esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Ablation Experiments  
To validate the efficiency of each implemented method, this section describes a series of ablation experiments conducted. Each method was progressively incorporated, and its impact on model performance was observed:  
* Baseline CNN Model (VGG-16): Served as the foundation for measuring the impact of additional techniques;
* MobileNetV2: Applied transfer learning with MobileNetV2 to assess performance changes using a lightweight model;
* MobileNetV2-LSTM: Evaluated the hybrid CNN-RNN method commonly used in current studies;
* MobileNetV2-CfC: Replaced the LSTM with Liquid Time-Constant networks to compare performance improvements;
* MobileNetV2-CfC-NCP: Assessed the proposed model's performance with the implementation of Neural Circuit Policy;
* MobileNetV2-CfC-NCP (Augmentation): Validate the effect of augmentation techniques on the proposed model performance.  
Figure 13 shows the results of the above models in terms of test accuracy and model complexity. Starting with the baseline CNN model using VGG-16, switching to MobileNetV2 results in a slight reduction in accuracy. However, MobileNetV2 significantly improves model efficiency by greatly reducing complexity. Combining MobileNetV2 with an LSTM enhances accuracy even with reduced complexity, demonstrating the effectiveness of the hybrid method. Replacing the LSTM with the CfC does not improve accuracy but slightly reduces model complexity, indicating that the liquid neural network is more efficient in this context. Adding NCP to the model further increases accuracy with only a small increase in complexity. Finally, training the model with augmented data significantly boosts its accuracy on the test dataset.  
Figure 12: Comparison of metrics between non-augmented and augmented models on the test dataset.
## 3 Methodology and Implementation
### Implementation
#### 3.4.2 Data Processing  
It is necessary to develop a program that downloads the data from the database and converts them to a table format for further analysis. Firebase Admin sdk for Python (available at [[https://firebase.google.com/docs/reference/admin/python/firebase_admin](https://firebase.google.com/docs/reference/admin/python/firebase_admin)]([https://firebase.google.com/docs/reference/admin/python/firebase_admin](https://firebase.google.com/docs/reference/admin/python/firebase_admin)), 24 June 2024) offers the functionalities of authorization and access to the database. Once the data are downloaded in dictionaries, they is formatted as tables using Pandas Python library (available at [[https://pandas.pydata.org/](https://pandas.pydata.org/)]([https://pandas.pydata.org/](https://pandas.pydata.org/)), 24 June 2024).  
After an initial observation of the data, we define a group of general categories that include generated sentences and pictograms created based on their meanings.  
* Care: Pictures and sentences that refer to personal care (hygiene, rest, affection, or clothing), such as \"_Lavar las manos_\" (wash hands) or \"_Ir al bano_\" (go to the toilet).
* Emotions: Pictures and messages relative to the user's emotions or physical state, such as \"_Yo estoy triste_\" (I am sad) or \"_Yo estoy cansado_\" (I am tired).
* Social interaction: Pictures and utterances that help the user to perform basic daily interactions (greetings, interjections, questions, explanations, etc.), such as \"_jHola amigos!_\" (Hi friends!) or \"_Ir a casa_\" (go home).
* Nutrition: Pictures and messages relative to food and beverages such as \"_Quiero corner_\" (I want to eat) or \"_Quiero agua_\" (I want water).
* Leisure: Pictures and sentences involved in leisure and learning activities (board games, electronic devices, playtime, etc.), such as \"_Ir al parque_\" (go to the park) or \"_Quiero dibujar_\" (I want to draw).
* Health: Pictures and sentences that express health issues, such as \"_Me duele el brazo_\" (My arm hurts) or \"_Dolor de cabeza_\" (headache).
* Name: Only for new pictograms, including all personal names registered by users.  
Additionally, we considered \"incoherent\" those sentences or pictograms that cannot be assigned to a single category (e.g., \"_Quiero corner quiero jugar_\", I want to eat I want to play) or misspelled words (e.g., \"wwww\"). A group of researchers manually labeled all the samples. The labeling process was later automated, taking advantage of the fact that many samples were repetitions of the same text.  
The final distribution of samples by category is represented in Table 8.  
Social interactions consistently concentrate a greater number of actions, either the formation of sentences or any other events linked to the administration of pictograms. It is interesting to note that all categories are consistent in their prevalence across the different events (sentences and new, deleted, or updated pictograms).
## 4 Discussion
### Testing in International Contexts  
Testing the model in an international context, where legal and infrastructural frameworks vary significantly, would provide valuable insights into its flexibility, scalability, and ability to address region-specific challenges. International testing can uncover region-specific risk factors, such as unique environmental conditions, regulatory constraints, or cultural approaches to project management. These insights can enhance the model's ability to adapt to diverse project environments. Legal and infrastructural differences across countries can reveal how the model performs under various compliance requirements, ensuring it remains relevant and practical in different jurisdictions. By validating the model across diverse infrastructural and geographical conditions, its potential as a global standard for risk management can be firmly established. Through international testing, the model can evolve into a universal framework capable of addressing the complexities of diverse infrastructure projects, thus ensuring its broad applicability and long-term impact.  
These research directions would not only increase the model's accuracy, but also broaden its application in risk management for infrastructure projects globally. Collaboration among researchers, policymakers, and industry stakeholders is essential to fully realize the model's potential in revolutionizing risk management for global infrastructure projects.
# Four-Channel Buck-Type LED Driver with Automatic Current Sharing and Soft Switching
## 2 Proposed LED Driver  
Figure 3 shows the proposed LED driver with soft switching. This LED driver was derived from [23] by adding one resonant coupled inductor to create two resonant inductors; by coupling odd output inductors together and even output inductor together to reduce the core size; and by adding differential-mode transformers to achieve current sharing. The proposed LED driver consists of four switches \(S_{1}\), \(S_{2}\), \(S_{3}\) and \(S_{4}\); four diodes \(D_{1}\), \(D_{2}\), \(D_{3}\) and \(D_{4}\); three energy-transferring capacitors \(C_{1}\), \(C_{2}\) and \(C_{3}\); three mutual inductances \(M_{r}\), \(M_{1}\) and \(M_{2}\); six self-inductances \(L_{11}\), \(L_{22}\), \(L_{1}\), \(L_{2}\), \(L_{3}\) and \(L_{4}\); and three differential-mode transformers \(T_{1}\), \(T_{2}\) and \(T_{3}\). The load is composed of four LED strings, \(LS_{1}\), \(LS_{2}\), \(LS_{3}\) and \(LS_{4}\).  
In the proposed LED driver, the four switches have the same duty cycle \(D\). The driving signal phases of four switches \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\), are \(0^{\circ}\), \(180^{\circ}\), \(0^{\circ}\), and \(180^{\circ}\), respectively. The value of \(D\) is limited to less than 0.5. The duty cycles for switches \(S_{1}\) and \(S_{3}\) are the same, equal to \(D\), whereas the duty cycles for switches \(S_{2}\) and \(S_{4}\) are the same, equal to 1-\(D\). Two blanking times per cycle are added between the two switches \(S_{1}\) and \(S_{2}\), and between the two switches \(S_{3}\) and \(S_{4}\), and their values are the same, called \(T_{d}\). In order to ensure that the four switches can reach ZVS turn-on, the parasitic capacitor across the switch must be discharged within the time of \(T_{d}\), and this switch must be turned on before this capacitor is charged again.
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 4 Experiments
### Hyperparameter Tuning
#### 4.4.1 Effect of Embedding Dimension  
According to Figure 4, through the evaluation metrics of the AUC and Logloss, we can see that the proposed model performs the best when the embedding dimension \(d\) is 16. Since the number of heads in the sequence-refiner layer is eight, \(d\) should be a multiple of eight. A lower \(d\) will decrease the number of model parameters and limit the model's expressive power. However, a higher \(d\) will lead to overfitting and thus a decline in performance.
# A Spatial 4-DOF Laser Collimation Measurement System
## 2 Optical Principle
### Angle-Measuring Optical Path  
As shown in Figure 5, another beam is reflected by beam-splitter BS1 and hits plane mirror M. After being reflected by plane mirror M, it passes through the polarizing beam-splitter (PBS1, PBS2), quarter-wave plate (QWP), beam-splitter (BS3), and half-transparent half-reflective mirror HW, which is reflected by HW, before hitting the polarizing beam-splitter (PBS2), where it is reflected and hits the CMOS2 image plane. When the half-transparent half-reflective mirror HW moves, the spot on the CMOS2 image plane moves, and this optical path is used to measure changes in the x/y direction angle, i.e., the pitch and roll angles.  
After the light source is collimated by the collimating mirror, it forms a parallel beam of light, which then becomes incident on the polarization beam-splitter, where the polarization state is separated. The transmitted light is P-polarized, while the reflected light is S-polarized. When the P-polarized light beam center is perpendicularly incident on the center of the semi-transparent half-reflective mirror, the outgoing light travels in the opposite direction to the incident light and is separated by the polarization beam splitter to achieve signal isolation. Then, it is focused by the convex lens to the center of the focal plane. If the semi-transparent half-reflective mirror undergoes a small angle of bending \(\alpha\), the reflected light will bend by \(2\alpha\) degrees, and the parallel light will be focused by the lens and converge on the focal plane. Finally, by calculating the change in the focus position of the image plane, the angle change can be characterized.  
As shown in Figure 6, when the target object moves, the angular cone prism RR moves accordingly, and the angular deflection angle and pitch angle of the angular cone prism in the x/y direction shift \(\alpha\) and \(\beta\), respectively. The beam is refracted through the plano convex lens L, and the angle between the beam and the central horizontal axis of the plano convex lens L is \(2\alpha\) or \(2\beta\). A laser is irradiated on the surface of the CMOS light-sensitive material and is received by it, with an offset of x. The beam forms a triangle with the central horizontal axis and the line on the focal plane of the plano-convex lens L, which can be determined using the following equation:  
\[x=f\tan 2\,\alpha \tag{3}\]  
\[y=f\tan 2\,\beta \tag{4}\]  
Figure 5: The pitch angle in the X direction and yaw angle in the Y direction, which are used to measure the optical path.  
When the alpha and beta are small, the following equation can be used:  
\[\tan 2\alpha\approx 2\alpha \tag{5}\]  
\[\tan 2\beta\approx 2\beta \tag{6}\]  
Then,  
\[\alpha=\frac{x}{2f} \tag{7}\]  
\[\beta=\frac{y}{2f} \tag{8}\]  
In sum, as long as the offset of the laser beam on the CMOS is obtained, it can be divided by the focal length \(f\) of the flat convex lens L twice to find the deflection angle \(\alpha\) and pitch angle \(\beta\).
## 3 Results
### Responses of SIF and VIsto Water Stress  
To explore the response of SIF, NDVI, NDRE and OSAVI to different water stresses, we calculated the mean values of SIF, NDVI, NDRE, OSAVI and SM from 5 April to 21 May. As shown in the Figure 11, different water stresses caused different effects on SIF and VIs, while the responses of the NDRE, OSAVI and NDVI to water stress were similar. By one-way ANOVA, SIF decreased significantly under mild drought, while the NDVI, NDRE and OSAVI did not change significantly under mild drought and decreased significantly only under moderate and severe drought. This indicated that SIF is more sensitive to drought.  
Figure 10: Seasonal variations of biomass under different water stresses. Each point and bar indicate the mean value \(\pm\) standard deviation (SD). DAP means days after planting. Values with different letters indicate significant differences at \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Plot** & **1000-Grain Weight (g)** & **Grain Weight (kg/m2)** \\ \hline T1 & 41.27 \(\pm\) 1.71 a & 0.55 \(\pm\) 0.02 a \\ T2 & 42.08 \(\pm\) 2.46 a & 0.51 \(\pm\) 0.04 a \\ T3 & 35.38 \(\pm\) 1.64 b & 0.37 \(\pm\) 0.02 b \\ T4 & 31.69 \(\pm\) 2.42 b & 0.31 \(\pm\) 0.03 c \\ \hline \end{tabular}
\end{table}
Table 3: Irrigation times and irrigation amounts of experimental plots under different water stresses. Each value indicates the mean \(\pm\) standard deviation (SD). In a column, values with different letters indicate significant differences at \(p<0.05\).  
To investigate the response of SIF and VIs to irrigation and precipitation in depth, we also analyzed the variation characteristics of SIF and VIs during the growing season. The NDVI, NDRE and OSAVI were relatively stable during the growing season, showing a trend of increasing and then decreasing, while SIF showed fluctuating changes, mainly due to the influence of irrigation or precipitation (Figure 12). The SIF showed a decreasing trend on 186 DAP and a sudden increase on 191 DAP, followed by a decreasing trend until the next irrigation, while the SIF showed similar changes on 199 DAP and 203 DAP. The NDVI, NDRE and OSAVI did not show frequent fluctuations, and only plot T1 showed fluctuations on 191 DAP. The above results indicate that SIF is sensitive to soil moisture and can respond to changes in the soil moisture after irrigation.  
Figure 11: The responds of (**a**) SIF, (**b**) the NDVI, (**c**) NDRE, (**d**) OSAVI and (**e**) SM under different water stresses. Values with different letters indicate significant differences at \(p<0.05\). The hollow blocks in the figure represent the average values, which were calculated using the data collected from 177 DAP to 223 DAP.
## 3 Result and Discussion
### Flow within the Leakage Vortex  
In order to investigate the flow within the leakage vortex, Figure 11 shows the relative Mach number contour on 95% span, which is located near the tip-leakage vortex core. It is observed that the tip-leakage vortex develops downstream along the suction surface and the flow direction of the leakage vortex is different from the main flow. As a result, there is an obvious bound. While the high-speed region appears in the throat regions, an obvious low-speed and a separated flow region appears on the suction side downstream it. Figure 11c shows that the separation that occurred in the leakage vortex core originates from the vortex breakdown [11].  
Figure 11: Contour of relative Mach number on 95% span for various backpressure.  
Figure 10: Span-wise scale and loss peak of the leakage vortex along the outlet Mach number.  
The separation region after breakdown stays near the suction-side corner region instead of flowing towards the pressure side. This is caused by the effect of the transverse pressure gradient, which is different from the tip-leakage vortex in the rotor of the compressor [18; 31; 32].  
Figure 11g presents that as the outlet Mach number increases, there is a shock wave in the passage clearly from the trailing edge of the adjacent blade to the blade suction side, termed as \"internal fishtail shock\" or \"incident shock\". The incident shock interacts with the leakage vortex in the passage and the interaction position moves downstream as the outlet Mach number increases.
## 3 Electricity Consumption Behavior Classification Based on CLB-BER
### Data Preprocessing
#### 3.1.1 Noise Recognition  
Noise is the abnormal data in the dataset. The existence of abnormal data makes training more difficult, makes the trained model deviate, and makes the direction of deviation uncertain, resulting in inaccurate results. In this paper, box plots are used to identify and mark the noise points. The mathematical expression formula of the box plots is shown in Equations (1)-(4).  
\[Q1=\begin{cases}\text{\emph{Median of}}\ X_{1},X_{2},\ldots,X_{(n-1)/2}&\text{ \emph{if n is odd}}\\ \text{\emph{Median of}}\ X_{1},X_{2},\ldots,X_{n/2}&\text{\emph{if n is even}} \end{cases} \tag{1}\]  
\[Q3=\begin{cases}\text{\emph{Median of}}\ X_{(n+3)/2},X_{(n+5)/2},\ldots,X_{n}& \text{\emph{if n is odd}}\\ \text{\emph{Median of}}\ X_{(n/2)+1},X_{(n/2)+2},\ldots,X_{n}&\text{\emph{if n is even}} \end{cases} \tag{2}\]  
\[\left\{\begin{array}{l}IQ=Q_{3}-Q_{1}\\ Vmin=Q_{1}-1.5\ IQR\\ Vmax=Q_{3}+1.5\ IQR\end{array}\right. \tag{3}\]  
\[V=\left\{\begin{array}{l}\text{\emph{Normal values,Vmin}}<V<Vmax\\ \text{\emph{Outliers,}}\ V>Vmax,\ V<Vmin\end{array}\right. \tag{4}\]  
where \(V_{min}\) is the lower end of the numerical range, \(V_{max}\) denotes the upper end of the numerical range, \(Q_{1}\) represents the lower quartile, \(Q_{3}\) is the upper quartile, and \(IQR\) is the interquartile range.  
Figure 4: Data processing flow chart.
###### Abstract  
In this paper, we present a novel approach to direction-of-arrival (DoA) estimation using two-row electronically steerable parasitic array radiator (ESPAR) antenna which has 12 passive elements and allows for elevation and azimuth beam switching using a simple microcontroller, relying solely on received signal strength (RSS) values measured at the antenna output port. To this end, we thoroughly investigate all 18 available 3D antenna radiation patterns of the antenna measured in an anechoic chamber with respect to radiation coverage in the horizontal and vertical direction and propose a generalization of the power-pattern cross-correlation (PFCC) algorithm involving a high number of multiple calibration planes (MCP) as well as specific combinations of radiation pattern sets. Additionally, a new way of RSS-based DoA estimation accuracy assessment, which involves thorough testing conducted along the elevation direction when RF signals impinging on the antenna arrive from arbitrary 0 angles, has been reported in this paper to verify the overall algorithm's performance. The results obtained for different signal-to-noise ratio (SNR) levels indicate that two-row ESPAR antenna can produce, even for low SNR values, accurate DoA estimation in the horizontal plane without prior knowledge about the elevation direction of the unknown RF signals by using appropriate combinations of only 12 3D antenna radiation patterns.  
Internet of Things (IoT); wireless sensor network (WSN); switched-beam antenna; electronically steerable parasitic array radiator (ESPAR) antenna; received signal strength (RSS); direction-of-arrival (DoA) estimation +
Footnote †: journal: Sensors
# Wireless Power Transfer System Based on Strapping Resonators
## 2 Analysis of the Strapped Resonator
The quality factor of the strapped resonators are also analyzed, since they are directly related to the transfer efficiency of the system. The quality factor of a resonator is defined as [17],  
\[Q=\omega_{0}\frac{W}{P_{loss}} \tag{1}\]  
In practice, the resonator is coupled to other components which will have an effect on the overall \(Q\). So the quality factor \(Q\) can be also expressed as,  
\[Q=\omega_{0}\frac{W}{P_{i\_loss}+P_{e\_loss}} \tag{2}\]  
where \(\omega_{0}\) is the resonant angular frequency, \(W\) is the average stored energy in the resonator, \(P_{i\_loss}\) is the power lost inside the resonator, \(P_{e\_loss}\) is the power coupled to outside the resonator. Disregarding external loading effects, the unloaded quality factor is defined as,  
\[Q_{0}=\omega_{0}\frac{W}{P_{i\_loss}} \tag{3}\]  
In a metal cavity resonator, \(P_{i\_loss}\) is the power dissipated in the cavity walls and \(P_{e\_loss}\) is the power coupled to outside of the resonator. Regarding external loading effects, the external quality factor can be expressed as,  
\[Q_{e}=\omega_{0}\frac{W}{P_{e\_loss}} \tag{4}\]  
The value of \(Q_{0}\) will decrease as \(P_{i\_loss}\) increases, while the value of \(Q_{e}\) decreases as the external coupling of the resonator increases. The values of \(Q_{0}\) and \(Q_{e}\) is generally different. The value of \(Q_{0}\) is  
Figure 3: Electromagnetic field distribution of \(\pi+1\) mode from the longitudinal section view: (**a**) Electric field distribution, (**b**) magnetic field distribution. difficult to measure but can be calculated by Eigen-mode simulation. The value of \(Q_{e}\) can be obtained by measurement. The values of \(Q_{0}\) of the \(\pi\) and \(\pi+1\) modes are calculated to be 450 and 700, respectively. To obtain \(Q_{e}\), a strapped resonator is fabricated and tested. A coaxial probe is used to excite the \(\pi\) mode and \(\pi+1\) mode resonances. By adjusting the position of the coaxial probe, the best impedance matching can be achieved with \(d_{4}\) = 8.5 mm. The \(Q_{e}\) factor is calculated using group delay [17],  
\[Q_{e}=\frac{1}{2}\pi f_{\tau}\tau_{\tau} \tag{5}\]  
where \(f_{\tau}\) is the resonant frequency and \(\tau_{\tau}\) is the reflected group delay at the resonant frequency. Both of them can be measured using a vector network analyzer (VNA). As shown in Figure 4, the resonant frequency and reflected group delay of the strapped resonator are measured using a VNA (E8361A, from Agilent). The measured results are shown in Figure 5. Two reflection zeroes at\(f_{\pi}\) = 650 MHz and \(f_{\pi+1}\) = 970 MHz mark the resonant frequencies of the \(\pi\) and \(\pi+1\) modes, respectively. The corresponding group delays at the two resonant frequencies are 81 ns and 181 ns. The values of \(Q_{e}\) are calculated to be 82 and 275 according to Equation (5). Figure 4: Measurement of the reflection characteristic of the strapped resonator. Figure 2:0 Measurement results.
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
###### Abstract  
Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets--FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models--BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set.  
fake news detection; machine learning; Transformer-based models +
Footnote †: journal: applied sciences