esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Evaluation Methodology for ICT Systems
### Risk Estimation
#### 3.4.2 Impact  
The impact factor is evaluated by a security expert based on the context of the TOE. Recognizing that not all vulnerabilities have equivalent effects, we treat the impact as a multidimensional measure that encompasses several domains: safety, financial, operational, privacy, and legal impact. For example, a vulnerability in an aircraft's control system impacts safety critically, whereas one in an in-flight entertainment system primarily affects financial and operational aspects. Following this multidimensional approach already validated in frameworks such as HEAVENS [33], EVITA [9] or MoRA [34] ensures that impact values are aligned with the context of the system.  
To reduce the subjectivity of the impact assessment, we use standardized scales that offer clear criteria for quantifying each aspect aligned with best practices and standards. This ensures reproducibility of the results of the evaluation and a harmonized comparison between products evaluated by different persons. For instance, Table 3 shows the scale for safety impact based on ISO 26262-3 standards [24]. Similarly, we have impact scales for the financial dimension (Table 4 following the classification of BSI-Standard 100-4 [26]). The Operational dimension (Table 5) is based on Failure Mode and Effects Analysis (FMEA) [25], adapting the vehicular defect severity categorization to classify the operational damages as done in HEAVENS. Finally, the privacy and legislation dimension (Table 6) follows the scale dimensions proposed in HEAVENS, although it could be also aligned with other proposals such as the Privacy Impact Assessment Guideline provided by BSI [27].  
The aggregate impact score is normalized on a scale of 0 to 10 (Table 7), facilitating an objective and scalable impact assessment. In this sense, the Safety and Financial impacts are given equal importance when estimating the overall impact level. The repercussions of these factors can be extremely severe for stakeholders (e.g., vehicle occupants may not survive, or organizations may face bankruptcy). In contrast, the impact of the operational and privacy and legislation parameters is comparatively lower concerning safety and financial damages. To accurately represent this during impact level estimation, corresponding factors were reduced by a magnitude of one for both operational and privacy and legislation parameters relative to the safety and financial parameters.
## 4 Description of the Proposed Topology Construction
### Selection of Leader Nodes
This approach sets up a threshold of distance, and nodes are not allowed to be leaders if the distance to their neighbours along the chain is beyond the threshold. From the above discussion, the ability to achieve optimal results in leader selection is a computationally rigorous task. Thus, instead of finding an optimal solution, the proposed topology uses a simple rule called Maximum Residual Energy First (MREF) for leader selection. This simple algorithm gives near optimal results for a lower number of nodes. As in the proposed topology, there are only a few lower-level leaders, making this algorithm perfectly suitable for selecting a higher-level leader. As the name suggests, MREF selects the node that has the maximum residual energy to be the leader for network operations. Residual energy information can be piggybacked with data messages as a part of the aggregated data. If every lower-level leader attaches its own energy level to data message and lets the BS find the maximum value, it will incur an additional \(O(n)\) overhead on every message. A better approach is to let every lower-level leader compare its energy level with the energy level attached to the incoming data message (if any) and send only the large one. The message overhead in this process is only \(O(1)\). For the lower-level leaders, the same selection procedures can be followed. However, since the communications of the lower-level leaders are not as energy intensive as for higher-level leader, it is proposed not to change lower-level leaders as frequently as higher-level leader. The benefits of using a slightly longer duration for selecting lower-level leaders include: (i) less communication overhead; (ii) reduced required time for leader selection at every round; and (iii) maximum utilization of the higher-level chain.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 2 Problem Description
### Basic Assumptions  
Based on the above problem description and the actual operation of CubeSat, in order to further simplify and standardize the scheduling problem, this paper makes the following assumptions and explanations for the scheduling model [28]: (1) The effect of space environmental factors (dust, particles) on the PV input power is not considered. (2) The operating state and power of the CubeSat are assumed to remain unchanged in each period; that is, the scheduling model can be discretized. (3) Assuming that the maximum power point tracking (MPPT) time is very short, the power oscillation problem near the maximum power point is not considered. (4) Parameters such as the entire orbit period of the CubeSat, the PV input power per period, the rated power consumption of each task, and the priority of the task are known quantities obtained in advance. (5) All missions, once activated, must be completed at once. (6) The CubeSat operates in the lifetime interval (within 1 year) without considering energy dissipation or additional power loss due to the aging of PV panels.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 2 Related Research
### LiteFlowNet  
LiteFlowNet is a lightweight version of the FlowNet2.0 architecture, which is designed to predict optical flow vectors between pairs of input images using convolutional neural networks. It is specifically optimized for execution on embedded systems or low-resource environments, offering accurate optical flow prediction while reducing model size and computational requirements. Cascaded flow inference is one of the characteristics used in LiteFlowNet. This approach involves performing multiple consecutive prediction steps to improve the accuracy of optical flow estimation. Typically, two or more prediction stages are employed, where each stage takes the results from the previous stage along with the original images as input. In the first prediction stage, the input image pair is processed to estimate an initial optical flow vector field. Then, the initial result is combined with the original images, and the combined result is passed as input to the second prediction stage. Additional prediction stages can be performed if needed, with each stage utilizing information from previous stages and providing progressively more accurate optical flow results. By incorporating cascaded flow inference, LiteFlowNet  
Figure 4: The structure of PWC-Net.  
Figure 3: The structure of SpyNet.  
leverages multiple prediction steps to refine and enhance its optical flow estimation. This technique allows for a smoother and more consistent output by iteratively improving upon initial predictions using richer contextual information.  
In summary, LiteFlowNet builds upon FlowNet2.0 but offers a lighter architecture that enables execution on embedded systems or low-resource computing environments. Cascaded flow inference enhances its performance by employing consecutive prediction stages that iteratively refine and improve optical flow estimation based on previous results and contextual information. Figure 5 shows the structure of LiteFlowNet.
## 2 Materials and Methods
### Validation of Digital Model  
A digital TRNSYS model was validated based on real measurements carried out during mobile energy unit operation (see Figure 6). A mobile energy unit ensured data logging with 1 min interval. For the validation, solar radiation on the horizontal surface [W/m\({}^{2}\)] and total electricity consumption [W] from 22 June 2021 15:00 till 23 June 2021 15:00 was used. During this period the diesel generator was switched off. At the start, the battery was fully charged (100%). The power injected to the battery translates into a positive energy balance, while the power going from the battery translates into a negative energy balance.  
\begin{table}
**Tent Parameters** & **Value** \\ \hline Roof and wall & U = 1.13 W/m\({}^{2}\)K \\ Floor & U = 0.87 W/m\({}^{2}\)K \\ Volume & 35.25 m\({}^{3}\) \\ Area & 20 m\({}^{2}\) floor; 48.6 m\({}^{2}\) walls and roof \\ Heat Loss & 73.32 W/K \\ Capacitance & 42.3 kJ/K \\ Weather & Riga, Latvia \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{3}{c}{**Timetable**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Occupant**} & \multirow{2}{*}{\begin{tabular}{c} **Hearing** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cooling T** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Light** \\ **(W)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Equipment** \\ **(W)** \\ \end{tabular} } \\ \cline{2-6}  & **Count** &
\begin{tabular}{c} **W** \\ **(met)** \\ \end{tabular} & & & & \\ \hline
12 a.m.–6 a.m. & 5 & 100 (0.9) & \(18\pm 2\) & 23 \(\pm\) 1 & - & - \\
6 a.m.–10 a.m. & 2 & 125 (1.1) & \(18\pm 2\) & 23 \(\pm\) 1 & 55 & - \\
10 a.m.–1 p.m. & 0 & - & \(5\pm 2\) & 27 \(\pm\) 1 & - & - \\
1 p.m.–3 p.m. & 5 & 125 (1.1) & \(18\pm 2\) & 23 \(\pm\) 1 & 55 & 300 \\
3 p.m.–8 p.m. & 0 & - & \(5\pm 2\) & 27 \(\pm\) 1 & - & - \\
8 p.m.–10 p.m. & 5 & 125 (1.1) & \(18\pm 2\) & 23 \(\pm\) 1 & 55 & 40 \\
10 p.m.–12 a.m. & 5 & 100 (0.9) & \(18\pm 2\) & 23 \(\pm\) 1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input data for energy system simulation.  
Figure 6: _Cont._  
As can be seen, the TRNSYS model data have a good correlation with measured data. The further evaluation of the system performance is based on the theoretical TRNSYS model.
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
## 3 Results and Discussion
### Removal of Sr\({}^{2+}\) from Aqueous Solutions
#### 3.2.2 Kinetic Studies of Sr\({}^{2+}\) Adsorption  
In order to obtain the equilibrium time of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) removal, we carried out the kinetic study of Sr\({}^{2+}\) adsorption at room temperature. As shown in Figure 2b, the removal efficiency of Sr\({}^{2+}\) (\(R^{\mathrm{Sr}}\)) (Equation (S6)) increased with time. It captured 87.4% of Sr\({}^{2+}\) ions within 24 hours, and the concentration of Sr\({}^{2+}\) ions was greatly reduced  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & \(R^{2}\) & \(q_{\mathrm{m}}\) **(mg/g)** & \(b\) **(L/mg)** & \(n\) & \(k\) \\ \hline Langmiur & 0.9938 & 42.60 \(\pm\) 1.43 & 0.02255 \(\pm\) 0.00 & - & - \\ Freundlich & 0.8912 & - & - & 4.8673 \(\pm\) 0.06 & 11.01 \(\pm\) 0.17 \\ Langmuir–Freundlich & 0.9929 & 40.91 \(\pm\) 2.62 & 0.02845 \(\pm\) 0.01 & 0.8932 \(\pm\) 0.16 & - \\ \hline \end{tabular}
\end{table}
Table 1: The isothermal adsorption fitting results of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\).  
Figure 2: (a) The equilibrium curves of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) adsorption fitted by the Langmuir (blue line), Langmuir–Freundlich (red line), and Freundlich model (black line) isothermal adsorption models (\(C_{0}^{\mathrm{Sr}}\) = 12.565–756.000 mg/L, pH = 6.03 –6.82); (b) kinetics of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) plotted as Sr\({}^{2+}\) concentration (mg/L) (black line) and removal efficiency (%) (blue line) vs. time \(t\) (min) under neutral conditions; (c) the pseudo-first-order kinetic model and (d) the pseudo-second-order kinetic model were used to fit the Sr\({}^{2+}\) adsorption kinetics of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\).  
from 3.425 mg/L to 0.429 mg/L (Table S2). Two classical kinetic models, the pseudo-first-order kinetic equation (Equation (S7)) and the pseudo-second-order kinetic equation (Equation (S8)) [58], were used to fit the experimental results (Table S3). Compared with the pseudo-first-order kinetics, the kinetic data were better fitted by the pseudo-second-order kinetic model with \(R^{2}\) of 0.96059 (Figure 2c,d). It is worth noting that the pseudo-second-order kinetic model is based on the assumption that the chemical interaction is the limiting step of the reaction. The chemical interaction here included the covalent electron or exchange electron reaction between the adsorbent and the metal ions in the solution at the solid-liquid interface [32]. This indicates that the Sr\({}^{2+}\) adsorption of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) was a prosperous chemical adsorption process rather than a mass transfer in solutions [59].
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 2 Methodology
### Transmission Tower Detection
Therefore, the ratio of length to width \(r_{j}\) is,  
\[r_{j}=\frac{l_{j}}{w_{j}} \tag{8}\]  
The aspect ratio thresholds are chosen based on the size of the transmission towers and the resolution of the images, and the MBRs that do not satisfy the aspect ratio are further filtered to obtain the exact MBRs. Additionally, the real transmission towers results can be denoted as \(\mathbf{T}=\big{\{}T_{j},r_{j}\geq T_{r},j=1,\ldots,M\big{\}}\), \(T_{r}\) is threshold of \(r\) for real transmission tower. The detailed procedures of the proposed Algorithm 1 are described as follows. **Input:** Amplitude SAR image \(\mathbf{z}\). **Output:** Detection results \(\mathbf{T}\). Step 1. Setting the window size for detecting pixels as \(\mathbf{W}\). Step 2. Calculating the SCR by using Formula (1) and SCR threshold to obtain potential transmission tower pixels \(\mathbf{P}\). Step 3. Calculating spatial density by using Formula (4) to obtain candidate transmission tower pixels \(\mathbf{C}\). Step 4. Calculating pixels distances by Formula (6) and distance threshold to obtain quasi-transmission towers \(\mathbf{G}\). Step 5. Generating convex-hulls \(\mathbf{H}\) of \(G_{j}\), constructing MBRs \(\mathbf{R}\) of the convex-hulls and set aspect ratio of MBRs by Formula (8). Step 6. Obtaining the detection results \(\mathbf{T}\). Figure 4: Sketch of the quasi-transmission towers selected based on pixel distance. Therefore, the overall flowchart of the proposed method is shown in Figure 6.  
Figure 5: (**a**) MBR. (**b**) MBR superimposed on the original image. (**c**) Sketch of the convex-hull and its MBR. Figure 6: The overall flowchart of the proposed algorithm.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Future Trends  
#### 8.3.1 Efficient NIDS  
Recent studies show the limitation of NIDS to detect zero-day attacks. New methods are proposed to improve the accuracy of NIDS, but no solution has been proved to be enough. Future research might be oriented toward the development of an up-to-date dataset that would represent the real environment and propose enough instances of each attack category. Another solution might be to develop a framework that would combine the most recent deep learning methods and use its results to improve itself by learning new features each time a new instance is encountered. In addition, to face the large computing resources that will be required for this framework, separating the NIDS into two parts can be a solution. One part that captures the traffic and analyses it and another part that will continuously train the model with the up-to-date datasets would improve the efficiency of how the resources are used in the NIDS. In addition, the second part which requires the biggest number of resources can be in the cloud to benefit from its powerful computing resources.
###### Abstract  
Underwater Wireless Sensor Network (UWSN) communication at high frequencies is extremely challenging. The intricacies presented by the underwater environment are far more compared to the terrestrial environment. The prime reason for such intricacies are the physical characteristics of the underwater environment that have a big impact on electromagnetic (EM) signals. Acoustics signals are by far the most preferred choice for underwater wireless communication. Because high frequency signals have the luxury of large bandwidth (BW) at shorter distances, high frequency EM signals cannot penetrate and propagate deep in underwater environments. The EM properties of water tend to resist their propagation and cause severe attenuation. Accordingly, there are two questions that need to be addressed for underwater environment, first what happens when high frequency EM signals operating at 2.4 GHz are used for communication, and second which factors affect the most to high frequency EM signals. To answer these questions, we present real-time experiments conducted at 2.4 GHz in terrestrial and underwater (fresh water) environments. The obtained results helped in studying the physical characteristics (_i.e._, EM properties, propagation and absorption loss) of underwater environments. It is observed that high frequency EM signals can propagate in fresh water at a shallow depth only and can be considered for a specific class of applications such as water sports. Furthermore, path loss, velocity of propagation, absorption loss and the rate of signal loss in different underwater environments are also calculated and presented in order to understand why EM signals cannot propagate in sea water and oceanic water environments. An optimal solk@ution for underwater communication in terms of coverage distance, bandwidth and nature of communication is presented, along with possible underwater applications of UWSNs at 2.4 GHz.  
UWSNs; sensors; underwater; water conductivity; water permeability; water permittivity; freshwater; seawater 201616890; doi:10.3390/s16060890
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 4 Application of Underwater Optical Wireless Communication  
In this study, a trial conducted in a lake environment is designed to validate the effective communication space of UWOC for the application of AUVs in automatically collecting data from fixed underwater nodes. The primary aim of this trial is to assess the practical value of employing UWOC in real-world scenarios involving AUVs and fixed underwater nodes. A schematic diagram illustrating the setup of the trial is presented in Figure 7.  
The experimental setup involved pre-configured communication parameters for the optical communication equipment, which was powered on and ready to operate. One of the optical communication devices was positioned at a fixed node, immersed in the lake at a depth of 7 m. The other optical communication device was carried by an AUV, as illustrated in Figure 8. The AUV followed autonomous navigation guidelines to enter the optical communication space and automatically transmitted data to the optical communication device at the fixed node. Throughout the experimental process, the AUV maintained a constant depth using a hovering control method and documented the optical communication status at different locations, as depicted in Figure 9. Upon receiving the command to cease communication, the AUV automatically withdrew from the optical communication zone, completing the necessary operations. The experimental results indicated that the AUV's actual effective communication space was largely consistent with the established communication space model, although a minor deviation was observed. This deviation amounted to approximately 90% of the predicted range of the model. It is surmised that this variation may be attributed to the impact of water waves caused by the AUV's movement, which led to diminished communication performance. Additionally, when the AUV exceeded the predetermined spatial range, noticeable attenuation of the communication signal was observed, validating the utility of the optical communication space model in  
Figure 7: Schematic diagram of the trial method.  
real-life scenarios and providing valuable insights for the practical application of optical communication systems in underwater environments.
## 6 Experimental Verification and Analysis
### Analysis of Inter-Satellite Ranging Accuracy
Compared to the conventional TDHD scheme, the CCFD-based pseudo-code ranging accuracy improves by 51.3%. At a \(C/N_{0}\) of 69.3 dBHz, the 1\(\sigma\) ranging accuracy is 0.8 cm under the CCFD scheme and 2.2 cm under the TDHD scheme. Compared to the conventional TDHD scheme, the CCFD-based pseudo-code ranging accuracy improves by 66.8%. In summary, the analysis demonstrates that introducing CCFD technology into the BeiDou inter-satellite link system can effectively enhance pseudo-code ranging accuracy. This approach serves as an effective measure to meet future demands for higher-precision orbit determination and time synchronization.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 2 OCTA Fundamentals
### Light Scattering from Red Blood Cells
In capillaries, where RBC flow is single-file, light forward scattered from RBCs is also backscattered from extravascular tissue (Figure 2B), creating axial multiple scattering tails (Figure 2D left box). In macrovessels, there are two important effects. First, RBCs tend to align their flat face parallel to the shear force, i.e., facing outwards along the vessel circumference (Figure 2C). The largest backscattering cross-section occurs when the shortest RBC dimension is aligned with the incident light. Therefore, the signal is enhanced at the top and bottom of the vessel lumen and reduced at the side (Figure 2D right box) [37]. At higher shear rates, RBCs elongate and the backscattering pattern disappears [38]. Second, for vessel lumens larger than a scattering length (\(1/\mu_{s}\)), multiple intravascular dynamic scattering events (green) can occur before detection. As OCTA images are created by post-processing OCT data, OCTA has an image penetration depth comparable to or less than OCT. This is typically \(\sim\)0.5-1.5 mm in most tissues, depending on the source wavelength and the sample optical properties [39; 40]. It is important to note that while OCTA visualizes blood vessels, the penetration depth of OCTA may be determined by the attenuation of both intravascular and extravascular tissue.
## 3 Datasets and Evaluation Metrics
### Hdr-Vqm  
The Quality Measure for High Dynamic Range Videos evaluates HDR quality by simulating stages of human visual perception. Its processing workflow typically includes signal preprocessing (e.g., applying perceptual quantizers), spatial decomposition (often using steerable pyramids or wavelets), contrast calculation within the resulting sub-bands, distortion estimation based on differences between features of the reference and test signals in these bands, temporal analysis (modeling eye fixations or temporal aggregation of distortions), and finally, aggregation (pooling) of distortions across bands and time into a single quality score. The complete algorithm details, including the specific decomposition and weighting methods, can be found in [96].