esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm
## 5 Gas Cyclone Design  
Cyclones are devices used for sizing, classification, and screening of particulate materials in mixtures with fluid (gases or liquid). Cyclones come in many sizes and shapes and have no moving parts. The mode of operation involves the process of subjecting the flowing fluid to swirl around the cylindrical part of the device. They impact the cyclone walls, fall down the cyclone wall (by gravity), and are collected in a hopper. The most important parameter of a cyclone is its collection efficiency and the pressure drop across the unit. Cyclone efficiency is increased through:  
1. Reduction in cyclone diameter, gas outlet diameter, and cone angle;
2. Increasing the cyclone body length.  
Capacity is, however, improved by increasing the cyclone diameter, inlet diameter, and body length. Increasing the pressure drop give rise to:  
* Increase in separation efficiency;
* Higher capacity;
* Decrease in the underflow to throughput ratio;
* Cleaner overflow.  
The design parameters include:  
- Feed rate \(Q\) m\({}^{3}\)/s;  
- Cyclone diameter \(\mathrm{D_{c}}\), m;  
- Set cut size \(d_{pc}\), \(\mathrm{\upmu m}\);  
- Cyclone efficiency;  
- Cyclone pressure drop, \(\mathrm{D_{P}}\);  
- Cyclone geometry (D (cyclone diameter), De(vortex finder diameter), h(cylindrical height), H(overall height), a(inlet height), b(inlet width), B(cone outlet diameter), S(vortex finder height));  
- Temperature, \({}^{\circ}\)C;  
- Pressure, N/m\({}^{2}\);  
- Viscosity, \(\mathrm{Ns/m^{2}}\);  
- Density fluid, \(\mathrm{kg/m^{3}}\);  
- Mass median diameter \(\mathrm{MMD}\), (\(\mathrm{\upmu m}\));  
- Geometric standard deviation GSD, \(\mathrm{\upmu m}\);  
- Number of cyclones;  
- Fluid density, \(\mathrm{kg/m^{3}}\);  
- Particle density, \(\mathrm{kg/m^{3}}\).  
Given the cyclone geometry (as shown in Figure 5) and the operating conditions, there are five design parameters that can be specified for a design.  
These parameters are:  
1. Feed rate, Q;  
2. Cyclone diameter, D;  
3. Set cut size, \(d_{pc}\);  
4. Cyclone efficiency;  
5. Cyclone pressure drop, \(\mathrm{D_{P}}\).  
Cyclone geometry:  
- a is the inlet height  
- b is the inlet width  
- D is the cyclone diameter  
- De is the vortex finder diameter  
- h is the cylindrical height  
- B is the cone diameter  
- H is the overall heightIt can be seen from  
- S is the vortex finder height.
## 4 Numerical Calculation Results and Analysis
### Discussion of Seismic Time-History Response Results
#### 4.3.3. Uplift and Slip between the Tank Bottom and Foundation  
In unanchored or partially anchored tanks, the uplift of the foundation may damage the piping connections which cannot adapt to the vertical displacements, damaging the plate shell structure due to excessive joint stress and leading to uneven settlement of the foundation. In this part, the uplift behavior of the tank bottom is investigated. Table 3 illustrates the maximum uplift of the tank reaches 7.75 mm under X + Y + Z excitation of the El Centro ground motion, which is 1.91 times larger than that of the X-direction excitation. The uplift time-history comparison of the tank bottom under El Centro ground motion is shown in Figure 9a. From the results, the vertical seismic component increases the uplift response of the tank. This phenomenon also occurs when the other two seismic waves are excited.  
Figure 9: Uplift and slip behavior of the tank bottom under El Centro ground motion: (**a**) The whole time-history curve comparison of tank bottom uplift; (**b**) time-history curve of the uplift amount during t = 8.0 s to t = 10.0 s; (**c**) uplift behavior of the tank (t = 8.66 s, under the X + Y + Z excitation, after a certain amplification); (**d**) change of tank position before and after the earthquake (under the X + Y + Z excitation, top view).  
The maximum slip value is 37.27 mm under X + Y + Z excitation of the El Centro ground motion, which is 3.39 times higher than that of the horizontal X-direction excitation. Figure 9 also gives a comparison of tank bottom's movement when the earthquake begins and stops under El Centro ground motion with X + Y + Z excitation. Under the excitation of the other two seismic waves, the slip response of the tank is much smaller than that by El Centro ground motion. It can be seen that the characteristics of the seismic wave have an obvious effect on the slip response of the tank. When considering multi-directional ground motions, both the uplift and slip response increases obviously. Especially, excessive tank bottom permanent sliding may cause damage to the tank or even slide out of the foundation. Hence, some restrictions in the tank bottom measures are necessary.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Trends, Transition Dates  
Transition times agree within +/ - 5 days among the sensors we surveyed. We are limited in some inferences in our dataset, as we do not have earlier canopy imagery either from our UAV or below-canopy sensors. We also acknowledge that segmented analysis that uses piecewise regression may inform these results differently than sigmoidal curves, which can also be fit to these data when the times series include more of the pre-green-up dormant phase. Given the absence of earlier (e.g., March/April) data, we could not use this approach. Thus, we can only assess when spring transition stabilizes, rather than estimating true start-of-season dates. We can assume that rather homogenous nature of the forest canopy observed contributed to this convergence. It is rather likely that in forests that are more heterogeneous, due to age, disturbance, or species composition, the uncertainty around this convergence could be inflated.
## 5 Numerical Results and Discussion  
This section presents various numerical results. First, the effects of various fading factors are discussed. Then, we compare Webb and Gaussian-approximated APD output and SER performance. Finally, key system parameters are properly designed to optimize communication performance.  
For a single transmission, where the link length, divergence angle, and atmosphere loss factor are fixed, the channel attenuation is constant. Therefore, for simplicity, we normalize the channel attenuation coefficient, i.e., \(h_{c}=1\). In addition, Table 1 summarizes other constant parameters used in simulations.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis
### Resist Distributed Denial-of-Service Attack  
The attacker can intercept the cipher text of message \(\text{m}_{1}\) that contains the timestamp \(\text{T}_{\text{MU}}\) and \(\text{ID}_{\text{HA}}\). However, the attacker cannot decrypt the cipher text and cannot forge the timestamp \(\text{T}_{\text{MU}}\) and specific object \(\text{ID}_{\text{HA}}\). Then, the attacker cannot use a distributed denial-of-service attack to attack the HA.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 1 Introduction  
Fifth-generation (5G) and 6G networks [1, 2] are expected to support a wide range of new technologies, such as drones and virtual/augmented reality, which require high bit rates, lower latency, and increased throughput [3, 4, 5]. The number of connected devices is increasing, resulting in a dramatic growth in traffic volume, causing anomalies such as network congestion, decreased quality of service, network delays, data loss, and blocking of new connections [6]. The network architecture should adapt to the volumes of traffic generated by various applications and use it for decision-making, taking into account several types of traffic with different service and priority requirements [7, 8, 9]. Artificial intelligence (AI) and machine learning (ML) are now trends for 5G networks that could provide more efficient and reasonable network planning and management [10]. AI and ML models could be trained on a large amount of data that service providers collect [11]. The collected data should be reliable, and the analysis carried out should be accurate [12].  
Network traffic forecasting is one of the tasks that use ML methods for effective network management [13, 14]. This task aims to identify potential problems before they occur, reduce service outages, manage user needs, and analyze user behavior in applications [15]. For example, traffic forecasting is used for smart power consumption by a base station [11]. In [16], this problem is considered based on network slicing, mobile edge computing, base station sleeping, and additional power during high-demand hours. Traffic forecasting is divided into short-term and long-term, but sometimes medium-term forecasting is also necessary [17].
# Sequential Movie Genre Prediction Using Average Transition Probability with Clustering
## 4 Experimental Results
### Performance Metrics  
As performance metrics, we consider (i) recall, (ii) precision, (iii) accuracy, and (iv) F1 score. To formally explain these metrics, true positive (TP) is the number of correctly predicted positive values, that is, the actual value is yes, and the predicted value is also yes; true negative (TN) indicates the number of correctly predicted negative values, that is, the actual value is no, and the predicted value is also no; false positive (FP) is when the actual value is no, but the predicted value is yes; false negative (FN) is when the actual value is yes, but the predicted value is no. The three metrics are described as follows:  
(i) _Precision:_ Precision is the ratio of correctly predicted positive answers to the total predicted positive answers.  
\[Precision:=\frac{TP}{TP+FP}. \tag{19}\]  
Figure 10: Subgenre trimming. In this example, we set \(\eta\)=0.5 and there are two types of performance metrics: precision and recall. We see that the minimum value of evaluation metrics \(P^{2}_{min}=0.6\) and \(P^{7}_{min}=0.55\) for cluster 2 and cluster 7, respectively. Hence, these clusters are not regarded as the trimming clusters. However, \(P^{3}_{min}=0.45<0.5\) for cluster 3, which is regarded as a trimming cluster.  
* _Recall:_ Recall is the ratio of correctly predicted positive answers to all answers in the actual class of answers. \[Recall:=\frac{TP}{TP+FN}.\] (20)
* _Accuracy:_ Accuracy is the ratio of correctly predicted answers to the total number of answers. \[Accuracy:=\frac{TP+TN}{TP+FP+FN+TN}.\] (21) Accuracy is a good measure when the values of false positives and false negatives of the data sets are almost the same.
* _F1 Score:_ This metric is a weighted average of precision and recall. Therefore, this score considers both false positives and false negatives as follows: \[F1 score:=\frac{2(Recall*Precision)}{Recall+Precision}.\] (22) The F1 score is usually more useful than accuracy when the values of false positives and false negatives of the data sets are quite different.  
Using the previously described data and performance metrics, we obtain various experimental results of the movie genre prediction in the following subsection.
# QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity
## 2 Related Works
### MAC Scheduling for eNBs
By this design, the proposed scheme can effectively decrease packet drop ratio. The above previous works design MAC scheduling methods for the MeNB and SeNBs. Table 2 summarizes the comparisons of previous works with the proposed QED in three aspects. Again, the proposed QED offers the most complete solutions for eNBs. More specifically, when allocating radio resources, the proposed QED scheme considers (i) the network situation (i.e., channel quality), (ii) fairness between traffic flows, and (iii) the packets' remaining transmission times. We can see that the scheme designed in reference [26] has features similar to ours, but it may not effectively allocate resources to GBR flows that are going to expire. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Consider QoS** & **Consider Fairness** & **Consider Packet Status** \\ \hline Ref. [22] & ✓ & ✓ & \\ \hline Ref. [6] & & & ✓ \\ \hline Ref. [7] & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of prior works with QED.
## 2 Materials and Methods
### Evaluation  
In this study, CNN and ConvLSTM were used as comparison experiments of CNN-LSTM models. The division of the training set, validation set and test set was 14:3:3. Data from 2000 to 2013 were used as the training set for training, and data from 2014 to 2016 were used as the validation set to verify the output models. Based on the trained model, rice yields were predicted for 2017, 2018 and 2019. Comparing the predicted yield with the statistical yield data allows the performance of the prediction model to be evaluated year by year as well as the overall prediction effect over 3 years. Root mean square error _RMSE_, mean absolute error _MAE_, percent error _PE_ and correlation coefficient \(R\) were chosen as evaluation metrics. The coefficient of determination R2 was also used in the study to evaluate the extent of spatial variation in predicted and observed yields. Equations (4)-(7), respectively, give the equations for _RMSE_, _MAE_, _PE_ and \(R\), where \(y_{i}\) is the predicted value, \(\hat{y}_{i}\) is the statistical value and \(n\) is the number of samples  
\[RMSE=\sqrt{\frac{\sum\
olimits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}{n}} \tag{4}\]  
\[MAE=\frac{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}}{n} \tag{5}\]  
\[PE=\frac{|y_{i}-\hat{y}_{i}|}{\hat{y}_{i}}\cdot 100\% \tag{6}\]  
Figure 3: The architecture of the CNN-LSTM model.  
\[R=\frac{\sum\limits_{i=1}^{n}{(y_{i}-\overline{y}_{i})(y_{i}-\overline{\tilde{y}}_{ i})}}{\sqrt{\sum\limits_{i=1}^{n}{(y_{i}-\overline{y}_{i})^{2}}}\sqrt{\sum \limits_{i=1}^{n}{(y_{i}-\overline{\tilde{y}}_{i})^{2}}}} \tag{7}\]
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 4 Measurement Results
### Path Loss Measurement  
We measured path loss in KT-1 Woongbi by considering transmit/receive power, antenna cable loss, and the designed monopole antenna gain. To verify our measurement results, we also considered formally verified radio propagation simulation, carried out by the international aerospace community [3]. The simulations in [3] were classified into six groups as follows: (A) intra-cab in and intra-flight deck; (B) Inter-Cabin; (C) Inter-Cabin-to-Lower Lobe and Inter Cabin-to-Flight Deck; (D) Inter-Cabin-to-Exterior; (E) Inter-Cabin-to-Landing Gear and Inter-Lower-Lobe to Exterior; and (F) Inter-Exterior.  
Figure 11 shows path loss measurements at 4.3 GHz. There is some variance among results at the same measurement point (e.g., P2-P3) due to hardware limitation and dynamic ranges of our measurement system. However, most of our measurement results range within those of Groups A-F. In particular, some measurements (e.g., P0-P1) showed similar path loss values with the results in [3] (e.g., Group D), which indicates the validity of our measurements.  
Figure 10: Communication regions in KT-1 Woongbi and an example of possible relay installation spots.  
**Remark 2**: _We obtained at most 1.9 m transmit range under the power constraint (i.e., 100 mW) in our test, as shown in Figure 11. One might think the transmission range is short. However, note that our experiment was done based on SDR devices (i.e., USRPs), which usually have less performance than commercial radio devices. Thus, the transmission range would not be an issue if we used commercial radio devices. Additionally, if the short transmission issue happened due to the path loss characteristic of the aircraft, then we could utilize relay nodes to overcome the short transmission range, as discussed in Remark 1._
## 6 Conclusions  
This study investigated the potential of an AR system for the rehabilitation training of individuals with lower limb amputations. We analyzed its effects on movement accuracy and subjective satisfaction by comparing training with and without AR feedback. Our findings demonstrate that while AR feedback did not universally enhance the joint range of motion across all exercises, it did improve the range in specific movements, namely standing arm abduction, standing trunk lateral flexion, and standing knee flexion to maximum height. Moreover, AR feedback significantly enhanced the consistency of exercise performance by reducing the standard deviation across most movements. Notably, participants who used AR feedback reported significantly higher subjective satisfaction across various aspects,including enjoyment, motivation, perceived effectiveness, and movement accuracy. This suggests that AR technology offers benefits beyond just improving movement accuracy, as it can also foster a more positive training experience, potentially leading to greater patient engagement and adherence to rehabilitation protocols.  
These results highlight the potential for developing and clinically applying customized AR-based rehabilitation training programs. Such programs could enhance the consistency of exercise movements and improve the subjective satisfaction of individuals with lower limb amputations. Future work should also explore the development of diverse AR-based training content and interaction modalities and evaluate their comprehensive impact on patients' functional recovery and psychological well-being within real clinical settings.  
Conceptualization, methodology, formal analysis, data curation and investigation, visualization, writing--reviewing, editing and investigation, Y.C.; methodology, investigation, J.K.; supervision, investigation, H.C.; project administration, funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Assistive Technology Commercialize R&D Project for Independent Living for People with Disability and Older People by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2024-00454355).  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Rehabilitation Engineering Research Institute (RERI-IRB-240831-1).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank all participants for their valuable contribution to this research.  
The authors declare no conflicts of interest.
## 4 Methodology for Dynamic Parameter Adaptation  
All bio-inspired methods used in these works use the same methodology for parameter adaptation, but adjustment is applied to each bio-inspired optimization method, because this methodology applies a fuzzy logic system to update one or more parameters during the execution of each algorithm, and on each iteration the fuzzy system is evaluated to calculate new parameters.  
To perform the calculation of new parameter values, the fuzzy system uses as input the percentage of elapsed iterations and the degree of dispersion of individuals from each bio-inspired method, and these metrics are now used as inputs for the fuzzy system as defined by Equations (10) and (11), respectively.  
\[Iteration = \frac{Current\ iteration}{Maximum\ of\ iterations} \tag{10}\] \[Diversity(S(t)) = \frac{1}{n_{s}}\sum_{i=1}^{n_{s}}\sqrt{\sum_{j=1}^{\frac{n_{s}}{ 2}}\left(X_{ij}(t)-\overline{X}_{j}(t)\right)^{2}} \tag{11}\]  
To put the methodology in perspective, the iteration number give us a percentage of what iteration we are currently in, so we can design rules to update the parameters depending if the algorithm is in early iterations or if is in final iterations, and, based on this knowledge, update the parameters of the bio-inspired optimization algorithm accordingly.  
The diversity give us a degree of how much are the individuals close to the global best individual, and with this we can control if we want that individuals get closer together or if we want that individuals move far away from the rest, in other words, we can control if the algorithm will use a global search (individual get separated for the rest) or if it use a local search (individual get close together).  
Combining these two metrics we can much better control the behavior of a bio-inspired optimization algorithm by controlling its parameters, this of course based on previous knowledge of the algorithm.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 2 Results and Discussion
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) is an unsupervised clustering method and could reduce the dimensionality of multivariate data and preserve most of the variance therein [31]. To get a clear distribution of the volatiles with the separation of the samples, PCA was applied to the data presented in Table 1, the first two principal components explained nearly 91% of the total variability of the GC-MS data set between the samples, is shown in Figure 3a. The corresponding loading weight plot, establishing the magnitude of each volatile component (variable), is illustrated in Figure 3b. Figure 3 plots the samples on the coordinate grid defined by the first two principal components and showed that PC1 and PC2 separated the _L. cubbea_ samples from the _P. cubbea_ samples.  
Principal component 1 (PC1) and PC 2, explained 47% and 44% of the total variance among the sample batches, showed that the cubeb berries discrimination based on vari  
Figure 3: PC1 vs. PC2 scatter plot for variability among the different cubbea samples: (**a**) bio-plots of PC1 & PC2 of the volatile compounds of different extracts (HD, SDE, and SPME) of _L. cubbea_ and _P. cubbea_; (**b**) relation between the volatile compounds (loadings).  
The extracts (HD, SDE, and SPME) of _P. cubbea_ was projected in positive PC2 and highly associated with \(\alpha\)-cubebene (A18, 0.4-0.52%), bicyclosesquiphellandrene (A27, 2.41-5.28%), camphor (B2, 17.59-20.6%), and linalool (C2, 14.89-21.31%). The extracts (HD, SDE, and SPME) of _L. cubbea_ were located in the negative PC 2 with high D-limonene (A10, 0-38.89%), 3,7-dimethyl-2,6-ocatadien (0.72-11.32%), cital (D3, 1.09-11.85%), and dodecanoic acid (F3) content (Figure 3b).  
In conclusion, 12 volatiles were identified as aroma-active compounds in both cubeb berries with mainly 'turpentine-like', 'fruity and flowery', 'herbal', and 'camphoraceous' flavors. Principal component analysis was further applied to the data of GC-MS, which differentiated and discriminated the two ambiguous cubeb berries according to their unique volatile compounds. _Litsea cubbea_ was characterized by higher level of d-limonene ('fruit, citrus' note, 0-38.89%), 3,7-dimethyl-2,6-ocatadien (0.72-11.32%), cital ('fruit, lemon' note, 1.09-11.85%) and dodecanoic acid (1.89-32.9%). \(\alpha\)-Cubebene ('herb' note, 0.4-0.52%) was identified as a marker compound for _Piper cubbea_ with higher camphor ('camphoraceous' note, 17.59-20.6%), and linalool ('flower' note, 14.89-21.31%) contents.