esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Literature Review  
Currently, there are various devices that comprise the suspension system of a vehicle. Active and semi-active systems use many control techniques for two objectives, passenger comfort and vehicle stability. In the literature, optimal control, adaptive control, sliding mode control, fuzzy systems, generic algorithms, etc., can be found. Nevertheless, other control policies have been proposed specifically for suspension systems.  
In [2], the control techniques Skyhook, Groundhook, and Mix-1-sensor for a semi-active suspension were compared. Such techniques are on-off, that is they only have two values corresponding to a soft or rigid damping force. Skyhook uses an imaginary damper between the body and the sky, trying to minimize the vertical acceleration of the vehicle body, but the contact between the tires and the ground is not very effective (stability control). Although this control strategy was proposed in [3] by [PERSON] in 1974, it is a reference in control laws focused on comfort. Groundhook is similar to Skyhook, but focuses on the vehicle stability, reducing the dynamic forces between the tires and the surface using a virtual damper between the tire and the road. Furthermore, Mix-1-sensor minimizes the acceleration of the sprung mass by measuring the oscillation frequency of the suspension and thereby determining high or low damping.  
On the other hand, in [44; 45; 46; 47], the study and analysis of semi-active suspensions was reported. For instance, in [44], a system consisting of a variable damper manipulated by a PID controller, placed in series with a passive suspension, was studied. The controller function is to adjust the damping coefficient of the damper to keep the body always stable. As the output variable, the displacement of the suspension is used. For the model used in this article, a NARX model (based only on the use of sampled inputs and outputs) was assumed.
# Micromachining in Powder-Mixed Micro Electrical Discharge Machining
## 3 Influence of Powder Characteristics
### Powder Concentration  
[PERSON] and [PERSON] [36] found that material-removal rate increases with the increase in powder concentration. In this experiment, copper was used as the tool electrode and Inconel 706 was selected as a workpiece material. Al powder was used as an additive in EDM oil in a concentration range of 0-40 g/L. They found that the material-removal rate increases with the increase of powder concentration, in this case at a concentration of 15 g/L. However, at a higher concentration--more than 15 g/L--the removal rate will decrease. This is due to the excessive powder deposited near the machining area. Instead of assisting electrical sparks to spread, excessive powder creates difficulty in removing the debris, and hence interrupts the machining cycle. [PERSON] et al. [44] and [PERSON] et al. [45] investigated that the material-removal rate and the quality of the surface machined was significantly increased with the introduction of graphite nanopowder. The suspended nano graphite powder breaks single sparks into many, small, uniform sparks to produce craters on the surface of the workpiece machine, resulting in the reduction of surface roughness.  
Figure 4: Research and development of power-mixed \(\upmu\)-EDM.  
Figure 3: Illustration of conventional EDM and power-mixed micro-electric discharge machining (\(\upmu\)-EDM).  
[PERSON] et al. [46] studied the effect of added carbon nanofibre into dielectric fluid in the material migration between tool electrode (tungsten) and workpiece (reaction-bonded silicon carbide). The result showed that it is necessary to introduce nanofibre at a concentration of 0.06 g/L in order to improve the surface quality and to prevent the deposition of tungsten material onto the workpiece surface. This can be explained by the fact that the nanofibre in the dielectric fluid initiated smaller discharge to form smaller craters, and that the smaller craters were an obstacle for tungsten debris to deposit on the crater surface. In terms of the effect of powder concentration on the tool wear rate, [PERSON] et al. [47] added graphite powder into dielectric fluid at concentrations of 0 g/L, 2 g/L and 4 g/L. They found that as the concentration of graphite powder increased, the sparking gap between the tool electrode and the workpiece increased. This phenomenon enhances flushing the debris out of the working area and enables a stable machining process, which further leads to the decrease in tool wear rate. The lowest tool wear rate was obtained at a concentration of 4 g/L.
## 3 Proposed Mitigation Architecture for an RF Front-End
### Optimization Solution  
To solve linear Equation (32), whose solutions \(\omega\) are a series of complex numbers, we make a transformation by assuming \(R=R_{re}+jR_{im}\), \(d=d_{re}+jd_{im}\), and \(\omega=\omega_{re}+j\omega_{im}\), where subscripts \"re\" and \"im\" mean the real and imaginary parts, respectively. Thus:  
\[\left[\begin{array}{cc}R_{re}&-R_{im}\\ R_{im}&R_{re}\end{array}\right]\left[\begin{array}{c}\omega_{re}\\ \omega_{im}\end{array}\right]=\left[\begin{array}{c}d_{re}\\ d_{im}\end{array}\right] \tag{33}\]  
\[\text{If }A=\left[\begin{array}{cc}R_{re}&-R_{im}\\ R_{im}&R_{re}\end{array}\right],\overline{d}=[{d_{re}}^{T}{d_{im}}^{T}]^{T}, \overline{\omega}=[{\omega_{re}}^{T}{\omega_{im}}^{T}]^{T},\]  
Equation (33) can be written as:  
\[A\overline{\omega}=\overline{d} \tag{34}\]  
In Equation (32), the odd-order Hammerstein model is set from 1 to 5 so that the first diagonal element of \(U^{H}U\) is \(\sum\limits_{n=1}^{N}|z_{1}(n)|^{2}\) and the last one is \(\sum\limits_{n=1}^{N}|z_{1}(n-M_{5}+1)|^{10}\). As a result of too large a power difference, \(U^{H}U\) and \(A\) easily tend to be ill-conditioned matrices. In this paper, WII [35] is used to deal with this ill-conditioned problem. The prerequisite of adopting WII is that \(A\) is a real symmetry non-negative definite matrix. Since \(R\) is a Hamiltonian matrix \(R=R^{H}\) and \(\omega^{H}R\omega=(U\omega)^{H}(U\omega)=|U\omega|^{2}\geq 0\), it can easily be verified. The final iterative formula is:  
\[\overline{\omega}^{(k+1)}=\overline{\omega}^{(k)}+(A+\lambda I)^{-1}( \overline{d}-A\overline{\omega}^{(k)}) \tag{35}\]  
where \(\lambda\) is the weighting factor. Although there is a matrix inversion step appearing in Equation (35), \((A+\lambda I)^{-1}\) does not update during the whole iteration procedure. Hence, unlike other methods, such as the recursive least squares method (RLS), \((A+\lambda I)^{-1}\) can be computed in advance and stays unchanged until the entire delay searching is finished. Therefore, the recurrence formula (Equation (35)) only contains some simple multiply-add operations, making it suitable for efficient realization in software-defined radio (SDR).
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 3 Methodology
### Machine Learning  
#### 3.2.1 Random Forest  
RF consists of a collection of multiple decision trees, each of which is generated by training on a random subsample of the data [45]. In RF, each decision tree is trained independently, meaning that different subsets of training data and features are used in the construction of each tree. This randomness helps reduce overfitting and improves the generalization ability of the model. The training process of RF involves randomness in two main aspects: data sampling and feature selection. Data sampling is achieved by randomly selecting a subsample of the training data with replacement to ensure that each decision tree is trained on a different subset of data. Feature selection involves randomly selecting a subset of features at each node of the decision tree for splitting. This randomness helps reduce the correlation between decision trees, thereby enhancing the performance of the overall model. During prediction, RF adopts a voting mechanism, where each decision tree predicts the outcome for a new sample, and the final prediction result is obtained by voting or averaging the predictions of all decision trees. This integrated approach effectively reduces the variance of predictions and improves prediction accuracy. RF has the advantage of being less sensitive to noise and overfitting and has the ability to handle high data dimensionality and multicollinearity. However, RF also has disadvantages such as high computational complexity and slow computation speed.  
Figure 7: Workflow of LSA.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
The framework described in this project could be implemented onto a ground station with a few hardware and software additions. The novelty of this framework comes from its portability, low-cost, and ability to expand for various other space-related projects. It uses a WiFi-enabled micro-controller to read online databases to retrieve public satellite data. Sensors are used to localise the apparatus to translate the reference of the satellites from a geocentric model to the reference frame of the device. This allows for the device to be placed in different orientations and different locations while still following the satellites accurately. The major contributions of this paper can be summarised as follows:  
* We propose a real-time, portable, cost-effective, and highly efficient object tracking framework that is able to detect objects in space with high accuracy. * We detail the system design and provide a complete implementation of the proposed framework with a hardware-based practical test-bed setup. * We examine the performance of the proposed framework using a practical use-case example. The rest of the paper is organised as follows. In Section 2, we discuss related works. In Section 3, we discuss our proposed framework for this system. In Section 4, we discuss the design and implementation of TrackInk. In Section 5, we discuss further system enhancements which could be applied to TrackInk, along with some interesting challenges we encountered during research. Finally, in Section 6, we conclude the paper with a discussion on future works.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
### Contribution  
The complex software environments of ADASs and their components, along with their large code base and extensive reliance on third-party components, make the process of mining and exploitation of BOF vulnerabilities challenging. In this work, we observed that ADAS devices commonly utilize unencrypted protocols for module communication. This provided an opportunity for us to extract information and features from raw communication traffic, enabling us to locate input stream and buffer data operations more efficiently. We achieved this by filtering out known protocols based on the captured device traffic, allowing us to more accurately locate data structures, input data passing, and buffer operations during the decompilation process. This approach reduces the workload of manual code auditing in the mining of BOF vulnerabilities. Based on the above situations, we proposed a communication-traffic-assisted ADAS BOF vulnerability mining and exploitation method.  
In detail, our contributions are as follows:We propose a method for mining and exploiting vulnerabilities in ADASs by analyzing their communication traffic. Our approach involves capturing and analyzing communication traffic, extracting feature fields, decompiling business applications, and locating data structures using these feature fields. By checking cross-references of the data structures, we can restore the entire data passing chain and analyze the data passing operations and buffer operations within this chain. Using these analyses, we can identify BOF vulnerabilities in ADASs.
2. To demonstrate the effectiveness of the proposed method and the severity of these vulnerabilities, we employed our method on several ADAS devices and mined the BOF vulnerabilities. Furthermore, we conducted a series of operations on a vehicle and captured the corresponding CAN messages. Subsequently, we replayed the communication traffic via the ADAS device and observed whether the vehicle repeated these operations. By exploiting the BOF vulnerabilities, we could execute console commands to replay CAN messages and map the attack to the physical world. Our experiment proves that BOF vulnerabilities in ADASs could interfere with the regular operation of intelligent connected vehicles.
# 3D Gravity Inversion on Unstructured Grids
## Appendix A  
As shown in Equation (1), the gravity anomalies cause by a tetrahedral is equal to the sum of the contributions from six edges. Taking the edge \(\mathrm{Q}_{1}\mathrm{Q}_{2}\) in Figure 1 for example, we can describe thrice rotations as follows:  
1. Rotate counterclockwise the x- and y-axes around the z-axis in Figure 1a by the angle \(\varphi\) until the rotated \(x\)-axis is coincident with the direction of the outward normal onto the x-y-plane.
2. Rotate counterclockwise the z- and x-axes around the \(y\)-axis by the angle \(\psi\) until the rotated \(z\)-axis is coincident with the outward normal direction of the triangle facet(Q1Q2Q3). With previous two rotations, we obtain the target coordinate system (X, Y, Z). Thus, the polygon is in the X-Y-plane, as shown in Figure A1b.
3. Rotate counterclockwise the X-and Y-axes around the \(Z\)-axis by the angle \(\theta\) until the rotated \(Y\)-axis is coincident with the direction of the outward normal on the edge Q1Q2 (as shown in Figure A1c).  
Refer to Figure A1c, Q1(\(x_{1}\), \(y_{1}\), \(z_{1}\)), Q2(\(x_{2}\), \(y_{2}\), \(z_{2}\)), and Q3(\(x_{3}\), \(y_{3}\), \(z_{3}\)) are three vertices of the triangle facet. For the three vertices, they can be either clockwise or counterclockwise in the X-Y-plane, because the grid generator stores these vertices in random order. However, the algorithm described in Section 2.1 require these vertices to be clockwise.  
We use the following method to judge the rank and rearrange vertices. the normal vector \(\mathbf{n}\) of the triangle facet is expressed as  
\[\mathbf{n}=\left|\begin{array}{ccc}\overset{\rightarrow}{i}&\overset{ \rightarrow}{j}&\overset{\rightarrow}{k}\\ x_{2}-x_{1}&y_{2}-y_{1}&z_{2}-z_{1}\\ x_{3}-x_{1}&y_{3}-y_{1}&z_{3}-z_{1}\end{array}\right| \tag{1}\]  
Q4(\(x_{4}\), \(y_{4}\), \(z_{4}\)) is the last point of tetrahedral but outside the X-Y-plane. Define \(t\) as the scalar product of the normal vector \(\mathbf{n}\) and vector Q1Q4 = (\(x_{4}-x_{1},y_{4}-y_{1},z_{4}-z_{1}\)), i.e.,  
\[t=\mathbf{n}\cdot\overset{\rightarrow}{\text{Q}_{1}}\overset{\rightarrow}{ \text{Q}_{4}} \tag{2}\]  
When \(t>0\), it means that \(\mathbf{n}\) and Q1Q4 are in the same direction, the three vertices are clockwise. When t \(<0\), however, the three vertices are counterclockwise. In this case, we need to rearrange the three input vertices to make them clockwise. After rearrangement, calculate the contribution of Q1Q2 as described in previous Section 2.1.  
Repeat the above process for every edge, and then we can obtain the gravity anomalies of a certain tetrahedral.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 5 Discussion of the Results  
Given that this paper analyzes the latency in multi-hop networks with Bluetooth standard, both the time that the _ping_ type data packets take to go from the first to the last node, as well as the time that the _pingback_ data packets take to go the other way round were measured. Moreover, two low power link policies were considered; _park_ and _sniff,_ and several periods of inactivity were tested, namely 100, 500, 1000 and 2000 ms. For all the tests, the amount of nodes in the network, and hence the amount of hops, was changed. An average of 320 samples per situation under test was considered in park mode, and 270 samples per situation in sniff mode.
## 4 Hybrid Check Matrix Construction (HC) Algorithm
### Energy Efficiency  
When communicating between two nodes, energy consumption occurs in two places, one is the transmission of information data (output power) and the other is the processing of data frames and error control. Therefore, the defined communication energy is equal to the sum of the energy consumed by the transmitted data and the energy consumed by the coding and decoding [34]. The design of the non-binary LDPC-CPM scheme parameters in the WSN is shown in Table 3. According to the simulation parameters in Table 3, the relationship between energy efficiency and Signal-to-noise (SNR) ratio is simulated. According to the above simulation diagram, when the BER of the system is required to be \(10^{-8}\), the SNR ratio of non-binary LDPC-CPM system is 2.4 dB. The SNR ratio of the uncoded system is 8 dB. Under the same code rate and length, the traditional coding and decoding scheme can reach the index requirement at 6 dB [35; 36; 37]. Under the same conditions, the non-binary LDPC-CPM scheme can reduce the transmit power by 42-60% compared with traditional scheme and reduce the transmit power by 55-75% compared with the uncoded system. Thereby the relationship between energy efficiency and SNR ratio is obtained, as shown in Figure 14.  
Figure 13: The influence of inner iterations on the performance of 8-ary LDPC-CPM system.
# Hybrid GA-SOCP Approach for Placement and
## 3 Proposed Hybrid GA-SOCP Optimization Approach
### Master Stage: Genetic Algorithm
#### 3.2.6 Fitness Function Evaluations and Replacement of the Population  
Once the offsprings have been created by the selection, recombination, and mutation stages, these are evaluated in the fitness function, i.e., optimization model (14)-(18). Let us suppose that the fitness function of the offspring \(\text{Os}_{i}^{t}\) is \(z\left(\text{Os}_{i}^{t}\right)\) and the fitness function of the offspring \(\text{Os}_{j}^{t}\) is \(z\left(\text{Os}_{j}^{t}\right)\). Subsequently, the potential offspring that will be part of the population is selected as the minimum of both fitness function values: (i) select \(\text{Os}_{i}^{t}\) if \(z\left(\text{Os}_{i}^{t}\right)\geq z\left(\text{Os}_{j}^{t}\right)\), or (ii) select \(\text{Os}_{j}^{t}\) otherwise.  
In order to decide whether the winner offspring is added to the population, two aspects must be verified: (i) is the fitness function of the winner offspring better than the worst individual in the population? If the answer is positive, then, (ii) is the winner offspring different from each of the individuals in the population? If the answer is also positive, then the winner offspring replace the worst individual in the population.  
Note that, if one of the previous questions has a negative response, then a new iteration is started without modification in the population.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 3 System Model and Problem Definition
### Migration Model
In time slot \(t\), we consider the migration of SI into or out of ES \(s_{i}\), where both service suspension and restoration demand computational resources. To avoid conflicts in resource usage, a period \(\phi_{t,i}\) within each time slot is dedicated exclusively to these processes, expressed as  
\[\phi_{t,i}=\text{Avg}\{C_{t,i},V_{t,i}\}, \tag{7}\]  
where \(C_{t,i}\) represents the average delay for SIs migrating into target ES \(s_{i}\), and \(V_{t,i}\) represents the average delay for SIs migrating out of the original ES \(s_{i}\). When the remaining time \(Rst_{t}^{k}<0\) for \(task_{t}^{k}\) and the task is incomplete, the SI of \(task_{t}^{k}\) must migrate to the cloud server. Given the cloud server's distance from the ES, the synchronization delay for SI migration is \(w_{t}^{k}=\frac{V_{t+1}^{k}+V_{W}^{m}}{B_{\Psi_{t}^{k},\text{cloud}}^{k}}+\mu\), where \(\mu\) represents the propagation delay. It is assumed that the cloud server possesses ample computational capacity to process the tasks and can return the results to the user in the subsequent time slot.
## 3 Numerical Results  
In this section, extensive numerical studies have been conducted to validate the present method through solving the eigenvalue problem of Equation (28) numerically by using MATLAB. For convenience, the numbers of double series terms are assumed to be the same and denoted by \(N\) (i.e., \(m=1,2,3,...,N\), \(n=0,1,2,3,...,N\)) and the two restrained edges have the same rotational fixity factor (i.e., \(r_{x0}=r_{xa}=r\)). It should be noted that the series solutions obtained by the present method are theoretically convergent to the exact values when \(N\rightarrow\infty\) while solutions with desired accuracy can be determined by finite terms. First of all, the convergence of the fundamental frequency parameter is shown in Figure 2 for the case of a square R-F-R-F isotropic plate with \(r=0.5\). The values are examined by truncating the series up to \(N=160\) since the computation time becomes very long on a standard personal computer when \(N>160\). From the results of the convergence study, the number of series terms \(N\) is taken to be 100 for all numerical results presented herein. Since the results of natural frequency of R-F-R-F plates are limited, the present method was first used to obtain the frequencies of simply supported plates and fully clamped plates. Two boundary conditions have been numerically computed and results are presented in Tables 1 and 2: (1) two opposite edges (\(x=0\) and \(x=a\)) simply supported and the other two (\(y=0\) and \(y=b\)) free (i.e., S-F-S-F); and (2) two opposite edges (\(x=0\) and \(x=a\)) fully clamped and the other two (\(y=0\) and \(y=b\)) free (i.e., C-F-C-F). As mentioned before, these two cases can be treated as two limit cases with rotational fixity factors, \(r=0\) and \(r=1\), respectively. It should be noted that \(r\
eq 1\) in Equation (27) and thus C-F-C-F is simulated by setting \(r=0.9999\) in this study. Table 1 tabulates the first six frequency parameters \(\Omega=\omega a^{2}\sqrt{\rho h/D}\) of isotropic plates. Results for orthotropic square plates are illustrated in Table 2. Different aspect ratios were investigated. Excellent agreements can be found from comparisons between the present predictions and previously published results for \(r=0\) and \(r=0.9999\) (i.e., S-F-S-F and C-F-C-F), respectively. Additionally, results for R-F-R-F plates with \(r=0.5\) are also provided for future comparisons.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Short-Reach Systems' Overview and Classification
#### 3.1.2 Inter-Data Center Links  
Inter-data center links are usually below 20 km and are typically employed to connect one data center to another and ensure data exchange between multiple DCNs [35,42]. Furthermore, they are usually referred to as DCIs [42]. Unlike what is entailed in intra-data center links, inter-data center links exploit the standard SMF (SSMF). This is mainly owed to the length scale, which is too long for the MMF implementation. The employment of MMF for this length scale can bring about an intermodal dispersion. In contrast, the length scale is relatively short for an optical amplifier deployment. As a result, an essential parameter  
Figure 2: Typical core, metro, and access network scenarios depicting intra-/inter-data center links. DCI: data center interconnects; F-AP: fog-computing-based access point; UE: user equipment; F-UE: “smart” user equipment; HPN: high-power nodes.  
for system optimization is receiver sensitivity. Furthermore, it is noteworthy that advanced photodiodes such as high-bandwidth avalanche photodiodes (APDs) offer considerably better receiver sensitivity compared with the conventional PIN PD. Furthermore, with an increase in the chromatic dispersion effects in conjunction with the baud rate and distance, the direct detection scheme eliminates the signal phase information, making the system nonlinear. Consequently, the chromatic dispersion effects have to be attended to by exploiting innovative signaling and DSP techniques. \(O\)-band transmissions are also a means of addressing this issue. This is due to the chromatic dispersion that is minimized at that wavelength band for the SSMF [35].