esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Overview of Advanced Persistent Threats (APTs)  
The targeted attack strategy used by a qualified and skilled adversary to maintain undetected access to critical information exfiltration for a lengthy period is known as an APT. There are various forms of APT assaults, including social engineering techniques such as spear phishing, SQL injection, malware, and watering hole attacks [3]. The term APT offers shorthand for what it is. Traditional assaults lack one or more of these traits.  
\"Advanced\" means the attacks are planned by a team of individuals with many resources, expertise, and funding. The assaults must be simple to be successful. It is common for an attacker to utilize phishing and readily available malware development tools [40; 41]. Nevertheless, when necessary, they utilize software, such as zero-day exploits, to target particular vulnerabilities and launch several attacks to gain access. \"Persistent\" attackers are desperate to access the victim's systems, applications, and resources. Resultantly, the intruder has full access to the system, including backdoors. If one connection is compromised, others may be opened and used to continue collecting sensitive information. Distinguishing between a threat and an opportunity is also important. Since they are more than software that runs independently, APTs pose a problem [12; 42; 43].  
There are two types of APTs, namely killing and leeching. Leeching occurs when an attacker passively gathers information from a target system without compromising it. An attacker may use a network sniffer to steal sensitive data. Leeching is a sneaky attack that gives attackers information they can use to launch more serious attacks. On the other hand, killing involves actively compromising and disrupting a target system.  
Malware can compromise a system, delete important files, or steal sensitive data. Killing is a more aggressive attack that can damage a target system and is easier to detect than leeching [44]. The primary difference between leeching and killing is the attacker's intent. Leeching is typically motivated by the desire to gain access to information or resources, while killing is motivated by the desire to disrupt or destroy systems and networks. Thus, the tactics used to defend against leeching and killing are different. Preventing unauthorized access is the focus of defending against leeching, while protecting against disruptions is the focus of defending against killing [44].
## 5 Investigation of the MIMO-Antenna Diversity Performance
### Total Active Reflection Coefficient (TARC)  
The TARC is another parameter used to measure the diversity. It depends on the antenna S parameters and defines the return loss in the MIMO antenna. For the case of a two-element MIMO antenna, it can be estimated using the following equation [35; 41], in which an ideal value of zero is desired:  
\[TARC=\frac{\sqrt{\left(\left|S_{11}+S_{12}e^{i\theta}\right|^{2}+\left|S_{21}+S_ {22}e^{i\theta}\right|^{2}\right)}}{\sqrt{2}} \tag{5}\]  
For the random excitation phase (\(\theta\)), different values of the TARC are obtained. Simulated TARCs for different excitation phases (\(\theta\)) taken from 0 to 180\({}^{\circ}\) with a step of 30\({}^{\circ}\) are illustrated in Figure 15.  
From these curves, it is obvious that the TARCs consistently exhibit values below \(-12\) dB in the entire FCC frequency band. Furthermore, all the curves within the family display a notable level of convergence, implying that the proposed antenna is not sensible to the phase variations. Consequently, this guarantees a substantial reduction in the mutual coupling between the different ports.
# Design and Implementation of a Lizard-Inspired Robot
## 4 Results of the Experiments and Discussion
### Forward Locomotion  
The second experiment verified the forward locomotion of the robot. The purpose of this experiment was to prove that the robot was capable of repeating the supposed forward locomotion. The parameters of the input angle function for the experiment were set as follows:  
\[\alpha =\frac{\pi}{6},\] \[\omega =10,\] \[\beta =0.\]  
The parameters represent the robot moving forward in a straight line. The locomotion time of the robot was set to 4 s. Forward locomotion was repeated 10 times. Table 5 shows the results of the experiments. In Table 5, \"traveling distance\" is defined as the distance from the starting point to the terminal point of \(O_{r}\). Additionally, Figure 20 shows the trajectories of each marker in the eighth experiment. Figure 21 shows a snapshot of the forward locomotion experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Experiments** & **Traveling Distance (mm)** \\ \hline
1 & 609.30 \\
2 & 605.71 \\
3 & 612.62 \\
4 & 610.21 \\
5 & 611.19 \\
6 & 614.14 \\
7 & 608.39 \\
8 & 611.76 \\
9 & 610.66 \\
10 & 612.75 \\ \hline Average & 610.67 \\ Standard deviation & 2.44 \\ \hline Ideal & 632.90 \\ Error ratio (\%) & 3.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the forward locomotion experiments.  
Figure 20: The trajectory of each marker in the eighth experiment of the forward locomotion experiment. (**a**) The overall view of the trajectories. (**b**) An enlarged view of the trajectory of \(O_{r}\). The robot coordinate \(O_{r}\) moved backward marginally because of the flexure of the carpet.  
From Table 5, the average of the 10 forward locomotion experiments was 610.67 mm, and the standard deviation was 2.44 mm. Hence, the fabricated robot possessed high repeatability for forward locomotion with respect to the parameters of the input angle function. In addition, the ideal distance of the forward locomotion obtained with the numerical simulation was 632.90 mm, and the error ratio between the average of the experiments and the ideal distance was 3.51%. Therefore, the proposed claw-shaped leg mechanism is effective for the developed robot, and the developed robot is capable of walking without the supporting legs slipping.  
The cause of the 3.51% error ratio was the backward movement of the robot due to the deflection of the carpet. As shown in Figure 22, the carpet consisted of cushioned fibers. The supporting legs kicked the ground to walk, and the fibers were deflected. Due to this deflection, the robot moved backward marginally. This backward motion was the cause of the error. In fact, Figure 20b shows that the robot moved backward marginally. Adding this backward distance to the traveling distance, the total distance became 636.83 mm, and the error ratio between the ideal distance and the total distance was 0.62%. This is a vast improvement in the accuracy. Hence, the error of the traveling distance caused the backward motion of the robot due to the flexure of the carpet.  
Figure 21: Snapshots of the forward locomotion experiment.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 5 Results and Discussion
### Classification Model
#### 5.2.1 Dataset Selection  
The dataset used in this study was obtained from [31]. It was obtained from a professional driver and comprised a total of 1.8 million data samples, providing three-axis acceleration and three-axis angular velocities across time. The dataset was labeled with different types of maneuvers, providing valuable information for training the classification model. The labeled maneuvers were (1) acceleration on a curve, (2) acceleration on a straight line, (3) fall-like maneuver, (4) harsh braking on a straight line, (5) degraded track, (6) fall in a curve, (7) fall in the roundabout, (8) fall on a slippery straight line, and (9) fall with leaning of the motorcycle.
## 1 Introduction
In this review, we revisit these eight areas to highlight the pros and cons of the critical parameters for NAFC MS analysis, with a focus on chromatographic clean up and separations prior to mass spectrometry detection of OSPW and environmental samples. We also expand the discussion to ten key parameters, including separation effects of salt content, the benefits of sample clean up, and a chromatography step prior to MS. Optimization and control of these parameters can lead to robust methods of analysis, while seemingly reasonable but variable settings can lead to irreproducible and ambiguous results. A good example of conflicting results was reported previously, where OSPW extracts were prepared, split, and identical samples were distributed to two laboratories. Each laboratory analyzed the extracts by measuring ions under either acidic or basic conditions [17]. Large differences were evident for the O\({}_{2}\) class depending on whether acidic or basic conditions were employed, regardless of the instrument used by the two laboratories (Figure 2). Of note, the most toxic O\({}_{2}\) class would be considered a minor component if only acidic conditions were used. In contrast, separation under basic conditions indicated that the O\({}_{2}\) class is by far the most abundant NAFC. While the observed differences can be explained by pH effects on separation of ions (discussed later in this review), the apparent results can lead to confusion about the levels of principal toxicants in the OSPW. Such discrepancies can pose difficulties to end-users of the data for toxicity assessment. \begin{table}
\begin{tabular}{l l l} \hline
**No.** & **Factor** & **Conclusions** \\ \hline
1 & Definition of total NAs & Use the classical definition of NAs \\ \hline
2 & Extraction phase, pH, temperature & Liquid–liquid extraction at pH 2 and room temperature with DCM as organic phase, or use ENV + SPE \\ \hline
3 & Use of surrogate standards & Use isotopically labelled model compounds as surrogate standards \\ \hline
4 & Minimum resolving power of instrument & 50,000 at \(m/z\) 200, acknowledging that potential interferences contribute to method uncertainty \\ \hline
5 & Use of derivatization & Do not utilize derivatization \\ \hline
6 & Polarity and mode of ionization & Negative-ion-mode ESI \\ \hline
7 & Suitable calibration standard and internal standard & Use commercially available Merichem NA mixture and at least one isotopically labelled internal standard \\ \hline
8 & Use of on-line or off-line fractionation of sample & Employ on-line chromatography prior to MS detection \\ \hline \end{tabular}
\end{table} 
Table 1: Factors affecting analytical results of classical NA quantitation methods (annotations refer to original publications) [16]. Figure 1: Examples of classical mono-carboxylic naphthenic acid (cNA) structures [18].
## 3 Materials and Methods
### Feature Importance Plot  
The feature importance describes which features are more helpful or important than other features in the data set. It can help to better understand the solved problem and sometimes lead to model improvements by employing the feature selection. Basically, feature importance is a technique that assigns a score to input features based on how useful they are at predicting a target variable. We computed the feature importance and the chi-squared test from our data set. It is represented in Figure 1 and Table 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variables** & \(\mathbf{p}\)**-Value** & **Association with Family Violence** \\ \hline Age & \(3.964\times 10^{-5}\) * & Associated \\ Gender & \(0.5029\) & Not Associated \\ Marital Status & \(8.886\times 10^{-6}\) * & Associated \\ Education & \(1.717\times 10^{-6}\) * & Associated \\ Profession & \(1.536\times 10^{-5}\) * & Associated \\ Family Type & \(0.7448\) & Not Associated \\ Family Members & \(0.1010\) & Not Associated \\ Number of Earners & \(0.4894\) & Not Associated \\ Head of Family & \(0.2999\) & Not Associated \\ Religion & \(0.3451\) & Not Associated \\ Wealth Status & \(8.053\times 10^{-5}\) * & Associated \\ Residence Location & \(1.164\times 10^{-5}\) * & Associated \\ Income before Coronavirus & \(5.058\times 10^{-9}\) * & Associated \\ Income after Coronavirus & \(1.979\times 10^{-11}\) * & Associated \\ Lost Job in Coronavirus & \(1.829\times 10^{-5}\) * & Associated \\ \hline \hline \end{tabular}  
* Indicates the statistical significance of the variable. If the \(p\)-value of the variable falls below the significance level (\(\%\)), then the variable is statistically significant.  
\end{table}
Table 3: Chi-squared table for the variables.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Coverage Monitoring  
The coverage issues mentioned in the above subsection need to be solved to provide a better cellular network. To rectify these issues it is first mandatory to identify the cause by using drive test method, performance measurements, and alarm and traces. To detect the weak coverage and overshoot coverage locations, unsuccessful handovers and terminated connections, a drive test is the best method [35]. The authors in Ref. [36], proposed a coverage mapping and cell boundary mapping techniques to solve the coverage issues. To monitor the network and to predict the network, network planning tools play an important role. ASSET radio planning tools are used to predict the network coverage and neighbouring cell planning techniques. RF planning tools [37] and RAN planning tools [38]  
Figure 2: (**a**) Single cell, (**b**) cell structure.  
are proposed in the literature to monitor the coverage. The coverage issues and network parameter optimization techniques are discussed in the following sections.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 5 Visual Servoing Controller Design  
### Relative-Position-Based Visual Servoing  
To realize responsive quick motion, we propose relative-position-based visual servoing, which calculates only the pose of the UAV relative to the target. Since only relative positions are necessary for our controller, the computational load of image processing on the onboard companion computer will be effectively decreased and we can get more available visual information in the same time duration. Figure 7 shows the relative-position-based visual servoing method we used in the experiment.  
Figure 5: Principle of PnP.  
Figure 6: Flowchart of high-speed target recognition.
## 3 Methodology
### Cartographic Scale-Variant Flight Planning
More specifically, two different overlap percentages were analyzed: (i) 90% and 70%, and (ii) 80% and 60%, for forward and side overlap, respectively. In this study, the correlation between the number of images and the flight altitude was examined based on the GSD. The graph in Figure 3 depicts this correlation. The curve started to flatten after a certain flight altitude, meaning that from 60 m and above (at a higher flight altitude), for the selected study area of five hectares using 80% frontal and 60% side lap, the number of images acquired does not change significantly. The scale-variant flight scenarios that were examined are presented in Table 1. Scenario 1 was at 60 m altitude resulting in images with GSD of 1.8 cm and 80% and 60% overlap. Scenario 2 was a flight from 60 m with 90% and 70% overlap and GSD 1.8 cm. Scenario 3 and Scenario 4 had an 80 m flight altitude, the GSD was 2.4 cm, and the forward and side overlap was 80% and 60% and 90% and 70%, respectively. The fifth scenario (Scenario 5) was the 100 m altitude (80% and 60%) with GSD 3 cm, and the sixth scenario (Scenario 6) was at the same altitude with 90% and 70% overlap.
## 4 UAV Path Planning with Cooperative Detection Using BLSSPIO  
### UAV Model  
Assuming that the path planning begins at time \(T_{start}\) and finishes at time \(T_{end}\), the time increment between waypoints is equal for UAVs. Given the waypoint number \(N\) (the meaning of this variable is the same as the spatial dimension of the BLSSPIO), the time interval between two points is \(T_{step}=(T_{end}-T_{start})/N\). In addition, the UAVs maintain constant altitudes and the global frame is simplified to \(X_{S}O_{g}Y_{g}\), and the velocity and position of UAV \(m\in\{1,2,\ldots,M\}\) at waypoint \(n\in\{1,2,\ldots,N\}\) are denoted as \[\left[V_{x}^{m,n},V_{y}^{m,n}\right]^{T},\mathbf{P}^{m,n}=\left[P_{x}^{m,n},P_{y}^ {m,n}\right]^{T}\text{, respectively. When the starting and ending points are }\mathbf{S}^{m}=\left[S_{x}^{m},S_{y}^{m}\right]^{T}\text{ and }\mathbf{E}^{m}=\left[E_{x}^{m},E_{y}^{m}\right]^{T}\text{, the navigation point is expressed as follows:}\] \[\mathbf{P}^{m,n}=\mathbf{P}^{m,n-1}+\mathbf{V}^{m,n}\cdot T_{step} \tag{18}\]  
In addition, a local coordinate \(X_{m}O_{m}Y_{m}\) is constructed, where the line connecting the starting and ending points is the \(X\)-axis. Figure 5 depicts the transition from the global frame to the local frame, and then the deflection angle \(\psi_{m}^{g}\) between two frames satisfies the following equation:  
\[\psi_{m}^{g}=\arctan\frac{E_{y}^{m}-S_{y}^{m}}{E_{x}^{m}-S_{x}^{m}} \tag{19}\]  
\[\left[\begin{matrix}\overline{V}_{x}^{m,n}\\ \overline{V}_{y}^{m,n}\end{matrix}\right]=\left[\begin{matrix}\cos\psi_{m}^{g} &\sin\psi_{m}^{g}\\ -\sin\psi_{m}^{g}&\cos\psi_{m}^{g}\end{matrix}\right]\left[\begin{matrix}V_{x} ^{m,n}\\ V_{y}^{m,n}\end{matrix}\right] \tag{20}\]  
\[\left[\begin{matrix}\overline{P}_{x}^{m,n}\\ \overline{P}_{y}^{m,n}\end{matrix}\right]=\left[\begin{matrix}\cos\psi_{m}^{g} &\sin\psi_{m}^{g}\\ -\sin\psi_{m}^{g}&\cos\psi_{m}^{g}\end{matrix}\right]\left[\begin{matrix}P_{x} ^{m,n}\\ P_{y}^{m,n}\end{matrix}\right] \tag{21}\]
# A Robust Control Algorithm of a Descent Vehicle Angular Motion in the Earth's Atmosphere
## 8 Numerical Example
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stabilization Accuracy**} & \multicolumn{2}{c}{**Consumption**} \\ \cline{3-7}
**Channel** & \multicolumn{2}{c}{**By Angles (\({}^{\circ}\))**} & \multicolumn{2}{c}{**By Velocities (\({}^{\circ}\)/s)**} & \multicolumn{2}{c}{**(Units \(\bar{Q}^{\text{R}TO}_{\text{alg}}\))**} \\ \cline{3-7}  & & \(\mathbf{\varepsilon}^{\text{trtr}+}_{\text{max}}\) & \(\mathbf{\delta}^{\text{all}}_{\text{err}}\) & \(\mathbf{\delta}^{\text{trtr}+}_{\text{max}}\) & \(\mathbf{\delta}^{\text{all}}_{\text{err}}\) & **In Channel** & **Total** \\ \hline \multirow{2}{*}{\(x_{v}\)} & ME & 1.438 & 0.727 & 0.453 & 0.147 & 0.235 & \\ \cline{2-7}  & _MSE_ & _0.162_ & _0.065_ & _0.062_ & _0.011_ & _0.026_ & \\ \hline \multirow{2}{*}{\(y_{v}\)} & ME & 0.078 & 0.120 & 0.264 & 0.108 & 0.151 & **0.944** \\ \cline{2-7}  & _MSE_ & _0.014_ & _0.007_ & _0.044_ & _0.011_ & _0.010_ & **0.148** \\ \hline \multirow{2}{*}{\(z_{v}\)} & ME & 1.412 & 0.771 & 5.492 & 1.427 & 0.578 & \\ \cline{2-7}  & MSE & 0.249 & _0.036_ & _1.030_ & _0.109_ & 0.143 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of lander stabilization processes without roll overturns according to the new algorithm. Thus, according to the results of statistical modeling in MATLAB for a new robust algorithm (Tables 5 and 7, Figure 4) with the output feedback matrix (41), such desired poles with characteristics (45) were found that, in comparison with the standard damping algorithm (Table 4, Figure 3), the accuracy of stabilization of the lander is doubled at approximately the same consumption.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 4 Mitigation of Corrosion
### Fly Ash and Silica Fume Additives/Binders  
The risk of corrosion and degradation of concrete structures in agricultural concrete can be mitigated by the integration of additives such as silica fumes and fly ash into traditional pozzolanic cement, as shown by [PERSON] and U-Tapao [28]. Concrete specimens labeled 10 FA, 25 FA, and 8 SA22 FA had variable mixes of supplementary cementing material (fly ash and silica fumes). The ratios of silica fume and fly have had a direct effect on the water demand, setting time, compressive strength and the average mass losses. The material characterization tests suggest that concrete sample  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{8}{c}{**Fiber**} & \multirow{2}{*}{**E-Glass**} & \multirow{2}{*}{**Hemp**} & \multirow{2}{*}{**Jute**} & \multirow{2}{*}{**Ramie**} & \multirow{2}{*}{**Coir**} & \multirow{2}{*}{**Sisal**} & \multirow{2}{*}{**Fax**} & \multirow{2}{*}{**Cotton**} \\  & & & & & & & & & \\ \hline Density (g/cm\({}^{3}\)) & 2.6 & 1.5 & 1.5 & 1.5 & 1.3 & 1.3 & 1.4 & 1.5 \\ Tensile strength (MPa) & 2400.0 & 550-900 & 400–800 & 500.0 & 220.0 & 600–700 & 800–1500 & 400.0 \\ E-Modulus (GPa) & 73.0 & 70.0 & 10–30 & 44.0 & 6.0 & 38.0 & 60–80 & 12.0 \\ Specific (E/d) & 29.0 & 47.0 & 7–21 & 29.0 & 5.0 & 29.0 & 26–46 & 8.0 \\ Elongation at failure (\%) & 3.0 & 1.6 & 1.8 & 2.0 & 15–25 & 2–3 & 1.2–1.6 & 3–10 \\ Moisture absorption (\%) & – & 8.0 & 12.0 & 12–17 & 10.0 & 11.0 & 7.0 & 8–25 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Material properties of selected fibers incorporated into FRPs [24].  
Figure 6: Apparent porosity in different sulfur concrete specimens [27]. “Reproduced with permission from [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON], Constr. Build. Mater.; published by Elsevier, 2011.”8 SF22 FA, which is made of 22% fly ash and 8% silica fumes. The superior performance of the cement sample with 8% silica fumes and 22% fly ash could be linked to the chemical composition of the binders, as shown in Table 12[28]. Silica fumes (SF) contains twice as much SIO\({}_{2}\) as fly ash (FA). In contrast, fly ash has slightly higher ratios of Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), K\({}_{2}\)O, and FeO\({}_{3}\), which augment the strength of the concrete structure. The graphs in Figure 6a,b show that 8 SF22 FA had the highest compressive strength and least mass losses.
## Abbreviations
### List of Nomenclature  
\begin{tabular}{l l} \(P_{EV_{0}}\) & The initial charging power of the electric vehicle. \\ \(P_{EV_{max}}\) & The maximum charging power of the electric vehicle. \\ \(\alpha\) & The charging battery time constant of the electric vehicle. \\ \(t_{max}\) & Signifies the total time needed to charge the electric vehicle's battery from zero \\ \(t_{max}\) & charge to maximum charge. \\ \(P_{N}\) & The probability coefficient of power consumption \\ \(\lambda\) & The success rate of a vehicle in accessing the charging converter. \\ \(\mu\) & The success rate of a vehicle in completing its battery charging. \\ \(\rho=\lambda/\mu.c\) & Stands for the number of charging stations for electric vehicles. \\ \(C\) & The number of BSSs for electric vehicles. \\ \(P_{PV}\) & PV system output power at the maximum power point. \\ \(P_{wt}\) & Wind turbine output power. \\ \(P_{(PV,STC)}\) & Nominal PV power at the maximum power point and standard conditions. \\ \(G_{T}\) & Radiation amount in standard conditions. \\ \(\gamma\) & Temperature coefficient. \\ \(T_{j}\) & The temperature of the solar cells. \\ \(N_{PVs}\) & The number of series modules. \\ \(N_{PVp}\) & The number of parallel modules. \\ \(V_{ci}\) & Lower cutoff speed. \\ \(V_{r}\) & Nominal speed of the wind turbine. \\ \(V_{co}\) & Upper cutoff speed. \\ \(P_{g}\) & Nominal power of the wind turbine. \\ \(C_{DG,t}^{Op}\) & The cost of operating in each time interval \(t\). \\ \(C_{DG,t}^{EM}\) & The cost associated with the pollution of the units in each \\ \(\lambda_{(CO_{2})}\) & Penalty factor for CO\({}_{2}\) production. \\ \(\lambda_{(SO_{2})}\) & Penalty factor for SO\({}_{2}\) production. \\ \(\lambda_{(NO_{2})}\) & Penalty factor for NO\({}_{x}\) production. \\ \(P_{ BSSs}(t)\) & The powers of the BSS. \\ \(P_{DC}(t)\) & The powers of DG. \\ \(P_{g}(t)\) & The powers of the grid. \\ \(P_{ch}(t)\) & Charging powers of BSS. \\ \(P_{dch}(t)\) & Discharging powers of BSS. \\ \end{tabular}