esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 2 ECM Components That Contribute to Tissue Engineering Strategies
### Proteoglycans and Glycosaminoglycans  
Proteoglycans are expressed ubiquitously in all ECMs and consist of a main protein core with one or more covalently attached, negatively charged glycosaminoglycans.  
Proteoglycans play a central role in the ECM, performing many structural and biological functions, including hydration of the ECM, homeostasis, structural organisation and regulating cell signalling cascades, such as those required for adhesion, migration and proliferation [63]. Glycosaminoglycans form an integral part of proteoglycan structure as unbranched, linear, repeating disaccharide chains [65], which include heparan sulfate, heparin, chondro sulfate, keratan sulfate, dermatan sulfate and hyaluronic acid. These 6 distinct glycosaminoglycan groups result from differences in structure of the disaccharide unit, such as their composition, size or variations in the position of sulfonylation sites. This element of variation explains the structural and functional diversity attributed to proteoglycans [63], which can be classified into four main groups: intracellular proteoglycans, cell surface proteoglycans, pericellular proteoglycans and extracellular proteoglycans (Table 2), all of which are bound by varying glycosaminoglycan chains [66].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Example** & **Gene Symbol** & **Glycosaminoglycan Chain** \\ \hline Intracellular & Serglycin & _SRGN_ & Heparin \\ \hline \hline \end{tabular}
\end{table}
Table 2: The four families of proteoglycans are primarily classified according to their location, gene/protein homology and the presence of particular protein units. This table aims to describe the proteoglycan classification system in a simplified form, highlighting the importance of cellular and subcellular location as well as the addition of specific glycosaminoglycan chains, which ultimately depends on the proteoglycan in question. Adapted from [66].  
Figure 4: Tropeelastin contains alternate hydrophobic and hydrophilic regions. Each region contains specific motifs that are involved in elastin fibre assembly induced by the enzymatic action of lysyl oxidase (LOX), producing mature elastin. Tropeelastin is synthesised inside the cell, where it binds to the elastin-binding protein (EBP), forming a transmembrane elastin receptor. This protein transports the tropoelastin to microfibrils which are found outside the cell, where the process of elastic fibre assembly starts. Tropeelastin is released from the EBP through galactosugar microfibril binding. Subsequently, lysine residues in tropoelastin undergo deamination by the LOX enzyme through oxidation. This forms crosslinking sites that result in the formation of mature elastin. Created with BioRender.com.
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 3 Results and Discussion
### Heavy Metal Leaching
#### 3.3.5 The Encapsulation of Pb  
All mixtures except one show Pb amounts below the EPA drinking water limit (Figure 8), in contrast with the MSWIFA and water mixture, which shows values three orders of magnitude greater than the limit. The exception is the mixture C70B0 which exceeds the EPA drinking water limit (which is 15 \(\upmu\)g/L) at all ages from 3 to 180 days. This finding implies that a mixture with cement and gypsum cannot encapsulate Pb to the required levels [16]. All groups encapsulate Pb very well, with encapsulation percentages above 99%. Pb encapsulation performance is in agreement with the literature [1; 6; 7; 8; 9; 10; 11; 22; 32; 36; 37; 38; 39; 40; 41; 42; 43]. Pb is known to be an amphoteric element, so Pb concentration is high under highly alkaline conditions (such as in C70B0), but low in more neutral conditions. Pb is not fixed within the C-S-H, but it can be adsorbed on the C-S-H surface. It is difficult to achieve  
Figure 7: The (**a**) Zn concentration and (**b**) encapsulation percentage for the leachate in all groups at all ages.  
efficient Pb immobilization using only cement [1; 9; 10; 22; 32; 36]. Pb is mostly stabilized through physical encapsulation of its precipitates by the hardened hydrate matrix [10].
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 5 Analysis of the Channel Access Prioritization of Packets
### Conditions for TSN-Compliant Scheduling  
When the traffic forwarding conditions are mapped to a time-discrete course of scheduling priorities, certain conclusions for a TSN compliant scheduling architecture in NG-RAN can be derived.
###### Abstract  
The most important item when indicating the mechanical properties of offshore elastomeric bearings is the shear modulus, and the method of measuring this is shown in EN 1337-3, a regulation related to offshore elastomeric bearings. In this work, we conducted an experimental and numerical study on an offshore elastomeric bearing to find its shear modulus. Shear modulus tests were conducted according to the procedure specified in EN 1337-3 Annex F, while simulations were performed using the finite element analysis (FEA) software, ANSYS. The main objective of this research work is to determine optimum analysis conditions for the simulation method that considers a nonlinear model for the elastomer material and predicts the experimental results accurately. We considered the Mooney-Rivlin (M-R) model that has two-parameter (2P), five-parameter (5P), and nine-parameter (9P) forms, depending on the number of terms in the series. We observed that the load-displacement graph is linear, and the percentage error between the results obtained with 2P and 5P M-R models is around 2.23% in the compression and 0.38% in the shear. The simulation results from 2P M-R model showed a good agreement with the experimental results with the correlation coefficient (R2) being 0.999 with an average error of about 2%. However, the deviation between the experimental and simulation results from the 9P M-R model is very high, with about 7%. Based on this study, we can say that the 2P M-R model can accurately predict the nonlinear behavior of hyperelastic material used in elastomer bearing. In addition, the shear modulus of elastic bearings for Class 3 Shore hardness was verified by comparing the numerical simulation values with those presented in EN 1337-3 Annex D.  
FPSO (floating production storage and offloading); offshore elastomeric bearing; shear modulus; shear stiffness; FEA (finite element analysis); experiment; numerical simulation +
Footnote †: journal: Applied Sciences
## 4 Proposed Architecture
### GradCAM Module  
By employing the GradCAM module discussed in Section 3.5, we highlight the key regions within the key points sequence that significantly contribute to the classification decision, enhancing the understanding and trustworthiness of our model's predictions. GradCAM enables the identification of temporal variations in the sequence most indicative of fall events. The resulting GradCAM heat map effectively provides visual cues, indicating the areas of the sequence with significant variations between fall and not fall events. By visualizing these salient regions, medical professionals gain profound insights into the decision-making process of our model, empowering them to validate the accuracy and reliability of its fall-event-detection capabilities.
# Agent-Based Model for Automaticity Management of Traffic Flows across the Network
## 3 Measures of Centrality in Networks  
A network is a set of nodes and links [6]. Modeling a network helps to determine the structural issues relevant at a given time. The topology of the network can be illustrated applying an adjacency matrix or adjacency list. The decision of which one has to be used depends on the analysis type.  
An adjacency matrix is a mathematical representation of a network, which allows the analysis of the clustering relation between the nodes. When search algorithms are used to enumerate components, the disadvantage of this matrix is the inefficient use of memory, but this is not a problem when a particular element is accessed [7; 8]. Different techniques for network interpretation and visualization are used nowadays; one of these techniques is based on clustering algorithms, in which the use of an adjacency matrix is more convenient.  
One of the measures that is applied to adjacency matrix is the centrality, which captures the hierarchy of the network nodes [9]. The awareness of the node relevance is crucial to manage any network because this will lead to the determination of the main nodes. The nodes are classified by their betweenness centrality. Eigenvalue centrality is an extension of degree centrality [8], and the topology of the network has an effect on both measures. The use of a node classification based on the centrality allows the network manager to realize the importance of the nodes. A definition of eigenvalue centrality of a node \(i\) is shown in Equation (1):  
\[x_{i}^{{}^{\prime}}=\sum_{j}A_{ij}x_{j}, \tag{1}\]  
where \(x_{i}^{{}^{\prime}}\) is defined by the sum of the \(i^{{}^{\prime}}s\) the centrality of i neighbors, and \(A_{ij}\) is an element of the adjacency matrix. The interpretation of eigenvalues tells if a node is growing or shrinking according to the amount of neighbors. In the model, this measure is used to identify the hierarchy of each node. Links allow communication to reach a remote node. Here, the betweenness centrality of a node has a relevant role. Clustering coefficient \(C_{i}\) describes how extended a node is, Equation (2) shows how to calculate it, where Equation (3) is the degree of the node \(k(i)\) and \(N(i)\) is the amount of neighbors of the node \(i\)[2; 10]:  
\[C_{i}=\frac{k_{i}(k_{i}-1)}{2}, \tag{2}\]  
\[k(i)=|N(i)|. \tag{3}\]  
In a heterogeneous network, the nodes with higher clustering degree are called hubs. In [11], it is affirmed that high degree nodes play a very significant role because the packets are routed through these hubs, and, in a social network, the people highly connected distribute information better than isolated people [12].  
Figure 1 shows a representation of hub nodes in a network where the size of the nodes is proportional to its degree, defined as \(k(i)=|N(i)|\), where \(N(i)\) is the amount of the neighbors of a node \(i\)[10], using this metric, a node hierarchy is obtained. The proposed agent-based model detects clusters of nodes when the flow preferences are assigned.
## 2 Proposed Framework and Experimental Methodology
### AES-128 Encryption Algorithm and Models  
Figure 2 presents the AES-128 encryption algorithm flowchart, detailing each encryption step. The process begins with loading a 128-bit plaintext block, followed by an initial AddRoundKey operation. A conditional loop checks if the current round (r) is less than the total number of rounds (Nr). If true, the algorithm performs the SubBytes, ShiftRows, MixColumns, and AddRoundKey operations sequentially, incrementing the round count until the final round is reached. In the last round, MixColumns is omitted, and the ciphertext is stored after the final AddRoundKey operation. This flowchart clarifies the AES encryption process, showing the iterative nature of the encryption steps and their importance in securing data.  
Figure 3 illustrates the CBC and CTR encryption modes used in AES-128. In Figure 3a, CBC mode shows that each plaintext block is XORed with the previous ciphertext block before encryption, making it inherently sequential and limiting parallel processing. Decryption reverses this process by XORing each decrypted block with the previous ciphertext. In Figure 3b, CTR mode demonstrates parallel-friendly encryption where a counter block  
Figure 1: Schematic of encryption and decryption process (a) CPU, (b) GPU. Note: Ellipses in values (e.g., ’...’) are used to indicate truncated representations for visual clarity.  
Figure 2: Flowchart for AES algorithm.  
(Nonce and Counter) is encrypted to produce a key stream, which is XORed with the plaintext blocks independently, enabling concurrent encryption and decryption operations. This independence highlights the CTR mode's suitability for GPU-based parallel processing.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 5 Conclusions  
The prediction of stuck pipe is very challenging due to the hybrid nature of the drilling process (which consists of different activities), the variability of geological conditions, the combined occurrence of adverse events etc.  
Three prediction methods of stuck pipe are established. In the first method, the neural network technology is used to calculate WOB and TOB in real-time. Combined with the torque and drag model, the real-time inversion of the friction coefficient is realized. The analysis method of stuck pipe trend based on the friction coefficient is finally established. In the second method, intelligent prediction of stuck pipe probability is realized by using mudlog data and LSTM network. Finally, a comprehensive prediction model is established, which combines knowledge-driven model (the first method) with data-driven model (the second method) using fuzzy mathematics theory.  
For future work, we plan to look for more stuck pipe samples. On the one hand, we can increase the stability and generalization ability of the intelligent prediction model. On the other hand, we will use more samples to determine a reasonable threshold of stuck pipe instead of setting thresholds artificially.  
Conceptualization, X.S. and S.Z.; methodology, S.Z., X.S. and Z.Z.; software, X.Y. and M.L.; validation, S.Z.; formal analysis, S.Z. and X.S.; investigation, S.Z. and Z.Z.; resources, M.L.; data curation, S.Z.; writing--original draft preparation, S.Z. and Z.Z.; writing--review and editing, S.Z. and X.S.; supervision, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (funded by National Natural Science Foundation of China, No. 2019 YFA0708300), the Strategic Cooperation Technology Projects of CNPC and CUPB (funded by China National Petroleum Corporation, No. ZLZX2020-03), the National Science Fund for Distinguished Young Scholars (funded by National Natural Science Foundation of China, No. 52125401) and Science Foundation of China University of Petroleum, Beijing (funded by China University of petroleum, Beijing, No. 246022 SZBH002).  
Not applicable.  
The data are not publicly available due to involve the information of Chinese oil fields and need to be kept confidential.  
The authors want to thank the National Natural Science Foundation of China and the CNPC for the financial support.  
The authors declare no conflict of interest.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 2 Antenna Model and Prototype  
The novelty of the proposed antenna is the compact size with the use of a flexible substrate material, which is suitable for in-body and on-body communications. The antenna structure is illustrated in Figure 1a. The prototype of the antenna is presented in Figure 1b. The antenna is printed on the front side of the flexible Rogers XT8100 substrate, having the following dimensions: width 20 mm, height 30 mm, and 50 \(\upmu\)m of substrate thickness.
## 2 Literature Review
### Traditional Approaches to Contingency Estimation  
Traditional contingency estimation often relies on expert judgment [5, 21], questionnaire surveys [4, 22], and simulation analysis [3, 23, 24]. Questionnaires have proven effective in identifying factors that could influence contingency estimates or cost overruns. The determinants of contingency funds, such as contract type, advance payment amount, and the availability of construction materials, are often revealed through questionnaire surveys [4, 17, 22]. Furthermore, expert judgment also plays a significant role in this process. For example, [PERSON] et al. [21] developed a fuzzy expert system for cost contingency estimation based on risk analysis and achieved reasonable accuracy. Similarly, [PERSON] et al. [5], informed by industry experts, identified cost overrun risks and developed a probabilistic risk network to guide contingency estimation.  
Conversely, Monte Carlo simulation and parametric modeling have also been applied to estimate contingency funds. [PERSON] et al. [23] used Monte Carlo simulation to capture the probabilistic nature of project costs. They assumed a normal distribution of project costs. The target cost was set as the mean of the project cost, while the planned budget was established at the 80 th percentile. The difference between these two values was then treated as an estimated contingency. Similarly, [PERSON] et al. [3] applied Monte Carlo simulation to manage cost contingency during project implementation. By assessing the contingency that arose in specific activities and the proportionate cost of these activitiescompared to the overall project, they calculated the remaining contingency. In addition to Monte Carlo simulation, [PERSON] et al. [24] used parametric modeling for contingency prediction and identified critical factors that affect contingency estimates, such as project characteristics, design performance metrics, and contract award processes. A regression model was then developed based on these factors.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 3 ESGM Model
### Object-Relation Extraction Module  
After passing through the gating unit, we obtain the fusion vector between the subject and the current word, standardize it through the ReLU activation function, use the vector obtained above as input, and use the relation as the mapping function to calculate the start and end position probabilities of the object corresponding to the relation. This object tagger first learns the input information through a linear layer, finally passes it through a fully connected layer into the sigmoid function, and outputs the probability of each word being the beginning and ending location of the object under the correspondence relationship. The formulas of specific implementation steps are shown in Formulas (10) and (11):  
\[p_{i}^{start\_o}=\sigma(ReLU(W_{start\_o}\cdot X_{i}+b_{start\_o})) \tag{10}\]  
\[p_{i}^{end\_o}=\sigma(ReLU(W_{end\_o}\cdot X_{i}+b_{end\_o})) \tag{11}\]  
where \(p_{i}^{start\_o}\) and \(p_{i}^{end\_o}\) respectively indicate the probability of the \(i\)th word being the beginning location and ending location of the object in the sentence, \(ReLU()\) represents the ReLU function, \(W_{start\_o}\), \(W_{end\_o}\),  
where \(P_{m}(s|x_{j})\) and \(P_{r}((r,o)|s,x_{j})\) are the results of Formula (8) and Formula (14), respectively, \(m\) and \(n\) denote the index of the \(m\)th subject and \(n\)th object in the sentence, respectively, M and N represent the respective quantities of subject and objects contained in the sentence, \(S_{m}\) indicates whether the binary label of the main language is used, and \(O_{n}\) indicates whether the binary label of the main object is used. The model uses the Adam optimizer [32] to optimize the loss function.  
The joint extraction approach can better integrate information between entities and relations. It can improve the consistency of the model and the precision of context association and relation type recognition while making full use of training data and being suitable for a variety of application scenarios. Therefore, joint extraction is widely used in the research and practical application of entity and relation extraction.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 5 Conclusions  
In this paper, four control strategies for the dual-boost inverter (DBI), a mainstream topology for single-stage PV microinverters, were reviewed and compared. In particular, the control strategies that were compared were classical linear control, FCS-MPC, flatness-based control, and sliding-mode-based control. Additionally, a detail review of the DBI model, the DBI's switching strategies, and the global DBI's control scheme were presented.  
The comparison of the control strategies showed that the linear controller, FCS-MPC, and flatness-based control require the largest amount of sensors (7), while sliding mode control requires only five sensors.  
On the other hand, the linear control presented the lowest averaged switching frequency (79,060 Hz) when compared to FCS-MPC (79,140 Hz), flatness-based control (79,980 Hz), and sliding mode control (85,680 Hz).  
Regarding the inductor's current ripple in each boost converter, sliding mode control showed the lowest value (5.10 A), closely followed by linear control (5.2 A) and flatness-based control (5.23 A). However, the inductor's current ripple in FCS-MPC (9.94 A) was almost twice that of sliding mode control. Thus, FCS-MPC resulted in being the worst alternative in terms of a possible ripple-related failure of the capacitors.  
Finally, the main goal of this power converter is to generate a sinusoidal wave form. The harmonic contents of the grid currents, obtained from applying the control strategies, were contrasted with the IEEE Std. 1547, and the result showed that all the control strategies complied with the standard. Additionally, the analysis of the grid current's harmonic content showed that the best-performing control strategy was sliding mode control with a current THD of 2.48%, while the other control strategies (linear control 3.85%, FCS-MPC 3.58%, and flatness-based control 2.59%) showed the lowest performance.  
In conclusion, sliding-mode-based control presented the best trade-off between its advantages and disadvantages; therefore, it was selected as the most promising control strategy for the DBI in single-stage PV microinverter applications.  
Conceptualization, D.L.-C., H.R. and S.K.; methodology, H.R. and S.K.; validation, D.L.-C. and H.R.; formal analysis, D.L.-C., H.R. and N.M.; investigation, D.L.-C.; resources, S.K. and F.F.-B.; writing--original draft preparation, D.L.-C.; writing--review and editing, H.R., N.M. and F.F.-B.; supervision, S.K. and J.R.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable.  
Data Availability Statement: Not applicable.  
Acknowledgments: The authors acknowledge the financial support provided by AC3E (ANID/ BASAL/FB0008), SERC Chile (ANID/FONDAP/15110019), and the Innovative Energy Technologies Center at the Universidad Austral de Chile, INVENT UACh.