esa-sceva/satcom-chunk-collection huggingface dataset:
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 2 Background
### Related Work  
Recently, there exist several works that focus on the study of blockchain in vehicular networks. In [7], the authors review the latest research activities of the blockchain-based vehicular environments by identifying research challenges and technical issues. They divide blockchain utilization into three layers: perception layer, networking layer, and application layer. First, the perception layer uses blockchain to solve the trust management issue and improve the perception accuracy on data sharing in the vehicular network. Second, the networking layer for network security and achieving a secure incentive mechanism solution. The last layer is the application layer for solving accountability, security, and privacy issues in a decentralized vehicular system.  
In [8], blockchain is utilized to form a decentralized trust management system in vehicular networks by allowing the neighboring vehicle to validate the received data using the Bayesian inference model for preventing inappropriate data from a malicious vehicle. Here, RSUs perform as miners to generate the block by employing joint PoW and PoS consensus algorithm. Moreover, the authors of [9] also propose a Bayesian network implemented in RSUs, which aims to easily detect fake information by calculating the subsequent probability of an event, taking into account pre-traffic probability, traffic event period, vehicle integrity, and the event collected from the vehicle.  
Additionally, to minimize attacks from malicious nodes in the consensus process, many works consider applying a consortium blockchain framework. In [10], the authors propose a consortium blockchain for a data sharing framework for the vehicular network to solve the risk of malicious tampering in a centralized data storage scheme. Here, they employ a smart contract in preselected nodes to manage data sharing and data storing in the system. The integrity and security of the data can be ensured by a digital signature technique when the vehicles upload their data. On the other hand, the block transactions are created and stored in the blockchain network by deploying a joint PoW and PBFT algorithm for its consensus mechanism. Moreover, the authors of [11] present a secure P2P data sharing system in VECNets by exploiting its smart contracts and applying the vehicle's reputation scheme for system efficiency. Here, they use joint PoW and PoS for consensus scheme and a three-weight subjective logic model for managing the vehicle's reputation. In order to motivate the vehicle node in providing accurate and timely data sharing, the authors of [12] propose a rewarding scheme using Ethereum smart contract incentives based on the calculation of vehicle reputation values. The authors proposeProof-of-Authority (PoA) consensus mechanism to validate the transactions and enhance the system performance.
## 4 Benchmarking
### Test on Max-Cut Problems
Therefore, the parameter \(p_{max}\) in the B-VNS algorithm was set at \(100/n\), which enabled the B-VNS algorithm to reach an equal maximum neighborhood structure, as in the VNS algorithm. We found that setting the iterations to \(0.2n\) was adequate to obtain good solutions, while reducing computation times. Unlike the tests for QUBO problems, we applied a non-equal condition by setting the chunk size \(C=0.9k_{max}\) for Helmberg-Rendl problems as well as for Burer et al. problems. This non-equal condition was used to investigate the impact of setting the chunk size to less than \(k_{max}\). This setting has a risk, that the B-VNS algorithm will be much less thorough than the VNS algorithm, but it was used with the aim to be faster. Table 3 shows the test results for Helmberg-Rendl problems, while Table 4 shows the test results for Burer et al. problems. The Mann-Whitney test with \(\alpha=0.05\) was conducted, as most samples did not satisfy the normality and homoscedasticity assumption. Although the B-VNS algorithm is much less thorough, the results show that the B-VNS algorithm was, still, able to provide solutions as satisfactorily as the VNS algorithm. Setting the chunk size \(C\) to smaller than \(k_{max}\) had an insignificant effect on the ability of the B-VNS algorithm, to achieve the objective values. Moreover, the B-VNS algorithm was shown to be substantially faster than the VNS algorithm, in all of the tested problems, regardless of the size. Therefore, the B-VNS algorithm was shown to clearly perform better than the VNS algorithm, on max-cut problems.
#### Article  
**Pituitary Adenylate Cyclase-Activating Polypeptide Protects Corneal Epithelial Cells Against UV-B-Induced Apoptosis via ROS/JNK Pathway Inhibition**  
[PERSON]  
1 Section of Anatomy, Histology and Movement Sciences, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy; [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (B.M.);some intracellular signaling, including the JNK pathway mediation of UV-B/ROS-induced apoptosis [8; 9; 10].  
The healthy cornea contains antioxidant molecules involved in the scavenging of ROS [11]. However, when UV-B-induced ROS overwhelmed the corneal antioxidant mechanisms, a reduction in its epithelium thickness was observed [12].  
Corneal epithelial damage affects millions of people around the world. Therefore, there is increased interest in discovering new strategies to repair corneal damage and promote proper corneal re-epithelialization.  
Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide belonging to the secretin and histidine-isoleucine (PHI) superfamily peptides. It exerts multiple activities in a tissue-specific manner by binding to G protein-coupled receptors comprising PAC1, VPAC-1, and VPAC-2 receptors [13; 14; 15]. Moreover, PACAP promotes the activity-dependent neuroprotective protein (ADNP) [16; 17]. Accordingly, NAP, the smallest active element of ADNP, plays a beneficial role, acting in a synergic way with PACAP [18; 19; 20]. PACAP is widely expressed in the nervous system and shows a neuroprotective role in several neurodegenerative diseases [21; 22; 23; 24; 25; 26; 27; 28]. The peptide and its receptors are distributed in different regions of the eye [29; 30; 31; 32]. The effects of the peptide in the visual system were repeatedly described, especially in the retina, where PACAP showed a neuroprotective role against different types of insult [33; 34; 35; 36; 37]. In the cornea, PACAP displayed different pleiotropic effects. It decreased corneal endothelial cell loss after transplantation, protected the corneal surface by stimulating tear secretion, and defended the endothelium against UV damage [38; 39; 40; 41; 42]. The involvement of PACAP in corneal epithelium protection against UV-B-radiation-induced ROS formation, and the underlying mechanisms therein, remains to be determined. Here, we assessed the expression of PACAP and its receptors in rabbit corneal epithelium. Then, we investigated its role in UV-B-irradiation-induced damage in rabbit corneal epithelial cells. Our results demonstrated that the peptide was strongly expressed in the corneal epithelial layer. Moreover, it affected UV-B-induced ROS generation and JNK pathway activation, counteracting apoptotic cell death induced by UV-B irradiation.
## 3 Data and Methodology
### Methodology
#### 3.2.1 CHTopoNER Model  
The CHTopoNER model includes a Chinese-roberta-wwm-ext layer [44], an improved SoftLexicon layer, and a TCNN-CRF layer composed of an IDCNN [45], a BiLSTM [46], and a CRF layer [47]. The model is shown in Figure 2.  
First, the Chinese text data were input. The model first converted the input text into character vectors using the Chinese-roberta-wwm-ext layer. Subsequently, improved SoftLexicon was used to obtain word-level semantic information. Next, the character- and word-level vectors were concatenated to fuse the semantic information on both levels. In addition, the model extracted and concatenated the semantic features of the vectors through a TCNN layer to obtain the forward and backward contextual dependency relationships of each character and word in the Chinese text, thereby exploring their potential semantic associations. Finally, the output of the TCNN layer was mapped to a predefined category space through a fully connected layer. The CRF layer was ultimately used to model and decode the label sequences because of its ability to consider the dependencies among labels, thereby improving the accuracy of sequence labeling.  
To avoid the out-of-vocabulary problem, possibly caused by SoftLexicon, the improved SoftLexicon improves the embedding weights of the corresponding words from the number of words matched in the training and verification sets into the calculated weights from the corresponding embedding obtained during pre-training using GLoVe [48].  
Figure 1: Method for parsing Chinese address text on the internet based on the integration of the CHTopoNER model and dynamic finite state machine (DFSM).  
Compared with BiLSTM, which can only retain contextual information but cannot efficiently process long sequences, the IDCNN uses a separable convolutional approach to reduce the number of model parameters, thereby reducing the computational complexity and adapting it for processing long sequences. Therefore, the CHTopoNER model integrates BiLSTM and IDCNN to form a TCNN network layer. The respective advantages of BiLSTM and IDCNN were fully exploited while avoiding their disadvantages. In the TCNN model, BiLSTM is primarily used to capture the contextual information of the sequence, whereas IDCNN is used to extract its local features. This integration method can be used for the accurate recognition of Chinese address elements.
## 6 Results
### Training and Testing Accuracy  
This section compares the training and testing accuracy of the proposed model against those of other models, primarily Probabilistic Matrix Factorization (PMF) [51], DeepWalk, GCNN, and OpGCN. The accuracy of these models was estimated in terms of the number of epochs, number of hidden units, and different batch sizes.  
Figure 7 shows that measuring the similarity can optimize the GCNN models to a greater extent. Thus, our proposed GCNN achieved higher accuracy as compared to the simple GCNN model and OpGCN model. Our proposed GCNN obtained a training accuracy rate of around 95% after 1200 epochs, compared to simple GCNN, with an accuracy rate of 78%, and OpGCN, with an accuracy of 93%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Time Complexity** & **Memory Complexity** & **Convergence Rate** \\ \hline DeepWalk & O (Lneh.log(\(n\))) & O (Lnh + Lh\({}^{2}\)) & 2200 Epochs \\ KGCN-Neighbour & O (Leh + Lnh\({}^{2}\) + rLnh\({}^{2}\)) & O (Lnh + Lh\({}^{2}\)) & 2100 Epochs \\ GCNN & O (Leh + Lnh\({}^{2}\)) & O (Lnh + Lh\({}^{2}\)) & 1800 Epochs \\ OpGCN & O (Lrnh\({}^{2}\)) & O (Lbrh + Lh\({}^{2}\)) & 1650 Epochs \\ Proposed GCNN & O (Leh + Lnh\({}^{2}\)) & O (Lbh + Lh\({}^{2}\)) & 1400 Epochs \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity (time and memory) and convergence rate comparison.  
Figure 7: Training accuracy against the number of epochs.  
Similarly, Figure 8 shows the comparison of the testing accuracy of all the previously mentioned models against the number of epochs. As we can see, our proposed model achieved a testing accuracy of 95.8%, as compared to OpGCN, with an accuracy of 92%, and GCNN, with an 88% accuracy. The testing accuracy of DeepWalk was improved compared with its training accuracy. The PMF model did not show satisfactory improvements in testing. The model's behavior in testing was estimated to be similar to the training behavior.  
Hidden units play an essential role in impacting the model's performance, and therefore, there is a need to analyze the accuracy and error rate compared to various hidden units. Figure 9 shows that the proposed GCNN's accuracy is the highest at HU_64 and can be considered optimal. We can observe from the graph that our proposed GCNN is quite impressive in comparison to simple GCNN and better than OpGCN due to the optimization and processing techniques applied to the model and data.  
Figure 8: Testing accuracy against the number of epochs.  
Figure 9: Testing accuracy against hidden units.  
Figure 10 shows the experimental results of selecting different batch sizes for testing the model's accuracy for new data. Then, this accuracy is compared with that of other models such as GCNN and DeepWalk. As we can see, our proposed model achieved a testing accuracy of 95.8% for Batch_256. This shows that the model's performance is better with a reasonably small size of batch. Similar behavior is observed for the other models.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle  
The deformation of an aircraft wing directly affects the reference datum of phase center, so only high-precision measurement of wing deformation can accurately obtain the reference of the phase center. At present, in the research of wing deformation measurement, researchers use laser measurement and visual imaging technology to measure wing deformation. Although the accuracy of this method is high, the method is based on the laser source and the laser source will have a short life after being used for a long time. Therefore, it is difficult to realize the full field measurement of the phase center of the array antenna on the large wing [7; 8; 9]. Some researchers also use the image pattern correlation technique (IPCT) to obtain the phase center of the airborne array antenna. This method can easily obtain the information of displacement vector field or strain field. However, when using high-speed cameras, noise and complex weather conditions will affect the measurement accuracy [10; 11; 12]. Therefore, to avoid the influence of the above adverse factors, the paper designs a real-time, high dynamic and high-precision method to measure the phase center of the array antenna. The method combines POS technology [13; 14] and fiber Bragg grating (FBG) sensor technology, which is helpful to improve the accuracy of measuring the phase center of array antenna. The core idea of the paper is to use the advantages of FBG sensing technology to assist the application of distributed POS technology, so as to provide a high-precision benchmark for distributed POS technology.  
Figure 1: Airborne ground-observation system.
## 4 Multidisciplinary Design, Analysis, and Optimization of an Adaptive Wing
### Adaptive Wing Aero-Structural Analysis  
A static aero-elastic analysis was performed to obtain the aerodynamic and structural data of the studied model. The structural data include the displacements, stresses, strains, and overall weight. Among the aerodynamic outputs, those of primary interest are the lift and drag coefficients, noted as \(C_{l}\), \(C_{d}\), respectively, and the pressure coefficient \(C_{p}\) at the wing upper surface. For the structural solution, the ANSYS software with a nonlinear solver was used.  
Design and manufacturing solutions seek to reduce the dynamic aero-elasticity effects. The wing prototype materials are chosen to have high stiffness properties, which is a solution to reduce the occurrence of dynamic instabilities and to delay the occurrence of possible flutter-related phenomena.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 1 Introduction
Motivated by the need to overcome these challenges, the SC and MS-SC decoding algorithms are of interest in this work due to their low computational complexity in polar codes. This work focuses on integer-based quantized SC decoding algorithms. This paper addresses the practical challenges of SC decoder implementation and makes the following key contributions:  
* Proposal of an All-Integer-Quantized MS-SC Decoder: We introduce an integer-based quantized min-sum SC (QMS-SC) decoder that eliminates floating-point computations, making it more suitable for hardware implementations. * Performance-Complexity Trade-off Analysis: We investigate the impact of integer-based quantization and demonstrate that 5-bit integer quantization approaches a performance comparable with high-precision MS-SC decoding. We also analyze the decoding performance over fading channels, showing that quantization beyond 5 bits leads to performance saturation. * Computational Complexity Reduction: The all-integer QMS-SC decoder reduces computational complexity by approximately 75% compared to the conventional SC decoder, significantly improving efficiency. The remainder of this paper is organized as follows: Section 2 presents the background and the system model. Section 3 discusses the related work. Section 4 discusses the role of integer quantization. The experimental results and the comparison in computational complexity are provided in Section 5. We discuss the implementation results of polar coding in Section 6. Finally, Section 7 provides the conclusions.
## 4 Materials and Methods
### Analysis of Cell Morphology and Attachment  
The morphology of cells and the microstructure of biomaterials were observed using scanning electron microscopy (SEM) as previously described [24]. hPDL-MSCs were seeded onto GPP and VSCM at the density of 5 \(\times\) 10\({}^{3}\)/well in 200 \(\upmu\)L of DMEM and cultured at 37 \({}^{\circ}\)C. After 3, 7, and 14 days, the samples were fixed overnight with 4% paraformaldehyde (ThermoFisher Scientific, Waltham, MO, USA) and washed with PBS to eliminate detached cells. The samples were then dehydrated by gradient ethanol washing. After replacing the remaining ethanol with hexamethyldisilazane (HMDS, Sigma-Aldrich, St. Louis, MO, USA), the sample surfaces were sputter-coated and examined under SEM (FEI Quanta 200, Hillsboro, OR, USA) at an accelerating voltage of 15-20 kV.  
The adhered hPDL-MSCs were also visualized by staining the nuclei and cytoskeleton structures using the focal adhesion staining kit (Sigma-Aldrich, St. Louis, MO, USA) according to the manufacturer's protocol. hPDL-MSCs with an initial seeding density of 5 \(\times\) 10\({}^{3}\)/well were cultured on GPP, VSCM, and tissue culture plate (TCP) in 200 \(\upmu\)L of DMEM for 3, 7, and 14 days. Cells were fixed with 4% paraformaldehyde for 15 min and permeabilized with 0.1% Triton-X100 (Sigma-Aldrich, St. Louis, MO, USA) for 5 min at room temperature. After blocking with 1% bovine serum albumin for 30 min, cells were stained with rhodamine-conjugated phalloidin and 4', 6-diamidino-2-phenylindole (DAPI) to visualize actin filaments and nuclei, respectively. Staining of focal adhesion was not performed because of the interference of material with the antibody. Cell morphology and attachment were qualitatively analyzed and observed with an ECHO Revolve fluorescence microscope (Echo, San Diego, CA, USA). No quantitative analysis was performed because the images were taken in 2D focal plains for 3D material. Therefore, such quantification will not be appropriate for assessing cell attachment.
# Ras Multimers on the Membrane: Many Ways for a Heart-to-Heart Conversation
## 5 Membrane Heterogeneity and Nanoscale Topography in Ras Dimerization
The same study showed enrichment and elevated nanoclustering of PIP2 with increasing membrane curvature and the opposite for PS [193]. Even though Ras itself lacks structural features to detect membrane curvature, curvature modulations result in changes in lateral segregation of membrane lipids which can be sensed by Ras with its ability to sort distinct lipid head groups and acyl chains. Computer simulations suggested that membrane clustering of tH-Ras may also induce membrane curvature [128]. Reorientation of Ras on the membrane in response to membrane curvature could lead to either the occlusion or free accessibility of the dimerization interfaces and effector domains (Figure 6C). This could explain differences in downstream signaling of the different Ras isoforms depending on their surrounding membrane topography [67]. A unique combination of lipid-protein composition and the associated membrane topography defines a specific type of membrane nanostructure (which is generally and casually called a 'nanodomain'). Among the potential nanodomains that Ras could be associated with and dimerize within, endocytic compartments represent an interesting class. Endocytosis, an indispensable process in eukaryotic cells, is driven by the interplay of local lipid and protein enrichment, membrane curvature and actin dynamics [194]. Clathrin-dependent endocytosis, for example, takes place in membrane regions enriched in PIP2 clusters that encourage membrane curvature [195]. The clathrin plaques and pits are further stabilized by actin filaments. Adaptor proteins are recruited to stabilized clathrin-lattices resulting in an additional increase in local PIP2 concentration creating a distinct membrane environment [194]. Incidentally, at least in the case of KRas, the membrane diffusion of Ras is coupled with endocytosis, where internalization of Ras likely occurs in membrane nanodomains that immobilize Ras (see also Figure 4) [130]. These nanodomains were previously hypothesized to be sites where Ras forms nanoclusters and recruits effectors [87; 133]. As such, it is not surprising that the signaling activity of some Ras isoforms depends on endocytosis [196]. At present, however, it is unclear which type of endocytic compartments are conducive to Ras dimerization. Defining these and other types of Ras-associated nanodomains will be an important step to understanding Ras dimerization in cells.
## 3 Results and Discussion
### Analysis of Volatile Compounds  
In order to evaluate the effect of Cava lees on the volatile fraction of breads, HS-SPME-GC-MS was performed. A total of 74 volatile compounds were identified (Table 4), including nine acids, 16 alcohols, 11 aldehydes, five ketones, 14 esters and eight terpenes. Bread volatile compounds may result from fermentation, lipid oxidation and Maillard and caramelization reactions [20; 23; 24; 25]. Alcohols, acids, esters, aldehydes and ketones were generated mainly during fermentation while some of them as alcohols, ketones and aldehydes come from lipid oxidation too. Lastly, Maillard and caramelization reactions originate pyrazines, pyridines, pyrroles, furans, sulfur compounds, aldehydes and ketones [22]. Additionally, the volatile compounds of Cava lees were also analyzed by HS-SPME (Table S1), since wine lees are able to retain aromatic substances such as esters, aldehydes, norisoprenoids, terpenes and some phenolic compounds [17; 26]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Compound** & **CAS-Num.