esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
While the distributed architecture reduces the computational burden in nonlinear systems, its communication mechanism's poor scalability limits its application. It is unsuitable for optimization scheduling in large-scale power grids with MCSs across vast geographic areas. In light of the limitations in the aforementioned studies, the main contributions of this study are summarized as follows:  
(1) Firstly, an operational model for thermal power units under RLVs was developed following the retrofitting of MSTS. By analyzing the output variation of thermal power units under such conditions, the beneficial impact of internal retrofitting with MSTS was demonstrated. Constraints for rapid ramping of thermal power unit output were incorporated to quantify the difference in net load caused by MSTS under RLVs and gradual output variation scenarios. Subsequently, a mathematical model was developed to depict the application of MSTS retrofitting in MCS optimization scheduling problems. (2) Secondly, for the operational scenarios of China's MCSs, a dynamic allocation method for PR revenues was proposed. This approach eliminates the limitation of the conventional method, where PR revenues of thermal power units are provided solely by the grid side and individual users. Instead, revenues are distributed among MCSs. The PR cost allocation (CA) for each CS is dynamically determined by the adjusted generation volumes based on tiered load rates across different time periods and the total compensation amount. (3) Finally, regarding algorithmic implementation, to achieve coordinated economic scheduling between the main grid and MCSs under China's regulated electricity market framework, the optimization problem is divided into two sub-problems: the main grid power optimization problem (Stage One) and the economic scheduling optimization problem for CSs (Stages Two and Three). Independent optimization is performed for the main grid and CSs. The economic scheduling optimization for CSs is carried out in a two-stage process to maximize the total revenue. In Stage Two, the total compensation amount for PR is fixed, which simplifies the relaxation techniques and computational methods introduced in Stage Three for addressing the nonlinear CA objectives.
## 4 Results and Comparison
### Heating Length and Temperature Rise  
The horizontal displacement of the final segment in a cantilever state is calculated as 23 mm (toward the pylons) before lifting the key segment for the considered partially earth-anchored cable-stayed bridge. Since this displacement does not give sufficient lifting space between the key segment and the adjacent segments, it is assumed that the space should be 100 mm. This means that the key segment is made with a shortened length of 154 mm. Hence, the required elongation length to be achieved by the heating process is 100 mm for each girder in a cantilever state.  
In order to determine the optimal regions for long-span bridges, three cases are considered with the same heating length but different heated regions near the pylons (Case-L1), middle region (Case-L2), and near the center of the main span (Case-L3)). By iterative analysis giving the required elongation length of 100 mm, the heating length and temperature rise are determined (Table 6 and Figure 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Thermal Coefficient (\(\alpha\))**} & \multirow{2}{*}{**Heating Length (\(L\))**} & \multicolumn{2}{c}{**Iterative Analysis**} \\ \cline{3-4}  & & & **Applied \(\Delta T\)** & \(\Delta L\) \\ \hline Case-L1, L2, L3 & \(1.2\times 10^{-5}\)/m/m/\({}^{\circ}\)C & 160 m & 54.9 \({}^{\circ}\)C & \(0.1\)**m** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Applied temperature rises (\(\Delta T\)) and heating lengths (\(L\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bridge Model** & **Reaction Force (\(\mathbf{k}\)N)** \\ \hline Model with self-anchored cable system (L-SE-1) & \(-\)289.3 \\ Model with partially earth-anchored cable system (L-EA-6) & 31,541.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Movement caused by removal of the longitudinal restraints in set back.  
Figure 6: Heating regions for the verification example bridge.
# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 3 Methodology  
Figure 3 illustrates the process of conducting the scientometric analysis of the literature in the field of \"event knowledge graph\", which includes data collection, author co-citation network analysis, journal co-citation network analysis, collaborative country network analysis, and keyword co-occurrence network analysis. The documents to be analyzed were collected by setting a combination of fields (e.g., \"Topic\" = \"event\" And \"Document type\" = \"review\" And \"Language\" = \"English\") as the conditions in searching the WoS database. The returned documents were further imported into CiteSpace to conduct the above-mentioned multiple-network analysis, based on which the representative scholars, core journals, hot research topics, and research trends in the event graph field can be identified. More details can be found in the following sections.  
\begin{table}
\begin{tabular}{c c c} \hline
**Relation** & **Meaning** & **Extraction Templates** \\ \hline Causal relation & One event (cause) causes another event (effect) to occur. & because, due to, because of, therefore, thus, result in, lead to, thereby, lie in, since, thanks to, due to the fact that \\  & Partial order relation in which two events & then, before, after, earlier, later, accordingly, subsequently, in consequence, consequently unless, “if \(\ldots\) then \(\ldots\)”, otherwise, \\ Conditional relation & One event is the condition for another event. & “provided/given/assuming/supposing/in the event/on the condition that \(\ldots\)”, as long as “not only \(\ldots\) but also”, at the same time, simultaneously, “either \(\ldots\) or”, alongside, together with \\ \hline \end{tabular}
\end{table}
Table 2: The commonly used event relations.
###### Abstract  
Energy storage systems (ESS) are key elements that can be used to improve electrical system efficiency by contributing to balance of supply and demand. They provide a means for enhancing the power quality and stability of electrical systems. They can enhance electrical system flexibility by mitigating supply intermittency, which has recently become problematic, due to the increased penetration of renewable generation. Flywheel energy storage systems (FESS) are a technology in which there is gathering interest due to a number of advantages offered over other storage solutions. These technical qualities attributed to flywheels include high power density, low environmental impact, long operational life, high round-trip efficiency and high cycle life. Furthermore, when configured in banks, they can store MJ levels of energy without any upper limit. Flywheels configured for grid connected operation are systems comprising of a mechanical part, the flywheel rotor, bearings and casings, and the electric drive part, inclusive of motor-generator (MG) and power electronics. This contribution focusses on the modelling and simulation of a high inertia FESS for energy storage applications which has the potential for use in the residential sector in more challenging situations, a subject area in which there are few publications. The type of electrical machine employed is a permanent magnet synchronous motor (PMSM) and this, along with the power electronics drive, is simulated in the MATLAB/Simulink environment. A brief description of the flywheel structure and applications are given as a means of providing context for the electrical modelling and simulation reported. The simulated results show that the system run-down losses are 5% per hour, with overall roundtrip efficiency of 88%. The flywheel speed and energy storage pattern comply with the torque variations, whilst the DC-bus voltage remains constant and stable within \(\pm 3\%\) of the rated voltage, regardless of load fluctuations.  
energy storage systems (ESS); flywheel; storage for residential premises; charge-discharge control strategy +
Footnote †: journal: Applied Soft. 2018, 8, 453; doi:10.3390/app8030453
## 4 Results and Discussion
### Case Study for DMSA in Static Scheduling  
#### 4.1.1 Scenario Setting  
The experimental cases involve several scenarios that include different numbers of satellites, different types of observations, and different distributions of observation targets. The number of mission targets ranges from 30 to 200. The target positions are randomly generated within the range of latitude \(-60\)-\(60^{\circ}\), and the observation type and priority (1-5) are randomly generated; the number of satellites ranges from 3 to 16. The type of satellites payloads in the constellation are randomly generated. The payload setting parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Optical Payload** & **Parameters** & **SAR Payload** \\ \hline SensorType & SimpleConic & MinElevationAngle & 15.2\({}^{\circ}\) \\ FOV & 5\({}^{\circ}\) & MaxElevationAngle & 51.9\({}^{\circ}\) \\ SlewRange & \(-40\)-\(40^{\circ}\) & ForwardExclusionAngle & 5.7\({}^{\circ}\) \\ Lighting & SunAng \textgreater{} 15\({}^{\circ}\) & AffExclusionAngle & 8.6\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Payload parameters.
## 3 Artificial Neural Network
### Data Preparation
#### 3.4.1 Feature Scaling  
Scaling features is one of the most important data transformations. Since the range of values of data may vary widely, it becomes a necessary step in data preprocessing while using machine learning algorithms. Most machine learning algorithms are poor at dealing with numerical attributes that fall within different ranges of the scale. This also applies to the analyzed dataset, where, e.g., the p\({}^{\prime}\) feature has values in the range from 30 to 415 and where the values of the \(\mathrm{G}/\mathrm{G}_{\mathrm{max}}\) feature range from 0.16 to 1.02.  
One of the two most common types of scaling are min-max scaling and standardization. Min-max scaling is commonly known as normalization. Normalization transforms the data in such way that the features are within a specific range, e.g., (0, 1). To do this, it subtracts the minimum value from the given value and divides the result by the difference between the maximum and minimum values. Standardization (also called z-score normalization) transforms data in such way that the resulting distribution has a mean of 0 and a standard deviation of 1. The standardization mechanism is as follows: we subtract the mean value from a given value and then divide it by the standard deviation, thanks to which the resulting distribution has a unit variance. Standardization does not limit the scaled values to a certain range, as is the case, for example, during normalization. It is also much less sensitive to outliers.  
The type of value scaling which will be the most appropriate in the analyzed task can be checked by substituting the normalized values, and then the standardized values, into the model. Target values usually do not need scaling. An important issue, that scientists often do incorrectly, is what values are scaled. As with all transformations, the scaling functions should be adjusted only to the training data, not to the entire dataset. Only after fitting against the training data can these functions be used to transform test data (and new cases).  
Figures 5 and 6 show the results already obtained from the ANN model. The figures show the MAE and RMSE values of the training and validation set (constituting 20% of the training set) for the data subjected to normalization and standardization.  
For the normalized training data: MAE = 0.2809, RMSE = 0.5069, and R\({}^{2}\) = 0.961; for the standardized data: MAE = 0.2032, RMSE = 0.4352, and R\({}^{2}\) = 0.979. For the ANN model aimed at determining the damping ratio based on other soil parameters, the standardization works better, and this scaling method will be applied to data in the final model version.
## 3 Materials and Methods
### Skeleton Tracking
The next condition, coplanarity, is defined as follows (Equation (2)):  
\[|(\text{R}\times\text{B})\cdot\text{N}|\leq\beta \tag{2}\]  
where R is the radius vector from the bone to point (P), N is the normal vector in point (P), B is the binormal vector in point (P), and \(\beta\) is the threshold. Among the bones in the skeleton that passed the three above tests against the point (P), the nearest one is selected and assigned to P. After completing the operation for all points, the values for unassigned points are populated using a median filter. Finally, a median filter with a fixed minimum number of neighbors equal to half the number of vertices in the neighborhood is applied. With this last filtration, small groups of incorrectly assigned vertices are fixed. On the grounds of the above segmentation, the scan in frame (t) is divided into fragments corresponding to individual bones. In order to track the bone transform in the next frame, the segment for the bone (B) is fitted to the scan in frame (t + 1) using the Iterative Closest Point method. Among the many variants of this algorithm, a Point-to-Plane [4; 5] version with rejection and optimization using the Levenberg-Marquadt solver [6; 7] was chosen. For the estimation of the correspondence between the set of segment points and the next frame scan, a random subset of the segment's point count is used. After establishing pairs of corresponding points in both clouds, pairs with distance further than a defined threshold are rejected. The choice of the Point-to-Plane objective function, as pointed out by [PERSON] [5], ensures faster convergence than the standard Point-to-Point algorithm. After fitting the segment to the scan in the next frame, the bone position is updated based on the input and output transforms of the segment from ICP, taking into account the motion constraints of the given bone. We consider three types of constraints: no constraint, translation constraints, and kinematic chain constraints. The first type is applicable in a case of the hip segment, from which the entire skeleton tree begins. The skeleton root can move freely, reflecting the change in position and orientation of the scanned subject. The second type, translation constraints, apply to all bones located lower in the skeleton tree, as the continuity of the bone chain must be maintained. The last type of constraint finds application in the case of leg and arm helper bones which do not take part in the segmentation process. Figure 7: Normal vector parallelism condition for point (P) with normal vector (N) and radius vector (R). In this case, position changes of the bone start point are allowed, but must maintain the distance to the preceding spine bone. In this way, the bone moves on the surface of a sphere with radius equal to the helper bone length and the helper bone transform is adjusted to close the skeleton chain.
# Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor
## 5 Real-Time Detection Using PCM  
### Overview and Threat Model  
In this section, we give an overview of the proposed detection method against cache side channel attacks, as well as its threat model. Our proposed method basically tried to detect the attack based on abnormal behavior of processors. For this, the method internally utilized an Intel PCM tool for the detection. The PCM tool collected a variety of hardware performance counters associated with the processor events in real time and provided the results through a file with CSV format. Our detection program repeatedly read relevant performance counters from the CSV file and checked whether some abnormal events (i.e., cache side channel attacks) happened in the processor. If so, the program further predicted what kind of attack was in progress based on classification-based machine learning algorithm. Since the detection program directly read some counters from the processor status registers, we required the program to run in privileged mode (i.e., root mode).  
We supposed that in practice, the detection program would be deployed and utilized in two possible execution environments: (1) a virtualized environment and (2) a single OS environment. In this article, we followed the threat models of other related studies [4; 5; 12; 14; 15; 16] for the execution environments. We describe each environment in detail, as well as its threat model in the following:  
* Virtualized environment: The detection program ran in one of the Virtual Machines (VMs) on the host (see Figure 3(a)). The attacker process was located on the same host, but ran in another VM. In our threat model, we assumed that the attacker owned its VM and had a root privilege to a guest OS in the VM. Despite its privileged access to its own VM, however, the attacker could not interfere with the detector program by conducting sabotage on the execution file or CSV files as the hypervisor logically separated these two VMs from each other. Cloud computing was one of the possible scenarios for the virtualized environment.
* Single OS environment: The attacker and the detector program ran in the same operating system on the host (see Figure 3(b)). In our threat model, we assumed that the attacker only had the user-level access to the OS in this environment. Owing to the privilege-based access control of the operating system, we could restrict access permission to any relevant resources such as CSV files so that only a root process including the detection program could access them. By doing so, the attacker still could not conduct sabotage on the executables and CSV files of the detector program.
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
###### Abstract  
The layout of directional high-level long boreholes in fracture zones for extracting pressure-relieved gas from goaf is a key technology to address the gas concentration exceedance in upper corners. To solve the gas exceedance issue at Fengtai Mine's upper corner, this study established a gas-solid coupling model using COMSOL Multiphysics 6.3 based on actual mine parameters. The research investigated the influence patterns of different extraction parameters (negative pressure, borehole diameter, and extraction duration) on coal seam gas pressure and effective extraction radius (critical gas pressure 0.74 MPa). The results demonstrate the following. The effective extraction radius shows positive correlations with extraction negative pressure, borehole diameter, and extraction time while exhibiting a negative correlation with gas pressure. When borehole diameter exceeds 203 mm, extraction negative pressure surpasses 25 kPa, and extraction duration extends beyond 90 days; the effective extraction radius stabilizes, with the gas pressure influence range ceasing to decrease significantly with further parameter increases. Field validation results showed that, during the observation period, both pure gas extraction volume and mixed gas volume exceeded those of high-level drainage galleries. The gas concentrations in both the upper corner and return airflow remained within safe limits, effectively resolving the gas exceedance issue. This achievement not only established an efficient gas control system for the Fengtai Mine but also provides valuable parameter optimization methods and engineering experience for similar coal mines. The implementation has significantly enhanced gas control efficiency and safety production levels in the mining area.  
gas extraction; gas-solid coupling; gas pressure; effective extraction radius; drilling parameters 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), Vol., pp. 1-10. External Links: Document Cited by: SS1.
## 3 Design of Fuzzy PID Controller for Servo Precision Press
### Determination of Domain and Related Parameters  
Degree of membership functions adopt Z-shaped, S-shaped, and triangular membership functions.  
Triangle Curve Function:  
\[f(x,a,b,c)=\left\{\begin{array}{l}0,x\leq a\\ \frac{x-a}{b-a},a\leq x\leq b\\ \frac{c-x}{c-b},b\leq x\leq c\\ 0,c\leq x\end{array}\right. \tag{2}\]  
In the formula, \(a\) and \(b\) determine the \"feet\" of the triangle, and \(c\) determines the \"peaks\" of the triangle.  
Figure 5: Structure diagram of fuzzy PID control system.  
Figure 6: Relationship between digital error and actual error.  
\(Z\)-shaped spline function:  
\[f(x,a,b)=\left\{\begin{array}{l}1,x\leq a\\ 1-2\left(\frac{x-a}{b-a}\right)^{2},a\leq x\leq\frac{a+b}{2}\\ 2\left(\frac{x-b}{b-a}\right)^{2},\frac{a+b}{2}\leq x\leq b\\ 0,x\geq b\end{array}\right. \tag{3}\]  
In the equation, \(a\) and \(b\) determine the two ends of the inclined portion of the curve. S-curve spline function:  
\[f(x,a,b)=\left\{\begin{array}{l}0,x\leq a\\ 2\left(\frac{x-a}{b-a}\right)^{2},a\leq x\leq\frac{a+b}{2}\\ 1-2\left(\frac{x-b}{b-a}\right)^{2},\frac{a+b}{2}\leq x\leq b\\ 1,x\geq b\end{array}\right. \tag{4}\]  
In the equation, \(a\) and \(b\) determine the two ends of the inclined portion of the curve.  
According to the basic principles of fuzzy control and the characteristics of this system, the domain of \(E\) and \(Ec\) is defined as \([-1,1]\). Seven fuzzy subsets of variables are defined: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (middle), and PB (positive large). The membership relationship of \(E\) is shown in Figure 7.
## 5 Benefits Study  
### Safety Benefits Analysis  
Scheme I is to use the soil bearing capacity between piles to transfer the load to deeper foundations, significantly reducing the lateral deformation of the soil between piles and foundation settlement. In the actual construction process, to ensure the safety of the buried high-pressure gas pipe, construction excavation should be mainly manual, and both sides of the pipe should not be excavated at the same time to avoid a large disturbance impact on the pipe. Improper construction techniques for plain concrete piles are prone to shrinkage, pile breakage and uneven pile strength. Combined with the actual geological situation, the silt-clay and sand layers will seriously affect the piling quality of the cement mixing pile; considering the extrusion of the soil on the pipe, the construction should be carried out by the long spiral borehole in-pipe pumping method.  
FLC in Scheme II is a light embankment method, which compensates for the additional stresses imposed by the upper fill and traffic loads by replacing the fill layer. Due to the small capacity of FLC, the additional stresses in the soft soil layer are smaller than the effective stresses, which achieves the purpose of reducing the settlement of the soft soil roadbed [31].  
Through analysis, it is found that the pile composite foundation is more complex than FLC construction technology, with lower safety and bigger impact on the surrounding environment, which requires various technical means to avoid the impact and interference with the existing buried high pressure gas pipe. After the construction of the piled composite foundation is completed, in order to meet the compaction requirements of the foundation, it is necessary to carry out layered rolling, and equipped with management personnel to monitor in real time; while FLC has high quality fluidity, pouring solidification is completed to meet the construction requirements, reducing the need for labour. It is concluded that Scheme II has better safety benefits.