esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study demonstrated the effectiveness of using an electronic nose (E-nose) with gas chromatography-mass spectrometry (GC-MS) for assessing oyster freshness. By integrating the E-nose with machine learning algorithms such as support vector machines (SVMs) and random forest (RF), we achieved rapid and reliable freshness predictions. Linear Discriminant Analysis (LDA) outperformed Principal Component Analysis (PCA) in classification accuracy, particularly at higher storage temperatures, indicating its suitability for distinguishing subtle variations in volatile compounds during spoilage. Significant changes in volatile compounds, including an upward trend in cis-2-(2-Pentenyl) furan, suggest potential markers for developing simpler freshness tests. This integration provides a cost-effective and non-destructive alternative to traditional GC-MS, enabling real-time quality monitoring suitable for large-scale and on-site applications. The integration of E-nose and machine learning provides a promising alternative to traditional GC-MS analysis, offering a cost-effective and non-destructive approach for real-time monitoring of seafood quality. Future research could further refine the E-nose's sensitivity to specific volatile markers and explore its application across other seafood products, contributing to improved food quality control and safety.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods13193110/s1](https://www.mdpi.com/article/10.3390/foods13193110/s1)]([https://www.mdpi.com/article/10.3390/foods13193110/s1](https://www.mdpi.com/article/10.3390/foods13193110/s1)), Tables S1-S4: Relative content of volatiles in oysters at 4 \({}^{\circ}\)C, 12 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C for different storage times.  
Conceptualization, B.W. and W.K.; methodology, B.W. and X.Z.; software, B.W. and K.L.; validation, B.W. and X.D.; formal analysis, B.W. and X.D.; investigation, B.W.; resources, B.W., G.W. and A.H.; data curation, B.W., C.W. and X.D.; writing--original draft preparation, B.W. and X.D.; writing--review and editing, B.W., W.K. and X.D.; visualization, B.W. and Y.W.; supervision, B.W., W.K. and X.Z.; project administration, B.W., W.K. and X.Z.; funding acquisition, B.W., W.K. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This project was independently supported by the \"Research on multi-scale flexible sensing and reliable traceability technology in cold chain process of prefabricated vegetables\" project (Grant No. GHYF2024022) and the \"2022 Yantai urban parities coordinated with university development project\".  
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.4 Call Initiated by User A (Caller)  
Once User A dialed a call, the IP address of the receiver was also captured. While this scenario differs in the case of sending text messages, the IP address of the receiver would never be known even if the users were on the same network. Hence, binding between both users is explicitly seen. It was observed that the Signal app connected to at least three servers to finally connect with the receiver or callee. When a user opens the app, it is connected to a Signal server. Next, the user opens a chat window of the receiver to place a voice call. Further, when a user dials a call, the client app makes a DNS query to the Simple/Session Traversal of UDP through NAT (STUN) server of Google (stunl.1.google.com) and the Traversal Using Relays around NAT (TURN) server (turn1.whispersystems.org). In response to these DNS queries, the STUN server provides a Google server such as (64.233.161.127) and the DNS turn query provides a server IP of whispersystems.org/signal server such as (52.47.185.9) as shown in Figure 9.  
Stun protocol is used for UDP calls to connect the electronic devices behind the NAT. This resolves the issues related to devices that are deployed between different NAT. Whereas TURN protocol is an extension of STUN. It is used to relay the messages between two devices after a connection is established. It can also be noted that binding is performed by the STUN server, while further communication is relayed between two devices and performed through the TURN server. These patterns were observed while the audio call was established, hence 1388 bytes of reassembled PDUs were sent to the Signal server to indicate audio call activity. When the binding request between the users was successful, it was observed that the private IP address of the other user was also visible. In Figure 10, the IP address of the receiver was 192.168.10.10, and the IP address of the initiating user was 192.168.137.206.  
The port numbers used by the TURN servers were 80 and 3478. In most cases, it was noticed that the public IP of the user was mapped with the TURN server. When the call was successfully established between the users, multiple UDP packets of length 22 were found that were transmitted between both users as shown in Figure 11. When the call ended, ICMP packets were sent from the caller to the server that showed that the destination host  
Figure 10: Receiver’s IP address during call initiated by User A.  
Figure 9: STUN and TURN servers during call initiated by User A.  
was unreachable. The traffic patterns related to the indication of ending a call are shown in Figure 12.
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 7 Conclusions  
This paper proposed the multipath routing algorithm EMRAR, which aims to strike a balance between transmission reliability and data accuracy in wireless sensor networks. It employs a multi-objective optimization with constraints, utilizing an improved immune genetic algorithm, IAAIA, to obtain the optimal solution. Simulation results verify that the proposed EMRAR algorithm outperforms existing algorithms in terms of transmission reliability, data accuracy, and energy consumption. Convergence time and end-to-end delay are also discussed. The findings suggest that the EMRAR algorithm is well-suited for scenarios with high requirements in data accuracy and transmission reliability at the receiving end, where real-time computing or delay sensitivity is less critical. In addition, the work in this paper provides a valuable method to optimize multiple objectives simultaneously in multipath routing for sensor networks, with potential applications in addressing similar challenges in WSNs.  
Our future research in this area will focus on applying the EMRAR algorithm to mobile ad hoc networks, such as VANET, where network topology dynamically changes due to vehicle movements. In such dynamic environments, calculating multiple path sets in real time to satisfy the evolving requirement is crucial. Hence, the impacts of node mobility and the real-time performance of the algorithm are issues that must be further considered.  
Conceptualization, F.D.; methodology, F.D., Y.M. and W.Y.; software, W.Y. and Y.M.; investigation, Y.M.; writing--original draft preparation, F.D. and W.Y.; writing--review and editing, Y.M. and X.Y.; supervision, F.Z.; project administration, B.N.; funding acquisition, F.Z. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: Key Technology Research of Early Fault Diagnose & Residual Lifetime (grant number 51975433) and Research on High-Speed and High-Precision Control Strategy of Linear Servo System under Periodic Disturbance (grant number 51975430).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 7: The comparison of average end-to-end delay under different data transmission rates.
## 5 Conclusions  
* Under dry sliding wear conditions, the COF vs. sliding distance curves of the ASF samples show three distinct regions: an initial sharp increase, followed by a gradual decrease and then a final steady state. The average COF and specific wear increase with increasing ASW particle size.
* All samples show relatively even wear tracks and similar wear progression behaviours. The wear is predominantly adhesive in L-ASF and abrasive in S-ASF and M-ASF. The samples also show behaviour indicative of delamination and oxidative wear.
* Lubrication leads to a much lower average COF and specific wear and a change from adhesive to abrasive wear compared to the dry tests. The porous ASW particles enhance the wear reduction in lubricated wear tests because they act as lubricant reservoirs, allowing for a continuous presence of lubrication.
* The heating of ASW particles leads to a significant change in wear behaviour for L-ASF, due to the compositional change upon heating, but no change for S-ASF and M-ASF. T6 treatment leads to a significant reduction in the COF and a lower specific wear due to the hardening of the Al matrix.  
Figure 6: Micrographs of worn surfaces **(a1,b1)**, micrographs of static partners **(a2,b2)** and wear track maps **(a3,b3)** for ESF samples under dry **(a1–a3)** and lubricated **(b1–b3)** sliding wear conditions.  
* The S-ASF and M-ASF samples have a superior wear resistance than the ESF sample because the Small and Medium ASW particles are stronger and harder than the E-spheres due to lower porosities. The ASFs experience much less plastic deformation, leading to higher COF and specific wear values than the ESF. The wear resistance of the L-ASF sample is inferior to the ESF sample because the non-heated Large ASW particles contain a significant amount of metal residue.  
Conceptualization, Y.Z.; Methodology, B.M. and Y.Z.; Formal analysis, B.M. and Y.Z.; Investigation, B.M.; Resources, Y.Z.; Data curation, B.M.; Writing--original draft, B.M.; Writing--review & editing, Y.Z.; Supervision, Y.Z.; Project administration, B.M. and Y.Z.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Low Carbon Eco-Innovatory at the University of Liverpool along with Ultromex Ltd.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to acknowledge the support of [PERSON] at the University of Chester for providing access to their Tribology Lab at Thronton Science Park.  
The authors declare no conflict of interest.
## 3 Uncertainty Propagation Approach of Constellation Phase  
The uncertainty propagation problem satisfying the dynamic constraints Equation (1) can be described as: Given the initial state \(\xi_{0}\) and the PDF \(p(t,\xi_{0})\) of the initial state deviation \(\delta\xi_{0}\), find the PDF \(p(t,\xi)\) of the state \(\xi(t)\) of the dynamical system at any moment or its statistical moments of each order. Generally only the first two order statistical moments of the deviation \(\delta\xi\), i.e., the mean and covariance matrix, are of interest.  
Because controllers, thrusters, and observers have a direct influence on the instantaneous state of the satellite, and the terminal state quantity we are interested in is the mean elements of the satellite, the samples are generated on the satellite's osculating trajectory,and the terminal samples appear around the mean trajectory. Therefore, the initial state errors are modeled as a probability distribution of three-dimensional position/velocity components \(x_{0}=[r,v]^{\mathrm{T}}\) of the two co-plane satellites at the moment \(t_{0}\). Since the short period effect is removed by averaging in the dynamics model, only long and long-period effects are retained, so the different initial positions of satellites in one orbital plane do not influence the evolution process. It means that the relative phase evolution of any two co-plane satellites can represent the divergence of this whole orbital plane. The relative phase \(\Delta\phi=\phi_{1}(t)-\phi_{2}(t)\) of the two satellites after time \(t\) is chosen as the terminal state quantity. The schematic diagram of two co-plane satellites' uncertainty propagation is shown in Figure 1. Since the orbits of LEO mega-constellations tend to be near-circular, the argument of latitude \(\phi=\omega+M\) is used to characterize the phase angle of a satellite. The evolution of the initial state to the terminal state involves, in turn, the transformation of the position-velocity components to the osculating elements, the transform between osculating elements and mean elements, and the semi-analytical propagation. The whole physical process shows strong nonlinearities, so the Monte Carlo is an appropriate analysis approach.
## 2 Numerical Simulation Methodology
### Definition of Materials and Boundary Conditions
#### 2.2.2 Boundary Conditions
The smoke outlet of the burner was considered as a mass-flow inlet with a velocity of 0.012 kg/s, and the flue gas fractions were calculated as 9.2% CO\({}_{2}\), 18.3% H\({}_{2}\)O and 72.5% N\({}_{2}\). The temperature of exhaust gas was set with a measured value. The top surface of frying stoves or stockpots was also regarded as a source of thermal plumes, while the exterior surface (smoke outlet) was set as the mass-flow inlet. The boundary parameters were similar to those of the cauldron stove. The radiation heat transfer between the stove and the human body can increase the human shell temperature directly. However, this paper mainly focused on the distribution \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Type** & **Pollutant** & **Detail Parameters** & **Reference** \\ \hline Pot (exterior) & Wall & - & T = 100 \({}^{\circ}\)C & \\ \hline Pot (top) & Mass-flow inlet & PM\({}_{2.5}\) & Release rate = 10\({}^{-7}\) kg/s; & [29] \\  & & & T = 100 \({}^{\circ}\)C & \\ \hline Pot & Velocity inlet & SF6 & \begin{tabular}{c} V = 0.29 m/s; \\ T = 100 \({}^{\circ}\)C; \\ Mass fraction = 10\% \\ \end{tabular} & [19] \\ \hline Gas burner & Velocity inlet & Exhaust gas & \
\begin{tabular}{c} V = 1.714 m/s; \\ T = 1200 K \\ \end{tabular} & [18] \\ \hline Pot & Velocity inlet & - &
\begin{tabular}{c} V = 0.161–0.192 m/s; \\ T = 200–300 \({}^{\circ}\)C \\ \end{tabular} & [31] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Schematic diagram of heat and mass transfer process of different stoves. (**a**) Cauldron stove; (**b**) Frying stove or stockpot. of indoor air temperature in the kitchen, which is mainly determined by convective heat transfer. In addition, the area of the stove surface is small compared with the kitchen volume. Therefore, radiation heat exchanges were not included in this paper [29]. For the exhaust fume system and make-up air parts, the boundaries of the exhaust hood, ESP plates and air duct were set as adiabatic and non-slip wall boundary conditions [34]. The voltage of the ESP collecting plate and wire plate was set as 5 and 25 kV, and the other wall boundaries were regarded as insulation. The particulate boundary of the collecting plate and wire plate was \"trap\", and that of the others was \"reflect\" [34]. A reverse velocity-inlet was imposed at the discharge outlet [11; 13; 17; 31]. A pressure-outlet boundary condition was imposed at the doors and fresh air inlet [11; 16]. Fluent could automatically treat them as pressure-inlet boundaries if the velocity vectors are pointed inside there. Zero-gauge pressure was given at all pressure-outlet boundaries. The boundary conditions of the turbulence specification were specified by the intensity and hydraulic diameter. The particulate boundary of outlet and doors was \"escape\". The boundary condition types and parameters for the case CCKEF system are shown in Table 2.
## 4 Results and Discussions
### Trending Areas of Application of Hybridized K-Means with MOA  
The trending areas of application of K-means with MOA hybrid algorithms reported in the reviewed literature include cluster analysis optimization, image segmentation, social network community detection, localization and sizing of flexible AC transmission system, routing protocols, color quantization, forecasting models, image compression, satellite image classification, facility location, intrusion detection, document information retrieval, and cloud networks load balancing. A summarized list of all the application areas identified in the cause of the study that are associated with the hybrid K-means algorithms is listed in Table 1.
## 4 Conclusions  
A novel OTDR technique, called DLFM-OTDR, has been proposed. It involves the intensity modulation on the probe pulse, where the modulation signal is the electrical digital LFM signal, and at the receiver, STFrFT is used to process the received signal and filter out noise. We theoretically analyzed and experimentally verified the working principle of DLFM-OTDR. Both the theoretical and experimental results showed that the OTDR spatial resolution is determined by the sweep frequency range of the LFM signal rather than the pulse width. In the demo experiment, a 7-dB improvement in the dynamic range and an approximately 10-times improvement in the spatial resolution were achieved compared to the conventional OTDR. Due to the bandwidth limit of the laser-driver circuit in the conventional OTDR development board, the sweep frequency range of the digital LFM signal ranged only from 4 to 8 MHz (\(B\) = 4 MHz). If a wide-bandwidth laser driver circuit generating a digital LFM signal with a broader sweep frequency range is adopted in DLFM-OTDR, the spatial resolution is expected to be further improved. In addition, the influence of the STFrFT window on the peak sidelobe ratio of the reflection peak was studied, proving that the use of a Hamming window can significantly suppress the sidelobes, but at the same time, the mainlobe would be widened by approximately 1.6 times.  
Figure 8: Measurement results of the Hamming short-time fractional Fourier transform window. (**a**) Considering only the primary linear frequency modulation signal in the backscattered signal and (**b**) adding the direct current component.  
Figure 9: Measurement results obtained by extracting the tertiary LFM signal. (**a**) Considering only the tertiary LFM signal in the backscattered signal and (**b**) adding the direct current component.  
**Author Contributions:** [PERSON] finished writing the whole manuscript and conducted experimental verification. [PERSON], [PERSON], [PERSON] and [PERSON] presented the idea. [PERSON] and [PERSON] are the co-first authors.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work is supported by Open Foundation of China Southern Power Grin and State Key Laboratory of Optical Communication Technologies and Networks (W.R.I.) and the Accelink Technologies Company Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Performance Evaluation
### Delay Impact on Detection Efficiency  
The crucial task is to control the time in terms of clone detection delay in the proposed multi-stage detection method. Parameter \"_d_\" (i.e., time out phase for receiving message) involves checking the delay in clone detection.  
As presented in Figure 14, a massive amount of clone mobile nodes is placed in networks, therefore the rise in the value of \(d\) will disturb the performance of the mobile nodes because of time delay. Results show that both are directly proportional to each other. Hence, detection delay will increase with rising the factor \"_d_\" significantly. The mentioned situation should be sorted out with utilizing the suitable clustering approach or by neglecting the nodes that move outside during the detection period.  
Figure 12: Lifetime of nodes.  
Figure 13: Detection time.
## 5 Error Behavior of the Reliability Estimation
### Influence of Interval Variables' Distributions  
The influence of the distribution types of interval variables can significantly influence the reliability estimation result, which should receive careful consideration. In order to study an element such an influence, the interval variables of Section 4.1 were assigned to different distributions (shown in Table 5) whose corresponding reliability estimation results are concluded in Table 6. It can be seen from Table 6 that the proposed method consistently gives accurate reliability estimations for all the studied distributions, which are therefore applicable to a wide range of distribution types for the interval variables. Such a result suggests that the partial decorrelation process adopted by the proposed method can decrease the influence of the distribution types of interval variables on the reliability, which also demonstrates the good robustness of the proposed method.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Failure Probability** \\ \hline MCS of \(10^{7}\) samples & \(P_{f}=0.0178\) \\ Interval MCS plus GA & \(\min P_{f}=0.0056\) & \(\max P_{f}=0.0427\) \\ The proposed method plus MCS & \(\min P_{f}=0.0172\) \\ FORM plus M-DRM & \(\min P_{f}=0.0048\) & \(\max P_{f}=0.0448\) \\ The proposed method plus FORM & \(P_{f}=0.0172\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results comparison of the different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter 1** & **Parameter 2** & **Variables Type** \\ \hline \(q\)(N/m) & 20,000 (mean) & 200 (standard deviation) & Normal distribution \\ \(l\)(m) & 12 (mean) & 0.12 (standard deviation) & Normal distribution \\ \(A_{S}\)(m\({}^{2}\)) & \(9.83\times 10^{-4}\) (mean) & \(9.83\times 10^{-6}\) (standard deviation) & Normal distribution \\ \(A_{C}\)(m\({}^{2}\)) & \(4.00\times 10^{-2}\) (mean) & \(4.00\times 10^{-4}\) (standard deviation) & Normal distribution \\ \(E_{S}\)(N/m\({}^{2}\)) & \(2.00\times 10^{11}\) (midpoint) & \(2.00\times 10^{9}\) (radius) & Interval distribution \\ \(E_{C}\)(N/m\({}^{2}\)) & \(3.00\times 10^{10}\) (midpoint) & \(3.00\times 10^{8}\) (radius) & Interval distribution \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of the variables in Section 4.1.