esa-sceva/satcom-chunk-collection huggingface dataset:
# A Case Study on Virtual HPC Container Clusters and Machine Learning Applications
## 4 ML on Docker
### Anomaly Detection Application
#### 4.2.1 Problem, Data, and Model
To find outliers, the data are subjected to the network, and then the reconstruction error is assessed, calculating how much the output data deviate from the input. A common measure of error in LSTM autoencoders is the mean squared error. It is necessary to establish a certain error threshold, beyond which the tested data are classified as anomalies. The underlying hypothesis is that if the network was infrequently trained using certain data or not at all, then accurately reconstructing these data would be difficult or impossible. Therefore, the larger the error, the more likely it is that the samples are anomalous. During network testing, detection was conducted on a large dataset (in a long sequence of measurements), and it was assumed that a certain percentage of anomalies existed within it, i.e., a percentile of the error distribution was rigidly chosen, with values to the left considered normal and those to the right as outliers. Figure 2: Training vs. test/inference pipeline. The implementation of the LSTM autoencoder and anomaly detection was programmed in Python using **TensorFlow**, an open-source platform dedicated to ML, especially deep learning of neural networks. TensorFlow allows for the use of state-of-the-art ML methods, providing a rich set of tools and libraries commonly used in this field. It enables the execution of time-consuming calculations in parallel on multiple processors and graphics cores. While TensorFlow itself offers the option of distributed computing on multiple machines, according to the authors of the paper [45], this task is not the easiest and requires significant input from the programmer. **Horovod** is a library that significantly simplifies the process of distributing calculations in TensorFlow. It supports the standard MPI for running parallel processes. Using it requires only minor modifications to the existing code. Internally, it operates by executing the _ring-allreduce_ algorithm between training epochs, which is used here to average the gradients of the neural network distributed across multiple computing nodes. At the same time, _ring-allreduce_ optimally utilizes the entire bandwidth of the computer network, while ensuring a sufficiently large buffer [45]. In short, in this algorithm, each of the \(n\) nodes communicates only with two neighbors. First, in \(n-1\) iterations, a node adds received values to its local buffer, and in the next \(n-1\) iterations, it replaces the values in its buffer with those received.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 3 Methodology
### Raw Data Collection  
After simulating our network models in different scenarios, the radio message tool was used to sniff and collect the radio messages transmitted between the nodes that would be analyzed with a 6 LoWPAN analyzer with a PCAP feature incorporated into the tool. Figure 4 presents the workflow of the data collection module. Subsequently, the collected data were processed by the Wireshark software and saved in comma-separated values (CSV) format.
## 5 Conclusions
For future work, incorporating physics-informed machine learning techniques could enhance the interpretability and reliability of the proposed methodology. By integrating domain knowledge into the learning process, physics-informed models can improve generalization, especially in cases with limited data. Additionally, while the current methodology does not utilize measurements during strong-motion phases, future work could explore integrating our approach with existing strategies that focus on capturing variations in damage sensitive features during high-intensity loading events. Since the frequency content of such vibration data is richer and contains more complex information, combining these insights with the transmissibility-based approach could contribute to a more comprehensive damage detection system capable of operating under a wider range of conditions. Conceptualization, B.G. and O.G. ; methodology, B.G. ; software, B.G. ; validation, B.G. and O.G. ; formal analysis, B.G. and O.G. ; investigation, B.G. and O.G. ; resources, B.G. and O.G. ; data curation, O.G. ; writing--original draft preparation, B.G. and O.G. ; writing--review and editing, O.G. ; visualization, B.G. ; supervision, O.G. ; project administration, O.G. ; funding acquisition, B.G. and O.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Scientific Research Projects (BAP) of Istanbul Technical University under Project No MGA-2024-45495. The funding for the experimental part of this study was provided by the Scientific and Technological Research Council of Turkey (TUBITAK) under project number 114M305 and by the General Directorate of Highways (KGM) under protocol number KGM/TK-10. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available upon request from the corresponding author subject to approval by the General Directorate of Highways (KGM) in accordance with the funding contract. (specify the reason for the restriction). **Conflicts of Interest:** Author [PERSON] was employed by the company MiTek Mitigation Technologies A.S. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Advantages and Limitations of EEG NFB  
EEG NFB is a non-invasive and painless method that does not require medication or surgery. Although the mechanism underlying neurofeedback training protocol is still not fully understood, the research indicates potential benefits, including improved attention, concentration, sleep quality, reduced anxiety and headaches [40]. It shows positive outcomes in conditions such as autism spectrum disorders, attention deficit hyperactivity disorder (ADHD) and other mental health disorders [18, 19, 20, 21, 41]. Despite these promising results, NFB has limitations. Key issues include insufficient data on long-term effects and whether learned positive behavioral patterns transfer to other behaviors [40]. Some studies suggest that positive effects may last up to 12 months for ADHD, but more research is needed to confirm these findings and understand their applicability to different NFB forms [40]. Furthermore, there is a lack of studies on behavioral patterns outside controlled settings and uncertainties about why NFB benefits are inconsistent across participants, possibly related to brain structure, putamen size and trust in the technology [40]. Additionally, the absence of control groups complicates distinguishing genuine effects from placebo responses. One of the main limitations is that not everyone is suitable for EEG NFB. For example, people with severe cognitive impairment cannot participate.  
Potential side effects of NFB include headaches, fatigue, nausea, dizziness, insomnia, agitation or anxiety [9].
## 1 Introduction
RF energy-harvesting technology is widely used in the Internet of Things, and many advanced energy harvesting technologies have been proposed in these applications [16; 17; 18; 19]. RF energy-harvesting systems face two significant challenges: system reliability and packet timeout probability. To address these problems, researchers have proposed several solutions, including improving system performance in wireless sensor networks through the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [20] and analyzing interrupt performance in wireless sensor networks [21]. In particular, [PERSON] proposed a system performance research method in [22] using two energy-harvesting protocols: the head harvesting energy of each timeslot (HHT) and the head harvesting energy of a dedicated timeslot (HDT). Researchers can choose a protocol that is more suitable for communication by contrasting the system performance of the HHT protocol under maximal ratio combining (MRC) and selection combining (SC) techniques with the HDT protocol under MRC and SC technology. According to [22], the HDT protocol harvests energy during a designated time slot, whereas the HHT protocol harvests energy at the head of the time slot. The energy-harvesting time is \(\alpha_{0}T\) and \(\alpha_{0k}T\), and \(\alpha_{0}=K\alpha_{0k}\), where \(K\) is the number of sensor nodes. The size of the transmitted packets is the same for both the HHT and HDT protocols in paper [22], where the size of the packet transmitted by the system is set to \(L\). Given that the HDT protocol harvests energy over a significantly longer period than the HHT protocol, this can lead to an unfavorable contrast. Moreover, the complex channel characteristics that apply to both MRC and SC technology are not taken into account in study. In response, we reformulate the optimization problem put out in [22] in order to solve these problems, and we offer competitive energy-harvesting strategies in this reformulated problem. To determine which protocol is best for the energy-harvesting system, different packet sizes are assigned to various protocols, and then, they are compared once more. The research findings of paper [22] show that the HDT protocol performs better than the HHT protocol. Our improved simulation results, however, show that HHT performs better and is more suited for RF wireless communication, particularly the HHT protocol using MRC technology. It is appropriate, and the following analysis provides a detailed examination.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Application and Scenarios
#### 2.4.1 Power Module Scenario  
The tag is placed with piezoelectric pointing to the road with the objective of obtaining as much oscillations as possible with the highest effect. With this background, we are able to check both the solar and the mechanical energy harvesting modules. Testing inside the lab does not give us the opportunity to check how energy harvesting techniques really work.  
Based on the application explained before, it is expected to obtain how both energy harvesting modules work, the time the device needs to charge and discharge the supercapacitor, and if it is capable of receiving and transmitting packages with the harvested energy. This energy is collected in the supercapacitor.  
Solar energy depends on the hours of daylight, although it is also important to know how much energy is captured when the tag is in a shady place during the day. The supercapacitor charge has been measured for several days to check these two scenarios.  
Mechanical energy depends on the amount of energy generated by the oscillation of the piezoelectric when a car passes next to the tag with different speeds. That energy was not measured with any external system. We have checked that the energy obtained from the wind caused by passing cars through the piezoelectric film, since the vibration associated to the ground, and transferred to the traffic sign, cannot be measured with this device. The mechanical module was tested with the tag in the same place. At this point, the solar panel is disconnected to isolate the techniques and obtain a measure related only to the mechanical module. This module has two outputs, as has been previously commented, BAT (connected to the supercapacitor) and SYS (connected to the regulator). They were monitored for several days.  
The tag starts to work when the voltage in the supercapacitor reaches 2.7 V, which goes against the 2.5 V of the theoreticians; this is due to component tolerances. That fact has been checked thanks to a LED that turns on when the system starts to work. With the energy of both techniques, the power module has to power the processing module to stay on and to transmit and receive packages. In the final version, the LED will not be assembled due to the question of consumption.
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 4 Methodology
### DGA String Extraction  
In general, to improve controllability and avoid being quickly blocked, most attackers will register their second-level domain name. For example, \"h7 smcnrwlddsdn34 fgy.info\" is the DGA domain name registered for the malicious code of Sality. However, registering a second-level domain name has a certain cost. Moreover, even if forged information is used, traceable information, such as IP, may be left, and there is a possibility of being traced by the source. At the same time, some attacks use dynamic domain name services to generate third-level domain names to save attack costs, such as \"blackshadespro.no-ip.org\". The second-level domain name and third-level domain name of the dynamic domain name service are not necessarily completely separated. Two types of domain names may be used simultaneously in one attack or different attacks launched by the same organization. In the domain name, this paper uses the string generated by DGA for detection and classification, such as \"h7 smcnrwlddsdn34 fgy\" in \"h7 smcnrwlddsdn34 fgy.info\", \"blackshadespro\" in \"blackshadespro.no-ip.org\", and so on. Therefore, we follow the following guidelines to extract the DGA domain name string from the domain name.  
1. If it is a second-level domain name, the second-level domain name part is extracted,
2. If it is a third-level domain name, first determine whether the second-level domain name is the domain name of the dynamic domain name service, such as \"no-ip.com\", \"afraid.org\", \"duckdns.com\", \"dnsdynamic.org\", \"dydns.net\", \"dynu.com\", etc., if so, the third-level domain name part is extracted.
3. If the second-level domain name in the third-level domain name is not the domain name of the dynamic domain name service provider, the longest string is extracted.
4. Otherwise, extract the longest string.
## Appendix A Properties of Local Mean Envelope of a Stationary Stochastic Process
### Example Signal: White Signal  
For a signal with independent but equally distributed samples, the correlation is  
\[r_{z}[k]=\mathbb{E}\{z[n]\cdot z[n+k]\}=\begin{cases}\mathbb{E}\{z^{2}[n]\}=m_{2},&\text{for }k=0\\ \mathbb{E}\{z[n]\cdot z[n+k]\}=(m_{1})^{2}&\text{for }k\
eq 0\end{cases}. \tag{20}\]  
Inserting this correlation into Equation (18), the second-order moment for the LME for a white signal is obtained as  
\[m_{2}^{T}=(m_{1})^{2}+\frac{m_{2}-(m_{1})^{2}}{T}=\mu_{z}^{2}+\frac{\sigma_{z} ^{2}}{T}. \tag{21}\]  
For large \(T\), the second-order moment \(m_{2}^{T}\) converges to the squared mean of the envelope.  
For an AWGN, the samples are zero mean complex normal distributed, and the envelope has a Rayleigh distribution. The \(\mathbb{E}\{z^{2}[n]\}=m_{2}\) is the power of the signal. The mean of the envelope is  
\[m_{1}=\mu_{z}=\mathbb{E}\{z[n]\}=\sqrt{\frac{\pi}{4}\cdot m_{2}}, \tag{22}\]  
and the variance is  
\[\sigma_{z}^{2}=m_{2}-\mu_{z}^{2}=m_{2}\cdot\Big{(}1-\frac{\pi}{4}\Big{)}. \tag{23}\]  
The normalised second moment given in Equation (19) is then  
\[\frac{m_{2}^{T}}{m_{2}}=\frac{\pi}{4}+\frac{1}{T}\cdot\Big{(}1-\frac{\pi}{4} \Big{)}, \tag{24}\]  
which converges to  
\[\lim_{T\to\infty}\frac{m_{2}^{T}}{m_{2}}=\frac{(m_{1})^{2}}{m_{2}}=\frac{\pi}{4} \tag{25}\]  
for large \(T\).
## 2 Outdoor Positioning in the IoT Ecosystem
### Common LoRaWAN-Based Localization Architectures
#### 2.4.2 Distributed Architecture  
With increasing the need for real-time processing and computing, fog computing appeared as a suitable solution. In Fog and Edge-AI-based architecture, the gateway layer act as a smart edge layer [36; 37]. At the smart edge layer, artificial intelligence algorithms are applied to enhance the quality of service (QoS). For example, different gateways can work in distinct environmental situations. Based on each particular environment, each edge gateway can determine environment error sources and reduce their effects on received signals. On the other side, some prepossessing (e.g., the adopted method by system and user agent for end-node localization discovering) and important evaluation (distance estimation) on receive data can be done on the edge layer. This mechanism helps to reduce latency (especially in emergency systems such as smart healthcare systems) and improve the overall system performance. As a result, location estimation error decreased. Then, the results are sent to the network server as a fog layer for aggregation and more advanced processing and forwarding to the cloud layer through the Internet. This method leads to efficient use of network bandwidths. Additionally, by increasing the number of functions and the number of connected devices to the fog and edge Layers, an additional orchestration layer is required for management purposes. The authors in [38] provide thorough functional and performance comparison with container orchestrators (in particular Docker Swarm and Kubernetes) under different real topologies in the fog-based cluster, using wired and wireless infrastructures. An example of an Edge-AI-based architecture is depicted in Figure 3.  
Figure 3: Fog and Edge-AI-based Architecture.  
Figure 2: Cloud-based architecture.
## 2 Materials and Methods
### Shallow Classification Algorithms
#### 2.3.2 Support Vector Machines (SVMs)  
[PERSON] [30] created the supervised machine learning algorithm known as SVM, which is used for classification, regression, and outlier detection. Owing to its robustness and significant accuracy performance with less computational power, SVM is generally implemented for solving classification objectives. The linear SVM approach involves representing each input data point as a point in an n-dimensional space, where n corresponds to the number of input dimensions. The classification operation is performed by identifying a hyperplane that effectively separates the data into two different classes.  
To provide a brief overview, SVM can be mathematically explained as follows.  
\[f(x)=w^{T}x+b \tag{5}\]  
In the above equation, \(x\) indicates the input feature vector of the classifier, \(w\) represents the weight vector of the hyperplane, \(T\) is the transpose, and the hyperplane position is expressed as \(b\).
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 4 Simulation Results  
This section provides numerical results to demonstrate positioning quality and achievable spectral efficiency with respect to different simulation parameters. In particular, we analyze the impact of fully-coordinated multi-point reception on the estimation accuracy and rate performance and compare it with the BS-selection method, where each active user is only assigned to the closest BS. We also evaluate localization accuracy and provide insight on the dependence of different simulation parameters on the achievable positioning quality. We further provide performance in-terms of spectral efficiency and corresponding outage with the compromise receive beam, evaluated over the increasing link-blockage probability.
# Low-Cost Active Anomaly Detection with Switching Latency
## 4 Numerical Results
### Scenario 2: \(s=\Omega(B)\)  
In this part, we compare the performances of DMSC policy, R-SPRT and CL-\(\tau cN\) policy for \(M=5\) and \(L=2\) in Scenario 2 where \(s=\Omega(B)\) as \(\alpha\to 0\).  
We consider the case where \(s/B=2\), which implies that the order of \(\triangle(M,L)\) (defined in (9)) does not change regardless of the values of \(\alpha\) or \(B\). We set \(\lambda_{f}=1\) and \(\lambda_{g}=3\), thus \(\frac{2D(g||f)}{L+1}\approx 0.96\), \(\frac{D(f||g)}{M-L}\approx 0.30\) and \(\triangle(M,L)\approx 1.56\). In this case, the DMSC policy always selects process \(m^{1}(n)\) to probe at each given time \(n\). We average the results over \(5\cdot 10^{4}\) trials. As shown in Figure 4, the R-SPRT significantly outperforms the DMSC policy for all values of \(B\) and the DMSC policy outperforms the CL-\(\tau cN\) policy.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space  
Following the successful division of the work space, it is easy to create the space \"Occupancy Map\", where the empty cubes represent the \"Free Spaces\" (FS). Based on this map, one can begin the obstacle-free route planning. A simple model of the above-mentioned method is displayed in Figure 6. Here, the drone is set to fly between residential buildings.  
The \"graph-like\", i.e., topological map of the free environment is created by connecting the center-points of the neighboring blocks in such a way that the connecting line cannot touch the occupied spaces (Figure 7, blocks marked with blue dots).  
Figure 6 shows a scaled 15 \(\times\) 15 \(\times\) 15-units WS, represented and partitioned by cube grid, where the Centre Points of the cubes are calculated and the occupied cubes are colored blue. SW support is ensured by the _Python_ package, with the different tools activated and suited for the requirements. Moreover, the Centre Points represent the nodes, while the connecting lines denote the edges of the graph. When the neighboring center points are connected, this results in 22 lines, 6 of which are the lateral adjacencies (shorter lines) and the rest are diagonal adjacencies (longer lines). The mathematical formula for length calculations is simply based on the Pythagorean axiom: \(\textit{length}\;e_{j}=\sqrt{\left(x_{\;i+1}-x_{i}\right)^{2}+\left(y_{\;i+1} -y_{i}\right)^{2}+\left(z_{\;i+1}-z_{i}\right)^{2}}\), where the indices \(<i,j\) are the number of nodes resp. edges.  
Figure 6: The scaled partitioned sample WS, with occupied blocks (blue dots) and Free Spaces (_FS_s).  
Figure 7: Creation of the graph-like (topological) map of the space, where the center points of the FS blocks are connected. Where the connecting line crosses (or touches) an occupied block (see red line), the connection is not possible.