esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk-Based Access Control Model: A Systematic Literature Review
## 6 Discussion

Table 4: _Cont._  
**RQ2: What are the risk factors used to build risk-based access control models? **  
One of the essential parts of a risk-based access control model is to choose the effective risk factors that determine access decisions efficiently. Many risk factors can be used to estimate the risk value associated with the access request to make the access decision dynamically and efficiently. To answer this research question, risk factors utilized in recent risk-based access control models were reviewed. Then, a brief overview of these risk factors is provided. This is followed by showing the risk factors extracted from retrieved/analyzed publications, as depicted in Table 5.  
* **Subject Clearance (Role)**: It represents the subject security level acquired from the system administrator. The most popular clearances in the military are Top Secret, Secret, Confidential, and no clearance. Different access permissions are granted according to the subject role in the organization. Each role is associated with certain permissions [58]. The higher the clearance granted, the lower the associated risk value. * **Resource Sensitivity**: It describes the sensitivity level of resources the user wants to access. Different sensitivity levels have different risk values. The higher the resource sensitivity, the higher the risk value if the access is granted to this resource [24]. * **Action Severity**: It characterizes the cost of a particular action on a particular resource in terms of confidentiality, integrity, and availability. So, different actions have different consequences and so have different risk values. * **Risk History**: It represents user previous risk values on a certain resource. It can be used to detect the future behavior of the user toward a certain resource. * **Trust**: It is similar to the risk history. It represents the subject/user trust toward a certain resource. Trust is classified into two categories: identity and behavioral trust. Identity trust is concerned
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 2 Backpack-Based Sensor System
### Inertial Sensor  
The Trimble APX-15 Unmanned Aerial Vehicle (UAV) was mounted for GNSS/INS. The Trimble APX-15 UAV (Figure 3) is an efficient GNSS-inertial solution for small UAVs. It weighs 60 g and is light enough to attach onto the backpack system. With GNSS signal integrated, APX-15 gives position, roll, pitch and heading output in 100 Hz, and IMU data in 200 Hz. This enables an accurate direct georeferencing of various sensor data. Detailed specifications of Trimble APX-15 UAV are described in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Specification**} \\ \hline Channels & 16 lasers \\ Range & Up to 100 m \\ Range Accuracy & Up to \(\pm 3\) cm \\ FOV (Vertical) & \(+15.0^{\circ}\) to \(-15.0^{\circ}\) (30.0\({}^{\circ}\)) \\ Angular Resolution (Vertical) & \(2.0^{\circ}\) \\ FOV (Horizontal) & \(360^{\circ}\) \\ Angular Resolution (Horizontal) & \(0.1^{\circ}\)–\(0.4^{\circ}\) \\ Rotation Rate & \(5\) Hz–\(20\) Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of Velodyne VLP-16.  
Figure 1: Backpack-based Multi-Beam Light Detection and Ranging (LiDAR) (MBL) sensor system.  
Figure 2: Velodyne VLP-16.
# Dynamic Analysis on the Parametric Resonance of the Tower-Multicable-Beam Coupled System
## 1 Introduction
However, the vibration of a single cable does affect the vibration characteristics of other cables through the indirect effect transformed by the beam or the tower [24], and the local resonance effect under the single-cable system cannot accurately reflect the resonance effect under the MCS of the cable-stayed bridge. More knowledge should be obtained through further studies on the nonlinear dynamics modeling of the full-bridge system [25]. In this paper, the shear difference in the beam section at each cable's anchored position is applied to simulate the effect of the bridge deck's bending stiffness and the indirect effect of adjacent cables (CEB). Considering the parabola alignment of the cable and the boundary of the system, the ordinary differential equations (ODEs) of the vibration system are derived based on the D'Alembert Principle and the Galerkin Method. The case study is adopted to verify the effectiveness of the modeling, while the mode shapes of the single beam obtained by this method and the finite element method are discussed in depth. The effect of the dynamic parameters obtained from the equation forms and that of the excitation sources on the parametric resonance have been further investigated through the numerical simulation based on the Fourth Runge-Kutta method.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### ADETECT MIMO Detector  
Our previously proposed deep learning-enhanced signal detection framework, called AIDETECT, is designed for efficient signal detection in MIMO systems. It offers improved accuracy and robustness compared to conventional and other DL-based methods while maintaining lower complexity. For AIDETECT, the detected signal vector \(\hat{\mathbf{s}}\) is determined by reducing the cost objective function \(f\) as  
\[\hat{\mathbf{s}}=\arg\min_{\mathbf{s}\in S^{T}_{t}}f((\mathbf{r}_{\text{Re}}, \mathbf{G}_{\text{Re}}),(\mathbf{r}_{\text{Im}},\mathbf{G}_{\text{Im}})) \tag{20}\]where \(f((\mathbf{r_{Re}},\mathbf{G_{Re}}),(\mathbf{r_{Im}},\mathbf{G_{Im}}))\) is a cost objective function that depends on \(\mathbf{r}\) and \(\mathbf{G}\) extracted by \(\mathbf{s}\). We set the AIDETECT method as our benchmark method along with the MLD, ZF, and MMSE detection methods [3].
## 3 Simulation
### Eliminating Time-Domain Pulses by Simulating MLS  
Based on the discussion in the previous section, we analyzed the reasons why the strong pulses in the output light were caused by the longest dwell time in the PRBS signal. This strong pulse greatly affected the stability of the output light in the actual fiber amplifier, and the strong pulse in the backward light would destroy the upstream amplifier. In order to suppress pulses caused by long dwell times, we used two models to adjust and optimize the PRBS: in model 1 (MLS1), the longest run (11111111) was replaced by \"101010101\"; in model 2 (MLS2), the second longest run (00000000) was replaced with \"01010101\". Both \(f_{cr}\) and the incident laser power remained constant.  
Figure 4 shows the time-domain characteristics of the original PRBS, MLS1, and MLS2 phase-modulated laser output in passive fiber and Stokes light. We can see that these three sequences did not change the periodic characteristics of the pulses after modulation, and the output light fluctuated periodically during each PRBS-9 period. However, the intensity of the output light was not same. It could be seen that when MLS1 and MLS2 were employed, the time-domain pulses of the output laser and backward Stokes light were greatly reduced. The output laser peaks of the MLS1 and MLS2 signals were reduced from \(\pm\)55% to \(\pm\)25% and \(\pm\)10% relative to the original PRBSs, respectively, and the peaks of Stokes light were reduced from 39% to 19% and 11%, respectively.  
The decrease in pulse intensity also proved that the strong pulses of the output light were caused by the long dwell time. When the bit sequence was a combination of 1 or 0, it was equivalent to the unmodulated state. At this time, SBS would accumulate amplification, and the longer the dwell time, the stronger the generated SBS time-domain pulse; when the long dwell time was broken, it hindered the establishment time of SBS and then effectively inhibited the generation of SBS. Our scheme could be further optimized to eliminate smaller pulses in the time domain, e.g., by replacing the third longest run with \"0101010\"; however, such a modification gradually reduced the randomness of the sequence, and the spectrum of the signal gradually lost the shape of the Sinc\({}^{2}\) envelope, which might lower the SBS threshold. MLS2 might be a compromise between maintaining sequence randomness and removing long dwell times. In the actual system, an AWG could be used to optimize the coding of PRBS signals for phase modulation and improve the stability of the output light in high-power amplification experiments.
## 4 Discussion
### General Conceptual Model of LST, SM, and Vegetation Productivity
Different types of soil can affect the SM available for plants, water balance in the soil, and subsequent vegetation conditions. Thus, although different soils can affect the relationships between optical/thermal data and direct measurements of SM [30], these remote sensing data have the potential to reflect the effect of soil water available for evapotranspiration on vegetation. The study area is an extensive and relatively homogeneous sand plain, being the main type of soil covered by the stations considered in this analysis [33]. Future comparison covering other semi-arid ecosystems can be useful for an intensive study of the interaction between these variables. Our study aimed to understand the impact of root zone SM during wet rainfall pulses on vegetation in semi-arid Mulga woodland through an integration of in situ measurements and optical/thermal satellite data. Future studies can explore the integration of multi sensors onboard satellites to evaluate scale effects on results. Lastly, the results suggest that the vegetation in Mulga woodland has a weak response to soil water availability during normal rainfall events (i.e., there is LST variability regardless of the few changes in SM). Most of the time, systems such as the Australian drylands are exposed to warm-to-hot annual temperatures and pronounced dry seasons. Mulga woodland has a tolerance to low water potential and efficiently uses SM [14]. The response of semi-arid vegetation to rainfall events generally occurs when the rainfall amount is larger than a threshold that would produce increases in evapotranspiration [14; 15]. Under this threshold, variations in LST are mainly associated with factors other than SM. More analyses are needed in the future to explore factors affecting surface energy balance in normal-to-dry periods.
## 3 Experimental Results and Discussion
### Elastic Modulus and Peak Strain  
The elastic modulus of FRC at different strain rates is shown in Figures 10 and 11. The results show that the elastic modulus of FRC increases with the increase in the strain rate, similar to the compressive strength and split tensile strength [1; 7; 18; 26]. The increasing range of the elastic modulus is different in FRC with different matrix strengths. In all cases, the elastic modulus with 0.2% fiber content is the highest for both the matrix strengths, similar to previous findings [6]. The lower the matrix strength, the more obvious the effect of the fiber is on the elastic modulus of the FRC [18]. The increase of the elastic modulus indicates that the ability of concrete to bear the elastic deformation of the load is reduced, which is beneficial to the non-destructive instability of the structure without causing excessive elastic deformation under load.  
The peak strain of fiber concrete at different strain rates is shown in Figures 12 and 13. The peak strain here refers to the strain corresponding to the peak stress. Contrary to the trends of the strength and elastic modulus, the peak strain of concrete decreases with an increase of strain rate. The peak strain of the fiber concrete can be enhanced under each strain rate compared with plain concrete, indicating  
Figure 11: Elastic modulus of FRC (C40).  
Figure 10: Elastic modulus of FRC (C30).  
that the addition of the fiber can improve the ability of the concrete to bear the deformation [15]. In fiber concrete, the peak strain with 0.2% fiber content is smaller than that with the other two fiber content levels, which is directly related to the maximum elastic modulus of concrete with 0.2% content.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
###### Abstract  
This research describes an experimental solution used for estimating the positions of pedestrians from video recordings. Additionally, clustering algorithms were utilized to interpret the data. The system employs the You Only Look Once (YOLO) algorithm for object detection. The detection algorithm is applied to video recordings provided by an unmanned aerial vehicle (UAV). An experimental method for calculating the pedestrian's geolocation is proposed. The output of the calculation, i.e., the data file, can be visualized on a map and analyzed using cluster analyses, including K-means, DBSCAN, and OPTICS algorithms. The experimental software solution can be deployed on a UAV or other computing devices. Further testing was performed to evaluate the suitability of the selected algorithms and to identify optimal use cases. This solution can successfully detect groups of pedestrians from video recordings and it provides tools for subsequent cluster analyses.  
ASPRET; DBSCAN; clustering: object detection; OPTICS; Python; K-means; UAV; YOLO 10.390/app12199485  
11 August 2022  
Accepted: 16 September 2022  
21 September 2022  
DPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Machine Learning Classifiers  
The ML classifiers employed during the comparative analysis are described below. Six classifiers were used in the study: the aforementioned NB, LR, k-NN, AdaBoost, RF, and SVM. The classifiers were considered due to their regular usage by other researchers in the RTA domain (as highlighted in Section 2.2) to construct robust models.
## 3 Results and Discussion
### Analytical Techniques  
The identification of potential products formed as a result of the chemical attack was further analyzed by XRD. Freeze-thaw damage of water-saturated concrete is a progress of physical attack. Hence, only sulfate-exposed specimen was analyzed by XRD.  
The XRD patterns of specimens 0 and S150 are presented in Figure 10. Calcium carbonate could be observed in the XRD pattern of S150. It is in accordance with the fact that decomposition of thaumasite occurs at about 60 \({}^{\circ}\)C during oven drying treatment. In addition, the peak of ettrinigite was more obvious in specimen S150, which indicates that ettrinigite was also a corrosion product. Based on XRD analysis, it can be indicated that chemical sulfate attack proceeds within the FT tests exposed to sulfate solution exposure. Both thaumasite and ettrinigite are produced in sulfate attacked specimens. Although the lower temperature may significantly slow down the reaction rate between sulfate ions and hydration products, this chemical attack still needs to be considered in the long-term services of RAC.  
Figure 10: XRD patterns of RAC in control condition (S0) and subjected to sulfate-exposed FT cycles (S150).
## 8 Accuracy of the Proposed Relations  
In Table 5, the results of the proposed damper derived from the proposed relations are compared with FE results. The proposed relations predicted the stiffness of the dampers with errors <11%. Except for the M_I damper (inelastic buckling), the ultimate strength exhibited errors <12%. In [39; 53; 54; 55; 56], the results of AISC_341 [57] were compared with FE results for predicting the shear capacity of shear links. In these studies, researchers presented relations [57] with errors of 50%. Therefore, the proposed relations in this paper for predicting the stiffness and strength of the proposed damper (as a shear link) are in good agreement with the FE results.
# An \(N\)-Port Universal Multimode Optical Router Supporting Mode-Division Multiplexing
## 4 Conclusions  
Since multimode waveguides can transmit different modes, MDM technology is considered an effective method to further increase the capacity of the communication systems. The optical router supporting MDM is an important part of the ONoC implements MDM. With the rapid development of MDM, OR based on MDM came into being. In this paper, we propose a model of MDM-OR that can realize MDM. After that, using this model, we construct the MDM-OR corresponding to the single-mode five-port optical router, and the transmission loss, crosstalk noise power, OSNR, and BER of the port pairs are analyzed. The simulation results show that when TE\({}_{0}\), TE\({}_{1}\), and TE\({}_{2}\) modes of 1550 nm are input, the transmission loss of the port pairs corresponding to the five-port MDM-OR is less than \(-10.64\) dB. In this case, the crosstalk noise power of the port pairs corresponding to the five-port MDM-OR is less than \(-17.45\) dBm. Correspondingly, in the five-port MDM-OR, the OSNR of TE\({}_{0}\), TE\({}_{1}\), and TE\({}_{2}\) modes can reach up to \(32.68\) dB after passing through different channels, the minimum value of BER is \(-4.58\) dB. From the simulation results, we can see that the MDM-OR based on the model in this paper has small transmission loss, and there will be a great prospect in the future in the field of ONoC that supports MDM.  
Conceptualization, Y.Y. ([PERSON]), Y.X. and Y.S.; methodology, Y.X., Y.Y. ([PERSON]) and L.D.; software, Y.Y. ([PERSON]), L.D. and X.J.; validation, Y.Y. ([PERSON]), Y.S. and J.C.; formal analysis, Y.Y. ([PERSON]); investigation, Y.Y. ([PERSON]), Y.Y. ([PERSON]) and T.S.; resources, Y.X.; data curation, Y.Y. ([PERSON]); writing original draft preparation, Y.Y. ([PERSON]) and Y.X.; writing review and editing, Y.Y. ([PERSON]), Y.S., Y.X. and B.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 13: The BER between different port pairs of the multimode router based on the TE\({}_{0}\), TE\({}_{1}\), and TE\({}_{2}\) mode.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 2 Directional Changes: An Introduction
### Dct1  
In 2012, [PERSON] et al. [26] presented a DC-based trading strategy named Zero Intelligence Directional Change Trading (ZI-DCT0). ZI-DCT0 runs a DC summary with a threshold named \"\(\Delta_{\text{xDC}}\)\". ZI-DCT0 has two trading rules:  
1. It initiates a trade at the DC confirmation point of a DC event. The type of trade can be either: counter trend (CT) or trend follow (TF). A CT (contrarian) trader makes a buy order while market exhibits a downtrend with the expectation that this downward trend will reverse. A TF (trend follower) trader opens their position with the expectation that the current trend will continue. In the case of CT, ZI-DCT0 opens a position against the market's trend. TF does the opposite. The user must specify the type of trade: either CT or TF.
2. ZI-DCT0 closes the position at the DC confirmation point of the succeeding DC event. When trading with ZI-DCT0, the trader must determine two parameters:
* The type of trade: CT or TF.
* The threshold \(\Delta_{\text{ADC}}\) to be used for conducting the DC summary.  
In 2015, [PERSON] [10] presented a trading strategy called \"DCT1\". The DCT1 was presented as an updated version of ZI-DCT0. The trading rules of DCT1 are the same as ZI-DCT0 (i.e., Rules (a) and (b) shown above); however, DCT1 is designed to automatically compute the two parameters: the DC threshold \(\Delta_{\text{ADC}}\) and the type of trade (CT or TF). Firstly, the trader defines a range of thresholds. Secondly, DCT1 automatically examines the profitability of each threshold, included in the specified range, using historical price data (as the training set). To this end, for each threshold value, the DCT1 applies the trading rules of ZI-DCT0 from two points of view: counter trend (CT) and trend follow (TF). In other words, during the training period, the DCT1 examines the profitability of all possible combinations of: (1) threshold, included in the range; and (2) the trade type (CT or TF). DCT1 returns the threshold \(\Delta_{\text{ADC}}\) and the type of trade (CT or TF) corresponding to the highest produced returns during the training period. It then uses these values to trade over the trading period.  
DCT1 was tested using high frequency data of the EUR/USD currency pair. The author reported that DCT1 was able to produce a rate of return of 6.2% during a testing period of one year (with bid-ask spread being taken into concern). The author did not report any: (a) comparison to a benchmark; (b) measurement of risk (e.g., _MDD_); or (c) evaluation of risk-adjusted metrics (e.g., Sharpe ratio).
## 3 Multi-Source Information Fusion Method
### Internal and External Leakage Diagnosis Based on Support Vector Machine  
#### 3.2.1 Relay Valve Leakage Fault Characteristic Parameters  
To diagnose the leakage modes more accurately, it is necessary to extract the fault characteristic parameters. In addition to the statistical mean shift mentioned in Section 3.1.2, and considering that the leakage will affect the response characteristics of the relay valve, eight fault characteristics shown in Table 2 are extracted; these fault characteristics correspond to the three stages of the relay valve: braking, holding, and releasing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Code**} & **Leakage Fault** & **Definition** \\  & **Characteristic Parameters** & **The CV1 pressure average of the holding phase** \\ \hline \hline \multirow{2}{*}{**A**} & **CV1 steady-state pressure** & **The BC pressure average of the holding phase** \\ \hline \multirow{2}{*}{**B**} & **BC steady-state pressure** & **The BC pressure average of the holding phase** \\ \hline \multirow{2}{*}{**C**} & **Following error** & **Difference between BC steady-state pressure and CV1 steady-state pressure** \\ \hline \multirow{2}{*}{**D**} & **Maximum** & **After the brake command is triggered, the maximum value of the BC pressure during the pressure holding phase** \\ \hline \multirow{2}{*}{**E**} & **Minimum** & **After the brake command is triggered, the minimum value of the BC pressure during the pressure holding phase** \\ \hline \multirow{2}{*}{**F**} & **Degree of volatility** & **After the brake command is triggered, the standard deviation of the BC pressure during the pressure holding phase** \\ \hline \multirow{2}{*}{**G**} & **Response time** & **Time required for BC pressure to rise from 10\% to 90\% of target pressure** \\ \hline \multirow{2}{*}{**H**} & **Delayed releasing time** & **From when the brake command is cancelled to when the BC pressure drops to 40 kPa** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Leakage fault characteristic parameters.  
Figure 3: Leakage detection process based on the Kalman and sequential probability ratio test (SPRT) Method.
## 3 Results and Discussion
### Effects of Environmental Media on Dissipation Energy Characteristics
#### 3.1.1 Hysteresis Loops of Tangential Force versus Displacement Amplitude
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environmental Media** & **Air** & **Acid Solution** & **Neutral Solution** & **Alkaline Solution** & **Deionized Water** \\ \hline Maximum tangential force (N) & 21.3 & 16.2 & 18.0 & 19.1 & 17.3 \\ Relative displacement (\(\mu\)m) & 33.6 & 58.5 & 54.3 & 51.2 & 49.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The maximum tangential force and relative displacement at loading cycles 20,000 in different environmental media.