esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 3 Materials and Methods
### Different Vanilla Processing Conditions  
2.1 Comparison of Volatile Components under Different Conditions during the Processing of Taiwan-Cultivated Vanilla  
This study sought to explore if the volatile components of vanilla pods differed according to their harvest maturity and processing conditions:  
(1) Different harvest maturity tests.  
More than eight months was required for vanilla pods to mature in Taiwan (the time from pollination to harvest of the pods). To test different harvest maturities, vanilla pods that were pollinated at 34, 38, and 42 weeks prior to harvest were prepared.  
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline \multicolumn{1}{c}{RI A  } & \multicolumn{4}{c}{**Compounds B **} & \multicolumn{4}{c}{**A E [FOOTNOTE:e]Footnote e: A-Newton Mingjan, \({}^{2}\) B-Thing Station, \({}^{3}\) C-Tayun Station, \({}^{4}\) D-Tayun Station, \({}^{5}\) D-Tayun Station, \({}^{6}\) D-Tayun Station  
(2) Different curing conditions.  
There are four stages of vanilla pod curing that include killing, sweating, drying, and conditioning. The Taoyuan Station used a fermentation machine (Metro C5 1 Series Holding/Proofing Cabinet, Wilkes-Barre, PA, USA) to replace the sweating and drying stages that require natural sunlight in traditional processes. The fermentation machine can better control the temperature and humidity to save processing time and provide improved quality control. This study conducted temperature adjustments for volatile components analysis at the following different curing stages:  
1. Killing temperature and time: fresh vanilla pod were tested under the following four conditions: vanilla pod's impression in hot water at 65 \({}^{\circ}\)C three times for 1 min each, 65 \({}^{\circ}\)C for 3 min, 70 \({}^{\circ}\)C three times for 30 s each, and 80 \({}^{\circ}\)C three times for 10 s each.
2. Sweating temperature: the vanilla pod were subjected to sweating at 40 \({}^{\circ}\)C and 45 \({}^{\circ}\)C in the fermentation machine after killing. The pods were treated at 40 or 45 \({}^{\circ}\)C for 8 h, then moved to room temperature for 16 h and temperature-changed for 10 days.
3. Microorganism treatment during the curing process  
Vanilla pods were treated with three different yeast strains (_D. bruxellensis_, _S. crataogenesis_, and _C. caaoi_) and a bacterial strain (_B. subtilis_) to compared the volatiles. The process to yeast treatment: while water content of the pods was closed to 38% during the drying stage, the pods were evenly attached to yeast solutions then air-dried. The concentration of yeast solutions was 1 \(\times\) 10\({}^{6}\)-1 \(\times\) 10\({}^{7}\) cfu/mL. Vanilla pods were treated once every 7 days, a total of 3 times, and finally placed in wooden boxes for condition. The method of _Bacillus_ treatment was: the pods were treated with the bacterial solution three times respectively after the killing stage, the sweating stage, and on the 14 th day of drying. The concentration of the bacterial solution was 1 \(\times\) 10\({}^{6}\)-1 \(\times\) 10\({}^{7}\) cfu/mL.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 2 Materials and Methods
### Methods
#### 2.2.2 Membrane Filtration  
Laboratory-scale equipment using a modular plate and frame system were used in membrane filtration. Polyacrylonitrile membranes (UF1-PAN, UF10-PAN and UF25-PAN), with a molecular weight cut-off of 1 kDa, 10 kDa, and 25 kDa, respectively, were used. These membranes were chosen based on preliminary investigation and the molecular weight of the substances in rose wastewater. The membrane area was 1250 cm\({}^{2}\)[28]. The initial volume of rose wastewater used for membrane filtration was 8 L. The pH of the initial rose wastewater was 4.1. Ultrafiltration was conducted under varying transmembrane pressures (0.2, 0.35 and 0.5 MPa), at a temperature of 50 \({}^{\circ}\)C, a feed flow rate of 330 dm\({}^{3}\)/h, and a volume reduction ratio of 2, 4, 6 and 8. Transmembrane pressure during ultrafiltration starts at 0.2 MPa; over 0.5 MPa, the change in permeate flux is minimal. The volume reduction ratio was chosen based on preliminary investigations, taking into consideration the fact that a ratio over 8 would result in too low of a permeate flux, which would prolong ultrafiltration and increase energy demand. The temperature and feed flow rate were chosen through preliminary investigations and the highest possible level of membrane equipment. Membrane cleaning was performed using a 0.5% sodium hydroxide (NaOH) solution, which is commonly used for removing organic fouling, scaling, and other contaminants from ultrafiltration membranes. The cleaning process was conducted at a temperature of 50 \({}^{\circ}\)C, which is the recommended range for effective chemical cleaning without compromising the integrity of the membrane. The cleaning solution was circulated through the membrane system at a pressure of 0.2 MPa, which is the operational pressure that ensures sufficient flow and contact time for the solution to interact with the fouling layer. The cleaning cycle lasted for 30 min, during which the NaOH solution was continuously circulated through the membrane modules to effectively dissolve and remove the fouling materials. Circulation was carefully monitored to maintain a constant temperature and pressure to ensure uniform cleaning efficiency. After the 30-minute cleaning period, the membrane was subjected to a final rinse with distilled water to remove any residual NaOH solution and to prevent any potential damage from prolonged chemical exposure. The rinse was performed until the pH of the rinse water returned to neutral (pH 7), indicating all cleaning agents were completely removed. To validate the cleaning process, key performance parameters such as flux were measured before and after cleaning to ensure that the membrane's performance was restored.
# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 6 Experimental Results and Discussion
### Result of Visual Sentiment Classification with the Fine-Tuned pre-Trained Models  
The existing pre-trained models used in our work (VGG-19, Resnet50V2, and Densenet121) were imported from the Keras library and the ImageNet weights were used to train them. The flatten class available in the Keras library was used to convert the max-pooling layer output into a one-dimensional array of vectors. A long feature vector was created and the flatten layer's output was normalized with a batch normalization layer. To reduce the problem of overfitting, a dropout layer was added and succeeded by a dense layer with 512 units. The network weights were penalized by adding the L2 regularization technique to avoid the effect of overfitting. The final dense layer (Softmax) had two units, corresponding to the number of sentiment classes (positive and negative).  
Figure 14: Feature map from Dense_Block1 of the DenseNet-121 model.  
Figure 15: Feature map from Dense_Block4 of the DenseNet-121 model.
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 3 Analysis of Synchronization Algorithm
### Feedback-Based Synchronization Scheme with Low-Resolution ADCs
The mean-square error (MSE) of frequency offset estimation with low-resolution ADCs can be further given by  
\[MSE_{b}=\frac{1}{n}{\sum\
olimits_{i=1}^{n}\big{(}\Delta f_{c}{{}^{\prime}}- \varepsilon\big{)}^{2}}, \tag{20}\]  
where \(\varepsilon\) is the normalized CFO. It is not difficult to see from Equation (10) that, when the amplitudes of the real and imaginary parts of the signal \(R[n]\) are close to zero, these small amplitude signals are over-amplified by low-resolution ADCs. In particular, in the case of one-bit quantization, the numerical result given by Equation (18) for estimating the frequency offset is generally not available due to its large error. Therefore, we adopt multi-bit quantization in the frequency offset estimation module. The synchronization framework with low-resolution ADCs is shown in Figure 4.  
The advantage of the Schmid and Cox (S&C) algorithm [30] is that it is not affected by frequency offset when it is used for frame synchronization or symbol synchronization. No matter how much the frequency offset is, the phase difference between the sampling points is a fixed value when the sampling interval is constant [31]. This is why we chose the S&C algorithm for analysis in the context of low-resolution quantization. This characteristic was advantageous to us for handling timing synchronization and carrier synchronization separately. Figure 4: Synchronization framework with low-resolution analog-to-digital converters (ADCs).
# Amortized Bayesian Meta-Learning with Accelerated Gradient Descent Steps
## 3 Meta-Learning with Amortized Bayesian Inference  
As previously depicted, the goal of meta-learning is to train a model that suits a collection of tasks; such a _meta-model_ works well on any task from this collection. Assume we are given \(T\) tasks for training, and the data are denoted as \(\mathcal{D}=\{d^{(t)}:=(x^{(t)},y^{(t)})_{t=1}^{T}\}\). Inputs are denoted as \(x\) and outputs as \(y\). \(\mathcal{D}\) can be elaborated as training data \(\mathcal{D}^{(t)}_{S}=\{(x^{(t)}_{in},y^{(t)}_{n})\}_{n=1}^{N_{t}}\) and test data \(\widetilde{\mathcal{D}}^{(t)}_{Q}=\{(\widetilde{x}^{(t)}_{m},\widetilde{y}^{( t)}_{n})\}_{m=1}^{M_{t}}\). According to [3], we denote the training samples as the _support set_ and the test samples as the _query set_. For each task \(i=1\cdots T\), \(\mathcal{D}^{(i)}=\mathcal{D}^{(i)}_{S}\bigcup\widetilde{\mathcal{D}}^{(i)}_{Q}\). We revisit the classical hierarchical variational Bayes probabilistic model in this section, and propose using the amortized Bayesian meta-learning scheme by implementing a function of \(x_{t}\) as the variational posterior.
###### Abstract  
In this study, when deriving thermoplastic polyurethane (TPU), the researchers replaced 1,4-butanediol (1,4-BDO) with 1,3-butanediol (1,3-BDO) as a chain extender and examined how the structure of the chain extender affected the final polymers. Regarding the raw materials for polymerization, three types of commercial polyols with the same molecular weight (Mn = 1000 g/mol), namely, poly (butyl acrylate) (PBA), poly (tetramethylene ether) glycol (PTMG), and polycarbonate diol (PCDL) were used. These polyols were used in conjunction with butanediol and 4,4'-methylene diphenyl disocyanate. Three groups of TPUs were successfully synthesized using one-shot solvent-free bulk polymerization. Compared with TPUs polymerized using 1,4-BDO, materials polymerized using 1,3-BDO are more transparent and viscous. Structural analysis revealed that no substantial differences between the final structures of the TPUs were present when different chain extenders were used. Thermal analysis indicated that compared with TPUs polymerized using 1,4-BDO, the glass transition temperature of those with 1,3-BDO was 15 \({}^{\circ}\)C higher. Examination of microphase separation in the structure by using morphological analysis revealed that compared with TPUs synthesized using 1,4-BDO, PBA, and PTMG synthesized using 1,3-BDO were relatively separated. PCDL synthesized using 1,3-BDO exhibited no morphological difference. Rheological analysis indicated PCDL synthesized using either 1,4-BDO or 1,3-BDO did not exhibit any obvious differences. In conclusion, TPUs synthesized using PCDL and 1,3-BDO exhibited thermal plasticity at room temperature (15-20 \({}^{\circ}\)C). Their basic application could be extended to the development of smart materials. In terms of further application, they could be used in shape memory and temperature-sensitive high molecular polymers.  
thernglish polyurethane; chain extender; thermal behavior; morphology; microphase separation; rheology Article
## 5 Discussion  
Once the results were analyzed, the methodology is structured according to the efficiency for the statistical monitoring of the process by the control charts and the capacity of the artificial neural networks to classify and interpret the source variation generated in the process. Regarding the statistical monitoring phase of the process, the operation of the MCUSUM graph focuses on accumulating the variability of past measurements and does not depend on a memory weighting like the MEWMA graph, thus achieving greater efficiency in detecting special variability in the manufacturing operation. With respect to the variability classification and interpretation stage, it is important to highlight the ability of multilayer perceptron ANN to classify nonlinear relationships in the data, such as the variability structure in the processes. On the other hand, a disadvantage of using the SVM is that the number of models required increases as the number of classes of the analyzed problem increases.  
The methodology is defined from the integration between the MCUSUM control chart and the multilayer perceptron ANN, allowing a strict and efficient surveillance of the critical quality characteristics monitored in the solder paste application operation by interpreting the variability with a sensitivity level of 92.41% and specificity of 91.16%. In this way, organizations will be able to define the source of variation in their processes in a reliable, expressive, and easy-to-interpret manner. The competitive advantage of the developed methodology is centered on being able to make pertinent adjustments to the manufacturing systems in a timely manner, guaranteeing process productivity, product quality, and customer satisfaction.  
As future work, classification models such as probabilistic and convolutional neural networks will be implemented with the intention of comparing their efficiency in the classification of variability in manufacturing processes against the results shown.  
Conceptualization, E.A.R.-S., V.F.-F. and M.T.-E.; methodology, E.A.R.-S. and Y.V.P.-P.; validation, E.A.R.-S., E.B.-S. and J.C.-S.; formal analysis, E.A.R.-S. and J.C.-S.; investigation, E.A.R.-S., Y.V.P.-P., E.B.-S., V.F.-F. and M.T.-E.; writing--original draft preparation, E.A.R.-S., writing--review and editing, E.A.R.-S. and Y.V.P.-P. 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 this study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Performance Evaluation and Comparison
### Results and Comparison  
In this section, we present a comprehensive evaluation and comparison of our LSTM-based intrusion detection system (IDS) across three distinct datasets: CICIDS2017, NSL-KDD, and UNSW-NB15. We provide detailed analyses of various evaluation metrics, including accuracy, precision, recall, and F1-score, as well as the visualization of loss curves, confusion matrices, and receiver operating characteristic (ROC) curves.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## Appendix C Proof of Theorem 3  
In the underlay OMA mode, the end-to end achieved rate of the channel \(\text{nel ST}\to\text{SR}\) is expressed as \(R_{\text{ST}\to\text{SR},x_{s}}^{\text{underlay}}=log_{2}\Big{(}1+\frac{|h_{ \text{SS}}|^{2}P_{S}}{|h_{\text{PR}}|^{2}P_{p}+\sigma^{2}}\Big{)}\). The outage probability of this channel with PU's interference is expressed as:  
\[ \begin{array}{l}P_{\text{ST}\to\text{SR}}^{out,x_{s},l}=Pr\Big{(}R_{\text{ ST}\to\text{SR},x_{s}}^{\text{underlay}}<R_{S}\Big{)}=Pr\Big{(}log_{2} \Big{(}1+\frac{|h_{\text{SS}}|^{2}P_{S}}{|h_{\text{PR}}|^{2}P_{p}+\sigma^{2}} \Big{)}<R_{S}\Big{)}\\ =Pr\Bigg{(}\frac{\rho_{th}}{|h_{SP}|^{2}}<\rho_{1},|h_{SS}|^{2}<\frac{\varphi_{ 5}(|h_{\text{PR}}|^{2}\rho_{p}+1)}{\frac{\rho_{th}}{|h_{SP}|^{2}}}\Bigg{)}+Pr \Big{(}\frac{\rho_{th}}{|h_{SP}|^{2}}>\rho_{1},|h_{SS}|^{2}<\frac{\varphi_{5}(| h_{\text{PR}}|^{2}\rho_{p}+1)}{\rho_{1}}\Bigg{)}\\ =Pr\Big{(}|h_{SP}|^{2}>\frac{\rho_{th}}{\rho_{1}},|h_{SS}|^{2}<\frac{\varphi_{ 5}(|h_{\text{PR}}|^{2}\rho_{p}+1)|h_{SP}|^{2}}{\rho_{th}}\Big{)}+Pr\Big{(}|h_{ SP}|^{2}<\frac{\rho_{th}}{\rho_{1}},|h_{SS}|^{2}<\frac{\varphi_{5}(|h_{\text{PR}}|^{2} \rho_{p}+1)}{\rho_{1}}\Big{)}\\ =Q_{3}+Q_{4},\end{array}  \tag{11}\]  
where \(Q_{4}\) is expressed as:  
\[ \begin{array}{l}Q_{4}=Pr\bigg{(}|h_{SP}|^{2}<\frac{\rho_{th}}{\rho_{1}},|h_{SS }|^{2}<\frac{\varphi_{5}(|h_{\text{PR}}|^{2}\rho_{p}+1)}{\rho_{1}}\bigg{)}\\ =\int_{0}^{\infty}\int_{0}^{\frac{\rho_{th}}{\rho_{1}}}\int_{0}^{\frac{\rho_{5}( \varphi_{p}+1)}{\rho_{1}}}\frac{1}{\lambda_{SS}}e^{-\frac{x}{\lambda_{SS}}} \frac{1}{\lambda_{SP}}e^{-\frac{y}{\lambda_{SP}}}\frac{1}{\lambda_{PRE}}e^{- \frac{x}{\lambda_{SP}}}dxdy\\ =\int_{0}^{\infty}\int_{0}^{\frac{\rho_{th}}{\rho_{1}}}\frac{1}{\lambda_{SP}}e^{ -\frac{y}{\lambda_{SP}}}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}} \Big{(}1-e^{-\frac{\varphi_{5}(\varphi_{p}+1)}{\rho_{1}\lambda_{SS}}}\Big{)} dydz\\ =\int_{0}^{\infty}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}\Big{(}1-e ^{-\frac{\varphi_{5}(\varphi_{p}+1)}{\rho_{1}\lambda_{SS}}}\Big{)}\Big{(}1-e^{ -\frac{\rho_{th}}{\rho_{1}\lambda_{SP}}}\Big{)}dz\\ =\bigg{(}1-e^{-\frac{\rho_{th}}{\rho_{1}\lambda_{SP}}}\Big{)}\bigg{(}\int_{0}^{ \infty}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}dz-\int_{0}^{\infty} \frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}e^{-\frac{x}{\lambda_{PRE}}}e^{ -\frac{\varphi_{5}(\varphi_{p}+1)}{\rho_{1}\lambda_{SS}}}dz\bigg{)}\\ =\Big{(}1-exp\Big{(}-\frac{\rho_{th}}{\rho_{1\lambda_{SP}}}\Big{)}\Big{)}\Big{(}1- \frac{\rho_{1}\lambda_{SS}}{\rho_{1}\lambda_{SS}+\varphi_{SP}\lambda_{PRE}}\cdot exp \Big{(}-\frac{\varphi_{5}}{\rho_{1}\lambda_{SS}}\Big{)}\Big{)}.\end{array}
## 2 Experimental Section
### Description of the \"COMMODORE\" Solution
#### 2.1.1 Sensor Design  
The concrete resistivity sensor is shown in Figure 2. The sensor is composed of five arrays of electrodes, measuring concrete resistivity between different electrode pairs. The approach is to design an array of electrodes, with the two-electrode measurement principle, where the spacing between arrays of electrodes closer to the concrete surface needs to be smaller to obtain a higher resolution of measurements close to the concrete surface. With such an electrode, it is possible to monitor concrete resistivity at four different concrete cover depths.  
Figure 1: High-level architecture of the hardware electronics and interconnections with the sensor board and antenna.  
Figure 2: Snapshot of the concrete resistivity sensor.  
The sensor was constructed with hard-plated gold electrodes on a PCB substrate. The fabrication technique is in line with standard PCB board manufacturing. Hence, there is potential for cost reduction with mass fabrication. The spacing between the electrode pairs over the concrete cover depth and their respective cell constants are given in Table 1. These distances were chosen to collect higher spatial resolution resistivity measurements close to the concrete surface, where the change in resistivity values is higher compared to the innermost depth (50 mm) of the concrete cover.  
The sensor operates at an excitation amplitude of 17 mV and frequency of 3 kHz. At this frequency, the phase angle is 0 degree, and therefore the impedance measurement of the ER can be used as a measure of the resistivity of the concrete. The sensor along with the readout and wireless communication module are enclosed in a compact housing. Initially, the value of 17 mV excitation voltage was chosen to design and calibrate the sensors. This value was carefully chosen for this electrode design so that the resulting current in concrete stayed within the instrumentation limits. The range of current measurement is from 2 nA to 90 \(\upmu\)A. Instrumentation details of the measuring electronics are provided in [26]. The designed electronics used in the final solution, which were applied in the concrete columns described in the study, provide a sine wave with an amplitude of 800 mV.
## 4 Results and Discussion
### Biochemical Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & **20 uE s\({}^{-1}\)m\({}^{-2}\)** & **50 uE s\({}^{-1}\)m\({}^{-2}\)** & **100 uE s\({}^{-1}\)m\({}^{-2}\)** & **140 uE s\({}^{-1}\)m\({}^{-2}\)** & **210 uE s\({}^{-1}\)m\({}^{-2}\)** & **300 uE s\({}^{-1}\)m\({}^{-2}\)** & **450 uE s\({}^{-1}\)m\({}^{-2}\)** & **15 mm** & **30 mm** & **Annular** \\ \hline \% of n-3 on TFAs & \(29\pm 0.1\) & \(15.9\pm 0.9\) & \(7\pm 0.3\) & \(6\pm 1.6\) & \(6.1\pm 1.6\) & \(8.3\pm 0.3\) & \(59\pm 0.3\) & \(6.4\pm 0.1\) & \(156\pm 0.9\) & \(12.7\pm 0.0\) \\ \hline \% of n-6 on TFAs & \(3.4\pm 0.1\) & \(27\pm 0.5\) & \(3.2\pm 0.8\) & \(2.4\pm 0.6\) & \(2.3\pm 0.3\) & \(4.1\pm 0.0\) & \(3.6\pm 0.1\) & \(2.5\pm 0.0\) & \(3.1\pm 0.0\) & \(4.6\pm 0.0\) \\ \hline \% of C25 n-3 & & & & & & & & & \\ (BPA) on TFAs & \(26\pm 0.1\) & \(13.3\pm 0.5\) & \(49\pm 0.3\) & \(4\pm 1.1\) & \(42\pm 1\) & \(56\pm 0.2\) & \(0\pm 0\) & \(44\pm 0.0\) & \(104\pm 0.6\) & \(9\pm 0.0\) \\ \hline \end{tabular}
\end{table} 
Table 3: Specific fatty acid classes in total fatty acids (TFAs) of the biomass obtained at the end of the batch cultivations grown under different photon flux densities and in reactors with different configurations with \(I_{0}=210\) μE s\({}^{-1}\)m\({}^{-2}\). n-3 and n-6 represent classes of fatty acids with the first unsaturation at the carbon 3 or 6. EPA: eicosapentaenoic acid.
## 2 Informative Frequency Band Selectors  
Real vibration signals are typically a mixture of sources with a complex spectral structure. The signal of interest is hidden in such a mixture. To improve the Signal-to-Noise Ratio, before demodulation (envelope analysis), an extraction of the SOI is needed. According to the literature, damage signature covers some frequency range around structural resonance [18]. To find the informative frequency band (IFB), some statistical indicators for impulsiveness detection are used. In this section, selected statistics are recalled.
## Appendix A  
This appendix explains and details the equivalences of the units used by some weather predictors in the International System of Units (SI). Table 2, shows the predictors used in this work with their units.  
For atmospheric pressures (_Ps_, _Pa_, SLP), it is common to obtain data in millibar units, which, according to the IAU (International Astronomical Union), although in continuous use, is obsolete. The SI unit for pressure is the pascal (Pa), equal to one newton per square meter (N/m\({}^{2}\)).  
Equivalence between bar and Pascal:  
1 bar = 100,000 Pa = 100,000 N/m\({}^{2}\)  
1 mb = 1 \(\times\) 10\({}^{-3}\) bar  
1 mb = 1 hPa = 100 Pa  
Geopotential height (_GPH_) is a vertical coordinate referring to the mean level of the Earth's sea whose units are meters and explains the variation of gravity with latitude and altitude. In meteorology, it is used to express the height at which a given atmospheric pressure (hPa) is found. A geopotential height chart for a single pressure level in the atmosphere shows the troughs and ridges (highs and lows), which can be seen in the weather charts.