esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A green procedure is described for supporting Pd nanoparticles on hydroxyapatite (HAP), which serves as a highly-stable heterogeneous catalyst displaying excellent activity for the aqueous expeditious reduction of nitroaromatics to the corresponding amines with sodium borohydride, and oxidation of primary and secondary alcohols by hydrogen peroxide with high yields and selectivities. The structural features of the prepared catalyst are confirmed by latest techniques including field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. The reusability of the heterogeneous catalyst was affirmed in the aqueous reduction of nitrobenzene and oxidation of cycloheptanol for six consecutive runs without significant loss of catalytic activity.  
hydroxyapatite; palladium nanocatalysts; heterogeneous catalysis; oxidation; reduction; alcohol; nitrobenzene +
Footnote †: journal: Applied Physics Letters  
1  
[PERSON], [PERSON], S.  
weak acid-base properties, preventing plausible side reactions caused by the supports; and (iii) the nonporous structure that facilitates overcoming problems that limit mass transfer [19; 20]. Indeed, HAP is a support with several appropriate properties with the formula Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\); specific features being its capability to form solid solutions and possessing a wide range of cationic and anionic substituents that can help stabilize the anchored nanocatalysts. Consequently, it facilitates the separation process for the immobilized nanocatalysts, thus enhancing their reusability [21]; various noble metal nanocatalysts including Au, Pd, Pt, etc., have been immobilized on the surface of rigid HAP support, thus exploiting the heterogeneity advantages and easy recovery and recycling of the adorned nanocatalysts [22].  
Many important chemical processes rely on the catalytic oxidation and reduction pathways in [23; 24; 25; 26], and traditionally these reactions are conducted using expensive homogeneous catalysts and toxic organic solvents often under gas pressure (e.g., oxygen or hydrogen) [27; 28; 29]. Alcohols are used as precursors for several acids, aldehydes, and ketones, wherein the oxidation of alcohols is necessary for producing paints, plastics, detergents, food additives, cosmetics, and drug intermediates [30]. On the other side, the reduction of nitroarenes to their corresponding arylamines is among the major organic transformations because of their use as one of the important precursors and intermediates for the preparation of pharmaceuticals, dyes, pigments, polymers, and agrochemicals [31; 32; 33]. Pd nanoparticles (NPs) have a huge potential role in these processes [34; 35; 36; 37]. In the present study, we synthesized Pd nanocatalysts supported on HAP (HAP-Pd) via a greener procedure using coffee extract in water. The activity of the HAP-Pd catalyst was verified in the reduction of nitroarenes in water and environmentally-friendly oxidation of alcohols. The obtained results showed that this supported Pd nanocatalyst is very effective as a heterogeneous catalyst.
## 3 Results and Discussion
### Validation  
The numerical results of velocity and temperature are compared with corresponding results proposed in various literature studies as shown in Figure 5. The velocity of coolant in the cooling channel owing to the Lorentz force generated by the MHD pump is considered as one validation parameter. The velocity validation is conducted for the applied current of 15 mA and magnetic field intensity from 2 mT to 20 mT. The present numerical results of velocity show closer agreement with linear fit of corresponding experimental velocity results proposed by [PERSON] et al. [43]. Under various conditions of magnetic field, the numerical velocity is validated with experimental velocity within maximum error of 2.52% and average error of 0.8%. Previous research studies proposed by [PERSON] et al. and [PERSON] et al. [44; 45] have compared the numerical results of velocity with the same experimental results of [PERSON] et al. To show the accuracy of present simulation results with corresponding experimental results, the simulated velocity results from aforementioned studies have been reflected in Figure 5a. As can be observed, the present numerical results of velocity show closer agreement and relatively lower error with experimental results compared to simulated velocity results presented in the literature studies for the same configuration. The simulated temperatures in the present study are compared with corresponding temperature results proposed by [PERSON] et al. under the same voltage conditions. The simulated temperature in the present study shows closer agreement to corresponding temperature results from the reference study, with a maximum error of 4.32%. The predicted results from the proposed numerical model are reliable as the validation is within the acceptable limit of 5%, hence, the numerical model could be adopted for further simulations in the present work.  
Figure 5: _Cont._
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping
### GDACS Tsunami Model Outputs
Locations legend (tsunami height): Green\(<\) 1 m, Orange 1–3 m, Red \(>\) 3 m. The right figure shows higher resolution calculation. S to 30.98\({}^{\circ}\) S) were expected further south than where they actually occurred (latitude in the range from 33.76\({}^{\circ}\) S to 29.82\({}^{\circ}\) S). Figure 6 shows a comparison of the measured and the estimated tsunami height. Figure 5: _Cont._  
Figure 5: Sea level measured (red) and calculated (blue) in Coquimbo (**a**), Valparaiso (**b**), Juan Fernandez Island (**c**). The red dotted lines in the plot represent the time of the earthquake. The calculations compared here are the high resolution for Coquimbo and Valparaiso and the coarse one for Juan Fernandez, for computational reasons considering the distance of the points from the epicenter. Figure 6: Measured (blue dots) vs estimated (red dots) wave heights along the coast. The measurement declared as “inundation depth” on the coast have been selected from the tsunami field survey dataset. The reason for the southern shift of the maximum impact is mainly due to the actual type of fault. In the minutes after the event a detailed fault mechanism is unknown and therefore a uniform deformation plane centered on the epicenter is assumed (Figure 7a). In reality the epicenter is only the starting point of the deformation which is not uniformly distributed along the fault plane. The finite fault model (Figure 7b) from the United States Geological Survey (USGS, [[https://earthquake.usgs.gov/earthquakes/eventpage/us20003K7a/finite-fault](https://earthquake.usgs.gov/earthquakes/eventpage/us20003K7a/finite-fault)]([https://earthquake.usgs.gov/earthquakes/eventpage/us20003K7a/finite-fault](https://earthquake.usgs.gov/earthquakes/eventpage/us20003K7a/finite-fault))) shows that the deformation started from the epicenter but most of it occurred northern off-centered. In general, the finite fault model is able to explain better the results of an event but unfortunately this solution is not available at the time of the event but only hours or days after the events. Additionally, the adoption of the output of a Finite Fault model requires detailed editing that cannot be performed automatically. One possible method to try to overcome the difficulties in the forecast of the initial source is the performance of several calculations by modifying the initial conditions with a certain range of possible values. A Monte Carlo type of analysis is under development to include in the possible calculations results the parameters uncertainty. It will always quite difficult to try to estimate the finite fault model as it is very complicated and impossible to forecast but at least a range of possible solutions can be identified.
## 2 Background Theory  
### Blockchain  
Blockchain is a distributed database of records or a public ledger of digital transactions that have been executed and shared between several participants [5]. Anyone in the blockchain network can monitor and verify the authenticity of the transactions. The consensus of a majority of the participants in the system will verify each transaction in the public ledger. Blockchain creates permanent records and histories of transactions. However, the permanence of the record is based on the permanence of the network. In the context of blockchain, this means that a large portion of a blockchain community would all have to agree to change the information and is incentivized not to change the data [5]. For example, in the blockchain, the attacker needs to control more than 50% of the network nodes to make the blockchain network compromised [6]. However, it is difficult and costly to have that amount of computing power at this time.  
The blockchain technology relies on two cryptographic methods, which are digital signatures and cryptographic hash functions. The digital signature provides the authenticity of a digital message. In the blockchain, it can be used to provide integrity, authentication, and non-repudiation. There are two properties required for the digital signature scheme [7]. The first property is straightforward; it means that valid signatures must be verified. For example, if someone signs a message with their secret key (\(sk\)), and his/her friend later tries to validate that signature over that same message using his/her public key (\(pk\)), the signature must validate correctly. The second requirement is that it is computationally infeasible to forge signatures. An adversary who knows your public key and gets to see your signatures on some other messages cannot forge your signature on some message for which he has not seen your signature.  
The cryptographic hash function will compute a hash value from the given input. In the blockchain, each block will store the hash value of the previous block. The change of one datum can be easily detected. If the adversary changes any data of one block, the next block's data will be changed. Therefore, the cryptographic hash function has an essential role in maintaining data integrity in the blockchain network. This function should have four properties:  
1. Deterministic means that the same input will always produce the same output.
2. One-way function means that the function can easily compute the input, but it is infeasible to revert it from the hash value [8].
3. Collision resistance is when it is difficult to find two different messages as input with the same hash value, for instance, \(m_{1}\) and \(m_{2}\), and \(hash(m_{1})=hash(m_{2})\)[9].
4. Pseudorandom means that a change in the input must result in an unforeseeable change of the output. If the hash value of the input two was four, the hash of three better not be six [10].
# A Blockchain-Based Trust Model for Uploading Illegal Data Identification
###### Abstract  
Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods.  
blockchain; trust model; smart contract; blockchain security +
Footnote †: journal: Applied Sciences
# Strain Response Characteristics of RC Beams Strengthened with CFRP Sheet Using BOTDR
## 3 Finite Element Modeling
### Interface Modeling  
When a bending load is applied to an RC beam strengthening with CFRP, de-bonding failure is one major failure that affects the structural strength. In order to simulate the failure along the interface between the concrete part and CFRP sheet, a large amount of interface modeling was presented [19; 20; 21]. In this study, the de-bonding failure between the concrete part and CFRP sheets was modeled using the tiebreak contact option. The tiebreak contact option, TIEBREAK_SURFACE_TO_SURFACE, is one tie contact option in LS-DYNA and works the same as general tie contact options before satisfying the failure criterion. The failure criterion used in this study is presented in Equation (1).  
\[\left(\frac{|\sigma_{n}|}{NFLS}\right)^{2}+\left(\frac{|\sigma_{s}|}{SFLS} \right)^{2}\geq 1 \tag{1}\]  
where \(\sigma_{n}\) is the normal stress, \(\sigma_{s}\) is the shear stress, NFLS is the normal failure stress and SFLS is the shear failure stress. Under tensile and shear loads, the normal and shear stresses in the interface are calculated. When the failure criterion is satisfied, the tie contact is released along the interface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Parameter** & **Magnitude** \\ \hline \multirow{4}{*}{Concrete} & Material model & MAT\_145\_SCHWER\_MURRAY\_CAP\_MODEL \\  & Density (kg/m\({}^{3}\)) & 2320 \\  & Compressive strength (MPa) & 26.0 \\  & Max aggregate size (mm) & 25 \\ \hline \multirow{4}{*}{Steel bar} & Material model & MAT\_024\_PIECEWISE\_LINEAR\_PLAISTICITY \\  & Density (kg/m\({}^{3}\)) & 7850 \\  & Young’s modulus (MPa) & 200,000 \\  & Poisson’s ratio & 0.3 \\  & Yield stress (MPa) & 460 \\  & Tangent modulus (MPa) & 2094 \\ \hline \multirow{4}{*}{CFRP Sheet} & Material model & MAT\_002\_ORTHOTROPIC\_ELASTIC \\  & Thickness per layer (mm) & 0.1 \\  & Density (MPa) & 2400 \\  & Longitudinal modulus (MPa) & 138,000 \\  & Transverse modulus (MPa) & 9650 \\  & In-plane shear modulus (MPa) & 5240 \\  & Out of plane shear modulus (MPa) & 2240 \\  & Poisson’s ratio & 0.4 \\  & Minor Poisson’s ratio & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
After failure arises, the interface behaves as a surface to surface contact. In order to simulate the interfacial separation accurately, it is necessary to determine the normal failure stress and shear failure stress through empirical models or proper tensile and shear tests. The failure stresses used in this study were determined through Equations (2)-(4) [23; 24]:  
\[N\!F\!LS=0.447(f^{\prime})^{0.55}\quad(\text{MPa}) \tag{2}\]  
\[S\!F\!LS=1.5\beta_{w}N\!F\!LS\qquad(\text{MPa}) \tag{3}\]  
\[\beta_{w}=\sqrt{\frac{2.25-\ b_{f}/b_{c}}{1.25+\ b_{f}/b_{c}}} \tag{4}\]  
where \(f^{\prime}\) is compressive strength of the concrete material, \(b_{c}\) is center to center spacing of CFRP strips and \(b_{f}\) is width of CFRP strip.
## 4 Experiment and Results
### Evaluation Metrics and Visualization
#### 4.3.2 Confusion Matrix  
Figure 10 shows an example image of a confusion matrix. This matrix is used to evaluate the performance of a classification model. It summarizes the relationship between predicted and true classes. The rows represent the true labels, and the columns represent the predicted classes. Each cell represents a count for a particular combination of true and predicted classes. The confusion matrix allows us to identify intuitively the classes that are incorrectly predicted by the model and helps us analyze the performance of each class. Thus, we can identify the strengths and weaknesses of the model and delve into various aspects of model performance.
## 2 Materials and Methods
### Data Acquisition and Understanding  
There are three main tasks addressed in this stage. First, the data are made available in the target analytical environment. Once there, they are explored to determine if their quality is good enough to answer the objective question. Finally, a data pipeline is configured to process new data or update them periodically [37]. In this study, the data are accessed from a relational database in the cloud under the Azure SQL service, which is updated daily. Access is achieved from Google Colaboratory using the pyodbc and pandas packages. For the development of this project, data from a five-year period were taken, and the records from the first 55 months were used for training and the rest for monitoring.
## 3 Data Fusion in the Integration of SINS/DVL/MCP/PS
### Data Fusion with the Subsystems
#### 3.1.1 Standard Kalman Filter  
The discrete random linear model without controlling terms can be described as follows:  
\[\left\{\begin{array}{c}\mathbf{X}_{k}=\mathbf{\Phi}_{k,k-1}\mathbf{X}_{k-1}+\mathbf{W}_{k}\\ \mathbf{Z}_{k}=\mathbf{H}_{k}\mathbf{X}_{k-1}+\mathbf{V}_{k}\end{array}\right., \tag{12}\]  
where \(\mathbf{X}\) is the state vector, \(\mathbf{Z}\) is the measurement vector, \(\mathbf{\Phi}\) is the system dynamic model (or state transition matrix), \(\mathbf{H}\) is the measurement matrix. \(\mathbf{W}\sim\left(0,\mathbf{Q}\right)\) denotes the system noise with the variance of \(\mathbf{Q},\ \mathbf{V}\sim\left(0,\mathbf{R}\right)\) denotes the measurement noise with variance of \(\mathbf{R}\), and \(k\) denotes the \(k^{th}\) time-step.  
When the system noise and measurement noise are non-related and \(\mathbf{Q}\geq\ 0\) and \(\mathbf{R}>0\), the vector \(\hat{\mathbf{X}}_{k}\) can be estimated by the standard Kalman filter. The equations in the standard Kalman filter are as follows:  
\[\left\{\begin{array}{c}\hat{\mathbf{X}}_{k,k-1}=\mathbf{\Phi}_{k,k-1}\hat{\mathbf{X}}_{ k-1}\\ \mathbf{P}_{k,k-1}=\mathbf{\Phi}_{k,k-1}P_{k-1}\mathbf{\Phi}_{k,k-1}^{\text{T}}+\mathbf{Q}_{k-1} \\ \mathbf{K}_{k}=\mathbf{P}_{k,k-1}\mathbf{H}_{k}^{\text{T}}\left[\mathbf{H}_{k,k-1}\mathbf{P}_{k,k -1}\mathbf{H}_{k,k-1}^{\text{T}}+\mathbf{R}_{k}\right]^{-1}\\ \hat{\mathbf{X}}_{k}=\hat{\mathbf{X}}_{k,k-1}+\mathbf{K}_{k}\left[\mathbf{Z}_{k}-\mathbf{H}_{k} \hat{\mathbf{X}}_{k,k-1}\right]\\ \mathbf{P}_{k}=\left[\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k}\right]\mathbf{P}_{k,k-1}\left[\mathbf{I}- \mathbf{K}_{k}\mathbf{H}_{k}\right]^{\text{T}}+\mathbf{K}_{k}\mathbf{R}_{k}\mathbf{K}_{k}^{\text{T}} \end{array}\right. \tag{13}\]  
where \(\hat{\mathbf{X}}\) is the estimation of \(\mathbf{X}\), \(\mathbf{P}\) is the matrix of state variance, \(\hat{\mathbf{X}}_{k,k-1}\) and \(\mathbf{P}_{k,k-1}\) are the one-step predictions at time \(k\) with \(\hat{\mathbf{X}}_{k-1}\) and \(\mathbf{P}_{k-1}\) at time \(k-1\), \(\mathbf{K}\) denotes the gain matrix, and \(\mathbf{I}\) is a unit matrix with corresponding dimensions.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 3 Proposed Framework
### Feature Extraction  
#### 3.2.1 Local Binary Pattern (LBP)  
Local pixel information from each frame can be computed using basic Local Binary Pattern operator [17]. The Local Binary Pattern (LBP) is calculated by checking the pixel values of an image or frame with a threshold value in the neighborhood area. The \(LBP_{P,R}\) generates \(2^{p}\) different output codes. However, the basic LBP has several drawbacks. It is susceptible to noise, as slight variation in the intensities of the neighbors can completely change the resultant binary code [9].
## 2 Bridge Dynamics in a VBI System and the Data Reconstruction Algorithm
### Signal Processing Methods
#### 2.3.2 Response Reconstruction with a Deep ResNet (Residual Network)  
The residual network (ResNet) was introduced [27] to mitigate the challenges of exploding and vanishing gradients in conventional neural networks, which can impede weight updates and lead to training stagnation. ResNet has become a well-utilized deep learning method [28; 29], facilitating the training of significantly deeper architectures. Distinct from traditional feedforward neural networks, a ResNet incorporates residual, or skip, connections that concatenate a layer's output with its subsequent layer's output. Specifically, a ResNet is constructed from multiple residual units. For a given residual unit \(l\) and its input \(x_{l}\), as illustrated in Figure 5, the output is formulated as follows:  
\[x_{l+1}=F_{l}\ (x_{l})+x_{l} \tag{18}\]  
where \(F_{l}\) denotes the residual function \(F_{l}\ (x)=W_{l,2}\cdot\sigma\ (BN(W_{l,1}\cdot\sigma(BN(x))))\). The \(BN\) and \(\sigma\) (i.e., the relu in Figure 5) function as pre-activation of the weight layers, and \(W\) is a linear projection function.  
Figure 5: Residual unit. [27].  
Further, the following can be obtained:  
\[x_{L}=x_{l}+\sum_{i=1}^{L-1}F_{i}\left(x_{i}\right) \tag{19}\]  
which can be expanded for deeper residual blocks \(L\) and shallower residual blocks \(l\). If the shallower residual blocks within the network can discern a reasonable representation, the network has the capability to bypass the remaining layers by utilizing the skip connections, effectively setting \(F_{l}\left(x\right)\) to zero for subsequent residual blocks.  
In this investigation, a ResNet was initially trained on high-frequency bridge responses induced by vehicle loading to emulate the transfer matrix across various cross-sections. The architecture of the ResNet is depicted in Figure 6.  
In this study, the ResNet comprised an input layer, five hidden layers, and an output layer. The network was designed to utilize the responses measured at two cross-sections to reconstruct the responses at a third cross-section. Accordingly, the input layer featured two inputs, corresponding to the two measured cross-sections, while the output layer generated a single feature output, representing the reconstructed response. Regarding the hidden layers, the first three layers were populated with 64 neurons each, the fourth layer contained 32 neurons, and the fifth layer comprised 16 neurons. The neurons between connected layers were fully interconnected. The activation function used was ReLU, which is suitable for signal prediction tasks as opposed to graph processing, thus eliminating the need for batch normalization (BN). Additionally, a skip connection was established from the output of the first hidden layer to the third hidden layer, facilitating the direct propagation of input features to aid in the learning process.
## 2 Traditional Encryption Techniques
### Theoretical Models and the Influence of Environmental Factors
Quantitative analysis indicates that heavy rain increases QKD error rates by up to 15%, while fog and aerosol concentrations contribute to photon scattering, reducing the effective communication range by 40% compared to clear weather conditions.