Composite Food Packaging
## 3 Results
### Factors Influencing the Process  
The delamination process is a complex chemical procedure influenced by numerous factors. The efficacy and efficiency of the process depends on various factors, with ultrasound identified as the most influential, followed by acid concentration. Intriguingly, the relationships between temperature and concentration or width and concentration are not statistically significant (Figure 4). These observations are corroborated by the \(p\)-values of T \(\times\) C (0.420079) and \(C\)\(\times\) a (0.0741109), as demonstrated in Appendix A.  
The duration of the delamination process is also affected by the relationships between temperature and ultrasound, and between nitric acid concentration and ultrasound. These relationships may impact the reaction rate and the extent of delamination. Consequently, meticulous control of these factors is imperative for achieving optimal results in the delamination process.  
In the absence of ultrasound, the dissolution rates of the aluminum and adhesive layers are similar. However, when ultrasound is employed, the aluminum layer dissolves substantially faster than the adhesive layer. This phenomenon can be attributed to the breakdown of the aluminum foil in the packaging layers facilitated by ultrasound. Additionally, a mixing process aids in removing the aluminum foil from the composite. The larger reaction surface area of the metal also results in accelerated dissolution in acid. It is crucial to note that prematurely halting the reactor before the adhesive layers are fully oxidized may yield suboptimal outcomes in the delamination process.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
###### Abstract  
The presence of microbial biofilms in the wounds affects negatively the healing process and can contribute to therapeutic failures. This study aimed to establish the effective parameters of cold atmospheric plasma (CAP) against wound-related multispecies and monospecies biofilms, and to evaluate the cytotoxicity and genotoxicity of the protocol. Monospecies and multispecies biofilms were formed by methicillin-resistant _Staphylococcus aureus_ (MRSA), _Pseudomonas aeruginosa_ and _Enterococcus faecalis_. The monospecies biofilms were grown in 96 wells plates and multispecies biofilm were formed on collagen membranes. The biofilms were exposed to helium CAP for \(1,3,5\) and \(7\) min. In monospecies biofilms, the inhibitory effect was detected after \(1\) min of exposure for \(E\). _facalis_ and after \(3\) min for MRSA. A reduction in \(P\). _areuginosa_ biofilm's viability was detected after \(7\) min of exposure. For the multispecies biofilms, the reduction in the overall viability was detected after \(5\) min of exposure to CAP. Additionally, cytotoxicity and genotoxicity were evaluated by MTT assay and static cytometry, respectively. CAP showed low cytotoxicity and no genotoxicity to mouse fibroblac cell line (3T3). It could be concluded that He-CAP showed inhibitory effect on wound-related multispecies biofilms, with low cytotoxicity and genotoxicity to mammalian cells. These findings point out the potential application of CAP in wound care.  
wounds; biofilm; plasma jet +
Footnote †: journal: Physics Letters B
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Control Group (Male**} & \multicolumn{3}{c}{**Experimental Group (Male**} \\ \cline{2-5}
**Variable** & \multicolumn{3}{c}{**Group A, _n = 26_)**} & \multicolumn{3}{c}{**Group B, _n = 24_)**} & \(p\) **Value \({}^{*}\)** \\ \cline{2-5}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Condyle length (cm) & \(1.76\) & \(0.43\) & \(1.86\) & \(0.37\) & \(0.365\) \\ \hline Coronoid length (cm) & \(1.49\) & \(0.48\) & \(1.55\) & \(0.36\) & \(0.608\) \\ \hline Ramus height (cm) & \(6.43\) & \(0.52\) & \(6.13\) & \(0.67\) & \(0.087\) \\ \hline Total ramus height (cm) & \(7.48\) & \(0.70\) & \(7.25\) & \(0.79\) & \(0.294\) \\ \hline Ramal width (cm) & \(3.69\) & \(0.55\) & \(3.58\) & \(0.33\) & \(0.421\) \\ \hline Mandibular body length (cm) & \(10.81\) & \(1.11\) & \(10.88\) & \(1.18\) & \(0.836\) \\ \hline Sigmoid notch depth (cm) & \(1.47\) & \(0.36\) & \(1.60\) & \(0.29\) & \(0.137\) \\ \hline Posterior ramus notch depth (cm) & \(0.24\) & \(0.13\) & \(0.30\) & \(0.16\) & \(0.110\) \\ \hline Anterior ramus notch depth (cm) & \(0.26\) & \(0.19\) & \(0.24\) & \(0.18\) & \(0.656\) \\ \hline Antegional notch depth (cm) & \(0.22\) & \(0.24\) & \(0.20\) & \(0.14\) & \(0.737\) \\ \hline Third molar width (cm) & \(1.39\) & \(0.12\) & \(1.41\) & \(0.15\) & \(0.608\) \\ \hline Retromolar space (cm) & \(1.54\) & \(0.59\) & \(1.10\) & \(0.58\) & \(\mathbf{0.010}\) \\ \hline Angle condude—coronoid process (deg) & \(31.04\) & \(5.38\) & \(28.75\) & \(5.06\) & \(0.129\) \\ \hline Gonial angle (deg) & \(124.81\) & \(7.48\) & \(122.33\) & \(8.76\) & \(0.287\) \\ \hline Inclination of lower posterior teeth (deg) & \(91.15\) & \(5.70\) & \(92.70\) & \(6.65\) & \(0.753\) \\ \hline Angle of impaction (deg) & \(90.93\) & \(8.69\) & \(69.46\) & \(32.66\) & \(\mathbf{0.002}\) \\ \hline Retromolar space/3M width (ratio) & \(1.09\) & \(0.39\) & \(0.77\) & \(0.39\) & \(\mathbf{0.006}\) \\ \hline \multicolumn{9}{c}{\({}^{*}\)using Student’s _t_-test.} \\ \end{tabular}
\end{table} 
Table 3: Comparison between male group A (MA) and the male group B (MB).
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 6 Experiment
### Experiment Setting
#### 6.1.3 Indicator Setting  
In order to evaluate the performance of an algorithm, we use three evaluation indicators: planning profit, revenue gap, and calculation time.  
The planned profit represents the average of the cumulative observed returns of all test cases in the task planning algorithm in a single planning scenario. The calculation method is as follows:  
\[p(A)=\frac{1}{N}{\sum}_{0}^{N-1}p_{k}(A) \tag{15}\]  
where \(p_{k}(A)\) represents the cumulative observation benefit of algorithm \(A\) in the k test case.  
The profit gap represents the average of the ratio of the cumulative observed return difference between the task planning algorithm and the benchmark reference algorithm to the cumulative reference income of the benchmark reference algorithm. The calculation method is as follows:  
\[G(A)=\frac{1}{N}{\sum}_{0}^{N-1}\frac{p_{k}(B)-p_{k}(A)}{p_{k}(B)} \tag{16}\]  
where \(p_{k}(A)\) represents the cumulative observation profit of algorithm \(A\) in the k test case.  
The calculation time represents the average value of the calculation time of all of the test cases of the person planning algorithm in a single planning scenario (from the start of planning to the generation of the observation plan). The calculation method is as follows:  
\[T(A)=\frac{1}{N}{\sum}_{0}^{N-1}t_{k}(A) \tag{17}\]  
where \(p_{k}(A)\) represents the calculation time of algorithm \(A\) in the k test case.
## 1 Introduction
By utilizing amplitude differences between reflections and diffractions, the multiple singular spectrum analysis (MSSA) is featured with simplicity and efficiency in separating diffraction to remove the intense reflections while retaining the faint diffraction energy [25]. The essence of this technique is to perform singular value decomposition (SVD), with greater singular values indicating reflection, medium values indicating diffraction, and smaller values indicating noise [26; 27]. However, determining the threshold of singular values that correspond to different wave components within a singular spectrum remains a challenge since GPR data are often featured with strong direct waves, coda waves, and ambient noise, which are inherently different from the seismic MSSA strategy. This could lead to a significant reduction in the resolution of the extracted diffraction profiles for GPR applications. To address these issues, this paper proposes a new technique for GPR diffraction separation, which incorporates multilevel wavelet transform (MWT) for preprocessing and multiple singular spectrum analysis (MSSA) for adaptive threshold determination using a partitioning-based \(k\)-means clustering algorithm [28]. This contribution feature allows for the differentiation of different components in the singular spectrum based on similarity attributes. To address the issue of decreased \(k\)-means clustering accuracy caused by the difference between the maximum and minimum singular values of the singular spectrum being approximately 2-3 orders of magnitude apart [29], we employ MWT to first preprocess the GPR dataset [30; 31; 32; 33]. MWT technique progressively decomposes the different components (i.e., direction, reflection, diffraction, and noise) into corresponding multilevel coefficients to enhance the accuracy of \(k\)-means clustering in coefficient profiles. These coefficients involve low-frequency approximate coefficients, high-frequency horizontal detailed coefficients, high-frequency vertical detailed coefficients, and diagonal detailed coefficients. Numerical experiments and field tests are conducted to validate the effectiveness and robustness of the proposed method. In particular, we showcase the dependence of the wavelet basis function on the separation performance, along with the analysis of clustering parameters selection and anti-noise performance.
## 3 Results  
### Study Participants  
First, two focus groups (\(N=4\) and \(N=4\)) were organized (September 2020) with CHWs. Then, in-depth interviews were conducted with a purposive sample of 13 CHWs and 11 clients (September-November 2020). Sociodemographic descriptors for the participating CHWs and clients are presented in Table 1. Some of the CHWs who participated in the focus group discussions were interviewed individually afterward.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & \multicolumn{2}{c}{**FGDs**} & \multicolumn{2}{c}{**In-Depth Interviews**} \\ \hline  & & \multicolumn{2}{c}{**CHWs**} & \multicolumn{2}{c}{**CHWs**} & \multicolumn{2}{c}{**Clients**} \\  & & \(N=8\) (\%) & \(N=13\) (\%) & \(N=11\) (\%) \\ \hline Sex & Male & \(3\) (37.5\%) & \(3\) (23.1\%) & \(4\) (36.4\%) \\  & Female & \(5\) (62.5\%) & \(10\) (76.9\%) & \(7\) (63.6) \\ \hline \multirow{3}{*}{Age (yrs)} & \(\angle 25\) & \(0\) & \(1\) (7.7\%) & \(0\) \\  & \(25\)-\(39\) & \(3\) (37.5\%) & \(7\) (53.8\%) & \(1\) (9.1\%) \\  & \(40\)-\(64\) & \(5\) (62.5\%) & \(5\) (38.5\%) & \(4\) (36.4\%) \\  & \(\angle 65\) & \(0\) & \(0\) & \(6\) (54.5\%) \\ \hline \multirow{3}{*}{Work or activity} & Student & \(0\) & \(2\) (15.4\%) & \(1\) (9.1\%) \\  & Worker/Servant/Self-employed & \(4\) (50\%) & \(6\) (46.1\%) & \(0\) \\ \cline{1-1}  & Job-seeking & \(1\) (12.5\%) & \(2\) (15.4\%) & \(1\) (9.1\%) \\ \cline{1-1}  & Reitened & \(2\) (25\%) & \(2\) (15.4\%) & \(6\) (54.5\%) \\ \cline{1-1}  & Disability & \(1\) (12.5\%) & \(1\) (7.7\%) & \(3\) (27.3\%) \\ \hline \multirow{3}{*}{Migration background} & No & \(8\) (100\%) & \(9\) (69.2\%) & \(7\) (63.6) \\ \cline{1-1}  & Yes, & Living \(<\)1 year in Belgium & \(0\) & \(0\) & \(1\) (9.1\%) \\ \cline{1-1}  & Living 1–5 years in Belgium & \(0\) & \(0\) & \(0\) \\ \cline{1-1}  & Living 6–10 years in Belgium & \(0\) & \(1\) (7.7\%) & \(1\) (9.1\%) \\ \cline{1-1}  & Living \(>\)10 years in Belgium & \(0\) & \(3\) (23.1\%) & \(2\) (18.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sociodemographic characteristics for participants of focus group discussions and in-depth interviews. FGDs (focus group discussions).  
In the following sections, the CMO configurations that resulted from the third analytical step are described. Finally, the discussion explicates how the CMO configurations are linked together in an overall program theory.
## 5 Conclusions  
A 3D numerical modeling algorithm is presented that utilizes mixed nodal and edge-based elements with Coulomb-gauged EM potentials in conductive anisotropic and magnetic media. This algorithm effectively reduces the number of unknowns compared to that of the nodal element method. The accuracy of this algorithm is validated by comparing its results with analytical solutions for a two-layer magnetic medium and is further confirmed through comparisons with results obtained from the edge-based FEM for a 3D conductive generalized anisotropic and magnetic model. Subsequently, several typical models are studied in detail. It can be concluded that:1. The algorithm, whether applied at low frequencies (0.001 Hz) or in cases of high resistive contrasts, demonstrates robust performance; it works well for modeling deep Earth and ocean models with strong resistivity contrasts.
2. The horizontal electric fields induced by Ex are predominantly influenced by conductivity in the x-direction, while the horizontal electric fields induced by Ey are primarily influenced by conductivity in the y-direction. Conductivity in the z-direction has a negligible impact on the horizontal electric fields. Additionally, it would be helpful to explain the effect of anisotropy on MT responses in practical surveys.
3. More attention on evaluating the influences of magnetic permeability in magnetic-rich areas is needed. The practical geological environment of Earth is extremely complex; the apparent resistivities and phases could be significantly distorted, or even reversed, due to the existence of non-zero magnetic susceptibility.  
Furthermore, this algorithm also provides a robust future forward modeling method for practical 3D MT inversion in conductive anisotropic and magnetic media.  
Conceptualization, T.X., Z.Z., M.Y. and J.Z.; methodology, T.X., Z.Z., M.Y., L.C., T.S. and J.Z.; formal analysis, T.X., C.G. and B.Y.; writing--original draft preparation, T.X. and Z.Z.; writing--review and editing: Z.Z., C.G. and B.Y.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
This study was co-funded by the National Natural Science Foundation of China (No. 42104078) and the Henan Province Science Foundation for Youths (242300420600, NSFRF240709).  
The data associated with this research are available and can be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.