esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Causes and Transmission Mechanisms of Railway Engineering Safety Risks
## 7 Discussion
[PERSON] [21] used the fuzzy interpretation structure model to build the fuzzy ISM model of the accident risks of the train service system, and they used the D-S evidence theory and Bayesian network to build an accident prediction model of the railway train service system, and combined with the actual train service system data, they conducted accident prediction and analysis for the train service system. In the above studies, most of the analysis methods of railway safety risks used by scholars were relatively simple. Although some scholars combined multiple analysis methods and put forward their own innovations in the research, they did not analyze the correlations between safety risks, resulting in certain deficiencies. With the development of science and technology, new technologies and new equipment are constantly introduced into railway operation and management. Nonetheless, the railway safety situation will be related to various factors such as the uneven quality of workers, the limited management level, and sudden and severe natural disasters. How to study the existing railway accident data, excavate the internal mechanisms, potential laws, leading factors, and transmission mechanisms of the accidents, and how to apply the subsequent discoveries and experience to actual operation management, so as to improve the overall safety level of the railway operation management system, has been the content of this paper. Firstly, the method of multi-objective index screening was used in this paper to make index screening more suitable for practical applications. Secondly, a Bayesian network was used, the structure of the Bayesian network was optimized, and the causal logic chain of the accident result was obtained. Then, based on the research results of this paper, we analyzed the impacts of personnel risk, facility and equipment risk, environmental risk, and management risk, respectively, and those risks combined. More precisely, we analyzed the causes of accidents and the relationships between them, and, in doing so, we made up for the lack of previous research on railway safety risks in the study of risk transmission mechanisms.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 4 Corrosion Detection Approaches
### Damage Analysis with NDE and SOM  
NDE (non-destructive evaluation) is a method that is used to detect the texture changes in a corrosion image. This method is also known as non-destructive testing (NDT) and non-destructive inspection (NDI). NDE enables the assessment of the characteristics of a component, material, or structure without damaging the subject itself. Moreover, the SOM (self-organizing map) is used to classify the corroded regions in the segmented NDE images. SOM is an unsupervised learning technique for the classification of similar patterns and features within the same class. The local parameters are modified using a group of neurons instead of the sample training of class membership. The texture variations of corrosion images can be classified using NDE and SOM.  
NDE and SOM were used in [79] to investigate deteriorations caused by rebar corrosion and corrosion-induced cracks. The electromagnetic wave radar of the NDE assessed the cover thickness of the specimens, which were 30 mm and 60 mm. The internal damage and corrosion rate were judged during the maintenance inspection. The electrolytic corrosionexperiment was conducted to evaluate the developed NDE and SOM techniques. According to the results, this technique managed to detect the internal crack region and rebar corrosion of concrete structures. Moreover, the SOM-based neural network [80] was used with ant colony optimization to identify the prestressed steel mechanism and corrosion evolution analysis. Additionally, the acoustic emission of NDE was used to characterize the signals from the prestressed steel. Four acoustic emission sources were used to identify four damaged areas on the prestressed steel strands. This showed that this technique was another alternative for corrosion analysis and detection.
## 3 An Indirect Measurement Method for Gas Consumption
### Key Technologies
#### 3.2.5 Correction by Gas Temperature and Pressure  
Diaphragm gas meters are measured by the volume of gas, but the volume is compressed or expanded by the influence of pressure and temperature [12]. For gas companies in China, the standard temperature of gas is 293 K and the pressure is 101.325 KPa. Therefore, it makes sense to correct the gas volume based on the gas temperature and pressure.  
The compression factor is used in the project to describe the state of the gas, which is described as [13]:  
\[pV=nZRT, \tag{5}\]  
where \(p\) is the absolute pressure of the gas, \(V\) is the volume of the gas, \(n\) is the amount of the substance, \(Z\) is the compression factor, \(R\) is the general gas constant, \(T\) is the thermodynamic temperature of the gas.  
For natural gas in the metered state and the same amount of natural gas in the standard state, the volume conversion Equation (6) can be obtained according to Equation (5), which can be represented as:  
\[V_{0}=\frac{p}{p_{0}}.\frac{T_{0}}{T}.\frac{Z_{n}}{Z_{g}}.V, \tag{6}\]  
where \(V\) is the volume of the gas under the metered state, \(V_{0}\) is the volume under the standard state, \(Z_{g}\) and \(Z_{n}\) are the compression factors in the working state and the standard state respectively, \(\frac{Z_{n}}{Z_{g}}\) can be expressed by the symbol \(K\). The pressure in urban gas pipeline belongs to the low pressure range, the compression factor has little change, so \(K\) can be considered as 1 [14]. Therefore, the Equation (6) can be simplified to the following:  
\[V_{0}=\frac{p}{p_{0}}.\frac{T_{0}}{T}.V, \tag{7}\]  
where \(\frac{pT_{0}}{p_{0}T}\) is defined as the volume correction factor in this paper.
## 2 Engineering Background  
### Geologic Conditions  
This study investigates the surrounding rock instability mechanism of a roadway induced by strong mining in an extra-thick coal seam and the synergistic control mechanism of blasting pressure relief and application of long bolts. The Wenjiapo Coal Mine (Binchang Mining Co., Ltd., Shaanxi Coal Chemical Group, Binzhou, China) with an annual output of 5 Mt is taken as a case study. The Wenjiapo Coal Mine is located in the eastern Binchang mining area in the administrative area of Binzhou City, Shaanxi Province, China (Figure 1a). The position of air return roadway 4106 (AR 4106) is shown in Figure 1b. There are five main lanes in the 41-panel area south of the working face. The western side is the design position of working face 4107 and the eastern side is working face 4105 (WF 4105). The width of the coal pillar between the working faces is 44.5 m. The Lujia-Xiaolingtai anticline is present in the southern part of the working face with a strike of ~46-51\({}^{\circ}\), which causes the working face to be generally higher in the south and lower in the north with a downhill slope of 1-4\({}^{\circ}\). The risk level of rockburst in the roadway is weak. The burial depth of the working face is 711-730 m with an average depth of 718 m. The coal seam is 9.5-11.32 m thick with an average thickness of 10.37 m, and is therefore classified as an extra-thick coal seam. The longwall top coal caring method is adopted for mining with a mining-to-caving ratio of 0.4. The roof in the working face is managed using the caving method.  
Figure 2 shows the stratigraphy of AR 4106. The immediate roof of AR 4106 is sandstone with a thickness of 1.33 m and moderately developed cracks; the main roof is fine sandstone with a thickness of 10.52 m and dense and hard lithology; the immediate floor is aluminum mudstone with a thickness of 6.26 m; and the main floor is mudstone with a thickness of 12.20 m. X-ray diffraction experiments were carried out on the aluminum mudstone floor, as shown in Figure 3. The results show that the rock mass contains 59.9% clay minerals. A 500-mm bottom-coal layer is reserved on the floor to retain the mudstone strength due to the serious roof water seepage during roadway excavation. The water source was mainly confined fissure water with an inflow of 10-20 m\({}^{3}\)/h during excavation.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 2 Related Work
### Contribution  
The most widely used methods to predict the link quality of WSN use input metrics acquired from the transceiver such as RSSI, LQI, and SNR or metrics calculated by software such as PRR, RNP, and EXT. Other algorithms seek to improve prediction accuracy using many metrics, historical data, and statistical trends. However, despite the reported successful results, some of these metrics cannot be easily acquired from the transceiver or are not readily available. In addition, the quality of a link can also be affected by other factors such as the distance from the transmitter to the receiver, the transmission rate, the data payload, the transmitter's power, and the presence of noise and interference, as well as the random presence of obstacles and objects that can attenuate, reflect, or scatter the radio frequency signal, consequently increasing the bit error rate (BER).  
For complex industrial environments, the application of a DNN classification algorithm is a better choice to predict LQE than other traditional methods based on statistical models and fuzzy logic [34; 35]. Moreover, the MLP model proposed in this paper compared to other machine learning models can help adjust the best WSN parameters, such as distance, transmission rate, power, and data payload in the transceiver. In addition, our work differs significantly from the current state of the art because, at the moment, there is no industrial solution to predict the LQE and the associated information reliability with alarm signals produced by modern wireless ANDON towers. The application of this technology will further the digitalization and evolution from a traditional factory to a smart factory within the Industry 4.0 model, particularly in developing countries, where the incorporation of new technology is slow and difficult.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Enhancing Communication
#### 4.1.2 Automated Reporting and Documentation  
The maritime industry relies heavily on accurate and timely documentation, including logs, reports, and compliance records. Traditionally, the creation and management of such documents are labor-intensive processes prone to human error and inconsistency. LLMs can automate the generation of reports by extracting relevant information from operational data and formatting it according to standardized templates [104; 105].  
By streamlining the creation of logs and reports, LLMs reduce the administrative burden on crew members, allowing them to focus on critical operational tasks. Automated documentation also enhances consistency and compliance with regulatory requirements by adhering to prescribed formats and content standards. Additionally, LLMs can assist in summarizing complex information, making it more accessible to stakeholders who need to review operational reports quickly [121].
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Multimedia Content Protection Techniques
#### 2.6.4 Multimedia Fingerprinting  
Unlike digital watermarking, which is incapable of tracing back the source of piracy, multimedia fingerprinting (also called transaction tracking) can trace back the identities of the pirates (colluders) upon finding an illegal copy. This traceability is achieved by embedding a unique user-specific information, known as a fingerprint, into different copies of the same content. A multimedia fingerprinting algorithm is a protocol between the content owner and the customer that involves three processes: fingerprint generation, embedding operation, and traceability of pirates from pirated/colluded copies.  
A multimedia fingerprinting scheme is expected to address the following constraints [34]:  
* Robustness: A fingerprint's robustness against signal processing operations is determined by the adopted watermark embedding method. Thus, a robust watermarking algorithm must be adopted so that the fingerprinting scheme can trace an illegal re-distributor after the digital content has been manipulated by common signal processing attacks.
* Collusion resistance: While digital fingerprinting may be effective at identifying a single adversary, multiple malicious buyers may collaborate to launch powerful collusion attacks against the fingerprinting system. By comparing their different versions, the colluders can attempt to identify the locations containing the fingerprint signal, remove the information from these locations and thereby create a copy that cannot be traced back to any of them. Thus, a fingerprinting scheme must be designed to withstand such collusion attacks.
* Quality tolerance: Fingerprinted content should have good visual quality and perceptual similarity to the original content.
* Embedding capacity: The capacity determines the length of fingerprint allocated to each user. The fingerprint is a binary string that can be long. Therefore, a digital fingerprint system should have a large enough embedding capacity to accommodate a full fingerprint.  
From the customer's point of view, a traditional fingerprinting protocol between him/her and the content owner is unattractive, because during the embedding procedure, the content owner obtains the identity information of the customer. This enables a malicious content owner to embed the identity information of the customer into any content without the customer's consent, and subsequently, accuse him/her of illegal re-distribution. To eliminate this threat, anonymous fingerprinting protocols were developed based on cryptographic tools (such as homomorphic encryption, secure multiparty computation or zero-knowledge proof protocols). A complete and sound anonymous fingerprinting protocol [35] is expected to provide buyer frameproofness, traceability, collusion resistance,anonymity, non-repudiation, dispute resolution and unlinkability. Recently, a growing number of anonymous and collusion-resistant fingerprinting techniques have been proposed for multimedia content.
# Secure and Efficient Authentication Scheme in IoT Environments
## 5 Evaluation
### Summary  
In this section, as depicted in Figure 11, we summarize the proposed scheme's performance in comparison with the existing schemes based on security necessities, cost calculation, and functional requirement factors using the earlier analysis results in Sections 5.1 and 5.2. The first, second, third, and fourth bars in Figure 11 represent computation, communication, storage cost, the types of security attacks prevented, and the number of additional security features (i.e., crucial functional requirements) supported. The analysis results in Figure 11 show that recent authentication schemes have high computation cost or high storage cost or high communication levels. Furthermore, the existing schemes suffer from various security attacks and lack other crucial security requirements, such as friendly password selection and its security and update policy. However, the proposed scheme eliminates all the previously mentioned security-related problems and vulnerabilities of the existing schemes and prevents 12 types of security attacks. Moreover,the proposed scheme also supports prevention against four more security attacks, which we explained in Section 4.1. Therefore, the proposed scheme removes security weakness from the recent existing schemes and prevents 16 types of major security attacks. Furthermore, the proposed scheme not only secures against 16 major attacks but also successfully improves security mechanisms by providing four additional security features (i.e., mutual authentication, session-key agreement, friendly password selection, and updates) at the same time, which is absent in recent existing schemes. In addition, the proposed scheme maintains the lowest computational cost of \(9330\times 10^{-1}\)\(\upmu\)s, the lowest storage cost of 384 bit, and the lowest communication cost of 1408 bit. Due to the lightweight nature of the proposed scheme, the proposed scheme is significantly more efficient in handling a high number (i.e., millions or zillions) of authentication requests in real-time IoT cloud scenarios. This lightweight flexibility helps the proposed scheme to achieve significant optimized usage of memory, electricity power, and computational power and efficiently reduce traffic on the communication channel and improve the response time of authentication requests. This is the significant contribution of this paper, which will play a substantial role while dealing with a huge organizational need.
## 2 Numerical Method and Validation
### Numerical Method and Boundary Conditions  
The numerical simulations in the present study are implemented on ANSYS-CFX platform. The numerical method is based on the finite volume approach [19] to solve high-fidelity Reynolds Averaged Navier-Stokes (RANS) equations. The equation is as follows:  
\[\frac{\partial U}{\partial t}+\frac{\partial F}{\partial x_{i}}=\frac{\partial G }{\partial x_{i}}, \tag{1}\]  
where \(U,F,\) and \(G\) are conservative variable, inviscid flux, and viscous flux, respectively. The viscous term is discretized by a second-order central difference scheme and a second order backward Euler scheme is adopted for time discretization [20]. The Shear Stress Transport (SST) [21] turbulence model is employed for its satisfactory simulative ability of typical flow phenomena including the low-speed turbulent boundary layer [22], transonic shock wave, and shock-boundary layer interaction and separation, which could occur in the low-aspect-ratio configuration. Free-flow conditions and no-slip boundary conditions are separately assigned to far-field surfaces and all solid surfaces [23].
## Abbreviations  
\begin{tabular}{l l} DFT & discrete Fourier transform \\ EFI & electric field instrument \\ EIA & equatorial ionization anomaly \\ EPB & equatorial plasma bubble \\ F10.7 & solar radio flux \\ GNSS & Global Navigation Satellite System \\ IBI & ionospheric bubble index \\ LAIC & lithosphere-atmosphere-ionosphere coupling \\ LEO & low Earth orbiting \\ LOC & local time \\ LP & Langmuir probe \\ Ne & electron density \\ PCC & Pearson correlation coefficient \\ PNG & Papua New Guinea \\ PSD & power spectral density \\ SFU & solar flux units \\ SIID & seismically induced ionospheric disturbance \\ STFT & short-term Fourier transform \\ TEC & total electron content \\ USGS & United States Geological Survey \\ UTC & Universal Time Coordinated \\ \(\lambda\) & geodetic longitude \\ \(\phi\) & geodetic latitude \\ \end{tabular}
# An Optimized Flexible Accelerator for Elliptic Curve Point Multiplication over NIST Binary Fields
###### Abstract  
This article proposes a flexible hardware accelerator optimized from a throughput and area point of view for the computationally intensive part of elliptic curve cryptography. The target binary fields, defined by the National Institute of Standards and Technology, are \(GF(2^{163})\), \(GF(2^{233})\), \(GF(2^{283})\), \(GF(2^{409})\), and \(GF(2^{571})\). For the optimization of throughput, the proposed accelerator employs a digit-parallel multiplier. The size of the digit is 41 bits. The proposed accelerator has reused the multiplication and squaring circuit for area optimization to compute modular inversions. Flexibility is included using three additional buffers on top of the proposed accelerator architecture to load different input parameters. Finally, a dedicated controller is used to optimize control signal handling. The architecture is modeled using Verilog and implemented up to the post-place-and-route level on a Xilinx Virtex-7 field-programmable gate array. The area utilization of our accelerator in slices is 1479, 1998, 2573, 3271, and 4469 for \(m=163\) to 571. The time needed to perform one-point multiplication is 7.15, 10.60, 13.26, 20.96, and 30.42 us. Similarly, the throughput over area figures for the same key lengths are 94.56, 47.21, 29.30, 14.58, and 7.35. Consequently, achieved results and a comprehensive performance comparison show the suitability of the proposed design for constrained environments that demand throughput/area-efficient implementations.  
hardware; accelerator; elliptic curve cryptography; point multiplication; FPGA +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Materials and Methods
### Model Parameter and Boundary Conditions  
The model is constructed using ANSYS DesignModeler three-dimensional modeling software (version number 24.1) to facilitate better interaction with Fluent (Figure 1). The dimension of the computational domain is 50 mm \(\times\) 80 mm \(\times\) 100 mm. The nozzle length is 3 cm. The outlet of the nozzle is elliptical in shape. The major axis of the ellipse, \(l\) = 5 mm, is fixed. The minor axis of the elliptical, \(d\) (the radius below the middle finger), is a variable. The cone angle of the nozzle exit is \(\theta\). The computational domain as a whole is divided by a hexahedral mesh. In order to ensure the accuracy of the calculation results, the mesh is refined in the middle part of the spraying area. The pressure inlet boundary conditions are used for the nozzle in the model, and the pressure outlet boundary conditions are used for the computational domain outlet. Symmetric boundary conditions are used for the rest of the domain. The velocity-pressure coupling is completed using the simple algorithm, and the discrete equations are in second-order windward format. To ensure that the flow at the wall can be accurately captured, the scalable wall functions is utilized.  
In order to study the effects of initial jet flow pressures, cone angles, and nozzle diameters on the flow characteristics of the jet, numerical simulation analysis is carried out under different working conditions. The specific working condition table is shown in the following Table 1.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 6 Selected MT-InSAR Techniques Using HPC or Cloud-Based Platforms
### MT-InSAR Processing via Cloud-Based Platforms and Services
It is essential to highlight that large amount of computing resources should not be confused with the concept of efficient use of the computing resources, where the former case does not necessarily imply high performance computing. As a general consideration, the main advantage of the CC-based investigations within the InSAR framework is the direct access to free available EO data. However, most of the works mentioned above lack the cost evaluation and the associated parallel performance evaluation, which remain undetermined, thus making a consistent comparison of the achievable computational performance very difficult. On the other hand, reproducible performance evaluation in cloud environments is currently a significant concern [223]. Although they allow minimizing data movement, the cloud-based data-centric InSAR approaches generally exhibits limited flexibility and usability from the user side. Nonetheless, emergent processing services somehow promote the increasingly multidisciplinary nature of applications. Therefore, the convergence of data-centric approaches and the high performance computing (HPC) paradigm remains crucial to the future [224].