esa-sceva/satcom-chunk-collection huggingface dataset:
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### How the Data Used to Train the Deep Learning Models Was Gathered/Generated?
#### 3.4.2 CTU-13 Dataset  
The CTU-13 dataset [94] was compiled in 2011, in CTU University, in the Czech Republic, and comprises real botnet, normal, and background traffic.  
The dataset is composed of 30 captures (corresponding to different scenarios) for several botnets samples. In each scenario, a specific malware with different protocols is used. After the capture, the authors analyzed the flow data in order to create the labels. There are four types of flows in the dataset: background, botnet, command and control channels, and normal. However, the dataset is unbalanced. For example, for a given scenario, there are 114,077 flows, where 37 (0.03%) is botnet traffic, and 112,337 (98.47%) of normal traffic.  
Two works found in this systematic review used this dataset [38; 40] to train deep learning models for anomaly detection. The authors made two different training/testing data partition. In the first partition, the CTU dataset was divided between training (80%), and testing (20%), both containing samples of every botnet. In the second partition, the botnet flows were divided into training and testing, i.e., the botnet flows that are present in the training set were not present in testing set.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### Collusion Attacks  
Collusion attacks can involve coordinated actions by multiple ASs and the sophisticated manipulation of inter-domain routing principles, such as ASs working together to inject or propagate malicious routing information. The distributed nature of these attacks across different geographic and administrative domains means that leveraging data from multiple vantage points can significantly enhance our detection capabilities. Observing the propagation of malicious announcements from various locations helps in identifying the collaborative pattern of these attacks, making their early detection more feasible.  
This criterion directly applies to collusion attacks, as they inherently involve collaboration between two or more ASs. A group-level analysis is crucial for uncovering the coordinated nature of these attacks, making them a prime candidate for detection techniques that analyze interactions and dynamics across multiple ASs. Collusion attacks exploit the trust relationships between ASs to propagate forged or malicious routing information. Understanding these attacks requires a deep analysis of the complex interactions and trust dynamics within the BGP ecosystem. Techniques that can analyze and understand these relationships are well suited to detecting such sophisticated attacks.  
The attackers in a collusion scenario use their advanced knowledge of BGP operations and existing trust relationships to manipulate routing information effectively. This manipulation is strategic and requires an in-depth understanding of the BGP's decision-making processes.  
Collusion attacks are designed to be stealthy, evading detection by conventional monitoring systems by appearing to be legitimate BGP announcements from trusted ASs. Our ability to detect these attacks benefits significantly from techniques that can analyze routing data from multiple perspectives to identify anomalies that single-point observations might miss.
## 2 Methodology
### Model Performance Metrics  
The performance of the ML models was assessed using recall, precision, and F1 score because they ensure that the model's diagnoses are reliable. The equations below are standard performance metrics in machine learning [32].  
Recall: Recall quantifies the ratio of accurately anticipated positive observations to all observations made during the actual class. It shows how well positive samples can be identified by the model. In medical screenings, for example, where missing a positive case (false negative) can be crucial, recall is crucial when the cost of false negatives is high.  
\[Recall=\frac{\text{True Positives}}{\text{True Positives }+\text{ False Negatives}} \tag{2}\]  
Precision: This metric quantifies the ratio of accurately predicted positive observations to all predicted positive observations. It shows how accurate positive forecasts are. When the cost of false positives is high, accuracy is essential. For instance, a false positive in a medical diagnosis could result in needless treatments.  
\[Precision=\frac{\text{True Positives}}{\text{True Positives }+\text{ False Positives}} \tag{3}\]  
F1 Score: The F1 score is calculated by taking the harmonic mean of recall and precision. It offers a balance between recall and precision, which is especially helpful for unbalanced datasets. When precision and recall must be balanced, the F1 score can be helpful, particularly when there is an unequal distribution of classes.  
\[F1\ Score=2*\left(\frac{Precision\ *\ Recall}{precision\ +\ Recall}\right) \tag{4}\]
## 3 System Model
### System Propagation Model  
In this paper, we mainly consider the effects of large scale fading (including free space loss, scintillation effect, cloud and rain attenuation, and oxygen absorption), small scale fading, transmission delay, and doppler effect under different beams. The difference of channel fading under different beams is mainly reflected in the difference in elevation angle, distance, and carrier frequency. We will conduct a detailed analysis below:
## 3 Results and Discussion
### Adsorption Studies  
Equilibrium data, commonly denoted as adsorption isotherm, are important to define the mechanism of the adsorption process accurately [26]. Here, the Langmuir, Freundlich, and Sips isotherm models were used to analyze the adsorption of dyes onto Zeo and Zeo-R.  
Figure 5 illustrates fitting of the experimental adsorption data with the aforementioned models. According to the Giles classification [27], the isotherms are type L-2 when MB and SA are tested as adsorbates, and type S-2 when MG is tested as adsorbate. L-type isotherms are characterized by a non-linear convex curve, which indicates lower availability of adsorption sites as the concentration at equilibrium increases and high affinity of the adsorbate for the adsorbent. As for S-type isotherms, the adsorption capacity of the solid increases as the adsorbate concentration rises.  
Table 5 summarizes the parameters obtained from the nonlinear plots of each isotherm. Fitting of the models to the experimental data was evaluated by the chi-square test (\(\chi^{2}\)) and the coefficient of determination (\(R\)). Most of the \(R\) values exceed 0.9 for the Langmuir model, which suggests that the model could fit the experimental results.  
The \(k_{L}\) values for the Langmuir model vary between 2.4 and 0.0006 L/mg. The \(k_{L}\) values are different when we compare the adsorption of dyes onto Zeo and Zeo-R (e.g., 2.4 and 1.4 L/mg for MB, respectively). Therefore, the adsorption capacity of the zeolites depends on the precise kaolin that is used as precursor for the synthesis of zeolites.  
The decrease in the \(k_{L}\) values could be related to molecular volume and steric hindrance. This is not surprising because zeolite A has a narrow pore aperture, which makes access very difficult for the dye molecules [28]. The results indicate that the affinity of the binding sites in Zeo-R increases for  
Figure 5: Equilibrium adsorption data for Zeo and Zeo-R as adsorbents and MB (**a**), SA (**b**), and MG (**c**) dyes as adsorbates.  
all the dyes probably because there are isomorphic substitutions in the parent kaolin. The \(k_{L}\) values vary between zero and 0.11 for SA adsorption onto Zeo and Zeo-R, suggesting that this dye has large affinity for solids.  
As previously reported by some authors, zeolite materials generally display high affinity for cationic dyes with very small molecular size, such as methylene blue. The behavior of MG, MB, and SA adsorption onto Zeo and Zeo-R shows that both zeolites have more affinity for MB, the dye with the smallest molecular size. Other authors have reported efficient removal of cationic dyes like basic blue 17, basic red 46, basic green 4, basic blue 9, basic violet 10, and basic yellow 28 by natural zeolites (e.g., clinoptiolite) [29; 30; 31; 32; 33]. In contrast, adsorption studies of anionic, acid dyes are very limited, and very low adsorption capacity values (0.5 mg g\({}^{-1}\)) have been reported [18; 34].
## 4 Applications of AI in Modern Communication Networks
### Security and Privacy
#### 4.3.1 Intrusion Detection and Anomaly Detection  
AI-based intrusion detection systems (IDSs) utilize advanced machine learning techniques such as neural networks and decision trees to analyze network traffic and detect anomalous behaviors indicative of cyberattacks. Models like Transformers can process large volumes of network data, achieving detection accuracies of up to 98% [75; 77]. These models are particularly effective in detecting sophisticated attack patterns and zero-day threats, where traditional methods may fail due to the dynamic and evolving nature of cyberattacks.  
As shown in Figure 6, the AI-based intrusion detection system (IDS), powered by Transformer models, consistently outperforms traditional IDS methods in terms of detection accuracy. The Transformer-based model improves over time, reaching detection accuracies up to 95%, while traditional IDS methods show more gradual improvement, peaking at 75%. The use of Transformers in AI-based IDSs allows for better real-time anomaly detection due to their ability to process and analyze complex network traffic patterns, making them highly effective for identifying both known and novel threats. In contrast, traditional IDS  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **AI Model** & **Efficiency Improvement (\%)** \\ \hline Bandwidth Management & Reinforcement Learning & 30 \\ Latency Reduction & Deep Learning & 50 \\ Resource Allocation & Neural Networks & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AI-driven resource allocation.  
approaches often struggle with new and sophisticated attack techniques, highlighting the need for advanced AI models in modern cybersecurity.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods  
### Preparation of the Randomly Oriented Carbon Nanotube Films  
#### 2.1.1 Preparation of the CNT Films  
Randomly oriented carbon nanotube films were prepared using the method previously reported [28]. Raw arc-discharged single-walled carbon nanotubes (SWNTs) were purchased from Carbon Solution Inc. (CSI, Riverside, CA, USA). This powder was obtained by high-voltage arc discharge, so these carbon nanotubes were not purified by a semiconductor tube. The dispersant (poly[9-(1-octanoyl)-9H-carbazole-2,7-diyl] (PCz)) was synthesized via Suzuki polycondensation for dispersion. PCz is an excellent polymer material for the dispersion and purification of carbon nanotubes. The carbon tubes obtained after the dispersion of PCz have the advantages of few defects and high semiconductor purity. At the same time, PCz also has the radius (chirality) selection for carbon nanotubes with a diameter of about 1-2 nm [29]. First, 200 mg of PCz was mixed with 100 mg of CNTs by adding 100 mL of toluene. The mixed carbon nanotube solution was sonicated for 30 min using a cell grinder (Sonics VC500, Sonics & Materials, Inc., Newtown, CT, USA) with a 50% duty cycle. The solution was centrifuged at 30,000\(\times\)\(g\) for 30 min to remove most of the multi-tubular bundles and insoluble material, and 90% of the top layer was removed, using a high-speed centrifuge model (Allega X-64R, Beckman Coulter, Brea, CA, USA) for the entire process. This was centrifuged at 50,000\(\times\)\(g\) for 2 h to remove trace amounts of metal nanotubes. The final supernatant was collected to obtain a high-purity solution of semiconductor carbon nanotubes. The dense carbon nanotube films were fabricated by the dip-coating method, and the high-resistivity SiO\({}_{2}\)/Si substrate was immersed in the collected supernatant for 48 h. After deposition, the substrate was blow-dried by high-purity nitrogen and baked in a tube furnace (Thermo Scientific Lindberg/Blue Moldatherm 1100 \({}^{\circ}\)C (Waltham, MA, USA)) for 30 min at 120 \({}^{\circ}\)C.
## 3 The Three-Dimensional UWB Antenna: Geometry, Computer Model, and Matching Parameters
especially in the high-frequency part of the considered frequency range. The obtained dependences are shown in Figures 3 and 4. As can be seen from Figure 3, the resonant frequencies of the characteristic modes (corresponding to characteristic angles of 180\({}^{\circ}\)) are sufficiently uniformly distributed over the frequency range. The structure of frequency dependences in the modal significances (Figure 4) shows that the antenna radiation characteristics at frequencies of 2... 4.5 GHz are mainly determined by the modal directional patterns of the first and second modes, whereas at frequencies of 4.5... 10 GHz they are determined by the second, third, fourth, and fifth modes. Moreover, modes 1 and 2 are the fundamental modes of the antenna part formed by almost closed ring-shaped conductor of variable width, and modes 3, 4, and 5 are the modes of the original asymmetrical exponential TEM horn supplementing the above-mentioned part of the antenna. It is customary to evaluate the effective radiation bandwidth of the modes by the frequency range in which the mode significance exceeds 0.707. From Figure 4, it can be seen that this frequency range is the shortest one (1 GHz) for mode 1, the longest one (7 GHz) for mode 2, and is equal to 5.5, 4.8, and 3.85 GHz for modes 3, 4, and 5, respectively. At the same time, Figure 3 shows that frequency ranges can be identified in the examined frequency range in which the inductive character of modes 1 and 2 balances the capacitive character of modes 3, 4, and 5, thereby leading to the reduction of the reactive energies of the modes and, as a consequence, the expansion of their effective radiation bandwidth. To expand the antenna matching band, we additionally analyzed the behavior of the characteristic modes (inductive/capacitive ones) in different frequency ranges. Then, according to recommendations [23] we increased the transverse size fragments of antenna with maximum currents for the first mode by adding a small volume of conducting elements with characteristic sizes of 0.5... 1 mm within several iterations. The analysis of distribution of currents of separate modes showed that the placement of the power supply unit on the antenna as proposed in work [23] is an optimal variant. As a result, we succeeded in expanding the antenna matching band by 5.4 % toward high frequencies (Figure 5). Figure 4: Frequency dependences of the modal significances of the first five characteristic modes. Figure 3: Frequency dependences of the characteristic angles of the first five characteristic modes.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 2 Overview of Flexible Wearable Heart Rate Monitoring Systems  
Flexible wearable heart rate monitoring systems represent the crystallization of materials science and biomedicine. These systems utilize special flexible materials to fabricate lightweight sensor electrodes and circuits that can conform to the skin. Such a design ensures the systems can acquire stable signals even when the human body is in motion. Meanwhile, biomedical technologies endow these systems with the capability to accurately monitor heart rate and maintain compatibility with the human body. By leveraging biosensing principles [40] to detect subtle physiological signals and employing biomedical algorithms to process the data for calculating clear heart rate values, they enable continuous and precise monitoring services, which are widely applicable to scenarios such as fitness, disease prevention, and health management.  
Given the significance of flexible wearable heart rate monitoring systems, this section provides a detailed introduction to such systems, covering their modular composition, modular functions, and the underlying principles of heart rate monitoring. Finally, this section places particular emphasis on the low-power design of flexible wearable heart rate monitoring systems.
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
## 2 Dataset  
In this research, the Case Western Reserve University (CWRU) benchmark bearing dataset [2,6] is used to experimentally evaluate the effectiveness of machine learning-based AFSMO that is diagramed in Figure 1. Figure 2 presents the experimental data acquisition system used to extract the signals in various conditions. To collect the vibration signals in normal and abnormal conditions, the vibration sensor (6205-2 RS JEM SKF) is used. This sensor collects normal and single point faults that are seeded on the drive-end bearings at various bearing locations as the ball fault (BF), outer fault (OF), and inner  
Figure 1: Proposed algorithm for FEDI (fault estimation, detection, and identification) in rolling-element bearing (REB).  
fault (IF), respectively. The sampling rate of the record data is 12 kHz under four different motor torques loaded under different rotation velocities from 1730 rpm to 1790 rpm. Table 1 illustrates the data description of the CWRU benchmark bearing dataset. Four different datasets are defined in this table which are categorized by motor torque loaded from 0 to 3 hp. Moreover, the vibration signals in healthy and faulty (BF, OF, IF) conditions have three different crack sizes (i.e., 0.007, 0.014, and 0.021 inches in diameter).
# Real-Time Kinematic Positioning Using Multi-Frequency Smartphone Measurements +
## 6 Conclusions  
This paper showcased the effectiveness of using smartphone measurements for RTK applications. The results showed that, even with the internal smartphone antenna, around 99% of fix solutions can be achieved under static and open sky conditions. Moreover, for all kinematic tests, the percentage of fix solutions was above 75%, even in challenging scenarios where the signal reception was difficult due to the presence of tall trees with rich foliage. In future work, the GNSS measurements will be combined with the ones from inertial sensors in order to enhance the performance in the presence of signal obstructions to match the ones encountered during the performed kinematic tests. Moreover, the identification of different types of environments will be performed in order to allow the tuning of RTK parameters on the fly.  
Conceptualization: M.S., G.L. and D.N.; Methodology: F.Z., M.S., G.L. and D.N., Software/Data Curation/Validation: F.Z., M.S., G.L.; Writing/Review/Editing: F.Z., M.S., G.L., D.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are available in this manuscript.  
Author [PERSON], [PERSON] and [PERSON] were employed by the company Topcon Positioning Systems Inc. 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.
## 2 Materials and Methods
### Methods
#### 2.2.2 Feature Extraction from Building Footprints  
The distribution and morphology of buildings vary significantly across urban, rural, and urban-rural fringe areas [1]. Therefore, we incorporated building footprints to capture these distinctions. Building footprints provide crucial information about the physical characteristics of structures across different regions. In urban areas, buildings are typically characterized by high-rise structures with a high density of distribution. In contrast, rural buildings are predominantly single-function, low-rise private residences clustered in villages [48]. These villages are spaced far apart, with minimal surrounding greenery and low building density. The urban-rural fringe combines features of both urban and rural areas, encompassing residential communities, private houses, and scattered commercial developments.  
To quantify these spatial patterns and structural characteristics, we extracted a comprehensive set of features from the building footprint data. These features included metrics related to building area, perimeter, structure ratio (perimeter-to-area ratio), height (number of floors), and estimated age. For each of these attributes, we computed the sum, mean, and standard deviation within each 250 m \(\times\) 250 m grid cell, resulting in a total of 15 dimensions of building-related features \(F_{building}\). These multidimensional building footprint features provided a nuanced characterization of the built environment across different urban and rural contexts. They captured not only the physical attributes of individual buildings but also the collective patterns and variations within each grid cell.
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 2 Materials and Methods
### System Model
The number of MPF basis functions which grant a negligible variance of the modeling error is close to the KLE case. The orthogonal sequences \(\mathbf{h}^{(k)}\) are obtained by solving the equation  
\[\mathbf{C}_{h}\mathbf{h}^{(k)} = \lambda_{k}\mathbf{h}^{(k)}, \tag{4}\]  
where \(\lambda_{k}\) is the eigenvalue associated with the eigenvector \(\mathbf{h}^{(k)}\). \(\mathbf{C}_{h}\) is the kernel matrix of the basis, constructed as described in [17; 18]:  
\[C_{h}(m,n)=R_{w_{w}}[(m-n)T+\tau_{0}], \tag{5}\]  
and \(\tau_{0}=\epsilon_{0}\) is some starting value. If it is assumed \(r_{w_{w}}(\tau,\tau_{1},\tau_{2})=\prod\left(\frac{\tau_{2}}{\tau_{\max}}- \frac{1}{2}\right)v_{w}(\tau_{1}-\tau_{2})v_{w}(\tau-\tau_{1})\), where \(\prod(\cdot)\) is the rectangular function, then:  
\[R_{w_{w}}(\tau)=\int\limits_{\tau_{1}=0}^{\tau_{max}+\tau_{0}+\tau_{w}}\int \limits_{\tau_{2}=0}^{\tau_{max}}r_{w_{w}}(\tau,\tau_{1},\tau_{2})\mathrm{d} \tau_{1}\mathrm{d}\tau_{2}. \tag{6}\]  
With these definitions, it can be shown that:  
\[E\left(\left\|e_{l,p}\right\|^{2}\right)= \lim_{L_{0}\to\infty}\sum_{l=0}^{L_{0}-1}\frac{e_{l,p}^{*}e_{l,p }}{L_{0}}=\sum_{k=K}^{P-1}\lambda_{k}^{2}\left\|h_{p}^{(k)}\right\|^{2}. \tag{7}\]  
The use of MPF offers the advantage of expanding the filtered stochastic process [17; 18] with fewer basis functions. Thus, instead of requiring \(K_{P}=\lceil(\tau_{p}+\tau_{w}+\tau_{max})(W+\beta)\rceil+~{}1\) prolate functions, it is necessary only to consider \(K=\left(\lceil(W+\beta)\tau_{\max}\rceil+1\right)<P\) MPFs.