esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Haptic Feedback (GQ3, FQ2)  
Haptic feedback plays a key role in medical simulations because it increases the simulation fidelity with beneficial effects on training. In particular, haptic feedback is an important add-on for virtual reality simulators because it improves the perception of virtual objects giving the user the illusion of touch. Many VR simulators recreate the sense of touch through haptic devices that apply forces, vibrations, or motions to the user during their interaction with the virtual environment.  
In particular, haptic technology can be used to simulate the tactile properties of tissues (i.e., the stiffness of a tissue is essential during a palpation procedure) and the manipulation of surgical instrumentation [47].  
With regard to question GQ3 (\"Does the simulator integrate haptic feedback?\"), [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] of the 23 studies integrate haptic feedback. The only system that does not offer such feedback is the simulator presented in [32] designed for the AR visualization of anatomical models for educational purposes.  
In more detail, answering question FQ2 (\"Which mode is used to convey haptic feedback? (actual interaction with physical components, haptic interface)\"), the largest part of the studies analyzed [22; 23; 24; 26; 27; 29; 30; 31; 33; 34; 36; 38; 41; 42; 43; 44] offer the trainee the possibility to use actual surgical instruments and/or manipulate/palapate physical anatomical replicas.  
Two of the reviewed simulators [39; 40] incorporate a haptic software module and deliver the simulated haptic experience via commercial haptic interfaces: Phantom Premium devices [39] and Phantom Omni devices [40] (Sensable Technologies, Woburn, MA). The main difference between the two studies is that haptic interfaces are used in [39] to interact with virtual models of organs superimposed on a physical model (a cat toy), whereas in [40] they are used to interact with a purely virtual representation of anatomy to mimic the insertion of a percutaneous needle and to mimic hand palpation.
## 5 Experimental Results
### Comparative Analysis
#### 5.2.4 Impact of Throughput  
Throughput in the wireless sensor network is defined as the measure of the amount of data that can be received in a certain amount of time. In Figure 9, the throughput of the proposed work is compared with several existing works with respect to the number of sensor nodes.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Simulation Time (s)** \\ \hline MiA-CODER & \(22\pm 1.0\) \\ SRC & \(30\pm 1.0\) \\ H3 & \(38\pm 1.0\) \\ \hline \end{tabular}
\end{table}
Table 7: Number of dead nodes analysis.  
Figure 8: Simulation time vs. average energy consumption.  
Figure 7: Simulation time vs. number of dead nodes.  
Figure 9 makes it clear that the throughput increases with the increase in the number of sensor nodes. The throughput of the proposed MiA-CODER mechanism is high due to the detection and coverage of holes in the network. The detection of holes is carried out by the virtual sector-based hole detection protocol (ViSHD) and the recovery of coverage holes is executed by implementing the multi-agent SARSA based on multiple parameters. The existing works lack proper recovery mechanisms which affect the throughput of those approaches.
## 4 Discussion
[PERSON] et al. [23] have used the fluorescence properties of caffeine to determine coffee quality using a fluorescence fingerprinting method. [PERSON] et al. [24] have determined the quality of Sidr honey using a fluorescence fingerprinting method by focusing on specific phenolic compounds (caffeic acid, chlorogenic acid, and ferulic acid). The accuracy of the identification model may be improved by combining fluorescence fingerprinting with chemical component analysis. This model has the potential to be applied to different food categories and to identify geographical origins, production areas, and other varieties. Fluorescence spectroscopy has been used to identify the geographic origin and other varieties of rice [15], varieties and geographic origin of olive oil [16], and botanical origin of Swiss honey [17]. Therefore, the effectiveness of fluorescence spectroscopy has been confirmed for a wide variety of foods, and its application for identifying kelp varieties may contribute to strengthening quality control and traceability in the food manufacturing industry. In future research, it is expected that the accuracy of identification will be improved by integrating other spectroscopic techniques, such as NIR. NIR is excellent for measuring the chemical components of food, and by combining it with fluorescence spectroscopy, more multifaceted analysis will be possible. By utilizing multimodal machine learning and integrating multiple spectroscopic data, it is expected that the accuracy of origin identification will be improved. In this study, only three types of kelp from Hokkaido were used, but the effects of differences in region and harvest time were not taken into consideration. In the future, the versatility of the model will be improved by using samples from other regions and different harvest times.
## 4 Numerical Setup
### Computational Domain and Boundary Conditions  
Half of the ship model was simulated considering lateral symmetry condition, with the boundaries of the computational domain defined as follows: the inlet boundary was positioned \(1.5L_{PP}\) away from the hull, while the top boundary was \(2L_{PP}\) above the mean waterline. The position of the outlet boundary is \(3L_{PP}\) behind the hull, the bottom boundary was set \(0.1L_{PP}\) below the hull, and the side boundary was located \(0.75L_{PP}\) from the symmetry plane of the ship model.  
The velocity inlet boundary condition was applied at both the inlet and top boundaries of the computational domain. The pressure outlet boundary condition was specified at the outlet boundary. A symmetry boundary condition was set at the symmetry plane of the ship model. Additionally, the no-slip wall boundary condition was assigned to the hull, bottom, and side boundaries of the ship model [20]. Figure 2 provides an overview of the boundary conditions used for numerical simulations in confined water. During the numerical simulations, the ship model was fixed while the speed of the incoming flow was equal to the negative value of the model speed.
## 2 Related Work
Hierarchical management and task distribution for target tracking is another proposed management method for WSNs in target tracking applications [19]. Minimizing computational, communication and sensing resources were the main aims in this study. Using acoustic signal energy measurements of individual sensor nodes to estimate the locations of multiple acoustic sources is another approach for target tracking [20]. They show that a maximum likelihood acoustic source location estimation method compared to existing acoustic energy-based source localization methods yields more accurate results and enhances the capability of multiple source localization. Using special types of Kalman filtering is another approach to overcome some of the problems of acoustic target tracking such as the problem of global time synchronization [5]. Multiple target tracking are discussed in different scenarios. Multi target tracking has been discussed with two different test case scenarios [21]. In the test cases, sensor nodes are placed in linear and lattice form. Basics of single target tracking and classification and multiple targets that are separated sufficiently in space or time, are discussed in some papers like [22]. They have used a technique called lateral inhibition to reduce computational and network costs while maintaining an accurate tracking. Beyond studying the various scenarios that are related to locating sensor nodes, some researchers have studied tracking objects with constant velocity with uncertain locations [23]. Using appropriate techniques can cause good time synchronization and localization accuracy that is essential for accurate target tracking [24]. Collaborative signal processing (CSP) is a framework that has been used for tracking multiple targets in a distributed sensor network. The key components include event detection, estimation and prediction of target location, and target classification [25]. Simultaneous localization, tracking and calibration based on Bayesian filter is made in 2-dimensional and 3-dimensional indoor and outdoor spaces with good accurate results [26]. Clearly, there is a rich set of research on the subject of target tracking using WSNs. Several methods like Kalman filtering, statistical methods and numerical methods had been deployed for solving the target tracking problem, but analytical geometric methods had not been used for target tracking. We have opted to use analytical geometric methods in order to attain better tuning of differing QoS parameters that are required and set by end-users at the application-level of WSNs but need to be realized at the middleware-level of WSNs.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 4 Materials and Methods
### HCEL Routing Protocol
#### 4.2.1 Network Deployment and Initialization  
The sequence diagram for the network deployment and initialization phase is shown in Figure 1. Initially, the sink node broadcasts its location information to all nodes in the network. This serves as a reference point for the other nodes to determine their positions relative to the sink node. Additionally, it allows the nodes to establish a communication link with the sink node. The two-hop nodes transmit essential information to the sink, including the unique identifier of the node, location coordinates, RSSI value, and residual energy. Simultaneously, each two-hop node shares its identifiers and location coordinates with other nodes. This exchange of information helps to establish a network topology and enables the nodes to identify their neighboring nodes. The one-hop sensor nodes send only their unique identifiers and locations to the sink, as these nodes have not participated in the data forwarding process (i.e., routing).  
Following these steps in the network deployment and initialization phase, the WBAN achieves an organized and coordinated setup. The information shared between the sink node and sensor nodes, along with the exchange of information among the two-hop nodes, establishes a foundation for effective communication and data transfer. This initialization phase ensures that the network is properly configured and ready for the subsequent data transmission and routing stages. Additionally, this phase in WBAN is designed to minimize the overhead in communication cost, further discussed in Section 5. In this case, data forwarding and routing decisions can be performed by giving the sink node essential information from the two-hop sensor nodes (i.e., location, RSSI, and residual energy). Through efficient assignment of parent nodes, the network reduces unnecessary communication overhead. This minimization of overhead conserves energy resources and enhances the overall efficiency and reliability of data transmission in WBAN. By striving to minimize the communication cost overhead, the network deployment and initialization phase lays the foundation for an optimized and energy-efficient WBAN system.  
Figure 1: Sequence diagram of the initialization phase.
## 4 Results and Discussion
### Prediction of Suspended-Sediment Transport  
#### 4.2.1 Q/c Curve  
Based on long-term measurement results, we can predict the transport of suspended sediments in a selected cross-sectional profile of a watercourse. If we know the water flow (Q) in a geometrically defined cross-section and with measured velocity vectors, as well as simultaneously determined concentrations (c) of suspended particles at selected points of the profile, we can predict the concentration over the entire profile and simulate the Q/c diagram. With the known Q/c function and a time-dependent flow Q(t), we can determine the annual balance (B) of transport of suspended particles and use the annual trends of flow Q to statistically predict the transport of suspended material through the selected profile. The described interdependencies are shown in Figure 6 for the Drava--Ptuj and Drava--Sturm locations, where the graphs also include the functional dependence, with R\({}^{2}\) (coefficient of determination) representing the fit estimate of the function to the measured values.  
Figure 4: The ratio between the concentrations of suspended material at depths of 2 m and 4 m, measured in three profiles of the River Drava (Profile 1, Profile 2, and Profile 3) at the monitoring site. Sturmov potok.  
Figure 5: Grain-size distribution curve of suspended material of a composite sample at the Drava Sturm measuring point.
## 4 Simulation Verification and Physical Experiment
### Observer Stability Verification  
Additionally, based on the parameters provided in Table 3 and Theorem 1, the corresponding calculations were carried out using the LMI toolbox in Matlab, yielding the following results:  
\[P=10^{5}\left(\begin{array}{ccc}4.1336&0&0\\ 0&4.1336&0\\ 0&0&4.1336\end{array}\right) \tag{27}\]  
\[Z=10^{8}\left(\begin{array}{ccc}-5.7161&0&0\\ 0&-5.7161&0\\ 0&0&-5.7161\end{array}\right) \tag{28}\]  
Accordingly, there exist a positive definite symmetric matrix \(P\) and a matrix \(Z\) satisfying condition \(A^{\rm T}P+PA-C^{\rm T}Z^{\rm T}-ZC<0\), thus ensuring the asymptotic stability of the designed IASMO.  
Furthermore, the fault location threshold is determined by the maximum value of \(\eta_{x}\) under the three switch fault conditions. Through multiple simulation experiments, the values of \(\eta_{1}\), \(\eta_{2}\), and \(\eta_{3}\) were determined to be 1.163, 1.274, and 1.561, respectively. Therefore, location thresholds \(\delta_{1}\) and \(\delta_{2}\) are set to 1.165 and 1.276. The criteria for locating faulty switches are detailed as follows:  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline DC side voltage U\({}_{\rm dc}\) & 300 V \\ DC side capacitance \(C_{1},C_{2}\) & \(5.6*10^{-3}\) F \\ Sampling time \(T_{s}\) & \(10^{-6}\) s \\ Three-phase filter inductance \(L\) & \(6*10^{-3}\) H \\ Three-phase load resistance \(R\) & \(8.3\,\Omega\) \\ \hline \end{tabular}
\end{table}
Table 3: Main parameters of T\({}^{2}\)3L inverter.  
\[F_{xi}=\begin{cases}T_{x1},&W_{x}<w_{x}\ \&\ \eta_{x}\geq 1.165\\ T_{x4},&W_{x}>w_{x}\ \&\ \eta_{x}\geq 1.165\\ T_{x2},&W_{x}>w_{x}\ \&\ \eta_{x}\in[1.12,1.165)\\ T_{x3},&W_{x}<w_{x}\ \&\ \eta_{x}\in[1.12,1.165)\\ T_{x1}\ \&\ \ T_{x3},&W_{x}<w_{x}\ \&\ \eta_{x}\geq 1.276\\ T_{x2}\ \&\ \ T_{x4},&W_{x}>w_{x}\ \&\ \eta_{x}\geq 1.276\end{cases} \tag{29}\]
# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 6 Conclusions  
In this study, we introduced ChurnKB, a knowledge base for churn analysis, integrating customer-generated textual data, such as chat logs, with churn analysis. By doing so, it captures cognitive and behavioural patterns indicative of churn risk. To enhance ChurnKB, we developed an interactive GenAI-enabled feature engineering approach, leveraging generative AI to refine its structure and content.  
We validated our approach through expert evaluation, using a structured questionnaire to assess ChurnKB's structure, content, and analytical outcomes. The results confirm its relevance and potential in improving churn prediction. However, some limitations were identified. Some features had weak relevance for detecting churn-related patterns, and linguistic indicators like \(I\), _absolute words_, and _certainty words_ were seen as neutral.  
Despite these challenges, integrating customer-company interactions into churn analysis improves upon traditional approaches based on demographic and product usage features. Our results demonstrate the effectiveness of ChurnKB-derived features, significantly boosting model performance, with XGBoost's F1 score increasing from 0.5752 to 0.7891 when combining textual and numerical data. This underscores the value of cognitive and behavioural insights in churn analysis. Future work will refine ChurnKB by addressing limitations and optimizing feature selection for further accuracy improvements.  
Future research could explore ChurnKB's application in banking, insurance, and e-commerce to assess its generalizability. Integrating real-time interactions, such as live chat and social media feedback, may enhance early churn detection. Additionally, explainable AI techniques could improve transparency and help businesses justify predictions. AI-driven knowledge bases like ChurnKB may shift churn management from reactive to proactive. They could also enhance customer experience and automate large-scale churn risk detection. These advancements may lead to more effective and responsible business decision making.  
Conceptualization, M.S. and A.B.; validation, M.S. and A.B.; formal analysis, M.S. and A.B.; investigation, M.S., A.B., X.Z., W.M., E.J.F. and A.J.; resources, M.S. and A.B.; writing--original draft preparation, M.S. and A.B.; writing--review and editing, M.S., A.B., W.M. and A.J.; visualization, M.S., A.B., A.H. and N.S.; supervision, A.B., X.Z. and J.F.; project administration, M.S. and A.B. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Survey-based evaluation of ChurnKB hypotheses. (**i**) Assessment of the initial churn taxonomy structure. (**ii**) Validation of new sub-instances (e.g., pronoun usage and absolute and certainty words). (**iii**) Evaluation of churn-related sub-instances and lexical lists. (**iv**) Assessment of the ChurnKB-enhanced classifier’s outcomes.  
Funding: This research received no external funding.
# A Contribution of Shortest Paths Algorithms to the NetworkX Python Library
## 5 Experiments and Results
### Results of Algorithms Tests
#### 5.1.4 RTAA* Algorithm  
Results and metrics of the execution of the RTAA* algorithm are presented in Figure 7.  
Real Time Adapative A* required slightly more time to find the path when compared to A* and produced an optimal path with a higher overall cost, while also using more memory. These results are consistent with the algorithm characteristics, since the tests were focused on a static and small graph environment, which goes against the capabilities of the RTAA* algorithm since no real time adaptations were made.
## 2 Methodology
### Determination of Reverberation Time  
#### 2.4.1 The First Averaging Method  
The measurement consists of the recording of sound pressure level. At the moment when it becomes stable, the sound source is turned off, and the meter measures the time when the background noise level is reached. The measurement can be applied with different dynamics. The most frequently recorded measurement is with the dynamics of 30 dB (Figure 4).  
Figure 3: View of the classroom during the measurement with an exemplary location of the sound source and two measurement points [27].  
Figure 2: Illustrations with two locations of the sound sources P1 and P2 and the arrangement of measurement points [27].  
For each frequency, \(n\) decay curve measurements are made. The reverberation time is determined in line with the following algorithm:  
1. We find the sound pressure level reduced by 5 dB from the initial value, which is the level of sound pressure at the moment when the sound source is turned off. The value is referred to as L\({}_{\rm p}\), and time t\({}_{\rm p}\) is read out for it (horizontal axis).
2. We find the value of the sound pressure level reduced by 25 dB from the L\({}_{\rm p}\) value, which is referred to as L\({}_{\rm k}\), and time t\({}_{\rm k}\) is read out for it (horizontal axis).
3. The reverberation time is determined based on the following relationship: \[T=\frac{60}{d}\] (1) where \(d\)--sound decay rate in dB/s, \(d=\frac{\Delta L}{\Delta\tau}\); \(\Delta L\)--difference in sound levels; \(\Delta\tau\)--increment of sound decay time.
4. The reverberation times for all measurements are arithmetically averaged, and the standard uncertainty is determined.
## 4 Conclusions  
In summary, a total of 165 volatile compounds were identified using SAFE and/or HS-SPME. Thirteen most important odorants were detected, including eight by SAFE-GC-O and eight by HS-SPME-GC-O; of these, five aroma-active compounds were identified by both methods. Sotolone, \(\beta\)-damascenone and 3-isobutyl-2-methoxyprazine, contributing to caramel-like, fruity, and red pepper aromas, respectively, were identified for the first time in doubanjiang. These compounds played key roles in the aroma quality of doubanjiang. In addition, methional, ethyl3-methylbutanoate, phenylethylal alcohol, 2-methoxyphenol, 3-methylbutanal, and phenylacetaldehyde also played important roles in the aroma quality of doubanjiang. These results will be useful to improve the aroma quality of this traditional food.  
Formal analysis, Z.L., L.D., C.Z; investigation, Z.L., J.J., S.Y.K.; writing-original draft preparation, Z.L. and H.-H.B.; writing-review and editing, Z.L. and H.-H.B.; supervision, H.-H.B.  
This work was funded by the Science and technology department of Sichuan province (2019 YFN0013), Young Scientist Exchange Program between The Republic of Korea and the People's Republic of China (2015), Youth Fund of Sichuan Province (2016)Q0048).  
The authors declare no conflict of interest.
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 5 Conclusions  
In summary, we proposed a new RRT-based path planning algorithm, Fast-RRT, to improve the speed and stability of finding the initial path. Therefore, two improvements, such as sampling in the unexplored space and random expansion, were performed. Furthermore, a new algorithm for finding a new near-optimal path was further proposed to obtain a near-optimal path by combining and adjusting multiple feasible paths.  
Compared with the RRT and RRT* algorithms, the proposed that Fast-RRT possesses remarkable advantages in speed and stability. For instance, within the Fast-RRT algorithm, the average search time and its variance to find a feasible path significantly reduced compared to the RRT algorithm. At the same time, the search speed of Fast-RRT for finding a near-optimal path is 20 times faster than the RRT* algorithm. Therefore, our Fast-RRT  
Figure 15: Comparison of the time required for Fast-RRT and RRT* algorithms to find an approximate optimal path when the gap between the approximate optimal path and the theoretical optimal path is different thresholds. It can be seen that compared with the RRT* algorithm, the Fast-RRT algorithm has an order of magnitude advantage in search time.  
algorithm exhibits great potential in practical motion planning applications. To further improve the performance of our algorithm, the combination of the Fast-RRT algorithm and other algorithms, such as Bidirectional RRT, Kinodynamic RRT*, and Information RRT*, will also be investigated in the future. At the same time, the application scenarios of our Fast RRT algorithm will also be extended from two-dimensional to multi-dimensional, as well as in actual motion planning tasks in future work.  
Conceptualization, Z.W. ([PERSON]) and Z.M.; methodology, [PERSON] ([PERSON]); software, Z.W. ([PERSON]) and W.Z.; validation, Z.W. ([PERSON]), Z.M. and W.Z.; formal analysis, Z.W. ([PERSON]); investigation, Z.M.; resources, Z.M.; data curation, W.Z.; writing--original draft preparation, Z.W. ([PERSON]); writing--review and editing, Z.W. ([PERSON]) and Z.M.; visualization, W.Z.; supervision, Z.M. and Z.W. ([PERSON]); project administration, Z.W. ([PERSON]); funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation (NSF) of China (No.61976014).  
Not applicable.  
The data presented in this paper are available on request from the first author.  
The authors declare no conflict of interest.