esa-sceva/satcom-chunk-collection huggingface dataset:
# Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
## 3 A Description of the Proposed Customer-Oriented Quality of Service Management Method for IBN
### QoS Improving with IBN Controller  
Traditional networks have limited capabilities to improve packet service methods. There is a moment when it is no longer possible to improve the QoS. In this case, a gradual switch to the IBN network is proposed. When operating the network, the network equipment fails and the best solution is to replace the nodes with support for both methods of service. Over time, the network will come to the conclusion that there will be separate segments that can be served only by controller. As a result, the entire network will switch to new equipment. The introduction of the controller is shown in Figure 19.  
The controller provides many benefits. For example, the controller implements routing and spreading tables instead of routers. In this case, the network becomes sensitive to changes in topology and load. The response time becomes minimal, which allows one to make almost instantaneous decisions. This applies to routing and billing. The controller collects statistics from network nodes. As a result, when requesting the installation of a new session, it will not be necessary to wait for a certain time for the correct installation of billing, which in turn improves the customer's satisfaction index.  
This also improves the balancer and the route selection algorithm. This is all due to the fact that network statistics are the latest. That is, if a remote node fails, the controller will know this in the upcoming iteration of the network.  
Distribution of routing tables by the controller improves several parameters at once. First, the time of establishing the consistency of such a network will be minimal, since each router will have a signal channel to the controller. Secondly, the network is exempted from the excess signaling information  
Figure 18: Proposed packet sorting method.  
distributed on the network. In traditional networks, the routing table is transmitted from each router to its neighbors, where it is compared and processed.
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 3 _Citrus_ Flavonoids: Structure, Classification and Biosynthesis
### Biosynthesis of Flavonoids
UDPglucose flavanone-7-\(O\)-glucosyltransferaseterase (UFGT:EC 2.4.1.115) and UDP-rhamnose flavanone glucoside rhamnsfarse (UFGRT) catalyze the conversion of the flavanone aglycones into their glucosides and rhamnosides [16]. Dihydroflavonol-4-reductase (DFR:EC 1.1.1.219) is a key enzyme required for both anthocyanin biosynthesis and proanthocyanidin accumulation and is involved in the conversion of dihydroflavonols to leucanthocyanidinis [17]. Flavan-3-ols, such as catechin and epicatechin, are synthetized from leucanthocyanidin and anthocyanin. Leucanthocyanidin can be converted to flavian-3-ol (for example, catechin) by leucanthocyanidin reductase (LAR:EC 1.17.1.3) or to anthocyanin by anthocyanin synthase (ANS:EC 1.14.20.4). Anthocyanidin as well can be converted to flavan-3-ol (for example, epicatechin) by anthocyanin reductase (ANR:EC 1.3.1.77), or glycosylated to form anthocyanin [18].
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
###### Abstract  
Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals has become the focus of numerous researches. In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed. High-order matrix founded on bispectrum are extended into structured datasets and transformed into the eigenvalue spectrum domain by GFT, so that features can be extracted from statistical quantities of eigenvalues. Spectral features have been computed to construct the feature vector. Support vector machine based on the radial basis function kernel (SVM-RBF) was used to classify different arrhythmia heartbeats downloaded from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia Database, according to the Association for the Advancement of Medical Instrumentation (AAMI) standard. Based on the cross-validation method, the experimental results depicted that our proposed model, the combination of bispectrum and 2D-GFT, achieved a high classification accuracy of 96.2%.  
ECG signal; feature extraction; high-order spectra; graph Fourier transform; cross-validation +
Footnote †: journal: Applied Sciences
# An Evaluation of the Efficiency of Compartmented
## Materials and Methods  
### Compartmented Alginate Fibres Production  
The compartmented fibres were spun from an emulsion of rejuvenator suspended in a water solution of sodium alginate. A 6 wt % solution of sodium alginate in de-ionized water was prepared for this purpose. At the same time, a 2.5 wt % poly(ethylene-alt-maleic-anhydride) (PEMA) polymeric surfactant solution was prepared by dissolving the copolymer in water at 70 \({}^{\circ}\)C and mixing it for 60 min. After the PEMA has been dissolved in the water, it was allowed to cool to room temperature (20 \(\pm\) 2 \({}^{\circ}\)C) and was combined with the rejuvenator, forming a healing agent solution, in PEMA/rejuvenator 1/1.5 proportion. Sodium alginate and PEMA/rejuvenator solutions were then combined in accordance with [PERSON] et al. [15] with a 70/30 rejuvenator/alginate proportion, found to be optimal for the compartmented alginate fibres. All of the solutions were mixed at 200 rpm for 60 s. It is important to note that the stirring rate and stirring time can be used to control the size of the rejuvenator droplets in the solution and thus the size of the rejuvenator compartments [7; 12; 16] (The lower the stirring rate and the shorter the stirring time, the larger the droplets; the higher the stirring rate and the longer the stirring time, the smaller the droplets).  
The emulsions were spun with a plunger-based lab scale wet spinning line in a conventional wet spinning process [12; 16; 17] to form the rejuvenator-filled compartmented fibres. More details on the fibre preparation and spinning process can be found elsewhere [17]. All chemicals used in the process were purchased from Sigma Aldrich (Zwijndrecht, The Netherlands), except for the rejuvenator, Modesel R20, which was provided by Latexfalt B.V. (Koudekerk aan den Rijn, The Netherlands).
###### Abstract
[17] found no difference between thermal and microwave heating. The results of some studies [18; 19] have demonstrated that the microwave-assisted thermal process was more effective than the conventional one under the same conditions for inactivating methylesterase, polyphenol oxidase, and peroxidase in cloudy apple juice, indicating the possibility of some additive non-thermal effects. A recent review [20] also provided additional details on the non-thermal effects of MW inactivation kinetics. Another recent study [21] found the combination of MW and ultrasound to be the most effective enzyme activity and quality parameters. It is well recognized that the activity and the thermal stability of many enzymes as well as microorganisms are also pH dependent. In one study, it was demonstrated that the \(\alpha\)-amylase activity in apple juice was dependent on pH, heat, and calcium ions [22]. From the preceding sections, it is clear that there is a need for a TTI that can be used under different heating conditions, such as pasteurization (low thermal resistance) or sterilization (high thermal resistance). \(\alpha\)-amylase has been found to be a good enzymatic TTI for kinetic studies. The objectives of this study were (1) to evaluate the kinetic parameters (decimal reduction time, D-values, and temperature sensitivity parameter Z-values) for the inactivation of \(\alpha\)-amylase-based TTI in different pH solutions during continuous-flow non-isothermal microwave heating conditions, and (2) to compare the results with those obtained from the conventional batch thermal treatment as well as the continuous thermal holding time kinetics.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture
### LoRaNET  
The implementation of a design specific network layer protocol was sought out given that the current LoRa protocol, LoRaWAN [37], does not include the functionality for the transceiver in the 433 MHz frequency band, which is the operating frequency that is to be used for the SIMMA project. Moreover, there is less flexibility while configuring the LoRa settings which may increase the transmission range, as well as a high packet overhead which incurs directly in the power consumption.  
LoRaNET is a proprietary network layer implementation, designed for this specific proposal, which uses the LoRa modulation, oriented for ultra-low power WSN, where the synchronization needs and bandwidth are not elevated. LoRaNET is able to implement a star network topology and defines two simple types of nodes:  
* Master node: Creates and manages the network, assigning network addresses to the other nodes and centralizing the message traffic;
* Slave node: Any other node that is part of the network.  
Every time a LoRa network is created, a series of basic parameters are established for its functionality. Within these parameters are the network ID, the maximum wait time for an answer within the network, the maximum wait time for a node to leave the network after the last message, and the number of attempts a message can be re-sent if there is no acknowledgment of message reception. These parameters must be adjusted accordingly with the LoRa RF settings, which alters the transmission range, as the time on air of a message may vary greatly.  
Once the network has been created, the master node can recruit any slave node within its reach as long as it is not part of any active network. If a message can be answered by several slave nodes simultaneously, as when recruiting, scanning the network or with any type of broadcast message, the answer is sent in pseudo-random intervals, reducing the possibility of RF packet collisions.  
When a slave node has been recruited, the master assigns him a 2 byte unique ID in the network, which is associated with the device's MAC address. If for any reason a slave gets disconnected, upon reconnection it will be assigned the same ID. The master node will always be assigned the 0x0000 address, and the 0 xFFFF address is reserved as a broadcast identifier.  
In the Table 1, a summary of the main commands implemented in the current version of LoRaNET is shown.
## 4 Prediction of Radiated Sound Power based on the DRSMs
### Prediction of Sound Radiation near the Sound Radiation Peak  
According to the conclusions mentioned in Section 3.4, in the frequency band near the sound radiation peak, the modal participant coefficients of the corresponding DRSMs are larger than that of some other modes. Therefore, only a few modes, including the DRSMs, have a major contribution to the vibration of the structure. The solving equations method can be used to calculate the mode participant coefficients, which need only the mode shapes of several important modes. Obtaining the participant coefficients is easier by using this method than by using the orthogonal decomposition method.  
For the previous cylindrical shell example, several modes with larger displacement contributions to the vibration of the structure, including the DRSMs, are selected, and the displacement equations are set up according to the principle of the solving equations method. The equations are solved to obtain the modal participant coefficients of the DRSMs according to Equation (3), and then the corresponding radiated sound power is calculated. The selection of measuring points on the surface of the cylindrical shell should be as random as possible to reduce the correlation between the displacement equations.  
In Figure 8, for the solving equations method (SEM), the DRSM (3,1) requires only two measuring points to calculate an almost accurate result for radiated sound power, the DRSM (2,1) requires only eight measuring points, and the DRSM (1,1) requires only 20 measuring points. The radiated sound power of each DRSM is calculated and compared with the total radiated sound power in their  
Figure 7: Comparison of the radiated sound power calculated by different prediction methods and comparison of the radiated sound power calculated by the same method with different number of measuring points.  
corresponding frequency band, as shown in Figure 8. Using only a few measuring points can also obtain good prediction results. As a comparison, Figure 8 also shows the prediction results obtained by using the orthogonal decomposition method (ODM). The DRSM (3,1) requires 4 \(\times\) 4 measuring points, the DRSM (2,1) requires 8 \(\times\) 4 measuring points, and the DRSM (1,1) requires 10 \(\times\) 8 measuring points. If the number of measuring points is reduced, then the result will be worse. This finding means that the solving equations method can effectively reduce the number of measuring points, compared with the orthogonal decomposition method. These modes should only be used to predict the radiated sound power in the corresponding frequency band, because they are not the DRSM in other frequency bands, and the prediction result may have errors. As a comparison, Figure 8 also gives the prediction results obtained by using the BEM. The vibration displacement data of 10 \(\times\) 22 measuring points is used as boundary conditions to calculate the radiated sound field. This result shows that the two other methods need much fewer measuring points than the BEM.
## 3 Results
### Coefficients of Variation (CoVs) during Signal Amplification  
The signals that were obtained during chip number-based signal amplifications were characterized with an average of 8.737% coefficient of variation throughout all the mAs, kVp, and SDD X-ray parameter variations. Amplifier board output signals also had an average coefficient of variation of 9.424% across the mAs, kVp and SDD parameter variations, as illustrated in Table 2.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**FT n.** & **VOC** & **CAS** & **MS\%** & **LRI** & **LRI lib** & **Rt** \\ \hline FT0347 & 2-Butanol, 2,3-dimethyl- & 594-60-5 & 83 & 649 & 645 & 2.31 \\ FT1087 & Hexanal & 66-25-1 & 94 & 800 & 801 & 5.31 \\ FT0867 & Furan, 2-butyl- & 4466-24-4 & 83 & 888 & 890 & 7.98 \\ FT1559 & Furan, 2-methyl-3-(methylthio)- & 63012-97-5 & 84 & 942 & 946 & 10.23 \\ FT0792 & Phenylacetaldehyde & 122-78-1 & 85 & 1037 & 1045 & 14.50 \\ FT1522 & unknown & & & 1074 & & 16.27 \\ FT1525 & (Z)-2-Hexanal diethyl acetal & 87383-46-8 & 81 & 1078 & 1077 & 16.49 \\ FT1527 & Decanal & 112-31-2 & 81 & 1171 & 1187 & 20.86 \\ FT1698 & 2-Nonenoic acid, methyl ester & 111-79-5 & 81 & 1189 & 1191 & 21.79 \\ FT1521 & unknown & & & 1270 & & 25.51 \\ FT1866 & unknown & & & 1326 & & 28.03 \\ FT2028 & unknown & & & 1462 & & 33.85 \\ FT2272 & Ethyl 4-\(t\)-butylbenzoate & 5406-57-5 & 80 & 1498 & 1487 & 35.42 \\ \hline \end{tabular}
\end{table} 
Table 1: Panel of 13 discriminatory features of seven mycobacteria species after random forest data reduction. Number of features, chemical name, CAS number, MS similarity (%), LRI calculated, LRI in NIST library (LRI lib) and retention time (Rt) are also reported. Figure 4: On the top, data feature reduction using RF algorithm, leading to generation of a panel of 13 most discriminatory features. On the bottom left, PCA of seven mycobacteria species plotting the panel of 13 features. On the bottom right, the dendrogram (top of the heatmap) depicts the relatedness amongst samples. Color scheme for samples is based on the mycobacteria species. Legenda: FT0347, 2-Butanol, 2,3-dimethyl-; FT1087, Hexanal; FT0867, Furan, 2-butyl-; FT1559, Furan, 2-methyl-3-(methylthio)-; FT0792, Phenylacetaldehyde; FT1522, unknown; FT1525, (Z)-2-Hexenal diethyl acetal; FT1527, Decanal; FT1698, 2-Nonenoic acid, methyl ester; FT1521, unknown; FT1866, unknown; 4-\(t\)-butylbenzoate. Myobacteria species and complex are reported in Section 2.2.1. Figure 5: Bar-plot of 13 discriminatory volatile features (FT). Legenda: FT0347, 2-Butanol, 2,3-dimethyl-; FT1087, Hexanal; FT0867, Furan, 2-butyl-; FT1559, Furan, 2-methyl-3-(methylthio)-; FT0792, Phenylacetaldehyde; FT1522, unknown; FT1525, (Z)-2-Hexenal diethyl acetal; FT1527, Decanal; FT1698, 2-Nonenoic acid, methyl ester; FT1521, unknown; FT1866, unknown; FT2028, unknown; FT2272, Ethyl 4-\(t\)-butylbenzoate. Myobacteria species and complex are reported in Section 2.2.1.
## 1 Introduction
Though the authors in [15; 24] have performed an admirable job in terms of accurate sum-rate analysis, their analysis procedures and the obtained sum-rate formulas are quite complicated and tedious. Therefore, how to evaluate the achievable sum-rate among the whole SNR regime with low complexity and acceptable accuracy is still well worth exploring. Moreover, all those IA performance analyses are based on VQ, whose codebook size grows exponentially with the quantized bits. For large channel matrices or high SNR regime, the increased quantized bit number results in unaffordable complexity when comparing the chordal distances between the target vector and all the codewords. Some researchers tried to reduce the VQ bits from different aspects, including optimizing the codebook design, quantizing the differential CSI, or designing new IA protocols to feedback precoders instead channel matrices [25; 26; 27; 28]. However, their efforts did not change the nature of the exponential complexity problem. Moreover, though SQ is simpler in principle [29], so far few works have investigated the theoretical performance analysis of IA with SQ-based feedback in depth. To cope with those challenges, in this paper, we focus on establishing a quantitative relationship between feedback bit numbers and the achievable rates of IA based on RVQ and SQ, and derive the concise closed-form sum-rate expressions for convenient performance evaluation. The achievable sum-rate of IA with three feedback types is analyzed, i.e., the RVQ-based CSI feedback case, the SQ-based CSI feedback case, and the mixed case with SQ-based CSI feedback and RVQ-based DI feedback. The closed-form sum-rate formulas of all three cases are derived, and the theoretical analyses are validated by simulation results. The rest of this paper is organized as follows. In Section 2, we introduce the system model including the CSI and DI feedback procedures. The proposed sum-rate analysis approaches with RVQ-based CSI feedback, SQ-based CSI feedback, and mixed CSI and DI feedback are introduced in Section 3-5, respectively. The simulation results are shown in Section 6, and Section 7 concludes the paper. Notice that in the following notations, normal letters denote scalar values, boldface lowercase letters and boldface uppercase letters represent vectors and matrices, respectively. \(\mathbf{A}^{H}\) denotes the conjugate transpose operator for matrix \(\mathbf{A}\).
## 3 Results
### Dissolved Pollution  
The four types of dissolved pollution are urban runoff, agricultural runoff, rural resident pollution, and livestock pollution, which caused TN, TP, NH\({}_{4}^{+}\)-N, and COD in each city. The term \"pollution load\" refers to the pollution dissolved by water in each pixel  
Figure 5: Land use and land cover and the main wastewater treatment plants in Henan Province. Figure 6: FVC data. and is the total value across the four pollution types. However, the total pollution refers to the pollution load multiplied by the area, and it is computed for four pollution types. First, we compare the pollution loads of the four types across different cities, as shown in Table 3.  
From Table 3, we can see that the dissolved pollution loads of Henan Province in 2021 were 57.1644 kg\(\cdot\)km\({}^{-2}\), 6.3586 kg\(\cdot\)km\({}^{-2}\), 36.1165 kg\(\cdot\)km\({}^{-2}\), and 21.4827 kg\(\cdot\)km\({}^{-2}\) for TN, TP, NH\({}_{4}^{+}\)-N, and COD, respectively. The total pollution amounts were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, NH\({}_{4}^{+}\)-N, and COD, respectively. The cities of Jiyuan, Luoyang, Xinyang, and Zhengzhou had relatively low pollution loads. Conversely, Puyang, Hebi, Luohe, and Kaifeng had relatively high pollution loads. For a single pollutant, Puyang and Luohe discharged the most TN, while Sanmencia and Luoyang discharged the least. For TP, Hebi and Luohe discharged the most, and Sanmencia and Luoyang discharged the least. For NH\({}_{4}^{+}\)-N, the cities discharging the most were Zhoukou and Luohe; the cities discharging the least were Sanmencia and Luoyang. The cities discharging high amounts of COD were Zhengzhou and Luohe, while those discharging low amounts were Sanmencia and Xinyang. More than half of the cities discharged more TN, TP, NH\({}_{4}^{+}\)-N, and COD than the average across the province. These cities are mainly distributed in the north and central parts of Henan province, i.e., Anyang, Puyang, and Hebi in the north, and Kaifeng, Shangqiu, Xuchang, Zhoukou, and Luohe in the center. The pollution load of each dissolved pollution type, including the pollution elements TN, TP, NH\({}_{4}^{+}\)-N, and COD, for urban runoff dissolved pollution is shown in Figure 7.  
From Figure 6, we can see that the city that discharged the most urban runoff was Zhengzhou: 0.1108 kg\(\cdot\)km\({}^{-2}\), 0.0116 kg\(\cdot\)km\({}^{-2}\), 4.6174 kg\(\cdot\)km\({}^{-2}\), and 36.4119 kg\(\cdot\)km\({}^{-2}\) for TN, TP, NH\({}_{4}^{+}\)-N, and COD, respectively. Figure 7 also shows that the pollution load was high in the north and low in the south. For agricultural runoff, the cities with the highest pollution loads were Hebi, Puyang, and Luohe. The TN and TP of Hebi were 253.736 kg\(\cdot\)km\({}^{-2}\) and 25.9367 kg\(\cdot\)km\({}^{-2}\), respectively. We can also observe that the pollution load was low in the southwest of Henan. For rural resident pollution, the cities discharging the most