esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Simulation Conditions
### LapTime
#### 3.2.2 Simulated Racetracks  
In order to simulate the energy gains with the proposed regenerative braking method, twenty-five racetracks were simulated in MATLAB software (Matlab R2022a version). The racetracks are simplifications of the real-life scenario since these are made up of a string of continuous points. These points represent a fixed theoretical optimal trajectory between the racing lines for each track, which can be seen in red in Figure 7. Each pilot can opt for different approaches to the same track. However, the LapTime software works with a predefined trajectory and calculates the fastest way to travel it. All the remaining trajectories are presented in Appendix A in Figures A1-A24, in which the track limits are seen in black and the trajectory is seen in red. The green arrow indicates the direction of travel in the starting point of each track.  
Figure 6: LapTime software fluxogram.  
Figure 7: Plan view and trajectory defined for the Circuit de Barcelona–Catalunya, Spain.  
The motorcycle's state of energy can be defined with the definition of mechanical energy without friction losses, as a simplification, represented in Equation (3). This equation states that mechanical energy variations (\(\triangle E_{M}\)) in an isolated system are null. Hence, kinetic energy (\(E_{Kintectic_{initial}}\)) and potential energy (\(E_{Potential_{initial}}\)) can only be transformed from one to another or transferred to another system to form \(E_{Kintectic_{final}}\) and \(E_{Potential_{final}}\). The body's mass is \(m=220\) kg, and the gravitational acceleration is \(g=9.81\) m/s\({}^{2}\). To create the most realistic scenario, altitude (\(h\)) has to be considered in the created tracks, as ignoring it will affect velocity \(v\) in Equation (4). This paper does not feature height values since the simulated tracks use two coordinates to create their plan view, as in Figure 7. This simplification leads to errors in the speed profile, braking values and consequently recovered energy results:  
\[\triangle E_{M}=0\ \rightarrow\ E_{Kintectic_{initial}}+E_{Potential_{initial}}=E_{ Kintectic_{final}}+E_{Potential_{final}} \tag{3}\]  
\[E_{kinetic}=\frac{1}{2}\ m\,v^{2}\ \ \ \ \ \ \ \wedge\ \ \ \ \ E_{potential}\ =\ m\,g\,h \tag{4}\]
## 7 Risk Analysis
### Tcu
#### 7.2.1 Tampering  
1. **Ports and Communications**  
_Security risk:_ The TCU is a controller that gathers and packages the GPS location, pictures, and sensor data from the ECU before sending them to outside services. Tampering assaults in the TCU have the power to alter the behavior of the device to the attacker's advantage. By changing the properties of the transmission route, communication attacks can be either physical or virtual. Ports for input and output must be secured against these attacks.  
Security measure: One common method for preventing tampering attacks is the usage of data integrity checks by leveraging cryptographic hash functions. The ECU can calculate a cryptographic hash of the collected data and send it to the TCU. Upon receiving the data, the TCU recalculates the hash using the same algorithm and compares it. If they match, it indicates that the data has not been tampered with during transmission, securing both the outgoing and incoming communications. A trusted platform module (TPM) can be used for speeding up the encryption and decryption process using hardware acceleration.
# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## 3 Digital Twin Model Construction of Wind Turbine Transmission System
### Construction of Kinematic Models for Transmission Systems  
The kinematic model of the wind turbine is the key to accurately mapping its motion state, and the digital twin model incorporating the kinematic model can accurately map the actual wind turbine operation in virtual space in real-time, driven by real-time operation data. Therefore, in this paper, the kinematic modeling of the wind turbine drive train is carried out. Construct a dynamic model based on the working principles of the central transmission system and related essential components of the wind turbine generator, as shown in Figure 5.  
Where \(\omega_{b}\) is the hub speed; \(\omega_{c}\) is the carrier speed; \(\omega_{p}\) is the planetary gear speed; \(\omega_{s}\) is the sun gear speed; \(\omega_{g1}\omega_{g2}\omega_{g3}\omega_{g4}\) are the speeds of gears 1, 2, 3, and 4, respectively. \(Z_{s}\), \(Z_{r}\), \(Z_{p}\), \(Z_{g1}\), \(Z_{g2}\), \(Z_{g3}\), \(Z_{g4}\), are the numbers of teeth on the sun gear, planet gear, ring gear, gear 1, gear 2, gear 3, and gear 4, respectively.  
Let \(\omega=(\omega_{b},\omega_{c},\omega_{s},\omega_{g1},\omega_{g2},\omega_{g3}, \omega_{g4},\omega_{g})\), and let \(\eta\) be the matrix representing the speed relationships between each component, then:  
\[\eta=\left[\begin{matrix}1&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\\ 0&0&1+\frac{Z_{r}}{Z_{s}}&0&0&0&0&0\\ 0&0&0&1&0&0&0\\ 0&0&0&0&\frac{Z_{g2}}{Z_{g1}}&0&0&0\\ 0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&\frac{Z_{g3}}{Z_{g4}}&0\\ 0&0&0&0&0&0&0&1\end{matrix}\right] \tag{14}\]  
Figure 4: Modeling of wind turbine drive train after lightweight.  
\[\omega^{\rm T}=\eta\cdot\omega^{\rm T}=\begin{bmatrix}1&0&0&0&0&0&0\\ 0&1&0&0&0&0&0\\ 0&0&\frac{Z_{\rm c}}{Z_{\rm c}}&0&0&0&0\\ 0&0&0&1&0&0&0\\ 0&0&0&0&\frac{Z_{\rm c2}}{Z_{\rm c1}}&0&0&0\\ 0&0&0&0&0&1&0&0\\ 0&0&0&0&0&\frac{Z_{\rm c3}}{Z_{\rm c4}}&0\\ 0&0&0&0&0&0&0&1\end{bmatrix}\begin{bmatrix}\omega_{b}\\ \omega_{c}\\ \omega_{s}\\ \omega_{g1}\\ \omega_{g2}\\ \omega_{g3}\\ \omega_{g4}\\ \omega_{g}\end{bmatrix}=\begin{bmatrix}\omega_{b}\\ \omega_{b}\\ \omega_{b}\Big{(}1+\frac{Z_{\rm c}}{Z_{\rm  
Therefore, the dynamic equation between the high-speed shaft and the generator is as follows:  
\[T_{g4}=J_{g}\ddot{\theta}_{g}+C_{2}(\dot{\theta}_{g}-\dot{\theta}_{g4})+K_{2}( \theta_{g}-\theta_{g4}) \tag{20}\]  
\[T_{g}=T_{g4}-C_{2}(\dot{\theta}_{g}-\dot{\theta}_{g4})-K_{2}(\theta_{g}-\theta_ {g4})=J_{g}\ddot{\theta}_{g} \tag{21}\]  
In Equations (20) and (21), \(J_{g}\), \(\theta_{g}\), and \(T_{g}\) represent the rotational inertia, angular displacement, and torque of the generator, respectively; \(T_{g4}\), \(C_{2}\), \(K_{2}\), and \(\theta_{g4}\) represent the torque, damping, stiffness, and angular displacement of the high-speed shaft, respectively.
## 3 Results and Discussion
### FeNP Antimicrobial Activity  
FeNP produced from Acacia _nilotica_ seedless pods extract antimicrobial activity was examined against four species of human pathogenic bacteria, and these are; _Escherichia coli_, _Staphylococcus aureus_, _Salmonella_, _Marsa_, and _Candidia_. It was interesting to observe the great ability of FeNP to inhibit the growth of the test organisms. Most of the tested microorganisms affected severely by FeNP; the inhibitory potentials of FeNP can be expressed in a descending order as follow; _Candidia_, _Staphylococcus aureus_, _Salmonella_, _Marsa_, and _Esherichia coli_ based on the diameter of the inhibition zone and the absorption at 600 nm (Table 2, Figure 12). It worth to mention that FeNP antimicrobial propensity observed clearly at higher concentration (20, 40, and 60 \(\upmu\)g), while at lower concentrations weak activity was detected. This is inconsistent with other studies on iron nanoparticles antimicrobial activity reported that iron nanoparticles have antibacterial activity at higher concentrations, and this activity can be further moderated by changing the surface potential and accessible surface functional groups. The changes cause a change in the interaction pattern at the FeNP bacteria interface, hence play a crucial role in determining the antimicrobial propensity of FeNP [29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**FeNP (\(\upmu\)g)** & _Escherichia coli_ & _Marsa_ & _Salmonella_ & _Staphylococcus aureus_ & _Candidia_ \\ \hline
10 & 11 \(\pm\) 0.2 mm & 17 \(\pm\) 0.2 mm & 19 \(\pm\) 0.2 mm & 17 \(\pm\) 0.1 mm & 17 \(\pm\) 0.3 mm \\
40 & 15 \(\pm\)0.1 mm & 20 \(\pm\) 0.2 mm & 22 \(\pm\) 0.1 mm & 22 \(\pm\) 0.1 mm & 25 \(\pm\) 0.1 mm \\
60 & 17 \(\pm\) 0.1 mm & 24 \(\pm\) 0.1 mm & 23 \(\pm\) 0.1 mm & 25 \(\pm\) 0.1 mm & 25 \(\pm\) 0.1 mm \\ \hline \end{tabular}
\end{table}
Table 2: Antimicrobial effect of Acacia _nilotica_ FeNP on human pathogens.  
Figure 11: Intraparticle diffusion model.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework
### Applications of MLTDAF in Counter-Terrorism
#### 8.4.5 Theoretical Approach  
The suggested architecture is a multi-layered procedure that starts with edge-level data collecting from both IT and OT devices. AI/ML models are then used to preprocess and analyze this data, identifying abnormalities and correlating events from various sources. Following the identification of an anomaly, alerts are ranked and routed to a response coordination module, which, depending on the type of incident, either escalates the issue to the relevant Security Operations Center (SOC) or OT security teams or automates the mitigation procedure.  
Despite still being theoretical, this approach will be further developed and defined in future work to ensure it is adopted as an effective countermeasure that can be used to thwart terrorist attacks, offering early detection and effective, robust, and sophisticated security measures and countermeasures against attacks targeting each layer. MLTDAF aims to be operated by a network of actors at many layers. OT security specialists focus on control-level events in SCADA and substations, while IT SOC personnel manage alerts at the enterprise and communication layers. AI-based support tools help reduce false positives and improve incident classification, while human analysts handle high-risk response decisions and facilitate cross-domain collaboration.
## 3 Proposed Clustering Routing Protocol
### Operation Phase  
In the operation phase, the information is transferred to the BS once the selection of optimal CHs has established the network connections. Data transmission occurs every round, where one round is completed every time the information is transferred from normal nodes to CHs, and then to the BS. After each round, the network is reconfigured by executing the YSGA algorithm to select the new optimal CHs and rebuild the network links. The process for choosing CHs and the optimal cluster network configuration is described in the next subsections.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 7 Conclusions  
This paper presents a Probabilistic and Deterministic Tree Routing Protocol called PDTR. It aims to root the packet toward the sink via optimal tree paths and save the nodes' energy for as long as possible. PDTR simulation results show a longer network lifetime, and less energy consumption, in comparison to the previously proposed protocols. That is to say that PDTR is a suggested routing protocol to prolong the network lifetime under the condition of sensor homogeneity.  
PDTR improves the deterministic tree strategy due to the fact that it builds the tree according to three probability distributions (hops-count, transmission distance, and residual energy). Hence the selection of optimal paths and the saving of energy. Moreover, PDTR improves the probabilistic tree strategy. First, this is due to the fact that it forwards data based on the product of three probability distributions controlled by parameters, two of them need to be updated by time (residual energy and hops-count); this update performs the use of paths when transmitting data, in comparison to the protocols based on predefined probability distribution. Second, it does not need much energy to be updated, in comparison to the other probabilistic protocols, because the update of PDTR happens only when any node loses \(\ell_{e}\) of its energy, hence a better performance in terms of energy consumption is obtained.  
Our work is based on hop-count distribution and residual energy, which sometimes need to be updated in the tree due to the change in sub-trees position and nodes' residual energies. This update needs some energy. Hence, our future work involves examining this fairness issue in order to enhance the PDTR and save the used energy to prolong the WSN lifetime.  
Conceptualization, R.G. and J.H.; methodology, R.G.; software, R.G. and S.D.; validation, J.H., M.A.A.A.-q.; formal analysis, R.G. and S.D.; investigation, J.H.; writing--original draft preparation, R.G.; writing--review and editing, R.G. and M.A.A.A.-q.; visualization, R.G. and S.D.; supervision, J.H.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Program through the National Research Foundation of Korea (NRF-2019R1A2C1005920) and by the National Natural Science Foundation of China (61775054).  
The authors declare no conflict of interest.
## 6 Discussion and Future Considerations
### Multi-UAVs  
Most of the works studied in this article assume only a single operating UAV, which is not practical or efficient for large-scale WSNs. The network becomes unstable if a single UAV experiences link failure [63]. On the contrary, multiple UAVs can reconstruct the network and find routes to deliver information to the destination should any link failure occur. Multiple UAVs could have a better completion time and can be used to cover large spaces than single UAVs [109]. Although multi-UAVs are more power-constrained [109], using multiple UAVs has the potential to improve the network performance and reduce AoI compared to a single-UAV implementation. Considering all the key features involved, optimizing UAV performance is more challenging for deploying multiple UAVs [63]. Mission planning for multiple cooperating UAVs visiting clusters of sensor nodes with a variable number of visits per cluster [104] can also be considered in the future. Multi-UAVs are capable of flying safely via collision avoidance [109]. Ensuring UAVs follow a particular formation while traversing a trajectory (not necessarily linear and avoiding collision) [109] is another potential research direction for future studies. However, if the trajectory is fixed, more attention should be given to sensor node scheduling.
## 2 Principles and Methods
### Noise Suppression Method Based on PSO-Optimized VMD
If these conditions are verified, output the optimal parameter combination. Perform VMD calculations on the signal under different particle positions to compute the fitness value minEp corresponding to each particle position. 4. Update Particle Positions and Velocities. Use Equations (9) and (10) to update the positions and velocities of the particles. 5. Calculate New Fitness Values and Output Optimal Parameter Combinations. Based on the updated particle positions and velocities, calculate new fitness values and determine whether to continue the iterative loop. If so, return to step (3); if the optimal parameter combination is found or the maximum number of iterations is reached, stop the iteration. * Perform VMD. After obtaining the optimal parameter combination, use these parameters to perform signal decomposition with the VMD algorithm, resulting in various IMF components.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 5 Simulation Results
### The Proposed Coordinated Optimization of IPGS
The downward adjustable capacity of the power system configuration under the two scheduling modes is shown in Figure 11.  
Based on the analysis of the downward adjustable capacity of the power system configuration under the two scheduling modes in conjunction with Figures 9 and 11, it can be found that during the low load period (1:00-7:00), When individually optimized, the downward adjustable capacity of the power system is mainly provided by the coal-fired power units G1, G2, and P2G1. During the formulation of the scheduling plan, the pressure limit of the natural gas system node 6 and the constraints of the gas intake of the gas storage equipment were not considered. As a result, the natural gas generated by P2G1 was 10.64 kcf and 25.46 kcf at 2:00 and 3:00, respectively, which could not be accepted by the natural gas system. The coordinated optimization adjusts the maximum conversion power of P2G1 in these two periods based on the amount of excess natural gas, so that the downward adjustable capacity provided by P2G1 at 3:00 is changed to be provided by P2G2. In order to compare the economics, the flexible scheduling costs of the two methods are given, as shown in the Table 3.  
Figure 11: Power system downward adjustable capacity in different dispatch modes: (**a**) Separate optimization and (**b**) coordinated optimization. From the data in Table 3, it can be seen that under the same scheduling flexibility requirements, when the IPGS is individually optimized, the unit's generation cost and adjustable capacity allocation cost are lower than the coordinated optimization, and since the natural gas system does not consider the effect of the conversion of active power and adjustable capacity of the P2G facilities, the adjustable capacity allocation cost of the P2G facilities is also reduced by 187$ compared to the coordinated optimization, which ultimately reduces the total cost of the power system by 1.65%. However, it also caused a gas load reduction of 368 kcf during the scheduling process of the natural gas system, which caused the total cost of the natural gas system to increase by 15.16% compared to the coordinated optimization. At the same time, due to the constraints of natural gas system operation constraints, 36 kcf of natural gas generated by the P2G facilities cannot be accepted by the natural gas system. The coordinated optimization of the IPGS reduces the gas load reduction to zero by adjusting the maximum active output of the gas-fired units and the P2G facilities, and the natural gas produced by the P2G facilities is also fully utilized. From the above data, it can be seen that with the increase of the proportion of gas-fired units, the scheduling scheme based on the constraints of the power system alone cannot give full play to the synergistic effect of the two energy sources. In order to improve the overall resource utilization level of the two energy systems, it is necessary to implement coordinated optimization scheduling.
## 2 Materials and Methods
### Experimental Setup  
#### 2.4.1 Benchmark Techniques  
From now on, the proposed approach will be referred to as GP_GAIN, whose prediction performance was compared against four techniques from the literature: two feature selection methods widely used in radiomics to select a subset of original features and two GP-based feature construction methods with multi-tree representation that have already been used in other contexts but never applied to radiomics. Both feature selection and feature construction consider the filter and wrapper approaches, whose descriptions are reported in the following:  
* FS_FWD is a wrapper feature selection approach based on sequential forward selection, where features are sequentially added to an empty set of features until adding extra features does not improve the AUC value of a logistic regression classifier [44].
* FS_mRMR is a filter feature selection technique based on the minimal redundancy-maximal relevance (mRMR) criterion, where dependencies between variables and class labels are measured with mutual information [45].
* GP_AUC is a GP-based wrapper feature construction method in which the fitness function is the AUC value obtained from a logistic regression classifier trained with the constructed features [40].
* GP_CORR is a GP-based filter feature construction method, where the Spearman correlation measures the redundancy between constructed features, while the relevance between features and class labels is calculated in terms of AUC. The fitness function is the difference between the relevance and redundancy averages [46].  
The obtained feature sets from these approaches were used to train six classifiers: logistic regression (LR), linear discriminant analysis (LDA), naive Bayes (NB), random forest (RF), support vector machine with Gaussian kernel (SVM), and multilayer perceptron (MLP) [47]. Moreover, the hyperparameters of RF, MLP, and SVM classifiers were tuned by Bayesian optimization to determine the best prediction models [48]. Table 2 summarizes the hyperparameter ranges of these classifiers. Furthermore, the parameter settings used for the three GP-based approaches are summarized in Table 3.
## 2 Materials and Methods
### PICOS Question  
The present review formulated a focused question using the PICOS criteria [23; 24]. Do patients with transverse maxillary deficiencies (P) treated with SARME (I) compared to NSRME (C) have an increased impact on the periodontal apparatus (O) as observed in human randomized controlled trials (RCTs), cohorts, and/or case series studies (S)?  
* Population: Patients with transverse maxillary deficiencies.
* Intervention: SARME.
* Comparison: NSRME.
* Outcomes: Prevalence/incidence of hard (e.g., DEH, fenestrations) and soft tissue (e.g., mucoginyal deformities/conditions) deficiencies. Secondary outcomes included probing depths (PDs), bleeding on probing (BOP), clinical attachment level (CAL), gingival recession (REC), clinical crown height (CCH), keratinized tissue width (KTW), tooth mobility, plaque index (PI), gingival index (GI), marginal bone level (MBL) or equivalent, root resorption, and/or dentinal sensitivity.
* Study design: Human RCTs, prospective/retrospective cohorts, and/or case series studies (>10 patients).
# Blind Image Deblurring Based on Local Edges Selection
## 4 Conclusions  
In this paper, we propose a blind image deblurring method based on local edges selection. The proposed edges selection method based on local information is new and proven to be more effective than using global information when capturing images with dense structures, noise, and blur.  
We use the new edges selection method to build the regularization term. Our model is simpler and more effective than other methods using image gradients. The image and blurring kernel are obtained simultaneously by alternating iteration. The experimental results show that the proposed blind deblurring method is effective for both gray value images and color images. Compared with other state-of-the-art deblurring methods, the quantitative results demonstrate the effectiveness of our method.  
There are still some limitations of the proposed method. In the deblurring process of the proposed method, we assume that the blur is stationary, but in practical application, the blurring kernels are sometimes time-varying and space-varying, which are much more complex. We will further focus on the deblurring related to more complex blurring kernel in the future.  
Conceptualization, Y.H. and J.K.; methodology, Y.H.; software, Y.H.; validation, Y.H. and J.K.; formal analysis, Y.H.; investigation, Y.H.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, J.K.; visualization, J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K.  
This work was supported by National Natural Science Foundation of China (Grant No. 31570713) and Beijing municipal construction project special fund.  
The authors declare no conflict of interest.
## 1 Introduction
### Manuscript Structure  
The structure of the remainder of this manuscript is organized as follows: Section 2 reviews related work on travel time prediction methods, including traditional methods, deep learning applications, and ensemble learning in traffic prediction; Section 3 details our proposed methodology, including the sliding time window-based short-term dynamic prediction mechanism and ensemble learning model design; Section 4 describes the experimental setup and analyzes the results, comparing the performance of different models in detail; Section 5 discusses additional details and implications of the results; Section 6 summarizes the research findings and discusses future research directions.