esa-sceva/satcom-chunk-collection huggingface dataset:
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Position Similarity
#### 2.3.1 Position Graph  
Generally, the position similarity is obtained by calculating the center distance between objects, where position graphs and Delaunay triangulation are commonly used methods [47; 48]. Micro scenes contain lines and polygons, and the positional relationship between features needs to be described separately. Delaunay triangulation has been widely used in the description of spatial relations and it meets the above requirements. We selected Figure 10a as an example; three types of triangles describing position relationships were obtained through the construction of Delaunay triangulation (Figure 13).  
Figure 12: Feature point set of the direction relationship.  
Figure 13: Construction of position graph.  
The first type of triangulation describes the position relationships between polygons, the second triangulation describes the position relationships between lines and polygons, and the third triangulation describes the position relationships between lines.
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 2 Related Work
### Program Understanding Model  
The program understanding model is the basis of intelligent vulnerability detection. There are two main forms of program understanding: sequence and structure. The sequential understanding model converts the source code into a sequence in a certain order, including character [28], token [29] and API [30]. It retains native information. However, it is affected by long-term dependency. The structural program understanding model includes Abstract Syntax Tree (AST) [24,31], Control Flow Graph (CFG) [32], Program Dependence Graph (PDG) [33], and Code Property Graphs (CPG) [34]. AST represents the syntactic structure of a programming language in a tree form. Through an AST, the syntax information can be obtained. CFG takes the codes with a sequential relationship as a basic block and concatenates them into an ordered graph based on their control dependency. CFG specifies the execution sequence of statements and the required conditions for a particular execution sequence. However, CFG failed to identify data flow information. PDG adds data dependency and control dependency on the nodes of CFG. The advantage of this structure is that statements that affect some sensitive operations can be easily and accurately identified. However, PDG loses the syntax information which is crucial for the detection of some types of vulnerability. Given that AST, CFG, and PDG have their own priorities, [PERSON] [34] proposed CPG that combines them into one graph. Although CPG provides accurate and detailed information, it compromises the efficiency of detection due to the increased data to be analyzed. Furthermore, structural representation is more complicated than sequential representation. In order to balance accuracy and efficiency, some intelligent vulnerability detection methods [25; 26] transform natural code sequence into a structured model. Then the structure model is transformed into a sequence model before embedding.
## 4 Experiments
### Experimental Setup  
#### 4.1.1 Datasets  
**KITTI:** We used the left/right image data provided by the KITTI 2015 driving dataset for the unsupervised learning-based depth-estimation network training. The KITTI dataset is a challenging real-world computer vision benchmark developed by utilizing an autonomous driving platform. The developers built a standard station wagon equipped with two high-resolution color/gray-scale video cameras. It is also equipped with a Velodyne laser scanner and a GPS positioning system to provide accurate ground-truth information. The raw dataset consisted of 61 scenes and included a total of 42,382 reconciled stereo image pairs. It also provides 3D point data for each image, which is used as target data in supervised learning and serves as the ground truth for performance measurements. We resized the provided image data and Velodyne depth map to a resolution of \(768\times 384\) before training, considering the effect on the batch-size decision according to the GPU memory size used for training. In addition, we expected that there would be less visual ambiguity in the training process due to the size of the course view and the medium view being \(192\times 96\) and \(384\times 192\), respectively.  
**CityScapes:** We also evaluated our model on the CityScapes dataset [14] to verify our model's generalization performance. The CityScapes dataset is a large-scale dataset containing a diverse set of stereo video sequences recorded from street scenes from 50 different cities, with high-quality pixel-level annotations of 5000 frames (fine annotations) in addition to a larger set of 20,000 weakly annotated frames (coarse annotations). It consists of 22,900 training images, 500 validation images, and 1525 test images. Since several images contain artifacts at the top/bottom of the images, and both left and right cameras unnecessarily captured some parts of the experimental car, the top 50 and bottom 224 rows of pixels are cropped to compensate. Cropping is also performed at the sides of the images to maintain the width-height ratio.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 5 Implication of Radiation Hormesis for Potential Therapeutic Study
### Inflammatory and Proliferative Diseases  
There is a long history of research into radiation-induced inflammatory and proliferative diseases. While cancer is one of the well-known consequences of ionising radiation, evidence has also been accrued showing that ionising radiation can induce inflammatory and other proliferative diseases even after exposure to sub-lethal doses [144]. Conversely, an increasing number of reports in the literature have explored the contradictory findings of LDIR, exploring its potential therapeutic effects in treating various inflammatory and proliferative diseases, particularly for the treatment of inflammation-related disorders, including gas gangrene, sinus infections, arthritis, tendonitis, and serious inflammatory lung conditions [145, 146, 147]. The natural radiation sources that account by far for the background radiation found in soil, water and air includes radon. It has been discovered that radon (a naturally occurring gas, found for instance at elevated levels in abandoned mines and spas) and ultra-low doses of X-ray and \(\gamma\)-ray exposures and \(\beta/\gamma\) exposures from uranium-bearing rocks offer an effective low-dose radiation therapy strategy to treat chronic inflammatory and proliferative diseases [148]. The mechanism by which LDIR modulates the inflammatory processes involves its immunomodulatory properties. Endothelial cells are known to play a pivotal role in regulating proinflammatory responses by recruiting the immune cells from peripheral blood to the inflammation side [149]. [PERSON] et al. (2019) indicated that a single low dose of X-ray radiation exposure (0.01-0.05 Gy) can result in non-linear dose-dependent effects in stimulating pro-inflammatory cytokines from endothelial cells including IL-8; G-CSF and PDGF-BB [150]. Several other in vitro studies have also revealed the anti-inflammatory effects of LDIR, including the reduction in leukocyte adhesion to endothelial cells, activation of the antioxidative system, decreased expression of inducible nitric oxide synthase (iNOS), and enhanced phagocytosis by macrophages in cells. In vivo studies have found that doses from 0.1 to 0.2 Gy have significantly increased CD8+ T cell production, reduced lung metastases from implanted syngeneic L1 sarcoma cells and increased NK cell numbers [151; 152].
# Service for Deploying Digital Twins of QKD Networks
## 3 Functional Overview
### QKD Network Digital Twin Orchestrator  
The QKD network digital twin orchestrator is responsible for deploying, configuring, and terminating any desired QKD network digital twin defined by the user of the service. As can be seen in Figure 2, a total of three steps must be completed to achieve this.  
1. The service user must describe the desired QKD network and deliver this description to the orchestrator. In particular, this document has to include parameters such as  
Figure 2: Outline of how the QKD network digital twin orchestrator service works. The numbers indicate the order in which the steps are carried out.  
the name and neighbors of each node, the ETSI API to be used, or the QKD protocol emulated when forming the quantum keys.
2. Once the digital twin orchestrator has the QKD network description, it communicates with the available physical infrastructure to instantiate the nodes comprising the digital twin of the desired QKD network. At this point, there are two possible scenarios. In the first case, the user provides the orchestrator with a set of pre-deployed machines (real, virtual, or virtualization containers) where he wants the digital twin of the QKD network to be deployed. If this is not the case, the ETSI NFV-compliant cloud and edge infrastructures (e.g., OpenStack-based) must be accessible to the orchestrator for it to automatically build the digital twin of the QKD network using standardized mechanisms to request the deployment of the virtual machines or virtualization containers needed to run the different QKD network nodes.
3. Lastly, the orchestrator installs the required software in order to emulate the complete behavior of the QKD nodes, including that which enables communication based on the ETSI API chosen by the user. In this last step, the orchestrator configures the digital twin and initializes the software that emulates the quantum channels and the QKD protocol used to form the quantum keys.  
Once these processes have been successfully completed, the QKD network starts operating and is available for any client application.
## 4 Data Analyses, Discussion and Findings
### Challenges  
Through semi-interviews and case studies, ten challenges of piping prefabrication including the standardization of design, the economies of scale, the prefabrication function of BIM software, insufficient information of the contractors, unavailable of fabrication shop, the quality and the availability of fittings and valves, authority of the procurement of some equipment, the capacities of workers, the design and construction specification, and policy were summarized. Then these challenges were surveyed through a questionnaire. All the 48 respondents provided valid answers to this question. More than 60% of respondents have selected at least three challenges. Meanwhile, some new challenges are supplemented during this survey. According to Figure 7, the five biggest challenges are the low level of standardization of design (58.3% of respondents), lack of preferential policy (52.1% of respondents), low-skilled workers (50% of respondents), the economies of scale (47.9% of respondents), as well as the availability of fittings and valves (41.7% of respondents). The design of piping systems is the last design activity after the building design and structural design. Therefore, the layout of the pipes and equipment in piping system is subject to the location and size of the column, the beam, and other building components. It is an inevitable result that a variety of elbows, branches, and reducers existed in the system. The design is separate from construction. The constructability is not in the scope of the objective of design. Through the interview with one of the largest MEP contractors in China, more than 100 thousand types of material were used in piping systems. The policies in recent years are more about prefabricated building and green building. However, policies related to the prefabricated building were limited primarily to the prefabrication of structural components such as slabs and walls. While the policies related to green building focus on the energy-saving in the operation and maintenance phase. There is a lack of policies to promote the piping prefabrication. The prefabrication and installation of pipes need smaller tolerances as the rework cost is high. Therefore, high skilled labors are needed. As the fierce competition among MEP contractors, it is difficult to achieve economies of scale. The availability of appropriate fittings and valves is a challenge to pipe prefabrication as different fittings and valves use different connectors, with different sizes. Other challenges are related to the BIM software for piping prefabrication, the support of the related specification, and the availability of fabrication shop. The result indicated that these challenges are easy to overcome.
## 3 Modeling Methods  
Three different types of modeling techniques were studied as part of the current investigation: multivariate linear regression (MLR), SVM-based techniques, and ANN-based techniques. Among these methods, MLR is a traditional parametric technique, while  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Short Notation** \\ \hline Number of samples & 225 & N.A. \\ \hline \multirow{4}{*}{Pipe diameter (m)} & Average: 0.091 & \\  & Min: 0.026 & \\  & Max: 0.265 & \\  & Standard deviation: 0.070 & \\ \hline \multirow{4}{*}{Average velocity (m/s)} & Average: 0.952 & \\  & Min: 0.107 & \\  & Max: 2.000 & \\  & Standard deviation: 0.591 & \\ \hline \multirow{4}{*}{Oil density (kg/m\({}^{3}\))} & Average: 921 & \\  & Min: 871 & \\  & Max: 987 & \\  & Standard deviation: 38 & \\ \hline \multirow{4}{*}{Oil viscosity (Pa.s)} & Average: 5.50 & \\  & Min: 0.16 & \\  & Max: 28.45 & \\  & Standard deviation: 6.79 & \\ \hline \multirow{4}{*}{Water density (kg/m\({}^{3}\))} & Average: 995 & \\  & Min: 985 & \\  & Max: 999 & \\  & Standard deviation: 3.43 & \\ \hline \multirow{4}{*}{Water viscosity (Pa.s) \(\times\) 10\({}^{-3}\)} & Average: 0.829 & \\  & Min: 0.496 & \\  & Max: 1.138 & \\ \cline{1-1}  & Standard deviation: 0.184 & \\ \hline \multirow{4}{*}{Water fraction} & Average: 0.370 & \\  & Min: 0.070 & \\  & Max: 0.844 & \\ \cline{1-1}  & Standard deviation: 0.163 & \\ \hline \multirow{4}{*}{Pressure gradient (kPa/m) *} & Average: 1.19 & \\ \cline{1-1}  & Min: 0.04 & \\ \cline{1-1}  & Max: 5.37 & \\ \cline{1-1}  & Standard deviation: 1.26 & \\ \hline \end{tabular}
\end{table}
Table 1: Experimental Parameters.  
SVM and ANN techniques are non-parametric machine learning (ML) techniques. Besides, the conventional model proposed by [PERSON] et al. [7], was also applied to the available dataset to compare its accuracy with the models of the present study.
## 3 Performance Analysis
### Transmission Throughput  
The normalized throughput is defined as the ratio of the successfully received bits/second of a given DCSK system to the transmitted bits/second of the proposed MCIM-SR-DCSK-CC system [1; 32]. The proposed MCIM-SR-DCSK-CC system can send N information bit streams per transmission period, i.e., \(\overline{T}=(U+RU)(1+m_{ci}+m_{cm})T_{c}\times 2\). \(N_{p}=1+m_{ci}+m_{cm}\) represents the number of bits transmitted per group.  
Thus, the normalized throughput can be calculated as [32]  
\[R_{t}=\frac{\left[\left(1-P_{\it{sys}}^{t}\right)^{N_{p}}N_{p}\right]/\overline {T_{t}}}{N_{p}/\overline{T}}=\frac{\left(1-P_{\it{sys}}^{t}\right)^{N} \overline{T}}{\overline{T_{t}}} \tag{44}\]  
where \(P_{\it{sys}}^{t}\) and \(\overline{T_{t}}\) represent the average BER and the time required to transmit N information bit streams for a given system \(t\in\left\{{\it{CIM-SR-DCSK-CC},{\rm{MCIM-SR-DCSK-CC}}}\right\}\), respectively. The time durations required for transmitting N information bit streams for the \({\it{CIM-SR-DCSK-CC}}\) system is \((U+RU)\frac{1+m_{ci}}{1+m_{ci}+m_{cm}}N_{p}T_{c}\times 2\).
## 3 Chaos-Based Parameter Estimation and Stability Analysis
### Chaos-Based Parameter Optimization and Bifurcation Diagrams
A deeper understanding of chaotic behavior in fractional iterative schemes supports the development of more reliable solvers for highly nonlinear engineering problems, including those with multiple roots, sensitive dependencies, or fractional-order characteristics. The integration of chaos with fractional iterative schemes offers several advantages:  
* Chaotic sequences enhance the exploration of the solution space, reducing the likelihood of premature convergence. * Fractional memory effects contribute to smoother iteration behavior and mitigate abrupt changes in the solution trajectory. * This combined strategy enables the application of iterative methods to challenging classes of nonlinear problems, such as those exhibiting multistability, sensitivity to initial conditions, or ill-posed behavior.
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
###### Abstract  
Fermented juice from the noni tree (_Morinda cirrifolia_) is a traditional medicinal product used by South Pacific Islanders to treat a wide range of ailments, including cancer, inflammation and obesity, as well as improving overall wellbeing. Many of its bioactive properties have been suggested to arise from the high antioxidant capacity and phenolic content found in the juice. However, there have been limited investigations into the phenolic profiles of noni juice produced locally in the Pacific. This study aimed to investigate the chemical composition and bioactive properties of noni juice. The first phase of this study used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to characterise the phenolic composition of five brands of commercial noni juice produced in the South Pacific region. A total of 21 phenolic compounds were putatively identified, with the most abundant generally being ruitin, 4-hydroxybenzoic acid and gentisic acid. Vastly differing phenolic profiles were found between the noni juice brands. Significant differences were also found in their antioxidant capacities and total phenolic contents. Of the three major phenolic compounds identified, gentisic acid showed the highest antioxidant activity (640% higher than Trolox). Additionally, the noni juice showed no significant anti-acetylcholinesteraseactive activity and no to moderate cytotoxicity against two cancer cell lines (HeLa and HT29). These results indicate that the phytochemical profiles--and hence, the expected bioactive properties--are likely to vary significantly between different noni juice brands. Furthermore, the anti-cancer activity of non-concentrated noni juice appears to be relatively low.  
Morinda cirrifolia; total phenolic content; targeted profiling +
Footnote †: journal: Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, QLD 4701, Australia  
1
Footnote 1: email: {[EMAIL_ADDRESS]}
# Circulating Current Reduction in MMC-HVDC System Using Average Model
## 2 MMC-HVDC System Structure
### MMC Control System
The decoupled system is represented as a first order transfer function:  
\[G_{s}=\frac{K}{1+s\mathrm{T}} \tag{25}\]  
The PI compensator can be shown as:  
\[G_{O}=K_{i}.\frac{1+sT_{i}}{sT_{i}} \tag{26}\]  
where \(K=\frac{1}{K}\) and \(T=\frac{l}{K}\). The open loop transfer functions of the current control loop can be shown as:\[G_{O}=K_{p}\bigg{(}\frac{1+sT_{i}}{sT_{i}}\bigg{)}\bigg{(}\frac{K}{1+sT}\bigg{)} \tag{27}\]  
The time constant \(T_{i}\) is selected as equal to the dominant time constant of the entire system, i.e., \(T_{i}=T=\frac{L}{R}\), to achieve a phase margin of \(60^{\circ}\) in order to meet the stability criteria. The proportional gain of the controller is chosen as \(K_{P}=\mathrm{L}/\tau\). The independent control of active and reactive power based on vector current control can be expressed as:  
\[P=\frac{3}{2}V_{d}i_{d}+V_{q}i_{q} \tag{28}\]  
\[Q=\frac{3}{2}V_{d}i_{d}-V_{q}i_{q} \tag{29}\]  
The \(q\)-axis component of voltage is set to zero, since phase locked loop (PLL) is synchronized to the grid voltage [35; 36]. \[P=\frac{3}{2}V_{d}i_{d} \tag{30}\]  
\[Q=-\frac{3}{2}V_{d}i_{d} \tag{31}\]  
The circulating current is generated due to a mismatch between the output voltage of different phase arms and the DC voltage, and it is a negative-sequence (a-c-b) current and twice the fundamental frequency. Due to the circulating current, RMS arm current increases, which results in higher power losses [37]. The expression for inner differential current given in Equation (9) can be extended to three phases:  
\[i_{diffa}=\frac{i_{dc}}{3}+i_{2f}\sin(2\omega_{o}t+\varphi_{o}) \tag{32}\]  
\[L\frac{d}{dt}i_{uq}(t)+\omega_{o}Li_{uq}=-V_{uq}(t)-V_{q}(t) \tag{24}\]0  
\[L\frac{d}{dt}i_{uq}(t)+\omega_{o}Li_{uq}=-V_{uq}(t)-V_{q}(t) \tag{24}\]1  
where \(i_{2f}\) is the double frequency circulating current and \(\varphi_{o}\) is the phase angle. As evident from Equations (32) to (34), the DC current (component) is needed to keep the SM capacitor energy at the required level. However, higher order harmonics may be needed inside the circulating current to reduce the ripple effect of the SM capacitor. The capacitor voltage balancing controller enables the energy variation in each sub-module to be shared equally (by each converter arm). The voltage of the SM should be kept approximately equal to its theoretical value \(V_{dc}/n\) (n is number of SMs in each arm), otherwise capacitor voltages will be unbalanced, and the AC output voltage will be difficult to control.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 2 State-of-the-Art
### Noise Sources in Amplifiers  
Various sources contribute to noise in optical amplifiers [15]:  
1. **Signal-spontaneous (sig-sp) beating:** Results from the mixing of the coherent signal with incoherent ASE in the same polarization. It has a flat frequency distribution and is proportional to gain and input signal power.
2. **Spontaneous-spontaneous (sp-sp) beating:** Involves copolarized spectral components of ASE, independent of input signal power and gain, and is more pronounced around the central frequency.
3. **Multipath interference (MPI):** Converts phase or frequency noise in the input signal to intensity noise in the output signal. It has low bandwidth and increases with input power and gain.
4. **Shot noise:** Random fluctuations in the photo-generated current in the photodetector, resembling white noise, and proportional to the average photo-generated current.  
For simplicity, we refer to the signal-spontaneous (sig-sp) beating contribution as _ASE noise_. The most significant noise in EDFAs is ASE, as both sig-sp and sp-sp beating depend on it, with sig-sp generally being dominant [16]. ASE occurs when excited erbium ions decay to the low-energy state before interacting with input signal photons, resulting in the spontaneous emission of photons with random phase and direction. Some of these photons align with the propagation axis, mixing with and amplifying the input signal, producing noise proportional to the amplifier's gain. Equations describing ASE noise are derived using carrier population and power formulas, with parameters obtained from complex experimental measurements. However, these parameters can change over time (e.g., due to aging), making one-time measurements unreliable long-term. Periodic measurements are required to maintain accuracy, which is expensive and impractical for in-use devices. Therefore, simpler models for ASE noise and other parameters are often preferred.