esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 4 Proposed Method
### Routing Using Multi-Commodity Flow Problem  
We propose a routing method that performs load balancing with multiple paths using the multi-commodity flow problem. This improves the throughput of the entire network. The protocol used is OpenFlow.  
The multi-commodity flow problem requires capacity on each edge. In OSPF, the cost that the router \(m\) uses for the link \((m,n)\) with neighboring router \(n\) can be manually set, but often follows Equation (4).  
\[\text{cost}(m,n)=\text{ceil}\left(\frac{\text{reference bandwidth(bps)}}{ \text{link bandwidth(bps)}}\right) \tag{4}\]  
Equation (4) is inversely proportional to the per unit time performance of the link. By making the capacity inversely proportional to the value of the OSPF cost, it can reflect the performance of the link.  
In addition, because it is necessary to know all the commodities to calculate the multi-commodity flow problem, the commodities are fixed from the beginning and the flow table is created by proactive control. For linear programming, we apply the method described in Section 4.1.  
The source and destination pairs and paths are registered for each commodity in the flow table of the OpenFlow switch. If there are multiple paths, a select-type group table is used. Select-type selects one of the ports defined as a group in a round-robin fashion. The allocation ratio parameter sets the amount of flow obtained from the multi-commodity flow problem. Table 2 shows an example of the flow table and group table when 10 flows flow into an OpenFlow switch, 3 flows flow out from port 1, and 7 flows flow out from port 2.
# Attosecond Time Delay in Photoionization of Noble-Gas and Halogen Atoms
###### Abstract  
Ultrafast processes are now accessible on the attosecond time scale due to the availability of ultrashort XUV laser pulses. Noble-gas and halogen atoms remain important targets due to their giant dipole resonance and Cooper minimum. Here, we calculate photoionization cross section, asymmetry parameter and Wigner time delay using the time-dependent local-density approximation (TDLDA), which includes the electron correlation effects. Our results are consistent with experimental data and other theoretical calculations. The asymmetry parameter provides an extra layer of access to the phase information of the photoionization processes. We find that halogen atoms bear a strong resemblance on cross section, asymmetry parameter and time delay to their noble-gas neighbors. Our predicted time delay should provide a guidance for future experiments on those atoms and related molecules.  
attosecond time delay; giant dipole resonance; Cooper minimum; asymmetry parameter; noble-gas atoms; halogen atoms Article
# Coupled Thermomechanical Responses of Zirconium Alloy System Claddings under Neutron Irradiation
## 2 Thermomechanical Modelling Framework
### Thermomechanical Models of Zr Cladding  
In the current work, pure Zr, Zr-4 alloy, Zr-2.5 Nb-0.5 Cu alloy, and N36 alloy were studied. The thermal conductivity of Zr alloy from room temperature to melting point was shown to be temperature dependent [22]:  
\[k=8.8527+7.0820\times 10^{-3}T-2.5329\times 10^{-6}T^{2}+2.29918\times 10^{3}T^{-1} \tag{8}\]  
Irradiation growth and creep of cladding are important mechanical behaviors under neutron irradiation [23]. The irradiation growth of Zr and its alloys were calculated according to a power-law empirical model [24; 25]. The axial strain is described as  
\[\varepsilon_{x}=A(\Phi t)^{n} \tag{9}\]  
The strain increment of the irradiation growth is given as  
\[\Delta\varepsilon_{z}=A(\Phi t)^{n}-A(\Phi(t-\Delta t)^{n}) \tag{10}\]  
where \(\varepsilon_{z}\) is the axial strain, induced by irradiation growth, \(\Phi\) is the fast neutron fluence rate, and A and \(n\) are the material parameters. The model parameters of irradiation growth for different Zr alloys are shown in Table 1. A = 1.2 \(\times\) 10\({}^{-21}\) N/cm\({}^{2}\), \(n\) = 0.794.  
Due to the volume conserving of the irradiation growth of the cladding [26], the growth in the other two directions was assumed to be consistent; the radial strain \(\varepsilon_{r}\) is given by  
\[\varepsilon_{r}=-\left(1-\frac{1}{\sqrt{1+\varepsilon_{z}}}\right) \tag{11}\]
## 3 Results and Analysis
### Overlapping-Droplet-Segmentation-Test Validation and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Segmentation Method** & **Type of Overlap** & **S1/\%** & **S2/\%** & \(\overline{S_{2}}\)/\%** & **Average Time/s** \\ \hline \multirow{3}{*}{Method of this paper} & SC & 7.32 & 92.68 & & \\  & NP & 8.57 & 91.43 & 91.35 & 2.66 \\  & PA & 13.33 & 86.67 & & \\ \hline \multirow{3}{*}{Based on contour solidity with watershed algorithm} & SC & 37.45 & 62.55 & & \\  & NP & 10.17 & 89.83 & 76.19 & 3.92 \\ \cline{1-1}  & PA & 49.83 & 50.17 & & \\ \hline \multirow{3}{*}{Improved concave point matching} & SC & 95.12 & 4.88 & & \\ \cline{1-1}  & NP & 23.81 & 76.19 & 68.82 & 2.33 \\ \cline{1-1}  & PA & 38.77 & 61.23 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation performance of overlapping-droplet segmentation under different methods. in the segmentation of parallel overlapping droplets, the segmentation accuracy of this paper's method is better than the other two methods, showing stronger performance. In terms of overall performance, the overall average segmentation accuracy of this paper's method reaches 91.35%, which is 15.16 and 22.53 percentage points higher than that of the contour solidity and watershed-based algorithm and the improved-concave-point segmentation method, respectively. This result indicates that the method in this paper has a significant advantage in segmentation accuracy, and its performance is significantly better than the existing segmentation methods. In terms of segmentation time, the method in this paper also performs well. Compared with the algorithm based on contour solidity and watershed, the segmentation time of overlapping droplets on each water-sensitive test paper is shortened by 1.26 s on average, which shows a higher processing efficiency. Although the segmentation time of this paper's method is slightly increased (by 0.33 s) compared to existing concave-point-matching algorithms, this small time difference has a negligible impact on the overall efficiency and performance. In summary, the method in this paper significantly outperforms the existing advanced segmentation methods in the field of fog droplets in terms of segmentation accuracy, efficiency, and robustness, and provides an efficient and reliable solution for the task of overlapping fog droplet segmentation.
## 2 Systems Description
It is then gathered and stored in the FWST. The saltwater is heated to (w2) when it leaves the dehumidifier, and it then flows via an ORC condenser, where it is partially warmed using waste heat from the ORC condenser to reach (w3) before passing through a humidifier to moisten the air coming from the DW of DCS. The incoming air carries some of the pure water out to the dehumidifier as it evaporates, and the remaining pure water is drained as a brine at the end of the process (w4). The schematic of the modified system (MGS-II) is shown in Figure 1b. The system can generate electricity, space cooling/ domestic heating, and fresh water. The system consists of the same components and operations as the MCS-I. In addition, the system is improved by adding second stage dehumid (2 nd Deh. ), a heat recovery system (HR), and a thermal energy storage unit to recover (TES-2) heat from brine and desalinated water to use in domestic heating applications. In MCS-II, the air leaves the first stage dehumidifier at (a11) and enters the second stage dehumidifier to reach (a12). The desalinated water yields at (w6) mix with the desalinated water produced from the first stage dehumidification (w7) to give the total desalinated water yields at (w8). After that, a heat recovery system is used to recover the heat energy from desalinated water (w8) and brine water (w4) to obtain heating water used for domestic applications via TES-2 to recover most of the waste heat. This helps to achieve system optimization and more efficient operation. The psychrometric cycle describing the processes of air through MCS-II is shown in Figure 2b.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 1 Introduction
### Importance of Corrosion Detection  
Corrosion in pipes is a critical issue in various industries, including oil and gas, water supply, and chemical processing. Corrosion can lead to leaks, structural failures, and significant economic losses. Therefore, early and accurate detection of corrosion is essential for maintaining the integrity and safety of infrastructure.
## 2 Methodology  
The data sets, their development, and statistics, and the architecture of the VOC-Net and network training and validation approaches are described in the following subsections. A schematic of VOC-Net for automated classification of experimentally collected spectra is shown in Figure 1.  
In the above schematic, an overview of how VOC-Net can be be integrated with a spectrometer is illustrated. A spectrum, measured using a THz spectrometer (described in the experimental data section), is fed to VOC-Net which consists of convolutional, pooling, and dense layers. VOC-Net broadly performs two tasks, extracting relevant features from the spectra and classification. Feature extraction is a process where a raw spectrum is reduced to a set of derived values which captures the spectrum's most relevant aspects, by preserving its distinctive and unique information. Classification is the process of categorizing the spectrum as belonging to a specific class or molecule. VOC-Net classifies by producing a set of raw score values that can be converted to softmax scores, which indicate the probabilities that the spectrum belongs to each class. VOC-Net outputs the class with the highest softmax score/probability; hence, classifying the input spectrum. The softmax scores, \(\sigma\), are given by,  
\[\sigma(\vec{z})_{i}=\frac{e^{\vec{z}_{i}}}{\sum_{j=1}^{K}e^{\vec{z}_{j}}} \tag{1}\]  
where, \(\vec{z}\) is the input to the softmax layer and K is the number of molecule classes.  
Figure 1: VOC-Net model for the automated classification of absorption spectra measured with a THz spectrometer.
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the CHISel Platform
Stage 5: Solution Development Technical Specifications:
* Use blockchain standards (e.g., ERC-721) to authenticate NFT-based artifact annotations. * Enhance the user interface with intuitive tools for layer creation, editing, and multi-user collaboration. Iterative Development:
* Test the platform with diverse user groups, including restoration experts, museum staff, and students, to refine usability and collaborative features. Marketing Strategy:
* Promote CHISel through academic conferences, cultural heritage events, and partnerships with museums and research institutions. Stage 6: Testing and Refinement Functional Testing:
* Validate multi-user collaboration features, ensuring real-time consistency and data accuracy. * Test blockchain integration for secure data tracking and NFT issuance. User Feedback:
* Gather feedback through structured interviews and surveys to evaluate the platform's usability, efficiency, and collaborative capabilities. * Address usability concerns, such as confusing interface elements or slow rendering. Compliance Check:
* Ensure alignment with cultural heritage preservation standards and data security regulations. Stage 7: Deployment and Support Launch Strategy:
* Launch CHISel with targeted outreach to cultural heritage organizations, emphasizing its collaborative and secure features. * Provide live support and documentation to assist users during the onboarding phase. NFT Placement and Accessibility:* [leftmargin=*]
* Offer NFTs representing key restoration phases or unique annotations, available through CHISel's integrated marketplace. * Ensure NFTs are accessible to all users through intuitive interfaces and wallets. * Compatibility and Maintenance:
* Regularly update the platform with new features and improvements, addressing user feedback and emerging needs. Stage 8: Evaluation and Iteration  
Performance Metrics:
* Track engagement metrics, such as the number of collaborative tasks completed and NFT transactions. * Assess the platform's impact on restoration workflows and research outcomes. Long-Term Improvements:
* Expand CHISel's capabilities to support other types of artifacts or collections. * Use analytics and user feedback to refine gamified elements and improve collaborative tools. Applying the framework to the CHISel platform enhances its potential as a tool for collaborative cultural heritage research and restoration. By integrating gamified workflows, blockchain for secure data management, and user-friendly collaboration tools, CHISel can streamline complex workflows and improve data accuracy. These enhancements not only foster interdisciplinary teamwork but also promote long-term preservation and accessibility of cultural heritage artifacts.
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
## 2 Materials and Methods
### Fitness Function
#### 2.5.1 Calculation of the Heat Deposition  
The first step in analyzing a treatment plan is to compute the heat deposition. The physical characteristics and the placement of the focal point are determined by considering the nonlinear behavior of the ultrasonic wave as it travels through heterogeneous tissue. The amount of thermal energy that is deposited at the focal point is proportional to the medium's absorption of ultrasonic energy. It is possible to get an exact forecast of the heat deposition by using various models such as basic ray-tracing models [44], simplified forward wave propagation models [45], or precise full-wave models [46]. Unfortunately, none of these models can be used for TEA optimization because of their lengthy execution times (on the order of minutes).  
As a result, some assumptions are required to simplify the problem. The initial assumption is that the focus's center may be located at the coordinates determined by sonication \(S_{i}=[x_{i},y_{i}]\). This is accomplished via the HIFU transducer's precise electrical steering of the ultrasound beam [47]. Second, the focus is assumed to be an elliptical shape in form, with the length of the axis proportional to the distance from the HIFU transducer (see Figure 4). Based on the power of the transducer, elliptical axes can be determined by the variance and used to estimate the volume rate of heat deposition using the Gaussian distribution of energy in the focus [48; 49; 50], i.e., the acoustic model is not utilized to compute the heat source term. Finally, in order to keep things simple, only two-dimensional issues are considered, and it is assumed that the transducer axes are always parallel to the domain axes. At this stage, the total energy deposited in the tissue is proportional to the sonication time \(t_{on}\).
# Analysis of Cyber-Intelligence Frameworks for AI Data Processing
## 2 Background of Cyber Intelligence  
### Concept and Characteristics  
The classic concept of intelligence is related to the quality of the mind that allows us to assimilate, understand, reason, make decisions, and form a vision or idea of a specific reality [9]. In relation to this, in the context of security, intelligence can be understood as \"_The product of the collection, evaluation, analysis, integration and interpretation of all available information that is immediately or potentially significant to planning and operations_\" [10]. Applying this definition to the domain of cyberspace approximates the concept of cyber intelligence, a term with increasing presence and relevance in today's society in the face of exponential criminal growth, which requires reactive but especially preventive responses. Cyber intelligence is therefore an emmently proactive discipline that uses various branches of information security to achieve its objectives (vulnerability management, threat management, incident response, etc.) [11]. This is achieved by developing products that support the decision-making process regarding the risks to which organisations are exposed. In this sense, [10] defines cyber intelligence as \"_Intelligence activities in support of cybersecurity. Cyber threats are mapped, the intentions and opportunities of cyber adversaries are analysed in order to identify, locate and attribute sources of cyber-attacks_\". This concept can therefore be grouped into three main branches of application in cybersecurity, which will be described later: _cyber threat intelligence, incident response_, and _vulnerability management_.  
The data used to produce the intelligence must be evidence-based and meet minimum quality criteria. It must be useful to the organisation based on its characteristics and Priority Information Requirements (PRIs) [11]. In addition, the data must be processable and manageable, a basic characteristic for processing them using machine-learning techniques [12]. Of particular interest in cyberspace is the concept of OSINT (Open-Source Intelligence Techniques) [13], which allows the management of existing data from all kinds of open sources to be processed for intelligence purposes. In order to be able to structure and process this information and, in particular, to represent it in a visual and user-friendly way, a large number of cyber-intelligence platforms have been developed in recent years that collect intelligence from various information _feeds_, generating exchanges mainly through the use of STIX [14] or TAXII [15] standards.
# Characteristics of Changes in Contractile Properties of Muscle after Anaerobic Exercise
###### Abstract  
Tensionyography (TMG) has been described as an effective method for identifying differences in muscle response to athletic training stimuli. However, there is a lack of studies that evaluated changes in muscle contractile properties in response to anaerobic effort and related these changes to Wingate test parameters in more than one lower limb superficial muscle. The main contribution of this work is to evaluate changes in the contractile properties of muscles in response to anaerobic exercise. In a group of 20 physically active men, the body composition analysis was performed using a Tanita DC-360 device. The anaerobic effort was assessed by a Wingate test (30 s), and the contractile properties of muscles were measured using a TMG S2 device. The TMG parameters were measured in selected superficial muscles of the dominant lower limb. The study also calculated indices of the differences (d) and the size of the effect (r). An anaerobic effort in the form of the Wingate test resulted in greater stiffness (Dm) in the vastus lateralis muscle (\(p\) = 0.0365; r = 0.47) and a delayed response to stimulus (Td) in the vastus lateralis (\(p\) = 0.0239; r = 0.51) and vastus medialis (\(p\) = 0.0031; r = 0.66) muscles. The half relaxation time (Tr) (\(p\) = 0.0478; r = 0.44) and sustained contraction time (Ts) (\(p\) = 0.0276; r = 0.49) in the rectus femoris muscle were also increased. In contrast, a prolonged stimulus-response time (Td) with a decrease in sustained contraction time (Ts) was observed in the gastrocnemius lateralis (Td: \(p\) = 0.0054; r = 0.64 and Ts: \(p\) = 0.0012; r = 0.74) and gastrocnemius medialis (Td: \(p\) = 0.0229; r = 0.52 and Ts: \(p\) = 0.0054; r = 0.64) muscles. A significant decrease in contraction time (Tc) (\(p\) = 0.0051; r = 0.63) occurred only in the soleus muscle. In addition, significant correlations were shown between selected changes in contractile properties of muscle and parameters of the Wingate anaerobic test. Anaerobic exercise has a significant effect on changes in skeletal muscle contractility parameters. TMG is an effective method for identifying differences in muscle response to sports training stimuli.  
tensionyography; anaerobic effort; contractile properties of muscle
## 6 Case Study: Application of the Method on LoA 4 Valet Parking with V2X
### Driving Strategy--AVP  
The driving strategy in AVP includes the global path planning from the entrance of the parking garage to the selected parking slot, the latter requiring V2X connection to the most up-to-date status of the available slots; the strategy also addresses the local path planning, which is defined in the vicinity of the final parking position, virtually blocking the path for other, simultaneously moving vehicles. Trajectory planning and execution requires precise localization from the vehicle, which can be done either by triangulation the position of the vehicle using locator sensor or using the AD sensor set for the execution of SLAM or other localization methods based on prior knowledge of the map of the infrastructure. Cloud-based computation is also possible for driving strategy, which requires the communication of the vehicle parameters and status to the central computing unit.
## 2 Theoretical Background
### The Role of Trust  
In this paper, user reputation is the main focus, however, the concepts of trust and risk are important issues. Reputation and trust (or trustworthiness) are commonly confused [9] and used as synonyms, even though their meanings are distinctly different. [PERSON] et al. [2] defined trust as \"the extent to which one party is willing to depend on something or somebody in a given situation with a feeling of relative security, even though negative consequences are possible\". Risk is often taken in the hope of some gain or benefit. Therefore, risk can be viewed as a situation where the outcome of a transaction is important to a party, yet the probability of failure is not zero [2]. By integrating the two definitions one can conclude the following: The amount of risk a party may be willing to tolerate is directly proportional to the amount of trust it has in the other party.