esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
###### Abstract  
This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner's dilemma, covert channels, qualitative metrics, and existing steganography techniques in computer communications. An architecture was developed to assess the effectiveness of this solution, and experiments were conducted, with their results presented. This contribution leverages established steganographic principles and seamlessly integrates with widely adopted IPsec protocols, offering a solution to improve covert communication within computer networks.  
Keywords:steganography; network security; data privacy; malicious traffic; network monitoring +
Footnote †: journal: _Applied_
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Single-Span Fiber System Setup  
We first simulated a single-span transmission link over SSMF. At the transmitter, the information bits of each polarization are independently encoded and the modulator maps the encoded bits either to 256 QAM or to 256-GS symbols. After the CM encoder, the signal was modulated using a root raised cosine (RRC) filter. Dual-polarization \(11\times 45\) GBd WDM channels are generated with 50 GHz spacing, yielding 720 Gbit/s raw data rate per wavelength. The corresponding line rate per wavelength is \(720\cdot R\), where \(R\) is the code rate of FEC code. For each polarization of each WDM channel, independent sequences of \(2^{18}\) symbols were transmitted. An ideal coherent receiver is used for detection and analog-to-digital conversion. Chromatic dispersion is digitally compensated by a fast Fourier transform based filter, then the signal is matched filtered and downsampled at the Nyquist rate.
## 2 Impedance Analysis of Inter-Turn Fault of Induction Motor  
### Mathematical Model of Induction Motor with Inter-Turn Fault  
Figure 1 shows the configuration of an induction motor under ITF conditions with Y and delta connections. In the event of an ITF in phase A, fault resistance appears in parallel with the winding of the faulty phase. This additional parallel path results in reduced impedance in the fault phase, leading to an imbalance in the per-phase impedance of the stator. The resulting asymmetry in impedance can be effectively utilized as a diagnostic feature for identifying ITF in induction motors. The impedance of healthy and fault phases can be expressed as follows:  
\[Z_{h}=R_{a}+j\omega L_{a} \tag{1}\]  
\[Z_{f}=\frac{R_{f}(R_{a1}+j\omega L_{a1})}{R_{a1}+j\omega L_{a1}+R_{f}}+R_{a2}+ j\omega L_{a2} \tag{2}\]  
where  
\[R_{a}=R_{a1}+R_{a2},\ \ L_{a}=L_{a1}+L_{a2}, \tag{3}\]\(Z_{h}\) and \(Z_{f}\) denote the impedances of healthy and fault phases, respectively. \(R_{a}\) and \(L_{a}\) represent the phase resistance and inductance of the healthy phase, respectively. \(R_{a1}\) and \(L_{a1}\) are the resistance and inductance of the fault phase with the fault resistance connected in parallel. \(R_{a2}\) and \(L_{u2}\) denote the resistance and inductance of the fault phase without the fault resistance. \(R_{f}\) is the fault resistance, and \(\omega\) is the electrical angular speed. By subtracting Equation (3) from Equation (2), the impedance of the fault phase is expressed as follows:  
\[\begin{array}{l}Z_{f}=\frac{R_{f}(R_{a1}+i\omega L_{a1})}{R_{a1}+i\omega L_{a 1}+R_{f}}+R_{a}-R_{a1}+j\omega(L_{a}-L_{a1})\\ =Z_{h}-\frac{(R_{a1}+i\omega L_{a1})(R_{a1}+i\omega L_{a1}-R_{f})}{R_{a1}+i\omega L _{a1}+R_{f}}.\end{array} \tag{4}\]  
Since the fault resistance is connected in parallel with the phase impedance, the fault phase impedance becomes lower than that of the healthy phase, as shown in Equation (4). This indicates that impedance imbalance occurs under ITF conditions, and the impedance of the fault phase is lower than that of the healthy phase. In the next subsection, the impedance analysis of the motor connection is provided to propose the fault diagnosis.
## 3 System Model
### Proposed CSTO Mechanism  
To reduce task transmission delay, we propose a novel task offloading mechanism called CSTO. As depicted in Figure 2, the traditional offloading mechanism requires the vehicle to offload all raw sensing data to the RSU, which consumes significant communication resources. Due to the redundant sensing data between the vehicle and the associated RSU, the vehicle can adopt the CSTO mechanism to offload the non-redundant sensing data to the RSU. Next, considering the differing perception perspectives of the RSU and the vehicle, the RSU needs to perform coordinate transformation preprocessing on the redundant sensing data. Finally, the RSU will compute the sensing tasks. Given the small size of the computed result, its transmission delay can be ignored [31].  
In this paper, we consider that the sensing tasks of the vehicle can be computed locally, offloaded to the associated RSU, or offloaded to another RSU via the associated RSU. Specifically, the vehicle sensing tasks can be computed locally to enhance the real-time performance of task processing. When the computational resources of the vehicle are insufficient, it can utilize the CSTO mechanism to offload tasks to the RSU for processing, thereby effectively reducing both transmission and computation latency. Additionally, when the computing resources of the associated RSU are insufficient, tasks can be further offloaded to another RSU for computation to reduce computation delay. To this end, we define \(\eta_{un}\in\{0,1,2,\ldots,N\}\) as the task offloading decision of the vehicle \(un\). Here, \(\eta_{un}=0\) indicates that vehicle \(un\) computes tasks locally, \(\eta_{un}=n\) means that vehicle \(un\) offloads tasks to the associated RSU \(n\), and \(\eta_{un}=v(v\
eq n)\) means that vehicle \(un\) offloads tasks to RSU \(v\) through the associated RSU \(n\).
## 4 Discussion  
Tables 3 and 4 compare the OLR and Extended Yale-B database execution times for the proposed hybrid Gabor Filter and the SSAE deep neural network, as well as the conventional SSAE method. According to Table 3, the proposed method takes less time to execute for the OLR datasets than the conventional SSAE model, with an average execution time of 0.2495 s for the proposed method and an average execution time of 0.2921 s for the traditional SSAE model. Furthermore, according to Table 4, the proposed method takes less time to execute than the conventional method of SSAE for the Extended Yale-B database, with an average execution time of 0.5495 s against the average execution time of 0.5746 s for conventional SSAE. Therefore, the proposed method of the hybrid Gabor filter and SSAE deep neural network is faster than the conventional SSAE method.  
As shown in Tables 5 and 6, the MSE for the proposed hybrid method using the OLR and the Extended Yale-B databases are lower than that of the conventional SSAE model. Accordingly, from the two databases, the MSE value of the proposed hybrid method is 0.0000, whereas the MSE values of the traditional SSAE model are 0.0009 and 0.0055 for both the OLR and the Extended Yale-B datasets, respectively. Furthermore, Table 5 shows that the proposed hybrid method achieved higher recognition rates with the test dataset on the OLR datasets than the conventional SSAE method alone. The proposed hybrid method  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Proposed Hybrid Method** & **Conventional SSAE** \\ \hline Samples & 76 & 76 \\ Error Rate (MSE) & 0.0000 & 0.0055 \\ Perfectly recognized images & 76 & 71 \\ Recognition rate (\%) & 100\% & 93.4211 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison for the Extended Yale-B dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SN of Images** & **Image ID in Database** & **Execution Time of Hybrid Method** & **Execution Time of Conventional SSAE** \\ \hline
1 & 01\_YB\_01 & 1.0825900 & 0.5950821 \\
2 & 02\_YB\_01 & 0.6231380 & 0.5880558 \\
3 & 03\_YB\_02 & 0.6030670 & 0.5713341 \\
4 & 04\_YB\_02 & 0  
is 100% accurate, whereas traditional SSAE is 98.75% accurate. Finally, the proposed hybrid method resulted in higher recognition rates on the test dataset from the Extended Yale-B database than the equivalent conventional SSAE model. The proposed hybrid method is 100% accurate, whereas conventional SSAE is 93.42% accurate.
## 3 Results and Discussion  
The discussion below explains how a new interferometry setup called far-field interferometry was initiated with fresh and worn Li-po batteries to determine the size of the scattering features in the neutron dark-field images. Neutron Talbot-Lau interferometry and Bragg edge imaging were then explored to visually observe battery degradation. Finally, neutron diffraction of a fresh and worn battery led to an understanding of the changes occurring on a chemical sense within the battery. Table 1 summarizes the experiments, facilities, battery capacities, and key results.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Expt.** & **Facility** & **Capacity/mAh** & **Summary of Results** \\ \hline Far-Field & NIST & 43, 90 & wear: 43 mAh may be \\  & & & optimum size for battery capacity and \\  & & & imaging \\ \hline Talbot-Lau & HZB & 15 & wear: 15 mAh is too small \\ \hline Bragg-Edge & HZB & 15 & wear: 15 mAh is too small \\ \hline Diffraction & SNS & 300 & chemistry: 300 mAh may be \\  & & & optimum size \\ \hline \end{tabular}
\end{table}
Table 1: Overview of Neutron Imaging and Diffraction Experiments.
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 3 Common Challenges in Autonomous Driving
### Narrow Perception  
To create the perception of the environment, autonomous vehicles primarily rely on onboard sensors and vehicle-to-everything (V2X) communication. The context of the environment is created by processing the sensory data and information obtained through V2X communication. However, the evident drawback of this approach is a limited comprehension of the environment, i.e., the perception of the world is constrained by the detection ranges and onboard deployed sensor capabilities. The scientific community,  
Figure 1: The cycle of autonomous vehicle core layers.  
standardizing bodies, and stakeholders have developed different strategies to solve this and related problems, such as creating a perception of the environment through external sources referred to as external or extended perception and disseminating it as needed. Despite addressing the core problem of narrow environmental understanding, a number of research questions and challenges are the following:  
* **CH 1:** How can a vehicle's external perception be created accurately? Will the environment's dynamics be recorded and sent (wherever necessary) in real time?
* **CH 2:** Can the classical local dynamic map be improved? If so, what extra information layers ought to be used and how will they help with the use cases of higher automation levels?
* **CH 3:** Do the 3 GPP Release 15 and 16 standardized interfaces and architectural components support the protocols for the interchange and fusion of external perception with onboard perception? If not, what could be the potential alternatives?
* **CH 4:** What fusion techniques could be employed to obtain a real-time and accurate picture of the environment if the perception is generated externally on the RSUs and then fused with the data from the onboard vehicles?
* **CH 5:** What is the optimal fusion strategy for accurately fusing the sensory data from heterogeneous sensors?
* **CH 6:** Data on autonomous vehicles come from a variety of sources, including the vehicle's sensors, sensors on other vehicles, RSU, etc. How to synchronize them is a significant challenge when dealing with a range of data sources. Should synchronization be handled by the storage system beforehand, or should we let the application developer do this?
## 4 Neural Network Architectures for Uniaxial Compressive Strength Prediction  
We implemented and compared four feed-forward neural network architectures to capture the complex, nonlinear dependence of uniaxial compressive strength (UCS) on water content, porosity, and density. Each model employs a distinct training algorithm and regularization scheme, which allows for a systematic evaluation of their convergence rates, generalization performance, and robustness to measurement noise:
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.2 Initial Hydraulic Heads  
Initial hydraulic head conditions refer to the groundwater level distribution in the system at the commencement of the simulation [23]. Distributed initial static hydraulic heads data collected from 11 observation wells of the current study are created by interpolating from the head values. The interpolated hydraulic heads distribution was then imported to MODFLOW at the start of the model run, and every cell in the model was designated its head value ([PERSON] et al., 2000). The land surface elevations at particular model cell are derived from 30 m \(\times\) 30 m DEM of the area, and then depth to static water level values from the wells database is subtracted from each DEMs obtained. The results of this calculation are used as initial prescribed hydraulic heads in each cell for the initial specification of head values.
## 7 Performance Evaluation
### Simulation Configurations  
In this section, we describe the simulation configurations for DRRF in both real-time and non real-time traffic scenarios. The metrics are the throughput (used to achieve three constraints: minimum rate guaranteed, maximum rate, and traffic priority) and percentage of overhead reduction. Note that the percentage of overheads was calculated from a MAC header and a fragmentation subheader. We do not consider an optional packing subheader and other subheaders. In terms of fairness, we used a well-known Jain Fairness index [17], which is computed as follows:  
\[f(x_{1},x_{2},...x_{n})=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n{\times}{\sum_{i=1} ^{n}x_{i}^{2}}} \tag{4}\]  
In this equation (Equation 4), \(x_{i}\) is the throughput for the \(i^{th}\) MS, and there are \(n\) MSs or \(n\) flows. For a real-time traffic class, we used the deadline or hard delay constraint as the metric _versus_ throughput fairness.  
Figure 4: Simulation Topology.  
Again, there are four main configurations: (1) DRRF, (2) DRRF+GWF, (3) DRRF Extensions, and (4) EDF-DRRF (See Table 5). For (1) and (3), the simulations were run from 0 to 10 seconds with 5 seconds of traffic duration. Flows start at 5 seconds, and then end at 10 seconds. There are ranging, registration, and connection setup processes during the first 5 seconds. The packet size was set at 1500 bytes. Each queue is 100 packets long; one queue per flow.  
For (2) and (4), the simulations were run from 0 to 22 seconds with 10 seconds of traffic duration. Flows start at 10, 10.02, 10.04, and 10.06 seconds, and then end after 20 seconds. The packet size was set at 500 bytes. At the base station, there is one queue for each mobile station, and each queue is 50 packets long.
# Residential Buildings Complex Boundaries Generation Based on Spatial Grid System
## 7 Conclusions  
The automatic generation of complex building boundaries, in contemporary research and engineering projects and practices, is heavily processing and data volume bounded. Even with the available processing power and storage capacity of commercial computer systems nowadays the interactive and reactive design is rather limited. The main challenge is the rapid generation of a large number of complex building boundaries in a reasonable time frame.  
The other dominant obstacle is that the majority of methods rely on the interior functional constraints as a driving force dominantly resulting in a low level of variability and simple rectangular geometry of generated building boundaries.  
As a basis for the automated generation of various building boundaries, the solution presented in this paper introduces a novel approach that ignores the internal (functional) and focuses only on the external (non-functional) impacts. The primary orientation on external impacts may be, at any instance, extended by suitable complementary traditional methodology. The applied research methodology and presented method rely on a developed extendible rule-based system that simplifies building boundaries creation by recursive application of formulated spatial grid generation algorithm. Based on starting parameter values (mainly the lot and building area spaces) the algorithm tends to create a set of grids that satisfy initial constraints by marking the individual grid cells as part of the building or empty.  
The presented conceptual framework model served as a foundation for creating a prototype software application that supports the experimental generation of grid arrays that are transformed into readable images of residential building boundaries. For the initial validation of the developed methodology, method, and algorithm, the concrete parametric resolution is set to 1 m.  
The comparative analysis has shown that the presented approach overcomes some of the limitations of previous related research that generate building boundaries in simple rectangular form or with limited variability. The proposed method, in its current stage, outperforms discussed existing methods concerning complex building boundary shape generation.  
Conceptualization, M.L.; methodology, A.P. and B.P.; software, M.L., A.P. and B.P.; validation, M.L.; investigation, resources, data curation, and visualization, M.L., A.P. and B.P.; writing--original draft preparation, M.L.; formal analysis, B.P.; writing--review and editing, A.P. and B.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 3 Results
However, the stress-strain curve of the diagonal-shaped mesh demonstrates a mechanical behavior more closely resembling that of vaginal tissue. \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Non-Medical Grade (_t = 9_)** & **Medical Grade (_t = 9_)** & _p Value_ \\ \hline
240\_2L & 2.56 \(\pm\) 0.110 & 2.135 \(\pm\) 0.083 & 0.029 \({}^{*}\) \\ \hline
240\_1L & 1.500 \(\pm\) 0.057 & 1.090 \(\pm\) 0.032  
Figure 8 compares the mechanical behavior of different 80 \(\upmu\)m diameter single-layer meshes, all tested within the comfort zone. These include a square-shaped medical-grade PCL mesh, a square-shaped non-medical-grade PCL mesh, and a diagonal-shaped medical-grade PCL mesh. When comparing the medical- and non-medical-grade materials, the medical-grade PCL shows a mechanical response closer to that of vaginal tissue. This makes medical-grade PCL the better option. For instance, the square-shaped medical-grade mesh demonstrates a maximum stress of 0.29 MPa, which is relatively close to vaginal tissue's maximum stress of 0.14 MPa. Among the meshes, the medical-grade 80 \(\upmu\)m mesh with a single layer most closely mimics vaginal tissue, making it the best choice in terms of mechanical compatibility. As for the geometries, the diagonal-shaped mesh holds promise, as its mechanical properties more closely align with vaginal tissue. Specifically, when comparing the two medical-grade meshes, the square-shaped mesh exhibits a maximum stress of 0.29 MPa, while the diagonal-shaped mesh shows 0.12 MPa, which results in a difference of around 14%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Secant Modulus (\(E_{\text{a}}\)) [MPa]**} & \multirow{2}{*}{**Variation (\%)**} \\ \cline{3-4}  & & **Non-Medical Grade** & & \\ \hline Square-Shaped Mesh & 80\_1L & 7.65 & 3.42 & 54\% \\ \hline Diagonal-Shaped Mesh & 80\_1L & \(\dots\) & 2.16 & \(\dots\) \\ \hline Sheep Tissue & & 3.96 & & \\ \hline Restorellé & & 17.61 & & 77.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Secant modulus for printed meshes, sheep vaginal tissue, and Restorellé® synthetic mesh at 10–20% elongation. Figure 7: Uniaxial stress–strain response of diagonal-shaped meshes printed with medical-grade PCL compared to sheep vaginal tissue and Restorellé mesh. Note: Fd_1L and Fd_2L refer to meshes printed with one layer and two layers, respectively.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 6 Discussion  
The assumptions made about drone (UAV) flight conditions in this study, including the use of only one satellite for comparison, constant-altitude travel in a straight line, and 2D analysis, significantly shape the interpretation and applicability of the results. Each of these assumptions simplified the real-world complexities of UAV operations and satellite communications, which may have influenced the validity and generalization of the findings.  
The use of a single satellite to compare with ground stations limited the analysis to a scenario where interference from other satellites or overlapping signals was non-existent. In reality, multiple satellites might be available, offering alternative communication links and affecting the signal quality due to interference. Relying on a single satellite may result in an overestimation of the signal reliability and strength, as it does not account for potential satellite malfunctions or suboptimal positioning relative to the UAV. The results might suggest better connectivity or fewer handovers than might actually occur in a multi-satellite environment where handover decisions could be more dynamic and complex.  
For the link budget, a straight-line path for the UAV was assumed. This may have led to an oversimplification of the analysis, potentially making the handover algorithms less effective in adaptive or complex flight patterns encountered in practical applications. The UAV, the satellite, and the center of the Earth were assumed to be in the same plane, which meant that the analysis was 2D. The exclusion of 3D spatial factors could have led to inaccuracies in predicting the signal strength and handover performance, making the results less applicable to scenarios where the UAV's relative altitude and geographic positioning vary significantly from the satellite.  
While the study's assumptions streamlined the modeling and analysis, making the complex problem of UAV-to-satellite communications more tractable, they introduced limitations that could affect the practical application of the research findings. Recognizing these limitations is crucial for interpreting the results and for considering further research that incorporates more dynamic and realistic flight patterns and satellite environments. This  
Figure 19: Received signal strength along UAV path.  
acknowledgment also guides the development of more robust and adaptive communication systems that can handle the complexities of real-world UAV operations.