esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 4 Results and Discussion
### Characteristics of Backflow in the Tee  
Backflow is widespread in non-isothermal mixing in the tee, and these phenomena are closely related to the momentum ratio of the main to branch flow. Therefore, to fully study these phenomena and understand the mechanism of the backflow in the tee, 12 groups of simulations with different momentum ratios were carried out and the flow field characteristics were analyzed. Detailed information on the simulations is listed in Table 4.  
The streamline diagrams of the three impinging jet cases were compared and the results are shown in Figure 12a-c. The streamline diagrams were drawn in the y-z plane at \(X=0\). Figure 12a-c shows that when the flow rate of branch flow is high enough, the upper fluid of the main flow is squeezed into a vortex and the phenomena of the backflow appear near the opposite sidewall. The higher the flow rate, the larger the eddy and the more obvious the phenomena of the backflow. When \(M_{R}\leq 0.1\), the backflow with large eddies occurs near the opposite sidewall upstream. The results are similar to the experiments conducted by several researchers, e.g., [PERSON] et al. (2016), [PERSON] et al. (2017), [PERSON] and [PERSON] (2018, 2019), and [PERSON] et al. (2019) In their research, the phenomena of the backflow occurred when \(M_{R}\leq 0.05\), \(M_{R}\leq 0.062\), \(M_{R}\leq 0.06\), and \(M_{R}\leq 0.05\), respectively. The difference may be the limit of observation methods in the experiment regarding the inadequate sensitivity of temperature sensors.  
As the velocity of the branch flow decreases, so does the vortex, and when \(M_{R}=0.2\), the phenomena of the backflow that appear near the opposite sidewall almost disappeared and gradually transitioned to the place of the right-angle structure upstream of the main flow, as shown in Figure 12d. As the velocity of the branch flow decreases and that of the main flow increases, the vortex formed by backflow becomes larger at the right-angle structure upstream of the branch flow, as shown in Figure 12e-f. Compared with the size of the eddies near the opposite sidewall upstream when \(M_{R}\leq 0.1\), the eddies shown in Figure 12d-f are relatively small, which may be the main reason that researchers have rarely observed the phenomena.
## 7 The Transformative Potential of Blockchain and Integration of AI in Education
### AI and Blockchain for Personalised Learning  
The integration of artificial intelligence (AI) and blockchain holds significant promise for revolutionising personalised learning in higher education. AI algorithms, combined with blockchain's secure and immutable data storage, can analyse vast amounts of learning data to create highly personalised educational pathways. These pathways can adapt in real time to a student's progress, learning style, and goals. One of the key advantages of this combination is the potential for continuous, adaptive learning. AI can analyse a student's performance across various subjects and tasks, stored securely on the blockchain, to identify strengths, weaknesses, and learning patterns. This analysis can then be used to adjust the difficulty, pace, and style of content delivery, ensuring that each student is consistently challenged at an appropriate level [46]. Moreover, AI-powered assessments, with results stored immutably on the blockchain, could provide a more comprehensive and nuanced view of a student's capabilities, potentially replacing or supplementing traditional grading systems [13].  
The blockchain component also allows for the creation of a lifelong learning record, securely storing a student's entire educational history, including formal degrees, micro-credentials, and informal learning experiences. This comprehensive record could be invaluable for employers and further education institutions, providing a more holistic view of an individual's knowledge and skills [47]. For an overview of applications of AI and blockchain in personalised learning, please refer to Table 5.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application Area** & **AI Role** & **Blockchain Role** & **Potential Benefits** \\ \hline \multirow{3}{*}{Adaptive Content Delivery} & Analyse learning patterns & \multirow{3}{*}{Securely store learning progress and preferences} & More engaging and effective learning experiences \\  & and style & & \\ \hline \multirow{3}{*}{Personalised Assessment} & Generate and grade adaptive assessments & Immutably store assessment results and learning trajectories & A fairer, more comprehensive evaluation of student capabilities \\ \hline \multirow{3}{*}{Skill Gap Analysis} & Identify areas for improvement based on performance data & Maintain a secure, verifiable record of skills and knowledge & More targeted learning recommendations and career guidance \\ \hline \multirow{3}{*}{Collaborative Learning} & Match students for peer learning based on complementary strengths and weaknesses & Securely manage peer interactions and contributions & Enhanced peer-to-peer learning experiences \\ \hline \multirow{3}{*}{Credential Verification} & Validate the authenticity of AI-generated personalised credentials & Store and provide easy verification of credentials & Streamlined hiring processes and reduced credential fraud \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Potential applications of AI and blockchain in personalised learning.
## 5 Conclusions  
Aiming at the problems of a large amount of point cloud data, long measurement times, and the low efficiency of splicing registration algorithms, a point cloud registration algorithm based on a binocular structured light measurement system with a low overlap rate was proposed. The proposed registration method can accurately register bullet point clouds at different resolutions with a low overlap. The algorithm in this paper was used to eliminate the noise points of the point cloud and realize the registration of the point cloud at different resolutions. The CAD bullet point cloud registration experiment and self-collected bullet point cloud experiment verified that the proposed algorithm can effectively apply low-overlap point cloud registration. At the same time, compared with the ICP, GICP, and TICP algorithms, the registration error of the proposed algorithm is more than 95% less than that of the ICP algorithm and its variant algorithms when the overlap rate is not less than 20%.  
**Author Contributions:** Conceptualization, Q.Z.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z., R.H. and Y.L.; formal analysis, Q.Z. and H.W.; investigation, Q.Z. and R.H.; resources, Y.L. and H.W.; data curation, Q.Z.; writing--original draft preparation, Q.Z. and Z.M.; writing--review and editing, X.H. and Z.W.; visualization, Z.M. and X.H.; supervision, Z.M. and Z.W.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 4 Advanced Methods for Extraction of Bioactive Compounds from Avocado Peel  
### Microwave-Assisted Extraction (MAE)  
Microwave-assisted extraction (MAE) is an improved maceration method for extracting bioactive compounds that use microwave energy. Typically, this method uses less  
Figure 1: Illustration of avocado structure and its potential bioactivity. References: [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14].  
Figure 2: Number of publications regarding extraction of avocado peels. (*) Data up to May 2024.  
energy, less time, less water, and less cost. As in maceration, a separation step (filtration) is necessary to separate the matrix from the extraction medium at the end of the extraction [15]. It is important to note that microwave-generated temperatures can degrade heat-sensitive components. Even though the thermal deterioration of the compounds cannot be avoided, it is still lower than any traditional extraction method. However, microwave extraction is also limited by the higher investment cost than conventional extraction. According to [PERSON] et al. [7], MAE has been successfully used to extract polyphenols from avocado peel. Temperature, duration, ethanol concentration, and solvent-sample ratio played significant roles in the yield of extractable phenols. The optimal MAE conditions were reported at the temperature of 130 \({}^{\circ}\)C, extraction time of 39 min, ethanol concentration of 36%, and solvent-sample ratio of 44 mL/g. The extract was reported to have contained 53 polar compounds. MAE extracted eight-fold higher phenols within similar conditions than conventional solid-liquid extraction. In addition, [PERSON] et al. [16] demonstrated that avocado peel extract contained procyanidins, catechin, and phenolic acids, exhibiting high antioxidant capacity. MAE can be conducted independently and be combined with another method, such as ultrasound-assisted extraction.
# Experimental Investigation into the Number of Phases in Debris Flows
## 6 Discussion
### Relative Motion of Fluid (Water) and Solids in Debris-Flow Bodies
However, a close examination of the components of the loading-induced rise in \(h\) for the observation tubes (\(\Delta h\)) reveals that it is derived entirely from the uplift of the tubes (\(\Delta h_{\rm P}\)) held up by the slurries, meaning that the contribution of the water movement under load to \(\Delta h\) is zero. In other words, the artificial mechanical forcing exerted around the loading tubes failed to drive the water toward the observation tubes, despite the proximity of the weight. This is consistent with the abrupt increase in \(R\) for the loading tubes and the stabilization of the rate of increase in _PCD_. It takes up to 220 min for the \"pressed\" \(h\) in the loading tubes to rise back to the level comparable to that of the observational piezometers, which is also the cause for the extension of the difference in \(R\) between the two types of piezometers and stabilization of the rate of increase in _PCD_. This indicates that the movement of water in the slurries was greatly hindered. Perhaps more importantly, the high \(R\) values (>1) for the loading tubes in all 36 relative-motion experimental runs were sustained for more than 510 min, and the durations of the \(R\) values (>0.7) for the 72 observation tubes in all 36 runs were also longer than 440 min, which far exceeded the durations of natural debris-flow events, especially the duration of individual debris-flow surges. Further, the higher \(h\) values for the loading tubes than the observational tubes (head difference), caused by loading, are maintained over a long period, ranging from 220 to >500 min, and the difference of between 0.20 and 1.23 (average 0.55, 108 data) in \(R\) between two types of piezometers, occurring after loading, lasts until the end of relative motion experiment (510 min). These results for \(R\) and \(h\) coincide with the low final _PCD_ of only 1.9-11.0%, and further demonstrate that movement of water relative to solids can hardly occur in debris flows. In summary, the results of the escape, displacement, and relative motion experiments immediately confirm that no relative motion of water (fluid) or solids occurs in debris flows, or is negligible. Water and solids in debris flows as a whole respond to applied stress, that is, they move at the same velocity and are deposited by freezing. Therefore, debris flows should be considered as single-phase flows.
## 6 Influence of Floods on GPS, GLONASS, and BDS CNR  
The two floods that occurred in Zhengzhou (DOY:201, 2021) and Xinxiang (DOY:203, 2021), China, are used to analyze the influence of flood on GPS, GLONASS, and BDS CNR. The GPS and GLONASS satellites were observed at Zhengzhou station, and the GPS and BDS satellite were observed at Xinxiang station. The sample rate of the Zhengzhou and Xinxiang data sets is 30 s and 1 s, respectively.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 4 Experiments and Discussion
### Loudness Compensation Experiment  
The loudness compensation experiment based on the gammachirp filterbank was carried out to verify the performance of the system, and the experimental results are given from two perspectives: objective evaluation and subjective assessment.
## 3 Materials and Methods
### Volatile Compound Measurement  
The extraction of volatile compounds was carried out using the headspace solid-phase microextraction (HS-SPME) technique [51]. A chopped sample of 3.00 g was added into a 20 mL headspace vial. 1,2-dichlorobenzene (100 mg/L) of 4 \(\upmu\)L was added as an internal standard, and the vial was sealed with a polytetrafluoroethylene (PTFE) silicone septum. The vial was then equilibrated for 10 min in a water bath at 45 \({}^{\circ}\)C. A divinylbenzene/carboxy/polydimethylsiloxane (DVB/CAR/PDMS) fiber was selected, exposed in the headspace of the vial, and extracted for 30 min at 45 \({}^{\circ}\)C. Finally, gas chromatography-mass spectrometry (GC-MS) was used for qualitative and quantitative analysis. A DB-was (0.25 mm \(\times\) 0.25 \(\upmu\)m \(\times\) 30 m) capillary column was used. The inlet temperature was 250 \({}^{\circ}\)C, and the SPME fiber was desorbed into the GC inlet for 5 min. The split ratio was set as 1:5. The temperature program of the oven was as follows: initial temperature of 40 \({}^{\circ}\)C, held for 2 min, increased to 90 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C /min, held for 5 min, then increased to 200 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C /min and to 230 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C /min, held for 10 min. Helium was used as the carrier gas at a flow rate of 1.5 mL/min. The interface temperature was 280 \({}^{\circ}\)C, and the ion source temperature was 230 \({}^{\circ}\)C. Electron energy was 70 eV. The scan mode was used with a mass scan range of 45-450 \(m/z\). Compounds were identified by searching the NIST 21 mass spectral library and retention indices (RI), with a similarity \(>\) 90%. The relative content of each compound was calculated by comparing its area to that of the internal standard. Three parallels were set for each treatment, and the results were calculated on a dry basis and further analyzed by taking the average value. The OVA value was calculated according to the following equation:  
\[\text{OAV}=\frac{C}{T}\]  
\(C\) is compound content (\(\upmu\)g/kg); \(T\) is the threshold value for the compound.
# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
## 3 Research on Automatic Target Recognition Algorithm Based on Geometric Primitives of Line Groups
### Line Detection  
Hough transform was used to extract the lines from the binary images processed by the edge extraction algorithm. Hough transform could extract all the lines in the image. Figure 12 shows the results of the line extraction of the binary image using Hough transform. It can be seen that there are many false lines, including lines that are too short caused by noise and a long line divided into multiple line segments. The line extracted by Hough transform needed to be processed accordingly to extract the main line, so as to reduce the interference caused by other false lines. The processing method was to first eliminate the false lines that were too short due to noise. The specific steps were as follows: set a fixed threshold value, and when the length of the line was less than this threshold value, it was identified as a false line, and it was eliminated.
## 6 Conclusions  
In this study, nine different fractal dimension configurations were introduced to the four-class BCI Competition IV-2a dataset using seven distinct FD methods: Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. Each combination/feature set was classified using five different machine learning models: LinearSVM, CART, GSVM, SVM with a polynomial kernel, and SGD. The outcomes presented in this work confirm the value of multi-method fractal dimension (FD) features in interpreting motor imagery signals. The novel contribution of the current study is underlined by the highest accuracy resulting from the Katz vs. box-counting vs. correlation dimension FD combination (KFD vs. BCFD vs. CDFD) classified by LinearSVM, which achieved an accuracy of 79.2%. This result surpassed the most recent method in the field--that is, the TWSB method (which achieved a score of 79.1%). Despite the addition of the stochastic gradient descent (SGD) model, linear support vector machine (LinearSVM) still dominated and outperformed all the other classifiers. The proposed FD-based pipeline has a level of performance that situates it alongside modern algorithms. This reinforces the idea that fractal dimension-based techniques offer a powerful approach to enhance motor imagery tasks utilizing the underlying dynamics of EEG signals for brain-computer interfaces.  
**Author Contributions:** [PERSON] supervised the research, analyzed the results, provided feedback, revised the draft, and approved the final version of the article. [PERSON] implemented the research, executed experimental work, analyzed the results, and revised the final article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in the BCI Competition IV 2a: [[https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)]([https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)), accessed on 20 January 2025 [62]. The code and Jupyter Notebooks can be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Discussion  
In this section, experimental results from the previous section are discussed. The results concerned prediction of the CPU usage of three VNFs, namely, firewall, OVS, and Snort via different ML models (MLR, SVR, and FNN). The regression plots for predictions (cf. Figures 7-9) suggest the superiority of the SVR model in terms of prediction accuracy for the considered VNFs. The prediction accuracy of the FNN for the firewall function is considerably low. Especially, the FNN is very inaccurate in predicting extreme (both low and high) CPU usage (cf. Figure 7c). Although prediction accuracy of FNN improves significantly for the OVS and Snort functions, it remains inferior to MLR and SVR in all the studied cases.  
Accuracy results in the form of the CDFs of error (Figures 10-12) are perhaps more useful for the design and analysis of SDN. Typically, the prediction performance of the ML tools is interpreted with a target or admissible prediction error. For the following discussion, it is assumed that up to a 2% prediction error is admissible. Then, it is of  
Figure 12: CDF of percentage prediction errors of FNN.  
interest to see how frequent the error in the ML predictions is admissible. For the SVR, the prediction error is admissible at least approximately 95% of the time for all the studied VNFs. On the other hand, both the MLR and FNN produce acceptable predictions only 60% of the time for their best cases, i.e., OVS.  
The computational nature or data set size of the VNF appears to have negligible effect on the prediction accuracy of the SVR. On the other hand, prediction accuracy of FNN shows significant variations with data set size. Moreover, for the acceptable error of up to 2%, MLR prediction accuracy improves as the computational intensity of the VNF increases i.e., predictions of OVS are more accurate than those of Snort (cf. Figure 10). However, such a clear trend with respect to computational intensity of the VNF cannot be seen in the FNN predictions (cf. Figure 12).  
The poorer prediction performance of FNN, especially in the case of firewall, can be attributed to the relatively small data set size for firewall CPU usage. The prediction performance of FNN can be improved by deploying a deeper ANN with multiple hidden layers. Moreover, the FNN can be complemented with principal component analysis for improving its prediction accuracy. Nevertheless, exploiting the true potential of an ANN typically requires a sufficiently large data set. Therefore, it will be an interesting future work to compare the prediction accuracy of the ML models for a larger and equal-sized data sets for each VNF.
## 3 Proposed Method
### Performance Metric  
Our masked face age estimation system was evaluated in a qualitative and quantitative fashion. As a quantitative performance measure, we used two standard metrics named mean absolute error (MAE) and cumulative score (CS). The predicted age and corresponding age ground-truth with the face image are presented as qualitative standards for the facial age estimation task.  
MAE: The system performance is reported as the mean absolute error in years. It calculates the mean of the absolute error amongst the predicted and ground-truth age values. A de facto standard for evaluating the performance of age estimation is standardized as MAE because most researchers choose it for the system evaluation. The MAE is calculated using Equation (3).  
\[MAE=\frac{\sum_{i=1}^{N}\left|y_{i}^{p}-y_{i}^{gt}\right|}{N} \tag{3}\]  
where \(N\) is the sum of test images, \(y_{i}^{gt}\) denotes the ground-truth age, and \(y_{i}^{p}\) implies the predicted age of the \(i\)th image, respectively.  
CS: The cumulative score is a measure based on the age error threshold value. It represents the percentage of test samples with an absolute age error less than a threshold value \(t\) among overall test samples. The cumulative score is described below by Equation (4):  
\[CS=\frac{N_{ac<t}}{N}*100\% \tag{4}\]  
where \(t\ \in\) [0, 10] and \(N_{ac<t}\) is the count of test images that possess an absolute age error less than the corresponding threshold value. The overall count of test images is denoted as \(N\).
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results  
#### 6.2.1 End-to-End Delay  
Figure 5 shows the average end-to-end delay between the participant and the root of DODAG for a number of hops. In general, the LLN network takes more time to transfer the data, due to its resource-constrained nature. The effective routing mechanism tackles these issues and increases the network performances. However, the end to end delay is one factor to deliver the sensor information on time to the sink time. It is noted that EDADA-RPL, LA-RPL, and RPL delays are 3.5 s, 3.9 s, and 4.2 s respectively, when the hop count number is 10. It is also observed that the delay increases as the number of hop count increases. It is due to a reduction in path breakage and the amount of redundant data transmission.