esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A majority of the data collected for fake news detection are written in English. As the spread of fake news has a negative impact on society, several studies have been conducted, and numerous technologies have been introduced to deal with such falsified texts. It is imperative to enable readers to distinguish between real and fake news.  
In this study, we proposed an improved exBAKE model by using pre-training based on a BERT model to accurately understand the contents of such articles. The results indicate that the model worked best on the FNC-1 dataset, which detected fake news by analyzing the relationships between headlines and the corresponding body texts of news articles.  
No automated tools had previously been created to check the authenticity of a news article in real time. Our proposed model will help readers and other journalists to avoid having to manually go through the process of distinguishing fake news from real news.  
In the future, we will experiment with various cases of fake news detection tasks using the pre-trained BERT model proposed in this study. We only analyzed the relationship between the headline and the body text of an article. Further experimentation is needed to apply data from other fake news detection tasks to BERT model, which will use additional news data in the pre-training phase.  
Conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing--original draft preparation, and writing--review and editing, H.J. and D.O.; validation, supervision, resources, project administration and funding acquisition, K.P., J.M.K. and H.L.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (IITP-2018-0-00705, Algorithm design and software modeling for judge fake news based on artifical Intelligence. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. NRF-2017M3C4A7068189).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **F1** & **AGR** & **DSG** & **DSC** & **UNR** \\ \hline Majority vote & 0.210 & 0.0 & 0.0 & 0.0 & 0.839 \\ TalosComb [21] & 0.582 & 0.539 & 0.035 & 0.760 & 0.994 \\ TalosTree [21] & 0.570 & 0.520 & 0.003 & 0.762 & 0.994 \\ TalosCNN [21] & 0.308 & 0.258 & 0.092 & 0.0 & 0.882 \\ Athene [25] & 0.604 & 0.487 & 0.151 & 0.780 & **0.996** \\ UCLMR [28] & 0.583 & 0.479 & 0.114 & 0.747 & 0.989 \\ featMLP [24; 25] & 0.607 & 0.530 & 0.151 & 0.766 & 0.982 \\ stackLSTM [25; 27] & 0.609 & 0.501 & 0.180 & 0.757 & 0.995 \\
**BERT** & 0.656 & 0.651 & 0.145 & **0.839** & 0.989 \\
**BAKE** & 0.734 & 0.667 & 0.463 & 0.822 & 0.986 \\
**exBAKE** & **0.746** & **0.684** & **0.501** & 0.813 & 0.988 \\ Upper bound & 0.754 & 0.588 & 0.667 & 0.765 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model performance. We improve the 0.14 F-score over prior state-of-the-art results.
# An Analysis of the Altitude Impact on Roots Compressor Operation for a Fuel Cell System
## 3 Results  
In this section, the main results from the simulations are presented and discussed. The analysis is divided into two parts: first, a detailed evaluation of the balance-of-plant operation and stack-compressor matching is performed at sea-level conditions; later, the impact of the altitude is evaluated for a net power range between 20 and 200 kW (or the maximum achievable value).
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 2 Characterization Technique
The schematic of the setup that implements the measurement technique is reported in Figure 3. The device under test (DUT) is biased by a DC source measure unit (SMU, HP 4155B) with a high resolution (4 \(\upmu\)V; 20 fA) and accuracy (V: 0.05%, I: 0.2%). On the input side, a commercial bias tee is used (Minicircuit ZX85-12G-S+, frequency range: 200 kHz-12 GHz), whereas on the output side a dedicated bias tee has been designed to support high-power levels (frequency range: 200 kHz-200 MHz, maximum voltage: 100 V, maximum current: 2 A, and power handling: 30 W). They provide DC-to-AC isolation and prevent low-frequency instabilities. Incident and reflected waves at the DUT ports are separated by two dual-directional couplers (frequency range: 10 kHz-400 MHz) and sent to a 12-bit 350-MHz 4-channel oscilloscope (Tektronix MSO54) for the acquisition. The input signal is provided by an arbitrary function generator (Tektronix AFG 3252). Regarding the load condition, it is important to stress that, by operating at a few megahertz, we can directly control the current-generator load. Therefore, once the class of operation to be investigated is defined, we can know and synthesize the corresponding load condition at the fundamental and harmonic frequencies. To this purpose, we designed and produced an electronically controllable tuner coupled with a frequency multiplexer, which controls the load impedance up to the third harmonic, allowing the synthesis of both conventional and high-efficiency classes of operation that require a harmonic manipulation of the load (e.g., class-F conditions). In case this is not sufficient, we can easily design a customized board that also implements the terminations required for the characterization at higher harmonics (if required) which can also be dimensioned to sustain high power levels, thus enabling the possibility of characterizing devices with very large peripheries. The setup is fully controlled by an internally developed software, that includes the possibility of implementing a stress-measurement routine to evaluate the degradation effects of the specific operating condition applied to the DUT. In this context, an important role is played by the calibration, which must be stable over time. This is easily achieved at megahertz frequencies, where the time-varying characteristics of instruments and components have negligible effects and provide a stable system for long periods of time.
## 5 Latency and Reliability Performance Evaluation in Wearable-Based MCPTT Applications  
In this section, we investigate the latency and the reliability performance in wearable-based mission-critical applications, more precisely, off-network MCPTT services using cellular-enabled smartwatches. The deployed smartwatches belong to one of the LTE-M device categories (LTE Cat M1 and LTE Cat M2), specifically, LTE Cat M2. In the following subsections, we refer to an LTE Cat M2 smartwatch as a UE.
## 4 Experiments
### Effectiveness of SSL
#### 4.2.4 Beyond ImageNet Pretraining  
Intuitively, training on more data can improve the representational and generalization capabilities of a model. To explore the boundaries of our proposed method, we also conducted experiments on a larger dataset, NAP+. One exciting finding is that we can obtain better results than with ImageNet pretraining while incurring much less consumption of computational resources. Without any modifications to the network, self-supervised training on the NAP+ dataset followed by fine-tuning on the NWPU dataset yields better results than ImageNet pretraining. Taking the ResNet-50, ResNeXt-50, and WRN-50 networks as examples, we achieved improvements of 0.84%, 0.89%, and 1.02%, respectively, compared with ImageNet pretraining at a 20% training ratio on the NWPU dataset. These experimental results reflect that better representations can be learned by using only remote sensing images. The CNN backbones learned in this manner are more specific for the remote sensing images.
# Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations
## 3 Results and Discussion
### Discussion  
The results presented for the Hertz FCNN validate its application in the postprocessing of AFM nanoindentation data of soft tissues. These findings are particularly significant, as certain curves may exhibit irregular shapes or be unsuitable for fitting with Hertzian mechanics due to their nonlinear nature or potential influence from AFM artifacts. Moreover, the FCNN provides predictions for indentation ranges up to 150 nm, surpassing the typical threshold analysed in classic model fitting approaches. Given that the models were trained on instances with a shared maximum indentation depth, their performance on curves with smaller indentations will not match the presented results. Nonetheless, a similar approach could be applied to train models using curves with varying indentation ranges. This would necessitate a substantially larger dataset to establish a robust and precise framework.  
Regarding the JKR regressor, not having any experimental data to verify the behaviour of these models in a real application is certainly a downsize, which leaves space to question if they would be able to generalize to experimental curves. Nonetheless, it has been shown that a deep learning approach is able to easily capture the shape of a force-indentation curve generated through JKR theory. By ensuring that these synthetic curves are good representations of nanoindentation retraction curves, this all indicates that this is a promising framework to study the adhesion energy of a wide range of samples, whose research is much more scarce than that of the elastic modulus.  
Despite the significance of the results presented, there remains considerable potential for further exploration in employing ML models to the field of AFM. Throughout this work, synthetic instances consistently shared the same tip radius and Poisson ratio. Nev  
Figure 10: Comprehensive outcomes for the JKR FCNN model with the best compromise between performance on predicting the [PERSON]’s modulus (left) and adhesion energy (right). (**a**) Predicted \(E\) values are in close agreement with the true labels. (**b**) Accuracy for adhesion energy predictions is acceptable, but lower than [PERSON]’s modulus predictions. (**c**) Distribution of percentage errors for material parameter \(E\). (**d**) Histogram with the percentage error distribution for adhesion energy.  
ertheless, these parameters could also be included as output variables, enabling models to be applicable across a broader range of experiments. To accommodate these variables, new models would need to incorporate them into the list of instance labels alongside the surface properties. Another challenge in AFM involves precisely identifying contact and detachment points. While an ML-based approach could be promising for addressing these difficulties, an effective model training may require experimental data with the location of these points precisely identified.
## 2 An Overview of Metaheuristic Algorithms
### Archive Multi-Objective Simulated Annealing Algorithm  
Archive multi-objective simulated annealing (AMOSA) is a global optimization algorithm adapted from the process of annealing in metallurgy. [PERSON] et al. [27] proposed the AMOSA algorithm based on the principle of the original Simulated Annealing (SA) algorithm [28]. In AMOSA, the Pareto dominance approach is adopted and uses the concept of an archive to store all non-dominated solutions. The archive size is limited with two parameters known as hard limit (HL) and soft limit (SL). The HL is the maximum size of the archive on termination, and it is equal to the number of non-dominated solutions required by the user; while SL is the maximum size to which the archive may be filled before clustering is used. The algorithm starts with the set of solutions randomly initialized and refined in the archive by using a hill-climbing technique. A solution is added in the archive if it dominates the previous one and exceeds the HL. If the archive reaches the SL size, then the well-known single-linkage clustering is used to reduce the size of the archive to HL in order to keep a diversity of non-dominated solutions [29]. In the main loop of AMOSA, three cases can occur in dominance:  
1. The current solution dominates the new solution and \(k\) points from the archive dominate the new solution. In this situation, a new solution can be accepted as the current solution with a given probability.
2. The current solution and the new solution are non-dominating with respect to each other. Here, the domination status of a new solution and members of the archive are checked through three situations: when a new solution is dominated by \(k\) points in the archive, the new solution is non-dominating with respect to the points in the archive, and when new solution dominates \(k\) points of the archive.
3. The new solution dominates \(k\) points of the archive. Here the new solution is selected as the current solution and also added to the archive, while all the \(k\) dominated points in the archive are removed. The process in the main loop is repeated through the number of iterations for each temperature, which is reduced to at each iteration using the cooling rate alpha until the minimum temperature is reached. Thereafter, the process stops and the resulting archive contains the final non-dominated solutions.  
AMOSA algorithm is capable of solving problems with many objective functions. It has been used to solve medical and engineering-related problems [30; 31], but so far there is no literature on AMOSA applied to solve evacuation problems.
# A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests
## 4 Simulations and Flight Tests
### Flight Tests
#### 4.2.2 Flight Test Results  
Figure 16 shows the trajectory curve of the UAV with full-wing configuration tracking four-sided paths during the experiment. Figure 17 shows the response curves of yaw angle, yaw angular rate, differential propeller thrust and ESO during the flight.  
Figure 16: The trajectory curve of the drone when tracking four-sided paths.  
Figure 17: The yaw angle (**a**), yaw angular rate (**b**), differential propeller thrust (**c**) and observer (**d**) curves during the flight.  
Figure 16 shows that the improved vector field trajectory tracking method still had a good control effect in the flight experiments. The maximum error of the controller was 12 m, and the average error was 5.06 m, which can meet the requirements of practical applications.  
The curve of 18-30 s in Figure 17a shows that the response time of the NDI-ADRC yaw angle controller was about 5 s, which was the same as in the simulations. When the command signal was stable at 10-18 s, the average tracking error was 0.91\({}^{\circ}\). Therefore, the NDI-ADRC yaw angle controller also had good control effects and control accuracy in the experiments. The curves of 18-25 s and 33-38 s in Figure 17b show that the yaw angular rate reached the maximum designed value 20\({}^{\circ}\)/s, but the overshoot was only 2\({}^{\circ}\)/s. Figure 17c shows that the output of the differential propeller thrust was very small, which corresponds to the simulation results. Therefore, the energy consumption of the differential propeller thrust was very small, which is suitable for the solar UAV with strict energy consumption requirements.
## 4 Discussion
As well, the scattering from this type of coverage was characterized by high volume scattering and double bounce intensities of the Yamaguchi decomposition (derived from ALOS-2 in 2016) at L-Band. The dielectric and geometric properties of the driftwood accumulations facilitate high intensity scattering at L-Band, thus this type of coverage is a \"rough\" target at L-band but not in C- and X-Band (i.e., because the logs are much larger than incident C- and X-Band microwaves). Even though the L-Band data showed limited value for characterizing the land cover classes HT, ST and WT using a single feature, the ALOS-2 quad-polarized and HH/VV-polarized data offered the feature space with the highest class separability; as indicated by the Transformed Divergence (TD) and squared Jefferys Matusita Distance (JD). However, since the ALOS-2 data were acquired at a steeper incidence angle and with a delay of six years, a true comparability of these results cannot be guaranteed. These results are therefore surprising, since one would assume a change of the land cover over time and an increasing dissimilarity between the reference and the PolSAR measurement with increasing temporal difference. Still, the ALOS HH/HV-polarized data acquired in 2010 showeda fairly good separability (Rank 17 in JD, Rank 18 in TD), and the data were observed to be more valuable for class separation than the C-Band (Rank 23 in JD, Rank 26 in TD), or X-Band (Rank 20 in TD, Rank 16 in JD) HH/HV-polarized data. All separability features indicated that the Kennaugh Matrix was the most favorable feature space among all examined decompositions, which is in accordance with the expectation that full PolSAR information is better suited for class separability than is available via Entropy/Alpha, or the Two/Three Component Decomposition models, for instance. Among the Model-based Decompositions, the Yamaguchi Decomposition of quad-polarized data exceeded the separability offered by the Two Component Decomposition models. Thus, cross-polarized information is important for class discrimination, even though volume scattering processes play a minor role for the tundra environment investigated. Thus, perhaps differences in roughness/geometry play a more important role.
## 3 Bibliometric Analysis
### Influential Aspects
#### 3.1.4 Influential Countries  
Concerning Table 4, the USA and China are leading in terms of citations and number of publications. It is seen that China has well over 400 publications, which is 41% higher than the USA's number of publications. Still, on the other hand, in the citations section, the USA has published some of the most fascinating and citable work in fintech and has secured 742 citations. China has 419 publications got 457 citations. There are 114 publications from South Korea; however, the country earned 411 citations. The UK is in the third position in terms of publications; however, UK publications obtained 394 citations. Indonesia and India are in the fourth and fifth positions in terms of publications; however, these countries are not on the citations list. In contrast, Italy and Taiwan are the last two countries in terms of the number of publications. However, a small number of publications from these two countries have significant contributions, as their publications are cited significantly.  
Figure 5 represents the top 10 corresponding author countries, and it is divided into two parts. The figure's first part shows that the orange colour represents many multiple country publications (MCP). These publications are collaborated on by at least one foreign country author. Bars in green represent single country publications (SCP), where correspondence and collaborations are from the same country [61].  
As shown in Figure 5, China has the highest number of both SCP and MCP. The country has produced 170 corresponding articles, of which 124 are SCP, and 46 are MCP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Total Citations** & **Country** & **Number of Publications** \\ \hline USA & 742 & China & 419 \\ China & 457 & USA & 296 \\ South Korea & 411 & UK & 189 \\ United Kingdom (UK) & 394 & Indonesia & 171 \\ Germany & 376 & India & 157 \\ Italy & 288 & Germany & 117 \\ Taiwan & 211 & South Korea & 114 \\ Switzerland & 185 & Australia & 97 \\ Hong Kong & 168 & Taiwan & 87 \\ Spain & 144 & Italy & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Core Countries in Terms of Number and Citations.  
Figure 5: Corresponding author country.  
USA comes in second place with 118 corresponding articles, 90 SCP, and 28 MCP. The exact amount of SCP is produced by Indonesia (ranked third); however, it lacks in MCP with only four publications. India (ranked fourth) and the United Kingdom (ranked fifth) have more inter-country collaboration than Indonesia. India has 9 MCP, and the United Kingdom has 23 MCPs.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 2 Related Works
[15] proposed BERT-based approaches to identifying malicious URLs, which mainly focused on creating a model that can effectively identify these URLs and improve the comprehension of the token relationships found in the URLs by using a BERT-based architecture. The authors used BERT's self-attention mechanism to record intricate associations by segmenting URL strings into word-level tokens. A thorough evaluation of the model's performance over a range of input data types, including binary and multi-classification tasks, was provided via the evaluation of the model on three different public datasets, Kaggle, GitHub, and ISCX 2016. With 98% accuracy on the Kaggle dataset, 96.71% accuracy on the GitHub dataset, and 99.98% accuracy in binary classification, the suggested system showed remarkable accuracy rates. Furthermore, the model's flexibility in various contexts was demonstrated by testing it with datasets from DNS over HTTPS and the Internet of Things. The effective application of a transformer-based model to a non-NLP problem is one of the paper's main contributions, offering a novel viewpoint on URL analysis in cybersecurity. The high accuracy rates show that the BERT-based method performs noticeably better than previous studies, and its quick decision-making on tested URLs makes it a viable option for real-time malware detection. Although the research achieved impressive results and model generalizability, it has some limitations related to morphologic attack, typo squatting attack, and character substitution attack. To address this issue, we need to integrate a deep learning model with the BERT model to combat these types of attacks. Malicious URLs and phishing detection were analyzed to evaluate the existing techniques and identify areas for improvement. Various approaches, including machine learning, deep learning, and hybrid models were reviewed. Models leveraging advanced architectures like Bi-LSTM, CNN, attention mechanisms, and ensemble learners demonstrated high detection accuracy due to their ability to capture both local and global features. Current researchers proposed security mechanisms such as feature-based methods, contextual analysis, and embedding techniques to address cyber threats like phishing attacks. However, many approaches faced limitations including reliance on outdated datasets, a lack of generalizability, and computational complexities. In contrast, hybrid models integrating neural networks and attention mechanisms offer stronger detection capabilities by effectively combining multiple feature representations. Moreover, models also simplify data handling and improve scalability, ensuring adaptability to evolving attack patterns. Despite their strengths, challenges like dataset biases, computational requirements, and parameter tuning remain areas for further exploration and refinement. Finally, Table 1 shows the summary of some related works.