esa-sceva/satcom-chunk-collection huggingface dataset:
# The _E. coli_ Global Regulator DksA Reduces Transcription during T4 Infection
## 3 Results  
### T4 Infections in the Absence of ppGrp or DKsA Produce Larger Plaques  
Plaque size can be used as an indirect measure of phage fitness since larger plaques can arise from better phage growth. T4 wt infections of different _E. coli_ wt strains typically yield plaques with similar sizes. For example, using two different _E. coli_ B strains, B606 wt or BL21(DE3) wt, we observed similar T4 wt plaques with minimal heterogeneity (average size of \(\sim\)96 pixels\({}^{2}\) and \(\sim\)81 pixels\({}^{2}\), respectively) (Figure S2a). In contrast, T4 wt infections of either B606 \(\Delta\)_dksA_ (Figure 1a) or B606 ppGpp\({}^{0}\) (Figure S2b) produced broad populations with both larger plaques and increased heterogeneity (average size of \(\sim\)147 pixels\({}^{2}\) and \(\sim\)144 pixels\({}^{2}\), respectively). An amber mutation in the T4 gene \(\it{motA}\) (T4\(\it{motA}\)\({}^{am}\)) is known to significantly impair T4 infection due to a dramatic decrease in middle gene expression and delayed replication ([25; 42]; reviewed in [23]). As expected, a T4\(\it{motA}\)\({}^{am}\) infection produced significantly smaller plaques than a T4 wt infection of the same strain (Figure 1b vs. Figure 1a). Surprisingly, larger T4\(\it{motA}\)\({}^{am}\) plaques were observed with either B606 \(\Delta\)_dksA_ (Figure 1b) or B606 ppGpp\({}^{0}\) (Figure S2c) (average size of \(\sim\)65 pixels\({}^{2}\) and \(\sim\)39 pixels\({}^{2}\), respectively) compared to B606 wt (average size of \(\sim\)7 pixels\({}^{2}\)).  
Although B606 \(\Delta\)_dksA_ grows more slowly than wt as cells enter stationary phase, we did not observe a marked difference in growth among wt, B606 \(\Delta\)_dksA_ and B606 ppGpp\({}^{0}\) during early to mid-log phase, when T4 infects (Figure S3). Consequently, we concluded that the differences in T4 plaque sizes did not arise from significantly different growth rates for the host strains.  
Figure 1: T4 infections in the absence of DKsA produce larger plaques. Density plots of plaque size for B606 wild type (wt; purple) and B606 \(\Delta\)_dksA_ (blue) infected with either T4 wt (**a**) or T4\(\it{motA}\)\({}^{am}\) (**b**). Plots shown represent one of three biological replicates.
###### Abstract  
The acoustic black hole (ABH) structures have the potential to achieve structural vibration suppression and noise reduction through the effect of the ABH on the concentration and manipulation of flexural waves. In this paper, a new solution is proposed to embed 2-D ABHs on the support plate to suppress the transmission of compressor vibration to the refrigerator body. The vibration and acoustic measurement experiment of the compressor, the support plate and the refrigerator body, and the coherence analysis of the vibration signals and acoustic signal are carried out to determine the influence of the compressor vibration on the vibration of the refrigerator body and the radiation sound of the back wall. The concentration and manipulation effects of 2-D ABH on flexural waves are verified by numerical simulation of flexural wave propagation in the time domain. FEM models of the original support plate and the damping ABH support plate are established to investigate the comprehensive effect of the 2-D ABHs and the damping layers on the vibration characteristics of the support plate through vibration modal and dynamic response analysis. Numerical simulation results show that the 2-D damping ABHs can suppress the vibrations generated by the compressor at specific frequencies in the middle and high-frequency bands from being transmitted to the refrigerator body through the support plate.  
acoustic black hole; vibration suppression; flexural wave; support plate; refrigerator compressor Article
###### Abstract  
The adsorption of Cu\({}^{2+}\) ions from an aqueous solution using AgNPs synthesized from _Convolvolvulus arrensis_ leaf extract was investigated. The characterization of AgNPs was investigated before and after the adsorption of Cu\({}^{2+}\) ions via Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM) analyses. The adsorbent contained various functional groups in addition to the AgNPs, which contributed to the Cu\({}^{2+}\) ions adsorption. The silver nanoparticle surface consisted of spherical particles and deep pores, which adsorbed numerous Cu\({}^{2+}\) ions. The influences of dosage, pH, and contact time on adsorption of 10 and 50 mg/L Cu\({}^{2+}\) at 298 K, and initial Cu\({}^{2+}\) concentrations at 298 and 323 K were studied. It was found that the highest percentage of Cu\({}^{2+}\) ions adsorbed from an aqueous solution was 98.99%; the aqueous solution had 10 mg/L of Cu\({}^{2+}\) ions and 0.2 g of AgNPs, at pH 12 and 298 K. A pseudo-second kinetics model offered the most accurate description of the process of adsorption. The process of Cu\({}^{2+}\) adsorption more resembled a Langmuir rather than a Freundlich isotherm model, including chemical and physical mixed adsorption (mixed adsorption) processes, and was exothermic and spontaneous.  
AgNPs; Cu\({}^{2+}\); green synthesis; adsorption; nanoparticles +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Sensory Evaluation  
#### 3.3.1 Electronic Nose  
According to Figure 5, the response values of the two samples in W1C, W3S, W3C, W6S, and W5C were very small (less than 5), indicating that the corresponding odors of the four sensors were very light. Sensors W1W, W2W, W2S, and W5S showed a relatively strong response value, indicating  
Figure 4: FTIR spectra of the microcapsules and their materials.  
Figure 3: Particle size of the spray-dried microcapsules.  
that the similar odors of nitrogen oxides, sulfides, and alcohols corresponding to those sensors were relatively strong in RJSR [35]. However, the response value of microcapsules (W1W 12.88 \(\pm\) 0.43, W2W 9.72 \(\pm\) 0.31, W2S 2.71 \(\pm\) 0.02, and W5S 3.54 \(\pm\) 0.22) was significantly smaller than that of the mixture (W1W 36.76 \(\pm\) 0.75, W2W 36.35 \(\pm\) 0.64, W2S 17.43 \(\pm\) 0.92, and W5S 17.43 \(\pm\) 0.21); thus, microencapsulation was able to mask these odors well. This was also demonstrated in LA (Figure 6a). The distribution of sensors W1C, W3S, W3C, W6S, and W5C was close to the coordinate zero point, indicating that their contribution to PC1 and PC2 was very small. On the contrary, W1W and W2W greatly contributed to LA1 (contribution \(>\) 0.5), while W1S and W2S greatly contributed to LA2 (contribution \(>\) 0.4). The contribution variances of PC1 and PC2 for direct electronic nose measurement (Figure 6b) were 97.2 and 2.6%, adding up to 99.8%. This means that the first two PCs were sufficient to explain the total variance of the datasets. In addition, as shown in Figure 6b, there was no overlap between the Mix. and the microcapsules. This selective microencapsulation phenomenon was also observed by [PERSON] et al. [10] and [PERSON] et al. [36], demonstrating that these two samples had differences in odor. The above results demonstrate that the odor of RJSR was successfully lightened.  
Figure 5: Radar image of the electronic nose results.  
Figure 6: Loading analysis (LA) and principal component analysis (PCA) results for the electronic nose: (**a**) LA and (**b**) PCA.
## 3 Results
### Results of Multiple Regression Model to Estimate VO2 max in Three Submaximal Exercise Tests  
Table 3 shows the results of multiple regression analysis to estimate the VO2 max. As a result of calculating the VO2 max regression model for the treadmill test, YMCA step test, and PACER test, the multiple correlation coefficients were 0.762, 0.770, and 0.847, and the explanatory power of the model was 58.0%, 59.2%, and 71.7%, respectively. The standard errors of the estimation were 4.545, 4.478, and 3.732 (mL/kg/min), respectively. Table 4 shows the formula for estimating the results of the VO2 max treadmill test, YMCA step test, and PACER test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Male (287)** & **Female (254)** & **Total (541)** \\ \hline Maximal GXT & & & \\ VO2 max (mL/kg/min) & 42.63 \(\pm\) 6.29 & 34.90 \(\pm\) 5.23 & 39.01 \(\pm\) 6.98 \\ Stable HR (bpm/min) & 73.44 \(\pm\) 10.10 & 77.22 \(\pm\) 8.88 & 75.21 \(\pm\) 9.73 \\ Maximal RE & 1.23 \(\pm\) 0.08 & 1.24 \(\pm\) 0.09 & 1.23 \(\pm\) 0.08 \\ Maximal HR (bpm/min) & 180.96 \(\pm\) 13.15 & 176.32 \(\pm\) 13.08 & 178.78 \(\pm\) 13.31 \\ Treadmill test & & & \\ Stage 3 HR (bpm/min) & 157.18 \(\pm\) 13.55 & 169.91 \(\pm\) 11.96 & 163.16 \(\pm\) 14.30 \\ YMCA Step test & & & \\ RHR (bpm/min) & 99.64 \(\pm\) 16.24 & 107.46 \(\pm\) 15.03 & 103.29 \(\pm\) 16.14 \\ PACER test & & & \\ Countries (\# laps) & 50.65 \(\pm\) 18.42 & 28.78 \(\pm\) 11.92 & 40.38 \(\pm\) 19.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximal GXT, treadmill test, YMCA step test, and PACER test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Treadmill Test** & **YMCA Step Test** & **PACER Test** \\ \hline Intercept & 68.828 & 60.400 & 43.418 \\ Sex & 8.428 & 9.138 & 5.114 \\ Age (years) & \(-\)0.249 & \(-\)0.229 & \(-\)0.094 \\ Height (cm) & 0.071 & 0.045 & \(-\)0.022 \\ Weight (kg) & \(-\)0.208 & \(-\)0.182 & \(-\)0.135 \\ Stage 3 HR (beat/min) & \(-\)0.137 & - & - \\ RHR (bpm/min) & - & \(-\)0.120 & - \\ Count (reps) & - & - & 0.229 \\ R & 0.762 & 0.770 & 0.847 \\ R2 & 0.580 & 0.592 & 0.717 \\ SEE & 4.545 & 4.478 & 3.732 \\ F & 147.801 & 155.466 & 270.900 \\ P & 0.000 & 0.000 & 0.000 \\ Durbin-Watson & 1.903 & 1.900 & 1.819 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple regression model to estimate VO2 max using the treadmill test, YMCA step test, and PACER test.
# A New Observation in Spiral and Curl Antenna Configurations above the Ground Plane
## 4 Conclusions  
We studied spiral and curl antennas at an antenna height \(h\) above the ground plane. First, a two-arm spiral antenna was analyzed using the moment method. It was found that the antenna at \(h<0.25\lambda_{0}\) radiates a CP wave with the help of the straight part of proper length. The spiral configuration parameters at \(h=0.15\lambda_{0}\) were found to be close to those of the curl antenna at the same height. Subsequently, a curl antenna was analyzed at \(h<0.15\lambda_{0}\). It was found that the configuration parameters optimized for CP radiation vary smoothly over the entire height range of the curl and spiral antennas, implying that the two antennas are essentially the same. It is emphasized that the curl antenna at \(h=0.10\lambda_{0}\) can be fabricated, but the spiral at the same height cannot be due to a minimal distance required between adjacent arms.  
Conceptualization, K.H.; software, K.H.; validation, M.H.; investigation, M.H.; resources, K.H.; data curation, M.H.; writing--original draft preparation, K.H.; writing--review and editing, K.H.; visualization, K.H.; supervision, K.H. and H.N.; project administration, K.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The numerical and experimental data used to support the findings of this study are included in this article.  
The authors declare that there are no conflict of interest regarding the publication of this article.
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 2 Materials and Methods
### Evaluation of Anti-Aging Performance  
#### 2.4.1 Aging Procedures  
The samples used for the ultraviolet aging were first aged by TFOT (Cangzhou, Hebei, China). The mass of TFOT-aged samples were 50 \(\pm\) 0.5 g, the diameter of the sample plate was 140 mm, the asphalt film thickness was approximately 3.2 mm, and the test temperature was stable at 163 \(\pm\) 0.5 \({}^{\circ}\)C for 5 h. Then, the UV aging simulation test was performed with a straight-pipe high-pressure mercury lamp, the UV radiation intensity was 2000 uv/cm\({}^{2}\), the UV wavelength was 365 nm, and the aging times were set up to three, six, and nine days. The sample mass of UV aging was 20 \(\pm\) 0.1 g and the test temperature was stable at 50 \(\pm\) 0.5 \({}^{\circ}\)C.
# Beyond Human Vision: Unlocking the Potential of Augmented Reality for Spectral Imaging
###### Abstract  
Spectral imaging is a broad term that refers to the use of a spectroscopy technique to analyze sample surfaces, collecting and representing spatially referenced signals. Depending on the technique utilized, it allows the user to reveal features and properties of objects that are invisible to the human eye, such as chemical or molecular composition. However, the interpretability and interaction with the results are often limited to screen visualization of two-dimensional representations. To surpass such limitations, augmented reality emerges as a promising technology, assisted by recent developments in the integration of spectral imaging datasets onto three-dimensional models. Building on this context, this work explores the integration of spectral imaging with augmented reality, aiming to create an immersive toolset to increase the interpretability and interactivity of the results of spectral imaging analysis. The procedure follows a two-step approach, starting from the integration of spectral maps onto a three-dimensional models, and proceeding with the development of an interactive interface to allow immersive visualization and interaction with the results. The approach and tool developed present the opportunity for a user-centric extension of reality, enabling more intuitive and comprehensive analyses with the potential to drive advancements in various research domains.  
augmented reality; spectral imaging; interactive visualization; photogrammetry; 3D Model Integration; spectral analysis Article
## 3 Results
### Sample-Predictive Optimum Controller  
The angular position and velocity were sampled at a rate simulating delays in processing and commanding onboard a real body, and the Sampled Predictive Controller performed very well. Selecting a time delay of 0.05 s with no noise, the controller performed very close to optimum levels, and performed modestly well (at least as good as our PD controller to be discussed shortly) in the presence of noise. Monte Carlo analysis revealed the same vulnerability to random conditions seen with the Continuously Updated Controller (particularly susceptible to random angular velocity conditions). The delay time was reduced to 0.025 s to regain a nominal performance. However, its performance mimics the continuously updated case so that we could consider real-world implementation of this controller, if the controller's application can accept the high angular velocity error and deviation.
_Applied_  
_sciences_  
A Multi-Layer Perceptron Network for Perfusion Parameter Estimation in DCE-MRI Studies of the Healthy Kidney  
**[PERSON] 1, [PERSON] 1,*\({}^{\text{\textregistered}}\)**\({}^{\text{\textregistered}}\), [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 2 and**  
**[PERSON] 2,3,4**  
1 Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland;  
[EMAIL_ADDRESS] (A.K.); [PERSON] (M.K.)  
2 Department of Health and Functioning, Western Norway University of Applied Sciences, 5063 Bergen, Norway; [PERSON].); [PERSON](A.L.)  
3 Department of Biomedicine, University of Bergen, 5009 Bergen, Norway  
4 Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5021 Bergen, Norway  
* Correspondence: [EMAIL_ADDRESS]  
Received: 28 June 2020; Accepted: 5 August 2020; Published: 10 August 2020  
**Abstract:** Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging technique which helps in visualizing and quantifying perfusion--one of the most important indicators of an organ's state. This paper focuses on perfusion and filtration in the kidney, whose performance directly influences versatile functions of the body. In clinical practice, kidney function is assessed by measuring glomerular filtration rate (GFR). Estimating GFR based on DCE-MRI data requires the application of an organ-specific pharmacokinetic (PK) model. However, determination of the model parameters, and thus the characterization of GFR, is sensitive to determination of the arterial input function (AIF) and the initial choice of parameter values. Methods: This paper proposes a multi-layer perceptron network for PK model parameter determination, in order to overcome the limitations of the traditional model's optimization techniques based on non-linear least-squares curve-fitting. As a reference method, we applied the trust-region reflective algorithm to numerically optimize the model. The effectiveness of the proposed approach was tested for 20 data sets, collected for 10 healthy volunteers whose image-derived GFR scores were compared with ground-truth blood test values. Results: The achieved mean difference between the image-derived and ground-truth GFR values was 2.35 mL/min/1.73 m\({}^{2}\), which is comparable to the result obtained for the reference estimation method (\(-\)5.80 mL/min/1.73 m\({}^{2}\)). Conclusions: Neural networks are a feasible alternative to the least-squares curve-fitting algorithm, ensuring agreement with ground-truth measurements at a comparable level. The advantages of using a neural network are twofold. Firstly, it can estimate a GFR value without the need to determine the AIF for each individual patient. Secondly, a reliable estimate can be obtained, without the need to manually set up either the initial parameter values or the constraints thereof.  
**Keywords:** dynamic contrast-enhanced MRI; kidney perfusion; glomerular filtration rate; pharmacokinetic modeling; multi-layer perceptron; parameter estimation
###### Abstract  
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.  
C-MAPSS; dynamic forgetting factor; ELM; OS-ELM; remaining useful life; stacked ELM; temporal difference +
Footnote †: journal: Applied Sciences
# Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks
## 5 Conclusions  
This article addressed the essential issue of side lobe interference in 5G sub-6 GHz networks using a unique collaborative beamforming strategy based on DQN reinforcement learning. Maintaining high-quality service becomes more dependent on efficient interference control as network densification rises to meet increasing bandwidth demands. In order to improve learning stability, our suggested method integrates experience replay memory and LSTM networks for time-series prediction into a two-antenna system with DQN-controlled phase shifters. With many base stations servicing randomly dispersed customers in an OFDM cellular network environment, this strategy proved to be much  
Figure 8: Served power comparison at each step.  
Figure 7: Interference power vs. successful step.  
better than previous approaches. Improved average sum-rate capacity (3.90), decreased average interference power (5.42 dB), higher SINR performance (5.15-29.18 dB range), and lower served power levels (average 15.73 dB) are among the major accomplishments. These findings demonstrate that, by reducing interference while preserving signal quality, our strategy may greatly improve 5G network performance, beating both conventional DQN and Q-Table techniques. Our method's ability to adapt to changing network circumstances shows promise for increasing spectrum efficiency in dense deployments. Future research might examine issues with practical implementation, scalability in bigger networks, and application to mmWave frequencies. With its more effective and flexible beamforming techniques for 5G wireless communications, this study offers a viable avenue for 5G and beyond network optimization.  
A.F.Y.M.: Conceptualization, methodology, formal analysis, validation, writing original draft; S.M.S.: Conceptualization, writing original draft; S.P.: Formal analysis, validation, visualization, review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Author Mr [PERSON] was employed by Innovative Skills LTD. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 The Proposed Combined Method and Principles
### Principle of STL  
STL is a time series decomposition algorithm based on local weighted regression. In the proposed combined method, STL is used to decompose load data into three components: a seasonal component, a trend component, and a residual component. The relationship between each component and the original load data is shown in formula (1).  
\[Y_{t}=S_{t}+T_{t}+R_{t} \tag{1}\]  
where \(Y_{t}\) represents the value of the original load data at time \(t\); \(S_{t}\) represents the value of the seasonal component at time \(t\); \(T_{t}\) represents the value of the trend component at time \(t\); \(R_{t}\) represents the value of the residual component at time \(t\).  
STL uses locally estimated scatterplot smoothing (Loess) to extract smooth estimates for three components; it consists of two recursive processes, the inner loop and the outer loop, and has good robustness. The inner loop, based on Loess, smooths the seasonal and trend components, while the outer loop computes robust weights based on the residual component to reduce the influence of time series outliers on the residuals [16]. The calculation formula for robust weights is shown in formulas (2) and (3).  
\[\rho_{t}=B\bigg{(}\frac{\mid R_{t}\mid}{6\text{median}(\mid R_{t}\mid)}\bigg{)} \tag{2}\]  
\[B(a)=\left\{\begin{array}{cl}\left(1-a^{2}\right)^{2}&\text{for }0\leqslant a<1 \\ 0&\text{for }a\geq 1\end{array}\right. \tag{3}\]  
where \(\rho_{t}\) represents the robust weight at time \(t\); median (\(\cdot\)) denotes the median function; \(B(a)\) denotes the Bisquare function; \(a\) represents the independent variable of the Bisquare function.  
In the next inner loop, the neighborhood weights in Loess will be updated based on the robust weights, following the update method shown in formula (4).  
\[v_{t,k+1}=v_{t,k}\rho_{t} \tag{4}\]  
where \(v_{t,k}\) represents the neighborhood weight at time \(t\) in the k-th inner loop.  
The flowchart of STL is shown in Figure 2.  
Figure 1: Flowchart of the proposed combined method.