esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Efforts
### e-Health Cloud Implementation Models  
Some efforts were invested in the e-Health Cloud implementation models. One example is a practical e-Health Cloud implementation model to support the construction of HIS for small healthcare providers who cannot afford to have their own HIS systems [60]. This solution imposes uniform standards of data sharing between existing HIS. The model suggests using a virtual private network (VPN) with the support of public networks such as the Internet to establish a private Cloud through which different hospitals can share HIS information. The VPN framework is easily implemented by adding a secure communication model to the existing HIS systems. This secure communication is used to connect to the Cloud for data transmission, storage and sharing.  
Another example is the introduction of the concept of shared and scalable infrastructure for e-Health Cloud across public and private networks [16]. The paper claims that to gain full leverage of the possibilities of offerings of the three layers in different private and public clouds, healthcare organizations will need to develop disciplined internal private clouds. These private clouds will, as technology evolves, exploit secure network techniques and virtualization tools to seamlessly merge locally hosted applications and services with those provided on the public Cloud, or the Internet, as well as those provided on private Clouds.  
[PERSON] [61] investigates utilizing a service modeling approach to model the requirements and design of different Service-Oriented Architecture (SOA) based services by using Service Oriented Modeling and Architecture (SOMA) and employing Service Oriented Modeling Framework (SOMF) modeling styles and assets. SOMF is a service oriented modeling language which serves as a tool for developing diverse service oriented models. The paper demonstrated the steps of building an SOA based healthcare application using SOM architecture and SOMF service modeling for handling the application requirements. It shows how to rapidly implement and evaluate e-health applications using this approach. Generally SOA can provide a good solution for facing some of the development and operation challenges facing the e-Health Cloud.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
###### Abstract  
Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few prediction models consider the characteristics of individual drivers, which may overlook the potential heterogeneity of driving behavior. In this study, a vehicle acceleration prediction model based on machine learning methods and driving behavior analysis is proposed. First, the driving behavior data are preprocessed, and the relative distance, relative speed, and acceleration of the subject vehicle are selected as feature variables to describe the driving behavior. Then, a finite Mixture of Hidden Markov Model (MHMM) is used to divide the driving behavior semantics. The model can divide heterogeneous data into different behavioral semantic fragments within different time lengths. Next, the similarity of different behavioral semantic fragments is evaluated using the Kolmogorov-Smirnov test. In total, 10 homogenous drivers are classified as the first group, and the remaining 20 drivers are classified as the second group. Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) are used to predict the vehicle acceleration for both groups. The prediction results show that the proposed method in this study can significantly improve the prediction accuracy of vehicle acceleration.  
driving behavior; Hidden Markov model; behavioral semantics; Kolmogorov-Smirnov test; vehicle acceleration prediction
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 4 Experimental Study
### Performance Evaluation
#### 4.2.3 Cache Hit Rates  
As the cache had limited space; it was advantageous to replace the cache with more valuable content to improve query performance (i.e., cache hit rate). In this evaluation, we measured the performance of query times in terms of better hit rates. We evaluated both of the datasets, LinkedCT and DYLDO. Figure 5 shows the hit rates achieved by the existing approaches. We utilized the access logs and based on the maintenance policy, each approach replaced the cache to keep it up-to-date. CAMP replaced the cache based on the access frequency and more frequent access queries were placed in a cache for future access. On average, CAMP achieved 82% hit rates as compared to the Eager (77%) and ChangeRate (70%). _PageRank_, _Eager_, _TTL_ and _Size_ performed worst in term of cache hit rates.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Software Architecture
#### 4.3.3 Confirmation Messages  
CnfMs transmit the receipt acknowledgment from the receiver (BS) to the transmitting UAV. The CnfM sends only the confirmation of the last received packet through the identification field. If any data message has not been received, it requests the retransmission of the IDs that were not received from the ID number.  
The CnfM format is presented in Table 4 and a detailed description of the fields that constitute the header is given as follows:  
* Identification: identifier of the last received packet;
* Message Type: it indicates the type of the packet, that is, type 3 in this case;
* Destination Node Address: identifier of the node that localized the boat;
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic pH Sensor Based on Fluorescent Radiometric Intensity Measurement
#### 4.1.3 Measurement System
It has been observed that the linearity and resolution are affected by the distance between the optical fiber and the sensor phase. In general, the greater the distance, the lower the upper limit of the linear margin of measurement and the lower the resolution. The light produced by the LED is brought to the detector by a plastic optical fiber measuring 1 mm in diameter, while the light emitted by the sensor is collected by an optical fiber measuring 1.5 mm in diameter, which is then bifurcated into two branches measuring 1 mm in diameter each, as shown in Figure 15. The reason for using a fiber with a greater diameter for the reception than for the rest of the fibers is to facilitate the collection of light. Doubling the fiber into two branches improves the results compared to using one excitation fiber and two receiving fibers--one to collect reflected light and the other to collect fluorescence light. A more detailed discussion of this approach can be found in [49].
# Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
## 2 Materials and Methods
### The Intrusion Scenario Discovery and Prediction Framework  
The main components (phases) of the proposed framework is illustrated in Figure 3:  
The descriptions of each component are listed as follows:  
1. Normalizing: Raw alerts are converted into a unified data structure for future analyzing;
2. Intrusion action extraction: A group of normalized alerts can correlated into a hyper-alert, which is called intrusion action in this paper, and should meet three requirements: first, they have the same source IP address and destination IP address; second, they have the same destination port or the same intrusion type; and last, they occur continuously in time order. The massive redundant raw alerts are aggregated in this process;
3. Online action clustering: Many extracted actions possess similar semantics, which will be merged based on the similarity calculating;
4. Intrusion session reconstruction: The time-ordered action sequence will be split into many subsequences by calculating the variance of the time gaps between actions, each subsequence  
Figure 3: The components of the proposed framework. The rectangles in blue are the main contributions of this paper.  
Figure 2: The learning process of the HTM.  
is named the intrusion session. Note that all the actions of a particular intrusion session are extracted from the same pair of physical devices. The extracted session will be refined in order to reduce more noise actions;
5. Intrusion session encoding: The intrusion session should be encoded before feed to the HTM algorithm. The session can be first converted into the action ID sequence, and the ID will be encoded into a bit array which is the HTM inner representation of that action. The learning phase of the HTM starts with the encoding of the ID sequences;
6. HTM online learning: The HTM learns the patterns of the action sequences and predicts the next step according to the current input, and the anomaly score which indicates the discrepancy between the predicted value and the input value. The predicted values and the anomaly scores will be updated in a particular matrix;
7. Intrusion scenario discovery: Based on the analyzing of historical predicted actions and the anomaly scores in the correlation matrix, the correlation strengths of intrusion actions are calculated by the auxiliary method; and
8. Intrusion prediction: The system will search through the matrix to determine the attacking paths that may happen in the future.  
The system is proposed based on our previous work [24], a few concepts, such as intrusion action, intrusion session, correlation graph, and methods, such as normalizing and intrusion action extraction, are inherited. In this paper, the data preprocessing is not discussed in detail.
## 3 Results
### Behavioral Intention to Use (BIU)  
Since the BIU data followed a non-normal distribution, the Wilcoxon-signed rank test was employed for the analysis. The obtained result indicated that no effect of hand gesture recognition and joystick control was found on users' BIU for the interactive interface.  
Regarding the difference in task completion time, a one-way ANOVA was used to examine its effect on BIU for the interactive interface in the conditions of hand gesture recognition, which had a statistically significant finding (F(1, 32) = 3.496, \(p\) < 0.05). Further analysis of the data shows that users using hand gesture recognition within 15 min and users using hand gesture recognition between 15 and 20 min have substantially different levels of BIU for the interactive interface. Users who completed gestural tasks in less than 15 min had a higher rating of their BIU for hand gesture recognition (M = 16.64, SD = 3.749), whereas users who accomplished the tasks in between 15 and 20 min exhibited a lower perception of it. On the other hand, for the conditions of joystick control, the statistics show that users have a better BIU for the interactive interface when they complete the tasks between 15 and 20 min and a lower level of BIU for joystick control if they use it within 15 min (see Figure 7). No effect was found for gender or college major.
# Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution
## 4 Simulation Experiment and Results Analysis
### Effectiveness Test of CGWO: Comparison with Other Optimization Algorithms  
In order to verify the performance of CGWO, this paper selects intelligent algorithms such as PSO, FA [46], FOA [47], GWO, and the proposed CGWO algorithm for comparison and analysis. The performance of all algorithms was evaluated according to the best value, mean value, and standard deviation generated by running the results on 23 test functions; for functions with non-fixed dimensions, the performance was tested with dimensions 30 and 100, respectively. These test statistics are presented below. The best experimental results in Tables 6-8 are shown in bold. Among them, the test results on the seven unimodal benchmark functions are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k\) & **Results** & \(f_{1}\) & \(f_{7}\) & \(f_{12}\) & \(f_{15}\) \\ \hline
**0** & Mean & **0** & \(4.922\times 10^{-4}\) & \(5.9110\times 10^{-3}\) & \(3.7223\times 10^{-3}\) \\
0.5 & Mean & **0** & \(5.417\times 10^{-4}\) & \(3.3055\times 10^{-3}\) & \(3.0463\times 10^{-3}\) \\
1.0 & Mean & **0** & \(2.720\times 10^{-4}\) & \(5.1541\times 10^{-3}\) & \(1.7624\times 10^{-3}\) \\
1.5 & Mean & **0** & \(3.230\times 10^{-4}\) & \(4.4289\times 10^{-3}\) & \(3.0011\times 10^{-3}\) \\
2.0 & Mean & **0** & \(2.429\times 10^{-4}\) & \(3.3075\times 10^{-3}\) & \(1.0698\times 10^{-3}\) \\
2.5 & Mean & **0** & \(3.157\times 10^{-4}\) & \(4.4238\times 10^{-3}\) & \(1.7514\times 10^{-3}\) \\
3.0 & Mean & **0** & \(2.907\times 10^{-4}\) & \(6.6250\times 10^{-3}\) & \(1.7448\times 10^{-3}\) \\
3.5 & Mean & **0** & \(3.646\times 10^{-4}\) & \(3.2158\times 10^{-3}\) & \(1.6956\times 10^{-3}\) \\
4.0 & Mean & **0** & \(2.353\times 10^{-5}\) & \(2.2255\times 10^{-3}\) & \(1.0748\times 10^{-3}\) \\
4.5 & Mean & **0** & \(\mathbf{1.843\times 10^{-5}}\) & \(2.1837\times 10^{-3}\) & \(\mathbf{4.1145\times 10^{-4}}\) \\
5.0 & Mean & **0** & \(2.154\times 10^{-4}\) & \(\mathbf{2.1836\times 10^{-3}}\) & \(1.0103\times 10^{-3}\) \\
5.5 & Mean & **0** & \(2.112\times 10^{-4}\) & \(5.3878\times 10^{-3}\) & \(1.7956\times 10^{-3}\) \\
6.0 & Mean & **0** & \(2.112\times 10^{-4}\) & \(4.4375\times 10^{-3}\) & \(1.0385\times 10^{-3}\) \\ \hline \hline \end{tabular} Those in bold are the cases where the result is the best.  
\end{table} 
Table 5: The test results of CGWO with different values of \(k\). Figure 7: Line plots of the results with different values of \(k\). It can be clearly seen from Table 6 that, for unimodal benchmark functions, the CGWO algorithm performs very well in both low and high dimensions. Specifically, for functions \(f_{1}\) to \(f_{4}\), CGWO achieves the theoretical optimal value when the dimension is 30 and 100; moreover, the best value, mean value, and standard deviation are all 0, and the solution performance far exceeds that of other algorithms.
## 2 Model of Dynamic Imaging
### Frequency Domain Image Filtering of Random Vibration  
In the process of frame-by-frame imaging, a certain error of velocity \(V_{e}\) of the optical camera is caused by the multi-source vibration of the aerospace payload, which is transmitted to the camera focal plane through the mechanical structure. After camera exposure, such error image shifts \(d_{is}\) can sharply destroy the output image sharpness. If the input image is a gaussian image, the line spread function \(LSF\), caused by random vibration, can be expressed as:  
\[LSF=-\frac{1}{\sqrt{2\pi d_{is}}}\exp\left(\frac{x^{2}}{2{d_{is}}^{2}}\right) \tag{16}\]  
The MTF of the filter system is obtained by the Fourier transform of the \(LSF\):  
\[\mathrm{MTF}(v)=\exp\left(-2\pi^{2}{d_{is}}^{2}v^{2}\right) \tag{17}\]  
In a linear system, the function of the MTF is approximated to be a digital filter, which is called a filter template. It realizes the response to various frequency components of the input spectrum in an image filtering system, and the output image spectrum \(O\left(f_{x},f_{y}\right)\) can be obtained from Formula (3)  
\[O\left(f_{x},f_{y}\right)=F\{\xi[g(x,y)]\}\exp\left(-2\pi^{2}{d_{is}}^{2}v^{2}\right) \tag{18}\]  
The degraded image \(o(x,y)\) can be obtained by the inverse Fourier transform of the output image spectrum:  
\[o(x,y)=\sum_{f_{x}=0}^{m-1}\sum_{f_{y}=0}^{n-1}O\left(f_{x},f_{y}\right)\exp \left[j2\pi\left(xf_{x}/m+yf_{y}/n\right)\right] \tag{19}\]  
where \(x=0,\;1,\;\dots,\;m-1\) and \(Y=0,\;1,\;\dots,\;n-1\) indicate that the size of the input image is \(m\times n\).
## 3 Results and Discussion
### Essential Oils Characterization by Gas Chromatography-Mass Spectrometry (GC-MS)
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & & & \multicolumn{2}{c}{**Percentage in the**} \\  & & & & & **Samples (\%)** \\ \cline{3-6}
**Identified Compound** & **R.T. (min)** & **RI\({}_{\text{exp}}\)** & **RI\({}_{\text{lit}}\)** & \multicolumn{1}{c}{_T. gummifera_} & \multicolumn{1}{c}{_T. villosa_} \\ \hline \(\alpha\)-Thujene & 7.78 & 919 & 911 & 0.3 & - \\ \hline \(\alpha\)**-Pinene** & **8.03** & **925** & **933** & **20.5** & 0.2 \\ \hline Camphene & 8.57 & 938 & 941 & 0.1 & - \\ \hline Sabinene & 9.58 & 963 & 961 & 1.8 & - \\ \hline \(\beta\)**-Pinene** & **9.69** & **966** & **964** & **27.1** & 0.1 \\ \hline \(\beta\)-Myrcne & 10.36 & 982 & 989 & 2.9 & 0.4 \\ \hline \(\alpha\)-Phellandrene & 10.85 & 994 & 995 & 0.4 & - \\ \hline \(3\)-Carene & 11.09 & 1000 & 1007 & 2.0 & - \\ \hline \(\alpha\)-Terpinene & 11.39 & 1007 & 1014 & 0.3 & - \\ \hline \(\alpha\)-Cyrene & 11.75 & 1016 & 1023 & 5.5 & 0.4 \\ \hline \(\beta\)-Phellandrene & 11.91 & 1020 & 1026 & 3.3 & - \\ \hline **Limonene** & **11.99** & **1022** & **1028** & - & **67.6** \\ \hline _cis_-\(\beta\)-Ocimene & 12.39 & 1032 & 1038 & 0.1 & - \\ \hline _trmus_-\(\beta\)-Ocimene & 12.85 & 1043 & 1048 & 0.3 & - \\ \hline \(\gamma\)**-Terpinene** & **13.30** & **1054** & **1060** & **33.4** & 1.3 \\ \hline n.d. & 14.53 & & & 0.1 & - \\ \hline Terpinolene & 14.62 & 1087 & 1087 & 0.6 & - \\ \hline Linalool & 15.18 & **1101** & **1101** & - & 0.1 \\ \hline French & 15.74 & 1114 & 1112 & 0.1 & - \\ \hline Terpinen-4-ol & 18.70 & 1187 & 1177 & 0.3 & - \\ \hline \(\alpha\)-Terpineol & 19.33 & 1202 & 1190 & 0.2 & - \\ \hline Fenchyl acetate & 20.71 & 1236 & 1232 & 0.1 & - \\ \hline n.d. & 23.73 & & & 0.1 & - \\ \hline
**Methyleugenol** & **28.93** & **1438** & **1415** & - & **29.6** \\ \hline Caryophyllene & 29.40 & 1449 & 1444 & - & 0.2 \\ \hline Germacrene D & 31.93 & 1512 & 1481 & 0.3 & - \\ \hline Elemicine & 35.02 & 1588 & 1566 & - & 0.1 \\ \hline n.d. & 36.78 & & & 0.1 & - \\ \hline
**Total identified** & & & & **99.7** & **99.9** \\ \hline \end{tabular}
\end{table} 
Table 3: Identification, retention time (min) and relative amounts (%) of compounds in the essential oils of _Tapusia gummifera_ and _Thapsia villosa_. Results are expressed as average percentage (%) of peak area \(\pm\) standard deviation (n = 3). Compounds are listed in order of elution from the HP-5 MS column. Major compounds are presented in bold. Abbreviations: n.d.: not determined, which means compounds that were not identified in the studied samples; R.T.: retention time of the eluted compounds expressed in minutes; RI\({}_{\text{exp}}\): experimental retention index calculated relative to n-alkanes series standards (C7-C30) on the HP-5 MS column; RI\({}_{\text{lit}}\): literature retention index on the similar phase column. RI\({}_{\text{exp}}\) and RI\({}_{\text{lit}}\) values associated with “.” were unable to be determined. Percentage values for compounds associated with “.” indicate that said compound was not present in the respective EO.
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 4 Industrial Internet of Things (IIoT) Platform for Hybrid Edge and Cloud Computing
### The Edge  
At the edge, the data come in from the physical world via sensors, and actions are taken to change physical state via various forms of output and actuators. The data processing, analysis, and evaluation are performed at the edge. The communications have real-time and deterministic behavior, while the quantity of the analyzed data is reduced.  
A triaxial accelerometer was embedded in the machine close to the cutting point and its signal was sent to a real-time controller located in the electrical cabinet of the machine. An expert algorithm which ran in this real-time programmable automation controller (PAC) processed this signal and when the machining becomes unstable, it commanded an action from the computer numerical control (CNC) to solve the vibration problem. This controller was surveyed by a higher-level cloud-based monitoring system, which was used as a follow-up and analytics tool. This surveillance was done through an IoT monitoring hub or gateway which gathered the data from the controller, managed them, and uploaded them onto the cloud. The physical platform used is shown in Figure 10.  
Figure 9: Block diagram of the Industrial Internet of Things (IIoT) platform for edge and cloud computing.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 4 Results and Discussion
### Criteria for Model Selection and Comparison  
* Random Forest Regression: Exhibits the lowest AIC and BIC values among the three models, indicating that it has the best fit to the data while maintaining a balance between complexity and performance. This indicates that a random forest is the most appropriate model for this dataset, effectively capturing the underlying patterns while minimizing overfitting.
* Lasso Regression: Comes in second place, exhibiting significantly lower AIC and BIC values than linear regression, but outperforming Random Forest. This indicates that Lasso is better than linear regression for fitting the data, likely due to its ability to perform regularization, which mitigates overfitting by penalizing large coefficients.
* Linear regression: Exhibits the highest AIC and BIC values, indicating it is the least effective model for this particular dataset. The model's simplicity may result in underfitting, as it fails to capture the complexities in the data compared to the more sophisticated models.
* Figure 20 illustrates a bar comparison between the two criteria, revealing a significant difference between them. The bars fall below the horizontal axes due to the negative values of the criteria.
# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 4 Results and Discussion
This is due to non-conflict parallel transmissions which are possible due to the proper allocation of priorities to member nodes. The algorithm proposed in this study achieves low latency due to the proper priority allocation of member nodes. Figure 21 shows the packet delivery ratio (PDR) performance of the proposed link scheduling algorithm according to link success probability (LSP). Figures 22 and 23 show the (PV and wind power) forecasting performance of the proposed link scheduling algorithm according to LSP. As shown in Figures 22 and 23, when the LSP increases, the RMSE of PV and wind power forecasting for all techniques is reduced, and among the different approaches, the proposed KRLS method produces the least RMSE. Moreover, it can be seen that a lower LSP results in a higher performance margin, and a higher LSP results in a modest drop in the performance margin. Figures 22 and 23 clearly indicate that the proposed KRLS approach performs better than the other ML methods. The proposed link scheduling algorithm for the WSN performs much better than the DeAMON with priority and DeAMON because of the reconfigurability when a parent node suffers from a node failure. In addition, the proposed algorithm can find an alternative parent node. Figure 21: A comparison of the proposed link scheduling algorithm with DeAMON according to LSP having 16 available channel offsets for the 6 TiSCH network shown in Figure 2.  
Figure 22: A comparison of the forecasting performance (PV power) based on proposed link scheduling according to LSP with 16 available channel offsets for the 6 TiSCH network shown in Figure 2.