esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 2 Materials and Methods
### Sensor Fabrication  
The RGO-based sensor fabrication started with the GO thin film assembly (Figure 1). First, 5 mg/mL GO aqueous solution was diluted with deionized (DI) water to create a 2 mg/mL GO aqueous solution. A total of 100 uL of 2 mg/mL GO solution was pipetted on the interdigitated electrodes and dried in an oven for 2 h at 50 \({}^{\circ}\)C. It should be noted that, before coating, the interdigitated electrodes were cleaned with DI water, acetone, and isopropyl alcohol to remove any residue. The interdigitated electrodes consisted of 10 finger pairs with 80 um of spacing and electrode width and metal layers of Ti/Cu/Ni/Au. Figure S1 describes the electrode dimensions in detail. Interdigitated electrodes increase the signal-to-noise ratio and the contact area between the electrodes and the conductive RGO film [38]. The GO thin film was then chemically reduced by L-AA solution, which is a green reductant (i.e., eco-friendly and non-toxic), at 80 \({}^{\circ}\)C for 90 min [39]. Next, four different types of metalloporphyrin solution were prepared by dissolving four different metalloporphyrin powders (CoTPP, FeTPP, MnTPP, and ZnTPP) in chloroform for 90 min of bath ultrasonication (80 W, 40 kHz) to create a 2 mg/mL concentration. RGO thin film samples were noncovalently functionalized by pipetting 15 uL metalloporphyrin solution onto the RGO thin film. The samples were left to air-dry in a funnel hood for 12 h. Finally, two electrodes were established by soldering two single-strand wires to the electrodes (Figure 2).
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### Angular Dispersion \(D(\theta_{ij},\theta_{r})\)  
Angular Dispersion describes how the signal is dispersed after reflection on the surface. In flat mirrors, the dispersion is minimal, and the signal follows the law of reflection, but in convex mirrors, the signal is dispersed in various directions, as we can see in the following expression:  
\[D(\theta_{ij},\theta_{r})=\frac{1}{2\pi}\frac{\cos(\theta_{r}-\theta_{ij})}{ 1-\cos(\theta_{r}-\theta_{ij})^{2}+\epsilon}, \tag{21}\]  
where \(\epsilon\) is a small value to avoid division by zero.  
Finally, the total received power at the receiver \(R\) due to reflections from the flat mirrors in the IRS can be expressed as follows:  
\[P_{r}^{\text{flat}}(t)=\sum_{i=1}^{M}\sum_{j=1}^{N}\Bigg{(}P_{s}\frac{A_{e}}{d_{ ij}^{2}}\cos^{m}(\theta_{ij})R(\theta_{ij})T_{ij}(\lambda)H_{ij}(t)\cos(\psi_{r}) \Bigg{)}. \tag{22}\]  
The total received power due to reflections from the convex mirrors in the IRS can be expressed as  
\[P_{r}^{\text{convex}}(t)=\sum_{i=1}^{M}\sum_{j=1}^{N}\Bigg{(}P_{s}\frac{A_{e}} {d_{ij}^{2}}\cos^{m}(\theta_{ij})F_{\text{conv}}(R_{ij})R(\theta_{ij})T_{ij}( \lambda)H_{ij}(t)\cos(\psi_{r})\Bigg{)}. \tag{23}\]
## 4 Numerical Validations
### Validation of the Damage Quantification Method  
After validating the characteristics of torsional wave-damage interaction, the proposed damage quantification method is evaluated. Two representative cases are investigated with different defects sizes. 5-cycle tone-burst excitation signals are used and their frequencies vary from 30 kHz to 80 kHz with an increment of 5 kHz. Other details are identical to those described in Section 4.1.  
Figure 6: Influence of damage width on the reflections from (**a**) the damage and (**b**) the end predicted by theory and validated by finite element simulations.  
In the first case, the width and depth of the defect are 39 mm and 0.7 mm, respectively. Three typical time-domain responses at different frequencies are shown in Figure 8a where the wave packets associated with reflections from the defect and end are identified. The complex Morlet wavelet transform is used to extract the wave amplitude and the \(SRC\) at different frequencies are calculated as shown by the dots in Figure 8b. With the proposed damage quantification method, a damage image is constructed in terms of the \(DI\) with respect to the defect width and depth in Figure 8c. By searching the largest \(DI\) in the damage image, the corresponding width and depth are identified as the damage size. In this case, the identified defect size is 39.5 mm in width and 0.665 mm in depth, which is very close to the actual damage size. Using the identified defect size, the theoretical result of the \(SRC\) is further calculated and plotted in Figure 8b. A good agreement between the theoretical result and finite element test result, which further confirms the efficacy of the proposed method.  
Figure 7: Influence of damage depth on torsional wave reflections: (**a**) typical time-domain signals; (**b**) reflection coefficients from the defect; (**c**) reflection coefficients from the end.  
The second case involves a defect of 60 mm in width and 1.05 mm in depth. Following the same procedure, the \(SRC\) at different frequencies are obtained in Figure 9a and the corresponding damage image is shown in Figure 9b. From the damage image, the defect is quantified as 60 mm in width and 0.98 mm in depth, which also exhibits high accuracy. With the identified damage size, the theoretical result of the \(SRC\) variation with respect to frequency also matches well with that from the simulations.  
To sum up, the characteristics of the torsional wave-damage interaction and the proposed damage identification method have been well validated with the finite element simulations. It is worth noting that the axial-symmetric defects are investigated to simplify the discussions. The theoretical analyses on the wave-damage interaction and the proposed damage quantification method also allow dealing with defects with complex cross-sections as demonstrated in Appendix A.  
Figure 8: (**a**) Typical responses at different frequencies; (**b**) variation of the \(SRC\) with respect to the excitation frequency; (**c**) damage image for the 39 mm-wide and 0.7 mm-deep defect.
## 3 Results and Discussion  
### Physicochemical Seawater Parameters of the Sampling Sites  
The physicochemical environmental parameters of seawater from both sampling sites for the four seasons are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Sampling Season** & **Evoikos Gulf** & **Saronikos Gulf** \\ \hline \multirow{3}{*}{pH} & Autumn & 8.1 \(\pm\) 0.02 & 8.2 \(\pm\) 0.02 \\  & Winter & 8.2 \(\pm\) 0.02 & 8.2 \(\pm\) 0.02 \\  & Spring & 8.0 \(\pm\) 0.02 & 8.5 \(\pm\) 0.02 \\  & Summer & 8.4 \(\pm\) 0.02 & 8.4 \(\pm\) 0.02 \\ \hline Salinity (ppt) & Autumn & 40 \(\pm\) 3 & 39 \(\pm\) 3 \\  & Winter & 40 \(\pm\) 3 & 39 \(\pm\) 3 \\  & Spring & 37 \(\pm\) 3 & 39 \(\pm\) 3 \\  & Summer & 36 \(\pm\) 3 & 37 \(\pm\) 3 \\ \hline TOC (mg C/L) & Autumn & 6.93 \(\pm\) 0.5 \({}^{\rm c}\) & 3.69 \(\pm\) 0.3 \({}^{\rm d}\) \\  & Winter & 7.13 \(\pm\) 0.5 \({}^{\rm c}\) & 3.96 \(\pm\) 0.3 \({}^{\rm d}\) \\  & Spring & 19.4 \(\pm\) 1.5 \({}^{\rm a}\) & 12.6 \(\pm\) 1.0 \({}^{\rm b}\) \\  & Summer & 19.2 \(\pm\) 1.5 \({}^{\rm a}\) & 12.8 \(\pm\) 1.0 \({}^{\rm b}\) \\ \hline TN (mg N/L) & Autumn & 0.16 \(\pm\) 0.01 \({}^{\rm e}\) & 0.06 \(\pm\) 0.01 \({}^{\rm g}\) \\  & Winter & 0.21 \(\pm\) 0.02 \({}^{\rm c}\)d & 0.08 \(\pm\) 0.01 \({}^{\rm f,g}\) \\  & Spring & 0.39 \(\pm\) 0.03 \({}^{\rm b}\) & 0.18 \(\pm\) 0.01 \({}^{\rm d}\) \\  & Summer & 0.50 \(\pm\) 0.03 \({}^{\rm a}\) & 0.25 \(\pm\) 0.02 \({}^{\rm c}\) \\ \hline NO\({}_{3}\)-N (mg N/L) & Autumn & 0.12 \(\pm\) 0.01 \({}^{\rm e}\) & 0.02 \(\pm\) 0.01 \({}^{\rm g}\) \\  & Winter & 0.18 \(\pm\) 0.01 \({}^{\rm c}\) & 0.04 \(\pm\) 0.01 \({}^{\rm f}\) \\  & Spring & 0.36 \(\pm\) 0.03 \({}^{\rm b}\) & 0.16 \(\pm\) 0.01 \({}^{\rm d}\) \\  & Summer & 0.41 \(\pm\) 0.03 \({}^{\rm a}\) & 0.18 \(\pm\) 0.02 \({}^{\rm c}\) \\ \hline NO\({}_{2}\)-N (mg N/L) & Autumn & \(<\)0.01 & \(<\)0.01 \\  & Winter & \(<\)0.01 & \(<\)0.01 \\  & Spring & \(<\)0.01 & \(<\)0.01 \\  & Summer & \(<\)0.01 & \(<\)0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Environmental seawater parameters (mean \(\pm\) SE) from both sampling sites for the four seasons.  
From Table 1, we can see that the pH and salinity do not show any notable differences for all seasons and both studied gulfs (Evoikos and Saronic). The levels of nutrients and organic matter and chlorophyll-a in the water showed a slight seasonal pattern, with the values of TOC, TN, nitrites, nitrates and chlorophyll-a being highest in summer. The main conclusion coming from the TOC and chlorophyll-a time series is that the Evoikos Gulf has a higher productivity than the Saronic Gulf. This favors the development of organisms that are dependent on the productivity of the area they live in, such as the oysters studied in this case [24]. Moreover, the best final yields for the oyster cultures in the bays with high concentrations of chlorophyll-a were directly correlated with better survival/performance rates [25].
## 3 Results and Discussion
### Analysis of Features  
Hypothesis tests are performed to understand the impact of predictors on maintenance performance metrics. For binary variables, an independent sample \(t\)-test statistic is used; for categorical predictors, an ANOVA (Analysis of Variance) test statistic is used; while for continuous variables, a correlation is used to determine linear dependency on target variables. Lastly, the results are reported using parametric and non-parametric statistical tests in cases where test assumptions are violated.  
Figure 4: Feature importance score.  
Figure 3: Predictive performance regression plots of the training set.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 3 E-Mqtt
### Overview of End-to-End Communication Methods
Once the PUBCOMP packet is received from the broker (13), the communication cycle on the publisher's end concludes. Upon reception of the PUBREL packet (14), the subscriber proceeds to remove the stored PUBLISH packet (15) and promptly generates a response by transmitting the PUBCOMP packet (16). Subsequently, the broker concludes the QoS 3 publishing process upon the receipt of the PUBCOMP packet and deletes the stored PUBREL packet (17). Within synchronous E-MQTT, the publisher remains in a suspended state starting from the moment the PUBLISH packet is dispatched (3) until the corresponding PUBREC packet is received from the broker (18). However, the publisher can transition out of this suspended state should a timer expire, prompted by exceptional circumstances like packet loss or subscriber failure. Conversely, in the context of asynchronous E-MQTT, the complete communication process unfolds asynchronously, devoid of such suspensions. In synchronous E-MQTT, the publisher and the subscriber are weakly coupled with the minimum number of waiting packets. However, if the publisher specifies a list of subscriber IDs as well as the number of waiting packets, it becomes strongly coupled to the designated subscribers. Conversely, in asynchronous E-MQTT, as the publisher does not need to wait for the PUBREC packet, the publisher and the subscriber are completely decoupled. Hence, applications employing E-MQTT possess the flexibility to opt for varying degrees of decoupling between publishers and subscribers, tailoring the approach to meet their specific requirements.
# Characterization of the Aroma of an Instant White Tea Dried by Freeze Drying
## 2 Results and Discussion
### GC-MS-O and OAV Analyses of Arona-Active Volatiles in the SDE Extract  
Previously, the major aroma-active compounds of a white tea have been shown to be (_E,E_)-2,4-nonadienal, hexanal, (_E,Z_)-2,6-nonadienal, (_E,E_)-2,4-decadienal, decanal, benzeneacetaldehyde, 2-pentylfuran, linalool, dimethyl sulfide, \(\beta\)-ionone, etc., in view of the higher aroma character impact (ACI, a ratio of OAV in a mixture) factors [26]. These aroma-active compounds are mainly aldehydes with six to ten carbons having green, fatty or fresh notes [30]. In order to investigate the aroma-active volatiles of the IWT, the SDE extract was further analyzed using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) combined with aroma extract dilution analysis (AEDA) and OAV determination. The SDE extract were sniffed to have 16 volatiles with noticeable notes based on GC-MS-O analysis (Table 2). These aroma-active compounds included 7 alcohols, 3 ketones, 2 aldehydes, 2 oxides, 1 hydrocarbon and 1 other. Among these volatiles, camphene (green note, FD = 64), linalool ( floral note, FD = 64), 2-hexanone ( floral and sweet notes, FD = 16), _trans_-\(\beta\)-damascenone (sweet note, FD = 64), benzeneacetaldehyde (sweet and honey notes, FD = 16) and safranal (green and woody notes, FD = 16) offered higher FD factor according to the AEDA analysis (Table 2). Meanwhile, most of these compounds had high OAVs, e.g., linalool, benzeneacetaldehyde, _trans_-\(\beta\)-damascenone and geraniol has OAVs of 21,451, 9505, 4,546,692 and 1616, respectively (Table 2). Despite some volatiles, e.g., hotrienol, \(\alpha\)-terpineol and geraniol had low FDs showed high OAVs, which could be related to the different thresholds in water and air [11]. In short, the GC-MS-O and OAV analyses indicated that camphene, linalool, _trans_-\(\beta\)-damascenone, 2-hexanone, benzeneacetaldehyde, safranal, 3-hexanol, _cis_-linalool oxide, _trans_-linalool oxide, hotrienol, geraniol, benzyl alcohol, \(\alpha\)-terpineol, phenylethyl alcohol, _trans_-\(\beta\)-ionone and indole were the major aroma contributors of the SDE extract. These aroma-active compounds were mainly alcohols as well as some aldehydes, which is obviously different from the aroma-active compounds of the white tea which with the aroma-active compounds mainly consisting of aldehydes with six to ten carbons [30].
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 2 Materials and Methods
### Color Space Conversion  
During the digital silk printing process, color information is transmitted through different devices to achieve color reproduction, which is one of the fundamental issues in the field of digital printing. Digital inkjet color management involves transferring image color data from one device to another with minimal color distortion, essentially moving color information from one color space to another. Its main purpose is to enable the conversion of colors between different color spaces [11], ensuring color accuracy when transferring and outputting colors between different media.  
In practical applications, there are many commonly used color spaces, such as RGB, CMYK, CIELAB, and HSV, etc. In the digital inkjet printing industry, there are three widely used color spaces--RGB, CMYK and CIELAB, where RGB and CMYK color spaces are device-dependent [12], and CIELAB is device-independent. Device-dependent color spaces have relative color coordinate values, which means that the same color values will display differently under different system conditions; device-independent color spaces are unrelated to any device and can be used to convert color spaces between different devices. The CIELAB color space is related to human visual color perception and is also a standard color space that computers can operate. To ensure color consistency, all color information used in this paper is unified into the CIELAB space [13]. The PANTONE TCX color was converted from RGB to CIELAB [14].  
Equations (1) and (2) are used to complete the conversion from the RGB color space to the XYZ color space.  
\[C_{linear}=\begin{cases}\frac{C_{\text{rgb}}}{12.92},&C_{\text{rgb}}\leq 0.04 045\\ \left(\frac{C_{\text{rgb}}+0.055}{1.055}\right)^{2.4},&C_{\text{rgb}}>0.04045 \end{cases} \tag{1}\]  
where C is R, G, or B.  
For D65 illuminant, the conversion follows the matrix conversion formula in Equation (2).  
\[\begin{bmatrix}\text{X}\\ \text{Y}\\ \text{Z}\end{bmatrix}=\begin{bmatrix}0.4124&0.3576&0.1805\\ 0.2126&0.7152&0.0722\\ 0.0193&0.1192&0.9505\end{bmatrix}\begin{bmatrix}\text{R}_{\text{linear}}\\ \text{G}_{\text{linear}}\\ \text{B}_{\text{linear}}\end{bmatrix} \tag{2}\]  
Then, in Equation (3), XYZ is converted into the CIELAB color space.  
\[\begin{cases}\frac{L*}{L*}=116f_{y}-16\\ a*=500\left(f_{x}-f_{y}\right)\\ b*=200\left(f_{y}-f_{z}\right)\end{cases} \tag{3}\]  
where:  
\[f_{x}=\begin{cases}\sqrt[3]{x_{n}},&\text{if }x_{n}>\epsilon\\ \frac{x_{n}+16}{116},&\text{otherwise}\end{cases}\  
\(\epsilon=0.008856\), \(\kappa=903.3\)  
For D65 illuminant, \(X_{n}=95.0489\), \(Y_{n}=100\), \(Z_{n}=108.8840\).
## 4 Final and General Remarks  
In the present review, the available studies regarding the use of PFA and the consequent impact on poultry products' yield and quality were summarized. The main focus was on the antioxidant capacity of PFA as potential alternatives to synthetic vitamins and, in particular, on their effect on the characteristics of poultry products. As presented in [PERSON] et al. [6], the inclusion of PFA as antioxidants in poultry diets can be effective in mitigating oxidative stress in vivo and in improving productivity and efficiency. Consistently with these conclusions [6], some post-mortem studies confirmed the capacity of grape pomace to reduce lipid peroxidation of poultry meat during refrigerated storage, with no effects on organ weight. A similar effect was exerted by oregano essential oil and oregano oil, the antioxidant efficiency of which was similar to or sometimes higher than VitE. The same PFA prevented a drop in VitE in tissues during preservation. Rosemary oil and rosemary plant alone or in combination with olive leaves or oregano essential oil reduced MDA and inhibited microbial growth during storage, at the same time exerting a sparing effect on VitE. However, the PFA effects were, in general, less pronounced than the VitE ones in poultry products. Hesperidin was also effective in reducing lipid oxidation in comparison with VitE, even when used in a lower amount. Tea catechins reduced TBARS values in poultry meat, but with a lower efficiency than VitE, while rosemary leaves and orange peel were more efficient at reducing TBARS levels than VitE. Coneflower, thyme, sage, and marigold extracts, along with sweet chestnut wood extract, increased the long-chain polyunsaturated fatty acid content of breast muscle. In addition, sweet chestnut wood extract improved meat color more than VitE, while organoleptic meat traits were significantly improved by the dietary addition of oregano aqueous extract. Thyme oil decreased carcass and organ weights while reducing TBARS values more effectively than VitE in a dose-dependent manner.  
Moreover, rosemary plant was more effective in reducing lipid peroxidation of eggs than VitE. The same effect was obtained by feeding olive leaves to the animals. Olive leaves generally increased long-chain polyunsaturated fatty acids in eggs, while a lowering of cholesterol and triglyceride levels in yolk was demonstrated by the addition of green tea and marigold powder/extract to diets.
## 3 Results and Discussion
### Amino Acid Analysis of PH and AH  
Results from amino acid analysis of PH and AH are shown in Table 3. Both hydrolyses contained similar amounts of TAA. The total contents of charged and hydrophilic amino acid residues (including Glu, Asp, Lys, Pro, Gly, Ser, Thr, Arg, and His) in both hydrolyses were up to about 68% of the TAA contents, which were relative to ice affinity and cryoprotective activity of antifreeze proteins [23]. About 27% of acidic amino acid residues (Glu and Asp) were found in both hydrolyses. The relative content of Glu, which contains strongly polar hydroxyl groups that are favorable for cryoprotective properties, in both hydrolyses reached about 15% [21]. PH and AH were also rich in Pro residues (about 13%) which also contributed to ice affinity [23]. Sericin hydrolyses (molecular weight of less than 3 kD) with cryoprotective activity were also rich in the amino acids Ser, Asp, Gly, Thr and Glu [24]. In ice-binding proteins from arctic yeast, aligned Thr/Ser/Ala residues were found to be critical for binding of ice [25]. The content of basic amino acid (Lys) in both hydrolyses were about 9%, which enhanced the stability if hydrogen bonds between the hydrolysate and the ice crystal. The total  
Figure 1: (**a**) Calibration curve of standard substances on TSK-gel G3000 PWXL column at 220 nm. The five standard substances were bovine serum albumin (67,000 Da), peroxidase (40,200 Da), ribonuclease A (13,700 Da), glycine tetramer (246 Da) and p-Aminobenzoic acid (137.14 Da), respectively; (**b**) Molecular weight distributions of PH (the hydrolysate prepared by Protamex hydrolysis) and AH (the hydrolysate prepared by Alcalase hydrolysis). PH and AH were eluted by 0.1 mol/L Na\({}_{2}\)SO\({}_{4}\) in 0.1 mol/L phosphate buffer (pH 6.7) at a flow rate of 1 ml/min and monitored at 220 nm at 25 \({}^{\circ}\)C.  
content of hydrophobic amino acid residues (including Phe, Met, Leu, Ile and Val) in PH was a little higher than that in AH. Previous researchers have verified that hydrophobic amino acid residues in fish protein hydrolysate helped to retain textures, water-binding properties and proportion of unfrozen water of frozen fish mince [5]. FAA composition results of PH and AH indicated that PH had higher FAA content than AH due to the exo- and endo-protease property of Protamex. PH also contained significantly higher content of free Lys (_P_ < 0.05), which has been reported to be cryoprotective [26]. Free Lys, combined with Arg, Asp and Glu, preferentially hydrated vulnerable proteins and bound free water, thereby enhancing cryoprotective abilities [26].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**AA** **(mg/g)**} & \multicolumn{4}{c}{**PH**} & \multicolumn{4}{c}{**AH**} \\ \cline{2-9}  & **TAA** & **Content** & **FAA** & **Content** & **TAA** & **Content** & **FAA** & **Content** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{Asp} & 99.8 & 12.33 & 0
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method
### Dimensionality Reduction of Fault Feature Construction  
Evaluating the gear fault state directly from noise-reduced vibration signals is challenging. In this context, time-domain features and frequency-domain feature parameters are initially selected. However, the abundance of feature parameters can potentially prolong subsequent identification and classification processes. To address this issue, a newly proposed dimensionality reduction method will be implemented to streamline and reduce the dimensionality of the fault information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifications** & **SNR/dB** & **RMSE** \\ \hline Soft threshold denoising & 17.0117 & 0.1119 \\ Hard threshold denoising & 18.7860 & 0.1252 \\ Threshold denoising in this article & 19.6544 & 0.0912 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR and RMSE values after noise reduction using CEEMDAN and various improved threshold functions.  
Figure 6: Comparison of the original noisy signal with the denoised signal. (**a**) Noisy signal. (**b**) Signal after CEEMDAN improved threshold denoising. (**c**) Signal after CEEMDAN soft threshold denoising. (**d**) Signal after CEEMDAN hard threshold denoising.