esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Key Aroma-Active Compounds Forming Different Flavor Characteristics of MT and FMT  
According to the flavor characters of key aroma-active compounds and the frequency of each flavor character, we screened out the dominant flavor characteristics of MT and FMT. The flavor properties of MT and FMT are shown in Figure 5. Both MT and FMT had green, sweet, fruity, fresh, citrus, fatty, and floral attributes, while MT had three unique flavor attributes (aldehyde, nut, and waxy), and FMT had the novel earthy, herbal, and mushroom flavor attributes. The key flavor compounds that contributed to the dominant flavor characteristics of MT and FMT are listed in Figure 5. Among these compounds, the upregulated compounds after fermentation were (E)-2-octanal, 2-ethyllexanol, isophorone, benzeneacetaldehyde, a-pinene, acetophenone, and trimethyl-pyrazine. In addition, the new-formed compounds were 1-octen-3-one, 1-octen-3-ol, citral, and (E)-2-nonenal. These upregulated or newly formed substances helped FMT maintain the pleasant sweet, fresh, fruity, and floral flavors while also making \"earthy\", \"herbal\", and \"mushroom\" to be the new dominant flavor characteristics of fermented tea. Mushroom aroma attribute has been widely concerning in the research of fermented teas [18; 34; 35]. 1-octen-3-ol and (Z)-4-heptenal have been considered to play an important role in the formation of mushroom aroma [18]. Moreover, according to our findings, the newly formed compound 1-octen-3-one was one of the most important contributors to the formation of the unique mushroom flavor of FMT, which has been rarely noticed in previous studies, and 1-octen-3-one also contributed to the formation of herbal and earthy flavors. After fermentation, \"aldehyde\", \"nut\", and \"waxy\" were no longer the dominant aroma attributes of the tea sample, which may be related to the decrease in the contents of heptanal, nonanal, octanal, and 6-methyl-5-hepten-2-one or the decrease of ROAV of some aroma compounds. According to KEGG analysis and previous research, phenylalanine metabolism, monoterenoid biosynthesis, and fatty acid metabolism may have contributed to the biotransformation of the key aromaticity compounds during the artificial fermentation by _E. cristatum_ (Figure 6) [14; 30; 36; 37; 38]. Among them, fatty acid metabolism might play a crucial role in the biotransformation of flavor substances.  
Figure 5: Key aroma-active compounds form the dominant flavor characteristics of MT and FMT. Compounds labeled in blue are downregulated after fermentation, compounds labeled in gray disappeared after fermentation, compounds labeled in red are upregulated after fermentation, and compounds labeled in saffron yellow are newly formed after fermentation.  
Figure 6: Probable metabolic pathway of the key aroma-active compounds. Compounds labeled in blue are downregulated after fermentation, compounds labeled in red are upregulated after fermentation, and those labeled in safron yellow are newly formed after fermentation.
## 3 Results
### Consumer Sensory Evaluation
\begin{table}
\begin{tabular}{l l l l l} \hline
**Sensory Term** & **HSRT-0** & **HSRT-200** & **HSRT-400** & **HSRT-600** \\ \hline Appeal & 6.19 \(\pm\) 1.7 a & 6.85 \(\pm\) 1.4 a & 7.02 \(\pm\) 1.4 a & 6.60 \(\pm\) 1.7 a \\ Overall liking & 6.51 \(\pm\) 1.6 a & 6.92 \(\pm\) 1.3 a & 7.04 \(\pm\) 1.4 a & 6.83 \(\pm\) 1.5 a \\
**Texture** & & & & \\ Tender & 2.04 \(\pm\) 1.9 a & 2.45 \(\pm\) 2.0 a & 2.58 \(\pm\) 1.8 a & 1.96 \(\pm\) 2.0 a \\ Chewy & 0.68 \(\pm\) 1.2 a & 0.53 \(\pm\) 1.0 a & 0.53 \(\pm\) 1.0 a & 0.64 \(\pm\) 1.3 a \\ Juicy & 2.40 \(\pm\) 1.8 a & 3.30 \(\pm\) 1.6 b & 3.25 \(\pm\) 1.6 b & 3.17 \(\pm\) 1.6 ab \\ Dry & 0.89 \(\pm\) 1.6 a & 0.32 \(\pm\) 0.8 b & 0.28 \(\pm\) 0.8 b & 0.21 \(\pm\) 0.63 b \\ Moist & 2.49 \(\pm\) 1.9 a & 3.55 \(\pm\) 1.4 b & 3.47 \(\pm\) 1.4 b & 3.34 \(\pm\) 1.5 ab \\ Hard & 0.34 \(\pm\) 0.9 a & 0.26 \(\pm\) 0.9 a & 0.40 \(\pm\) 1.0 a & 0.34 \(\pm\) 1.1 a \\ Firm & 1.43 \(\pm\) 1.7 a & 0.81 \(\pm\) 1.2 a & 1.17 \(\pm\) 1.7 a & 1.49 \(\pm\) 1.9 a \\ Soft & 2.23 \(\pm\) 2.0 a & 2.98 \(\pm\) 1.8 a & 2.98 \(\pm\) 1.7 a & 2.94 \(\pm\) 1.9 a \\ Elastic & 0.91 \(\pm\) 1.5 a & 0.57 \(\pm\) 1.2 a & 0.57 \(\pm\) 1.3 a & 1.13 \(\pm\) 1.6 a \\ Gummy & 0.74 \(\pm\) 1.2 a & 0.58 \(\pm\) 1.1 a & 0.75 \(\pm\) 1.3 a & 0.94 \(\pm\) 1.6 a \\ Flake-like & 1.21 \(\pm\) 1.8 a & 1.36 \(\pm\) 1.7 a & 1.11 \(\pm\) 1.6 a & 0.92 \(\pm\) 1.4 a \\
**Appearance** & & & & \\ Light colour & 2.40 \(\pm\) 1.9 a & 1.77 \(\pm\) 2.0 a & 1.40 \(\pm\) 1.7 a & 1.72 \(\pm\) 2.0 a \\ Intense red colour & 1.47 \(\pm\) 1.8 a & 1.83 \(\pm\) 1.9 a & 1.68 \(\pm\) 1.8 a & 2.23 \(\pm\) 1.9 a \\ Shiny surface & 2.28 \(\pm\) 1.8 a & 2.96 \(\pm\) 1.8 a & 2.53 \(\pm\) 1.8 a & 2.91 \(\pm\) 1.7 a \\ Matte surface & 1.36 \(\pm\) 1.8 a & 0.77 \(\pm\) 1.5 a & 0.91 \(\pm\) 1.5 a & 0.89 \(\pm\) 1.6 a \\ \hline \end{tabular}
\end{table} 
Table 3: The appeal (1 = not appealing at all, 9 = very appealing), overall liking (1 = I do not like it at all, 9 = I like it very much) and Rate-All-That-Apply (1 = slightly applicable, 5 = very applicable) results (average \(\pm\) standard deviation) of hot smoked rainbow trout (HSRT) samples obtained with consumer sensory evaluation (n = 53). For both HSRT and CSRT, several texture attributes describing the perception of water content were observed to be the most susceptible to HPP. In HSRT, juiciness and moistness significantly increased, and dryness significantly decreased already after treatment at 200 MPa. However, for juiciness and moistness, the scores slightly decreased again at 600 MPa. Juiciness and softness were significantly lower in CSRT-600 in comparison to CSRT-0 and CSRT-200. Instead, hardness and firmness showed an opposite trend, being significantly higher in CSRT-600 than in CSRT-0 or CSRT-200. In comparison to juiciness and softness, moistness in CSRT was more steeply reduced, CSRT-400 already showing significantly lower scores when compared to CSRT-0.
## 3 Power Allocation Optimization and Problem Formulations
### Power Allocation Formulation
A block diagram of the MMIMO systems that demonstrates the power allocation for the training phase and the data phase is shown in Figure 2.  
Figure 2: A diagram that illustrates the power allocation for the training phase and the data phase.
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
## 4 Discussion
As can be seen, the errors resulting from the proposed method based on the use of the accelerometer built-in the smartphone can be compared favourably with those reported in [26], in which an external accelerometer sampled at 10 kHz was used. As it has been pointed out, the proposed method determines the motor speed by means of the frequency corresponding to the rotation of the mechanical shaft of the motor. But the method did not require the use of features unique to induction motors. Accordingly, although the tests were carried out with an induction motor, the proposed method should also be able to measure the speed of other kinds of electric machines and rotating devices. The success of the proposed method relies on the premise that the frequency corresponding to the highest peak of the RMS value of the vibration signal spectrum, \(\omega_{F}\), matches the fundamental (first harmonic) of the rotor mechanical frequency. In a very low percentage (less than 3%) of the about 450 vibration signal samples analysed, and mainly related to the use of the converter, the frequency corresponding to the peak of the RMS value of the vibration signal spectrum corresponds to a frequency that does not match the mechanical frequency of the rotor shaft. In these scarce cases, the identification hypothesis is not fulfilled, and the rotor speed is not properly estimated. An alternative extended method that allows treating even these very infrequent or exceptional cases is then proposed. The extended method only requires the analysis of the spectra of the vibration signals, \(F_{REF}(\omega)\), corresponding to each reference axis, \(REF=\{X,Y,Z\}\), and the identification of the frequencies corresponding to the peak of the three axial vibration signal spectra, \(\omega_{F\_{REF}}\): \(|F_{REF}(\omega_{F\_{REF}})|=\text{MAX}\{|\,F_{REF}(\omega)|\,|\}\). That is, the highest amplitude frequency peak for each axis.
## 5 Performance Evaluation
### Effects of the Number of Agents
This trend highlights that as the number of agents increases, FLDQN exhibits a decreasing average running time compared to DQN, where each agent runs independently. The efficiency gains can be attributed to the weight-sharing mechanism employed in FLDQN, where all agents utilize the weights of a single network learned during the training process. This approach reduces the learning time by minimizing the number of parameters that need to be learned as discussed previously. Our proposed approach assumes a common set of actions among agents while providing each agent with unique observations and hidden states. Furthermore, agents can be trained concurrently, thanks to the parameter-sharing scheme, which takes individual observations into account while leveraging the collective experiences of all agents. This feature clearly positions our suggested approach to lower computation costs and accelerate the training process. In conclusion, DQN operates independently, with each agent working in isolation without the ability to influence or share information with others. This inherent lack of inter-agent communication and parameter sharing in DQN can result in inefficiencies during the learning process. As a consequence, each agent may experience varying convergence times, potentially leading to instances where some agents take more or less time than others to complete their learning objectives. Unlike cooperative approaches, such as FLDQN, where agents can share experiences and insights through parameter sharing, the independent nature of DQN may limit its ability to benefit from collaborative learning dynamics, impacting its overall efficiency and adaptability in multi-agent scenarios. To conclude, Figures 7 and 8 suggest that increasing the number of agents in FLDQN further enhances the overall system performance. Figure 7: Comparison of average travel time between DQN and FLDQN by varying numbers of agents. Performance in the system is dependent on the number of agents.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)
### Password Update Phase  
If the \(USR\) wishes to update or change their password as \(PWD^{New}\), the following steps are followed:  
* The \(USR\) selects a service provider for changing their password.
* The \(USR\) and the service provider are first authenticated mutually. After successful authentication, the \(USR\) updates their password as \(PWD^{New}\).
* The \(USR\) makes the smart card compute: \[A_{RI}^{New}=\left(A_{RU}\oplus PWD\oplus PWD^{New}\right)\] (35)
* The smart card replaces \(A_{RI}\) with \(A_{RU}^{New}\) in its storage.
# Novel Insights on Plant Extracts to Prevent and Treat Recurrent Urinary Tract Infections
## 6 Plants Commonly Used in the Treatment of rUTI
### Herbal Chinese Medicine
#### 6.1.1 Arctostaphylos uwa-ursi  
Leaf extracts from _A. uwa-ursi_, an herb found in Europe and northern America and named \"grape of the bear\", obtained Germany's governmental approval for treatment of UTI [6]. The leaves contain flavonoids, tannins, terpenoids, iridoids, and arbutin, a glycoside with recognized antibacterial activity [26]. _Uvacin_ and _Uva-E_ are commercial products based on uva-ursi extracts from leaves and have been reported to be effective in preventing rUTI. However, other herbal formulae with this plant have dose-dependent activities against uropathogens and may serve as an essential adjuvant treatment to antibiotic prophylaxis in reducing rUTI.
## 3 Results and Discussion
### DLS Analysis  
The size distribution of the as-prepared powder in aqueous colloidal dispersion was measured via DLS. Figure 2 presents the experimental size results.  
After the analysis, two different diffusing species with a distinct hydrodynamic radius distribution were revealed. The average hydrodynamic radius was estimated at \(\sim\)237 nm, with two main peaks at 275.7 nm and 5076 nm being present. The first peak was attributed to nanoscale N-TiO\({}_{2}\) particles, whereas the second was attributed to nanoparticles tending to agglomerate. The intensities of these peaks were 96.1% and 3.9%, respectively, while the polydispersity index (PDI) was 0.327. It is noteworthy that the size measured via this technique was that of the hydrodynamic radius in an aquatic environment; therefore, the particles tended to form colloids, aggregate into larger particles, and exhibit a larger average size than when in dry powder form [35].  
Figure 3 depicts the zeta potential distribution measured for the N-TiO\({}_{2}\) particles dispersed in an aquatic environment.  
Through the zeta potential measurement, the electronic charge of the particles was estimated. When TiO\({}_{2}\) or metal oxides in general are dispersed in water, there is a negative surface charge. The higher the zeta potential value, the grater the stability of the dispersion will be, so these measurements are of critical importance in the field of toxicology and related applications. The exact charge on the particle is dependent on the isoelectric point (IEP) in the aqueous suspension [36; 37]. Here, the zeta potential result for the N-TiO\({}_{2}\) powder was \(-\)20 mV, with a zeta deviation of 4.07 mV--a result considered to be moderately stable in literature [38]. All of the DLS experiments were repeated three times to ensure the reproducibility of the results.  
Figure 3: Zeta potential distribution for N-TiO\({}_{2}\) particles in water dispersion.  
Figure 2: Size distribution for N-TiO\({}_{2}\) powder dispersed in water.
## 5 Summary and Conclusions  
Concepts of the PERS based on an EHF were studied. Additionally, as shown in this analysis, it was possible to increase the efficiency of the EHF. It was evident that double HMGs with PERS could regenerate a large amount of gravitational potential energy and could guarantee the control performance of the boom. Some useful conclusions were achieved as follows:  
1. According to the special working style, the structure of double HMGs with PERS was presented. The discrimination method of the working mode of the proposed double HMGs with PERS for EHF was feasible.
2. The actual descending velocity of the forks was consistent with the trend of the handle signal. The deviation of the descending velocity was small and the following ability was good. The overall pressure fluctuation was small and the stability of the speed control was good when the boom descending velocity of EHF was low.
3. When the system switches between different working modes, both the speed and pressure fluctuations were small and had little influence on the performance of EHF.
4. With the increase of the load mass and descending velocity, the regeneration efficiency increased. The maximum efficiency was up to 74%.  
Though the test results show that the proposed structure could realize the energy regeneration, the volume efficiency of the hydraulic motor affected the regeneration efficiency. The optimization of the HMG is worth further studying.  
[PERSON] proposed the idea of potential energy regeneration for heavy forklift. [PERSON] developed the structure and working mode. [PERSON] analyzed the data. [PERSON] wrote the paper. [PERSON] did the experiment. [PERSON] checked and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 51875218 & 51905180), Excellent Outstanding Youth Foundation of Fujian Province (No. 2018J06014), Industry Cooperation of Major Science and Technology Project of Fujian Province (No. 2019H6015), Natural Science Foundation of Fujian Province (No. 2018J01068 & 2019J01060), and STS project of Fujian Province (No. 2018T3015). This work also has been supported by Fujian Southchina Heavy Machinery Manufacture Co., Ltd.  
The authors declare no conflict of interest.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 4 Fair Editions of Logistic Regression
### Maximum Entropy Logistic Regression with Demographic Parity Constraints  
While standard Logistic Regression aims to maximize the likelihood of observed class labels, the authors of [21] proposed a novel approach, Maximum-Entropy Logistic Regression, which maximizes entropy while enforcing linear fairness constraints. These constraints specifically minimize the covariance between model predictions and the protected attribute, thereby ensuring adherence to the Demographic Parity fairness criterion. Additionally, their Logistic Regression variant is integrated with a backward Stepwise Fairness-Aware Feature Selection algorithm, designed to iteratively remove features that contribute most to disparities in demographic parity. The proposed model is evaluated on five real-world benchmark datasets widely used in fairness-aware machine learning: Adult Income, Bank Marketing, COMPAS, German Credit, and Law School Admission. Experimental results demonstrate that the model performs comparably to Disparate Impact-Free Logistic Regression [18].
# Investigation of a Precise Control Scheme for Rice Quality
## 3 Results and Discussion
### Milling Quality
#### 3.2.2 Head Rice Yield  
The head rice yield (HRY) serves as a direct indicator of the quality of milled rice, thus playing a crucial role in both rice grading and evaluation of drying quality. As shown in Table 2, the HRY values range from 68.3% to 72.3% across different processing methods. According to the ANOVA results presented in Table 4, all experimental factors exhibit significant influences on the HRY with the exception of air velocity (V). Figure 2d illustrates that drying temperature is the dominant factor affecting the HRY, followed by tempering ratio. At a significant level of \(p<0.05\), no interaction terms were found to be significant, while the quadratic terms T\({}^{2}\), RH\({}^{2}\), V\({}^{2}\), and TR\({}^{2}\) have notable effects. The HRY regression equation was derived by eliminating non-significant interactions and quadratic terms (Table 3). The R\({}^{2}\) value of the regression model is 0.9118, with a CV value of 0.47, indicating that the model exhibits excellent fitting ability and reliability.  
Figure 10: Interaction effects of TR and RH on ACP: (**a**) 3D surface and (**b**) contour.  
Figure 9: Interaction effects of RH and MC on ACP: (**a**) 3D surface and (**b**) contour.
## 7 Conclusions  
Underwater acoustic communication is a promising form of technology. The concept of cognitive underwater acoustic networks originated from cognitive radio and is the basis for underwater communication technology. One of the difficulties in applying cognitive radio technology to hydroacoustic communications is that the radio channel is very different from the hydroacoustic channel, which is one of the most complex and variable channels. Thus, a full understanding of the characteristics and physical effects of the hydroacoustic channel is needed for better simulation and a solid foundation for the next step of spectrum sensing. Considering the shortage of underwater spectrum resources, spectrum detection technology must be urgently advanced. However, because the research in this paper is not in the hydroacoustic channel, the existing hydroacoustic channel model is used for the simulation experiments. Because the hydroacoustic signal in the channel will be affected by a variety of factors, such as absorption loss, attenuation, and multipath effects, there are many challenges in applying the energy detection algorithm from radio technology to underwater communication. Understanding the different characteristics of underwater channels and selecting a suitable transmission model for underwater channels would allow the traditional two-threshold detection algorithm to be better applied to underwater communication. In this study, improvements were made in the traditional double-threshold energy detection algorithm; the proposed algorithm takes into account signals for which the energy values are between the two thresholds and uses a co-operative sensing algorithm to detect the signals step by step. A simulation comparison of several conventional energy detection algorithms was performed separately, and the results show that the proposed algorithm achieves its optimal effect when the number of users and the signal-to-noise ratio are different.  
Methodology, J.Z. and R.Z.; writing--original draft preparation, J.Z.; review and editing, J.Z. and R.Z.; supervision, R.Z. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tianjin Intelligent Manufacturing Special Fund Project--Underwater Heterogeneous Node Communication and Positioning Integrated Ad Hoc Network System Research and Development, Project No. 20201207 and Tianjin Sino-German University of Applied Sciences Video Image Intelligent Analysis and Processing Technology Innovation Team and Tianjin \"131\" Innovative Talent Team.: 201912.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Control Allocation  
Analyzing the control handover process and simulation results [40; 41] reveals three key factors influencing control right allocation in cross-domain cooperation. These factors include the radar detection capability of the air relay control agent, the connectivity of the control data link, and the connectivity of the return data link between the air relay control agent and the load agent. To ensure reliable handover and stable control guidance, this study developed a mathematical model for control allocation based on these three aspects.
## 2 Related Work
### Component Reliability Models
Researchers study the information transfer from sink node to sensor node [16; 17; 18] and/or the opposite direction [19; 20; 21]. Although the levels are different, it is undoubted that these component parameter estimations are crucial foundations of the reliability analysis at the path-level and systemlevel of WSN. In addition, higher network connectivity usually leads to higher resistance to node failure because it can provide multiple alternative connectivity paths.