esa-sceva/satcom-chunk-collection huggingface dataset:
# Protective Effects Induced by a Hydroalcoholic _Allium sativum_ Extract in Isolated Mouse Heart
## 3 Results and Discussion
### GC-MS Analysis of VOC Fraction
#### 3.1.1 Garlic Powder
\begin{table}
\begin{tabular}{l c c c c} \hline
**Compound** & **Class** & **Area\%** & **RI** & **RI\({}_{\text{L}}\)****a** \\ \hline Diallyl trisulfide (allitridin) & SCC & 22.0 & 1305 & 1300 \\ Propenyl propyl trisulfide & SCC & 0.16 & 1316 & 1314 \\ Eugenol & Alcohols & 0.54 & 1366 & 1357 \\ Methyl 1-propenyl tetrasulfide & SCC & 0.93 & 1369 & 1368 \\ Allyl 1-methylthio propyl disulfide & SCC & 0.85 & 1386 & 1387 \\ Daily tetrasulfide & SCC & 6.80 & 1548 & 1539 \\ Tridecyl methyl ketone & Other & 0.15 & 1700 & 1697 \\
2,4-dimethyl-5,6-dithia-2,7-nonadienal & SCC & 3.10 & 1789 & 1788 \\ Myristic acid & Other & 0.11 & 1795 & 1794 \\ FAE (unidentified) & FAE & 0.40 & 1823 & - \\ Phthalate diisobutyl & Other & 0.18 & 1876 & 1858 \\ Methyl palmitate & FAE & 1.26 & 1926 & 1925 \\ Phthalate dibutyl & FAE & 0.39 & 1969 & 1960 \\ Ethly palmitoale & FAE & 0.70 & 1972 & 1975 \\ Ethyl palmitate & FAE & 9.68 & 1995 & 1993 \\ Propyl palmitate & FAE & 0.31 & 2089 & 2077 \\ Methyl linoleicate & FAE & 2.62 & 2096 & 2093 \\ Methyl elaidate & FAE & 0.56 & 2101 & 2107 \\ Ethyl linoleicate & FAE & 17.8 & 2161 & 2164 \\ Ethyl oleate & FAE & 4.93 & 2165 & 2173 \\ Ethyl steater & FAE & 0.16 & 2193 & 2198 \\ Methyl-9-12-heptadecidenoate b  & FAE & 2.70 & 2242 & - \\
29 unknown & & 10.6 & & \\ \hline
**Class** & & & **Area\%** & \\ \hline Alcohol & & & 3.7 & \\ SCC & & & 42.8 & \\ FAE & & & 41.5 & \\ Others & & & 1.4 & \\ \hline \end{tabular}  
* RI reported in literature b MS-only identification method. SCC: sulfur-containing compounds. FAE: fatty acid esters.  
\end{table} 
Table 2: _Cont._  
Figure 2: Distribution of the principal classes of volatile compounds detected (HS-SPME conditions: T = 80 \({}^{\circ}\)C; teq = 20 min, tsa = 15 min). In the optimized conditions (Table 2), dailyl trisulfide is the most abundant component (22.0%), followed by ethyl linoleate (17.8%), ethyl palmitate (9.68%), diallyl tetrasulfide (6.80%), and diallyl disulfide (6.18%). Diallyl trisulfide is also the major analyte extracted by the PDMS-DVB fiber in the same analytical conditions (37.52% in Table S1). The alcohol-selective CAR-PDMS phase extracts allyl alcohol very efficiently (31.04%), probably arising from allilin degradation [32], as well as 2,3-butanediol (28.06%). It is noteworthy that the VOC distribution was not altered by the four-month room temperature storage (Table S2).
## 2 Methods  
The microwave breast tumor detection system proposed in this paper, shown in Figure 1, can be divided into three steps: signal acquisition, pre-artifact removal, and MICF-DMAS imaging to obtain the image map from the preprocessed signal:  
The MICF-DMAS algorithm consists of three steps: time delay, computing the MICF, and multiplying the MICF using the DMAS. First, the distanc  
Figure 1: Microwave breast tumor detection system.  
transmitting antenna to the focal point of the imaging region and then to the receiving antenna is calculated and converted to a time delay. The tumor-scattered signals from all channels are reverse-delayed and synthesized to focus on a specific focal point of the imaging region within the breast. In the second step, the ICF of each focal point in the imaging region is calculated, and multiple ICF values are multiplied to obtain the MICF. The ICF indicates the coherence between signals, with a higher ICF indicating a higher coherence between signals. Detecting the start and stop moments of tumor signals in different channels allows identifying pairs of tumor scattering signals without time overlap and pairs of signals with tumor scattering signals occurring with time overlap in different channels. For pairs of tumor scattering signals that do not overlap in time in different channels, the start and stop times of the tumor signals in either channel are used as the boundaries of the time window. The signal pairs are then summed and multiplied by the time window to obtain the ICF. After this, the ICFs of multiple pairs of signals are multiplied to obtain the MICF. At the tumor focal point, the coherence between signals is higher, and multiple ICFs of higher values are multiplied. At other focal points, the MICF is multiplied by one small ICF for each signal pair with low coherence. The MICF has the advantage of amplifying the weight difference between the tumor focal point and the other focal points. Finally, the MICF is multiplied by the focal point intensity calculated using the DMAS algorithm. This reduces the intensity of the less coherent clutter focal points in the image and maintains the intensity of the more coherent tumor focal points.
## 4 Methodology
### The 7-Parameter Transformation
#### 4.3.1 Transformation Parameters
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(t_{x}\) (m) & \(-\)546.499 \\ \(t_{y}\) (m) & \(-\)162.314 \\ \(t_{z}\) (m) & \(-\)469.397 \\ \(\mu\) (ppm) & 4.417 \\ \(r_{x}\) (\({}^{\prime\prime}\)) & 5.906 \\ \(r_{y}\) (\({}^{\prime\prime}\)) & 2.075 \\ \(r_{z}\) (\({}^{\prime\prime}\)) & \(-\)11.508 \\ \hline \hline \end{tabular}   
The procedure of reversible transformation (from HDKS to HTRS96 and back to HDKS) using two sets of parameters involves transformation from HDKS to HTRS96 based on the first set of parameters, and then transformation of the resulting point from HTRS96 back to HDKS based on the second set of parameters. For point P with the coordinates in HDKS:  
\[P_{HDKS}= \begin{bmatrix}X\\ Y\\ Z\end{bmatrix} = \begin{bmatrix}4,485,995.037\\ 1,296,375.198\\ 4,329,893.947\end{bmatrix} \]  
the mentioned procedure results in the following:  
\[P_{HDKS}= \begin{bmatrix}X\\ Y\\ Z\end{bmatrix} = \begin{bmatrix}4,485,995.023\\ 1,296,375.216\\ 4,329,893.956\end{bmatrix} \]  
By comparing these two points, it is evident that their coordinates are not entirely equal. In the second case, if only one set of transformation parameters is used (from HDKS to HTRS96) and the return to the HDKS coordinate system is performed by using the inverse expression (Equation (52)), the output point in HDKS has the following coordinates:  
\[P_{HDKS}= \begin{bmatrix}X\\ Y\\ Z\end{bmatrix} = \begin{bmatrix}4,485,995.037\\ 1,296,375.198\\ 4,329,893.947\end{bmatrix} \]In this case, the obtained coordinate is completely equal to the original coordinate in HDKS. This proves that a 7-parameter transformation is one-to-one reversible only if a single set of parameters is used with Equation (52).
# Evaluating the Effect of Road Surface Potholes Using a Microscopic Traffic Model
## 3 Performance Evaluation  
In this section, the performance of the ID and proposed models is evaluated over a 1 km circular road for 200 s with a platoon of 31 vehicles. The explicit Euler technique with time step 0.5 s is used to implement both models. The simulation parameters are given in Table 1.  
It is important to select realistic model parameters. Therefore, the parameters considered are the same as in [41]. For the proposed model, \(\tau=0.5\) s means \(\frac{\tau}{\tau_{N}}<1\) indicating an aggressive driver, \(\tau=6.0\) s means \(\frac{\tau}{\tau_{N}}>1\) indicating a sluggish driver, and \(\tau=3.0\) s means \(\frac{\tau}{\tau_{N}}=1\) indicating a typical driver [1]. The distance headway is \(h=21.0\) m [11], the safe distance headway is \(h_{s}=5.0\) m [42], and the time headway for both models is \(T=1.0\) s [43]. The value of \(\delta\) ranges between 1.0 and \(\infty\) and is typically 4.0 [15], hence the ID model is evaluated for \(\delta=1.0,\ 4.0\), and 200. The maximum normalized density is \(d_{m}=0.5\).  
In terms of size, potholes can be categorized as small, medium, or large with widths between 0.5 and 3.5 m and depths between 0 and 0.3 m [1]. Potholes with a width less than or equal to 0.70 m are classified as small, with a width greater than 1.7 m as large, and in between as medium. The pothole dimensions considered are given in Table 2.
## 2 Related Work  
### Adaptive Neuro Fuzzy Inference System  
ANFIS is an adaptive system that combines the fuzzy inference capabilities of the Takagi-Sugeno model with the parameter-learning capabilities of neural networks. It uses neural networks to implement fuzzification, fuzzy inference, and defuzzification in fuzzy control by automatically extracting rules from input-output sample data and reasoning between fuzzy inputs and fuzzy outputs. Through learning and optimization processes, it improves the accuracy and generalization ability of the model and achieves automatic adjustment of fuzzy inference control rules. Table 2 shows a comparison of neural networks, fuzzy logic systems, and ANFIS techniques, providing a clearer understanding of their similarities and differences.  
ANFIS has been applied in various fields, such as system control [22], pattern recognition [23], data mining [24], medical biology [25], and regression prediction [26]. However, there has been no precedent for the use of an ANFIS network for value assessment in home appliance recycling. The advantage of neural networks lies in their ability to self-learn and self-organize, as well as their generalization ability, enabling them to learn and process input data by adjusting their weights. However, an ANFIS network is also a \"black box model\", making it difficult to express and understand human knowledge. On the other hand, fuzzy logic systems can use expert experience for reasoning, but they have difficulty in learning, and the reasoning process may be vague. ANFIS combines the advantages of the above two approaches. In the evaluation of waste household appliances, owing to the ambiguity of the expressions of various attributes of waste household appliances, such as new, relatively new, super old, very damaged, etc., the use of fuzzy networks is suitable to handle fuzzy language.
## 3 Materials and Methods
### Headspace-SPME (HS-SPME) and Analysis of Volatile Compounds  
Volatile organic compounds were analyzed using HS-SPME with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) StableFlex fiber (50/30 \(\upmu\)m thick, 2 cm long; model 57348-U; Supelco Inc., Bellefonte, PA, USA). The volatile compounds were extracted as reported previously [29], with some modifications. Briefly, a sample of ground rice (0.5 g) was placed in a 10 mL crimp-type HS vial and analyzed using a GC-TOF-MS (Pegasus BT Flux, LECO, St. Joseph, MI, USA). Before analysis, the SPME fiber was conditioned at 270 \({}^{\circ}\)C for 30 min. Then, the vial containing the rice sample was heated in an oven at 80 \({}^{\circ}\)C for 5 min, with a desorption time of 3 min. The injection port, ion source, and transfer line temperatures were set to 250 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C, respectively. Helium was used as the carrier gas, and the flow rate was 1.00 mL/min. Individual samples were automatically injected into an Rtx-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Restek, Bellefonte, PA, USA) at a splitless mode. The GC oven temperature program was set as follows: the initial oven temperature was set to 40 \({}^{\circ}\)C, held for 1 min, then increased to 250 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, and maintained for 10 min. The data were acquired over an \(m\)/z mass range of 35-400, and the acquisition rate was 20 spectra/s. The data were analyzed using the Chroma TOF software (version 5.50, LECO, St. Joseph, MI, USA), and the peaks were identified based on the MS data by comparing with in-house libraries, NIST, and Wiley9 (Figure S9 and Tables S5 and S12).
## 2 State of the Art
### Metamodelling Protocols
Additionally, the need for integrating the key enabling technologies (KETs) into the design process of manufacturing systems [64] and the corresponding orchestration could be enhanced through the use of a BPMN structure, while an alternative approach, that of service-based manufacturing systems [65], could be integrated. The lack of an overall framework though is evident. Figure 2 illustrates this issue, by representing different phases of manufacturing where a metamodel could be used. The interactions between the phases are also shown. It is logical that the capabilities and constraints of the technologies define their applicability. However, it is also the connectivity with each other that drives their choice since they will be used complementarily to create, eventually, a production system. Thus, it may be inevitable that the classification does not only contain the definition of families per use, but also a system that manipulates meta-information on the technologies' capabilities. In summary, a tree-like structure is adopted in conjunction with an information management system. To begin with, industrial entities could be broken down into nine categories: processes, cell, auxiliary functions, monitoring and control, operation and design line level functionalities, information and communication technologies, Industry-4.0-related concepts, materials and resources, and finally, policies and strategies. The fifth industrial revolution (Industry 5.0) is treated herein tentatively as a strategy. At this point two important notes are made: (i) these categories could be enriched in the future and (ii) not all technologies can be uniquely classified. An example of the second fact is that the Internet of Things could fit under both ICT and Industry 4.0. In addition to this, subcategories can be defined, in a tree-like structure, by also mentioning their characteristics in terms of meta-information. This kind of meta-information can be used in various phases of the production, such as the design and the operation of the factory, or the integration of new technologies. To this end, a Unified Manufacturing Meta-Modelling Mark-up Language (UML4.0) can be defined, following the patterns of the RMON MIB tree-like structure and ASN.1, potentially with XML encoding rules. In addition to all of the protocols mentioned above, a taxonomy of methods has to be formed. This could be performed in analogy similar to RMON1 and RMON2, as illustrated in Figure 3.  
Figure 3: Analogy between SNMP taxonomy and current approach. Figure 2: Three distinct phases and the corresponding languages that can be developed. SNMP, MAPE and architecture description languages (ADL) are mentioned.
## Materials and Methods
### Methods  
All participants received the following evaluations within four weeks of the liver histology study: hemogram, liver biochemistry, assessment of viral markers, ARFI, and TE. All related data were officially reported in the hospital's electronic database prior to obtaining the liver histology report. The FIB4 index was calculated according to the method published by [PERSON] et al. [6]. The FIB-4 values were calculated automatically using the formula: age (years) \(\times\) AST [U/L]/(platelets [10\({}^{9}\)/L] \(\times\) (ALT [U/L])\({}^{1/2}\)).  
TE measurements (FibroScan 502 touch machine; Echosens) were obtained according to the manufacturer's standard protocol. After at least three hours of fasting, ten measurements were done from the right intercostal space with the patient in the supine posture. Selection of the M or XL probe was determined by the device. Liver steatosis was measured by the control attenuation parameter (CAP) value during the TE study. ARFI (Acuson S2000; Siemens Healthcare, Erlangen, Germany) imaging was conducted according to the published guidelines by obtaining measurements in two locations [25], with slight modification [7; 18]. Briefly, two locations from the right intercostal space were selected for assessment; one was at the right upper liver and the other was at the right lower liver based on optimal 2D imaging. At each location, 10 measurements were performed with the area of interest perpendicular to the skin. The mean of the medians from the two locations was recorded. Patients maintained slow spontaneous breathing during the measurements. Most of the measurements were conducted by an ultrasound technologist with 25 years of experience.  
The liver fibrosis status in the non-biopsy group was estimated according to our experience in ARFI studies (in press, JMU 2019). The cutoff values for the estimated fibrosis scores of \(<\)2, 2, 3, and 4 were as follows: 1.25, 1.32, 1.79 m/s for the NBNC group; 1.22, 1.32, 1.46 m/s for the CHB group; and 1.23, 1.37, 1.71 m/s for the CHC group. The severity of liver fibrosis in the biopsy group was determined according to the METAVIR fibrosis grading system [26]. For patients with non-alcoholic fatty liver diseases (NAFLD), diagnosis and fibrosis grading were based on the criteria published by [PERSON] et al. [27]. All TE data were correlated with the ARFI measurements and then stratified according to the CAP value.
## 3 Materials and Methods  
The methodology for conducting this research is focused on obtaining pollutant emissions produced by the vehicular operation of a roadway. Currently, different software programs apply these models to calculate vehicle emissions. Some of these were tested for this research. A description of them is provided below:  
* HDM-4 is a model developed for road management that allows for calculations of the amount of pollutant emissions in the form of chemical substances [73].  
* COPERT 3 version 2.1 is software used to calculate road transport emissions. This program classifies vehicles into categories and subcategories, according to the type of fuel, vehicle weight, size, engine technology, etc. [74].
* MOBILE 6.0 calculates emission factors for specific vehicle types; the estimation of emission factors depends on conditions, such as the ambient temperature, travel speed, operating modes, fuel volatility, and proportion of distances traveled by each vehicle type [75].
* CALINE 4 is a dispersion model for measuring air quality [76].  
It is worth noting that for the purposes of this research, HDM-4 software has been chosen as the calculation tool, as its application has been successful in over 100 countries, both developed and developing, and provides results for each generated chemical substance. On the other hand, the HDM-4 SEE (social and environmental effects) module works according to the formulas established by [10]. The formulation of emission models in HDM-4 is based on vehicle-specific fuel consumption, and fuel consumption is dependent on the vehicle characteristics, speed, and road conditions.  
In this regard, Figure 3 outlines the methodological approach for quantifying emissions from road operations.
# NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
## 4 Numerical Experiments
### Inverse Pendulum Problem with Noisy Data  
In this section, we introduce Gaussian noise to the experimental data collected for the inverse problem. The noise was sampled from the Gaussian distribution:  
\[P(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-(x-\mu)^{2}}\big{/}2\sigma^{2} \tag{9}\]  
For this experiment, we chose to set the mean value (\(\mu\)) to 0 and the standard deviation of the noise (\(\sigma\)) to 0.1. As depicted in Figure 5, we trained the PINN model using the ADAM optimizer. However, we encountered an issue where the loss value failed to decrease after 400 epochs. This suggested that the optimizer had become stuck in a local minimum, which is a common problem associated with the ADAM optimizer when presented with noise.  
Figure 4: Inverse pendulum problem: the top figure shows the comparison between the true value with the predicted value from PINN trained by NSGA-PINN method. The figure on the bottom shows the prediction of constant value \(k\).  
Figure 5: Inverse pendulum problem with data noise: Group of figures from left to right columns show each loss value by using different training method with noisy data.  
To address this issue, we implemented the proposed NSGA-PINN method, resulting in significant improvements. Specifically, by increasing the diversity of the NSGA population, we were able to escape the local minimum and converge to a better local optimum, where the initial condition constraints were more effectively satisfied.  
By examining Table 2, we can see a clear numerical difference between the two methods. Specifically, the table shows that the PINN trained by the ADAM method has a total loss value of 0.017, while the PINN trained by the proposed NSGA-PINN method has a total loss value of 0.0133.  
Finally, in Figure 6, we quantify uncertainty using an ensemble of predictions from our proposed method. This ensemble allows us to compute the 95% confidence interval, providing a visual estimate of the uncertainty. To calculate the mean value, we averaged the predicted solutions from an ensemble of 100 PINNs trained by the NSGA-PINN algorithm. Our observations indicate that the mean is close to the solution, demonstrating the effectiveness of the proposed method. When comparing the predicted trajectory from the PINN trained with the NSGA-PINN algorithm to the one trained with the ADAM method, we found that the NSGA-PINN algorithm yields results closer to the real solution in this noisy scenario.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 4 Study on the Relationship between Physical Layer Parameters and the Performance of LoRa  
A numerical model of the physical layer of an LoRa system is shown in Figure 2. This model is mainly used to simulate and evaluate the performance of an LoRa communication system through the AWGN channel. The LoRa PHY frame structure is the preamble part, which is used to synchronize the receiver with the incoming data stream. In the default explicit mode of operation, the number of bytes in the header part specifies the forward error correction (FEC) code rate, payload length, and 2 byte cyclic redundancy check (CRC) in the frame. This part allows the receiver to discard packets with invalid headers. The header part is coded at a code rate of 4/8, and the code rate of the payload is specified in the PHY header. The payload part is the data to be transmitted and the CRC part is used to check byte errors for cyclic redundancy and protect the payload. The input bits are first encoded using a Hamming code. Then, the bit stream is whitened, interleaved, and Gray indexed, and all SF bits are mapped to 2 SF bits. Figure 1 presents a time versus frequency diagram of the LoRa waveform. The extended bits are further transferred to the Invert Fast Fourier Transformation (IFFT). Finally, the time domain signal is modulated by the chirp signal. The signal arrives at the receiving end after passing through the evaluation channel, and synchronization, frequency offset estimation, and compensation are performed before demodulation. Figure 3a illustrates the time versus frequency diagram when there is no  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SF**} & \multirow{2}{*}{**BW**} & **Time-on-Air** & **Data Rate** & **Sensitivity** \\  & & **(msec)** & **(kbps)** & **(dBm)** \\ \hline
7 & 125 & 44.29 & 5.47 & \(-\)123 \\
7 & 250 & 22.14 & 10.94 & \(-\)120 \\
7 & 500 & 11.07 & 21.88 & \(-\)117 \\
8 & 125 & 88.58 & 3.13 & \(-\)126 \\
8 & 250  
noise in the channel. The time versus frequency diagram when the SNR is \(-3\) dB in the AWGN channel is shown in Figure 3b. After the received signal is de-chirped, and then sent to the Fast Fourier Transform (FFT), the maximum value is found from all the values, and the maximum value is the demodulation result. Finally, Gray indexing, de-interleaving, de-whitening, and Hamming decoding are carried out to recover the information.