esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### NTN Architectures  
In order to provide the services presented above, several NTN architectures are proposed and detailed in the following.  
Depending on the position of the gNB functionalities, it is possible to consider three basic cases: the transparent architecture, the regenerative architecture and the split architecture. In the last case, the gNB functionalities are split in the distributed unit (DU) and in the central unit (CU). The DU is located on-board and the CU is left on the ground. Finally, for each of them, it is also reported their protocol stack architectures both for user plane (UP) and control plane (CP), allowing to know in which NTE each protocol function is closed.
## 5 Discussion
### Vulnerabilities Mitigation
#### 5.2.2 Hack
This example reinforces the importance of Supply Chain Management (SCM) in cybersecurity. In large and technologically complex companies, keeping the systems up to date may be challenging. As a consequence, attackers may exploit known vulnerabilities in the systems. Thus, it is fundamental to establish a patch-management program to timeously update and secure the organization's assets. For zero-day attacks, which explore previously unknown vulnerabilities, a security patch has not yet been published by the product developer, and signature detection is ineffective. As an alternative, ML methods can detect such intrusions based on the perception of suspicious activities that differ from the expected baseline, which may enhance the detectability for novel attacks. Data Loss Prevention (DLP) solutions also contribute to data security and avoidance of data breaches. Such technologies detect and deter unauthorized data transfers, including preventing PII data breaches. However, it is important to note that DLP is ineffective in detecting data exfiltration through steganographic techniques. When there is a need to publish statistical metrics related to a dataset, but concerns about preserving the privacy of the individuals within the dataset are paramount, leveraging differential privacy can be a valuable and appropriate approach to addressing this challenge. Differential privacy, introduced by [PERSON] [72], provides a framework for releasing aggregate information about a dataset while adding noise or perturbation to the data so that individual records remain private and indistinguishable. This ensures that sensitive information is protected and that statistical knowledge can be derived without compromising the privacy of the data subjects, whilst maintaining the utility of the data [73]. Because of this balance between utility and privacy enhancement, differential privacy has applicability in several areas, and is used by the US Census and by big companies such as Google, Apple, and Microsoft [74].
## 3 Results
### State Space: Absolute Camera Coordinates (Path Learner)
#### 3.1.1 State and Action Space  
* The discrete world to explore is a \(N\times M\) grid where the state is defined by the position of the camera in one of these \(N\times M\) locations.
* The number of possible states is therefore \(N\times M\).
* The set of possible actions \(A\) is the same for all states \(s\in\mathbf{S}\), \(\mathbf{A}(s)=\{\ Move\ Left\ (ML),\ Move\ Down\ (MD),\ Move\ Right\ (MR),\ Move\ Up\ (MU)\}\).
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### Experiments
#### 3.3.1 Data  
We tested our algorithm on both real and synthetic datasets. The real dataset was downloaded from the FIMI repository [35; 36], and is called \"webdocs\". It contains close to 1.7 million transactions (each transaction is a web document) with 5.3 million distinct items (each item is a word). The maximal length of a transaction is about 71,000 items. The size of the dataset is 1.4 gigabytes (GB). A detailed description of the \"webdocs\" dataset, that also includes various statistics, can be found in [36].  
The synthetic dataset was generated using the IBM data generator [37]. We have generated six million transactions with an average of ten items per transaction--a total of 100,000 items. The total size of the input data is 600 MB.
# Remediation Opportunities for Arsenic-Contaminated Gold Mine Waste
## 3 Mitigating Arsenic-Contaminated Mine Waste
### Global Arsenic Concentrations
Three sites were chosen for sampling, a former mining site, a smelter site, and a sludge deposit site that has naturally been covered with a soil layer [59]. Soil samples resulted in As concentrations ranging from 860 to 5751 mg/kg (Table 1). The Czech Republic is also greatly affected by historic mining activities in the northwest; Jachymov is home to the Giftkies arsenic deposit [60]. [PERSON] et al. [60] investigated contaminated soils and reported As concentrations ranging from 74 to 3697 mg/kg (Table 1). The European Union recommends that As soil concentrations should not exceed 20 mg/kg for agricultural soils [61; 62]. In Australia, [PERSON] et al. [63] studied Au mining sites in Bendigo, Ballarat, and Mount Egerton; samples taken from the three sites were combined and homogenised. [PERSON] et al. [63] reported As concentrations ranging from 1210 to 22,000 mg/kg (Table 1). The National Environment Protection Measures (NEPMs) for the Assessment of Site Contamination are national guidelines used to investigate soil and groundwater contamination in Australia for human and ecosystem health [5; 64]. The NEPM Health-Based Investigation Level A (HIL A) provides a standard for residential land with a garden and easily accessible soil [5; 64]. The NEPM HIL A reports that As concentrations should not exceed 100 mg/kg, and, if this limit is reached, contaminated soil requires further investigation [5; 64]. Arsenic concentrations found in regional Victoria at some mine waste sites exceed the HIL A by a minimum of 10-fold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Soil Type** & **As (mg/kg)** &
\begin{tabular}{c} **Legislated** \\ **Values (mg/kg)** \\ \end{tabular} & **References** \\ \hline Giant Mine, Yellowknife, & & 156–5760 & & \\ Northwest Territories, Canada & & (mean 1550) & 12 & [21; 53] \\ Giant Mine, Yellowknife, & & 11–7680 & & 12 & [21; 53] \\ Northwest Territories, Canada & & (mean 177) & & \\ Nova Scotia Gold Mining & & 200–77,000 & 12 & [52; 53] \\ District, Canada & & & & \\ Meguma Terrane, Nova Scotia, & & 318–76,500 & 12 & [32; 53] \\ Canada & & & 162–12,483 & \\ USA & Soil & (median 73) & 5–20 & [65; 66] \\ Morro do Ouro, Minas Gerais & & 26–699 & & [67] \\ State, Brazil & & 1085–35,372 & & [68] \\ Delita, Cuba & & & & \\ Cornwall, & Soil & 67–4550 & 20 & [55; 57] \\ England & & 23–471 & 20 & [56; 57] \\ Cornwall, & & & 43–486 & \\ England & dust & (mean 149) & 20 & [56; 57] \\ Southern, Tucany, Italy & Soil & 5–2035 & & [69] \\ Castromil Mine, & & 31–6909 & & [58] \\ Portugal & & (mean 820) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global arsenic concentrations in mine waste and mine-impacted soil.
# Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications
## 2 Background and Related Work
### Mobile Application Vulnerability Analysis
Moreover, it can scan applications to detect leaks of sensitive information and assess network security issues. Research studies, such as those conducted by [9, 14, 15], have demonstrated the efficacy of MobSF in uncovering a wide array of vulnerabilities in mobile applications. The Quick Android Review Kit (QARK) is an open-source static analysis tool designed specifically for evaluating the security of Android applications. In contrast to AndroBugs, which primarily focuses on known vulnerabilities, QARK conducts a more in-depth examination by inspecting the source code of the application. By employing static code analysis methods, QARK can detect various security issues such as insecure communication channels and inadequate encryption implementations. Moreover, a study conducted by [15] applied QARK to assess a selection of banking applications, successfully identifying vulnerabilities associated with insecure data transmission and weak user authentication mechanisms. This research highlights QARK's efficacy in identifying security flaws that could be exploited by potential adversaries. While QARK provides a more thorough analysis compared to AndroBugs and MobSF, it is not suitable for users who are unfamiliar with Android application development to interpret the findings. These tools, in conjunction with SAST and DAST, offer a valuable collection of resources for researchers and cybersecurity experts to detect and mitigate vulnerabilities that could be present in mobile banking applications. The choice of the most suitable toolset depends on variables such as the unique features of the application, the proficiency of the analyst, and the extent of thoroughness required in the evaluation process.
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
###### Abstract  
During tunnel construction, the ground properties, initially evaluated, are continuously assessed and verified through back analysis. This procedure generally requires many numerical analyses, so a metamodel based on artificial neural networks has been used to reduce the number of analyses. More datasets can be used to create more reliable metamodels. However, there are no established rules regarding the optimum number of datasets for a reliable metamodel. Metamodels predicting the vertical displacement of the tunnel crown using five ground parameters (unit weight (\(\gamma\)), uniaxial compressive strength (UCS), material constant m\({}_{i}\), geological strength index (GSI), and coefficient of lateral pressure (K)), with 3, 4, 6, 8, and 10 values per property, were created to confirm the reliability of the metamodel based on the number of datasets in this study. Metamodels using 6 and 8 values for each property showed 5% and 1% mean absolute percent errors, respectively. These numbers of each of the properties would be appropriate for developing the metamodel. Among the five parameters, only the results of the global sensitivity analyses of GSI and K are higher than 0.9. According to these results, it is verified that assessments based only on these parameters are sufficient in the back analysis.  
metamodel; artificial neural networks; back analysis; reliability; tunnel excavation Article  
Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation  
10.3390/app12146851
## 3 Data and Methods
### Landslide-Influencing Parameters
Current velocity increases as the water depth increases and can be classified into 3 classes: 0-0.5 m/s, 0.5-1 m/s, and 1-1.5 m/s. Liquefaction is the most serious geological hazard in the Yellow River Estuary. There are hundreds of liquefaction zones in the study area, which were discovered by geophysical explorations. Liquefaction zones are mainly distributed between a 6 m to 12 m water depth, where the strength of hydrodynamic action is the strongest. Liquefaction is divided into 4 classes: not easy to liquefy (liquefaction depth < 0.5 m), slightly liquefy (0.5 m < liquefaction depth < 2 m), moderate liquefy (2 m < liquefaction depth < 4 m), and serious liquefy (liquefaction depth > 4 m). Seabed sediments are easy to slide after liquefaction as their bearing capacity reduces greatly. Erosion is divided into 4 classes, which are the stable zone (<0.02 m/s), slight zone (0.02-0.05 m/s), moderate zone (0.05-0.1 m/s), and serious zone (0.1 m/s). The serious zone and moderate zone are mainly distributed in a water depth of less than 12 m.  
Human engineering activities are mainly offshore production platforms, submarine pipelines, cables, and so on in this study area. They can be divided into 4 categories: core zone, buffer zone, potential-impact zone, and no-impact zone. The actual scope of various engineering structures is named the core zone. The buffer zone is the core area extending 500 m outwards. The potential-impact zone is where the buffer zone extends another kilometer outward, and other areas are the no-impact zones.
## 3 Related Works
### Hardware Configuration and Alternatives  
In this study, we utilized the Tello EDU drone, which is an accessible and educationally friendly platform equipped with essential features for computer vision and machine learning applications. The Tello EDU drone is configured with a 5 MP camera capable of streaming 720p video at 30 frames per second, complemented by Electronic Image Stabilization (EIS) [49]. It also features a Vision Positioning System (VPS) [50] to facilitate precise hovering and includes an Intel 14-Core Processor for high-quality footage. This drone offers programmability through the Tello SDK, allowing integration with Python scripts for automated control and image-processing tasks.  
For regions where the Tello EDU is not available, several alternative drones offer similar capabilities. One such alternative is the Robolink CoDrone [51], which supportsvisual coding options like Blockly and additional features such as a physical controller and an LED screen. The Crazyflie 2.1 [52] is another versatile platform, suitable for advanced users with technical knowledge, offering highly customizable and programmable options. The Airblock drone [53] is modular and fun to build, making it ideal for STEM education and creative projects. Finally, the Parrot ANAFI [54] offers a high-quality 4K camera and versatile programming options and is suitable for professional applications.  
To ensure the project can be replicated with different hardware, we recommend several specifications for replacement drones. The camera quality should have at least 720p resolution to ensure sufficient detail in captured images. The drones should also have programmability with access to a comprehensive SDK supporting Python or similar programming languages. Built-in WiFi operating at 2.4 GHz is necessary for connectivity to a local computer to execute drone commands and facilitate autonomous decision-making. Additionally, the drones should feature on-board processing capabilities to handle real-time image stabilization and processing tasks. For stabilization and control, a built-in IMU and sensors are essential to maintain flight stability and accurate height measurement.  
The information about these drones in Table 1 may vary by country and availability. Additionally, these drones might not be easily found in all regions due to security and regulatory restrictions. Table 1 provides a detailed comparison of the different drones, highlighting their key features, programming capabilities, and additional functionalities. By carefully evaluating these factors, you can choose the drone that best aligns with your specific needs and budget. By adhering to these specifications, the project can be effectively implemented using various drones available in different regions, ensuring broader accessibility and applicability.
## 4 Performance Analysis  
The vertical FSO system as given in Table 1 is assumed to analyze the transmission performance with the simulation results. The vertical link distance is set to 20 km for a HAP-UAV operating in minimum wind speed. The receiver sensitivity for NRZ-OOK modulation is assumed to be \(-20\) dBm to achieve a high data rate greater than tens of Gbps. The refractive structure at the ground is assumed to be \(1\times 10^{-12}\) m\({}^{-2/3}\) to analyze the effect of stronger atmospheric turbulence for the vertical FSO link. The distance between transmitters is set to 2 m to achieve spatially independent channels. The standard deviation of the pointing jitter is assumed to be 20 \(\mu\)rad from [21]. The detected misalignment error is analyzed for the transceiver configurations of the ground and the UAV terminals. The analysis of the transmission performance as the number of transceivers is presented for bi-directional communication FSO links.
## 5 Experiment Validation
### Case 1: Bearing Outer Race Fault
As shown in Figure 16, the comparison result visually exposes the disadvantages and limitations of the other three traditional methods. As seen in Figure 16c, the background noise is partly eliminated and some periodic impulses can be observed by the EEMD method. However it is quite hard to find the location of the fault characteristic frequency from the envelope spectrum in Figure 16d. The waveform and envelope spectrum of the WP method is separately plotted in Figure 16e,f, where the defective frequency can't be recognized. Figure 18 shows the fast kurtogram of outer-race fault signal, and the selected center frequency and bandwidth are 10,666 Hz and 4066 Hz. Then the filtered signal by kurtogram method and the envelope spectrum are separately presented in Figure 16g,h. It can be seen that the kurtogram method doesn't work well in removing noise and identifying the defective impulses. Moreover, to visually quantify and assess the performance of different four methods, the two indicators are separately applied to the analyzed result by Equations (30) and (31). As shown in Table 3, the SNR(\(f_{op}\)) and kurtosis value of the proposed method are larger than other three traditional methods, indicating that the proposed method is suitable for bearing fault detection. In summary, the  
Figure 17: The envelope waveform of the filtered signal in time domain. Figure 18: The fast kurtogram of outer-race fault signal. comparison result evidently exposes the merit of the proposed method in eliminating background noise and extracting features for the outer race fault vibration signal.
## 2 System Model  
The SIMO channel model which is showed in Figure 1 can be described as follows:  
\[\left\{\begin{array}{l}\mathrm{x}_{1}(n)=\mathrm{s}(n)*\mathrm{h}_{1}(n)+ \mathrm{w}_{1}(n)\\ \vdots\\ \mathrm{x}_{K}(n)=\mathrm{s}(n)*\mathrm{h}_{K}(n)+\mathrm{w}_{K}(n)\end{array}\right. \tag{1}\]  
where \(*\) represents convolution, \(K\) is the number of outputs, \(w(n)=\left[\mathrm{w}_{1}(n),\mathrm{w}_{2}(n),\ldots,\mathrm{w}_{K}(n)\right]^ {T}\) represents the noise which obey the Gaussian distribution with mean value of zero and variance of \(\sigma\). \(\mathrm{s}(n)\) is the transmission signal of length \(N\), \(x(n)=\left[\mathrm{x}_{1}(n),\mathrm{x}_{2}(n),\ldots,\mathrm{x}_{K}(n)\right]^ {T}\) represents the received signal, \(h(n)=\left[\mathrm{h}_{1}(n),\mathrm{h}_{2}(n),\ldots,\mathrm{h}_{K}(n)\right] ^{T}\) represents the CIR, We assume that the length of CIR is \(L\). Without considering the influence of noise, the received signals of every output have the following cross-relationship:  
\[\mathrm{x}_{i}(n)*\mathrm{h}_{j}(n)=\mathrm{x}_{j}(n)*\mathrm{h}_{i}(n),1\leq i \
eq j\leq K \tag{2}\]  
Equation (2) can be expressed as a matrix form, as shown as:  
\[X_{i}(n)\mathrm{h}_{j}(n)=X_{j}(n)\mathrm{h}_{i}(n) \tag{3}\]  
where \(X_{i}(n)=\left[\mathrm{x}_{i}^{1}(n),\mathrm{x}_{i}^{2}(n),\ldots,\mathrm{x}_ {i}^{L}(n),\ldots,\mathrm{x}_{i}^{L}(n)\right]\), \(\mathrm{x}_{i}^{l}(n)=\mathrm{x}_{i}(n-l)\) is the cyclic convolution matrix of size \(N\times L\).  
According to Equation (3):  
\[\mathrm{X}^{k}(n)=\left[\begin{array}{cccccc}0&\cdots&0&X_{k+1}(n)&-X_{k}(n )&\cdots&0\\ \vdots&\ddots&\vdots&\vdots&0&\ddots&\vdots\\ \underbrace{0&\cdots&0}_{k-1\text{ }blocks}&\underbrace{X_{K}(n)}_{K-k+1\text{ } blocks}&0\cdots-X_{k}(n)\\ \end{array}\right] \tag{4}\]  
Every block of Equation (4) is a matrix of size \(N\times L\). We can obtain the following, as shown as:  
\[\mathrm{X}(n)\mathrm{h}(n)=0,\mathrm{X}(n)=\left[\mathrm{X}^{1}(n),\ldots, \mathrm{X}^{K-1}(n)\right]^{T} \tag{5}\]  
Adding constraints by setting \(\mathrm{h}_{1}\left(\lfloor L/3\rfloor\right)=1\), where \(\lfloor\cdot\rfloor\) represents the operation of rounding down. The purpose of the constraints is to avoid zero solution, which will introduce the delay and gain to the final channel estimation results inevitably. However, in practice, the delay and gain will not affect the performance of the system. Equation (5) can be expressed as:  
\[-b=\Psi\tilde{h} \tag{6}\]  
where \(b\) is the column \(\lfloor L/3\rfloor\) of the matrix \(X(n)\), \(\Psi\) is the matrix of all the columns in \(X(n)\) except \(\lfloor L/3\rfloor\), \(\tilde{h}\) is all elements except \(h_{1}\left(\lfloor L/3\rfloor\right)=1\) in \(h\). The CIR obtained by Equation (6) needs to meet two identifiable condition, which is described in detail in [25]. One condition is that the transmission function of each subchannel in \(h(n)=\left[h_{1}(n),h_{2}(n),\ldots,\ h_{K}(n)\right]^{T}\) has no common zero, and the other condition is that the autocorrelation matrix of the transmission signal \(s(n)\) is full rank.
## 1 Introduction
In [18], architectural configuration and operation principles for the metro-access network based on a ring architecture are presented and described together with protection mechanisms for different fiber failure scenarios. Various WDM-PON architectures utilizing only passive components in remote nodes are analyzed from a viewpoint of with the traffic protection securing in [19]. A motivation to this contribution consists of two aspects. First, there exist many scientific papers and research contributions devoted to WDM-PON network designs with various architectures, topologies and components. Only a small part of them consider a practical managerial significance along with practical applications. Thus, they arouse no concern from network operators. In some cases, it is obvious that presented network designs will never be realized and implemented. Second, many network operators devote themselves to a transition to advanced WDM-PON networks. For careful and responsible reviewing and considering diverse approaches, they are coming out above all from own experiences with real customer traffic provisioning in their passive optical infrastructure. They rarely consider other various variants advantageous from long-term aspects for reasons of missing scientific works with qualified examinations of a real WDM-PON network implementation and deployment together with protection scheme mechanisms. Considering both aspects, we decide to create such skillful, flexible and adaptable software tool that can accommodate network operators' demands and can realize a scientific base necessary for qualified and professional decisions associated with a development and transition to next-generation passive optical networks utilizing advanced wavelength division multiplexing techniques. By this way, we want to provide an option of comparing different traffic protection scenarios for advanced WDM-PON network designs, evaluating their possible realizations from a viewpoint of the total optical power budget and analyzing their network availabilities with accommodation to concrete optical access infrastructures with specific set of optical components, parameters and values.
## 3 Methodology
### Storage and Loss Modulus Analysis  
The second set of tests concentrated on assessing the storage modulus (G\({}^{\prime}\)) and loss modulus (G\({}^{\prime\prime}\)) across the same frequency range. The storage modulus (G\({}^{\prime}\)) reflects the elastic component of the material's response, indicating its ability to store energy during deformation, while the loss modulus (G\({}^{\prime\prime}\)) represents the viscous component, showing how much energy is lost as heat.  
These moduli were measured throughout the frequency sweep, and their values were plotted against angular frequency (\(\omega\)) at both temperatures. The results illustrated whether the bitumen exhibited more elastic (solid-like) or viscous (liquid-like) behavior under dynamic conditions. Higher G\({}^{\prime}\) values indicate greater energy storage elastically, whereas higher G\({}^{\prime\prime}\) values suggest a more viscous behavior.  
This analysis offered detailed insights into how the bitumen's energy storage and dissipation change with frequency, particularly under high-temperature and high-stress scenarios. The test results at 15 \({}^{\circ}\)C and 25 \({}^{\circ}\)C facilitated a comparative analysis of the material's behavior across different temperatures.