esa-sceva/satcom-chunk-collection huggingface dataset:
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 5 Implementation and Evaluation
### Time Performance  
We analyze the detection time of different algorithms and HMM-R which is under different windows and different ratios. It showed that HMM-R performs better at detecting time than other algorithms, and the detection time of HMM-R is related to the size of the sliding window and the rate of attack.  
When the attack ratio is 0.025, Table 6 shows the detection time of each algorithm when the size of the window is 40, of which the BP and SOM algorithms have the longest detection time, indicating that the neural network algorithm is not suitable for the data center network with low delay flow. The data in Table 7 is the detection time of HMM-R under different windows. It shows that the training time of HMM-R increases with the increase of windows. It can be concluded that the HMM-R detection time is related to the size of the sliding window, so the time complexity of the HMM-R can be reduced by adjusting the size of the window at a certain ratio. Table 8 shows the HMM-R detection time at different attack ratios. It can be seen if the attack ratio increases, the detection time will increase gradually. This is due to the increase of attack ratio, which increases the attack rate and the number of attack packets, thus increasing the number of observed sequence states, that is, the observed state matrix becomes larger and eventually leads to an increase in the time complexity. Therefore, in a certain detection window, HMM-R time complexity is also related to the attack ratio.
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 4 Modelling Process of the Proposed AEM
### Thevenin Equivalent Circuit for the Arm on Blocked Model  
Under blocked mode, all IGBTs on the arm are switched off, and the current path relies on the direction of the current. As shown in Figure 7a, when the current is positive, all capacitors on the arm are inserted in series into the arm, and the corresponding Thevenin equivalent circuit is depicted as the dark-green part in Figure 7c. Here,  
\[R_{\text{C, BIk}}=\sum_{x=1}^{N}R_{\text{C0,x}}=\frac{N\Delta T}{2C_{0}} \tag{38}\]  
\[V_{\text{Arm, EQ, BIk}}(t-\Delta T)=\sum_{x=1}^{N}V_{\text{C0,EQ},x}(t-\Delta T )=NV_{\text{C0,EQ}}(t-\Delta T)=R_{\text{C, BIk}}i_{\text{Arm}}(t-\Delta T)+Nv_{ \text{C0}}(t-\Delta T) \tag{39}\]  
Figure 6: The Thévenin equivalent process: (**a**) The arm equivalent circuit based on arm current; (**b**) The arm equivalent circuit with \(R_{\text{C, EQ2}}\) discrete companion model; (**c**) Thévenin equivalent circuit of the arm; (**d**) The equivalent circuit of the MMC.  
where, \(v_{\text{C0}}(t-\Delta T)\) denotes the equivalent history voltage source of SM average capacitor voltage. Similarly to Equation (10), \(V_{\text{C0, EQ}}(t-\Delta T)\) is equal to \(V_{\text{C0, EQ}},x(t-\Delta T)\). For negative current, alternatively, the current only flows through the diode \(D_{2}\) as shown in Figure 7b,c.  
As shown in Figure 7c, the blocked-state Thevenin circuit retains two static elements: the diodes \(D_{1}\) and \(D_{2}\) with negligible conductive resistances. The ON or OFF states of \(D_{1}\) and \(D_{2}\) directly depend on the EMT simulation programs, and do not need users to imitate their switching action. The solution for the equivalent capacitor voltage under blocked model is similar to the solution under de-blocked model, and is not discussed in this paper for the sake of conciseness.
## 2 Feature Enhancement Point Cloud Data Resampling
### Nonuniform Voxel Mesh Resampling  
The voxel mesh resampling, similar to lattice point sampling, is a grid of point clouds, with \((X_{\text{min}},X_{\text{max}}),(Y_{\text{min}},Y_{\text{max}}),(Z_{\text{ min}},Z_{\text{max}})\) as the range of values for the three -dimensional directions of the point cloud, forming a rectangular enclosing box on the space, whose length, width, and height are:  
\[L=X_{\text{max}}-X_{\text{min}}W=Y_{\text{max}}-Y_{\text{min}}H=Z_{\text{max}}-Z_ {\text{min}} \tag{1}\]  
In the 3D space, the mesh can be divided by unit length, which can eventually be divided into \(N\) voxel spaces, which in turn can be replaced by the centers of all points in the voxel space.  
Although the voxel mesh resampling method ensures that the point cloud shape remains unchanged, the feature information is easily lost at some corners where the curvature changes significantly, so the geometric sampling method is introduced to reduce the number of point clouds where the local curvature changes drastically. Geometric features can be used based on the Difference of Normal (DoN) method to first perform point cloud segmentation, segmenting two parts of the point cloud with obvious curvature change and gentle curvature change, and taking different sizes of voxels' down-sampling for the two-point clouds, respectively. For the point cloud with obvious curvature change, a smaller sized grid is used to retain more points. For the point cloud with insignificant curvature variation, a larger sized grid is used to reduce the number of point clouds and improve the computational efficiency.  
Suppose the radii of two different neighborhoods of point \(p\) are \(r_{l}\) and \(r_{s}(r_{l}>r_{s})\), then the difference between the normal is calculated as shown in Equation (2):  
\[\Delta\hat{n}(p,r_{s},r_{i})=\frac{\hat{n}(p,r_{s})-\hat{n}(p,r_{i})}{2} \tag{2}\]  
where \(\hat{n}(p,r_{i})\) and \(\hat{n}(p,r_{s})\) are the normal vectors calculated from the radii.  
The specific process is as follows.  
1. The DoN operator is calculated for the point cloud after de-contextualization, which is divided into two parts: those with drastic curvature change and those with gentle curvature change.
2. Different sizes of resampling are used. For the part with large curvature change, a smaller size voxel mesh is used for resampling to retain the detail information to the maximum extent, and for the part with gentle curvature change, a larger size voxel mesh is used for resampling to minimize the number of plane points, and the number of points in the two parts after sampling is summed.
3. Compare the sum of the two parts of the point cloud with the sampling space; if it is larger than the threshold value, the sampling size is adjusted to a larger size for the part with obvious curvature change and recalculated; if it is smaller than the threshold value, the sampling size is adjusted to a smaller size and the area with gentle curvature change is recalculated until the value range is satisfied.
## 1 Introduction
The major contributions of our work can be summarized as follows:  
* Based on the standard Transformer architecture, we propose a two-layer Transformer network for CSI feedback, which is capable of better characterizing the CSI and thus improving the recovery accuracy;
* We adopt higher dimensional feature representation to improve the quality of feedback and increase the number of attention heads to jointly attend to information from different representation subspaces at different positions;
* The warm-up cosine training scheme is introduced for quicker convergence and stronger capability to learn high-resolution CSI features.
###### Abstract  
Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.  
Ant-colony optimization; pheromone termite; congestion avoidance; energy consumption; end-to-end delay +
Footnote †: journal:  
00 2023 by the authors. Liconssee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)]([https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0))).
## 2 TCAPLL Architecture
### Integration Filter  
The GPS/INS coupling is a non-linear system, and as seen in the previous section, most of the MEMS INS errors have non-Gaussian noise. Therefore an EKF is used as the integration filter in the TCAPLL architecture. The EKF applies the Taylor series expansion for the non-linear system and observation equations, and takes the first-order terms to apply the well-developed linear Kalman filter theory, where the probability density function (PDF) is approximated by a Gaussian model [15]. This approach is adopted to derive a set of linear differential equations that define the INS error states. Details can be found in [16]. The state vector of the TCAPLL EKF is composed of 29 states and is given as:  
\[\delta\mathbf{\dot{x}}=\mathbf{F}\delta\mathbf{x}+\mathbf{G}\mathbf{w} \tag{6}\]  
where \(\mathbf{F}\) is the dynamic matrix, \(\mathbf{G}\) is the noise matrix, \(\mathbf{w}\) is the Gaussian white noise vector and \(\delta\mathbf{\dot{x}}\) is the augmented error state vector, expressed as:  
\[\left(\delta\mathbf{x}\right)_{29\times 1}=\left[\delta\mathbf{r}_{b \times 3}^{l}\quad\delta\mathbf{v}_{b\times 3}^{l}\quad\delta\mathbf{a}_{b \times 3}^{l}\quad b_{ab\times 3}\quad SF_{ab\times 3}\quad b_{g\times 3} \quad\delta b_{g\times 3}\quad SF_{g\times 3}\quad\delta t_{b}\quad\delta t_{d} \right]^{T} \tag{7}\]  
where \(\delta\mathbf{r}_{b\times 3}^{l}\), \(\delta\mathbf{v}_{b\times 3}^{l}\), and \(\delta\mathbf{a}_{b\times 3}^{l}\) are the position, velocity and attitude errors in the local-level frame, which is chosen to be the ENU (East, North, Up) in this paper. \(\delta t_{b}\) and \(\delta t_{d}\) are the GPS receiver oscillator bias and drift. Their differential equations are written as [17]:  
\[\delta\dot{t}_{b}=\delta t_{d}+\eta_{b}^{osc} \tag{8}\]\[\delta\dot{t}_{d}=\eta_{d}^{osc} \tag{9}\]  
where \(\eta_{b}^{osc}\) and \(\eta_{d}^{osc}\) are Gaussian-distributed white noises. As stated before, the difference between the raw pseudoranges and Doppler measurements from the GPS tracking loops, together with the INS ranges and Doppler frequencies, form the input of the centralized integration filter. The derivation of the analytical parameters of the state vector and the measurement model of the TCAPLL integration filter can be found in [18]. It is important to mention that the well-known INS mechanism equations are used to convert the output of the IMU into a PVA solution and further propagate it in time. In the TCAPLL architecture, we used quaternions for attitude representation and propagation.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 4 Control System
### Aerial Manipulator Control  
At the inner most level, the UAV is controlled through cascade attitude and rate controllers. The input to these controllers is the desired orientation and based on the state, the output is the vector of the rotors' angular velocities. The second level of control, which uses the inner attitude control loop, consists of two additional cascades, the position and the velocity control. These controllers receive a referent position and velocity feed-forward value to generate the desired vehicle orientation and thrust. The manipulator joints are controlled through standard Proportional, Integral, Derivative (PID) controllers; however, in a real-world setting, servo motors with integrated control are typically used.  
As mentioned earlier, it is important to track the desired force after contact with a wall is achieved. To accomplish this, an adaptive impedance controller is employed to generate an appropriate setpoint for the position controller. This controller receives a trajectory supplied by the mission planner, which steers the aerial manipulator towards the sensor mounting target on the bridge.
## 5 Optimization of Load Distribution in Artificial Joints and Orthopaedic Implants  
Load transfer from internally implanted devices to their surrounding bone tissues has been of great importance and interest. The frequent usage of metal for implanted materials creates a large stiffness difference between prosthesis and bone tissue, known as the stress shielding effect, and often leads to failure of the implant due to the large stresses created at the interface [83]. Methods of design to overcome the consequences of this effect were developed, such as a design with porous structure to provide a gradient modulus distribution for the purposes of minimizing the stress shielding effect and extending the lifespan of the implant [84].  
Implant design has also included the formulation of the optimal contacting surfaces through which the load bearing is transmitted across the joint. An example is the knee joint, whereby a criterion of minimal slippage (or gliding) is utilized for achieving minimal shear stresses [85]. For the hip joint replacements, optimization techniques based on machine learning have been suggested [86]. Efforts were also aimed to optimize and individualize the positioning parameters of a femoral component in order to facilitate its accurate implantation [87].
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 5 Temperature Calibration  
After the strain FBGs were glued to the ferrite cores, and before potting, the ferrites were heated in 10 \({}^{\circ}\)C increments in an oven. All temperature FBGs were also calibrated before being inserted into the pad. The peak reflected wavelengths recorded at each temperature allowed \(\Delta\lambda_{B\_T}\) to be determined prior to the thermal testing of the pads. A thermometer was placed beside the FBGs in the oven to measure their temperature to within 0.1 \({}^{\circ}\)C. Before the calibration was performed, the attached sensors were heated to 5 \({}^{\circ}\)C above the maximum calibration temperature for a few hours and cooled down, to relax any residual stresses in the system prior to the calibration [22]. Figure 8 below shows that the typical shift in peak reflected wavelengths as temperature is increased from a room temperature of 23.3 \({}^{\circ}\)C to 110 \({}^{\circ}\)C. \(\Delta\lambda_{B\_T}\) for strain FBGs was around twice that for temperature FBGs, because the thermal expansion of the optical core is only 0.55 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C compared to the 11 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C of ferrite [36]. An accurate measurement of \(\Delta\lambda_{B\_T}\) is critical for obtaining accurate strains, because \(\Delta\lambda_{B\_T}\) accounts for around 90% of the total wavelength shift, \(\Delta\lambda_{B\_T}\), during the thermal testing of the pads.  
Figure 8: Typical wavelength shift (\(\Delta\lambda_{B\_T}\)) of a free FBG and an FBG attached to ferrite due to changes in temperature.  
Figure 7: Schematic of the instrumentation on the ferrites of the pads. Ferrites were numbered 1 to 4, as indicated by the bold underlined numbers. Sensor locations along each ferrite are indicated by letters A, B or C. The side of the ferrite the sensors are on (i.e., top or bottom) is indicated by colour. The type of sensor is indicated by shape.  
Apparent strain measured by resistive strain gauges due to the stress-free thermal expansion of ferrite was determined using the same method used for the measurement of CTE described in Section 3.1. This temperature induced apparent strain from each strain gauge was subtracted from the measured strain of the corresponding gauge during the thermal loading of the pad to obtain true mechanical strain on the ferrite.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 3 Numerical Simulation  
Due to the complexity of the underground geological conditions, various undesirable geological bodies will be encountered during tunnel construction, which will do harm to the construction. The undesirable geological structures often encountered in tunnel construction mainly include lithological interfaces, fault fracture zones, karst geological bodies, composite strata, etc. [39]. They are further explained in Table 1.  
Figure 2: The flow chart of adjusted frequency domain FWI method.  
The tunnel cavity will interfere with ahead prospecting of seismic waves. Therefore, forward simulation of seismic ahead prospecting should be carried out firstly without anomalous objects in front of the tunnel face, and the influence of the tunnel cavity on the forward simulation results should be studied. This is also conducive to the identification of the seismic wavefield response characteristics of typical undesirable geological bodies in the next part. The observation system is shown in Figure 3.
## 5 Frequency Regulation by DFIGs Integrated by VSC-HVDC  
### Derated Mode of DFIG  
When ignoring the active power loss of the DFIG and assuming that the induction generator and converters provide a fast response, the output of the DFIG can be approximated to the captured power of the wind turbine (WT), i.e., \(P_{\rm wt}\). The relationship of \(P_{\rm wt}\) to the rotor speed, i.e., \(\omega_{\rm wt}\), is shown in Figure 4, where \(v_{\rm ci}\) is the cut-in wind speed \(v\), \(v_{\rm lm}\) and \(v_{\rm hm}\) are the critical values between the slow, the medium, and the high ranges of \(v\). The subscript max denotes the maximal value.  
Under the maximum power point tracking (MPPT) mode, the WT operates along the curve ABC, while under the derated mode, the DFIG operates along the curve DEF to reserve power for frequency regulation.  
The DFIG may participate in the frequency regulation as the SGs do, by applying the droop control that enables the DFIG to adjust its power output under circumstances of frequency deviation. In this case, \(P_{\rm wt}\) is given by,  
\[P_{\rm wt}=P_{\rm wt,der}-K_{\rm DFIG}\Delta f \tag{14}\]  
where subscript der denotes the derated mode, and \(K_{\rm DFIG}\) is the coefficient of the droop control that adjusts the captured power of WT \(P_{\rm wt}\) in response to the system frequency deviation \(\Delta f\).  
With the virtual inertia control to the DC capacitor and the variable frequency control applied to the VSC at the wind farm side, \(\Delta f\) is sensed by the DFIG indirectly through  
Figure 4: Captured power versus rotating speed of WT.  
\(\Delta U_{\text{DC}}\), thus (14) is modified to (15) and included in the DPF model to estimate the frequency regulation capability of the DFIG integrated through the VSC-HVDC.  
\[P_{\text{wt}}=P_{\text{wt,der}}-K_{\text{DFIG}}\Delta f_{\text{wt}}=P_{\text{wt,der }}-K_{\text{DFIG}}K_{\text{f}_{\text{wt}}/\text{U}_{\text{DC}}}\Delta U_{\text{DC}} \tag{15}\]
## 3 The Proposed Approach  
This section introduces a novel hybrid detection methodology specifically designed to identify and mitigate the risks posed by black hole and gray hole attacks within mobile ad hoc networks (MANETs), utilizing the dynamic source routing (DSR) protocol. The proposed approach combines the strengths of multiple detection techniques into a coherent and multi-faceted defense mechanism uniquely tailored to addressing the inherent security challenges of MANETs. The methodology is structured into three integrated components:  
This component forms the foundation of the hybrid approach, establishing a baseline of normal network behavior through statistical models. By continuously monitoring network traffic and comparing real-time data against this baseline, the system can detect deviations indicative of potential attacks. The use of statistical anomaly detection ensures the early identification of unusual patterns that may signify black hole or gray hole activities.  
Building upon the initial anomaly detection, this component employs advanced data mining techniques, including clustering and classification algorithms, to analyze and categorize nodes based on their behavior. These techniques allow for the isolation of nodes that exhibit suspicious behavior patterns, further refining the accuracy of the detection process. The use of K-means clustering and decision trees helps distinguish legitimate and malicious nodes, enhancing the robustness of the detection system.
## 2 Materials and Methods
### In Vitro Antioxidant Activity  
_Agastache rugosa_ extracts were used to determine antioxidant activity in the same manner as in the TPC and TFC assays. The DPPH radical scavenging activity (RSA) was evaluated based on previous reports by [PERSON] et al. [40], with slight modifications. Briefly, 0.1 mL of 0.2 mM DPPH, which was dissolved in 99.9% methanol, was added to a 96-well plate, and 0.1 mL of the extracts was added to each well at 6 concentrations: 31.25, 62.5, 125, 250, 500, and 1000 mg/L. After incubation in the dark for 30 min, the absorbance was measured at 517 nm using a UV-Vis spectrophotometer. For the control, 70% methanol, which was used for the extraction solvent, was added instead of the sample extracts.  
The ABTS RSA was evaluated following the protocol previously reported by [PERSON] et al. [40], with slight modifications. Briefly, 7 mM ABTS powder was thoroughly dissolved in potassium persulfate (2.5 mM) solution, which was prepared in deionised water, and the mixture was incubated in the dark for 16 h. After incubation, the absorbance of the ABTS buffer was adjusted to 0.7 \(\pm\) 0.002 at 734 nm, and 0.15 mL of the ABTS buffer was transferred to a 96-well plate. Then, 0.05 mL of _A. rugosa_ extract was added to each well, ranging from 31.25 to 1000 mg/L, and incubated in the dark for 3 min. The decrease in absorbance was measured at 734 nm using a UV-Vis spectrophotometer, and 70% methanol was used in place of the sample extracts for the control. The sample's RSA was calculated based on a previous report by [PERSON] et al., and the plotted curve was used to indicate the amount of antioxidants needed (mg/mL) to reduce the initial free radical concentration by 50% (IC\({}_{50}\)).