esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fundamentals of InSAR and Interferometric Geometry  
To support the interpretation of the PS-InSAR analysis results, this section introduces the fundamental physical and geometric principles of Interferometric Synthetic Aperture Radar (InSAR).  
Radar signals transmitted from satellites are reflected back from the Earth's surface, carrying both amplitude and phase information. The phase component is highly sensitive to changes in the distance between the satellite and the ground surface, allowing for the detection of subtle displacements through a differential phase analysis over time [15].  
The primary output of InSAR processing is an interferogram, which visualizes phase differences as fringe patterns. These fringes indicate the relative displacement between acquisition dates, typically projected along the satellite's Line-of-Sight (LOS) direction. However, the measured phase difference is affected not only by surface displacement but also by topographic variations, atmospheric delay, orbital errors, and noise. Therefore, a reliable deformation analysis requires correction procedures, such as orbital refinement, Atmospheric Phase Screen (APS) mitigation, and phase unwrapping [16].  
Figure 3 illustrates the fundamental imaging geometry of repeat-pass InSAR. In the fundamental setup, a radar signal with a specific wavelength is transmitted from a satellite to a ground target point P, and the backscattered signal is received by the onboard sensor. Within the SAR data, these signals are stored as complex numbers comprising both amplitude and phase, known as a Single Look Complex (SLC) image. The SLC image represents radar data acquired from a specific satellite position during a single pass.  
When the satellite passes over the same region again along a nearly identical orbit, a second SAR image is acquired from a slightly displaced antenna position. The distance between the first antenna position and the ground target point P is denoted as R, while the distance from the second antenna position to the same ground point is expressed as R'. Although the satellite maintains a consistent orbital path, there exists a spatial separation between these two imaging locations, referred to as the baseline (B). It is defined as the distance between the antenna positions and forms an angle \(\alpha\) (alpha) with the horizontal plane. The satellite altitude is represented as H, and the look angle between the satellite and the ground target is denoted as \(\theta\) (theta).  
Due to this baseline, a slight geometric offset arises between the two imaging positions. This offset causes a phase difference between the corresponding pixels of the two SAR  
Figure 3: Principle of Interferometric Synthetic Aperture Radar (InSAR).  
images, which reflects the variation in signal travel paths between the satellite and the ground surface. This phase difference serves as the fundamental observation in InSAR analysis for deriving surface deformation or topographic features.
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 2 Materials and Methods
### GIS-Based Fault Rupture and Cable Distress Assessment  
#### 2.4.1 Numerical Modeling of Cable Distress  
The structural performance of the cable due to offshore fault-cable intersection has been simulated employing the static standard module of the ABAQUS finite element (FE) software [57]. Beam elements have been used to represent the cable, whereas the cable-soil interaction has been simulated by means of pipe-soil interaction (PSI) elements along the axial and vertical directions. PSI elements usually lead to more accurate and realistic results compared to the conventional soil springs. The two nodes of PSI elements are attached to the cable, while the other two represent the soil, thus facilitating the assessment of soil resistance.  
A cable of typically infinite length has been taken into consideration to minimize the effect of undesired boundary conditions at the edges of the FE model. The fault movement is imposed on the one end of the cable and the corresponding far-field PSI nodes, whereas the other end of the cable and the PSI nodes are fixed. Figure 2 depicts the employed FE modeling of the cable. It is noted that only the case where the rupture bedrock is not covered from soft seabed sediments has been simulated herein. The impact of the presence of soft sediments on fault rupture propagation and the corresponding distress of offshore CLs has been numerically examined by the authors' group [58].
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
###### Abstract  
Water quality may be affected by aspects such as pollution from industries, agricultural fertilizers and pesticides, and waste produced by humans. This contamination can affect the produce of the fields irrigated by untreated water. Therefore, it is necessary to add a treatment process in irrigation systems. In this paper, an architecture, communication protocol, and a data analysis algorithm for a wastewater treatment system intended for irrigation are presented. Our system includes a smart group-based wireless sensor network that is able to detect high salinity levels and pollution stains, such as oil spills. When contamination is detected, the water is led into auxiliary canals that perform the biosorption process to treat the water and dump it back into the main canal. Simulations were performed to assess the amount of data stored on the secure digital (SD) card, the consumed bandwidth, and the energy consumption of our proposal. The results show the system has a low bandwidth consumption with a maximum of 2.58 kbps for the setting of two daily data transmissions of the node in the last auxiliary canal. Furthermore, it can sustain the energy consumption in adverse conditions, where the node with the highest energy consumption reaches the lowest energy value of 12,320 mW/h.  
sensor network; LoRa; water quality; contamination; precision agriculture +
Footnote †: journal: Applied Physics Letters  
1
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations  
In this section, we focus on existing and open source activities on 5G-MEC testbeds for vehicular communications. This section is organized into three subsections: Section 3.1 describes a set of important 5G-MEC testbeds, whereas Section 3.2 focuses on 5G-V2X testbeds. Section 3.3 presents the features of the selected 5G-MEC V2X testbeds. We describe the deployment strategies and their respective use cases for each of the considered testbeds.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 1 Nicotinamide Adenine Dinucleotide (NAD+)  
NAD+ is a fundamental metabolite required for multiple oxidation-reduction (redox) reactions to maintain cellular homeostasis and energy production [1, 2, 3]. The NAD+ molecule is a dinucleotide composed of adenosine monophosphate (AMP) linked through a phosphate group to nicotinamide mononucleotide (NMN), which, in turn, is made up of the phosphate group, a ribose ring and nicotinamide [1, 4]. The electrochemical properties of the nicotinamide moiety are essential for the classical redox functions of NAD+. Thus, the nicotinamide group can accept a hydride anion (H+, 2e\(-\)), reducing the molecule to NADH. In contrast, NADH is oxidised to NAD+ by donating that hydride anion [5, 6]. Alternatively, the ribose ring of the AMP moiety of NAD+ can be phosphorylated by the enzyme NADK (NAD kinase), generating NADP+ (nicotinamide adenine dinucleotide phosphate), which can be reduced to NADPH [7]. All forms of NAD (NAD+, NADH, NADP+, NADPH) act as electron acceptors and donors in multiple redox reactions in the cell (Figure 1).  
The NAD+ oxidised form is ubiquitous and the form most abundant in cells. However, its availability varies depending on intracellular factors, such as subcellular compartment, tissue and cell type, and intracellular glucose levels, but also on extracellular factors, such as caloric intake or expenditure, ageing or circadian rhythms. For example, it has been observed that free NAD+ concentrations in the nucleus and in the cytosol are similar since both compartments are connected, while the free NAD+ content in mitochondriais much higher (approximately 100 uM in nucleus/cytosol vs. 250-500 uM in mitochondria), ensuring a quick local regulation of NAD-dependent redox processes [8; 9]. NAD+ concentration can also vary greatly during the day because of the string regulation by the circadian rhythm. The circadian clock complex CLOCK:BMAL1 induces the transcription of NAMPT, which is the limiting enzyme in the biosynthesis of NAD+, thus increasing the production of NAD+ every 24 h. SIRT1, which is an important NAD+-consuming factor, counteracts this effect by deacetylating BMAL1 and the promoter histones of other regulatory genes [10; 11; 12].
## 4 Simulation Result and Discussion  
### Test System  
This study uses IEEE 9 Bus and New England 39 Bus test systems, as shown in Figures 9 and 10, respectively, for the STVS assessment. The IEEE 9 Bus test system consists of three generators, three loads, six lines, and three transformers. The detailed parameters of this test system can be found in [39]. The New England 39 Bus test system consists of 10 generators, 19 loads, 34 lines, and 12 transformers. The detailed parameters of this test system can be found in [40]. The nations that are leading the way in renewable energy typically have a penetration rate of 20-30% [41]. For this reason, the RESs considered in this study are PVG and WTG, which are assumed to supply 25% of the total electrical demand of the test system. The data and model for RESs are taken from [42; 43]. For the IEEE 9 Bus test system, each solar and wind farm of 39 MVA are connected to bus 8. Similarly, for the New England 39 Bus test system, the size of the solar and wind farm at bus 6, 15 and 18 are 297, 263, and 202 MVA, respectively. This study uses the built-in wind farm model and PVG model available in DIgSILENT power factory software [44]. TDS in DIgSILENT power factory software were used to create the data set. To examine the performance of various deep learning and machine learning models, the Keras module with TensorFlow backend in Python is utilized. All the calculations are completed on a PC with an AMD Ryzen 5 2600 Six-Core processor running at 3.40 GHz and 16 GB of RAM.  
Figure 9: IEEE 9 Bus System.
###### Abstract  
Globally, plant viral infection is one of the most difficult challenges of food security, where considerable losses in crop production occur. Nanoparticles are an effective control agent against numerous plant pathogens. However, there is limited knowledge concerning their effects against viral infection. In the present study, the green synthesis of zinc oxide nanoparticles (ZnO NPs) using aqueous leaf extract of _Mentha spicata_ was achieved. X-ray diffraction patterns confirmed the crystalline nature of the prepared ZnO NPs. Dynamic light scattering and scanning electron microscopy analyses revealed that the resultant ZnO NPs were spherical in shape with a particle size ranged from 11 to 88 nm. Fourier transmission infrared spectroscopy detected different functional groups, capping and stability agents, and showed Zn-O bond within wavenumber of 487 cm\({}^{-1}\). Under greenhouse conditions, the antiviral activity of biological synthesized ZnO NPs (100 ug/mL) against Tobacco mosaic virus (TMV) was evaluated. The double foliar application of the prepared ZnO NPs, 24 h before and 24 h after TMV-inoculation, was the most effective treatment that showed a 90.21% reduction of viral accumulation level and disease severity. Additionally, the transcriptional levels of PAL, PR-1 (salicylic acid marker gene), CHS, and POD genes were induced and up-regulated in all ZnO NPs treated plants. Notably, the results exhibited that aqueous extract of _Mentha spicata_ was an effective reducing agent for the green synthesis of ZnO NPs, which showed significant antiviral activity. Finally, the detected protective and curative activity of ZnO NPs against TMV can encourage us to recommend its application for plant viral disease management. To our knowledge, this is the first study describing the antiviral activity of the green synthesized ZnO NPs.  
Mentha spicata; Zinc Oxide Nanoparticles; green synthesis; XRD; DLS; FTIR; SEM; antiviral activity; gene expression +
Footnote †: journal: Applied Soft. 2020, 10, 5054; doi:10.3390/app10155054
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 4 Experimental Evaluation and Analysis
### Test Results of Actual Components Dataset
In Figure 12e, YOLOv11 accurately detects the side and loose defects with high confidence (side: 97.4-99.9, loose: 93.8), showing strong performance on larger, well-defined targets. However, it fails to detect the hole defect, confirming its poor performance on small anomalies, as reflected in its low AP-bbox-50-hole score of 0.187 in Table 3. Despite its speed and effectiveness in detecting large targets, YOLOv11 exhibits limited reliability in capturing tiny features. Defect R-CNN, as shown in Figure 12f, consistently delivers the best visual performance, detecting all three defect types with near-perfect confidence scores (\(\geq\)99.9). This outcome corresponds with its quantitative results in Table 3, where the model achieves high scores across all categories. These results reflect the effectiveness of the proposed architectural design, which improves the model's ability to capture fine structural details and edge information in CT images. Overall, the detection outcomes in Figure 12 are in strong agreement with the quantitative performance reported in Table 3. The consistent dominance of Defect R-CNN across both visual and numerical evaluations highlights its robustness, generalization capability, and suitability for industrial CT defect detection scenarios that require high precision and stability. Figure 11: Annotations of three defect types (side, loose, hole) in a reconstructed CT image.
## 4 Experiment and Analysis
### Impact of Measurement Error  
The measurement point was set as \(M\) = 41, and the initial guess = 0.1 m. Impacts of different measurement errors (\(\sigma\) = 0.0, 0.1, 0.5, 1, and 2 \({}^{\circ}\)C for case 1 and \(\sigma\) = 0.0, 0.1, 0.5, and 1 \({}^{\circ}\)C for case 2) were analyzed. The inversion results of different measurement errors are shown in Figures 8 and 9.  
The results are as follows:  
* when \(\sigma\) = 0.0 \({}^{\circ}\)C, the average relative error was 0.92%;
* when \(\sigma\) = 0.1 \({}^{\circ}\)C, the average relative error was 1.03%;
* when \(\sigma\) = 0.5 \({}^{\circ}\)C, the average relative error was 2.35%;
* when \(\sigma\) = 1.0 \({}^{\circ}\)C, the average relative error was 3.03%;
* when \(\sigma\) = 2.0 \({}^{\circ}\)C, the average relative error was 4.40%.  
Figure 8: Identification results of different measurement errors for inversion results of case 1.  
Figure 9: Identification results of different measurement errors for inversion results of case 2.  
Case 2:  
* when \(\sigma\) = 0.0 \({}^{\circ}\)C, the average relative error was 0.95%;
* when \(\sigma\) = 0.5 \({}^{\circ}\)C, the average relative error was 2.31%;
* when \(\sigma\) = 1.0 \({}^{\circ}\)C, the average relative error was 2.79%.  
The results showed that when there were some measurement errors, the inversion results were still satisfactory. When the measurement error was less than 1 \({}^{\circ}\)C, the accuracy of the inversion solution could still meet the general engineering requirements, and the larger the measurement error, the more distorted the inversion results. Therefore, it was beneficial to improve the inversion results to improve the measurement accuracy as much as possible in the experiment.
## 4 Conclusions  
The transmission of data requires more energy consumption; thus, it is important to have an efficient energy routing algorithm for WSN. Thus, optimal routing methodology, namely the moth flame algorithm and salp swarm algorithm are used in this work. Initially, the SNs which are deployed are organized in a lattice and the multi-criteria clustering is utilized for selecting the appropriate CH. The proposed approach is compared with existing MFO, SSO, Fuzzy PSO, and LEACH techniques, in terms of energy consumption, throughput, end-to-end delay, latency, and packet delivery ratio, and this approach performed well. Subsequently, the combined bio-inspired algorithm would be a better substitute for energy-efficient routing for the WSN. As a result, the hybrid routing method obtained minimal energy consumption with 0.3 mJ, a throughput of 0.97 Mbps, a delay ranges between 3 s to 8 s, a minimal latency value of 3.1 s, and a maximum packet delivery ratio of 97.7%. These results provide the highest energy efficiency of the proposed system. The future scope of this work may be mobile nodes that would be taken into consideration for WSN-based mobile and IoT.  
Conceptualization, J.V. and P.S.M.B.; methodology, S.K.M.; validation, S.B.; resources, M.K.; data curation, P.J.; writing--original draft preparation, J.V. and P.S.M.B.; writing--review and editing, S.K.M. and P.J.; visualization, J.C.B.; supervision, S.B., M.K., P.J. and J.C.B.; project administration S.B., M.K., P.J. and J.C.B. All authors have read and agreed to the published version of the manuscript.  
Researchers Supporting Project number (PNURSP2023R195) Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R195) Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
Figure 8: Analysis of network life-time vs. number of nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **Proposed** & **MFO** & **SSO** & **Fuzzy-PSO** & **LEACH** \\ \hline
100 & 1550 & 1313 & 1295 & 1312 & 1112 \\
200 & 1708 & 1401 & 1429 & 1388 & 1376 \\
300 & 1981 & 1698 & 1696 & 1630 & 1253 \\
400 & 2298 & 1792 & 1765 & 1891 & 1547 \\
500 & 2391 & 2173 & 2001 & 2121 & 1920 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Network lifetime comparison with different routing protocols.
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 3 Preparation Stage  
### Target Wind Profiles  
As pointed out by previous full-scale field measurement studies [3; 5; 8; 22], the profiles of the TWF are highly dependent on the meteorological background and the local terrain effect. A variety of wind profile models have been proposed to quantitatively describe the TWF caused by different mechanisms. This study selects four widely recognized models of twisted wind angle for discussion, namely, (1) the Ekman spiral model for the TWF induced by Coriolis force [3; 23], (2) the inverse proportion model [3] and the exponential law model [8] for the TWF induced by mountainous terrain, and (3) the logarithmic law model [4] for the TWF induced by tropical cyclones over coastal areas.  
In particular, for the Ekman spiral model, the profiles of the wind speed in the longitudinal and lateral directions, denoted by \(u\) and \(v\), respectively, and the twist angle, \(\theta\), are expressed by:  
\[u(z)=u_{g}\bigg{[}1-e^{-\frac{z}{h_{E}}}\cos\left(\frac{z}{h_{E}}\right) \bigg{]}-v_{g}e^{-\frac{z}{h_{E}}}\sin\left(\frac{z}{h_{E}}\right) \tag{1}\]  
\[v(z)=v_{g}\bigg{[}1-e^{-\frac{z}{h_{E}}}cos\left(\frac{z}{h_{E}}\right)\bigg{]} -u_{g}e^{-\frac{z}{h_{E}}}sin\left(\frac{z}{h_{E}}\right) \tag{2}\]  
\[\theta(z)=tan^{-1}\bigg{(}\frac{v(z)}{u(z)}\bigg{)} \tag{3}\]  
where \(u_{g}\) and \(v_{g}\) are the gradient wind speeds in the longitudinal and lateral directions, respectively, and \(h_{E}\) is the Ekman height determined by the Coriolis parameter and the turbulence viscosity coefficient.  
By setting the maximum twist angle at the ground level at \(30^{\circ}\) and that at a height of \(500\) m at \(0^{\circ}\), Figure 2 depicts the twist angle profiles obtained from the above four models. It is readily observed that the twist angle following the inverse proportion model rapidly drops to a value below 5\({}^{\circ}\), which is a very small angle corresponding to negligible TWF effects, as the height reaches 100 to 200 m, and those following the exponential law and the logarithm law models decrease to approximately 5\({}^{\circ}\) at a height of 200 m. In contrast, the twist angle expressed by the Ekman spiral model remains higher than 5\({}^{\circ}\) until the height reaches 400 m, indicating that the twisted wind effect represented by such a model is much more prominent than those represented by the three other models. Therefore, to consider the most prominent twisted wind effect for conservative purposes, this study selects the twist angle profile expressed by the Ekman spiral model as the target profile for the generation of the TWF in the wind tunnel.
## 3 Results and Discussion
### Anisotropic Structures  
#### 3.4.1 Fabricated Specimens  
As shown in Figure 15, two types of test specimens containing either material A or B and one specimen designed to have anisotropic properties by applying a half-and-half structure were fabricated. Anisotropic structures were classified to measure resilience properties into anisotropic-1 and anisotropic-2 according to the impact direction of the steel ball, which rotated 90 degrees; that is, out of the eight sides of the regular octagon, which was the shape of the internal structure, only two sides perpendicular to the curing surface were printed with soft material B, while the other sides were printed with hard material A. The printed specimens were classified as anisotropic-1 when the ball impact test was performed at the perpendicular direction to the curing surface. When the ball impact direction was parallel to the curing plane by rotating the printed specimen by 90 degrees, this was referred to as anisotropic-2.
## 6 Model Application and Deployment
### Model Deployment and User Interaction Design  
We have developed a straightforward command-line interface that enables users to input queries and receive immediate responses generated by the model, as illustrated in Figure 16. This process encompasses several pivotal steps:  
* Loading the Model and Tokenizer: Ensuring the pretrained GPT model and its corresponding tokenizer are efficiently loaded in the deployment environment, facilitating the encoding and decoding of text data.
* Interactive Loop Design: Implementing an infinite loop that waits for user input, where each iteration involves transforming the user's query into the input format required by the model. The model is then invoked to generate an answer via the generate function, which produces a response of a predetermined length based on the user's question and instantly presents it to the user.
* Simplified Text Generation Function: For rapid demonstration purposes, we provide the generate_text function. This function directly generates text without incorporating circuit-breaking logic, making it suitable for preliminary model performance validation and swift iterative testing.
* Text Inspection and Circuit-Breaking Handling: The generate_text_with_circuit_breaker function subjects the text generated by the generation function to quality inspection and applies circuit-breaking measures to obtain a final response that adheres to the intent of the input query, ensuring relevance and coherence.