esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 4 Discussion  
### Comparing Experiments of Algorithms  
#### 4.1.1 Simulation Experiments  
The algorithm proposed in this paper is compared with LMS and FxLMS algorithm by simulation. The input signal is the recorded transformer noise. The order of LMS algorithm is set as 32 and the step size is 0.03, and the order of FxLMS algorithm is set as 32 and the step size is 0.05. The parameters of adaptive tracking algorithm are set as follows: the initial phase is 10 degrees; the initial amplitude is 0.1 mV, and the adjustment step of the phase perturbations \(\mu_{k\text{-}\varphi}\) is 1; the adjustment step of amplitude perturbations \(\mu_{kA}\) is 0.01, and the maximum adjustment limit is 50; the minimum error limit is \(\varepsilon_{\text{min}}\)=10\({}^{-3}\). The simulation results are shown in the Figure 7.  
Figure 6: Flowchart of disturbance tracking module.  
The convergence time to steady state of the algorithms proposed in this paper is 0.007, while it is 0.029 seconds and 0.014 seconds of LMS algorithm and FxLMS algorithm respectively. The steady-state error of the proposed algorithm is a little larger than that of the LMS algorithm and the FxLMS algorithm. This is because the elimination target of the proposed algorithm is the five main transformer noises, while the LMS algorithm and the FxLMS algorithms use all noise as the elimination target. Although residual noise by the proposed algorithm is larger, the noise has been greatly reduced with the shortest convergence time.  
There is an inconsistency and contradiction between the step size and the convergence speed in the LMS algorithm [3]. Its convergence speed is the slowest of the three algorithms in the simulation experiment. Since perform secondary channel parameter estimation is needed in the FxLMS algorithm, when the algorithm is applied to open sound field, the influence of the secondary channel parameter estimation and acoustic feedback phenomenon may lead to the performance degradation, or even instability of the algorithm [21]. In addition, the algorithmic stability of the LMS algorithm and the FxLMS algorithm is closely related to the power spectrum of the input signal [21; 22]. When there is pulse signal in the input signal, the LMS algorithm and the FxLMS algorithm are unstable. However, because the algorithm proposed in this paper sets the error limit and the maximum number of adjustments, even if the pulse noise causes the current calculation to not converge, it will not cause the algorithm to be unstable.
## 3 Results and Discussion
### Force-Position Control  
The force-position control was considered as last control concept and allowed the combination of the two previous strategies. The force-position control allowed to verify whether it is possible to vary the gap during the process or to set it to constant values after the process start. The control loop has been designed to operate as a force control until a certain gap dimension \(gap_{switch}\) is reached, and then to switch to position control (see Figure 14). Both positions \(X_{set}\) and forces \(F_{set}\) at the start and end of the sheet metal were again controlled separately from each other.  
Figure 15 shows the resulting joint gap (a) and forces (b) over time. A compression force of \(-30\) N was used as \(F_{set}\) until a gap width of 0.2 mm (\(gap_{switch}\)) was reached. After reaching \(gap_{switch}\) at IP3, the gap was controlled to stay constant (\(X_{set}\) = 0.2 mm). The acting compression force of \(-30\) N at process start led to a longer time \(\Phi\) until the gap started to increase as expected based on the force-controlled process discussed in Section 3.3. Afterwards, the gap width started to grow for IP2 and IP3 while the forces remained constant due to the closed-loop control. The gap was kept constant after reaching \(gap_{switch}\) by adjusting the forces that can be seen clearly in Figure 15b.  
Figure 14: Closed-loop force-position control.  
Figure 15: Closed-loop force-position control (\(\mathrm{v_{w}}\) = 1 m/min, \(\mathrm{P_{L}}\) = 0.4 kW): **(a)** gap measurement via inductive probes; and **(b)** force measurement via load cells.  
This demonstrated that it was also possible to react to different events during welding. A relatively low welding speed of 1 m/min have been considered in the results shown so far, which is why the possibility of scaling to higher processing speeds will be considered in the following.
# Novel Aspects of Polynucleotide Phosphorylase Function in _Streptomyces_
## 1 Introduction  
Polynucleotide phosphorylase (PNPase, EC 2.7.7.8) was the first enzyme shown to synthesize polyribonucleotides [1], and for some time, it was thought to be the bacterial RNA polymerase. The enzyme was subsequently characterized in _Escherichia coli_ and other bacteria, and was shown to catalyze the following reaction:  
\[(\mathrm{p}^{5}\mathrm{N}\mathrm{N}^{3}\mathrm{OH})_{\chi}+\mathrm{Pi}\leftrightarrow (\mathrm{p}^{5^{\prime}}\mathrm{N}^{3}\mathrm{OH})_{\chi-1}+\mathrm{pp}^{5^{ \prime}}\mathrm{N}\]  
where N is any of the four bases found in RNA [2, 3]. As written, the reaction depicts the phosphorolytic degradation of RNA chains, and this activity appears to reflect the major function of PNPase in vivo. The reaction is reversible, however, and PNPase will synthesize polyribonucleotide chains, using nucleoside diphosphates (NDPs), rather than triphosphates, as substrates. The polymerizing activity of PNPase played an important role in the synthesis of polyribonucleotides used to unravel the genetic code [4, 5].  
PNPase is found in all bacteria examined to date, except _Mycoplasma_, and is also present in eukaryotic organelles [6]. The enzyme has not been identified in Archaea [7]. In _E. coli_, PNPase and RNase II are the major 3\({}^{\prime}\)-exonucleases involved in RNA degradation [8]. In addition to its degradative function, PNPase plays a role in the bacterial response to environmental stresses, such as cold shock [9, 10, 11], is involved in biofilm formation [12, 13] and virulence determination [14, 15], and the activity of the enzyme is modulated by a number of small molecule effectors, at least in vitro [16, 17, 18, 19].  
_Streptomyces_ are Gram-positive, soil-dwelling bacteria, notable for their ability to form spores and for their capacity to produce antibiotics [20, 21]. Nearly 70% of all antibiotics used in clinical and veterinary medicine worldwide are synthesized as natural products by members of the genus [22]. Of particular relevance to this review, a number of biochemical and genetic features of _Streptomyces_PNPase distinguish it from its counterparts in other bacteria. In what follows, the functions of PNPase in _Streptomyces_ will be explored. The reader is referred to several excellent reviews as sources of additional information on PNPase from _E. coli_ and other bacteria [2; 3; 23; 24].
# Safety Assessment of Microwave Breast Imaging: Heating Analysis on Digital Breast Phantoms
## 4 Discussion
### Comparison with Existing Studies About the Safety of MI
In [18], a SAR evaluation on a hand-held impulse-radar breast cancer detector, designed for placement on the breast with the patient in the supine position, has been conducted to ensure patient safety. With an output power of 0.045 mW transmitted through a single antenna at a time, the maximum 1g averaged SAR values in the breast were found to range from 0.0118 W/kg to 0.0246 W/kg across the frequency range from 3.1 GHz to 10.6 GHz. Due to significant differences in the architecture and positioning of the device relative to the breast, these results cannot be directly extended to or compared with the analysis conducted in the current work. In concluding the overall analysis, as shown in [17], it would be beneficial to conduct experimental trials to validate these results; however, the current MI system does not support SAR or temperature measurements. Nevertheless, Sim4 Life software has been validated multiple times in dosimetric and thermal studies, for example, in [29], and the fact that the found temperature increases are extremely low reaffirms the fact that even significant variations between simulated and measured data would not compromise the safety of the diagnostic examination. Rigorous computational methods, coupled with conservative safety margins, provide a robust framework for assessing potential thermal risks in medical imaging. Moreover, a cross-platform validation has been performed by comparing the Sim4 Life simulation of a simplified test case with an equivalent model implemented in CST Microwave Studio (version 2023, [https://www.3 ds.com/products/simulia/cst-studio-suite](https://www.3 ds.com/products/simulia/cst-studio-suite) accessed on 1 March 2025). The same transmitting horn antenna model has been implemented in both platforms, with a target consisting of a cylindrical phantom containing homogeneous material with electrical conductivity of 2 S/m and relative permittivity of 20. The comparative analysis revealed consistent results between these independent computational platforms, confirming the reliability of the presented results.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
## 3 Results and Discussion
### Nanoemulsion  
Nanoemulsions were formulated using chitosan as a stabilizing agent, employing the low-energy phase inversion composition method. The formulations incorporated chitosan concentrations ranging from 0.05% to 0.5% by weight. The resulting formulations were homogenous and exhibited a milky white appearance, with no signs of creaming or oil separation observed after 24 h.
## 3 Experimental Results
### Flexural Strength Analysis
The force equilibrium is expressed as Equation (8) based on Figure 14.  
\[0.85f^{\prime}_{\ c}ab+A^{\prime}_{\ s}E_{s}\varepsilon_{cu}\frac{c-d^{\prime} }{c}=A_{s}f_{y}+A_{ps}f_{ps}\cos\alpha \tag{8}\]  
\[f_{ps}=\big{(}\varepsilon_{se}+\Delta\varepsilon_{ps}\big{)}E_{p}\leq f_{py} \tag{9}\]  
where \(f^{\prime}_{\ c}\) is the compressive strength of the concrete; \(a\) is the Whitney stress block depth; \(b\) is the beam width; \(E_{s}\) is the elastic modulus of the reinforcing bar; \(A^{\prime}_{s}\) and \(A_{s}\) are the areas of the compressive and tensile reinforcements, respectively; \(\varepsilon_{cu}\) is the compressive strain of the concrete extreme fiber at the ultimate state; \(c\) is the neutral axis depth; \(d^{\prime}\) is the distance from the beam top to the centroid of the compression steel; \(f_{y}\) is the yield strength of the tension steel; \(A_{ps}\) is the area of the external steel rod; \(f_{ps}\) is the stress acting on the external rod; \(\alpha\) is the angle of the external rod with respect to the horizontal axis after deformation; \(\varepsilon_{se}\) and \(\Delta\varepsilon_{ps}\) are the initial effective strain and increased strain on the external rod, respectively; and \(E_{p}\) and \(f_{py}\) are the elastic modulus and yield strength, respectively, of the external rod. The values of \(E_{s}\varepsilon_{cu}(c-d^{\prime})/c\) and \(f_{ps}\) are equal to those of \(f_{y}\) and \(f_{py}\), respectively,  
Figure 14: Stress and strain distribution at ultimate state. \[L_{p}=\sqrt{\left(L/2-d_{h}\right)^{2}+\left(d_{p}-d_{v}\right)^{2}} \tag{3}\]0 Curvature distribution at ultimate state. when the compressive reinforcing bar and external rod yield. \(f^{\prime}_{y}\) is the yield strength of the compressive reinforcing bar. Finally, the flexural strength \(M_{n}\) is expressed by Equation (10):  
\[L_{p}=\sqrt{\left(L/2-d_{h}\right)^{2}+\left(d_{p}-d_{v}\right)^{2}} \tag{3}\]1  
Table 5 shows a comparison between the analytical and measured moment capacities for each specimen. A sensitivity analysis was also conducted with respect to the four loading steps: \(\varepsilon_{cu}=0.003\), \(\varepsilon_{cu}=0.005\), \(P_{\max}\), and at the end of the test. No significant difference in the predicted moment capacities was observed. It was recommended in previous studies [40; 46] that an ultimate compression strain of 0.005 be conservatively adopted for the purpose of rehabilitation. In this sensitivity analysis, the predicted moment capacities for \(\varepsilon_{cu}=0.005\) were closer to the measured moment capacities than those for \(\varepsilon_{cu}=0.003\). The average and standard deviations between the predicted and measured capacities were 0.84 and 0.08, respectively, for \(\varepsilon_{cu}=0.005\).
## 2 Materials and Methods  
### CT-Image Contrast Detail Phantom  
The CT phantom (CDP) is manufactured of PMMA with drilled holes whose diameters increase gradually and diagonally from the centre where the diameter (d) = 1 mm to the edge where \"d\" = 12.5 mm following the pattern 1, 2.5, 5, 6, 7.5, 9, 10, 11 and 12.5, respectively, as shown in Figure 1. The variation in diameters of the holes enabled assessing contrast-detection capabilities of the CT scanner at a wide dynamic scale of spatial resolutions. CTDP is a cylindrical phantom of 15 cm radius and 20 cm thickness. CT scans were acquired across the depth of the phantom. The holes were concentrated around the centre of the cylinder across a depth of 4 cm, as shown in Figure 2. These holes were filled with Iodine contrast (milli-Molar (mM) in water with concentrations that generally range from 2-4.1% to resemble different x-ray attenuation conditions and vary according to the CT testing needed. CT machine's ability to precisely image the tissue or object with variable contrast and resolution properties was evaluated through the phantom's CT slice.  
Figure 1: Shows the holes pattern in the Polymethyl Methacrylate (PMMA) CT phantom. The blue circle shows the centre from which rays of holes extend from the centre to the phantom periphery filled with 3.2% iodine concentration. The red oval shows the variation in holes size from 1 mm to 12.5 mm.
## 2 Experiments
### Experimental Results
#### 2.5.1 Coplanar Crack Growth Rate  
When mode III and mode I are mixed, the equivalent SIF range at \(\varphi=0^{\circ}\) in Figure 5 can be expressed as [14]  
\[\Delta K_{\mathrm{s}}=0.5\big{[}\Delta K_{\mathrm{I}}^{2}(1-2\
u)^{2}+4\Delta K _{\mathrm{III}}^{2}\big{]}^{0.5} \tag{5}\]  
where \(\
u\) is the Poisson ratio. When \(\
u=0.3\), it becomes  
\[\Delta K_{\mathrm{s}}=0.5\big{[}0.16\Delta K_{\mathrm{I}}^{2}+4\Delta K_{ \mathrm{III}}^{2}\big{]}^{0.5} \tag{6}\]  
Figure 7 shows the growth rate data obtained for RP, consisting of the RP1, RP2, and RP3 results, with respect to \(\Delta K_{\mathrm{s}}\). The data are observed to be divided into two groups by \(\delta\). When the Paris-type law was applied to the data for the same \(\delta\), the following equations were obtained:  
\[\frac{da}{dN}=C(\Delta K_{\mathrm{s}})^{m} \tag{7}\]  
where \(N\) is the number of cycles. For \(\delta=90^{\circ}\), \(C=2.52\times 10^{-11}\) and \(m=2.93\) and for \(\delta=30^{\circ}\), \(C=1.15\times 10^{-10}\) and \(m=2.26\). The \(R\)-squared value, i.e., the coefficient of determination (\(R^{2}\)), is 0.973 and 0.917. In each case, a good correlation could be obtained even when the \(\Delta K_{\mathrm{III}}/\Delta K_{\mathrm{I}}\) differed.  
Figure 7: Coplanar crack growth rates against \(\Delta K_{\mathrm{s}}\) for RP.  
Next, the growth rate data obtained for RF, comprising of the RF1 and RF2 results, were correlated with respect to \(\Delta K_{\rm s}\), as shown in Figure 8. In this case, the correlation was poor. The growth law was as follows:  
\[\mathrm{d}a/\mathrm{d}N=7.40\times 10^{-10}(\Delta K_{\rm s})^{1.62}\ (R^{2}=0.638) \tag{8}\]  
Although not shown in the figure, when \(\delta\) was constant, the growth rates increased with \(\Delta K_{\rm III}/\Delta K_{\rm I}\). When \(\Delta K_{\rm III}/\Delta K_{\rm I}\) was constant, the growth became faster owing to the increased \(\delta\).  
Figure 9 shows the FCG rate data as a function of \(\Delta K_{\rm s}\) when the \(\Delta K_{\rm III}/\Delta K_{\rm I}\) was 1.0, for all the specimens. WT exhibited the highest growth rate, followed by RP and RF.
## 3 Results and Discussion
### Characterization of AuNPs
#### 3.2.3 Particle Size Analysis  
DLS  
The size distribution and hydrodynamic residue of plant-based AuNPs can be detected by DLS analysis (diffraction light scattering). As shown in Figure 3B, the average size obtained from DLS analysis is about 91.28 nm according to the size distribution graph. Accordingly, the particle size obtained by DLS is larger than the values obtained by TEM and XRD (Table 1). This phenomenon could be explained by the fact that TEM analysis is performed on dry particles, as opposed to DLS (carried out in an aqueous solution), which measures the diameter in the hydrated state (hydrodynamic diameter) [65]. In addition, the diameter obtained by DLS is affected by the non-homogeneous distribution of NPs in the colloidal solution and the coating agent around the NPs surface, which may interfere with size calculation [66,67]. The average size of gold nanoparticles fabricated by aqueous extract of _Benincasa hispida_ was close to 70 nm when measured by DLS analysis, whereas a much smaller size was obtained by TEM analysis (i.e., 22.2 nm) [41]. The average particle size of gold nanorods synthesized by biomass filtrate of _Nostoc ellipsosporum_ was 436.6 nm and 173 nm when obtained by DLS and TEM analyses, respectively [53]. Despite the \"increase\" in relative particle size of AuNPs in their hydrated state, the particles maintained their nanometric order.  
Moreover, the polydispersity index (PDI) can be calculated using DLS data. A suspension can be considered homogeneous when the PDI (ranging between 0 and 1) remains below 0.4; on the other hand, when PDI exceeds 1, the distribution is fully heterogeneous [68]. Herein, the PDI value of green synthesized AuNPs in the colloidal solution was 0.3, meaning that dispersion was homogeneous.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Size Measurement By:** & **Average Size** \\ \hline TEM & 15.11 \(\pm\) 8.5 nm \\ XRD & 18 nm \\ DLS & 91.28 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average size of green synthesized AuNPs calculated by TEM, XRD, and DLS.  
Figure 3: Characterization of AuNPs. (**A**) XRD analysis; (**B**) DLS measurements; (**C**) zeta potential analysis.
## 4 System Modelling
### Road Reservation Matrix  
Let \(\Omega\) be a given set of road segments (5), and \(T\) a given set of successive time slots (6), the process of reservation of \(\Omega\) during \(T\) is managed using road reservation matrix, denoted as \(M_{\Omega,T}\), which contains the weights of all the road segments of \(\Omega\), during all the time slots in \(T\), since the weights change dynamically based on the road demands, \(M_{\Omega,T}\) is managed and updated by the local DIFTOS server that is responsible for the region that incorporates \(\Omega\).  
\[\Omega=\{r_{0},r_{1},r_{2},\ldots,r_{N-1}\} \tag{5}\]\[T=\left\{t_{0},t_{1},t_{2},\ldots,t_{M-1}\right\} \tag{6}\]  
\[M_{\Omega,T}=\left(w_{r,t}\right)_{m\times n} \tag{7}\]  
where \(w_{r,t}\) is the weight of the road segment \(r_{i,j}\) during time slot \(t\), as defined in (3).
## 2 Materials and Methods
### Flux Tower Measurements
#### 2.1.2 Flux Tower Data Processing  
At the site described above, an EC system was installed on one of the towers (40.0167\({}^{\circ}\)N, 116.3833\({}^{\circ}\)E) to measure the carbon dioxide (CO\({}_{2}\)) exchange between the land surface and the atmosphere from September 2014 to April 2017. The EC system comprises a three-dimensional (3D) sonic anemometer (CSAT3, Campbell Scientific, USA) that measured 3D wind velocity, two temperature and relative humidity probes (HMP155A-L15) that collected the temperature and relative humidity, and a closed-path gas analyzer (Models 911-0010, Los Gatos, USA) for monitoring of the H\({}_{2}\)O/CO\({}_{2}\)/CH\({}_{4}\) density, etc. The installation height of the EC sensor was 21.6 m. The EC measurements were sampled at a frequency of 10 Hertz, and the turbulence data were stored using data loggers (CR3000, Campbell Scientific, USA) and processed every 30 minutes. A mini weather station was also installed beside the tower to record data on the air temperature, precipitation, radiation, wind speed/direction, and relative humidity every second.  
The tower carbon flux data from January 2015 to December 2016 (a total of 2 years) were used in this study. EddyPro software ([[https://www.licor.com/env/products/eddy_covariance/eddypro.html](https://www.licor.com/env/products/eddy_covariance/eddypro.html)]([https://www.licor.com/env/products/eddy_covariance/eddypro.html](https://www.licor.com/env/products/eddy_covariance/eddypro.html))) was used to correct the raw EC data conventionally, including coordinate rotation, Webb-Pearman-Leuning (WPL) correction, and frequency response calibration, etc. In addition, the half-hourly flux data were u* (friction wind speed) filtered and storage calculated by the u* threshold, gap-filled, and flux partitioned using the REddyProWeb online processing tool ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWeb)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWeb))). The ReddyProc tool used the nighttime net ecosystem exchange (NEE\({}_{\text{night}}\)) as nighttime respiration, and the model of NEE\({}_{\text{night}}\) and air temperature at nighttime was determined using nonlinear optimization. After that, daytime respiration (R\({}_{\text{d}}\)) was estimated using the daytime temperature. Finally, GPP was calculated as the difference between the daytime NEE and R\({}_{\text{d}}\). The GPP in the output data files derived from the ReddyProc tool included the original and gap-filled results. The percentage of high quality gap-filled GPP data in the calibrated and validated years was 44% and 54%, respectively. Therefore, the original data and the most reliable 44% gap-filled GPP data in the calibrated year were used for the parameter estimations of LUE-based and SIF-based models. All the gap-filled GPP data in the validated year (including 54% of the most reliable data) were used for model validation in this study.
## 3 Attitude Controller Design
### Experiment Results and Discussion  
Attitude control is essential for under actuated rigid body vehicles, such as airplanes, surface vessels, helicopters, and multi-rotor aerial vehicles. Attitude control is often designed as the inner loop of path or trajectory control, named as hierarchical control. \(\eta_{2d}\) is the output of the higher layer controller. As a fundamental function of attitude control, \(\eta_{2d}=[0,0,0]^{\top}\) is an essential state that needs to be stabilized under the influence of disturbance, such as hydrodynamic moments generated by constant fluent or turbulence, and collision.
## 1 Introduction
The system detects a feature point on a dynamic object using an enhanced lightweight YOLOv5-based method [22]. After that, position is estimated using the feature points that were kept, and a dense point cloud map is produced to satisfy navigation and path planning requirements [23]. The experimental findings demonstrate the good real-time performance and ability of the algorithm presented in this research to increase the system accuracy in dynamic circumstances [24]. The rest of this paper is organized as follows. The general architecture of the SLAM system used in this work is described in Section 2, along with the guiding ideas and purposes of each module. The elements of the target detection network and the improvement techniques are covered in detail in Section 3. Section 4 provides a description and analysis of the comparative experiments that were conducted to evaluate the system's performance. In Section 5, the research of this paper is discussed. Finally, in Section 6, the work is summarized, along with an outlook for future work.