esa-sceva/satcom-chunk-collection huggingface dataset:
# IR and Raman Dual Modality Markers Differentiate among Three _bis_-Phenols: BPA, BPS, and BPF  
[PERSON]  
1 USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Avenue, Beltsville, MD 20705, USA; [EMAIL_ADDRESS] (W.S.); [EMAIL_ADDRESS] (J.Q.); [EMAIL_ADDRESS] (M.K.)1 USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Avenue, Beltsville, MD 20705, USA; [EMAIL_ADDRESS] (W.S.); [EMAIL_ADDRESS] (J.Q.); moon.  
and accurate screening method is needed to detect these chemical additives in plastic and plastic products when present at levels that may not require extraction or pre-concentration.  
Spectroscopy is an efficient, sensitive, and highly effective technique for in situ and in-line product screening for chemical and biophysical markers of changes to products. Recent developments and applications of vibrational spectroscopic technologies to authentication issues in agricultural commodities and foods include utilizing (1) Fourier Transform (FT) infrared (IR) and Raman to detect botanical additives and chemical contaminants in food powders [11; 12]; (2) FT-near-infrared (NIR) to detect pork adulteration in real products [13]; (3) FT-Raman to detect whey added to milk powder [14]; (4) NIR to authenticate honey [15]; and (5) FT-IR spectroscopy combined with chemometrics to authenticate functional food oils [16; 17].  
Both IR and Raman provide unique spectral fingerprints of a chemical. IR and Raman spectral data are routinely reported as complementary, except that distinguishing which spectral lines in either provide the most spectral information on identity is most often unknown. Since they are structural analogs, all three plasticizers (BPA, BPS, and BPF) will have vibrational modes which would be redundant and perhaps indistinguishable from each other. Collecting both IR and Raman spectra signals can provide both fingerprints and a complete cross-comparison of relevant vibrational modes. The objectives of this study are to achieve the following:  
1. Identify spectral wavenumbers in which IR and Raman signals for the same vibrational mode are most different in normalized relative intensity.
2. Use these dual-modality data as a marker to determine the most sensitive signal ratio which is specific to BPA, BPS, and BPF.
3. Assign these wavenumbers to vibrational modes characteristic of individual compounds.
## 6 Performance Evaluation
### Performance Analysis of CL-APCC
#### 6.1.3 The average energy consumption of each data packet  
The average energy consumption (AEC) of data packets received by BS reflects the length of the network's lifetime. The routing protocol is set as AODV and the number of nodes is 100. We respectively obtain the statistical data by random sampling nodes for the network with CL-APCC and without CL-APCC. AEC is calculated as Equation (37):  
\[E_{{}_{T_{\text{x}}}}\left({L_{Data}}\right)=E_{{}_{\mathit{elce}}}\times{L_{ Data}}+\varepsilon_{{}_{\mathit{amp}}}\times{L_{Data}}\times{d^{\,2}}\]  
\[E_{{}_{Rx}}\left({L_{Data}}\right)=E_{{}_{\mathit{elce}}}\times{L_{Data}}\]  
\[n_{{}_{\mathit{average\_hop}}}=\frac{\sum\limits_{i=1}^{D_{\mathit{total}}}{n_ {{}_{i\_hop}}}}{D_{\mathit{total}}} \tag{37}\]  
\[E_{{}_{\mathit{average}}}=E_{{}_{T_{\text{x}}}}\left({L_{Data}}\right)\times n _{{}_{\mathit{average\_hop}}}+E_{{}_{Rx}}\left({L_{Data}}\right)\times\left({n _{{}_{\mathit{average\_hop}}}}-1\right)\]  
Figure 12: The average queue length of node in the network.  
The parameters in the Equation (37) are described as follows. \(E_{Tx}\)(\(L_{Data}\)) is the sending energy consumption of data packets, \(E_{Rx}\)(\(L_{Data}\)) is the receiving energy consumption of data packets, \(n_{average\_hop}\) is the average hops of data packets, and \(E_{average}\) is the average energy consumption.  
Figure 13 shows that the average energy consumption of each data packets received by BS.
## 4 Performance Evaluation
### Communication Overhead  
The exchanged authentication messages size (communication overhead) is an important parameter in the evaluation of performances; the communication overhead is defined as the sum of message lengths (in bits) transmitted and received by a device in the authentication process. Here, the protocol uses six messages to achieve the authentication. Communication overhead is calculated as follows:  
M1 (128 bits) + M4 (128 bits) + M5 (384 bits) + M6 (384 bits) + M7 (576 bits) + M8 (240 bits)  
Communication overheads of different protocols are given in Table 2. The proposed protocol presents a limited overhead storage and limited communication cost comparatively to other protocols as illustrated by the graph in Figure 10.  
Figure 9: Authentication phase.
## 3 Results
### Numerical Simulation Verification by Experiments for Two to Three Tested DN 150 UFMs  
Different from the large diameter of the UFM, the UFMs of DN150 adopt a more conservative experimental working condition, under which only one and two tandem arrangement is set up for verification experiment. For two UFMs in tandem arrangement, the relative measurement errors \(E\) are plotted in Figure 7 as a function of flowrate \(Q\). Similar to 4.1, both the experimental data (distributed symbols) and numerical results (continuous lines), as shown in Figure 7, expressed as an obvious negative power-law relationship. Also, we can note that the increase in \(n\) can induce an overall shift in the \(E\)-\(Q\) curve compared with DN250.  
Figure 6: Relative measurement error \(E\) as a function of flowrate \(Q\) for four to five tested flowmeters: (**a**) four tested UFMs; (**b**) five tested UFMs.  
Compared to different diameter and inlet velocity, Re can be calculated using \(\mathrm{Re}=4\rho Q/\
u\pi D\), where \(D\) is the pipe or UFMs internal diameter, v is the kinematic viscosity of the fluid, and \(Q\) is the inlet volume flow. Although the density and the kinematic viscosity of natural gas change at different times, they can be considered to be constant on the same bypass pipeline. Here, comparing to three tandem arrangement of DN250, the Re of DN150 comes to 1.67 times of that at the same inlet volume flow rate in the theory. Thus, in the previous overall shift theory, three tandem arrangement of DN150 are more likely to get terrible \(E\)-\(Q\) curve, which we can get proof from Figure 7. Simulation result shows that the overall shift offset of the \(E\)-\(Q\) curve is larger than DN250. This suggests that as the gas flows through smaller diameter UFMs, the flow distortion becomes more sensitive when \(n\) increases. However, it is still within the allowable range of relative error. Thus, three tandem arrangement of DN150 is numerically proved to be valid.
## 3 Results
### Associations with Clinical and Radiological Variables  
Statistical analysis revealed that the patients with clinically significant PCa were significantly older (65.08 (SD = 7.24) vs. 62.82 (SD = 7.4) years, \(p\) = 0.007) and harbored significantly lower prostate volume (44.25 (SD = 17.83) vs. 54.27 (SD = 24.89), mL, \(p\) < 0.001) as compared to cases with the clinically insignificant disease. No significant difference was identified in PSA, PSA density level, and target lesion size between targeted and systematic biopsy groups (all \(p\) > 0.05).  
Most cases with clinically significant disease 54.64% (53/97) were detected by the first targeted core, while the second and third targeted samples detected an extra 16.49% (16/97)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**PI-RADS-3**} & \multicolumn{2}{c}{**PI-RADS-4**} & \multicolumn{2}{c}{**PI-RADS-5**} & \multicolumn{2}{c}{**Total, \(n\) = 283**} \\ \cline{3-10}  & & \(n\) & \% & \(n\) & \% & \(n\) & \% & \(n\) & \% \\ \cline{3-10}  & No PCa & 7 & 63.63 & 57 & 30.00 & 8 & 9.76 & 72 & 25.44 \\ GG score after targeted & GG1 & 4 & 36.37 & 80 & 42.10 & 30 & 36.59 & 114 & 40.28 \\ prostate biopsy & GG2 & 0 & 0.0 & 35 & 18.42 & 27 & 32.92 & 62 & 21.90 \\ GG3 & 0 & 0.0 & 16 & 8.42 & 12 & 14.63 & 28 & 9.89 \\ GG4 & 0 & 0.0 & 2 & 1.05 & 5 & 6.10 & 7 & 2.47 \\ \hline Total & & 11 & 100.00 & 190 & 100.00 & 82 & 100.00 & 283 & 100.00 \\ \hline \multicolumn{10}{c}{Chi-Square Tests, the value of \(p\) < 0.001} \\ \hline \multicolumn{10}{c}{PI-RADS v2.1—prostate imaging reporting and data system version 2.1} \\ \hline \end{tabular}
\end{table}
Table 2: Different prostate cancer grade group detection rates according to PI-RADS scores. GG—prostate cancer grade group.  
Figure 3: Sensitivity and specificity of targeted and systematic biopsy in diagnosing ISUP \(\geq\)2 prostate cancer by (**a**) mpMRI/US-targeted prostate biopsy; (**b**) Adapted systematic prostate biopsy. AUC—Area Under the Curve.  
and 28.87% (28/97) of cases, respectively. For 78.95% of patients with additional biopsies, the regions of interest (ROI) were greater than 0.5 cm\({}^{3}\) or 8 mm in diameter. According to mpMRI, the most common ROIs were in the peripheral zone of the prostate (66.10%), followed by the transition zone (27.51%) and fibromuscular stroma (6.39%). There were no statistically significant differences between clinically relevant and non-significant PCa detection rates using target and AdSB, depending on lesion location on mpMRI.
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## 1 Introduction
### Motivation  
Machine learning has a wide application. Feed forward back propagation neural network is one of the popular machine learning approaches [19]. It has the ability to overcome the constraints of the existing mathematical models and helps in problem-solving based on the complementary nature of both modelling and practices.  
In addition, machine learning has played an important role in water-related engineering applications, such as reservoir operation [20; 21], water management [22; 23], turbine operation [24; 25], water distribution network [26; 27], sediment settling velocity prediction [28], intelligence hydrological model [29; 30], rheological prediction model [31; 32] etc. Therefore, it is believed that such a method is also applicable for the study of motion characteristics of the single bubbles.  
By formulating machine learning models that are able to predict the final velocity, drag coefficient, and shape of single bubbles with an acceptable level of accuracy, it may beneficial to the researchers, engineers, and hydrologists while dealing with the issues related to bubbles.
## 3 Proposed Stereoscopic Image SR Method with View Incorporation and CNN
### Global Reconstruction Constraint to Incorporate the Right View  
For a given LR right view, \(\mathbf{Y}\), there may be many HR right views \(\mathbf{X}\) owing to the extremely ill posed character of SR. We consider that the reconstructed HR right view \(\mathbf{X}\) should be retained with the LR right view \(\mathbf{Y}\) in terms of the MR stereoscopic image observation model. However, the estimated right view image \(\mathbf{X}_{1}\) obtained in the first stage may not satisfy this condition. Therefore, the global reconstruction constraint is enforced for incorporating the right view by projecting \(\mathbf{X}_{1}\) onto the solution space of \(\mathbf{Y}=\mathbf{DBX}\). It is computed as:  
\[X^{*}=\underset{X}{\operatorname{argmin}}\|DBX-Y\|_{2}^{2}+\|X-X_{1}\|_{2}^{2} \tag{11}\]Thus, the solution can be computed by iterative back projection. The updated equation is:  
\[X_{t+1}=X_{t}+\
u\Big{[}B^{T}D^{T}\big{(}Y-DBX_{t}\big{)}+(X-X_{1})\Big{]} \tag{12}\]  
where \(X_{t}\) denotes the reconstructed right view image after the \((t-1)\)-th iteration and \(\
u\) denotes the step size with the value of one.  
We use \(\mathbf{X}^{*}\) from the aforementioned optimization as the ultimately reconstructed right view image. On one hand, image \(\mathbf{X}^{*}\) is as close as possible to the estimated right view image \(\mathbf{X}_{1}\), obtained by estimation of the view difference image in the first stage. On the other hand, it satisfies the global reconstruction constraint in the second stage.
## 3 Computational Approach
### Weight Calculation
#### 3.1.1 Tag-Based Weight  
This study is predicated on the belief that the regularity with which a user employs a particular tag serves as a reliable indicator of their level of interest or preference for that specific tag. Essentially, the more often a user utilizes a tag, the greater their inclination or fascination towards the subject or concept signified by that tag. Hence, this study defines the tag-based weight of a user (\(u\)) to a resource (\(r\)) as follows [27]:  
\[W_{tag}(u,r)=\frac{\sum_{t_{r}\in tag(u,r)}fr(u,t_{r})}{\sum_{All}fr(u,t_{i})} \tag{1}\]  
The equation presented employs the notation _fr(u,tr)_ to depict the frequency with which a user \(u\) attaches a specific tag to a resource r. Here, _tr_ symbolizes the collection of tags linked with the resource r at the instance when the user u earmarks or bookmarks it. Essentially, the formula captures the tagging behavior of the user towards a particular resource, helping to infer user interests or preferences based on their tagging patterns. As per the definition of tag weight, the resulting value is a real number between 0 and 1. A higher tag weight suggests a greater level of interest from the user towards a particular resource.
## 3 Results and Discussion
### Structural Response
#### 3.2.3. Ductility Analysis  
The ductility property of a structural element is an important factor that reveals its reliability. This property can be described as the capability of a structural element to plastically resist loading between its yield to ultimate points [47]. The evaluation of the yield point can be made by graphical and equivalent toughness approaches [48].  
In the current study, the deflection (\(\mu_{D}\)) and curvature ductility parameters were calculated to investigate the ductility behavior of beams with different \(\rho\). Here, \(\mu_{D}\) was evaluated as the ratio of the ultimate deflection (\(\delta_{u}\)) to yield deflection (\(\delta_{y}\)). The beam's load-deflection curves were employed to estimate these deflections. According to the widely used approach [49; 50; 51], a straight line was created between the origin point and 50% of the ultimate load (\(P_{u}\)) that extended to 80% \(P_{u}\), as depicted in Figure 10(a). The deflection at this load (0.8 \(P_{u}\)) was defined as involving the beam's yield of reinforcement (\(\delta_{y}\)), while defection corresponds to the same load but on the descending branch, which was assumed to be equal to the ultimate deflection, \(\delta_{u}\) The rationale for this assumption is that all UHPC beams exhibited flexural stability issues when the strength of the beams reduced by 20% \(P_{u}\) after the ultimate load.  
Figure 10(b) shows the method for evaluating the curvature ductility (\(\mu\)), which is an energy-based approach. Here, \(\mu\) was calculated by Equation (15). This method has been employed by various investigators (e.g., [48]) in the past.  
\[\mu=\frac{A_{u}}{A_{y}}=\frac{0.375M_{u}\varnothing_{y}+0.875M_{u}\big{(} \varnothing_{u}-\varnothing_{y}\big{)}}{0.375M_{u}\varnothing_{y}} \tag{15}\]  
Table 6 summarizes the results of the performed ductility analysis of the UHPC beams. This table shows that the percentage difference of \(\mu_{D}\) for the duplicated samples were between 1.95% and 11.12%; it was in the range of 30.44% to 76.21 for \(\mu\). The relatively high percentage difference for \(\mu\) was likely due to the bilinearization assumptions of the \(M-\varnothing\) curve, as it exhibited high material nonlinearity. With respect to the control beam, the means of \(\mu_{D}\) and \(\mu\) for the two duplicated samples of each beam set were evaluated (as given in parenthesis in Table 6) to emphasize the influence of \(\rho\) on the ductility of UHPC beams. This table suggested that as \(\rho\) increases, the \(\mu_{D}\) exponentially increases (Figure 12). Table 6 illustrates that both B3 and B4 had inferior curvature ductility with respect to B1. The reason for this phenomenon would be the quick closure of the \(M-\varnothing\) curve for these beams due to compressive failure of their concrete at top surface or tensile failure of bars prior to achieving high ductility.  
Figure 10. (a) Moment–curvature of beams and (b) ultimate moment–reinforcement ratio relation.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 2 Piezoresistive Mechanisms  
The resistance variation of a sensor caused by deformation is known as piezoresistivity. In other words, piezoresistive strain sensors can transform strain changes into resistance changes of the sensor material. Considering that the piezoresistive materials used for SHM are almost composites that contain carbonaceous or metallic nanomaterials as conductive fillers and polymers as substrates, this review mainly focuses on the piezoresistive principles of those composites. The primary piezoresistive principles are mainly divided into four categories, namely, the interface contact effect, quantum tunneling effect, percolation network and crack propagation.
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## 1 Introduction
### Pyrolysis Char as a Source of Carbon Black  
Among the three primary pyrolysis products, the solid residue--pyrolysis carbon black--has received relatively limited attention, despite constituting 30-40 wt% of the pyrolysis products. The properties and output of pyrolysis products are influenced by the tire class that is processed, as well as the pyrolysis reactor configuration and conditions (temperature, pressure, and heating rate). Despite the substantial production of rCB in tire pyrolysis, a straightforward and cost-effective recycling technology for this material remains elusive. Therefore, the valorization of rCB stands as a pivotal criterion for the cost-effectiveness and environmental friendliness of tire pyrolysis.  
Carbon black from pyrolysis is frequently incinerated with other hazardous wastes, presenting a significant obstacle to the widespread adoption of tire pyrolysis technology globally. Given the global quantity of waste tires, developing an economically and ecologically sustainable technology for processing and utilizing rCB could result in substantial savings in primary raw materials, such as oil and natural gas, as sources of carbon black.  
According to the classification standard D1754 [13], carbon black grades are designated based on their surface area and structure, which are essential for identifying specific carbon black properties. The ASTM standard D3053 [14] establishes carbon black grades for tire manufacturing industries, distinguishing between hard blacks (e.g., N110-N330) with a high BET surface area and structure, used for tire treads, and soft blacks (e.g., N550-N774) with a lower surface area and structure, employed in tire carcass. Specialty products often require very high-quality carbon black.  
In an ideal scenario, rCB would be reused for new tires. However, this application is hindered by the abundance of inorganic impurities in rCB, mainly resulting from rubber mixture additives during tire manufacturing, such as fillers (e.g., silica fume, TiO\({}_{2}\)), vulcanizing agents (e.g., sulfur, ZnO), and other auxiliary compounds. These impurities, along with non-volatilized volatiles like pyrolysis oil and PAH adsorbates, diminish the free BET surface area, lower rCB structure, and compromise other quality standards [8].
## 5 The SNR of MFIR Filters
### The SNR and Optimal Ordering for MFIR Filters
#### 5.3.1 General MFIR SNR Expressions
3.3 SNR Performance of a Linear Phase MFIR Filter Approximating the Squared Magnitude Response of a Real Pole Filter  
Figures 8 and 9 show the SNR performance of linear phase MFIR filters approximating the squared magnitude response of real pole filters with \(|\lambda|\) in the interval [0.1, 1). For each \(|\lambda|\) value, the number of stages, \(P\), is kept the same as in Section 5.3.2. There is no objective comparison possible between this MFIR approximation and the IIR filter, since the IIR filter is not a linear phase filter and the MFIR filter approximates the _squared_ magnitude response of the pole indicated on the horizontal axis. However, it is clear that the round-off noise performance is comparable with the real pole non-linear phase MFIR approximations. The ordering has no impact when L\({}_{2}\) bound scaling is used and the noise performance with L\({}_{2}\) bound scaling is always better than absolute and infinity bound scaling. Infinity bound scaling and absolute bound scaling have the same performance. Reverse ordering yields again better results than forward ordering. However, the maximum difference between the two orderings is rather small (3.3 dB). Compared to the non-linear phase approximation, the difference between L\({}_{2}\) bound scaling and the other scaling methods are somewhat larger. .3.4 SNR Performance of an MFIR Filter Approximating a Complex-Conjugate Pole Pair Filter in Cascade  
Figure 10 shows the \(\left(\sigma_{e}^{2}\right/\sigma_{in}^{2})SNR\) values when realizing pole pairs with a magnitude \(r=0.9\) and angles \(\theta\) in the interval [0, \(\pi\)) in case of L\({}_{2}\) bound scaling. Figures 11 and 12 show the results in case of infinity bound scaling and absolute bound scaling respectively. In Figure 13 the \(\left(\sigma_{e}^{2}\right/\sigma_{in}^{2})SNR\) values are calculated for the pole magnitudes: 0.8, 0.85, 0.9 and 0.95 approximated with \(P=5\), 6, 7, and 7  
stages respectively in case of infinity bound scaling and reverse ordering. The surface plots are the \(\left(\sigma_{e}^{2}\middle/\sigma_{in}^{2}\right)\)_SNR_  values for the MFIR filters. The half circle shaped curves are the \(\left(\sigma_{e}^{2}\middle/\sigma_{in}^{2}\right)\)_SNR_  values for the corresponding IIR filters. Figure 14 compares the noise performances of the approximation of the complex-conjugate pole pair with a magnitude \(r=0.9\) and any angle between 0 and \(\pi\), for several scaling methods and orderings. All pole magnitudes between \(\left|0.8\right|\) and \(\left|0.99\right|\) in steps of 0.01 have been calculated but are not all shown here. The results shown in the figures are however representative for all combinations of scaling methods, pole magnitudes and angles that were calculated. After extensive analysis of the data, the following conclusions can be drawn for the MFIR approximation of a complex-conjugate pole pair filter realized using the cascade structure.