esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation of the Proposed UniChain System
### Adding a New Academic Record Via a University Node  
The procedures of adding a new academic record starts after establishing a stewardship between the university and the student nodes and thus having a shared RC. First, internal encryption in the university node begins the process of adding a new record. When a new record is created by a university node, that record will be transferred to the DB Manager. It creates an access link (AL) of a free location in the university existing database and hashes both the generated access link \(h(AL)\) and the record \(h(EAR)\).  
The DB Manager forwards the created access link, and the student record to the Cipher/Decipher Manager for encryption. The Cipher/Decipher Manager generates a symmetric key (SMK), encrypts the new record and link with that key and then encrypts that generated symmetric key with the public keys of the: university, student and set of proxies. The Cipher/Decipher Manager sends the encryptedrecord to the DB Manager to store. In addition, all other encrypted data will be sent to the DB Manager to create a log indicating the creation of the new record since the history of all access will be stored in the blockchain to provide a full view of all events that happened to each record. The hash of the created log will be calculated and stored in the DB Manager for block verification later. Thus, ensuring the integrity of data since if any part of the data is changed, all involved nodes will notice the alteration. Then, the log will be sent to the Cipher/Decipher Manager for encryption with the public key of the university node.  
The university node sends the student's ID to its SRHC that will return the associated RC address. The university node then sends the record information (filename of the record, hash value of the access, and hash value of the student's record, the encrypted symmetric keys \(E_{pk}(SMK)\), and the log) to the RC. The RC stores the filename of the record, the hash value of the access link, and the hash value of the student's record. The RC then creates a new ACC for the record and forwards the encrypted symmetric keys \(E_{pk}(SMK)\). The ACC auto-creates the access and permissions information for the record, i.e., student and university permissions, and then sends its address to the RC for its reference. On the other hand, the LC updates its entries with the received encrypted log, the associated Ethereum address of the university node, the \"new log\" status to indicate that the new log has not been added to the blockchain yet, and the timestamp of the last status update. At the end, the encrypted access link is sent to the student over HTTPS who will store that link in its Cipher/Decipher Manager and will be used when the student would like to read his/her record. Additionally, when the new record is created, the university node notifies the NC to updates the associated degree of the university node. The NC informs the REM to add the value of the added record to the node's degree and to return it to perform the update.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Performance Metrics Analysis
#### 4.3.2 Waiting Time (W) Reduction  
* Medium Single Service (B) prevents system overload, ensuring steady processing.
* Bulk service significantly reduces waiting time, as seen in Table 3, where W is minimized at lower \(\alpha\)-cut values.
* Low Vacation (C) ensures continuous service, eliminating excessive delays.
###### Abstract  
With the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existing studies often address FFS scheduling and automated guided vehicle (AGV) path planning separately, resulting in resource competition conflicts, such as equipment idle time and AGV congestion, which prolong the manufacturing cycle time and reduce system energy efficiency. To solve this problem, this study proposes an integrated production-transportation scheduling framework (FFSP-AGV). By using the adjacent sequence modeling idea, a mixed-integer linear programming (MILP) model is established, which takes into account the constraints of the production process and AGV transportation task conflicts with the aim of minimizing the makespan and improving overall operational efficiency. Systematic evaluations are carried out on multiple test instances of different scales using the CPLEX solver. The results show that, for small-scale instances (job count \(\leq\)10), the MILP model can generate optimal scheduling solutions within a practical computation time (several minutes). Moreover, it is found that there is a significant marginal diminishing effect between AGV quantity and makespan reduction. Once the number of AGVs exceeds 60% of the parallel equipment capacity, their incremental contribution to cycle time reduction becomes much smaller. However, the computational complexity of the model increases exponentially with the number of jobs, making it slightly impractical for large-scale problems (job count > 20). This research highlights the importance of integrated production-transportation scheduling for reducing manufacturing cycle time and reveals a threshold effect in AGV resource allocation, providing a theoretical basis for collaborative optimization in smart factories.  
Keywords:integrated scheduling; flexible flow shop scheduling; makespan; AGV scheduling; mixed-integer linear programming; exact algorithm +
Footnote †: journal: Applied sciences
# A Global Review of PWR Nuclear Power Plants
## 2 International Development of the PWR Design
### American PWR Evolution
#### 2.2.1 Westinghouse PWR Design  
After checking the technical feasibility of the technology at _Shippingport_, it began its commercial development. Two new nuclear power plants, _Connecticut Yankee_ and _San Onofre_, began commercial operation in 1968 and featured a PWR design supplied by the Westinghouse Company under a turnkey contract [20]. In the case of the Connecticut Yankee nuclear power plant, a 4-loop PWR design and a generation capacity of 490 MWe were chosen (Figure 7d). The vessel of this reactor was at that time the longest in the world, and was designed and built by the company _Combustion Engineering_. In contrast, _San Onofre_ had a 3-loop PWR and 365 MWe electrical output design (Figure 7c) [21; 22]. That same year, 1968, the first nuclear power plant in Spain, _Jose Cabrera_, came into operation, which had a Westinghouse PWR with 150 MWe electrical output [23], becoming the only operational 1-loop PWR around the world (Figure 7a). Following these first plants, the North American company developed in  
Figure 6: A 3D view of a PWR design with RCS and main components.  
parallel throughout the 1960s and 1970s the variants PWR 2-loop (Figure 7b), PWR 3-loop (Figure 7c), and PWR 4-loop (Figure 7d).  
Starting in the 21 st century, the US company has continued to bet on PWR design, developing Generation III design AP1000, 2-loop 1150 MWe PWR (Figure 7b) that uses the forces of nature and simplicity of design to enhance plant safety and operations and reduces construction costs [24; 25], AP1000 design continues to use prove components, and the inherent safety and simplicity of the AP600 design. The AP600 design was Westinghouse's initial start-up as Generation III PWR design, but predictions of a lack of competitiveness for this design prompted the company to develop the AP1000 [26].
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 3 Applicative and Comparative Analysis for In Situ Measurement of Cylindricity
### Experimental Verification and Analysis of Parallel Scan EST
In particular, when there are significant systematic interferences in the measuring system, such as inconsistencies in sensitivity and linearity, angular and positional errors of five probes, an obvious distortion of the measured median line profile will occur. Even more, the larger the total number of the cross-section in the cylinder is, the more serious the distortion is. Comparatively, if the outputs of Probes 1-4 are used to measure and reconstruct the cylindrical profile, this EST will become an integrated EST of the three-point roundness and sequential two-point straightness. Here, the sequential two-point straightness EST [18] is executed for the detection of the LSC vector of each cross-sectional profile of the cylinder. Let us see Equation (6), \(\mathbf{\Theta}_{cx}(J,1)\), which is the first harmonic vector of the additional \(X\) directional error motions induced by the tilt error motions of the cylinder around the \(Y\)-axis of each position, should be omitted, and the double integration calculations will be avoided. At this moment, the tilt error motions of the cylinder cannot be removed although the influences of various interferences on detecting the LSC vector of each cross-section are weakened. In engineering practices, the tilt error motions of the cylinder are much smaller, particularly in case if the cylinder is long and the rotation speed is low. Figure 9 shows the curved median line profiles measured by the outputs of Probes 1-4. Obviously, the measured profiles are in good consistency in six experiments, and the variation of the extracted LSC vector of each cross-sectional profile is within the range \(\pm 1\pm 1\upmu\)m, which shows a convincing proof of the accuracy of the parallel scan EST for measuring cylindrical profile. Figure 8: Median line profiles measured by the parallel five-point EST. The accuracy of the extracted LSC vectors of sequential cross-sectional profiles by the four-point EST is higher than that extracted by the five-point EST if the tilt error motions of the cylinder are small and negligible. Figures 8 and 9 show that the profiles of the curved median line measured by two parallel ESTs differ slightly, and the conformity of the spatial median line measured by the parallel four-point EST is validated to be higher in the six experiments, which implies that the tilt error motions of the cylinder of the experimental system is small. Therefore, the parallel four-point EST is more suitable for in situ measurement of cylindricity of the large-scale roller, where the tilt error motions of the cylinder are indeed small in case that the cylinder is long and the rotation speed is low. On this basis, the cylindrical profile can be accurately reconstructed by Equation (7).
## 3 Results
### Isolated Bioactive Compounds in Melastoma Leaf Extract Using HPLC
[PERSON] and [PERSON] [28] also found that the amount of quercetin is large compared to rutin in the methanolic extract of _Tecomella undulata_. Plant polyphenols, such as flavonoids, are a class of secondary metabolites that are also collectively known as \"Vitamin-p\" [28]. The chemical structure of flavonoids consists of a C15 (C6-C3-C6) skeleton, and they are grouped into four major classes according to the position of the aromatic ring in the phenyl-benzopyran rings [29]. They are widely distributed throughout the plant kingdom, and, to date, there are about 300 known flavonoids [30]. Quercetin and rutin are considered the two main and most significant flavonoid compounds. Quercetin is known as 5,7,31,41-tetra hydroxy flavonoid. Meanwhile, according to [PERSON] and [PERSON] [31], rutin is the rhamnoside of the flavonoid quercetin (quercetin-3- rutinoside or 3,3\({}^{\prime}\),4\({}^{\prime}\),5,7-pentahydrox flavones-3-rutinoside), and it is present in several plant parts. They both possess important phytochemicals and medicinal properties [28]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Peak No.** & \begin{tabular}{c} **Retention Time** \\ **(min)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak Area** \\ **(mau)** \\ \end{tabular} &
\begin{tabular}{c} **Compound** \\ **Identification** \\ \end{tabular} \\ \hline \multirow{3}{*}{Optimized _Melastoma_} & i & 1.978 & 10,273.4 & - \\  & ii & 2.535 & 3207.2 & - \\ \cline{1-1} Extract & iii & 7.770 & 391.7 & Rutin hydrate \\ \cline{1-1}  & iv & 8.769 & 1489.9 & Quercetin hydrate \\ \hline \hline \end{tabular}  
1 mg/1 mL of extract in HPLC-grade methanol.  
\end{table} 
Table 6: The retention time and peak area of the optimized mixture extract. Figure 6: The overall chromatogram for the mixture of _Melastoma_ leaf extracts. For example, they provide several defense protections against ROS in humans. Moreover, flavonoids display a remarkable role in various pharmacological activities, such as antioxidant, anti-inflammatory, anti-diabetic, and anti-cancer effects [31]. Therefore, these significant bioactive compounds of rutin and quercetin in the mixture of _Melastoma_ leaf extracts provide therapeutic value as the natural antioxidants against attacks caused by reactive oxygen species.
# A Quantum Approach for Exploring the Numerical Results of the Heat Equation
## 2 Methodology
### Converting Heat Equations to Quantum Form
#### 2.2.2 The Use of Quantum Gates for Propagation  
To propagate the temperature distribution in quantum form, we needed to design quantum gates that simulate the evolution of the system governed by the Hamiltonian operator \(H\). The Hamiltonian for the heat conduction problem in discrete form models the differential operator from the heat equation.  
To implement the time evolution of a quantum state corresponding to the heat equation, we used the Trotter-Suzuki expansion. This method allows one to approximate the time evolution operator, which greatly simplifies its implementation on a quantum computer. To evolve the quantum state \(|\ \psi(t)\rangle\) over a small time step \(\Delta t\), we used the time evolution operator \(U\), which is related to the Hamiltonian \(H\) as follows.  
\[U=e^{-iH\Delta t}, \tag{6}\]  
where \(\Delta t\) is a small time step. The Hamiltonian \(H\) in discrete form for the heat equation can be written as  
\[\mathrm{H}=-k\sum_{i=1}^{N-1}\bigl{(}\sigma_{i}^{+}\sigma_{i+1}^{-}+\sigma_{i }^{-}\sigma_{i+1}^{+}-2\sigma_{i}^{x}\bigr{)}. \tag{7}\]  
Here, \(\sigma_{i}^{+}+\) and raise and lower operators \(\sigma_{i}^{-}\)----raise and lower operators, a \(\sigma_{i}^{x}\)--Pauli-Z operator acting on the \(i\)th qubit.  
For a small time step \(\Delta t\), the time evolution operator can be approximated using the Trotter-Suzuki expansion. The Trotter-Suzuki decomposition allows us to represent the evolution operator of a complex system as a product of the evolution operators for simpler subsystems. For the Hamiltonian of thermal conductivity \(H\), we can identify components that can be conveniently implemented on a quantum computer. Let us consider the decomposition into two Hamiltonians \(H_{1}\) and \(H_{2}\), corresponding to the interaction of neighboring qubits and the self-interaction of each qubit, respectively.  
Interaction of neighboring qubits:  
\[H_{\rm I}=-\rm k\sum_{i=1}^{N-1}(\sigma_{i}^{+}\sigma_{i+1}^{-}+\sigma_{i}^{-} \sigma_{i+1}^{+}). \tag{8}\]  
Self-interaction of each qubit:  
\[H_{\rm 2}=\rm k\sum_{i=1}^{N-1}(2\sigma_{i}^{\rm Z}). \tag{9}\]  
Then, the time evolution operator can be approximated as follows:  
\[\rm U\!\!=e^{-iH\Delta t}\approx(e^{-\frac{iH_{\rm I}\Delta t}{m}}e^{-\frac{iH_ {\rm I}\Delta t}{m}}\ldots e^{-\frac{iH_{\rm I}\Delta t}{m}})^{m}, \tag{10}\]  
where \(H=H_{\rm I}+H_{\rm 2}+\ldots+H_{p}\)--decomposition of the Hamiltonian into parts that are easier to implement. Here, \(m\) is the number of times used to repeat the product of the exponentials to improve the accuracy of the approximation. The larger \(m\), the more accurate the approximation of the Trotter-Suzuki expansion. In the limit at \(m\rightarrow\infty\), the approximation becomes accurate.
## 2 Methodology
### Proposed Network Structure  
The structure of the deep network proposed in this study is illustrated in Figure 4. First, a dimension-reduction-based autoencoder is used as a preprocessing step that helps to detect the change point by reducing the multi-sensor turbofan engines to a single signal. The input sample is shaped into a 2-dimensional tensor as \(N_{tw}\times N_{ft}\), where \(N_{tw}\) and \(N_{ft}\) denote the time sequence length and the number of the features, respectively. The details of the input data preparation are described in Section 3.1.  
A 1D-CNN is employed in the first layer to extract spatial features from the fused sensor measurement and the newly added feature. Then, a max-pooling layer is used for reduced complexity and overfitting. The 1D-CNN layer configuration is \((N_{F},(F_{L}\times 1))\), where \(N_{F}\) is the number of filters and \(F_{L}\) is the filter size. In order to keep the size of the feature map fixed, zero-padding is used. The pooling layer resizes each feature map independently by utilizing the max operation and \(F_{P}\) is the pooling size. An LSTM is used for revealing temporal information from the extracted features and \(N_{C}\) denotes the number of LSTM cells. Next, a fully connected layer smooths the feature matrix and maps these obtained feature vectors to the target labels of the samples. \(N_{N}\) is used to represent the number of neurons used in the fully connected layer. In the end, a fully connected output layer with one neuron is placed to make RUL predictions.  
ReLU is the activation function, and the Adam algorithm is the optimization algorithm for the training network.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Technical Development
#### 4.2.1 Uyghur
In this table, the DNN-HMM is the hybrid DNN system; Chain-TDNN is a variant of the hybrid system with a 1D temporal dilated CNN employed; BLSTM-CTC/attention is an end-to-end system, with both the CTC loss and the attention-based sequence-to-sequence architecture; Transformer-CTC and Conformer-CTC are both based on CTC training with Transformer and Conformer as the encoder blocks, respectively; and Conformer-CTC-MTL introduces multi-task learning on the basis of Conformer-CTC.
###### Abstract  
The objective of this research was to determine the content of total phenols, total flavonoids, and the antioxidant and antimicrobial activity of the ethanolic extracts of propolis obtained by two methodologies during in vitro digestion. Ethanolic extracts of propolis were obtained by ultrasound and maceration and the yield and content of the bioactive compounds, as well as their antimicrobial and antioxidant activity, were evaluated. Yields higher than those reported in other investigations (71.6%) were obtained. The highest content of phenols and flavonoids in the ethanolic extracts was 34,406.6 mg GAE/100 g in propolis from San Pedro, obtained by maceration (SP M), and 19,523.2 mg QE/100 g in propolis from Teotitlan, obtained by ultrasound (TU), respectively, being higher than what is established in Mexican regulations. The antioxidant and antimicrobial activity of the extracts was not affected by the method of obtaining. At the end of the in vitro digestion there was an 80% loss of the phenolic content and a 90% loss of the flavonoid content. Therefore, antioxidant activity was affected. On the other hand, ultrasound improves the obtaining of bioactive compounds. In vitro digestion decreases the content of bioactive compounds; therefore, their functional properties are affected. Thus, it is important to consider technologies that allow extracts to be protected from in vitro digestion conditions.  
propolis; phenolic compounds; flavonoid; ultrasound; maceration; microbial inhibition +
Footnote †: journal: Medical Physics
## 4 Security in 5G Infrastructure
### Vulnerabilities in 5G System  
The presented security mechanisms address critical aspects of the 5G system. However, the 5G system complexity and novel use cases have exposed it to vulnerabilities not previously encountered.  
Vulnerabilities span the RAN, Core, and UE, but we will focus on vulnerabilities in the 5G core network. Replay attacks on N1 and N2 interfaces, API vulnerabilities on the Nnef interface and Nnrf interface, and stability tests against DDoS attacks are primary concerns.  
SBA, reliant on APIs, introduces vulnerabilities due to poor developer implementation. Common API web server issues are exploitable, potentially compromising critical infrastructure or causing a DoS attack.  
Numerous vulnerabilities stem from factors such as IoT/M2M implementations [36], multistack environments [37], open-source programs [38], improper API usage [39], third-party apps [40], and insufficient testing [41]. Cloud deployment introduces shared security responsibility and necessitates additional security controls beyond 3 GPP [27].  
Different works on vulnerability analysis of 5G systems have identified threats to various NFs. For example, [PERSON] et al. [10] examine critical 5G interfaces, providing a set of possible vulnerabilities and threats. Comprehensive vulnerabilities are also detailed in [27], categorized as shown in Table 1. It is worth noting that this section specifically addresses the subset of threats pertinent to the scope of the presented study.
# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 2 Materials and Methods  
### Problem Formulation  
In a knowledge graph, knowledge is represented as triples, which are either relation or attribute triples [19; 20; 21]. Relation and attribute triples are expressed as:  
\[T_{R}=\left\{(h,r,t)|h,t\in E,r\in R\right\} \tag{1}\]  
\[T_{A}=\left\{(e,a,v)|e\in E,a\in A,v\in V\right\} \tag{2}\]  
where \(E\) is the set of entities, \(R\) is the set of relations, \(A\) is the set of attributes, \(V\) is the set of attribute values, \(T_{R}\subseteq E\times R\times E\) is the set of relation triples, and \(T_{A}\subseteq E\times A\times V\) is the set of attribute triples. Therefore, a KG can be expressed as \(KG=\left(E,R,A,V,T_{R},T_{A}\right)\).  
Based on this expression for KGs, entity alignment can be described as follows: given a pair of KGs--KG\({}_{1}\) and KG\({}_{2}\)--the aim of entity alignment is to search for an entity pair \(\left\langle e_{1},e_{2}\right\rangle\), where \(e_{1}\) and \(e_{2}\) refer to the same real-world entity, with \(e_{1}\in KG_{1}\) and \(e_{2}\in KG_{2}\).
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
###### Abstract  
Traditional holographic printing techniques prove inadequate when only input data are available. Therefore, this paper proposes a new artificial-intelligence-based process for generating digital holographic stereograms from a single black-and-white photograph. This method eliminates the need for stereo cameras, photogrammetry, or 3D models. In this approach, a convolutional neural network and deep convolutional neural field model are used for image colorization and a depth-map estimation, respectively. Subsequently, the colored image and depth map are used to generate the multiview images required for creating holographic stereograms. This method efficiently preserves the visual characteristics of the original black-and-white images in the final digital holographic portraits. This provides a new and accessible method for holographic reconstruction using limited data, enabling the generation of 3D holographic content from existing images. Experiments were conducted using black-and-photographs of two historical figures, and highly realistic holograms were obtained successfully. This study has significant implications for cultural preservation, personal archiving, and the generation of life-like holographic images with minimal input data. By bridging the gap between historical photographic sources and modern holographic techniques, our approach opens up new possibilities for memory preservation and visual storytelling.  
holography; photogrammetry; 3D models; depth map; image colorization; holographic stereogram printer