esa-sceva/satcom-chunk-collection huggingface dataset:
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 4 Analysis of Wheel-Rail Stick-Slip Characteristics
### Vehicle-Track Space Rigid-Flexible Coupling Model
#### 4.1.2 Validation of Model  
Using the measured data of rail vibration accelerations, this section verifies the effectiveness of the vehicle-track space rigid-flexible coupling model. The measured interval is located in the line Section 1. Firstly, the rail surface irregularity was measured by the corrugation acquisition instrument, and then the measured rail surface irregularity was added to the rail model for calculation. In the model, the vehicle speed was set as 55 km/h according to the actual operation situation, and the rail measuring point was located on the top surface of the inner rail bottom. The calculation and measurement results of rail vibration accelerations at the measuring point section are shown in Figure 6. It can be seen that the calculation and measurement results are in good agreement, which indicates that the model is credible.  
Figure 5: Diagram of vehicle-track space rigid–flexible coupling model.  
Figure 6: Comparison of calculation and measurement results. (**a**) Rail longitudinal vibration acceleration, (**b**) Rail lateral vibration acceleration. (**c**) Rail vertical vibration acceleration.
## 4 Chemical Methods for Synthesis of Magnetic Ferrite Nanoparticles
### Sol-Gel Method
Furthermore, the polyol method enables the creation of water-dispersible ferrite nanoparticles, characterized by a uniform size distribution, superparamagnetic properties, and a blend of small average size with high saturation magnetization. Magnetic nanoparticles were synthesized from iron(III) acetylacetonate and iron(III) nitrate at Fe\({}^{3+}\) concentrations of 0.06 mol/L, 0.10 mol/L, and 0.15 mol/L [189]. Polyols are highly beneficial in the synthesis of nanoparticles, and their adaptability spans low-molecular-weight compounds such as ethylene glycol, which is excellent for reducing and solubilizing metal salts, diethylene glycol, and triethylene glycol, with their higher boiling points and suitability for high-temperature reactions, to high-molecular-weight polyethylene glycols, available in various molecular weights, which provide flexibility and help prevent the agglomeration of nanoparticles [191; 192]. [PERSON] et al. demonstrated that NiFe\({}_{2}\)O\({}_{4}\) nanoparticles can be successfully synthesized using nickel and iron nitrates with glycine as fuel. Glycine-nitrate gel acts as an ideal matrix for trapping metal ions [192]. Magnetic ferrite nanoparticles created through the polyol method extract contaminants in water treatment. For instance, iron ferrite oxide nanoparticles can adsorb heavy metals and organic pollutants, facilitating their removal from water with magnetic fields [120]. [PERSON] et al. demonstrated that CoFe\({}_{2}\)O\({}_{4}\)-APDS effectively degrades methylparaben (MeP) under visible light. Their study showed nearly complete degradation (99.9%) of 10 mg/L MeP within 100 min using 0.2 g/L CENPs and 2 mM ammonium peroxydisulfate (APDS) at pH 7 [193]. Moreover, composites such as magnetic ferrite nanoparticles have been engineered for improved pollutant extraction. These nanoparticles provide a large surface area, high reactivity, and simple separation process, rendering them highly efficient in water purification [194; 195; 196].
# Root Canal Preparation of a Commercial Artificial Tooth versus Natural Tooth--A MicroCT Study
## 1 Introduction
(2020), it was concluded that replicas obtained from microCT scans better reproduced the internal anatomy, and in particular, TrueTooth(r) was the best replica for access cavity preparation training and root canal negotiation [5]. TrueTooth(r) (DELabs) is a 3D-printed replica of human teeth, designed for teaching access, negotiation, shaping, irrigation, and obturation for root canal iterative training [17], that is radio-opaque with a pulp-colored medium inside each canal space, which can be dissolved with sodium hypochlorite (NaOCl) [18]. TrueTooth(r) replica #15-001 is a maxillary molar with a distobuccal canal with a severe curvature and a large mesiobuccal canal, ribbon-shaped, with a moderate curvature [17]; these features are adequate for the purposes of the present study. Nonetheless, it has been reported that, in 3D-printed teeth, it is difficult to remove the support material from parts with features having fine details [7; 8]. In this way, canals could be filled partially or totally with the support material, and the effect of this material on microCT 3D analysis should be considered [15]. To the authors' knowledge, there are no studies that (1) compare the time and pecking movements needed for the preparation of TrueTooth(r) with NTs, (2) investigate if TrueTooth(r) is adequate for the evaluation of the canal preparation using microCT, and/or (3) analyze the effect of the support material on the variables of interest. Hence, the aim of this study is to evaluate the preparation of TrueTooth(r) with the PTG system and compare this to NTs in the following aspects: (1) the time and number of pecking movements needed for the root canal preparation; (2) the root canal volume increase; and (3) if the pulp-colored medium has any effect on the 3D analysis by microCT in order to assess if TrueTooth(r) can be used in endodontic training and/or microCT studies. The null hypothesis is that the time, pecking movement, and root canal volume increase are equal between TrueTooth(r) and NTs and that the TrueTooth(r) pulp-colored medium has no effect on the variables of interest.
## 2 Experimental Procedure  
### Digital Image Correlation Technique  
Digital image correlation method relies on observations of random speckle patterns on the specimen surface. Image patterns are recorded before and after deformation of the specimens, then they are digitized and stored in a computer. The undeformed and deformed images are divided into small regions called \"subsets\", with each subset containing a group of pixels. Digital image correlation is used to match the subsets on the undeformed image with their corresponding subsets on the deformed image, as shown in Figure 1.  
The cross-correlation function is chosen for this study, and it is defined as Equation (1):  
\[F(x_{m},y_{n},u_{x},u_{y},\frac{\partial u_{x}}{\partial x^{\prime}},\frac{ \partial u_{x}}{\partial y^{\prime}},\frac{\partial u_{y}}{\partial x^{\prime }},\frac{\partial u_{y}}{\partial y^{\prime}})=1-\frac{\sum\limits_{i-\frac{ \beta}{2}-\frac{\beta}{2}-\frac{\beta}{2}}^{\frac{\beta}{2}}\left[\left(f(x_{m +i}g_{n+i})-\overline{f}\right)-\left(g(x_{m+i}g_{n+i})-\overline{g}\right) \right]}{\left[\sum\limits_{i-\frac{\beta}{2}-\frac{\beta}{2}-\frac{\beta}{2 }}^{\frac{\beta}{2}}\left(f(x_{m+i}g_{n+i})-\overline{g}\right)^{2}\right]^{ \frac{1}{2}}} \tag{1}\]  
Figure 1: Initial subset (ABCD centered on \(P\)) and deformed subset (A*B*C*D* centered on \(P\)*).  
The \(f(x_{i},y_{j})\) represents the gray level value at coordinate \((x_{i},y_{j})\) of the undeformed image, while the \(g(x_{j}^{*},y_{j}^{*})\) represents the gray level value at coordinate \((x_{j}^{*},y_{j}^{*})\) of the deformed image, the subset size is \(N\times N\). The coordinates \((x_{i},y_{j})\) and \((x_{j}^{*},y_{j}^{*})\) are directly related by the deformation occurring between the two images. If the deformation occurs in two dimensions parallel to the camera, then the coordinates are related by Equation (2):  
\[\begin{array}{l}x^{*}=x+u_{x}+\frac{\partial u_{x}}{\partial x}\Delta x+ \frac{\partial u_{x}}{\partial y}\Delta y\\ y^{*}=y+u_{y}+\frac{\partial u_{y}}{\partial x}\Delta x+\frac{\partial u_{y }}{\partial y}\Delta y\end{array} \tag{2}\]  
The \(u_{x}\) and \(u_{y}\) are the displacements of the subset center in the \(x\) and \(y\) directions, respectively, and \(\Delta x\) and \(\Delta y\) are distances from the subset center to any point in the subset \((x,y)\).
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 4 High-Level System Design
### Data Exchange Protocols  
To facilitate the seamless exchange of data between different components of the system and external services, standardized data exchange protocols will be employed. Examples include JSON-RPC for communication between the front-end interface and the blockchain network and the InterPlanetary File System (IPFS) for decentralized storage and retrieval of metadata associated with NFTs.  
To enhance the system's interoperability and data integrity, we implement the following advanced data exchange protocols and mechanisms:  
1. Blockchain Oracles: We integrate blockchain oracles to enable secure communication between our smart contracts and external data sources or systems. This includes: - Chainlink for reliable off-chain data retrieval and verification; - Band Protocol for cross-chain data oracle functionality; - Custom oracles for specific global registry integrations (e.g., the ISBN database).
2. Decentralized Storage Solutions: - In addition to IPFS, we incorporate Filecoin for long-term, incentivized storage of larger datasets; - We implement Swarm for the resilient and censorship-resistant storage of critical metadata.
3. Cross-Chain Communication Protocols: - Integration of Polkadot's cross-chain messaging (XCMP) for interoperability between different blockchain networks; - Implementation of the Cosmos Inter-Blockchain Communication (IBC) protocol for seamless data exchange across heterogeneous blockchain systems.
4. Standardized API Interfaces: - Development of RESTful APIs adhering to OpenAPI specifications for easy integration with existing systems; - Implementation of GraphQL APIs for flexible and efficient data querying.
5. Secure Data Transit: - Utilization of Transport Layer Security (TLS) for all data transmissions; - Implementation of JSON Web Tokens (JWT) for secure authentication and information exchange.
6. Data Integrity Verification: - Integration of Merkle Tree proofs for efficient verification of large datasets; - Implementation of zero-knowledge proof systems (e.g., zk-SNARKs) for privacy-preserving verification of sensitive data.
7. Real-time Data Synchronization: - Utilization of WebSocket protocol for real-time updates and notifications; - Implementation of Server-Sent Events (SSE) for efficient one-way real-time data flow.
8. Interoperability with Legacy Systems: - Development of custom API adapters for seamless integration with existing global registry systems (e.g., ISBN, ISSN, DOI databases); - Implementation of ETL (Extract, Transform, Load) processes for bulk data migration and synchronization.  
These advanced data exchange protocols and mechanisms ensure that our system can efficiently and securely communicate with various external systems and data sources, maintaining data integrity and enabling real-time updates across different platforms and registry systems. This comprehensive approach to data exchange enhances the overall interoperability and reliability of our NFT-based global digital registry system.
# Special Issue: Smart Service Technology for Industrial Applications
## 4 Production Data Analysis and Statistical Decision-Making  
In this subfield, four papers were published, introducing the following highlights:  
Fuzzy Quality Evaluation Model Constructed by Process Quality Index:  
In this paper, a model of fuzzy quality evaluation was developed by the process quality index. This model can cope with the problem of small sample sizes arising from the need for enterprises' quick response, which means that the accuracy of evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model was built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors [16].  
Process Quality Evaluation Model with Taguchi Cost Loss Index:  
Many statisticians and process engineers are dedicated to research on process capability indices, among which the Taguchi cost loss index can reflect both the process yield and process cost loss at the same time. Therefore, in this study, the Taguchi cost loss index was used to propose a novel process quality evaluation model. In addition, this paper adopted the mathematical programming method to find the confidence interval of the Taguchi cost loss index, and then employed this confidence interval to perform statistical testing and to determine whether the process needed improvement [17].  
Novel Service Efficiency Evaluation and Management Model:  
This paper proposed a standardized concept with a service operation efficiency evaluation index. This index is not only convenient and easy-to-use, but it also has a one-to-one mathematical relationship with the performance achievement rate. The advantage of the simple and easy-to-use point estimate can be maintained, and the risk of misjudgment caused by sampling errors can be reduced as well, which is helpful for the service industry to move towards the goal of intelligent innovation management. This method is not only applicable to the performance evaluation of the multi-workstation service operation process, but also to the performance evaluations of other service operations [18].  
Statistical Hypothesis Testing for Asymmetric Tolerance Index:  
An asymmetric tolerance index is a function of the average of the process and the standard deviation. It is difficult to obtain the confidence interval of the index. Therefore, this study adopted [PERSON]'s inequality and [PERSON]'s theorem to find the combined confidence region for the average of the process, as well as the standard deviation. Then, this study adopted the mathematical programming to find the confidence interval, and also employed this confidence interval for statistical hypothesis testing [19].
## 2 Communication Network Infrastructure  
The infrastructure of the communication network aims to establish a physical link between network devices by using wireless optical communications technology, which is challenging since it relies on the line of sight (LOS).  
The proposed infrastructure at the level of a planet, as shown in Figure 1, will be replicated for all other significant bodies within the asteroid belt. It includes:  
* Area Border Router (ABR)
* Five Lagrange Subnet Routers (LSR)
* Swarm of Planet Monitoring Nanosatellites (PMN)
# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 2 Related Work
### Phrase Table Combination
Their experimental result showed that the highest BLEU score was obtained by grow-diag with a score of 20.9, and the lowest BLEU score was obtained by intersection with a score of 19.1. Those investigations showed that the best symmetrization of word alignment can differ for each language pair. This conclusion confirmed the language-specific characteristic of the symmetrization of word alignment. Symmetrization of word alignment is also dataset-specific because the same language pairs with different datasets can obtain different BLEU scores [9; 12]. [PERSON] and [PERSON] [12] explored two types of test sets: in-domain and out-of-domain. The exploration was conducted using six symmetrizations: grow-diag-final, grow-final, union, grow-diag, grow, and intersection. In-domain means the test set type was the same domain as the training dataset. In contrast, out-of-domain uses a different type of test set from the training dataset. Their experiment was implemented in three language pairs: Spanish-English, French-English, and Dutch-English. In an example of Spanish-English, a higher BLEU score of 30.63 was obtained using grow-final in the in-domain test set. Using the same symmetrization, Spanish-English obtained a lower BLEU score of 25.00 in the out-of-domain test set. Their results suggested that the symmetrization of word alignment can differ for each type of test set (dataset-specific). [PERSON] [9] also suggested the same conclusion. They explored six datasets: NIST2002, NIST2003, NIST2004, NIST2005, NIST2006, and NIST2008. The test was conducted using nine symmetrizations (grow-diag-final-and, intersection, union, grow-diag-final, grow-diag, grow, scctotgt, tgttsrc, and grow-final) in the Chinese-English language pair. Given an example of a baseline system that used the same symmetrization, i.e., grow-diag-final-and, in two datasets (NIST2002 and NIST2005), the BLEU scores obtained for both datasets were 31.56 and 25.82. Their results confirmed the dataset-specific characteristic, in agreement with [PERSON] and [PERSON] [12]. The symmetrization of word alignment can be applied either in direct translation [9; 12] or pivot approaches [1; 2; 3; 4; 6; 13; 14]. No studies have compared the different strategies in direct translation and pivot approaches. Considering this gap, we applied a phrase table combination that uses different symmetrizations of word alignment in pivot approaches based on the highest BLEU scores. Our consideration was based on [8; 9; 10; 12], which stated that the symmetrization of word alignment is language-specific and dataset-specific as different language pairs with different datasets have different BLEU scores. Lastly, our strategy employs the available parallel corpora of src-trg, src-pvt, and pvt-trg without removing the phrase pair that could be a potential candidate in the translation process.
# Photovoltaic Solar Cells: A Review
## 2 Solar Cells
### p-n Junction Solar Cell
#### 2.4.3 PV Cell under Illumination  
When the incident light is absorbed in the electronic semiconductor forming a p-n junction, it generates pairs of negative and positive mobile charges (e\({}^{-}\) and h\({}^{+}\)). The electrons move out of the n-area to the metal contacts across the negative contact then through the load. Next, the electrons move back into the positive contact and finally to the p-area where they can recombine with holes, as shown in Figure 5.  
Figure 4: The flow of majority carriers in a forward-biased diode.  
As mentioned earlier, the behaviour of the p-n junction diode is employed in solar cells. Therefore, the basic qualitative discussion and mathematical analysis used for diodes are also be applied to the p-n junction PV cell. Without illumination, a PV cell has the same electrical characteristics of a large diode. Under illumination, the output current is simply a summation of the current under the dark condition '\(I_{d}\)' and photocurrent '\(I_{ph}\)' [25], which is expressed as:  
\[I=I_{ph}-I_{d} \tag{10}\]  
To summarize the generated currents:  
* The net flow of the electrons and holes in a p-n junction semiconductor under equilibrium conditions will generate two currents: '\(I_{diff}\)' and '\(I_{drift}\)'. These currents balance and cancel each other at the equilibrium state.
* If an external source is deployed to the p-n junction, the generated current is the diode current '\(I_{d}\)'.
* Under illumination, the p-n junction will present another current called light or photocurrent '\(I_{ph}\)'.  
The equivalent circuit of the solar cell is presented below. In real cells, power is dissipated through a series resistance '\(R_{s}\)' created due to the ohmic contact in the front surface and shunt resistance '\(R_{sh}\)' due to leakage current. Therefore, for a practical solar cell, the equivalent circuit includes '\(R_{sh}\)' added in parallel with the diode and '\(R_{s}\)' added in series with the load as given below, Figure 6[31, 32, 33].  
Therefore, the above equation can be written as follows:  
\[I=I_{ph}-I_{d}-I_{sh} \tag{11}\]
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 2 Materials and Methods
### Seamless Weather Integration for TBO
#### 2.2.1 Structure of 4 DT-Wx Prototype System
In this study, an interpolation method utilizing adjacent model data was applied to extract the altitude and time values corresponding to each point \(n\) of the trajectory. Specifically, the altitude and time of point \(n\) along its trajectory were calculated through an interpolation function \(f_{interpolation}\), which uses the data from the immediately preceding model point (\(n-1\)) and the immediately following model point (\(n+1\)) as an input to estimate the values at point \(n\), as described in Equation (2):  
\[k_{n},\ t_{n}=f_{interpolation}(n-1,n+1) \tag{2}\]  
where \(k_{n}\) is the trajectory altitude at point \(n\) and \(t_{n}\) is the trajectory time at point \(n\). In this study, three interpolation techniques were applied and compared for the interpolation mentioned above [32; 33; 34; 35; 36; 37]:  
* Linear interpolation estimates the intermediate values using a straight line between two points, assuming a linear relationship between the given data points. * Spline interpolation uses polynomials to smoothly connect data points, applying quadratic or cubic polynomials to each interval. * Inverse distance weighting (IDW) interpolates by weighting the values of the surrounding data points inversely proportionally to the distance. The closer the data point, the greater the weight, and this is used to estimate intermediate values. These methods were selected due to their widespread use in meteorological applications, relative simplicity, and proven effectiveness. Each technique offers a balance between computational efficiency and the ability to handle various weather data characteristics. These approaches provide a robust performance without undue complexity, making them well-suited for real-time weather data processing in trajectory-based operations. Additionally, as an option, a 3 \(\times\) 3-size buffer (in the buffer-on state) can be set at relevant points to monitor the surrounding conditions. In this study, we define the two states of buffer configuration as follows: the buffer-on state is denoted as \"BOn\", and the buffer-off state is denoted as \"BOff\".