esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, the resulting two-dimensional refractivity was employed as the input into the MATLAB Parabolic Equation Toolbox (PETOOL) for airborne synthetic aperture radar (SAR) applications [28; 29]. The propagation path losses, according to the air-to-ground SAR scenario between Pohang and Heuksando in Korea, were simulated, and the illuminated electromagnetic field intensities on the terrain in relation to the atmospheric conditions were examined.
## 3 Results  
### Percentage of Crossings  
Figure 3 shows the percentage of crossings for all experimental conditions. In general, participants based their decision on the movement of the vehicle, in terms of speed and distance, crossing mainly during trials where the vehicle speed decreased. Although slight differences in the percentage of crossings could be observed, namely between no sound and the other two auditory conditions, it is not possible to state that the type of sound emitted by the vehicle, or its absence, has influenced the percentage of crossings. The very low percentage of crossings in the constant speed stimuli should also be highlighted. The participants did not feel, overall, that it was safe to cross when a vehicle signals no intent of slowing down, and in the particular case of the gasoline condition, none of the participants decided to cross during in these conditions.  
These observations were confirmed by a two-way repeated-measures ANOVA, which analyzed the role of auditory condition and the vehicle speed pattern as factors affecting the crossing decision. The auditory condition did not significantly influence the participants' percentage of crossings, F(2, 18) = 0.90, \(p\) = 0.43, while main effects were found for speed pattern, F(2, 18) = 1580.54, \(p\) < 0.01, \(\eta^{2}\) = 0.99. Bonferroni post-hoc test indicated that the percentage of crossings did not significantly differ between the stop (m = 96.67%; sd = 6.61%) and slow down (m = 98.33%; sd = 4.61%) patterns, but they were significantly higher than that regarding constant speed pattern (m = 2.67%; sd = 7.85%).  
Figure 3: Percentage of crossings and respective mean, standard error as a function of auditory condition, per vehicle speed pattern.  
A significant auditory condition \(\times\) speed pattern interaction on participants' percentage of crossings was also found, F(4, 36) = 2.89, \(p\) = 0.04, \(\eta^{2}\) = 0.24. However, considering the results shown in Figure 3, the effect of this interaction was mainly due to the great relevance of the speed pattern effect on the percentage of crossings and not exactly to that regarding the interaction between the two variables, as shown by the small effect size value (\(\eta^{2}\)). Bonferroni post-hoc test indicated the percentage of crossings is only significantly different when the auditory conditions at constant speed (electric: m = 2.00%; sd = 4.22%; gasoline: m = 0%; sd = 0%; no sound: m = 6.00%; sd = 12.65%), slow down (electric: m = 100.00%; sd = 0%; gasoline: m = 99.00%; sd = 3.16%; no sound: m = 96.00%; sd = 6.99%), and stop (electric: m = 98.00%; sd = 4.22%; gasoline: m = 99.00%; sd = 3.16%; no sound: m = 93.00%; sd = 9.49%) patterns were compared. No significant differences existed between the percentage of crossings referring to the different auditory conditions verified for the stop and the slow down patterns.
## 1 Introduction
However, lateral motion may influence the corresponding quantitative data. With the aim of minimizing this influence, [PERSON] et al. [10] extend the concept of resonant Doppler OCT by moving the OCT scanning unit lateral to the sample. Protective coatings are very important for various objects. Corresponding thickness measurements are essential for determining the quality and degradation of coatings. The ability of OCT in comparison with state of the art methods for determining degradation effects are presented by [PERSON] et al. [11]. Coating thickness measurements for the automotive industry using a line field OCT approach are presented by [PERSON] et al. [12]. As outlined above, dynamic processes can be investigated using OCT because of the fast imaging speeds provided by the technique. These dynamic processes may occur at different time scales and are investigated in two papers of the special issue. [PERSON] et al. [13] use OCT to investigate the rather fast alveolar dynamics in an in vivo animal model. On the other hand, [PERSON] et al. [14] use full field OCT in combination with dynamic, mechanical, and molecular contrast with the aim of providing tissue- and disease-specific information on a microscopic level and improving the sensitivity/specificity of disease diagnosis. The depth range of spectral domain OCT instruments is generally limited and may not be sufficient for a specific application. For an extension of this depth range, [PERSON] et al. [15] use two separate reference arms in combination with a phase modulation. The latter allows the reconstruction of the full complex signal and a doubling of the depth range, which is doubled again through the implementation of the second reference arm. With all these contributions, this special feature issue underlines the very active and rapidly growing field of OCT. Meanwhile, various applications of the technology have been demonstrated and further improvements of instrumentation may be developed in the future. This may pave the way for additional applications of this powerful imaging technique. I would like to take this opportunity to thank all the authors for their contribution to this special feature issue. In addition, I would like to thank the reviewers for their time and efforts in reviewing the manuscripts. Reviewing can very often be a thanksless task, but I believe that it represents an essential part of the scientific publishing process as it significantly improves the overall quality of the papers that are published. Finally, I would like to acknowledge the hard work of the organizational and editorial team of Applied Sciences. Without their excellent support this special feature issue would not have been possible. The author declares no conflict of interest.
# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 1 Introduction  
Smartphones have become an essential part of our daily lives and are considered personal devices for individuals. Initially, smartphones were primarily intended for business [1]; however, in a few years, they have replaced computers as the most important device connected to the Internet [2], leading to a high demand for smarter applications or functionalities to meet consumer needs.  
Artificial intelligence has been a topic of controversy in all areas since its implementation in computer video games. This technology has opened to many fields of knowledge, making a solid path for smartphones to perform many tasks that make people's lives easier, such as recognising places through a photograph taken from the device's camera, interpreting voice commands, biometric pattern recognition to unlocking devices, automatic emergency calls when a medical mishap occurs, etc. In addition, artificial intelligence can strike the right balance between hardware and software, allowing for more sophisticated functionalities. Therefore, it is possible to process, analyse and implement the optimal configuration of resources to increase the lifetime of devices and balance the hardware features of cameras to take pictures in extreme lighting conditions or, on the contrary, in low light for right photography. In addition, artificial intelligence has been part of many devices in recent years such as so-called smart homes, which can be managed automatically via a mobile app. Companies such as Google and Amazon have created innovation in common devices that we use in our homes, such as lamps, speakers, appliances, etc., as well as smart home devices that help people with everyday tasks, with products such as Alexaor Google Assistant. However, with the constant demand for new functionalities, the currently used artificial intelligence algorithms are becoming obsolete, and as improvements are made, their computational cost and demand for resources increases, which means that optimisation methods must be sought to be implemented in low-resource devices.  
This paper presents a review of the main artificial intelligence algorithms that can be adapted for mobile devices to fulfill image and text classification functionalities, as well as providing a comparison of the main algorithms and methods analysed. The rest of this paper is organised as follows. Section 2 describes the challenges and problems that may arise when implementing machine learning in mobile devices. Section 3 details the architectures that can be used to implement machine learning on mobile devices. Section 4 describes the algorithms that can be implemented for image and video detection. Section 5 describes the algorithms that are used for text analysis on mobile devices. Section 6 describes the different frameworks available to implement machine learning on mobile devices. Section 7 mentions how federated learning works, and finally, Section 8 presents the conclusions of the work.
## 4 Simulation Results and Discussion
### Comparison between Constant and Tuning Parameters of VSG
#### 4.3.1 PV Power Variation  
The PV power in Figure 13 is used to realize the high PV power variation. The result of the frequency response with the online updating parameter is compared to the VSG constant parameters. Figure 19 is the system frequency response of the adaptive parameter by BSA and the constant parameter. It shows that with the tuning parameter, the system has smaller frequency overshoot compared to the constant parameter. Although the improvement is small, it proves the promising performance of the adaptive parameter.  
Figure 16: Total active power demand.  
Figure 17: Frequency respond: VSG Vs Droop control.
### Research Contribution  
Existing mathematical tools that are employed to explore the power quality concerns of the inverters (DC-AC converters) become complicated if they are used in power quality concerns of direct AC-AC converters. An accurate analytical and simple approach that we call pulse selective approach (PSA) as reported in [17; 26] is employed to compute the harmonic contents for AC-DC converters and direct frequency changers but they are not yet practically validated. In this approach, a waveform that apparently seems to be non-sinusoidal is decomposed to its parents' sinusoidal components during some selected periods of time. The power quality concerns of each sinusoidal wave in the selected period are evaluated; then their results are merged to have the harmonic contents of that entire waveform. According to the authors' best knowledge, this approach is not employed for power quality evaluation in the direct single-phase and three-phase AC voltage controllers. Therefore, in this research article, the harmonics contents of the output voltage for various AC voltage control schemes are analytically computed. The computed harmonics contents are validated through practical and simulation results. The MATLAB/Simulink based environment is employed to simulate the harmonics contents for direct AC-AC converters by carefully selecting the period as an inaccurate selection of the period of a waveform leads to inaccurate results. In a nutshell, the contribution of this research article is the successful application of the proposed (analytical) pulse selective approach to AC-AC voltage converters for computing harmonic contents and then the validation of mathematically computed results through simulation and experimental results.
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 2 Electromagnetic Interference on Low Voltage Lines--Network Model
### Robustness of a Simple Communication Network System
Figure 4: Other examples of interference, such as a wave caused by lightning, adding a signal that changes a large sequence of bits (**top**), or multiplying the voltage of the original square by an undefined factor (**bottom**). External interferences, their magnitude, and shapes can be unexpected and out of control. Figure 3: An interference originated by an external source strikes the line at a random node, propagating through the line. It may happen at the time when the squared signal is passing, distorting the signal that reaches the receiver R (**top**) or maybe dislocated in time, changing the original signal sequence at R (**middle**). If a noise hits the node (**bottom**), the squared signal could be completely distorted. If external interferences can affect the functional features level, internal structure features can affect it as well. Two examples in Figure 5 illustrate how internal structure features can damage or destroy a message--that is, how the functional features will be affected by the structure and at what amount. Line structural variations could modify the signal sent by S in such a way that R could misunderstand it, as shown by the first example (Figure 5 top). The electrical line may present a different and increasing capacitance from node N5 and beyond, significantly distorting the signal along its length, and the traveller square voltage signal is reshaped (by obvious illustrational purposes the figure exaggerates the representation). The second case shows a failure that intermittently interrupts the line-segment between N5 and N6, and no signal achieves the receiver R when the N5-N6 link is off (Figure 5 bottom). Figure 5 shows that physical structure features may have varying degrees of robustness, i.e., from relatively distorting the system performance (the functional processes still work) to completely outages (no processes occurring at all). In short, the robustness of the system illustrated in Figures 3-5 is a result of two combined factors, which we call **structural** and **functional**. The structure depends on the features related to the system physical structure, and the functional factor has to be with the processes running on that structure. Intrinsically interrelated, the two factors could define degrees of the system robustness. Defining a degree of system robustness is a subject that escapes from the purpose of this paper and will not be addressed in this work. Instead, a model to dynamically deal with the system is developed later, showing ways to enhance the low voltage line robustness and sustainability.
## 4 Experiment and Result
### Evaluation Metrics  
We considered a number of performance metrics to assess our model. We used Fasttext, Word2 Vec, and different N-grams (Unigram (U), Bigram (B), Trigram (T), UBT (U+B+T), character 3-gram (C3 Gram), character 4-gram (C4 Gram), and character 5-gram (C5 Gram)) with TF-IDF word embedding methods on the benchmark dataset to find the different features and compare the proposed model with different baseline models. To evaluate the baseline model, we randomly selected 80% of samples for training and 20% of samples for testing purposes. In addition, we used ten-fold cross-validation and chose the best performance results. The confusion matrix depicts a summary of the classification results on the testing dataset based on known true values. It provides a summary of the model's success as well as effective results for true positive, false positive, true negative, and false negative. The proposed model is evaluated using accuracy (A), precision (P), recall (R), and F1-score (F).  
Accuracy: The classification accuracy rating, known as the accuracy score, is calculated as the percentage of accurate predictions to the total number of predictions. Here, we define accuracy (A) using the formula in Equation (1):  
\[\text{A}=\frac{\text{True Positive}+\text{True Negative}}{\text{Total Number of Predictions}} \tag{1}\]  
Precision: Precision is defined as the number of true positive outcomes divided by the total positive outcomes, including those that are misidentified (P). Equation (2) is used to calculate precision:  
\[\text{P}=\frac{\text{True Positive}}{\text{True Positive}+\text{False Positive}} \tag{2}\]  
Recall: The recall is measured as the proportion of true positive results to the actual positive samples that should have been detected. The recall is computed using Equation (3):  
\[\text{R}=\frac{\text{True Positive}}{\text{True Positive}+\text{False Negative}} \tag{3}\]  
F1-score: The F1-score determines the model's accuracy in each class. The F1-score metric is usually used when the dataset is imbalanced. Here, we use the F1-score as an evaluation metric to demonstrate the adequacy of our proposed method. The F1-score is computed using Equation (4):  
\[\text{F1}=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{4}\]  
Figure 7: The loss (during training and validation) of both frozen and unfrozen AugFake-BERT models on the original dataset. (**a**) Loss of BERT (Frozen) Model; (**b**) Loss of BERT (Unfrozen) Model.
## 3 Results and Discussion
### Emissions Characteristics
#### 3.2.3 Toxic Aromatic VOCs, BTEX
In other words, the high combustion temperature due to the rapid burning with increased oxygen content of palm oil biodiesel after the late ignition delay of the main injection reduced benzene. [PERSON] et al. [71] studied the pyrolysis and oxidation of benzene under various combustion conditions. They showed that temperature greatly affected the pyrolysis of benzene. Phenyl radicals produced by benzene at high temperature broke the aromatic rings and made C\({}_{2}\) and C\({}_{4}\) species. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Engine Load** & **Test** & **Benzene** & **Toluene** & **Ethylbenzene** & **Xylene (m, p, o)** \\ \hline
**(Nm)** & **Fuel** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
0 & PD0 & 26.7 & 8.9 & 3.7 & 16.7 \\  & PD10 & 27.1 & 8.1 & 2.6 & 9.3 \\  & PD30 & 29.9 & 7.1 & 2.1 & 11.1 \\  & PD100 & 36.5 & 6.3 & 1.5 & 5.9 \\ \hline
40 & PD0 & 38.0 & 8.1 & 3.2 & 10.0 \\  & PD10 & 38.5 & 6.0 & 1.9 & 7.2 \\  & PD30 & 43.0 & 6.2 & 2.0 & 8.6 \\  & PD100 & 28.8 & 7.1 & 2.2 & 10.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: BTEX results under different test conditions. Figure 6: Toxic aromatic VOCs, BTEX: engine load of (**a**) 0 Nm, and (**b**) 40 Nm. As shown in the above studies, applying biodiesel and its blends has a great effect on the generation of aromatic VOCs. The results of the emission trends of VOCs are all different. In particular, researchers have found different results of the emission characteristics of benzene. This likely affects the generation of BTEX as a result of the fuel, but it depends on the conditions of the experiment, the type, and the condition of the engine.
# A Novel Inverse Simulation Method of Helicopter Maneuvering Flight
## 2 Degeneration Method
### Degeneration Based on a Transitional Vector  
We assume that a prescribed time history exists of a transitional vector \(\mathbf{\eta}(t)\) that can directly express \(\dot{\mathbf{y}}_{\text{DES}}\) in the form:  
\[\dot{\mathbf{y}}_{\text{DES}}=\mathbf{g}^{*}\Big{(}\mathbf{\eta}^{(0)}(t),\ \mathbf{\eta}^{(1)}(t), \ldots,\ \mathbf{\eta}^{(n)}(t)\Big{)} \tag{5}\]  
Meanwhile, each element \(x_{i}\) in the state vector \(\mathbf{x}\) can be expressed in the form:  
\[x_{i}^{(r)}=h_{r,\ i}\left(\mathbf{\eta}^{(0)}(t),\ \mathbf{\eta}^{(1)}(t),\ldots,\ \mathbf{\eta}^{(n)}(t)\right) \tag{6}\]  
where \(i\) denotes the \(i\)th element in the state vector \(\mathbf{x}\), \(r\) is the time derivative order of \(x_{i}\), which equals 0 or 1, and \(n\) is the time derivative order of \(\mathbf{\eta}(t)\).  
Substituting Equation (6) into Equation (1) gives:  
\[\mathbf{h}_{1}\Big{(}\mathbf{\eta}^{(0)}(t),\ \mathbf{\eta}^{(1)}(t),\ldots,\ \mathbf{\eta}^{(n)}(t) \Big{)}-\mathbf{f}\Big{(}\mathbf{h}_{0}\Big{(}\mathbf{\eta}^{(0)}(t),\ \mathbf{\eta}^{(1)}(t),\ldots,\ \mathbf{\eta}^{(n)}(t)\Big{)},\ \mathbf{u}\Big{)}=\mathbf{0}_{N \times 1} \tag{7}\]  
where \(\mathbf{h}_{r}(\cdot)=[h_{r},1(\cdot),\ h_{r},2(\cdot),\ \ldots,\ h_{r},N(\cdot)]^{ \text{T}}\), and \(N\) is the dimension of \(\mathbf{h}_{r}(\cdot)\). Thus, the first-order differential equation, i.e., Equation (1), can be degenerated into these nonlinear equations. The time history of the control vector, \(\mathbf{u}\), can be attained by solving Equation (7) in each simulation time step. It makes sense, and improves the numerical stability, because the nonlinear equations can be easily solved, for example, using the Levenberg-Marquardt algorithm for nonlinear least-squares minimization [27], without evaluation of \(\mathbf{g}_{x}(\cdot)\).
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 2 Related Works
### Hybrid Models  
To overcome the intrinsic locality of convolution operations and the insufficient low-level details in global self-attention, [PERSON] et al. [44] proposed a hybrid model named TransUNet. They replaced the last few layers of the encoder in UNet with ViT blocks to capture global features. Their model achieved higher segmentation accuracy than both pure Transformer-based and CNN-based models on the Synapse multi-organ segmentation dataset [45]. [PERSON] et al. [46] constructed UNETR in a similar manner and later extended it with Swin Transformer blocks [40] to create Swin UNETR [21]. Compared to UNETR, Swin UNETR demonstrated better performance across various 3D medical image segmentation datasets [47]. [PERSON] et al. [48] developed a more efficient 3D medical image segmentation model upon the UNETR, where the ViT modules were replaced by efficient paired attention blocks to enhance feature extraction capabilities and parameter efficiency.  
To address the challenges of multimodal feature fusion and computational efficiency in brain tumor segmentation, [PERSON] et al. proposed SSCFormer [49]. This model revisited the ConvNet-Transformer hybrid framework from scale-wise and spatial-channel-aware perspectives, enhancing the ability to capture hierarchical features in volumetric medical images. [PERSON] et al. proposed MM-UNet [50], a novel cross-attention mechanism between modules and scales for brain tumor segmentation, to overcome the obstacles of limited receptive fields and insufficient global feature integration in traditional U-Net architectures. MM-UNet achieved superior performance and outperformed existing state-of-the-art methods. In response to the computational inefficiency of existing multimodal brain tumor segmentation models, [PERSON] et al. introduced SuperLightNet [51], a lightweight parameter aggregation network designed to minimize computational load while maintaining high accuracy. By optimizing the encoder-decoder structure, SuperLightNet reduced parameters by 95.59%, improved computational efficiency by 96.78%, and enhanced memory access performance by 96.86%, while still achieving a 0.21% increase in segmentation accuracy compared to leading methods. These studies highlight the ongoing evolution of brain tumor segmentation models, emphasizing cross-scale feature fusion and computational efficiency as key directions for future research.