esa-sceva/satcom-chunk-collection huggingface dataset:
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 5 Threat Analysis and Risk Assessment (TARA)
### Attack Feasibility Rating  
The attack feasibility rating could be evaluated using one of three approaches: attack potential-based, Common Vulnerability Scoring System (CVSS)-based, and attack vector-based approaches. Due to the limitations in knowledge about the system elements, technologies, and actors at the time of this research and the unavailability of an advanced system architecture design, the attack vector-based approach was utilized.  
Criteria for Evaluation  
The attack feasibility rating was determined by assessing the predominant attack vector in the attack path. The scale for ratings considered the following aspects:  
* **Attacker's Experience:** This denotes the level of experience and skill that the attacker possesses. More experienced attackers might find certain vulnerabilities easier to exploit.
* **The Ease of Reproducing the Attack:** This denotes the complexity involved in replicating the attack. Simpler, more straightforward attacks are rated as more feasible.
* **Approach Used:** In this research, the attack vector-based approach was applied. This approach evaluates how accessible the attack vector is, considering whether it is network-based, local, adjacent, or physical.
* **The Context of the Attack:*
* This rating scale includes the following.
* high feasibility, as remote access via the network is easier and less resource-intensive.
* medium feasibility, involving access from a nearby network or system.
* low feasibility, requiring physical proximity to the asset.
* very low feasibility, necessitating physical access to the asset, which is often more challenging and less desirable for attackers.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 5 Simulation Results  
In this section, we evaluate the performance of our proposed SC-AmBC along with the conventional TD-AmBC. The simulation scenario is set as a wireless local area network (WLAN) using 2.4 GHz industrial, scientific and medical (ISM) band [57]. Applications for the AmBC would be smart home or IoT environments where massive numbers of RF devices are seamlessly integrated into existing wireless infrastructure. For AmBC, AP transmits IEEE 802.11g Wi-Fi OFDM signals [2] while tags harvest these ambient RF signals [51] and upload data streams by switching load impedances [9]. Then, RF tag in SC-AmBC modulates \(\widetilde{M}\) load impedances with non-orthogonal sparse codeword, while that in TD-AmBC modulates \(M\) impedances with TDMA [2; 23]. Then, the AP in SC-AmBC receives the illuminated signals experiencing fully-correlated DBC [12; 13; 18; 19; 20; 21; 23] and detects data streams from these signals via the proposed D-MPA. For simplicity, we assume a system model with a single antenna at AP and no forward-error correction (FEC) codes, but extensions of our model to such scenarios are done in a straightforward manner. The AmBC is numerically evaluated by Monte Carlo simulations, since it is challenging to derive a closed-form theoretical performance in multi-dimensional constellation [39] and in the DBC [13] especially for low SNR. System parameters are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **TD-AmBC** [2; 23] & **SC-AmBC** \\ \hline \multirow{3}{*}{Modulation} & Load impedances & \(M\) & 2 \\  & Constellation dimension & 2 & \(2K\) \\  & Constellation symbols & \(M\) & \(\widetilde{M}\) \\ \hline \multirow{3}{*}{Detection} & ISI exploitation & no & yes \\  & MAI exploitation & no & yes \\  & Complexity & low & medium \\  & BER & high & low \\ \hline \multirow{3}{*}{Networking} & Max. sum throughput & \(R_{b}\) & \(\lambda R_{b}\) \\  & Max. connectivity & \(K\) & \((\begin{smallmatrix}K\\ K_{1}\end{smallmatrix})\) \\ \cline{1-1}  & Duty cycle & \(1/K\) & \(K_{1}/K\) \\ \cline{1-1}  & Latency & high & low \\ \cline{1-1}  & Multiple access & orthogonal & non-orthogonal \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the conventional TD-AmBC and the proposed SC-AmBC.
## 3 Results and Discussion
### Hydroplilicity  
Water contact angle (WCA) was determined to verify changes in hydrophilicity for R-HIPS membrane surfaces (Figure 3). The contact angles decreased from 83.8\({}^{\circ}\) to 66.2\({}^{\circ}\) with an increasing sulfonation degree from 0 to 5%. This reduction was due to the presence of strongly hydrophilic -SO\({}_{3}\)H groups, which enhanced membrane surface interaction with water, facilitating the filling of air voids and enhancing overall wetting ([PERSON] et al., 2017). In general, porosity, pore size, and hydrophilicity are key factors that determine water permeability and solute rejection properties of membranes ([PERSON] et al., 2017). Higher pore size generally allows for increased flux, but it may compromise the membrane ability to reject solutes. On the other hand, a significant challenge in filtration processes is membrane fouling, which occurs when unwanted materials accumulate on the membrane surface or within its pores, due to, among other factors, membrane hydrophobicity. Fouling not only reduces filtering efficiency but also decreases the average membrane life and increases operating costs ([PERSON] et al., 2017). Thus, these properties (porosity and hydrophilicity) are crucial for optimizing membrane performance in applications such as water treatment for dye and salt elimination and separation processes, where organic suspended matter is present.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Membrane** & **Contac Angle (\({}^{\circ}\))** & **Water Uptake (\%)** & **Weight Loss (\%)** & **Porosity (\(c\))** & **\(\mathbf{r_{m}}\)** & **Flux (L/m\({}^{2}\)h)** \\ \hline R-HIPS & 83.8 \(\pm\) 1.034 & 2.2 \(\pm\) 0.5 & 1.2\(\pm\) 0.33 & 75.0 & 4.0 \(\pm\) 0.32 & 6.0 \(\pm\) 0.84 \\ R-HIPS-3 & 74.8 \(\pm\) 0.26 & 3.6 \(\pm\) 2.9 & 3.2 \(\pm\) 0.61 & 63.9 & 3.8 \(\pm\) 0.08 & 4.3 \(\pm\) 0.13 \\ R-HIPS-5 & 66.1 \(\pm\) 0.87 & 10.6 \(\pm\) 0.4 & 6.0 \(\pm\) 0.95 & 67.0 & 3.3 \(\pm\) 0.67 & 3.1 \(\pm\) 1.02 \\ HIPS-R ([PERSON] et al., 2018) & 81.7 \(\pm\) 3.42 & 51.7 \(\pm\) 5.5 & - & 52.3 & 32.4 \(\pm\) 1.12 & 180.6 \(\pm\) 0.71 \\
30 wt\% ([PERSON] et al., 2019) & 83.0 & - & - & 62.5 & 50-500 & 8.5 \\
35 wt\% ([PERSON] et al., 2019) & 91.0 & - & - & 61.6 & 50–250 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Water contact angle, water uptake, weight loss, % porosity, radius pore size, and flux of R-HIPS membranes.  
Figure 3: Water contact angles of R-HIPS, R-HIPS-3, and R-HIPS-5 membranes.  
Furthermore, HIPS, R-HIPS-3, and R-HIPS-5 membrane swelling tests support the results found by contact angle measurements. HIPS, R-HIPS-3, and R-HIPS-5 showed a higher water uptake as SD increased (see Table 2). After immersion for 7 days in water, R-HIPS and R-HIPS-3 showed water uptake values of 2.2% and 3.6%, respectively, while R-HIPS-5 exhibited a significantly higher uptake of 10.6%, following a similar trend to the contact angle measurements. Moreover, weight losses after immersion were 1.2, 3.2, and 6.0% for R-HIPS, R-HIPS-3, and R-HIPS-5 membranes, respectively (Table 2).
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 4 Experiment  
This section evaluates the performance of the proposed GEO space object detection algorithm and compares it with several state-of-the-art models. Further, the inference performance of the proposed method based on NVIDA Jetson Nano is presented. The specific experimental details are elaborated, including the establishment of experiment platform and parameter setting. This section highlights the corresponding results and analysis.
# Vehicle Lane Change Models--A Historical Review
## 1 Introduction
So, the lane change prediction model can be made more accurate if the driver's state and also relevant information about the car and the surrounding vehicles are factored in. Lane change models are mainly divided into four types: empirical models, physical models, cognitive models, and mixed models. The lane change experience model is built on the basis of the driver's subjective experience and intuition, so it will be affected by the differences between individual drivers, lane change habits, and subjective biases. The physical model of lane changing is mainly modeled based on vehicle dynamics and road conditions, and the accuracy of the model is affected by the vehicle type, road conditions, and driving style. The lane change cognitive model is built on the basis of the driver's cognitive processes and behavior. The accuracy and reliability of the model depend on the influence of the driver's cognitive ability and behavioral habits, while the influences of the driver's emotions, attention, and fatigue state on lane change decisions cannot be considered. A single lane change model can provide effective lane change prediction results under specific scenarios and conditions. However, when a lane change is performed in a more complex traffic environment, it is necessary to combine the empirical model of lane changing, the physical model of lane changing, and the cognitive model of lane changing to form a mixed lane change model, which is more advantageous and effective. The contributions of this study are as follows: In order to further optimize lane change models, we reviewed the different algorithm types in the empirical, physical, cognitive, and mixed models of lane changing; discussed the advantages and disadvantages of each algorithm; and identified the problems that remain be improved according to the advantages and disadvantages of these algorithm models. The purpose of this study is to provide a variety of algorithm models for the researchers of vehicle lane change models, so that they can be inspired and achieve multi-dimensional thinking in the development process of lane change models, which will promote the further development of vehicle lane change model algorithms.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
###### Abstract  
After cell culture medium is treated with low temperature plasma (LTP), the liquid is rich in reactive oxygen and nitrogen species (RONS), and becomes plasma-activated medium (PAM). PAM, as the supplier of RONS, can affect the angiogenesis of cells. The purpose of this study is to investigate the effects and related mechanism of PAM on human umbilical vein endothelial cells (HUVECs). Cell viability and cell cycle were evaluated after HUVECs were treated with PAM for 24 h. Changes in cell angiogenesis, migration and adhesion, secretion of cytokines such as VEGF and bFGF, expression of VEGFR-2 and phosphorylation of the key proteins in the MEK/ERK signaling pathway, concentrations of H\({}_{2}\)O\({}_{2}\) and NO\({}_{2}\)\({}^{-}\) in PAM and in cells were also investigated. The results showed that PAM obtained by LTP treatment had dual effects on the angiogenesis of HUVECs: PAM obtained by short-term LTP treatment promoted the angiogenesis of HUVECs, while PAM obtained by long-term LTP treatment inhibited the angiogenesis of HUVECs. The mechanism may be that PAM treatment changes the content of RONS, affects the VEGF-VEGF-2 signaling pathway, and ultimately affects the angiogenesis of HUVECs.  
low temperature plasma; vascular endothelial cells; reactive oxygen species; angiogenesis +
Footnote †: journal: Medical Science
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
###### Abstract  
Unmanned Aerial Vehicles (UAVs) have become crucial components of building air-ground integrated networks for sixth-generation mobile communications. UAV-related technology has advanced quickly in both military and civilian contexts. Problems with long-distance and dynamic communication need to be fixed in the non-terrestrial transmission channels of high-altitude UAVs. For long-distance UAV dynamic non-terrestrial transmission channel modeling, a spatial statistical channel model-ray tracing (SSCM-RT) hybrid channel model is proposed and verified, and SSCM-RT channel simulations incorporating spatial consistency analysis are carried out to realize the description of the communication process in the dynamic scenario of the non-terrestrial transmission in symmetric and asymmetric situations. The SSCM-RT model can output channel state information continuously, which is helpful for the quantitative description and characterization of the UAV channel in practical settings.  
U 2023 15 1955 195 1
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 4 System Model and Route Selection
### Delay Measurement
The CC sends a list of available D2D routes to nodes in small cells (SCs) to reduce interference among routes [4] and increase traffic offload from the macrocell (MC) base station (BS) [2]. In [3; 12], traffic offload from the backbone route to small cell nodes has shown to reduce end-to-end delays. * D2D communication has shown to enable multihop communication for delivering a high-quality video stream, contributing to an increased network capacity at the expense of a higher end-to-end delay [4]. Each node can access both cellular channels (in which the node is the PU) and cognitive channels (in which the node is the SU) to improve spectrum efficiency and quality of service (QoS) [6; 7]. * Route selection enables a source node to select a route to a destination node while garnering the benefits of traffic offloading from a MC BS to SCs [27]. The source node uses either: (a) a route that goes through the MC BS; or (b) a route that uses D2D communication that goes through intermediate nodes; or (c) a direct D2D source-to-destination communication if they are in proximity. The MC BS monitors traffic among nodes and provides available routes to the source nodes. The proposed scheme has shown to increase traffic offloading using D2D communication under observation and control from MC BS, leading to an increased throughput.
## Appendix A Basic Concept of Allan Variance  
How to compute Allan variance would be subsequently stated in this section. Assuming that \(N\) stands for the consecutive data points; the data is formed with a group of \(n\) consecutive data with \(n<\frac{N}{2}\), and each of the member in the group represents a cluster; the cluster is related to a time length of \(\tau\), which is given by \(\tau=nt_{0}\), where \(t_{0}\) is the sampling interval of the data. Given  
\[\bar{\Omega}_{k}\left(\tau\right)=\frac{1}{\tau}\int_{t_{k}}^{t_{k+1}}\Omega \left(t\right)dt \tag{12}\]  
where \(\Omega\left(t\right)\) stands for the instantaneous output rate of the incoming data, \(\bar{\Omega}_{k}\left(\tau\right)\) denotes the average value of the cluster outputs which start from the \(k\)th data point, and \(n\) samples are contained in the cluster from \(t_{k}\) to \(t_{k+1}\) where \(t_{k+1}=t_{k}+\tau\). Again, given  
\[\theta_{k+n}=\theta_{k}+\int_{t_{k}}^{t_{k+1}}\Omega\left(t\right)dt \tag{13}\]  
where \(\theta_{k}\) denotes the outputs in terms of the incoming data which is given by \(\theta_{k}=\theta\left(kt_{0}\right)\). For example, the respective gyro and accelerometer sensors can output the corresponded angles and velocities. Again, given  
\[\bar{\Omega}_{k}\left(\tau\right)=\frac{\theta_{k+n}-\theta_{k}}{\tau},\ \ \ \bar{\Omega}_{k+1}\left(\tau\right)=\frac{\theta_{k+2n}-\theta_{k+n}}{\tau} \tag{14}\]  
Finally, the discrete form of the Allan variance estimation is defined as [63; 65]  
\[\sigma^{2}\left(\tau\right)=\frac{1}{2\left(N-2n\right)}\sum_{k=1}^{N-2n}\left[ \bar{\Omega}_{k+1}\left(\tau\right)-\bar{\Omega}_{k}\left(\tau\right)\right]^ {2} \tag{15}\]  
or  
\[\sigma^{2}\left(\tau\right)=\frac{1}{2\tau^{2}\left(N-2n\right)}\sum_{k=1}^{N- 2n}\left(\theta_{k+2n}-2\theta_{k+n}+\theta_{k}\right)^{2} \tag{16}\]
## 3 Results
### Soil Moisture Content  
Soil moisture content is the percentage of water in the soil compared to dry soil.  
For satellite observations, radar and a radiometer are required to obtain an adequate solution. For the MERRA-2 system, soil moisture data were provided using the Soil Moisture Active Passive Observatory (SMAP) system, which is a radiometric and radar instrument.  
The approach to soil moisture measurement uses a combination of the radar spatial resolution and the radiometer accuracy with simultaneous measurements of surface radiation and backscattering. Instruments measure parameters in the upper layers of soil to provide a global assessment of the soil moisture. Since some parts of the land are heavily covered with vegetation, the calculations include an automatic algorithm for extrapolation between the values obtained at different times of the day at different positions of the satellite relative to the Earth's surface.  
The final reanalysis data provide soil moisture values in different units [51]. The first is in dimensionless units of relative saturation for different layer depths. The second is soil moisture content in volumetric units of m\({}^{3}\)/m\({}^{3}\), considered as the volume of water in the soil volume (including all solid material, water, and air). In both cases, soil moisture variables are provided for the top 0-100 cm layer. Summary data are also presented for different soil layers, 0-5, 10-100, and 134-853 cm [48]. An example of the soil moisture values is shown in Figure 18. The atlas includes 63 different soil moisture characteristics based on the analysis of data for the period 1980-2021.  
Figure 17: Mean values of temperature (\({}^{\circ}\)C) of the upper 10 m of soil for the period 2000–2021.
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction
### Methodologies Used to Analyze Pedestrian Behavior
#### 1.3.2 Simulation Studies  
Researchers have also performed simulations of street-crossing tasks in an immersive virtual environment to study how divided attention affects pedestrian behavior. Thirty-six participants crossed unsigned intersections using a manual treadmill in a virtual setting. They were either undistracted, engaged in a hands-free phone call, or listening to music. The findings indicated that participants found it more difficult to cross safely while on a phone call than when listening to music, even though they took longer to start crossing when talking. These challenges were mainly due to difficulties in recognizing crossing opportunities within the time limit [13].  
Another study examined the impact of visual-manual and auditory-cognitive distractions on pedestrian safety during crossings in a controlled environment using a CAVE-basedsimulator. Sixty participants performed crossing tasks under various distraction conditions and time pressures. The results showed that both distraction types negatively affected safety. Visual-manual distractions were responsible for slower crossing speeds without significantly changing gap acceptance, while auditory-cognitive distractions led to earlier crossings and smaller gaps accepted. Time pressure pushed participants to accept smaller gaps and reduced crossing durations, resulting in riskier decisions and fewer near-collisions compared to baseline conditions. These findings highlight the complex effects of distractions and time constraints on pedestrian safety and provide valuable insights for future research [12].
## 7 Conclusions and Future Work  
In this paper, research efforts in this thesis have concentrated on the path planning of a mobile robot against unknown static obstacles. The work is presented experimentally in order to produce reliable results. A four omni-wheeled mobile robot (4 OWMR) is used as the mobile platform to give simultaneous rotating and sideways motions, particularly in small spaces. To manage the robot during navigation and enable localization in indoor maps, low-level position and velocity control loops are used. To recognize the surrounding obstructions, a LIDAR sensor is included. During indoor navigation, a user interface (GUI) is designed to monitor the robot and use path-planning algorithms.  
Local path planning is implemented with four path planning algorithms: PRM, hybrid A*, RRT, and RRT*. Optimization using a genetic algorithm is applied to the generated paths to improve the generated paths. Based on these trials, comparison research evaluating the performance of the 4 OWMR in various environments is done in terms of success rate, velocity, travel distance, duration, and convergence absolute error. Hybrid A* is proven to be relatively better than the other algorithms while using the weighted sum ranking technique. In convergence ranking, the Hybrid A* is generally second in all maps (only behind RRT*). Also, it is ranked first in energy optimization rankings. The overall ranking results of the Hybrid A*, when compared to other techniques, are 81.67% against RRT, 80% (PRM), and 71.67% (RRT*).  
For future work, more complicated and complex maps would be tested experimentally. Further optimization techniques would be implemented to obtain more optimized paths. In addition, the implementation of machine learning techniques would be sought after to provide smoother paths, lower energy consumption and higher accuracy in convergence. Several hardware modifications will be sought after to get smoother paths and avoid possible discontinuities in path generation and slow sampling rates. The utilization of faster programming languages like python will be investigated for future implementation.  
Conceptualization, M.R.A.A.; methodology, A.A. and M.R.A.A.; software, M.M.M. and A.A; formal analysis, A.A; investigation, M.M.M. and M.R.A.A.; writing--original draft, A.A.; visualization, M.M.M.; supervision, A.A and M.R.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Detection of Defects on Metal Surfaces Based on Deep Learning
## 2 Methodology
### Image Processing
In the final stage of the study, the Otsu thresholding method was applied to separate defects from the background and enable the detection of these defects independent of the background type. This method examines the relationship between the pixels in the image and automatically determines the optimal threshold value that minimizes the within-class variance of both classes (defect and background). This makes the defects more prominent. In the Otsu thresholding method [24], the pixels in the image are divided into two distinct classes: one representing the background and the other representing the defects. Classification is based on intensity distribution. The histogram of the image represents the proportion of each gray-level intensity relative to the total number of pixels. The method calculates the most suitable threshold value from the histogram to maximize the distinction between the classes. For this calculation, the weighted averages of the classes are used and compared to the overall mean intensity of the image. The goal is to maximize the variance between the two classes, thereby making them as distinct as possible. In this way, the threshold value that maximizes this variance is determined and used to separate defects from the background. \[C_{1}:\ \ 0\leq i\leq t\ \ \ \ \Rightarrow\ \ \ \text{Background}\] \[C_{2}:\ \ t<i\leq 255\ \ \ \Rightarrow\ \ \ \text{ Foreground (Defect)}\]  
\(C_{1}\) and \(C_{2}\) represent two separate classes. One of them is the defect class, while the other is the background class. Image with pixel values ranging from 0 to 255 (\(i\)). T In this way, the defect in the image is separated independently from the background.