esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 3 Preliminaries
### Our Approach  
The CAN authentication in MAuth-CAN meets two goals: (1) No receiver can open any message that does not go through a centralized authentication of MAuth-CAN, (2) The CAN controller for message authentication is never enforced to leave CAN bus by consecutive and numerous transmission errors by intention.  
In this paper, we show why MAuth-CAN never fails to meet the above goals. To simplify the above goals, we present sufficient conditions in theorems, which should be satisfied to meet the goals (Section 4.4, and then prove them by model checking (Section 5)). We use a high-level model of MAuth-CAN, where the reaction of the authenticator to the attack message is highlighted (Section 5.1). Then, using model checking, we prove that the authenticator of MAuth-CAN passes no attack message without verification even under even consecutive attacks if the sufficient conditions in Theorem 1 consisting of Lemma 1 and Lemma 2 are satisfied. We present a low-level formal model of MAuth-CAN in the MAC level of the data link layer of CAN, which is detailed enough to be able to reflect actual behaviors of CAN. This model ensures our verification is practical enough to provide valid proofs of security of MAuth-CAN (Section 5.2). Then, we prove Theorem 2 by proving Lemma 3, which is the essential property of MAuth-CAN assumed by Theorem 2. Finally,we show that our verification results are consistent with the actual implementation of MAuth-CAN (Section 6).  
In the following subsection, we present our model method technique, model checking using UPPAAL.
# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 2 Proposed Framework
### Optimization Process
The goal is a Gaussian that ranges from 60 to 120 degrees, with a unit value of 90 degrees:  
\[tan(\frac{\theta_{hor}}{2})=\frac{Dist_{hor}}{2D_{S}} \tag{1}\]6  
This variable's purpose is to create missions like those shown in \[tan(\frac{\theta_{hor}}{2})=\frac{Dist_{hor}}{2D_{S}} \tag{1}\]7 The green mission has \(D_{min}\) = 1 m, \(D_{S}\) = 3 m, \(N_{Vert}\) = 4, while the red mission has \(D_{min}\) = 0.5 m, \(D_{S}\) = 1.5 m, \(N_{Vert}\) = 8. It is noticed that the mission in green is more distant from the surface (\(D_{S}\) greater), having less points of image capture because of greater \(D_{min}\). The number of horizontal bands is also reduced by the smaller number of vertical waypoints. \[tan(\frac{\theta_{hor}}{2})=\frac{Dist_{hor}}{2D_{S}} \tag{1}\]8 Incidence angle calculation. With this fitness configuration, the search for better parameters can use any metaheuristic algorithm. For instance, Genetic Algorithm (GA) [46], Particle Swarm Optimization (PSO) [47], Bat Algorithm (BA) [48], Ant Colony (AC) [49], or other methods can be used in the optimization. Table 1 summarizes the constants with their equivalent units. \(N_{Vert}\in\mathbb{N}\) and \(N_{Waypoints}\in\mathbb{N}\) represent numerical values for the number of vertical waypoints and the total of waypoints, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**Constant** & **Explain** & **Dimension** \\ \hline \(D_{min}\) & Horizontal Distance between two images & m \\ \hline \(D_{S}\) & Distance to Surface & m \\ \hline \(N_{Vert}\) & Number of Vertical Waypoints & \\ \hline \(\theta_{hor}\) & Opening angle of the camera—Horizontal & \({}^{\circ}\) \\ \hline \(\theta_{vert}\) & Opening angle of the camera—Vertical & \({}^{\circ}\) \\ \hline \(Dist_{hor}\) & Field of View—Horizontal & m \\ \hline \(Dist_{Vert}\) & Field of View—Vertical & m \\ \hline \(Coverage_{hor}\) & Intersection between two images—Horizontal & \% \\ \hline \(Coverage_{Vert}\) & Intersection between two images—Vertical & \% \\ \hline \(D_{vert}\) & Vertical displacement between two horizontal transfers. & m \\ \hline \(G_{Time}\) & Time Fitness Gain & [0, 10] \\ \hline \(G_{Photometric}\) & Photometric Fitness Gain & [0, 10] \\ \hline \(h_{max}\) & Maximum surface height & m \\ \hline \(h_{min}\) & Minimum surface height & m \\ \hline \(D_{T}\) & Distance traveled in each horizontal transfer & m \\ \hline \(N_{Waypoints}\) & Total number of mission waypoints & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of constants. Figure 8: Mission example.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Ministatellites
#### 3.2.1 NovaSAR-1 (400 kg)
remote sensing perspective, temperature or moisture conditions, as well as solar illumination, tend to be consistent in ground targets if observed at the same time of the day. Although a Dnepr rocket may launch up to 3 NovaSAR at once theoretically, multi-satellite launches have not been realized for NovaSAR and other minisatellites; further miniaturization was necessary, which was achieved by microsatellites introduced in the next section. The notable milestone of NovaSAR, however, is that SSTL became the first satellite manufacturer with a full Earth-observation line-up covering radar instruments as well as infrared and visible-wavelength payloads (e.g., Disaster Monitoring Constellation in 2002). Continuing its successful contracts of optical satellites with several national governments, SSTL was able to commercialize NovaSAR's product with Australia and the Philippines, heralding the advent of a global SAR data market [87].
## 4 Results
### Comparison of Derived SSTs with Other SST Product  
In order to examine whether the derived SST coefficients can be operated properly in the entire COMS region, we estimated SSTs based on the present NLSST algorithm on 26 March 2015. Since there was considerable cloud coverage in the mid-latitude region as well as in the tropical regions, all SST images were composited to produce a daily SST field by taking the average, as shown in Figure 13a. In order to compare the satellite-derived SST with L4 SSTs, the OSTIA SST on the same date is illustrated in Figure 13b. Our overall comparison indicated that the two SST images were quite similar in terms of temperature ranges and oceanic features. For a deeper comparison, the estimated SST images were enlarged to concentrate on the seas around the Korean Peninsula, as shown in Figure 12c,d. Both SST images presented large-scale oceanic features such as the Kuroshio Current and subpolar front in the East Sea (called Sea of Japan). The COMS/MI-derived SSTs expressed small-scale features in detail, such as the fine structure of the Kuroshio meandering, and mesoscale eddies in the vicinity of the subpolar frontal region, which was in contrast to the smoothed and filtered composite SST field of OSTIA. The details of the small-scale and fast-changing features are unlikely to appear in the L4 SST  
Figure 12: Spatial distribution of (**a**) root mean square errors (RMSEs) and (**b**) mean biases of MCSSTs and (**c**) RMSEs and (**d**) mean biases of NLSST from in situ temperatures in bins of \(5^{\circ}\times 5^{\circ}\) for the period from April 2014 to March 2015.  
fields due to their long repeating period (usually more than one day), as well as to the large swath gap of the polar-orbit satellite [(69; 70)].  
The OSTIA SST is one of the best SST composites; it combines all available satellite data and in situ measurements over the course of a day. However, its current product does not include any SSTs from geostationary satellite data covering this particular region. Thus, it is believed that the SST product may depend on AMSR2 SSTs from microwave remote sensing in the optimal interpolation procedure. In light of this, the present SSTs of COMS/MI, which are derived several times a day, can contribute to the capability of data sampling in the daily SST product over the full-disk region by overcoming the limitations of observations by other near-polar orbiting satellites. Eventually, the present SSTs are anticipated to be operational not only in daily SST composites, but also for diverse applications in the atmospheric and oceanic fields.
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 4 Basin Amplification Models Based on CNN
### Performance Indicators  
In order to better evaluate the effectiveness of the trained CNN model, the mean absolute error (_MAE_), root mean square error (_RMSE_), and the determinant factor (\(R^{2}\)) were used as accuracy metrics; see Equations (17)-(19).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-f(x_{i})\rvert \tag{17}\]  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{1}{n}\big{(}f(x_{i})-y_{i}\big{)}^{2}} \tag{18}\]  
\[R^{2}=1-\frac{\sum\big{(}y_{i}-f(x_{i})\big{)}^{2}}{\sum\big{(}y_{i}-\overline {y}\big{)}^{2}} \tag{19}\]  
The combined use of _MAE_, _RMSE_, and \(R^{2}\) provides a more comprehensive evaluation of the results. _MAE_ and _RMSE_ focus on the accuracy of the predictions, while \(R^{2}\) focuses  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Basin Sample**} & **Training Data Set** & **Validation Data Set** & **Testing Data Set** \\ \hline Number & 756 & 605 & 120 & 151 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The division of samples.  
Figure 13: CNN model structure optimization process.  
on the explanatory power of the predictive model. Therefore, when assessing the predictive performance of a model, these three metrics are usually considered together to obtain clearer, more comprehensive, and more reliable evaluation results. The smaller the values of _MAE_ and _RMSE_, the better the prediction accuracy of the CNN model. The range of the decision factor (\(R^{2}\)) was in the range [0-1], and its value closer to 1 indicated a better fitting effect.
## 4 Simulations on the Trapping System
### Electrical Simulations
#### 4.1.2 Residual Axial Radiofrequency  
In the simulations, the trap electrodes have been considered as perfectly machined and aligned. Nevertheless, this is an ideal case, since misalignments or imperfections may occur during the fabrication or the assembly. Regarding the Paul trap, for instance, any deviation of the blade electrodes from the ideal case causes the presence of a nonzero RF field along the axial direction. The approximate solution to the equation of motion for a particle with mass \(m\) and charge \(+\epsilon_{0}\) in a Paul trap in the presence of an additional DC field can be written as [34]:  
\[x_{i}(t) \approx \bigg{(}x_{0,i}+x_{1,i}\cos(\omega_{i}t+\varphi_{i})\bigg{)}\, \left(1+\frac{q_{i}}{2}\cos(\omega_{RF}t)\right)\quad\text{with}\quad i=x,y,z\]where \(x_{1,i}\) is the equilibrium position of the ion along the ith direction, \(x_{0,i}\) is the position shift from \(x_{1,i}\) due to the stray electric field, \(\omega_{RF}\) is the RF frequency, and \(\omega_{i}\) is the secular motion frequency. Consequently, the micromotion amplitude along the ith direction can be estimated as \((x_{0,i}\,q_{i})/2\). Ideally, along the RF electrodes' axis, the parameter \(q_{z}\) is null near the trap center; instead, it assumes nonzero values if the finite electrodes' length and misalignment effects are introduced. In order to evaluate \(q_{z}\) and the residual axial micromotion amplitude, a modified version of the trap assembly, having the blade electrodes displaced from their exact position, was simulated. Since the mechanical tolerance on the blades' position was 5 um, the angular deviations were neglected in the simulation, while the linear displacements were considered equal to the tolerance for taking into account the worst possible case. This evaluation showed a residual axial micromotion amplitude of about 0.5 nm, which corresponded to a micromotion energy of about 1 uK.
# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Direct Monitoring-Based Studies  
This section includes main properties and outcomes only direct monitoring-based studies listed in Table 1. In Table 2, devices, sensing mechanisms, the methods of sensor signal processing, and the target property of the direct monitoring studies. Additionally, scour validation tests, and experiments on real bridges are tabulated here. The assessments, outcomes of the studies, performance, and efficiencies are provided at the end of this section. The research listed in Table 2 mainly focused on the monitoring device they used. [49], [50], [48], [44], and [45] aimed to measure scour depth with the sensors they used. [52] monitored sediment deposition processes. On the other hand, [44] searched for the rigid body motions of the piles and piers, using velocity sensors. [51] and [55] tried to locate and track the movement of smart rocks they deployed along the scour hole. Vertical displacement of the rock into the hole revealed the depth of scour. Direction known and unknown smart
## 5 Conclusions  
Big data give many opportunities along with many challenges in building knowledge management systems, especially in healthcare, a knowledge-intensive industry. In this study, we have proposed a healthcare knowledge management system to improve medical diagnosis decision making in the context of big data and artificial intelligence. It ensures the systematic knowledge development process in which knowledge exploration is performed through machine-learning algorithms and knowledge exploitation is performed through the application of machine-learning models for medical diagnosis. All of these exploration and exploitation activities are conducted in a big data environment. The system is built with the most popular big-data-processing technologies such as Spark, HDFS, HBase, and Kafka.  
We illustrate the healthcare knowledge system for the detection and classification of hypertension and brain hemorrhage. The decision tree models are used in the knowledge layer of the system with an accuracy of over 84% for high blood pressure detection and over 92% for high blood pressure classification. In the process of building a decision tree, we rely on the Feature Importance to remove unnecessary data fields in improving model accuracy and optimizing the model training time. The deep neural network with Faster R-CNN Inception ResNet v2 is used in the knowledge layer for brain hemorrhage detection with mAP of 0.68. The data used in the study are collected from several hospitals in the Mekong Delta of Vietnam and health-monitoring devices. The expansion to use other large databases such as the Premier Hospital Database is necessary for this system to be able to take advantage of medical knowledge in different regions. Afterward, we will be able to expand the scope of our research with a wide range of diseases to better serve public healthcare.  
Conceptualization, T.-C.P. and A.-C.P.; methodology, A.-C.P. and T.-C.P.; software, A.-C.P.; validation, T.-N.T. and T.-C.P.; formal analysis, A.-C.P.; investigation, A.-C.P. and T.-C.P.; resources, A.-C.P. and T.-C.P.; data curation, T.-N.T.; writing--original draft preparation, A.-C.P.; writing--review and editing, T.-N.T. and T.-C.P.; visualization, T.-N.T.; supervision, T.-C.P. project administration, A.-C.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available on request by contacting the corresponding author.  
The authors declare no conflict of interest.  
Figure 16: Average precision (AP) of four brain hemorrhage types.
## 3 Approach
### Storing Transaction Pool  
The sensor device (DeviceSensor) encrypts the periodically measured environment sensor data (SensorData\({}_{\text{i}}\)) into DeviceKey\({}_{\text{n-1}}\) and transmits them to the registered gateway device (Devicegateway). The transmitted SensorData\({}_{\text{i}}\) contain environment sensor values and DataCount for identification. Whenever the sensor device transmits data, it executes ModResult(SensorData\({}_{\text{i}}\),16) and stores the result in TransactionPool\({}_{\text{Sensor}}\) along with the counter number. The result of ModResult is one of the values from 0 to F. Until BlockCondition\({}_{64}\) is satisfied, each ModResult of SensorData\({}_{\text{1}}\), SensorData\({}_{\text{2}}\), SensorData\({}_{\text{3}}\),..., SensorData\({}_{\text{64}}\) is saved into TransactionPool. The TransactionPool accumulates up to 64 values from 0 to F, which are the modular results. The Devicegateway also executes ModResult (SensorData, 16) for received data and stores the result in TransactionPoolgateway along with the counter number.  
If there is no packet loss, TransactionPool\({}_{\text{gateway}}\) and TransactionPool\({}_{\text{Sensor}}\) should have the same transaction pool. On the other hand, if there is a lost packet, the TransactionPool\({}_{\text{gateway}}\) shows the lost data. Figure 7 shows the mechanism of storing Transaction Pool. The pseudo code is as Algorithm 1.  
Figure 7: Transaction pools stored by sensor device and gateway device.
## 6 Results
### Wind Profile 13 m/s Turbulence A  
The requirements for pitch control are relaxed due to a wind speed lower than the nominal value most of the time, causing the generator speed not to rise above the reference speed frequently. However, there may be some peaks in the wind speed, and the response that a given controller offers might improve the behavior of the system.  
Figure 16: Comparison of the evaluated controllers for a wind profile of 18 m/s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MI (MN\(\cdot\)m)** & **GI (MW)** & **SpI (rpm)** \\ \hline Baseline PI & 60.0260 & 1.1988 & 275.4546 \\ Improved PI & 53.8895 & 0.3808 & 83.3007 \\ Improved PI + Fuzzy A & 52.3816 & 0.2943 & 63.0921 \\ Fuzzy B & 55.9004 & 0.6653 & 150.2268 \\ Improved PI + Fuzzy C & 53.3247 & 0.2614 & 55.4029 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Indexes for simulations with wind profile 3 (18 m/s A).  
Figure 15: Comparison of the evaluated controllers for a wind profile of 15 m/s.  
The NREL baseline PI can cope with the variations of the wind speed as these are not big enough to lead the generator speed above its nominal value consistently, as seen in Figure 14. The added Anti Wind-Up achieves a more constant pitch reference without losing control capabilities, although there are almost no differences between them. With respect to the PIs, fuzzy logic controller A produces a more stable pitch reference. Similar results are obtained with Fuzzy B and Fuzzy C.  
As previously stated, in this scenario, the pitch control has little to no impact on the system behavior as the performance indexes are similar for the five controllers (Table 7). However, two of the fuzzy logic controllers start to exhibit their capabilities to improve the PI results, as the speed index (SpI) is around 10% lower (better) for them.
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 3 System Model
### Analysis of Power Consumption  
Figure 6 demonstrates the average power consumption set in contradiction of \(P_{max}\) in assessing 20 IoT devices at 10 iterations for ZFBF, Fixed MMSE, UFBF, JBDA, ANN, KNN, ACO, IACO-C and IACO. In \(P_{max}\leq 30\) dBm, it detected that IACO needs extra power at \(L\leq 35\) compared to existing methods, since it requires additional activated RRUs to confirm the provided data rate. The IACO method steadily rises to persistent power consumption whenever the \(P_{max}\) expands.  
Once extra RRUs are installed, power consumption starts to rise that subsequently decreases the energy efficiency of the network. Therefore, the proposed IACO activates lesser RRUs to confirm QoS requirements with minimized power consumption. \(L\) executes equally with proposed IACO, which needs additional power to manage larger complex optimization issues. At the end, energizing additional RRUs for the IACO not only enhances the transmit power considerably but also increases energy efficiency. Figure 6 clearly shows  
Figure 5: Analysis of VIM6G IoT for varying BS antenna count for varied values such as (**a**) 66, (**b**) 64 and (**c**) 80.  
Figure 6: Analysis of Power Consumption.  
that the proposed IACO accomplishes better results in energy-efficiency optimization when compared with existing methods.
## 5 Experimentation
### Message Generation Distribution
#### 5.3.2 Scenarios' Message Generation Distribution  
Table 1 summarizes the message creation distribution for the different scenarios. The Taxis scenario uses an ISTH distribution that replicates two working days, where the messages are generated each 10 to 60 s uniformly distributed for each working day during eight peak hours. With all the scenario specifics, it is considered a low- to medium-congestion scenario.  
The Info5 scenario uses the aforementioned message generation distribution. Given the message generation distribution and the fact that the nodes congregate around the events of the congress, it is considered a medium- to high-congestion scenario.  
The Campus scenario uses an ISTH distribution resembling a workday where, during the peak hours, messages are created every 10 s. Hence, this scenario is considered a high-congestion scenario.  
For the Emergency scenario, during the first eight hours, messages are generated at the high rate of each 10 s using a CBR distribution followed by eight hours of low-rate message generation (every 80 s). Therefore, Emergency is a variable-congestion scenario.