esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper analyzes the vibration characteristics of the MRCS using the Rayleigh-Ritz method and Gram-Schmidt orthogonal polynomials. The study discusses the effects of boundary conditions and preload states on the natural frequency and modal loss factor and verifies the convergence and validity of the model. The results of the study are as follows:  
Figure 12: Comparison of theoretical and experimental vibration patterns of the MRCS: (**a,b**) \(n=1\), \(m=2\); (**c,d**) \(n=1\), \(m=3\); (**e,f**) \(n=3\), \(m=2\).  
1. The boundary conditions at both ends have a significant impact on the natural frequency and modal loss factor of the MRCS. The C-C boundary condition has the highest natural frequency and damping ratio, while the F-F boundary condition has the lowest. ii. The Pasternak elastic foundation has a significant effect on the natural frequency and modal loss factor of the MRCS. In low-order modes, the natural frequency increases with increasing \(k_{i}\) (\(i\) = 1, 2, 3, 4), and the modal loss factor decreases with increasing \(k_{i}\) (\(i\) = 1, 2, 3, 4). In higher-order modes, the effect of \(k_{i}\) (\(i\) = 1, 2, 3, 4) on the natural frequency and modal loss factor is the same as that of the artificial spring. Additionally, the radially applied position of the Pasternak elastic foundation does not affect the natural frequency and modal loss factor. iii. The preload displacement has a significant effect on the natural frequency and modal loss factor of the MRCS. The natural frequency increases with an increasing preload displacement, and the modal loss factor decreases with an increasing displacement. Furthermore, the effects of internal and external preload on the natural frequency and modal loss factor are the same.  
Conceptualization, methodology, investigation, software, formal analysis, and visualization, as well as writing the original draft, Y.B.; Providing resources, reviewing and editing the manuscript, and validating the data, P.D.; Managing data curation and assisting with investigation, K.F.; Supervising the project and managing resources, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Data Acquisition and Processing
### Acquisition of COVID-19 Information  
Nowadays, drugs against COVID-19 are divided into two categories according to their targets (genes) [32]. The first is to act on the immune cells of the human body to enhance the immune function of human. The second is acting on the COVID-19 itself, binding receptors, and the enzymes needed for its replication. Through gathering the information of COVID-19, we have screened out four targets (genes) of COVID-19 with clear function and high reliability. They are RNA dependent RNA polymer (RdRp) [32], ACE2 [32], pp1 ab [33], human immunity virus type 1 protection (pol) [34]. Therefore, we link the COVID-19 entity to KG through four drug-gene interactions: COVID-19-RdRp, COVID-19-ACE2, COVID-19-pp1 ab and COVID-19-pol.
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 2 Materials
### Steel Plate  
Two steel plates with different roughness (i.e., smooth and rough) are used as the structural material in the Martian simulant-structure interface, as presented in Figure 3. The size of the steel plates is 140 mm \(\times\) 100 mm \(\times\) 11 mm. In this study, the rough steel plate is made by gluing a 2 mm thick layer of the mixture of epoxy resin and Fontainebleau sand (30 g of epoxy resin per 100 g of sand passing from the sieve of 0.63 mm). While no sand is glued on the smooth plate.  
The roughness of the steel plates is measured along with two directions of the plate surface (shear direction and perpendicular to the shear direction). The roughness parameters of arithmetic average height (\(R_{\text{a}}\)) and largest peak-to-valley height (\(R_{\text{max}}\)) are summarized in Table 3. The \(R_{\text{a}}\) values of the smooth and rough steel plates are 1.0 \(\upmu\)m and 45.1 \(\upmu\)m, respectively, as shown in Table 3. The \(R_{\text{max}}\) values of the smooth and rough steel plates are 7.9 \(\upmu\)m and 277.0 \(\upmu\)m, respectively (Table 3). Normalized roughness (\(R_{\text{n}}\)) is used for evaluating the interface roughness, in agreement with the literature [10; 11; 12; 13; 14; 15; 16]:  
\[R_{\text{n}}=\frac{R_{\text{max}}}{d_{50}} \tag{1}\]  
The \(R_{\text{n}}\) values of the smooth and rough steel plates are 0.14 and 4.86 (Table 3).
## 1 Introduction
Concerning the determination of micro- or nanoparticles, additional techniques based on thermal analytical methods coupled with mass spectrometry [22; 23] or vibrational spectroscopy, including Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy [24; 25; 26; 27; 28], can be applied to determine information about the sample composition. Whereas pyrolysis-gas chromatography coupled with mass spectrometry has proven to be effective for nanomaterial analysis [11; 29], both FTIR and Raman micro-spectroscopy are non-destructive techniques suitable for the detection of sub-micromaterials, providing detection limits of about 20 \(\upmu\)m and 1 \(\upmu\)m [30; 31], respectively. Raman micro-spectroscopy is frequently used as a valid alternative to infrared spectroscopy due to its common advantages of high efficiency, negligible interference from water, and intrinsically higher spatial resolution. In addition, liquid chromatography-mass spectrometry (LC-MS) is expected to evolve as one of the most versatile and reliable analytical techniques for the characterization of NPs, offering a valuable tool to advance the knowledge of the properties and behavior of nanomaterials and to monitor their eventual long-term effects on health and the environment, which have not been predicted so far [32]. With reference to micro- and nanoplastics, the LC-MS technique plays an important part in the context of their pollution, since it can be used not only for particle detection, identification, and quantification but also for the determination of organic contaminants in micro- and nanoplastic samples by studying sorption/migration phenomena [33]. In the context of a research project that aimed to develop innovative analytical methods to characterize the chemical load present in polar regions, with a focus on micro- and nanoparticle contamination in Antarctic snow, in this study, TEM/EDX and Raman micro-spectroscopy were exploited to qualitatively evaluate critical parameters such as the size, morphology, and chemical composition of individual particles and their aggregates at the micro/nanoscale. This information can be of pivotal importance to assess the occurrence and distribution of small micro- and nanomaterials in remote regions, identifying potential sources, and their impact on the environment.
# A Guidance for Blockchain-Based Digital Transition in Supply Chains
## 4 Findings
### Concerns  
Interviewees also underscored several risks related to blockchain implementation in SCs. The main concern in SC operations is about the current designed systems; in fact, a blockchain-based system in the SC is a centralized system, which reduces the decentralization capacity.  
Furthermore, interviewees have a clear consensus on energy issues for the implementation of these emerging systems. In this instance, blockchain technology might powerfully impact industry applications, but it is not a green technology, and environmental issues need to be considered in advance. Accordingly, innovation and research in this field may help reduce energy consumption and increase performance. However, this would be a responsibility issue for the whole research community that is working on energy-intensive technologies such as blockchain.  
Furthermore, common concerns highlighted by interviewees are related to the government, consortiums, share costs, legal implications, new technology lawyers, lack of efficiency, expensive resources, and cost of computational power. In addition to these issues, interviewees mentioned the barriers regarding energy questions around the world, the negative impacts of the technology on global warming, the high expectations from potential implementation in the industry, and the concern about possible exit strategies from blockchain data platforms. Other criticisms are more oriented to technical issues and drawbacks such as energy use, low performance (slower than other platforms/systems in use), migration paths, scale-up capability, and other core technology issues.  
Interviewees pointed out that, at this stage, blockchain products are data platforms centralized by one or more big companies. This design does not simplify the creation of any value in a chain. Interviewees remarked that blockchain solves only the consortia building process for value chains. In this instance, companies play in a centralized way, deploying platforms as a tool to manage the complexity of stakeholders for international SCs.  
Moreover, the common awareness among interviewees is about the infancy and complexity of blockchain. Whereas blockchain does not represent an integrated solution thus far, practitioners are struggling to determine how complex and how big the future blockchain market will be. Even though start-ups would play a fundamental role in blockchain development and innovation, interviewees are skeptical about blockchain applications for small- and medium-sized enterprises (SMEs). This skepticism is related to the adoption of international SCs for multinational companies, where the involved stakeholders would implement the technology to increase a specific operational performance in a wide and complex ecosystem. Thus, in this instance, start-ups would have difficulties finding the right business space. However, interviewees remarked that highly relevant SMEs play in this emerging market. SMEs have agility, dynamism, and openness to boost innovation at a further stage, pushing faster digital transformations and new skills.
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
###### Abstract  
The introduction of the maritime autonomous surface ship (MASS) to the maritime industry will open up a new era and bring about a new paradigm shift in terms of cost efficiency, maritime accidents, and human resources. Various studies are currently being conducted to realize the MASS. Understanding the scope and direction of these studies will be of great help for future MASS research. In this study, the current development status of technologies for autonomous ships is identified, and considerations and directions of improvement are suggested for six major research fields that cover all technological issues of MASS. Firstly, the results of the regulatory scoping exercise (RSE) on the International Maritime Organization (IMO) conventions to accept MASS are identified; in particular, human elements are identified as vital issues to be considered for the design and operation of MASSs. Secondly, various studies on the decision-making system are identified, and the future direction is suggested. Thirdly, in terms of ship design and propulsion system, design changes for autonomous cargo ships are investigated, with their potential impacts to be considered. Fourthly, the communication system will need to be robust and supported by multiple systems to minimize potential risk with third-party infrastructures, and suitable protection of systems, networks, and data will be required as an integral part of the safety system for cybersecurity. Fifthly, issues of maintenance and repair are identified, with a maintenance strategy to be considered. Lastly, hazard analysis of the autonomous ship is explored, and system-theoretic process analysis (STPA) and the functional resonance analysis method (FRAM) are identified as the most representative new methods that can be used for hazard analysis of autonomous ships.  
maritime autonomous surface ships (MASSs); autonomous vessels; shore control center (SCC); regulatory scoping exercise (RSE); human elements; risk assessment; HAZID +
Footnote †: journal: Applied sciences
## 3 Results
### Approving of the Constructed Models  
The torsion with tension test was conducted experimentally and simulated using the FE method to approve the constructed models. As shown in Figure 10a, the experimental and calculated torque vs. torsion curves are in good accordance (the average absolute relative error was 6.9%). The traces of the severe plastic deformation are seen on the fracture surface. The failure proceeded after large plastic deformation by torsion and with insignificant localization due to tension forces.  
After the deformation, the microstructure is fully recrystallized due to large plastic deformation before the fracture (Figure 10b,c). The average grain size was 10 \(\pm\) 4 and 7 \(\pm\) 3 \(\upmu\)m in the center and near the surface of the sample, respectively. These values are in good accordance with the calculated values. The grain size value obtained by FE simulation changed from 11.3 to 8.5 \(\upmu\)m in the samples' cross-section's radial direction.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.7 Neural Networks  
The ANN is based on a human neuron, is formed by layers, and has the ability to solve problems without the need for programming. For example, to perform a simple calculation in traditional programming, one would have to enter data and a formula to solve the calculation. In contrast, the ANN will be able to predict the result of the calculation without the need for programming. To achieve this, the neural network must be trained, evaluated, and validated with examples [27; 37; 50; 51; 52; 53]. The structure of an ANN can be seen in Figure 5.  
Figure 5: Structure of an Artificial Neural network.  
There are many types of ANN architectures, and one of the most common ones for detecting NTLs is the multi-layer perceptron ANN with backpropagation, which is generally used as a binary classifier [4; 16; 17]. In [93], a study that employs neural networks for detecting NTLs is presented; the model achieves a success rate of 67.5%. The use of neural networks has allowed the transformation of one-dimensional energy consumption data into a two-dimensional form, which is then used to construct a neural network algorithm. This establishes the behavior of energy consumption, which is utilized for detecting potential cases of fraud [54].
## 1 Introduction  
Friction drilling is a hole production method, in which the metal cast sheet materials are drilled without cutting since a material sample heats up with the effect of the friction in the area of the conical tool and material contact. The generating temperature softens the sample, and then it is pushed downward by the tool, where the tool proceeds into the material to generate the bushing formation, increasing the connection strength in sheet materials. Therefore, investigation of the bushing formation is the main goal of TFD operations [1]. It is generally desired to obtain bushings only with a cylindrical shape, having no cracks and petal formations, in order to increase the connecting strength [2]. Higher bushing length leads to a greater screwing area as well as an increase in the connection strength of sheet materials [3]. Bushing shape, especially bushing height and wall thickness, depends on both the thickness of the drilled material (t) and the diameter of the hole (d), and hence it depends on the ratio of the drilled material and the diameter of the hole [4]. Bushing shape is completed in five steps and categorized according to the geometrical regions of the conical tool and proceeding dimension of the tool into the material [5]. The bushing height is measured to be approximately 2-3 times of the (t) [6].  
A decrease in bushing height leads to an increase in the wall thickness of the bushing formation, which provides a greater screwing area by connecting sheet materials [7; 8; 9; 10]. The height of the bushing depends on (d) rather than (t). The height of the bushing does not change with (t) for selecting constant hole diameters [11]. However, cracks and petal formations on the bushings cause the strength of the connections to decrease. Furthermore, deformation and fracture of the drilled material cause the occurrence of petal formation and cracks on bushing shape. Better selected tool geometry provides improved bushing formations [12].  
During the TFD process, the bushing shape is formed with the help of the required temperature that occurs between the tool--workpiece interface owing to the friction. Due to this rise in temperature, the material thermally softens, and the yielding point decreases to facilitate plastic deformation to provide the bushing formation. The appropriate process temperature is generally between 1/2 and 2/3 of the melting temperature of the drilled material [13; 14; 15].  
The main purpose of this paper is to investigate the effects of t and d on the geometrical dimensions of the bushing shape, such as bushing height and its wall thickness. Furthermore, the investigation of the proportion of the volume of the bushing shape to the total volume of the evacuated material is a crucial result. Therefore, this proportion was investigated experimentally in the TFD of the A7075-T651 aluminum alloy.
# Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
###### Abstract  
The trade-off between the design phase and the experimental setup is crucial in satisfying the accuracy requirements of large deployable reflectors. Manufacturing errors and tolerances change the root mean square (RMS) of the reflecting surface and require careful calibration of the tie-rod system to be able to fit into the initial design specifications. To give a possible solution to this problem, two calibration methods--for rigid and flexible ring truss supports, respectively--are described in this study. Starting from the acquired experimental data on the net nodal co-ordinates, the initial problem of satisfying the static equilibrium with the measured configuration is described. Then, two constrained optimization problems (for rigid or flexible ring truss supports) are defined to meet the desired RMS accuracy of the reflecting surface by modifying the tie lengths. Finally, a case study to demonstrate the validity of the proposed methods is presented.  
large deployable reflector; tie adjustment; experimental setup Article
## 2 Communication System Model and Theory
### Correlation Cost Factor-Based Updates to Matched Filters Reception Method for M-SS (CCF-MSS)
#### 2.3.3 Theoretical Derivation of the Receiver Decoding Process
The processed signals are then input into the spreading code index library for correlation operations, yielding  
\[R_{i,c,j}= <\tilde{r}_{c,j}(t),c_{j}(t)>,j=1,\ldots,M \tag{17}\] \[R_{i,s,j}= <\tilde{r}_{s,j}(t),c_{j}(t)>,j=1,\ldots,M\]  
\(R_{i,c,j}\) and \(R_{i,s,j}\) represent the correlation results of the \(i\)-th symbol with the \(j\)-th local spreading code, and the noise component becomes negligible after correlation despreading. The sum of the two yields is  
\[R_{i,j}=R_{i,c,j}^{2}+R_{i,s,j}^{2} \tag{18}\] \[= <a_{i}[0]c_{i,b_{i}}(t-\tau_{i}(t)-iT_{s}),c_{j}(t)>^{2}\]  
The decoding is completed after outputting the despreading result \(j\). Here, \(j\) represents the despreading result of the transmitted \(i\)-th. At this point, the carrier phase shift \(\phi_{i}(t)\) has been eliminated and the remaining \(\tau_{i}(t)\) represents the time-domain expansion or compression of the spreading code caused by Doppler effects, which can be mitigated by resampling the signal.
## 5 Conclusions  
In this paper, a probabilistic, multi-objective approach for CM that considers both voltage stability and wind energy was proposed. P-VSCOPF was formulated as a multi-objective problem not only for maximizing social welfare but also for improving the voltage stability margin. It can be used to assist both ISOs and planners in visualizing the nonlinear relationships between power transfer levels and voltage stability margins via the MOPSO algorithm. MCSs were applied to select the input variables of both wind speed and load by using the probability distribution functions. The problem was solved using 1000 trials of an MCS. The VSI was calculated to increase the accuracy of the sorting and ranking technique in the contingency analysis. The optimal location of a wind farm, with a special emphasis on CM, was also determined using the CDF. The influence of the wind output variations on the congestion price, which is related to both the LMPs and the NCPs, was analyzed. The VSM, ALC, and TML were employed as measures of voltage stability. To illustrate its effectiveness, the proposed approach was both demonstrated and compared to the conventional OPF and VSCOPF using the modified IEEE-24 bus system with wind farms. The simulation results revealed that the P-VSCOPF achieved discernible advantages over all of the other OPF methods. It was able to not only minimize the LMP differences and lower the NCP for CM but also increase the VSM. Therefore, the information provided by the proposed approach can be useful in both the planning and operation of various power systems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **TTL (MW)** & **System Losses (MW)** & **TML (MW)** & **ALC (MW)** & \(\textit{Pays}_{\text{ISO }}\) (\$/h)** & **VSM (MW)** \\ \hline Conventional OPF & 3056.97 & 52.63 & 3246.19 & 189.22 & 1049.95 & 364.37 \\ VSCOPF & 3139.86 & 42.39 & 3555.11 & 385.25 & 820.13 & 640.87 \\ P-VSCOPF & 3222.77 & 41.92 & 3638.04 & 415.36 & 812.26 & 685.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of optimal solutions of three methods. Figure 10: Voltage profiles of three methods. **Acknowledgments:** This research was supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute. (grant number: R15 XA03-55). **Author Contributions:** [PERSON] proposed the content of this paper and [PERSON] coordinated the proposed manuscript approach. **Conflicts of Interest:** The authors declare no conflict of interest.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Dimensioning in Optical Networks
#### 5.3.5 AI and Machine Learning for Statistical Modeling of Optical Networks
The integration of these advanced computational techniques empowers network operators with prescriptive insights, moving beyond reactive troubleshooting to proactive optimization [87]. As optical networks evolve towards disaggregation and open architectures, the role of AI/ML in statistical modeling will become even more critical, providing the intelligence required to navigate increasing complexity and unlock the full potential of future optical communication systems. AI and ML are also transforming optical network management by enabling sophisticated statistical modeling of traffic and QoT [88; 89]. These techniques predict network congestion, identify anomalies, and optimize routing dynamically, moving beyond traditional rule-based systems [88]. By analyzing vast datasets of network performance, AI/ML models can proactively mitigate issues like signal degradation and crosstalk, ensuring high QoT and efficient bandwidth utilization [89]. This intelligent automation leads to more reliable, resilient, and cost-effective optical networks, crucial for handling ever-increasing data demands [91].