esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Measurement Uncertainty Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sources of Uncertainty** & **Symbol** & **Type** & **Probability Distribution** & **Standard Uncertainty** \\ \hline Calibration of the sensor & & & & \\ (Including the contribution of systematic error & \(u_{\alpha l_{ex}}\) & B & Rectangular & 0.454 arc-sec \\ and the uncertainty of reference autocollimator) & & & & \\ Resolution of sensor & \(u_{res\_x}\) & B & Rectangular & 0.057 arc-sec \\ Drift & \(u_{drf\_ex}\) & B & Rectangular & 0.242 arc-sec \\ Repeatability & \(u_{exp\_x}\) & A & Gaussian & 0.301 arc-sec \\ Standard uncertainty of \(\varepsilon_{y}\) & \(u_{ey}\) & & & 0.599 arc-sec \\ \hline \end{tabular}
\end{table} 
Table 2: Uncertainty of the inclination angle around the Y-axis of the proposed precision level. As the final result, the combined uncertainties of the proposed precision dual-axis level were evaluated to be 0.762 arc-sec and 0.599 arc-sec in the \(\varepsilon_{x}\) and \(\varepsilon_{y}\) direction, respectively. The expanded uncertainties were thus evaluated to be 1.506 arc-sec and 1.174 arc-sec in the \(\varepsilon_{x}\) and \(\varepsilon_{y}\) direction (coverage factor \(k\) = 2, 95% confidence), respectively.
## 1 Introduction  
Advancement in computer and network technology have made it easier than ever to store digital multimedia files, including music, photos, and movies, and to transfer and share them with others. With billions of digital photos exchanged on social media sites every day, the introduction of mobile phones has only accelerated this trend [1]. But as technology has advanced, so too have nefarious activities, such as the unauthorized duplication and dissemination of digital data. To prevent such criminal exploitation of digital data, numerous studies are being conducted, especially in terms of preserving the authenticity and integrity of multimedia data. Although security enhancements for the commonly used JPEG-1 images have proliferated [2, 3], the same level of progress has not been achieved for JPEG 2000 images. The JPEG Committee (SC29/WG1) has defined the JPEG Privacy and Security procedures for JPEG file formats, such as JPEG 2000, JPEG, and JPEG, in awareness of this problem. A wide range of subjects are covered by the international standard, including encryption, watermarking, privacy protection, digital signatures, authentication, and compression-based security. Users can safeguard their photographs from unwanted access, manipulation, and exploitation by using the framework the standard offers for safe and private image sharing and storage. Additionally, it offers standards for communicating privacy policies and managing metadata to guarantee openness and give users control over their personal information. All things considered, JPEG Privacy and Security highlight the significance of privacy and security in the digital imaging sector and offer an extensive range of tools and advice to protect the confidentiality and integrity of image data. Therefore, one of the main goals of JPEG Privacy and Security is to identify both image data and metadata forgeries in JPEG files [4].
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion
### Generalizability of the Proposed System  
To evaluate the generalization of the proposed system, we used the system trained on the WHAM! dataset to directly enhance the noisy speech from the CHiME-4 dataset; the results are shown in Table 7. The proposed system outperforms the other methods significantly on all the metrics for both simulated data and real-world data, indicating its superior generalizability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**2 Stage Training** & **MFMVDR** & **PESQ-NB** & **STOI** & **SI-SNR (dB)** & **SDR (dB)** & **WER (\%)** \\ \hline \hline ✗ & ✗ & 2.79 & 0.94 & 13.24 & 13.69 & 21.80 \\ ✗ & ✓ & 2.39 & 0.90 & 9.20 & 9.60 & 50.80 \\ ✓ & ✗ & 2.79 & 0.94 & 13.47 & 13.92 & 19.90 \\ ✓ & ✓ & 2.84 & 0.94 & 13.50 & 13.92 & 19.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of two-stage training strategy and MFMVDR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{5}{c}{**CHiME-4 SIMU**} & \multicolumn{5}{c}{**CHiME-4 REAL**} \\  & **PESQ-NB** & **STOI** & **SI-SNR (dB)** & **SDR (dB)** & **WER (\%)** & **WER (\%)** \\ \hline noisy & 1.74 & 0.81 & 5.07 & 5.18 & 76.60 & 81.80 \\ LSTM [17] & 2.45 & 0.89 & 12.21 & 12.84 & 45.80 & 61.90 \\ Conv-TasNet [20] & 2.24 & 0.89 & 10.86 & 12.56 & 47.50 & 62.80 \\ DPRNN [21] & 2.47 & 0.90 & 10.17 & 13.19 & 38.30 & 45.40 \\ proposed & 2.69 & 0.93 & 14.23 & 14.87 & 30.50 & 41.40 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other methods on CHiME-4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **\#Para (M)** & **PESQ-NB** & **STOI** & **SI-SNR (dB)** & **SDR (dB)** & **WER (\%)** \\ \hline noisy & - & 1.63 & 0.77 & \(-\)2.76 & \(-\)2.67 & 73.20 \\ LSTM [17] & 16.41 & 2.50 & 0.89 & 9.77 & 10.43 & 30.60 \\ Conv-TasNet [20] & 8.66 & 2.27 & 0.92 & 11.44 & 11.94 & 40.10 \\ DPRNN [21] & 2.59 & 2.68 & 0.94 & 13.09 & 13.65 & 20.10 \\ proposed & 15.44 & 2.84 & 0.94 & 13.50 & 13.92 & 19.60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other methods on WHAM!.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods  
### Overall Flowchart  
Figure 1 illustrates the flow of the proposed method.  
Initially, in the learning phase of the location fingerprinting method, a radio wave propagation simulation is conducted in an urban environment. Since the radio wave propagation simulation only provides sparse data due to its computational cost constraints, regression-based radio wave propagation modeling is employed to supplement continuous data. This approach enables the utilization of optimization techniques for continuous functions, such as particle swarm optimization and genetic algorithms, in subsequent steps.  
Next, in the estimation phase, pattern matching between the location fingerprint database and the received signal from the target is performed. Based on the matching results, the positioning error is calculated and evaluated. Subsequently, an optimization algorithm is employed to iteratively update the UAV flight trajectory and conduct evaluations. The trajectory with the optimal evaluation value is determined as the optimal flight path. The above procedures are executed sequentially.
# An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design
## 2 Improved NSGA-II Methodology with Dual-Sorting Approach
### Implementation of the Genetic Algorithm  
The genetic algorithm was implemented entirely in MATLAB R2021b and coupled with the Monte Carlo-based particle transport code JMCT [21] to evaluate the radiation dose objective functions for various shielding configurations. MATLAB was used to perform key genetic algorithm operations, including encoding, decoding, sorting, selection, and mutation, as illustrated in Figure 1. During the optimization process, the decoded design parameters were automatically converted into GDML-format geometry models, and simulation input files were generated based on a predefined structural hierarchy. JMCT was then used to calculate the radiation dose objective values for the specific shielding geometry. To enhance simulation efficiency, a global variance reduction technique based on weight windows was applied at each generation and integrated into the transport simulations. This strategy enabled optimized allocation of computational resources and accelerated convergence.  
This study employs independent random numbers for coding. To evaluate the robustness of the dual-sorting NSGA-II method, two cases with different mutation strategies were considered:  
* Case 1: Each gene segment within a shield solution is evaluated independently to determine whether it should undergo mutation, with a mutation probability of 10%;
* Case 2: In each shield solution, only one gene segment is randomly selected for mutation.  
The selection of the next generation of \(N\) parent individuals follows a tournament selection algorithm, where the top \(N/4\) shield solutions from the current population \(C_{t}\) are directly retained as part of the parent sets \(P_{t+1}\) and \(P_{t+1}\). The remaining \(3N/4\) shield solutions are determined through five rounds of tournament selection.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Dataset Collection
#### 3.1.3. Generated Datasets  
In the subsection on generated datasets, the purpose of generating datasets is to improve the generalization ability and robustness of the model. In the real world, financial data are often imbalanced, particularly in scenarios of financial attack detection where normal transactions far outnumber fraudulent ones. To address this issue, data generation techniques are used to balance the datasets. A primary method employed is the synthetic minority over-sampling technique (SMOTE) [30]. SMOTE generates new samples by interpolating among minority class samples, thereby balancing class distribution. This method enhances model performance in handling imbalanced data. The basic idea of SMOTE is to find the \(k\) nearest neighbors for each minority class sample and then generate new samples through random interpolation between these samples. Assuming a minority class sample \(x_{i}\) with its nearest neighbor set \(\{x_{i1},x_{i2},\ldots,x_{ik}\}\), a new sample \(x_{new}\) is generated as follows:  
\[x_{new}=x_{i}+\lambda\cdot(x_{ij}-x_{i}) \tag{6}\]  
Here, \(x_{ij}\) is a nearest neighbor of \(x_{i}\), and \(\lambda\) is a randomly chosen parameter within [0, 1]. Through these methods, not only high-quality datasets are obtained, but the model's robustness and generalization ability are also enhanced through data generation techniques. This is crucial for the development and testing of the Merkle-Transformer model.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 2 Theoretical Background  
In this study, we propose a prediction model that can provide ocean weather for ship path planning. The model was developed using deep learning, particularly machine learning. The model predicts eight parameters of ocean weather (i.e., SST, wave height, wave period, wave direction, wind speed (U component), wind speed (V component), current speed (U component), and current speed (V component)) worldwide. For this, an advanced model should be used, rather than a general deep neural network. Therefore, a denoising AutoEncoder and convolutional LSTM (simply, ConvLSTM) that can train 2D time-series data were used. To predict ocean weather for the entire world, the ConvLSTM, which can set the input data as a large area, was used. In addition, if the ConvLSTM is used, each parameter of ocean weather can be used as a channel image; hence, it is possible to predict the ocean weather by considering the causality for each other. Secondly, if the training of models is performed using limited training data, the delayed prediction problems may occur. It can be solved by using AutoEncoder. Therefore, we proposed a prediction model that combines the AutoEncoder and the ConvLSTM. This section introduces the process from essential deep neural networks to the convolutional LSTM and the AutoEncoder.
# Review on Complete Mueller Matrix Optical Scanning Microscopy Imaging
## 3 Mueller Matrix Applications
### Material Science
Conventional polarization microscopy study the polycrystalline structures of rocks using simple cross polarizers. It reveals the presence of different minerals by rotating the sample, resulting in intensity changes which comes from the difference in birefringence and the thickness of the crystals. The advantage of moving into an MM configuration is the capability of quantifying the absolute value of D and as well as the orientation of the mineral without moving parts.
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 2 Related Work
### Reinforcement Learning for Cyber Security  
Sophisticated cyberattacks require protection mechanisms that are responsive, adaptable, and scalable. Reinforcement learning has been extensively proposed to address these problems [19]. In recent years, reinforcement learning has been gradually applied in payload generation and attack detection. However, most of the research is demonstrated on datasets and not applied to web applications, and implementing these methods needs to meet many constraints.  
[PERSON] et al. [20] explored the possibility of using reinforcement learning to generate SQL injection payloads to evade web application firewalls (WAFs). They created a reinforcement learning environment for WAF evasion tasks and evaluated various mainstream WAF products using a proximal policy optimization (PPO) algorithm. Our framework successfully discovers the number of evasive payloads per WAF in our experiments and can significantly outperform baseline strategies. Finally, we extract common patterns from the discovered evasion payloads and discuss weaknesses/flaws of existing WAF products as well as suggested improvements.  
[PERSON] et al. [21] proposed Suggerster, a reflected XSS payload detection tool based on Q-learning. Suggerster sends a series of strings to a web application and observes the response. Then, the agent is trained to generate attack strings using a multi-objective reinforcement learning (MORL) environment framework with a parameterized action space. However, this process requires human participation, including analyzing observations and calculating rewards, which hinders Suggester from fully automated penetration testing.  
[PERSON] et al. [22] proposed a reinforcement learning-based XSS attack detection model by training adversarial examples and detection models together. They first mined the adversarial examples of the detection model based on the adversarial attack model of reinforcement learning. Then, they alternately trained the detection model and the adversarial model. After each round, the newly generated adversarial samples were marked as malicious samples for retraining the detection model.  
[PERSON] et al. [23] proposed the _link_, a black-box web scanner based on reinforcement learning. _Link_ uses a web crawler to obtain the input point of the target web application and then uses two strategies of generation and mutation to generate the payload. They used a reinforcement learning algorithm to discover reflected XSS vulnerabilities. However, the black box scanner cannot judge the coverage of the scanned input points in the web application.
## 4 Routing Layer
### Reliable Routing  
In this sub-section, the major USN routing protocols focusing on reliability proposed to date are reviewed. Reliability in routing for UWSNs is usually achieved by maintaining different copies of the packets being sent through different paths towards the destination. A greedy and straightforward technique to implement this is packet flooding.  
One protocol that implements the flooding technique is directional-flooding-based routing (DFR) [109]. This protocol has been proposed in order to improve reliability. DFR avoids flooding by means of performing the routing tasks for one message with only a reduced number of nodes. This protocol assumes that all nodes have location information, and the forwarding nodes are selected as a function of the signal strength. Results show that DFR throughput is a function of the set of forwarding nodes selected for the link.  
Instead of flooding, in two-hop acknowledgments (2H-ACK) [110], two instances of one message are maintained in the USN without an additional load, increasing network reliability.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 3 Proposed Algorithm
#### System Architecture  
The system architecture describes the interactions between different components of the proposed system. The main components are the mobile money applications (i.e., G-MoMo SIM Serial Number Generator Application, G-MoMo IT Support Application, G-MoMo Agent Application, and G-MoMo Customer Application), mobile money application server, mobile money databases (i.e., main database and FIDO database), network, mobile money services, mobile money IT support staff, mobile money agents, mobile money users, biller systems, payment service provider systems, point-of-sale devices and access devices (computers, tablets, and smartphones). The information about mobile money IT support staff, agents, and users is stored in the main database. The generated public keys are stored in the FIDO database, and private keys and biometric templates are stored in smartphones. The databases for the mobile money applications run on the mobile money application server to ensure security since the server checks requests to and from the databases. Mobile money services are used to transfer information between G-MoMo applications. With the G-MoMo applications, mobile money IT support staff can log in, register new mobile money agents, add new smartphones for the subscribers, add other new mobile money IT support staff, generate statistics showing the total number of registered subscribers, and log out. Mobile money agents can log in, register new mobile money users (customers), deposit money into customers' accounts, confirm money withdrawal through QR code scan, check balance, account management (change credentials such as PIN and fingerprint), and log out. Mobile money users can log in, withdraw money, send money, pay bills, check balance, check mini statements, account management (change credentials such as PIN and biometric fingerprint), and log out. The authentication security in G-MoMo applications is enforced on the application layer using a PIN, OTP, and biometric fingerprint. The security of PIN and OTP is ensured by SHA-256, biometric fingerprint by FIDO, and the QR code and records in the databases using Fermet encryption. All the components work together to attain the systems' goal. Figure 6 shows the system architecture for the proposed scheme.  
Figure 6: System architecture for the proposed system.
## Appendix A
Finally, after returning to original variables (\(\xi=\Delta_{21}kz\), \(z_{0}=z\), \(z_{1}=z/\Delta_{21}k\), \(\frac{\partial}{\partial z}=\Delta_{21}k\frac{\partial}{\partial\xi}+\frac{ \partial}{\partial z_{0}}+\frac{1}{\Delta_{21}k}\frac{\partial}{\partial z_{1 }}+O(\Delta_{21}k^{-2})\)), we obtain the following set of equations:  
\[-\left(\frac{\partial U}{\partial z_{1}}+i\gamma^{2}(-|U|^{2}U-3U^ {*2}W+4U|V|^{2}-2U|W|^{2})\right)-L_{1}^{(0)}u_{1},\]3 \[-\left(\frac{\partial U}{\partial z_{1}}+i\gamma^{2}(-|U|^{2}U-3U^ {*2}W+4U|V|^{2}-2U|W|^{2})\right)-L_{1}^{(0)}u_{1},\]4 (A9) \[-\left(\frac{\partial U}{\partial z_{1}}+i\gamma^{2}(-|U|^{2}U-3U^ {*2}W+4U|V|^{2}-2U|W|^{2})\right)-L_{1}^{(0)}u_{1},\]5with respect to \(U,V,W\), and the functions \(u_{1},v_{1},w_{1}\) are the solutions of the linear Schrodinger equations:  
\[ \begin{split}&\frac{\partial u_{1}}{\partial z}+iD_{1}\frac{ \partial^{2}u_{1}}{\partial t^{2}}=0,\\ &\frac{\partial v_{1}}{\partial z}+v_{21}\frac{\partial v_{1}}{ \partial t}+iD_{2}\frac{\partial^{2}v_{1}}{\partial t^{2}}=0,\\ &\frac{\partial w_{1}}{\partial z}+v_{31}\frac{\partial w_{1}}{ \partial t}+iD_{3}\frac{\partial^{2}w_{1}}{\partial t^{2}}+i\Delta_{31}kw_{1} =0,\end{split}  \tag{10}\]  
and the expansion series (A1) transforms into the following form:  
\[ \begin{split}& A_{1}=U+\frac{1}{\Delta_{21}k}\Big{(}\gamma(U^{*} Ve^{-i\Delta_{21}kz}-V^{*}We^{i\Delta_{21}kz})+u_{1}\Big{)},\\ & A_{2}=V+\frac{1}{\Delta_{21}k}\Big{(}-\gamma(U^{2}+2U^{*}W)e^{ i\Delta_{21}kz}+v_{1}\Big{)},\\ & A_{3}=W+\frac{1}{\Delta_{21}k}\Big{(}3\gamma UVe^{-i\Delta_{21 }kz}+w_{1}\Big{)}.\end{split}  \tag{11}\]  
Let us stress that without taking into account the wave with tripled frequency (\(A_{3}=W=w_{1}=0\)), Equations (A9)- (11) reduce to the corresponding ones, derived in [51]. As we analyse the THG, then we assume that the SH and TH are absent in the input section of a medium. Therefore, the complex amplitudes \(A_{2}\) and \(A_{3}\) are equal to zero in this section. This results in the following conditions with respect to functions \(U\), \(V\), \(W\):  
\[U|_{z=0}=A_{1}|_{z=0}=A_{10}(t),\,V|_{z=0}=A_{2}|_{z=0}=0,\,W|_{z=0}=A_{3}|_{z= 0}=0. \tag{12}\]  
and to functions \(u_{1}\), \(v_{1}\), \(w_{1}\):  
\[u_{1}|_{z=0}=w_{1}|_{z=0}=0,\,v_{1}|_{z=0}=\gamma A_{10}^{2}(t), \tag{13}\]  
respectively. At writing these conditions, we compare the corresponding terms in a series with respect to the small parameter \((\Delta_{21}k)^{-1}\) in (11). Since the initial distributions of the complex amplitudes \(A_{j}\), \(j=1,2,3\) do not contain the terms at \((\Delta_{21}k)^{-1}\), then the corresponding terms in (11) should be chosen equal to zero. Then, the Equations sets (A9) and (10) reduce to the set of equations (9). The reason is that the SH amplitude \(V\) equals zero (\(V\equiv 0\)) due to both its initial condition (12) and its including in all terms in the second equation of the equations set (A9). The same analysis shows that \(u_{1}\equiv 0\), \(w_{1}\equiv 0\). Therefore, the representation (11) transforms to the representation (8).
# A 5G-Based VR Application for Efficient Port Management
## 4 Demonstration and Performance Evaluation
### Experiment Setup  
The new smart port architecture proposed in this paper can simulate the real-world scenario through VR streaming integrated with additional information about equipment, machine, and road in the port so that the operator can view immersive information of any area of the port at any time and from anywhere. Through 5G, the data collected by the cameras can be transmitted to the remote operator side with ultra low delay thus effectively improving the operation efficiency. It can greatly facilitate the loading and unloading of port cargo, support autonomous driving, reduce labor intensity, and avoid operational safety risks. Virtualizing the information in the port through VR supports the interconnection of everything in the port while observing and retrieving information in real-time.  
In the proposed system, multiple sources of information are collected from massive sensors deployed around the smart port. The staff can remotely monitor and control the objectives such as an autonomous truck in real-time through the VR glasses. The system leverages mature driverless trucks technology that can achieve autonomous driving under specific routes in specific environments, and the staff only needs to regularly overhaul the ground as well as the sensors on the trucks. In Figure 12, we demonstrate easy control of a truck traveling around the smart port and observe the container information of each workshop in the port in real-time through the first-person operating platform and simulate the operation of the port in various situations in real-time.  
Figure 12: _Cont._  
Figure 12: Simulation scenario of 5G + VR. (**a**) Warehouse A; (**b**) Warehouse B; (**c**) Indoor information panel; (**d**) Outdoor information panel.  
Besides the demonstration on the entire smart port level, a series of experiments are conducted to investigate the feasibility of the proposed 5G + VR architecture within the application of remote crane control. The following section first describes how the real-world environment of the smart port is mapped to the VR environment, and how the data collected from various sensors and PLC controllers are transmitted between both environments via 5G + HTTP to synchronize the states in controlling the truck to move the cargo and the crane to grab the cargo. Detailed network analysis is then performed to study the QoS of the network and its feasibility to support the proposed architecture. Section 4.3 illustrates the QoE analysis of the demonstrated smart port VR system with an extensive subjective test.