esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 4 Results and Analysis
### All-Class Classification  
The categorical cross-entropy loss function was used to train the MLP model across 30 epochs using the ADAM optimizer, with a learning rate of 0.001 and a batch size of 128. The MLP classifier performed wonderfully, achieving 94% AUC, 94% accuracy, 73% precision, 72% recall, and a 66% F1 score.  
With a batch size of 128 and a learning rate of 0.001, the hybrid CNN + LSTM model was developed, employing the trained model and the binary cross-entropy transfer functions. The hybrid model's precision, accuracy, F1 score, and recall score were 75%, 94%, 70%, and 71%, respectively, while its AUC was 97%. Table 4 below displays the performance of all classification models, including AUC, recall, precision, F1, and accuracy.  
Figure 17: Hybrid model confusion matrix for attack classification.  
Figure 18: Hybrid model ROC curves for attack classification.  
Figure 19 displays the confusion matrix for the MLP model. The matrix rows represent occurrences of a predicted class, while each column represents an actual class. In other words, 9325 out of 9711 were accurately indicated as being in class 0, 6499 out of 7460 as being in class 1, 974 out of 2884 as being in class 2, 2192 out of 2421 as being in class 3, and 39 out of 67 as being in class 4.  
The ROC curves of the MLP model are shown in Figure 20 for each class. The class with the most significant AUC is class 0, with a value of 0.96, followed by 0.97, 0.89, 0.97, and 0.93 for the other classes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AUC** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
**MLP** & 0.94 & 0.94 & 0.73 & 0.72 & 0.66 \\
**Hybrid model** & 0.97 & 0.94 & 0.75 & 0.71 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the two models for all-class classification.  
Figure 20: MLP model’s ROC curves for all-class classification.  
Figure 21 displays the confusion matrix for the hybrid model. The matrix rows represent instances in a predicted class, while the matrix columns represent actual classes. For example, 9045 out of 9711 were accurately predicted as class 0, 6818 out of 7460 were correctly forecasted as class 1, 1383 out of 2884 were correctly predicted class 2, 1937 out of 2421 were correctly predicted as class 3, and 42 out of 67 were precisely predicted as class 4.  
With AUC values of 0.98 for class 0, 0.99 for class 1, 0.94 for class 2, 0.95 for class 3, and 0.97 for class 4, the ROC curves for the hybrid model are shown in Figure 22 for each class.
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
## 4 New Electrical Configurations and Operation Modes
### Operational Circumstances for the Pilot Network  
It is known that the PN falls within one of the three following different circumstances: Circumstance 1 (C1) is the most usual, and this occurs when the PN, or at least part of it, is supplied by  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  
the External Grid (EG) without experiencing grid eventualities; Circumstance 2 (C2) is when the PN, or at least part of it, operates isolated from the external grid without experiencing grid eventualities (this means that back-up resources, like an IDPR or a diesel generator, ensure the security of supply for consumers and the power balance of the PN, or part of it.); finally, Circumstance 3 (C3) is when the PN, or part of it, experiences grid failures, e.g., a blackout or a short-circuit (including also situations when the PN, or part of it, is not supplied because it is undergoing scheduled maintenance tasks).  
According to the presented operational circumstances and the disposition of switches and back-up resources in the PN (see Figure 2), the grid can be divided into three sectors, as depicted in Figure 6. Sector 1 (S1) and Sector 3 (S3) can be operated in isolated mode since they are equipped with IDPRs (including a BESS) and/or a diesel generator. For instance, by being able to operate isolated, they can fall under the operational circumstances C1 and C2. Conversely, Sector 2 (S2) can only operate while connected to the external grid or with the support of other sectors, since this sector is not equipped with IDPRs or a BESS. Table 3 collects all possible scenarios for the PN, according to the operability of sectors and the state of links between them [30; 31; 32; 39].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Links between**} & \multicolumn{3}{c}{**Operationality**} & \multicolumn{1}{c}{**Circumstances**} \\ \hline
**EG-S1** & **S1-S2** & **S2-S3** & **S1** & **S2** & **S3** & \\ \hline C & C & C & O & O & O & C1 \\ C & C & D & O & O & N & C1 + C3 \\ C & D & D & O & N & N & C1 + C3 \\ \hline D & C & C & O & O & O & C2 \\ D & C & D & O & O & O & C2 \\ D & C & O & O & O & N & C2 + C3 \\ D & D & C & N & O & O & C2 + C3 \\ D & D & D & O & N & N & C2 + C3 \\ D & D & D & N & N & O & C2 + C3 \\ \hline C & C & D & O & O & O & C1 + C2 \\ C & D & C & O & O & O & C1 + C2 \\ C & D & D & O & N & O & C1 + C2 + C3 \\ \hline \hline \end{tabular}  
* C: Connected; D: Disconnected; O: Operating; N: Non operating.  
\end{table}
Table 3: Pilot Network scenarios depending on state of links and operability of sectors [30; 31; 32; 39].  
Figure 6: The Pilot Network divided into three sectors [30; 31; 32; 39].
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments
### Comparison with SLAM  
SLAM-based approaches rely on only visual odometry or its combination with inertial measurements. For a fair comparison, we chose a recent monocular SLAM approach. Considering that SLAM solutions are relative, we initialized the camera pose with the altitude and attitude of the platform. For comparison, we use ORB-SLAM3, the most recent version of the ORB-SLAM series [11; 12; 13].  
In Figures 11 and 16, we show horizontal and vertical geopositioning results generated from our approach (blue trajectory) and ORB-SLAM3 (green trajectory), respectively, compared with reference onboard GPS sensor and altimeter readings (red trajectory). We can see from the figures that the relative solution of SLAM results in drift in the both horizontal plane and altitude without any external correction. The error for the altitude estimation is much higher than the horizontal error due to the better spatial distribution of landmarks in the horizontal plane.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 5 Discussion  
As the commercialization of AI technology expands, we are tasked with protecting sensitive data, ensuring trustworthy AI decision-making, and maintaining robust system security. In this paper, we propose a resilient and efficient defense system against adversarial attacks. The proposed DiNo-Net learns adversarial robustness indirectly through knowledge distillation, effectively reducing the learning time. Additionally, we suggest that increasing the temperature can expand the loss surface around the ground truth, which can be effective for feature denoising. With a defense success rate of 72.7%, DiNo-Net demonstrated excellent defensive performance. In addition, we proved that the defense effect could be maximized while minimizing the increase in model parameters through 11.24 million model parameters. Furthermore, as opposed to the conventional defense approach combining adversarial training and denoising, the training duration was reduced by over 35 min, thereby confirming the impact of training time reduction. Although DiNo-Net does not provide a complete solution for adversarial attacks, we present a new method for expanding the loss surface of adversarial examples, thereby offering a direction for future research to effectively eliminate adversarial perturbations.  
Moreover, our research primarily focuses on the field of computer vision. As such, the effects of denoising in other domains where adversarial attacks can occur may differ from the vision domain (e.g., signal processing). Consequently, our method requires additional validation for application in other domains. This presents an opportunity for future research to explore the adaptability of our method across various fields.
## 3 Simulation Testing Scenarios and Results  
The system is tested under the balance case between the DC load and PV in Table 2, and the procedure is as follows:* The perturbation signal is injected at \(t\) = 0.4 s after the system startup.
* The islanding condition occurs at \(t\) = 1.2 s after the system startup. The model parameters are as follows:
* The maximum power (each PV) is \(P_{\text{max}}\) = 50 kW.
* Switching frequency of the converter is \(f_{s}\) = 10 kHz.
* The DC bus voltage is \(V_{dc}\) = 500 V.
* The under voltage threshold is \(V_{dcmin}\) = 450 V.
* The islanding threshold is 0.2346 [16].
* The perturbation duration is \(T\) = 8 ms.
* The perturbation signal factors at normal condition in the normal case are \(n\) = 0.9-1.1.
* The perturbation signal factors at normal condition in the cancellation case are \(n\) = 1.1-0.9 for PV1 and \(n\) = 0.9-1.1 for PV  
The result in the normal scenario is shown in Figure 5, and the cancellation scenario result is shown in Figure 6. Figure 5 shows that the islanding condition is detected after 56(ms). The IDM cannot detect the islanding condition when the cancellation problem occurs. In closing, injecting two perturbation signals in the opposite directions can make the cancellation occur completely.  
The impact of the injected signal cancellation on the islanding detection method is significant based on the results. The islanding detection method cannot detect the islanding phenomenon in the cancellation scenario.
# Structural Assessment of Endodontic Files via Finite Element Analysis
## 2 Materials and Methods
### Finite Element Modeling of Physical Tests
#### 2.2.5 Dissipated Energy Density for Fatigue Life Estimation  
[PERSON] et al. asserted that the hysteresis energy per cycle holds significance in estimating the low cycle fatigue life of shape memory alloys due to the stabilizing trend observed in the hysteresis curve after multiple cycles [24]. Specifically, in the case of uniaxial loading, the count of cycles until failure is determined by the dissipated energy as measured using hysteresis curves. [PERSON] et al. have validated this proposed approach in scenarios involving torsional loading [25]. Furthermore, [PERSON] et al. have extended this methodology to cases of multiaxial loading [26].  
Based on the findings from the reviewed literature, the dissipated energy proves to be a convenient parameter for assessing the fatigue life of endodontic files, which are commonly subjected to intricate loading patterns. It is important to note that different endodontic file sets are typically manufactured from various NiTi alloys and undergo different heat treatments. Hence, establishing a calibrated relationship between dissipated energy and fatigue life is necessary. The computed hysteresis energy can also be used to compare different endodontic file designs made from the same material or evaluating various surgical techniques applied to an identical configuration.  
In the current study, the dissipated energy density, also referred to as 'hysteresis energy density,' has been employed to predict the overall fatigue behavior of the F1 and F2 endodontic files and identify the location of failure. Currently, the hysteresis energy is used for comparison purposes, while the calibration of the relationship between dissipated energy and fatigue life is not within the scope of this study.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 2 Background
### Digital Twin  
In recent years, the DT concept has garnered global attention. It involves creating a virtual or digital replica of a physical object, such as machinery, buildings, farms, fields, plants or even human beings, using real-time data from these physical entities. This approach allows for comprehensive analysis, simulation, and optimisation. The term digital twin, introduced by [PERSON] in a 2003 presentation and later elaborated in a white paper [21], has provided the foundation for its development across various domains. Digital models are created by collecting real-time data from IoT solutions, such as sensors and other sources. DT systems are different from other systems as they not only simulate predicted futures but also provide real-time insights. Unlike simulations [22], DTs use real-time data and can provide quick and valuable information by incorporating the latest available data.  
Although DT technology originated in manufacturing [23], its application has broadened to sectors such as healthcare [24], energy [25], urban planning [26], and recently in agriculture [27]. In agriculture, digital twins have revolutionised practices by simulating farming cycles such as soil conditions, weather patterns, crop growth, and pest infestations and utilising real-time data for decision-making. This approach allows for customised recommendations, optimising crop yields, efficient resource use, and minimising environmental impacts.  
Digital twins improve precision farming by creating accurate digital replicas of farms, enabling remote monitoring and management. Farmers can customise irrigation and fertilisation plans and accurately predict harvesting times. As advanced predictive tools, these models help manage risks like crop diseases and pest infestations effectively. Integrating artificial intelligence and machine learning [28] further provides advanced analytics and predictive insights essential for sustainable food production and climate change adaptation.  
Even though introducing DTs in agriculture offers many benefits, adopting this technology faces challenges [29], including the need for significant initial investment, a robust technological infrastructure, and concerns regarding data privacy. Despite these limitations, the future of DTs in agriculture and beyond looks promising, with potential advancements such as enhanced integration with advanced technologies.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 6 Considerations for Enhancing ISO/IEC 29192-2 Lightweight Ciphers
Its efficient design and low resource requirements make it a viable alternative for embedded systems, even though it does not follow the ARX paradigm. While these ciphers are not yet standardized under ISO/IEC 29192-2, their performance in energy-critical applications suggests they may be valuable candidates for future standardization. Comparing these with current ISO-standard algorithms like CLEFIA, PRESENT, and LEA may offer new insights into optimizing energy efficiency, security, and implementation complexity in the evolving landscape of IoT security. The ISO/IEC 29192-2 lightweight cipher standard plays a crucial role in defining secure and efficient cryptographic solutions for resource-constrained environments, particularly in IoT systems. However, recent studies and this paper indicate that some of the standardized algorithms, particularly PRESENT and CLEFIA, may not be the most optimal choices in terms of energy efficiency and hardware performance. Given the increasing demands for secure and energy-efficient cryptographic solutions in IoT, a reassessment of the standard is necessary to ensure that it aligns with modern cryptographic advances and real-world constraints. In recent years, several newly developed lightweight cryptographic algorithms have demonstrated better security and hardware efficiency compared to the existing ISO/IEC 29192-2 ciphers. For example, the SIMON and SPECK cipher families, developed by the U.S. National Security Agency (NSA), have been widely analyzed and shown to offer strong security properties while maintaining high performance on both software and hardware platforms [38]. These ciphers have been adopted in various applications due to their low computational complexity and optimized execution on embedded processors. Such advancements suggest that lightweight cryptographic standards should be updated to incorporate more efficient and secure algorithms, ensuring that IoT systems benefit from the latest developments in cryptographic research.
## 3 Results
### Structural Characterization  
Figure 6a displays the Raman spectra of the four nanostructures, here we can see that the main vibration modes correspond to the tetragonal anatase phase because modes E\({}_{\text{g(1)}}\), B\({}_{\text{1g}}\), A\({}_{\text{1g}}\) + B\({}_{\text{1g}}\), and E\({}_{\text{g(3)}}\) were located at 154 cm\({}^{-1}\), 391 cm\({}^{-1}\), 509 cm\({}^{-1}\), and 638 cm\({}^{-1}\), respectively [16; 27]. TiO\({}_{2}\)-nt had the mode E\({}_{\text{g(1)}}\) with higher intensity compared to the other TiO\({}_{2}\) films. The E\({}_{\text{g(1)}}\) mode is originated from the O-Ti-O bending vibration, while its larger vibration intensity is mainly achieved by the high periodicity of tubular nanostructures [27; 33]. An important cause can be the thickness of film because, as can be observed in Figure 6a, the intensity of E\({}_{\text{g(1)}}\) mode increases with the thickness of the film [33; 34]. These results are in agreement with XRD patterns of Figure S1. From the XRD pattern, it can be observed that (101) plane of the anatase phase had a higher intensity for the nanotubes compared to the other films. Another possible cause is the crystalline size because it has already been reported that the intensity of the E\({}_{\text{g(1)}}\) mode peak increases when the crystalline size decreases [1; 34]. Moreover, in Figure 6a we noticed that this mode was displaced because the peak must be localized at 146 cm\({}^{-1}\)[1; 33; 34].  
Figure 6b shows the relationship of the displacement of the E\({}_{\text{g(1)}}\) mode with the thickness of the film and single-ionized oxygen vacancies. As can be seen, the displacement of this mode increases as the thickness of TiO\({}_{2}\) film increases. This fact would demonstrate that the displacement of this vibration mode is a thickness effect. Moreover, we can observe that oxygen vacancies affect the intensity of the E\({}_{\text{g(1)}}\) mode but in a smaller proportion than the thickness of the film. As already suggested, the different crystalline sizes and the confinement of acoustic phonons originate this displacement [27; 34].
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
## 3 Approaches for Exciplex-Based WOLEDs
### WOLEDs Based on Exciplex Emitters
#### 3.2.4 WOLEDs Exploiting Exciplex and Fluorescent Emitters  
To achieve high-efficiency WOLEDs exploiting exciplex and fluorescent emitters, it is required that the exciplex emitter can well take advantage of the TADF mechanism to harvest the triplets via the RISC procedure, since the fluorescent emitters cannot make use of the triplets. In such way, single-EML structures are more easily to manipulate the excitons distribution. Similar to single-EML hybrid WOLEDs with blue exciplex hosts, the exciplexes are simultaneously functioned as the blue emitter and the host of fluorescent guest emitter in single-EML WOLEDs exploiting exciplex and fluorescent emitters. Thus, the concentration of fluorescent guest emitters is also low to ensure the incomplete energy transfer from the blue exciplex host to the fluorescent guests via the Forster energy transfer [176].  
In [PERSON]'s work, they also realize the WOLED by exploiting exciplex and fluorescent emitters [172]. They have developed a single-EML fluorescent WOLED by doping a low concentration of 0.3% 2,8-di-tert-butyl-5,11-bis(4-tert-butylphenyl)-6,12-diphenyltetracene (TBRb) complementary guest in the blue exciplex CDBP: PO-T2T host, achieving the maximum EQE and PE as high as 20.8% and 75.4 lm W\({}^{-1}\), respectively. For the emission mechanism, the incomplete energy transfer ensured that the blue light could be generated by the exciplex host while the complementary light by TBRb, where the energy transfer is Forster type rather than Dexter type due to the low concentration of TBRb, as displayed in Figure 7. In addition, exciplex host has the low HOMO of donor and high LUMO of acceptor, which reduce the guest trapping effect, yielding the nearly unity of exciton utilization ratio.
# Dual-Band Shared-Aperture Antenna with Pattern and Polarization Diversity
## 1 Introduction
As [22] shows, a dual-layer MS-based dual-band antenna is created, while the high-frequency (HF) band is restricted to 5.69-5.91 GHz. In [23], a compact dual-band design is suggested, which inserts an FSS between LF and HF antennas and requires a high profile. Another PRS antenna is utilized to integrate microwave and mmW bands in [24]. In [25], a millimeter wave PRS is embedded into a microwave patch antenna, which realizes a large frequency ratio and covers 3.41-3.59 GHz (5.14%) and 26.62-30.26 GHz (12.8%) at the same time. Although most antennas realize dual-band radiation, the bandwidth is relatively small, and most of them can only achieve dual unidirectional radiations. This paper proposes a dual-band co-aperture antenna with pattern diversity, covering 2.09-11.61 GHz (9.52 GHz, 138.9%) and 21.6-29.6 GHz (8 GHz, 31%). The antenna consists of a hollow cone, a Vivaldi antenna, and a medium polarization converter. The proposed antenna has a good omnidirectional radiation pattern in the low-frequency frequency band and a directional radiation pattern in the high-frequency band. In the low-frequency band, vertical polarization radiation is performed by the hollow cone. By adding the Vivaldi antenna at the bottom of the cone, a high frequency is generated. Then, a medium polarization converter is added above the cone to achieve circular polarization in the high-frequency band by the Vivaldi antenna. The paper has five sections. Section 1 gives the introduction, and the antenna design process is described in Section 2. The simulated results are discussed in Section 3, and the design guidelines and comparison are presented in Section 4. Finally, a conclusion is obtained in Section 5.