esa-sceva/satcom-chunk-collection huggingface dataset:
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
###### Abstract  
The integration of deep learning with optical flow estimation in Particle Image Velocimetry (PIV) represents an emerging solution. Extensive research indicates that deep learning has potential to match or outperform state-of-the-art classical algorithms in efficiency, accuracy, and spatial resolution. However, current learning-based methods, which rely on cost volumes and convolutions for flow regression, are limited to local correlations. This limitation hinders the capture of global information. While extensive iterative refinements enhance the quality of prediction flows, they also result in a linear increase in inference time. To enhance both efficiency and accuracy, we propose a global matching method for PIV. This method directly compares feature similarities to identify correspondences between images and generate estimated flows. The underlying idea is to first extract initial features of particle image pairs, then enhance these features through a Transformer specifically designed for PIV, and perform operations for feature correlation matching, followed by global optical flow propagation and optimization. Additionally, higher-resolution features are introduced for refinement. By employing both synthetic and experimental data, including benchmark sets and data from turbulent wave channel flow experiments, we demonstrate that global matching method in PIV achieves superior efficiency and accuracy compared to existing learning-based methods.  
particle image velocimetry; transformer; optical flow; deep learning Article
## 3 Result  
### The Effect of Different N Management Practices on Grain Yield  
The N rates (R) and N application methods (M) had a significant impact on grain yield and its components (Table 1). Under N1, N2, and N3, the F treatment significantly increased the grain yield compared to that of T, while there was no significant difference between the two treatments under N0. The grain number per spike under N1 in F treatment was significantly higher than T. The spike number and 1000-grain weight under N2 showed a trend of F \(>\) T, while there was no significant difference in grain number per spike between them. The spike number in F treatment under N3 was significantly higher than that of T. Furthermore, under the T treatment, the grain yield of N3 was significantly higher than others in 2022-2023, while there was no significant difference between N3 and N2 treatments in 2023-2024. Obviously, under the F treatment, both seasons showed a trend of N2 \(>\) N3 \(>\) N1 \(>\) N0.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Treatment** & **Grain Yield** & **Spike Number** & **Grain Number per Spike** & **1000-Grain Weight** \\ \hline \multicolumn{4}{c}{**(kg ha\({}^{-1}\))**} & **(\(\times\)10\({}^{4}\) ha\({}^{-1}\))** & & **(g)** \\ \hline \multirow{8}{*}{2022–2023} & TN0 & 6656.04 \(\pm\) 155.77 g & 488.07 \(\pm\) 15.89 e & 35.61 \(\pm\) 0.54 e & 43.51 \(\pm\) 0.09 a \\  & FN0 & 6672.03 \(\pm\) 112.55 g & 485.33 \(\pm\) 2.31 e & 35.60 \(\pm\) 0.53 e & 43.50 \(\pm\) 0.23 a \\  & TN1 & 8265.19 \(\pm\) 89.28 f & 532.08 \(\pm\) 4.00 d & 38.89 \(\pm\) 0.35 d & 43.24 \(\pm\) 0.12 a \\  & FN1 & 8696.97 \(\pm\) 10.89 e & 524.00 \(\pm\) 4.00 d & 40.61 \(\pm\) 0.54 c & 43.37 \(\pm\) 0.12 a \\  & TN2 & 9963.57 \(\pm\) 53.08 d & 592.00 \(\pm\) 4.00 c & 43.26 \(\pm\) 0.13 a & 42.41 \(\pm\) 0.02 b \\  & FN2 & 11,019.41 \(\pm\) 17.71 a & 637.33 \(\pm\) 4.62 a b & 43.56 \(\pm\) 0.51 a & 43.44 \(\pm\) 0.07 a \\  & TN3 & 10,256.69 \(\pm\) 56.82 c & 628.00 \(\pm\) 0.00 b & 42.00 \(\pm\) 0.05 b & 41.91 \(\pm\) 0.22 c \\  & FN3 & 10,669.20 \(\pm\) 14.49 b & 646.67 \(\pm\) 4.62 a & 42.90 \(\pm\) 0.46 a & 41.92 \(\pm\) 0.27 c \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yield components and grain yield under different treatments.  
The Effect of Different N Management Practices on Canopy Photosynthetic Effective Radiation Interception Rate  
The canopy photosynthetic effective radiation interception rate (CaR) of different treatments is shown in Figure 3, which gradually decreased with the increase of days after anthesis. There was no significant difference in CaR under N0, between T and F. Under the same N application rate, the CaR of F treatment was higher than that of T at 0, 7, 14, 21 and 28 d after anthesis. In addition, the CaR under T treatment after anthesis showed a trend of N3 > N2 > N1 > N0. The CaR under F treatment at 0, 21, and 28 d also showed a trend of N3 > N2 > N1 > N0, while there was no significant difference between N2 and N3 treatments at 7 and 14 d after anthesis.
## 2 Simultaneous Localization and Mapping
### FastSLAM 2.0
#### 2.1.2 Data Association  
After acquiring the exteroceptive data, it is necessary to match the current observations to the features already stored in the map and/or initialize new landmarks. Such a problem can be solved with various approaches depending on the available sensor setup and the navigated scenario. As mentioned in Section 1, in this dissertation, we tackle data association through the JCBB method, a probabilistic approach for jointly associating multiple non-uniquely identifiable observations. Generally, the complexity of the data association step strongly depends on the number of observations and map features. If data association is performed particle-wise, meaning that different particles carry different matches for the same observations, its complexity also depends on the number of particles.  
Section 2.2 tackles the data association problem in more detail.
## 4 Results
### Integrated Analysis of Findings  
Quantitative and qualitative analyses of the chatbot's three interaction modes provide a comprehensive view of the strengths and limitations of each mode across varying task complexities. The menu-based mode demonstrated the highest efficiency, particularly for simple and complex tasks, which is consistent with the typical efficiency of structured chatbots in straightforward interactions. However, its effectiveness declined in complex tasks, owing to limitations in handling nuanced queries. Qualitative feedback indicated that the hybrid-based mode's flexibility allows users to switch between text and menu options based on preference, making it adaptable for various educational tasks. Thus, simplifying the decision pathways could enhance efficiency and create a more seamless and intuitive user experience. As a result, while the menu approach supports efficient task completion, it would benefit from greater adaptability to handle more complex scenarios, aligning with the findings of previous studies on chatbot interfaces [76].  
The hybrid-based mode is highly adaptable, excelling in complex tasks where users could benefit from flexible, multimodal interactions. However, qualitative feedback revealed potential cognitive overload when switching between text and menu options, which may explain its slightly lower efficiency scores despite high overall satisfaction. Lastly, the text-based mode achieved the highest effectiveness scores in complex tasks, owing to its open-ended input capability. However, its performance lagged in more straightforward queries, as users expressed frustration with the rephrasing requirements. Therefore, while text input can enhance the accuracy in complex inquiries, future iterations could refine NLP to handle more straightforward tasks better, reducing the need for user rephrasing and improving ease of use [77]. The findings of both the quantitative and qualitative methods align with the overall proposed measures and their relationships to each measure, as depicted in Figure 6.
## 4 Experiments and Results
### Data Processing  
When processing the datasets, we took a series of steps to ensure the quality and suitability of the data. Firstly, we segmented the text and counted the frequency of the words to understand the characteristics of the data better. Subsequently, we created a stop word list to remove some lower-importance words, such as you, I, he, hello, here, and there. Since our dataset involves telecom fraud, the text information may contain some sensitive information, such as names, ID numbers, phone numbers, bank card numbers, social media accounts, etc. To ensure data security, we used regular expressions to anonymize this sensitive information and replace it with common identifiers, such as \"name\", \"ID card\", and \"telephone number\".  
Additionally, considering that telecom fraud often involves inducing victims to download specific software, we uniformly replaced the software names with the generic term \"software\". Moreover, we found that some URLs were present in the text. To protect data privacy, we replaced these URLs with \"URL\".  
Furthermore, to address potential misspelling issues in fraud-related texts, we employed a Chinese misspelling correction algorithm to help restore the text content. For example, \"  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Training Set** & **Test Set** \\ \hline Sports & 5000 & 1000 \\ Entertainment & 5000 & 1000 \\ Home Furnishings & 5000 & 1000 \\ Property & 5000 & 1000 \\ Education & 5000 & 1000 \\ Fashion & 5000 & 1000 \\ Current Affairs & 5000 & 1000 \\ Game & 5000 & 1000 \\ Technology & 5000 & 1000 \\ Finance and Economics & 5000 & 1000 \\ \hline Total & 50,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of THUCNews dataset statistics.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Number** & **Example** \\ \hline Illegal Promotion & 2573 & \% DIGIT \% \  
Lastly, to standardize the length of our dataset, we added or removed some text appropriately to ensure a general length of the data. These preprocessing steps helped to ensure that our dataset was clean and secure and provided a reliable foundation for subsequent analysis and modeling. During the experiment, we divided the dataset by category to ensure that the training and testing sets had the same proportion of samples from each category. For the FBS dataset and our dataset, we allocated 80% of the data for training and 20% for testing, ensuring that there was no overlap between the testing set and the training set.
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 5 Perspectives  
The strategies of bakery products reformulation in order to reduce salt are nowadays of a great interest to an international level, supported by the authorities and research community to benefit consumer health. It represents a great challenge for the bakery industry, since sodium chloride is one of the raw materials for bakery products with important effects on their technological and sensory properties. Due to the important role of NaCl it is difficult to reduce or to eliminate it completely from bakery products. However, baking industry undertakes efforts to reduce the salt content from baked products.  
In the salt reduction strategies will be taken into account the economic and technical criteria, but also the acceptability of the bakery products with a low salt content by consumers.  
Gradual reduction of salt from bakery products may be one of the best solutions for the industry and consumers in the future without significant costs. Moreover, the use of different salts with similar effects from the technological point of view (KCI, MgCl\({}_{2}\), CaCl\({}_{2}\), calcium lactate, calcium gluconate, etc.) to those of NaCl addition may play an important role of salt reduction in baked products but their uses are limited due to unpleasant taste. Some taste enhancers (amino acids, yeast extracts, glutamates, hydrolyzed vegetable proteins nucleotides, fermented sugars, etc.), B4 vitamin may be used in the future to the industrial level in combination with other salts to improve the salty taste of baked products.  
To obtain bakery products with a low salt content the NaCl may be totally replaced with sea salt (a natural mix of NaCl, KCl, MgCl\({}_{2}\)) with a low sodium content but it use may be limited due to it higher cost which may be problem in poor and developing countries in which bakery products are also low cost ones.  
In order to produce bakery products with low salt amount, with technological and sensory characteristics comparable to bakery assortments obtained with a normal salt content, in the future it is recommended to continue researches to combine different ingredients with similar technological effects as those of NaCl which can improve each other their salty taste, to optimize production recipes and to implement different industrial processes such as encapsulation, inhomogeneous distribution of salt in the bakery process, etc.  
Therefore, further researches are needed to obtain bakery products assortments with a low salt amounts, with good technological characteristics, by complying with WHO recommendation and consumers demand.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Microstructure Characterization Methods
#### 2.4.5 X-ray Diffraction (XRD) Analysis  
The X-ray diffraction (XRD) investigations were carried out to identify phases in the CuW composites, both prior to and following the addition of graphene using X-ray Diffractometer (D/MAX-2600, Rigaku, Japan) operating at 40 kV and 40 mA with Cu K\({}_{\alpha}\) radiation and a scan rate of 0.02\({}^{\circ}\) s\({}^{-1}\) in a 20 range of 30\({}^{\circ}\)\(\sim\)90\({}^{\circ}\).
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 4 Conclusions
In the NRTK mode, the positioning accuracy resulted between the two aforementioned RTK configurations and was in agreement with the horizontal accuracy of 3 cm declared by the NRTK service provider. Last but not least, inaccurate height determination was noticed in RTK surveying by using two low-cost receivers, of which the mobile receiver spins around the base station. A source of this issue was identified in accumulating PCO/PCV errors of the used low-cost antennas, as the highest errors mostly occurred when antennas were in the opposite orientation. Similar to low-cost single-frequency receivers [25], low-cost dual-frequency receivers do not meet their nominal performance in urban areas. However, the positioning accuracy achieved in static and RTK/NRTK modes justifies their use in land surveying applications, such as cadastral surveying and mapping, which require sub-decimeter horizontal accuracy. Static positioning using low-cost receivers (both base station and mobile receiver), which achieves baseline accuracy better than 1 cm, is suitable for monitoring applications, including hazard and atmosphere monitoring. Although low-cost solutions, which are tailored for land-surveying, are not available on the market yet, this market is expected to rapidly grow soon. **Author Contributions:** Conceptualization, N.W. and T.H. ; methodology, T.H. ; formal analysis, N.W., T.H. and G.M. ; resources, N.W. and A.K. ; data curation, N.W. and A.K. ; writing--original draft preparation, T.H. ; writing--review and editing, N.W. and A.K. ; visualization, T.H. and G.M. ; supervision, T.H. ; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript. **Funding:** Low-cost receivers were funded by the research support funds of the Wroclaw University of Environmental and Life Sciences, Poland. T.H. was supported by the European Union's Horizon 2020 research and innovation program under Marie Sklodowska-Curie Grant 835997. **Acknowledgments:** We thank Leica HxGn for providing access to Smartnet service and ASG-EUPOS for sharing GNSS observations from reference stations. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The Proposed HSTMAC Framework and System Model
### Foundation of the HSTMAC Protocol  
We evaluate the performance of a MAC protocol in the view of resource utilization maximization. The concept of _capacity_ is defined as the maximum amount of resource blocks used for successful data transmissions. We will show how packet size affects the system capacity.
## 2 Preparation
### Data
#### 2.2.1 Field Measurement Data  
We conducted field measurements at 56 forest plots (Figure 1a-d,f,g) between 30 August to 15 September in 2012 and at 18 forest plots (Figure 1e) during 10-18 August 2013. These 74 plots consisted of 24 needle leaf forest plots, 21 broadleaf forest plots, and 29 mixed forest plots. The average diameters at breast height (DBH) ranged from 10 cm to 15 cm and the average tree heights ranged from 15 m to 25 m. In each forest plot, we recorded the center location using global positioning system (GPS) [26] working in differential mode, and manually measured the forest structure parameters including LAI, DBH, tree height, and tree crown size in two cross directions for all trees with DBH > 5 cm. The effective LAI (LAIe) of the forest stand was measured using LAI-2200 (Li-Cor, Inc., Lincoln, NE, USA) [27] under diffuse light conditions. We measured forest LAIe at five spatially-balanced locations within a plot and two repeats were made for each measurement, with one above canopy and four below canopy readings using two sets of LAI-2200 with the same type and calibration procedures. For above canopy measurements, measurements were obtained using the LAI-2200 being placed at the open space near forest plots; while for below canopy measurements, the instrument was horizontally held about 1.2 m above the soil background for the sake of reducing the influences of understory vegetation. Then, the LAIe value for each measurement was calculated by using the below and above canopy measurements. At last, the LAIe of a plot was calculated as the average of LAIe measured at five different locations within this plot. The clumping index was obtained using the Tracing Radiation Architecture of Canopy (TRAC, 3 rd Wave Engineering, Nepean, ON, Canada) [28] in direct sunlight condition to convert LAIe to true LAI. In addition, we collected data on the environmental conditions of plots including slope, aspect, and elevation.
## 2 A Distributed Optical Fiber Sensor System Based on BOCDA  
Figure 1 shows the BOCDA system. The continuous wave (CW) light from the laser diode (LD) is divided by a 50/50 coupler into the pump and probe arms. The pump wave is chopped by an electro-optic modulator (EOM) for lock-in detection, and it is launched to a sensing optical fiber after being amplified by an Er-doped fiber amplifier (EDFA). The probe wave is generated by a single-side band modulator (SSBM) and a microwave synthesizer, where the frequency offset between the pump and probe waves is swept near the Brillouin frequency of the sensing optical fiber within a 1 GHz span. The probe wave is amplified by another EDFA, and then it is propagated along the sensing optical fiber in the opposite direction to the pump wave. A sinusoidal frequency modulation is applied to the LD by a LD current modulator to create a correlation peak (CP), the sensing position, within the sensing optical fiber. The probe wave with Brillouin gain is detected by a photo detector (PD), and the BOCDA signal is acquired by a data acquisition board (DAQ) after being processed by a lock-in amplifier. The frequency of the LD current modulation is adjusted to change the measurement position along the sensing optical fiber, while the spatial resolution, which is determined by the modulation parameters of the LD [10; 11], is set to 3 cm for all measurements. After the Brillouin frequency shift, which is the change in the central frequency of the Brillouin gain spectrum determined for each position by embedded software, local strain is calculated by applying a pre-acquired coefficient of 20 \(\upmu\)\(\upmu\)/MHz.  
Figure 1: _Cont._  
The BOCDA system is packaged in a single portable box for easy handling and access to the sensing optical fiber. The optical fibers of the pump and probe arms protrude from the system box, and are spliced to the sensing optical fiber to minimize the reflection from connecting points for noise suppression.
## 3 Materials and Methods
### Performance Metrics  
The confusion matrix shown in Table 6 depicts the evaluation of various performance metrics of a classifier. True positives are the number of responses proportional to the positive class that were correctly predicted as positive. True negatives are the number of responses equal to the negative class that were correctly predicted as negative. False positives are the number of responses equivalent to the negative class but predicted as positive. False negatives are the number of responses equal to the positive class but predicted as negative.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Hyperparameters** & **Value Range** \\ \hline \multirow{2}{*}{SVM} & Complexity parameter, C & 1–7 \\ \cline{2-3}  & Type of kernel & PolyKernel, PUK, RBF \\ \hline \multirow{3}{*}{KNN} & Number of neighbors, K & 1, 3, 5, 7 \\ \cline{2-3}  & Distance weighting & No distance weighting, \\  & & 1/distance, 1-distance \\ \hline \multirow{3}{*}{J48} & Confidence factor, C & 0.25, 0.50, 0.75 \\ \cline{2-3}  & Minimum number of instances per leaf, M & 1–3 \\ \hline \multirow{3}{*}{AdaBoostM1} & Base classifier & Decision stump, J48 \\ \cline{2-3}  & Number of iterations, I & 10, 20, 30 \\ \hline \multirow{3}{*}{Bagging} & Base classifier & REPTree, J48 \\ \cline{2-3}  & Number of iterations, I & 10, 20, 30 \\ \hline \multirow{3}{*}{ROTF} & Base classifier & J48, Random Forest \\ \cline{2-3}  & Number of iterations, I & 10, 20, 30 \\ \cline{1-1} \cline{2-3}  & Removed percentage, P & 40, 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters and their value range of machine learning models.  
The performance metrics such as accuracy, sensitivity (recall), specificity, precision, F-measure, the area under the ROC curve, cost, and training time of the machine learning models are computed using the confusion matrix in Table 6.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 2 The SR Process for CLLC Resonant Converter
### Below-Resonant State  
Figure 3a shows the waveforms of the resonant converter operating in below-resonance mode, which is referred to as PO mode [25] in the literature. The time \(t_{0}\sim t_{1}\) denotes the dead time of the MOSFET, and the optimal turn-on time of the synchronous rectifier gate signal is \(t_{1}\) and the optimal turn-off time of the synchronous rectifier gate signal is \(t_{2}\). The primary resonant inductor current \(i_{L\
u 1}\) and the magnetizing inductor current \(i_{L\
u}\) are equal during \(t_{2}\sim t_{3}\).  
At the \(t_{0}\) moment, the gate drive signal of \(S_{2}\) and \(S_{3}\) drops low. \(S_{2}\) and \(S_{3}\) turn OFF instantly if the \(C_{ds}\) of the MOSFET is not taken into account. The inductor current of the primary side \(i_{L\
u 1}\) flows through the body diode of \(S_{2}\), \(S_{3}\). \(L_{\
u 1},C_{\
u 1},L_{m}\) participate in resonance; the primary side ceases to transfer energy to the secondary side. \(L_{\
u 2},C_{\
u 2}\) participate in resonance in this state. At moment \(t_{1}\), the \(L_{m}\) stops resonating and is clamped at \(nU_{o}\).  
At the \(t_{1}\) moment, the primary switch tube \(S_{1},S_{4}\) turns ON, and the rectifier tube \(S_{5}\), \(S_{8}\) also turns ON simultaneously. At the \(t_{2}\) moment, the current \(i_{L\
u I}\) equals the current \(i_{L\
u n}\), the resonant inductor is no longer clamped by the magnetizing inductor and participates in the resonance again, and all the secondary-side switches are turned OFF.  
At the \(t_{3}\) moment, the primary-side switching tube \(S_{1},S_{4}\) turns OFF and continues to repeat the symmetrical half-switching cycle. In summary, the key to accurate SR in below resonance is accurately calculating the duty cycle \(D_{SR\_Duty}\). The resonant frequency point can be considered a special below resonance, and SR can be implemented in the same manner as below-resonance SR.  
Figure 2: Incorrect conduction of rectifier signals. (**a**) Late turn-on, (**b**) early turn-off, (**c**) early turn-on, (**d**) late turn-off.