esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### 5G Cryptomining Attack Detection Scenario
#### 9.1.1 Cryptomining Attack Description  
One of the most discussed threats to 5G is the possibility of leveraging the computational resources provided by the network to perform cryptomining attacks. Cryptomining is a process of validating transactions on a decentralized cryptocurrency blockchain. The attack works by creating a botnet of devices, called miners, which are used to validate transactions and receive rewards in the form of digital currencies, such as Ethereum (ETH) and Monero (XMR).  
Figure 8: Integration of GAN synthetic traffic into ML model.  
The attacker can use devices that are already infected with malware or can infect new devices, hijacking their resources to create the botnet for mining. Attackers can use various ways to infect devices, such as spreading malicious links on social networks, using phishing attacks, or spreading malicious applications.  
In addition, the attacker has to choose the cryptocurrency to be mined and the mining pool that they want to join to validate transactions. A mining pool is a service that allows miners to combine their resources to validate blocks of transactions and receive rewards in exchange. Once the attacker has collected all the pieces, they can set up the botnet to mine cryptocurrencies for the criminal's benefit.  
To detect cryptomining traffic in a timely and accurate manner, the network is the best place to identify these types of attacks [39]. However, detecting cryptocurrency mining activity on the network can be challenging, as encryption methods have been adopted in most of today's network protocols. For example, the attacker can use the SSL/TLS encryption protocol to hide the cryptomining protocol in the payload of the encrypted communication. For this reason, classical techniques of Deep Packet Inspection (DPI) or identification of mining pool domain names (in the case of using encrypted SNI or web proxies) are ineffective in detecting mining activity in today's networks and more sophisticated techniques are needed to prepare today's cybersecurity professionals to deal with these problems in real-life situations.  
To effectively mitigate these threats, ML/DL techniques can be used to train models that can accurately identify the presence of cryptomining traffic in real-time, even if it is encrypted, using a set of salient flow features collected at the network and transport levels [39]. In this exercise, we ask the student to attempt rule-based detection of the presence of encrypted and non-encrypted cryptomining traffic on the network using traditional NIDS, such as Snort. Afterward, we ask the student to take advantage of an ML/DL-based cryptomining detector provided as part of the STA module to more effectively identify the presence of cryptomining traffic on the network.
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 4 Results  
### Simulation of Initial Structure with and without Circular Inclusion  
The first step was to search for the presence of bandgaps in the geometry with and without the circular inclusion, see Figure 3. The circular area inside the structure has a radius of 50 mm and consists of the same material with the outer part of the geometry.  
The response for various directions and frequencies, indicating band gaps and including the first eight eigenfrequencies, are shown in Figure 4a,b.  
The bandgaps that are formed from the geometry with the inner circle, Figure 4a, are two. The first band gap is between the third and fourth eigenfrequencies (1160-1250 Hz) and the second is between the fifth and sixth eigenfrequencies (1370-1840 Hz). Similarly, there are two band gaps located on the diagram of Figure 4b, one between the third and fourth eigenfrequencies (1020-1240 Hz) and the second is between the fifth and sixth eigenfrequencies (1340-1680 Hz).
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 4 Methodology
### Deep Feedforward Neural Network  
Recently, the neural network is widely used in the prediction of traffic volume and proposes the development of deep learning [39; 40; 41]. The DFNN is a deep learning model comprised of an input layer, several hidden layers, and an output layer [42; 43; 44]. The quantity of hidden layers defines the depth of the architecture [45]. The topological structure of the DFNN is shown in Figure 2.  
Figure 1: The flow diagram of the designed framework.  
The theory of the DFNN is available in past research [44; 45; 46]. In this section, we introduce the activation function and objective function used in the DFNN algorithm.  
Firstly, the rectified linear unit (ReLU) function was selected as the activation function of hidden layers and the output layer, considering that the ReLU function has a higher computing efficiency because it only activates a fraction of the neurons in each epoch. The ReLU function has been proven to be effective at avoiding gradient vanishing and overfitting, and serves as the preferred choice when developing a neural network to solve multiple problems except for the binary classification [46; 47]. The ReLU function is shown in Equation (1).  
\[f(x)=\left\{\begin{array}{ll}0&\quad x<0\\ x&\quad x\geq 0\end{array}\right. \tag{1}\]  
Then, the objective function was built by minimizing the loss function of mean square error, as in Equation (2).  
\[\min\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}+\lambda\cdot R (\theta) \tag{2}\]  
where \(y_{i}\) represents the actual highway passenger volume and \(\hat{y}_{i}\) represents the predicted highway volume. \(N\) is the number of predicted samples. \(R(\cdot)\) is a regularized constraint, represented by the \(L2\) norm of the parameter \(\theta\), which is solved by the gradient descent method. \(\lambda\) is the coefficient of regularized constraint \(R(\cdot)\).
## 3 Experimental Measurements
### Experimental Set-Up
This conversion is based on converting 1 mW to 1 \(\upmu\)V in a 50 \(\Omega\) system, which defaults to 107 dBm = 1 \(\upmu\)V. The result, in dB\({}_{\text{i}}\)V, represents a voltage value that enables comparison of signals based on their voltage levels rather than power levels. This can be more useful for certain analyses and applications, such as compatibility and interference studies in EMF. Subsequently, dB\({}_{\text{i}}\)V is converted to dB\({}_{\text{i}}\)V/m, providing a measurement of electric field strength in units of decibel microvolts per meter. This unit is essential for quantifying the field strength in a specific space and considers the distribution of the field around the source. \[\frac{\text{dB}\upmu\text{V}}{\text{m}}=\text{dB}\upmu\text{V}+\text{AF} \tag{10}\]  
where dB\({}_{\text{i}}\)V is a unit of absolute level relative to a voltage of 1 \(\upmu\)V (microvolt) and AF represents the antenna factor. The antenna factor is important for converting the voltage value into the field strength value. It indicates how efficiently an antenna converts an EMF into an electrical signal (voltage) at a specific frequency. This conversion process allows the measured voltage to be transformed into a more useful metric that shows the amount of EMF strength present at a particular point relative to the antenna. The resulting dB\({}_{\text{i}}\)V/m is used for more accurate and relevant comparisons of exposure to EM radiation in different environments or under different conditions. The last step is to convert dB\({}_{\text{i}}\)V/m to V/m (volts per meter), which represents the absolute value of the electric field strength. This conversion is crucial for the final exposure and safety assessment, as volts per meter is the standard unit for expressing EMF strength in international standards and guidelines. \[\frac{\text{V}}{\text{m}}=10^{(\frac{\text{dB}\upmu\text{V}-120}{20})} \tag{11}\]  
In the previous conversion step, dB\({}_{\text{i}}\)V/m represents the electric field strength value in decibel microvolts per meter. The value 120 is used as the reference value to calibrate the scale between dB\({}_{\text{i}}\)V/m and V/m. This value serves as the conversion factor that transforms dB\({}_{\text{i}}\)V/m into a linear V/m scale. This conversion step yields the resulting electric field strengths in units that are directly relevant to engineering specifications and health regulations. V/m is directly applicable for evaluating whether EMF levels exceed the limit values established by health and safety standards, which is essential for protecting human health from potential adverse effects of high levels of EM radiation.
# Startup Sustainability Forecasting with Artificial Intelligence
## 3 Results  
### Survey Findings  
The survey revealed that the organizations are unanimously interested in adopting AI solutions, with a preference for outsourcing the development to external software providers. Similarly, organizations tend to track and keep records of startup data. These data mostly include information about the founders (personal websites and CVs), business plans, pitch decks, and other media related to the startups.  
Additionally, they record startup deliverables that indicate progress, including frequent revisions of the business model canvas and the value proposition canvas. Whereas a significant amount of data seem to be collected, the organizations do not take full advantage of them. Specifically, expert analysts are well aware of the value of the data that they are collecting, and well educated in the potential of AI.  
The requested value proposition involves improved and informed decision support that can lead to faster processing times with fewer errors. The optimal strategy for startup evaluation was described as a hybrid between human and artificial intelligence, each providing a unique view of the investigated startups. Quantitative metrics should be used to evaluate these strategies, including the return on investment (ROI) of the investing decisions recommended by the AI system.  
Several forecasting targets were discussed, including profitability, growth, general valuation scores, and future investments. However, the survey revealed that forecasting startup sustainability and longevity should have the most significant impact for decision-makers. Investors heavily question the integrity and longevity of startups before making decisions. Naturally, sustainability depends on multiple factors that must be considered. It is affected mostly by the cohesion of the founding team, the burn rate and runway of the company, and, of course, the potential for profitability and growth. Furthermore, the desirability of the developed product or service by the market and the feasibility of its implementation are critical factors of success, and therefore, also longevity.
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 4 Simulation and Experimental Results
### Discussion of the Algorithm Limitations  
In the last decade, an explosive growth has been witnessed of the GNSS, Starlink, and other satellites. This brings opportunities for the passive radars, but an effective selection of satellites also becomes a fundamental issue to be considered. The passive radar could not, after all, achieve a real-time processing of all satellite sources. As shown in Figure 15, we have summarized the signal processing of the GNSS-based multistatic radar. The most computational section lies in the two-dimensional coherent integration of the range compression and Doppler processing, because of the high sampling frequency in the fast-time domain. Hence, the computational burden is approximately proportional to the number of the selected satellites.  
Figure 14: CMF method selection result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Q** & **2** & **5** & **10** & **15** & **20** \\ \hline Number of CHO selected satellites & 31 & 31 & 31 & 31 & 31 \\ RMS @ CHO method /m & 1.52 & 3.81 & 7.61 & 11.42 & 15.20 \\ RMS @ all 55 satellites /m & 1.46 & 3.65 & 7.30 & 10.93 & 14.57 \\ RMS @ random selection of 31 sat/m & 1.93 & 5.09 & 9.34 & 14.10 & 19.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CHO method selection results.  
Based on the above, two algorithms are proposed for the transmitter-selection problem of the passive radar when they are more than enough. The effectiveness of both algorithms has been verified by the simulation and experimental results. However, the two methods have their own advantages and limitations. First, the two methods are under a same premise that the approximate target position is known, which is generally a target-tracking mode, however, when the target position deviation is too large, the performance of proposed methods can be affected. Further analysis will be conducted in the follow-up work.  
Second, for the CMF method, the positioning accuracy after selection is also limited by the choice of the covariance matrix construction method and the matrix fusion algorithm. While for the CHO method, once the multistatic geometry is certain, the number of satellites filtered out are determined, which lacks the flexibility. Nevertheless, a combination of the two methods can address this problem, that the CMF could be applied after the CHO selection.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 4 Field Survey Results and Discussion
### Deterioration Patterns in Failure Conditions
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Hollow Concrete**} & \multicolumn{3}{c}{**Gypsum**} & \multicolumn{3}{c}{**Autoclaved**} \\ \hline
**Total Number of Samples** & \multicolumn{3}{c}{**41.00**} & \multicolumn{3}{c}{**33**} & \multicolumn{3}{c}{**30**} \\ \hline  & **n** & **\%** & **mean** & **n** & **\%** & **mean** & **n** & **\%** & **mean** \\ \hline Cracks on the Surface & 6.00 & 15\% & 83.33 & 11 & 27\% & 76.00 & 12 & 29\% & 73.33 \\ Crack at Partition-Wall Junction & 15.00 & 37\% & 80.00 & 13 & 32\% & 74.62 & 18 & 44\% & 75.88 \\ Crack at Partition-Ceiling Junction & 10.00 & 24\% & 70.00 & 22 & 54\% & 67.73 & 12 & 29\% & 71.82 \\ Crack Around Door Frame & 12.00 & 29\% & 78.18 & 21 & 51\% & 73.16 & 10 & 24\% & 68.89 \\ Stability of Electrical Installations & 10.00 & 24\% & 82.00 & 17 & 41\% & 72.94 & 11 & 27\% & 81.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the Impact of Failure Conditions on Partition Performance. This table provides the count of observed samples (n), the proportion of samples exhibiting defects in failure conditions (%), and the average performance score (mean) across different partition typ. Figure 11: Performance Degradation of Autoclaved Concrete Block Partitions in Failure Conditions. Figure 12: Performance Degradation of Gypsum Board Partitions in Failure Conditions. Figure 13: Comparative Analysis of Partition Performance by Failure Mechanism. Each radial axis represents a distinct type of failure, showing the impact of each one on overall partition type performance.
## 3 Methodology and Data
### Results of Qualitative Interviews
#### 3.2.4 Internal Users' Context  
The internal users' context includes the reasons behind the adoption of innovative technology from an individual application perspective, which includes the employees' capability and attitude.  
* Performance Expectancy: Most of the respondents said they felt the convenience of broadband mobile applications for personal use. They also use broadband mobile applications on private mobile devices for business or to communicate with their company colleagues, customers, or suppliers. More than half of respondents believed that broadband mobile applications could improve the efficiency and convenience of their work (P1-3, A2-3, I1-2, N2) and enhance customer satisfaction (P1-3, A1-3, I1). Some respondents also said they will recommend the use of broadband mobile applications to colleagues, superiors, or management (A1-2, P1-3, I2-3).
* Effort Expectancy: Respondents P1 and P3 reported that most of their clients' staff already have a considerable understanding of the adopted programs before their deployment. Therefore, it would not require much effort in implementation. Respondentdents A2 and I1 stated that the purpose of using broadband mobile applications is to simplify some operating processes. Employees did not feel any pressure before adoption, and some colleagues even thought that the new technology would reduce the pressures of time and mistakes. Respondent N2 reported that colleagues are very busy in their daily work, and do not think that broadband mobile applications will bring convenience but that they could generate extra loadings and interference.
## 4 Future Research Challenges
### Carrier Aggregation  
An optimal SS framework to allocate multiple resources efficiently among users is crucial for our future SS systems [237]. Multiple CCs across the available spectrum can be utilized to create a wider bandwidth channel to increase the network data throughput and overall capacity [238]. Moreover, an application-aware resource allocation scheme is needed for the users of HetNet to achieve fragmented spectrum allocations and aggregate licensed and unlicensed carrier spectra [239]. A Clear Channel Assessment (CCA) may be performed in response to the uplink grant to determine the availability of an unlicensed spectrum [240]. Moreover, the Licensed Assisted Access (LAA) method is the latest approach presented by 3 GPP that can exploit high-spectrum bandwidth to address the limitation of the current 5G network [241]. Various Machine Learning (ML)-based resource allocation techniques can also be applied. For instance, a deep learning method can be used to overcome the resource allocation management of BS by performing fractional spectrum access proactively and selecting the channel dynamically [242]. Lack of dynamic control of wireless network resources leads to unbalanced spectrum loads and introduces capacity bottleneck. Therefore, a solution similar to extended Dynamic Spectrum Access (eDSA) is needed to provide quality load balancing in available spectrum bands, traffic allocation, and capacity enhancement through the aggregation of current resources [243]. Moreover, some AI-based solutions for resource management must be proposed; for example, Evolutionary Programming (EP) algorithm [244].
## 3 Methodology
### Modified GRU Model  
GRU is one of the most widely used models for time series forecasting, known for its efficiency, high accuracy, and simple structure. In this study, we enhance GRU by replacing its linear hidden layers with dynamic heterogeneous graph attention layers to develop the final prediction model. As illustrated in Figure 4, the linear hidden layers of the update gate and reset gate in the basic GRU structure are substituted with dynamic heterogeneous graph attention layers. Through the modification, the dynamic learning ability of GRU is strengthened and the efficiency and accuracy of the proposed model are enhanced. The underlying principles of the improved GRU model are as follows.  
Figure 3: The schematic diagram of traditional graph attention and heterogeneous graph attention, where different line types stand for various edge types. (**a**) Traditional graph attention which line types are the same. (**b**) Heterogeneous graph attention with different line types.  
As shown in Figure 4, the improved GRU model retains the basic structure of the standard GRU model, primarily consisting of the update gate and reset gate. The update gate Z controls the amount of historical information used, while the reset gate R determines the extent to which the historical state influences the current state. The update gate and reset gate in the classic GRU model are represented as follows:  
\[R_{t}=\sigma(x_{t}W_{rx}+h_{t-1}W_{rh}+b_{r}) \tag{7}\]  
\[Z_{t}=\sigma(x_{t}W_{zx}+h_{t-1}W_{zh}+b_{z}) \tag{8}\]  
where \(R_{t}\) and \(Z_{t}\) represent the reset gate value and update gate value at time t, respectively, and \(\sigma\) is the sigmoid activation function, \(x_{t}\) is the input vector, \(W_{rx}\) and \(W_{rh}\) are weight matrixes of the reset gate, \(W_{zx}\) and \(W_{zh}\) are weight matrixes of the update gate, and \(b_{r}\) and \(b_{z}\) are biases.  
In addition, the content in the brackets represents the MLP hidden layer of GRU. In this paper, the dynamic heterogeneous graph attention is used to replace this layer. The update gate and reset gate after substitution are described as follows:  
\[\widetilde{HA}=\mathrm{HeterAttention}\left(G_{dynamic}\right) \tag{9}\]  
\[R_{t}=\sigma\left(\widetilde{HA}[x_{t}]+h_{t-1}W_{rh}+b_{r}\right) \tag{10}\]  
\[Z_{t}=\sigma\left(\widetilde{HA}[x_{t}]+h_{t-1}W_{zh}+b_{z}\right) \tag{11}\]  
where HeterAttention is the heterogeneous attention process, and \(\widetilde{HA}\) is an intermediate variable representing the results given by the dynamic heterogeneous attention layer.  
Given that the expression of \(Z_{t}\) and \(R_{t}\) are obtained, the candidate hidden state \(\widetilde{h}\) and the final hidden state \(h\) can be calculated as below:  
\[\widetilde{h}_{t}=\tanh h\left(\widetilde{HA}[x_{t}]+(R_{t}\odot h_{t-1})W_{ h}+b_{h}\right) \tag{12}\]  
Figure 4: The structure of the modified GRU model.  
\[\mathrm{h_{t}}=(1-Z_{t})\odot\widetilde{\mathrm{h_{t}}}+Z_{t}\odot\mathrm{h_{t-1}} \tag{13}\]  
where \(\odot\) means matrix dot product, that is, the multiplication of the corresponding position elements of two matrices.
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 2 System Design and Process Introduction  
The core aim of this study is to design an underwater AUV terminal processing strategy based on an optical vision system that can improve the transmission efficiency by transmitting point clouds instead of transmitting image data, so as to achieve the purpose of compressing the image data. The system performs calculations from the video stream to the 3D point cloud at the terminal AUV, and the main flow chart of the system is shown in Figure 1.  
First of all, the leader ship with the sonar positioning and navigation system achieves the detection and location of the target, then the AUV's route is planned and driven from the main ship to collect the video stream of the target.  
Next, the video stream is preprocessed to obtain the image sequence through 'key frame extraction' and 'image enhancement' processes. We firstly filter the blurred frames with the Laplace variance algorithm and extract key frames using the clustering algorithm. The key frame extraction step allows more efficient 3D reconstruction based on images. A detailed description of this stage can be seen in Sections 3.1 and 3.2. Next is the image enhancement module. In this step, different enhancement methods are used for images with  
Figure 1: Underwater AUV terminal processing strategy based on optical vision system.  
various problems, including color degradation and too much noise. This step reduces the number of incorrect feature matches and allows for more robust global rotation estimations. We give a detailed description of this stage in Section 3.3.  
The last step is the 3D reconstruction, whereby the point clouds can be generated from the image sequence through the 'SFM and PMVS' process. We present a more efficient rotation-averaging method based on L1 RA [25] raised in 2013 by [PERSON] in our algorithm, namely LTS (least trimmed squares), which is a robust regression method that is integrated into the rotation averaging. Our experiment show its efficiency, while a detailed description is shown in Section 6.1. Then, we introduce a clear global SFM-PMVS pipeline in Section 5.