esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 6 Experimental Setup and Results
Our final test accuracy was around 98.3% for Urdu numerals and 96.04% for Urdu characters. Table 4 depicts the comparison of our proposed approach with other techniques for the same task. It is noteworthy that the results shown in blocks (last two red-colored rows of Figure 16) were quite similar regardless of the number of hidden neurons in the case of Urdu handwritten character classification. Figure 14: Effect of (a) batch size and (b) learning rate on accuracy. Figure 15: Confusion matrices and respective performance graphs in Urdu handwritten numeral classification. (**a**) Accuracy result with 10 hidden neurons; (**b**) best validation performance with 10 hidden neurons; (**c**) accuracy result with 30 hidden neurons; (**d**) best validation performance with 30 hidden neurons; (**e**) accuracy result with 50 hidden neurons; (**f**) best validation performance with 50 hidden neurons. Figure 16: Confusion matrices and respective performance graphs in Urdu character classification. (**a**) Accuracy result with 10 hidden neurons; (**b**) best validation performance with 10 hidden neurons; (**c**) accuracy result with 30 hidden neurons; (**d**) best validation performance with 30 hidden neurons; (**e**) accuracy result with 50 hidden neurons; (**f**) best validation performance with 50 hidden neurons. The experiments were also performed using variations of the \(n\)-fold cross-validation approach in order to avoid any confusion regarding the ratio of training and testing data. The confusion matrices for Urdu handwritten numerals are given in Tables 5 and 6 showing average accuracy of 92.7% and 95.6%, respectively using 10-fold and 8-fold cross validation. Similarly Tables 7 and 8 show results of Urdu handwritten characters (shown in groups in Figure 10) with 10 fold and 8 fold cross validation. Overall it was observed that our proposed model of CNN showed better predictive accuracy compared with other classification models.
## 2 Materials and Methods  
### Datasets Used in the Study  
Satellite observations from GOSAT and MODIS, model simulations from CarbonTracker, and ground-based measurements for validation from TCCON for 5 years from 1 June 2009 to 31 May 2014 were collected. The specifications of the used datasets are shown in Table 1. To highlight the differences of seasonal cycle over various land cover types, we selected nine different regions of interest where there are distinctive correlations of seasonal cycle between XCO\({}_{2}\) and terrestrial biosphere parameters. Figure 1 shows the locations of these regions overlapped with land cover map. Table 2 gives detailed specifications of these regions. In addition, XCO\({}_{2}\) data from 12 TCCON sites (shown in Figure 1) and model simulations are obtained to investigate the uncertainty of GM-XCO\({}_{2}\) datasets used in this study.  
Figure 1: Regions of interest selected from global land region from 45\({}^{\circ}\)S to 60\({}^{\circ}\)N overlapped with land-cover data in 2009 obtained from Europe Space Agency (ESA, [25]). Nine different regions of interest are outlined as red rectangles, including Forest in Northern Canada (FnC), Forest in Eastern Russia (FeR), Cropland in Western Russia (CwR), Cropland in Northern China (CnC), Cropland in Northern India (CnI), Cropland in Eastern Brazil (CeB), Grassland in Central Africa (GeA), Mixed forest in Southern Africa (FsA) and Shrub-land in Northern Australia (SnA). Six regions in the Northern Hemisphere, including FnC, CwR, FeR, CnC, CnI, and GeA, are used for correlation analysis of seasonal cycle between NDVI and XCO\({}_{2}\) in Section 3.2, while five regions, including CnI, GeA, CeB, FsA, and SnA, are used for correlation analysis of seasonal variations between LST and XCO\({}_{2}\) in Section 4.2. Twelve selected sites of TCCON are marked with black dots and used for evaluating mapping data in Section 4.1.2.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 8 Conclusions  
Studying the operation of the Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr protocols, the three ZKP protocols have been successfully implemented with the necessary checks to ensure that the calculations carried out in each case are the ones indicated and that the code is as simple as possible. The primary focus of this paper was to accomplish the following goals:  
* Creating an application that manages the Verifier and Tester roles: The developed application has two main interfaces, one server and one client, which act as the Verifier and Prover, respectively. The Prover connects to the port where the Verifier is waiting for a connection, starting at the moment the ZKP authentication protocol is chosen.
* Testing the security of the protocols against brute-force attacks: The Prover interface includes a brute-force authentication option, where it will try to authenticate not with the private key, but under the assumption that it is unknown by trial and error.
* Allowing the application to be executable: The application has been developed in Python code, which allows it to be executed on different operating systems.  
The tests carried out on the application have not only allowed us to verify its correctness but have also shown that it can be a useful tool for studying the chosen ZKP algorithms; it allows the user to follow its execution step-by-step and to analyze the factors that make them vulnerable to brute-force attacks in a practical way.  
In conclusion, our paper has introduced the concept of Zero-Knowledge Proof (ZKP) protocols and their use in authentication in public-private key encryption systems. ZKP protocols ensure that sensitive information such as passwords and private keys are not vulnerable to interception or attack. This is especially relevant in applications such as online banking, e-commerce, and secure communication where the security of the underlying encryption system is paramount. As such, the study and implementation of ZKP protocols have become increasingly important in ensuring the privacy and security of online transactions. We have implemented and evaluated three ZKP protocols--Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr--using an application that checks their security against brute-force attacks. By providing a clear explanation of the math behind these protocols and offering practical examples of their application, we hope to make the study and implementation of ZKP protocols more accessible to a wider audience, contributing to the development of more secure online transactions.
## 4 Economic Evaluation
According to this design, a new battery has to be bought every year because the design was carried out with this efficiency. The rest of the equipment has roughly a life of 20 years of use, as many researchers have supported [63; 64]. For this reason, the investment period is 20 years, as has been shown in Table 2.  
The aim of the economic evaluation is obtaining the value of the above-mentioned parameters for comparing the systems (NPV, IRR, CRF, LCOE); the results are shown in Table 3. For this, a series of parameters is required including fixed capital investment, working capital investment (10% of the fixed capital investment), invested funds, benefits, amortization, and cash flow (see Table S4 in Supplementary Materials). Currently, the conventional system presented a more sympathetic economic part, since the NPV and IRR is higher, 2164 and 12.31%, respectively. The main barrier for the El-FC system is their high initial investment cost and the reliability of novel energy technologies. According to the economic results, the LCOE value of our system is higher than the conventional system due to the novelty of our system, which involves an anion exchange membrane water electrolyzer that has been little developed so far, corresponding to a high cost (see Table 3). However, the values of L
## 4 Discussion
### Model Relevance with Different Shifting Test Intervals  
Our experiments suggest that the CatBoost model remains relevant even when trained on older data, indicating a certain resilience against concept drift in the sense that the model does not simply \"expire\" after a time gap. However, we also observe that the model performance varied across different testing windows: when the same model was applied to data from different future intervals, the predictive metrics would rise or fall. The primary reason for this was not a loss of validity in the training data; rather, the data to which the model was applied possessed changing characteristics over time. In other words, while the model does not become obsolete, its efficiency can vary based on when exactly we evaluate or deploy it. Such variations shows the importance of future-based testing scenarios (as opposed to artificial splits) to capture more realistic performance estimates. Although our study found only moderate differences, in more volatile environments these shifts could be larger, underscoring the need for periodic checks or incremental updates to maintain peak performance in real-world churn prediction.
# Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
## 2 Materials and Methods
### Basic Theory of Acoustic Emission Technology
#### 2.1.2 Waveform and Parameters of Acoustic Emission Signal  
The AE signal is a non-smooth signal, which can be divided into burst type and continuous type [22]. Not only due to the complexity of the AE waveform signal collected in the actual bridge environment, but also considering the existence of reflection, refraction, and mode transformation during transmission, the received signal differs from the actual signal. In addition, it is difficult to describe the original AE signal accurately. Currently, the common method to describe the AE signal includes using the characteristic parameters of AE in the simplified waveform to describe the AE signal [23]. A simplified diagram of the AE signal is shown in Figure 1.  
Figure 1: Acoustic emission simplified waveform characteristic parameter model.  
According to the simplified waveform of the AE signal, the main characteristic parameters include amplitude, duration, energy, rise time, ringing count, average signal level, event and threshold. The energy of AE signal is the area under the energy envelope, with the unit of ms*mV [23].
## 5 Results and Discussions  
We tested the performance of the proposed algorithm by conducting simulations involving graphs with various numbers of nodes, i.e., 10, 15, and 20 nodes, as shown in Figures 3-5, respectively. For each graph, four variations of connectivity were simulated to show the success of finding solutions. We denoted the graph, together with the connectivity of variations, by using the code \"CONFIG \(A\)-\(B\)\", where \(A\) is the number of nodes and \(B\) is the label of a variation. For instance, the first variation of the graphs with 10, 15, and 20 nodes were denoted as CONFIG 10-1, CONFIG 15-1, and CONFIG 20-1, respectively. Note that all edges were bi-directional.  
Three significant aspects were evaluated. The first aspect was the influence of minimum allowable arrival time on the solution's existence and the convergence of the solutions. The second was the correlation between the average degrees of all nodes, the number of vehicles, and the number of nodes to the success of finding solutions. The last aspect was the accuracy of the resulted minimum traveling time of the slowest vehicle, according to the variation of the evaporating constant and its effect on the convergence of the search results.  
For evaluating the first and third aspects, we established simulations for three vehicles, where the 1 st, 2 nd, and 3 rd vehicles started from Nodes 4, 1, and 3, respectively. Each vehicle had four speed options, i.e., 0.1, 0.5, 1, and 1.5 m/s. Meanwhile, for evaluating the second aspect, we applied two until seven vehicles on each connectivity configuration. Vehicles 1 until 7 were started from Nodes 4, 1, 3, 5, 10, 7, and 8, respectively. For one simulation, we applied 3000 iterations of searching the optimal solution.  
Figure 3: Graph with CONFIG 10-1 configuration (10 nodes).
## 3 Distributed Optimized Scheduling of EGHS
### Outer Layer PCCP Algorithm  
The constraint in Equation (5) is processed as follows [20]:  
\[\left(b_{ij,d+1}^{t}\right)^{2}+\Delta x_{ij}\text{M}_{2}\left(f_{ij,d}^{t} \right)^{2}-\left(b_{ij,d}^{t}\right)^{2}\leq 0 \tag{26}\]  
\[-\left(b_{ij,d+1}^{t}\right)^{2}-\Delta x_{ij}\text{M}_{2}\left(f_{ij,d}^{t} \right)^{2}+\left(b_{ij,d}^{t}\right)^{2}\leq 0 \tag{27}\]  
In the above, Equation (27) is a convex constraint. The relaxation of Equation (28) is as follows:  
\[\left(b_{ij,d}^{t}\right)^{2}-\left[2b_{ij,d}^{t,(k-1)}b_{ij,d+1}^{t}-\left(b_ {ij,d+1}^{t,(k-1)}\right)^{2}\right]- \tag{28}\]  
\[\Delta x_{ij}\text{M}_{2}\left[2f_{ij,d}^{t,(k-1)}f_{ij,d}^{t}-\left(f_{ij,d}^ {t,(k-1)}\right)^{2}\right]\leq\delta_{ij,d}^{t}\]  
In the above, \(b_{ij,d+1}^{t,(k-1)}\) and \(f_{ij,d+1}^{t,(k-1)}\) are the pressure value and airflow of the pipeline position \(d+1\) at time \(t\) after the \(k-1\) iteration; \(\delta_{ij,d}^{t}\) is a relaxation variable with a value greater than or equal to 0.  
The constraint in Equation (21) is processed as follows:  
\[f_{\text{GT},\;it}\geq h_{2,i}P_{\text{GT},\;it}^{2}+h_{1,i}P_{\text{GT},\;j, t}+h_{0,i}\quad i\in\Omega_{\text{GT}} \tag{29}\]  
Since unnecessary gas consumption will increase the cost of energy supply to the system, the constraint in Equation (29) is always tight. In order to keep the relaxation domain tight, the relaxation variables are added to the inner optimization objective function:  
\[W^{(k)}=f+\rho^{(k)}\sum_{t\in T}\sum_{(i,j)\in\Omega_{\text{pipe}}}\sum_{d\in \Omega_{d}}\delta_{ij,d}^{t} \tag{30}\]  
In the above, \(W^{(k)}\) is the objective function value including the penalty function obtained by the \(k\)th optimization; \(\rho^{(k)}\) is the penalty factor for the \(k\)th iteration of the outer PCCP; \(\Omega_{d}\) is the set of relaxation variables.  
The convergence conditions of PCCP are as follows:  
\[\left\{\begin{array}{l}\left|W^{(k)}-W^{(k-1)}\right|\leqslant\varepsilon_{1 }\left|W^{(k-1)}\right|\\ \max(\delta)\leqslant\varepsilon_{2}\end{array}\right. \tag{31}\]  
In the above, \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are convergence determination parameters; \(\delta\) is the relaxation variable matrix; If the convergence conditions are not met, the penalty factor is updated as follows:  
\[\rho^{(k+1)}=\min\Bigl{(}v_{c}\rho^{(k)},\rho^{\max}\Bigr{)} \tag{32}\]In the above, \(\rho^{\text{max}}\) is the maximum penalty factor; \(v_{\text{c}}\) is the dynamic adjustment coefficient.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### Rayleigh Optical Properties  
Rayleigh optical thickness vertical profiles were calculated by following the recommendations of [PERSON] et al. [PERSON] et al. (2016). Rayleigh optical thickness for layer \(l\) of thickness \(\Delta z\) is given by the product of the layer molecular number density, \(N_{l}\), the layer Rayleigh scattering cross section, \(\sigma_{Ray,l}\), and \(\Delta z\):  
\[\tau_{Ray,l}=N_{l}\sigma_{Ray,l}(\lambda)\Delta z \tag{8}\]  
The layer molecular number density, \(N_{l}\), was derived from the G5 NR layer temperature and pressure using:  
\[N_{l}=N_{s}\frac{p_{l}}{p_{s}}\frac{T_{s}}{T_{l}} \tag{9}\]  
where \(N_{s}=2.546899\times 10^{19}\) cm\({}^{-3}\), \(T_{s}=288.15^{\circ}\) K, and \(P_{s}=1013.25\) hPa are the molecular number density, temperature, and pressure for standard air conditions. The Rayleigh scattering cross section for incident unpolarized light is given by:\[\sigma_{Ray,l}(\lambda)=\frac{24\pi^{3}(n^{2}(\lambda)-1)^{2}}{\lambda^{4}N_{g}^{ 2}(n^{2}(\lambda)+2)^{2}}F_{l}(air,\lambda) \tag{10}\]  
Here \(F_{l}(\lambda)\) is the King Factor for the depolarization of air that takes into account the effects of anisotropy of air molecules. It depends on the molecular composition of air, and can be calculated from a weighted average of the depolarization terms for the major constituents of air:  
\[F(air,\lambda)=\frac{78.084F(N_{2})+20.946F(O_{2})+0.934F(Ar)+C_{CO_{2}}}{78.08 4+20.946+0.934+C_{CO_{2}}} \tag{11}\]  
\[F(N_{2})=1.034+3.17\cdot 10^{-4}\lambda^{-2} \tag{12}\]  
\[F(O_{2})=1.096+1.385\cdot 10^{-3}\lambda^{-2}+1.448\cdot 10^{-4}\lambda^{-4} \tag{13}\]  
\[F(Ar)=1.00 \tag{14}\]  
\[F(CO_{2})=1.15 \tag{15}\]  
For our forward model simulations we assumed 300 ppm CO\({}_{2}\) (\(C_{CO_{2}}=0.030\)). Note Equation (11) does not include water vapor. [PERSON] et al. (2008) and [PERSON] et al. (2014) indicate that ignoring water vapor effects can result in Rayleigh optical thickness errors on the order of 0.01% to 0.2%, comparable to the errors associated with assuming a constant CO\({}_{2}\) concentration.  
The calculation of Rayleigh scattering cross section (Equation (10)) also requires the refractive index of air, \(n(\lambda)\), which we estimated with the following:  
\[(n-1)\cdot 10^{8}=8060.77+\frac{2481070}{132.274-\lambda^{-2}}+\frac{17456.3}{39.32957-\lambda^{-2}} \tag{16}\]  
The Rayleigh scattering expansion coefficients (\(B_{Ray,l}\)) were taken from Table 1 in [PERSON] (2010), and depend on the depolarization ratio of air, which was simply set to 0.03.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 4 Pre-Processing of Data and Selection of Neural Model
### Spatio-Temporal Prediction
#### 4.4.2 LSTM Neural Network Tuning  
Finally, we fit the multilayer LSTM neural network according to the two experiments specified in Section 5 which consider 7 and 11 input stations with 1 and 24 h as prediction outputs, respectively. The network needs to be specified because the numbers of input and output variables varies. In the case of the experiments with seven stations and 1 h as output, the network R36 was taken as the base considering \(\{200,100,50\}\) neurons and dropout rates  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**E’**} & \multirow{2}{*}{**N’**} & **E** & **N** & **PM\({}_{2.5}\)** & \multirow{2}{*}{**...**} & **PM\({}_{2.5}\)** & \multirow{2}{*}{**E** (Es-02)**} & **N** & **PM\({}_{2.5}\)** & \multirow{2}{*}{**...**} & **PM\({}_{2.5}\)** & \multirow{2}{*}{**...**} & **PM\({}_{2.5}\)** \\  & & **(Es-02)** & **(Es-02)** & **(t-23)** & & **(Es-02)** & **(t-0)** & **(Es-03)** & **(Es-03)** & **(t-23)** & **(t-0)** & **(t-0)** & **(t-24)** \\ \hline C & D & C & D & \(-1\) &... & \(-1\) & E & F & 25.1 &... & 18.2 &... & 32.1 \\ \hline E & F & C & D & 35.1 &... & 32.8 & E & F & \(-1\) &... & \(-1\) &... & 35 \\ \hline G & H & C & D & 25.3 &... & 30.4 & E & F & 17.4 &... & 22.9 &... & 24.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Structure of dataset used in spatio-temporal experiments.  
of \(\{0.1,0.3,0.5\}\) in a greedy way, to avoid trying an exponential number of combinations. In addition, the entry of a fourth layer was allowed. The selection was based on the value of \(R^{2}\) on a validation set given by the 30% of the original test set (details in Section 5). The experiments indicated that the network with the best results turned out to have four LSTM layers with \(\{200,50,100,30\}\) neurons with dropout rates of \(\{0,0.5,0.5,0.5\}\), respectively. We call this network LSTM-4.The training of the proposed model typically manages to control overfitting by considering the indicated dropout rates. As an example, Figure 5 shows the evolution of the mean squared error (MSE) loss function using the LSTM-4 network for the 1 h forecast at Cerro Navia station using six closest stations as input. In the graph it can be seen that the training and validation loss curves tend to converge.
## 5 Numerical Example
### Example 5--Industrial Application: Flexible Coupler  
The TO concepts for the manufacture and assembly of a welded industrial pipe are applied to evaluate the best topological configuration of a metal clamp, which is part of the member pipe tool called a flexible coupler. The Brazilian company called YYY Engineering [26] manufactures the model, where in Figure 15a can be seen the flexible coupler in the assembly of a tube-curve type, which has been obtained via an \"empirical\" process through CAE (Computer Aided Engineering) analysis and Figure 15b illustrates the design domain and boundary conditions for the flexible coupler composed of clips specially developed to form an adjustable strap according to the size of the pipe. TO is performed with the SESO method, which applies a Finite Element Analysis to a 180 \(\times\) 75 \(\times\) 8 geometry containing 57,120 hexahedral finite elements. The material properties are [PERSON]'s modulus \(E=2.1E5\) MPa and [PERSON]'s ratio \(\
u\) = 0.3, the filtering radius is 1.5 mm, and the optimization parameters are \(RR=ER=0.01\). The loads have a magnitude of 7.5 kN and are applied at four different points, as shown in Figure 15. The optimal topology is shown in Figure 16 with front and cross-sectional views, which was achieved with a final volume fraction of 0.25 and compliance of 7.333 kN\(\cdot\)mm. After obtaining the optimal structure, tests of deflection and straining of the component were done using SolidWorks. The model optimized via SESO and post-processed in SolidWorks achieved a volume reduction of approximately 62% of the one proposed by [26], and Figure 17 shows that the model created through the TO procedure had seven cavities, compared to the three cavities of [26].  
Figure 14: Surface graphs for optimal topology: (**a**) SERA, (**b**) SESO, (**c**) SIMP, and (**d**) ESO.  
Figure 16: Topology optimal: (**a**) frontal view—SESO and (**b**) cross view—SESO.  
Figure 15: (**a**) metal clamp model, [26]; (**b**) Design domain and boundary conditions.