esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 2 Material and Methods
### Multifrequency Impedance Measurement  
All of the measurement instruments and measuring methods used for the experiments were designed and prepared to meet the research requirements. The impedance was measured on 21 extracted teeth that had been treated and cleaned using a variable frequency voltage signal generated by a programmable digital frequency synthesizer. The signal varied from 100 Hz to 20 kHz, and the impedances at different frequencies were collected using automatic data collection (ADC) and stored in a computer. Figure 1 presents a schematic of the whole measurement process.  
Figure 2 illustrates the physical diagram of the measurement process and its constituent parts. The 21 tooth samples were single root canal teeth from 21 adults of different ages, including incisors, canines, and molars. The teeth were preprocessed in vitro by soaking them in a 2.5% sodium hypochlorite solution and removing the periodontal tissues and attachments, such as dental calculus. The root canal was washed after opening and removing the pulp. The crown of the tooth was polished as a reference plane. A tooth fixation device was used to keep the relative position of the tooth constant during the experiment, ensuring the consistency of the data. A precise file translation device was used to strictly  
control the change of distance by increments of 0.5 mm with an accuracy of 1 \(\upmu\)m. One of the files of types #15, #25, and #40 was fixed on the file translation to be located in the root canal. The root canal length measurement prototype was designed to generate sine sweep signals with multiple frequencies and select the impedance values. The impedance ratios of different frequency combinations could then be obtained through data processing in the prototype.  
For each tooth, we changed the file distance from the root tip by \(+5\) mm to \(-1\) mm (where \(+\) denotes that the file does not reach the apical foramen and \(-\) denotes that the file exceeds the apical foramen).  
Two theoretical tests were verified during this process: the impedance decreased with increasing frequencies when the file was in the same position; the closer the file was to the apical foramen, the smaller  
Figure 1: Diagram of the measurement process.  
Figure 2: Experimental equipment.  
the impedance ratio, and the larger the frequency difference, the smaller the ratio. The detailed verification is explained in the following results section (pre-verification).
## 3 Data and Method
### InSAR Calculation in Cloud Platform
#### 3.2.1 GPU-Assisted InSAR Processing Module  
The InSAR data processing involves two primary modules: interferometry processing and InSAR time-series analysis. This study utilizes GPU parallel technology developed under the CUDA framework to optimize the key time-consuming steps in interferometric modules. These steps include geometric coregistration, resampling, and enhanced spectral diversity (ESD) [35]. The Sentinel-1 SAR satellite constellation employs terrain observation using the progressive scans (TOPS) mode to acquire IW SLC products. In this mode, the varied squint angle in the azimuth direction introduces an azimuth-varied Doppler centroid frequency, imposing stricter coregistration requirements. An inaccurate coregistration leads to phase jumps between bursts. Therefore, a very high azimuth registration accuracy is needed [36]. However, precise coregistration methods have high computational complexity. To attain this level of accuracy, distinct coregistration methods for initial and fine coregistration are necessary, typically utilizing the precision orbit-based geometric positioning method and the ESD method, respectively. Geometric coregistration entails determining the azimuth/range offset between an image pair by back-projecting DEM into master and slave image coordinates [35]. Subsequently, the slave image is resampled with the master image, followed by the utilization of ESD to refine the azimuth shifts derived from geometric coregistration, thereby enhancing registration accuracy further. ESD leverages the phase difference in the overlapping area of master and resampled slave images between adjacent bursts within a subswath to estimate misregistration residuals [36; 37].  
The CUDA implementation of geometric coregistration is structured into three stages [35]: data initialization, offset estimation, and warp function estimation. During the data initialization, both the DEM and orbit-fitted polynomials are transferred to GPU global memory. The GPU constant memory is utilized to store the variables, such as the bounding box and intervals of the DEM product. In the offset estimation stage, DEM pixels are radar coded into master and slave image coordinates on the GPU. Finally, the least squares (LS) fit is performed on the GPU. Resampling is achieved by two kernels: DerampDemod and Interpolation. Before kernel execution, the slave image and deramping parameters are transferred into the GPU global memory. After calling the DerampDemod kernel, the estimated phases and the deramped slave image are stored in the global memory and subsequently used by the Interpolation kernel. During 2D interpolation, the interpolation convolution kernels and warp functions are transferred into the GPU global memory. In the ESD approach, overlaps, along with the image azimuth sampling frequency and Doppler centroid frequency difference, are transferred into GPU global memory.
###### Abstract  
Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce **TSET** (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model's understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named \"semantic transformation\" to fortify the model's grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10).  
Knowledge Base Question Answering; Text-to-SPARQL; semantic parsing; further pretraining; Triplet Structure
# Transverse Vibration of Clamped-Pinned-Free Beam with Mass at Free End
## 1 Introduction  
High-rate dynamics are defined as events having amplitudes greater than 100 \(g\) over durations less than 100 ms [1]. Some examples of high-rate systems may include civil structures exposed to blast, passenger vehicles experiencing collisions, and aerial or spacecraft vehicles subjected to ballistic impacts. Such systems have the potential to experience rapid changes in mechanical configuration through damage. Economic investments and lives could be saved if fast detection of parameter changes can be accurately quantified [2]. A variable input observer has been studied by the authors as a potential solution to increasing convergence times through richer inputs [3]. However, there is a need to validate high-rate structural health monitoring (SHM) methods [4].  
An experimental testbed has been designed and built to test and validate SHM methods systems experiencing high-rate dynamics. The development of an experimental testbed is critical, because the experimentation on real-life high-rate systems would be complex, difficulty to verify, and potentially very costly. This testbed design incorporates a cantilever beam with a roller that restrains the displacement in the vertical direction and is allowed to move freely along the length of the beam. Additionally, the mass at the free end of the beam can be dropped through the de-energizing of the electromagnet that detaches the mass from the beam. The roller is a moving cart that provides a changing boundary condition while the mass drop provides a sudden change in mechanical configuration. This system is easily controllable and repeatable in a laboratory setting.  
To develop analytical solutions for this beam structure, the Euler-Bernoulli beam theory is applied. The system is modeled as clamped-pinned-free with a point mass at free end.  
To the best knowledge of the authors, this configuration has not been previously studied analytically nor experimentally. There is no mention of this beam configuration in the book authored by [PERSON] [5]. The clamped-pinned-free without mass [6] and the clamped-free with mass at free end without pin [7] have been studied. In more recent years, researchers have investigated the free vibration of multi-span beams with flexible constraints [8], axial vibrations of multi-span beams with concentrated masses [9], multi-span beams with moving masses [10], and multi-span beams carrying spring-mass systems [11,12].  
Using beam theory, Section 2 derives the transcendental equation. A generalized form is presented that is applicable to any pin location and any mass value. Derived results are verified through comparison between well known cases in literature. Section 3 calculates the eigenvalues for normalized pinned location for various mass ratios. Section 4 calculates the mode shapes for several different pinned locations, while Section 5 compares the results from the theoretical calculations to experimental data.
## 1 Introduction  
In the last few decades, a lot of investigation has been done on the synthesis and properties of porous P(S-DVB) beads. The activated swelling method, developed by [PERSON] et al. was utilized for the synthesis of monodisperse porous particles in the range of 1-20 \(\upmu\)m [1; 2; 3]. Recently, a lot of research has been conducted to study the different properties of monodisperse polystyrene microspheres with porous structure [4; 5; 6; 7; 8; 9; 10; 11], since they have high specific surface area, tunable pore morphology, mechanical stability and strong adsorption [12; 13; 14]. The widely-used techniques for the synthesis of porous P(S-DVB) beads include activated swelling [15], seeded emulsion polymerization [16], precipitation polymerization [17], template imprinting [18; 19] and membrane techniques [20; 21].  
Due to lower back pressure, a more regular flow regime in the column and higher resolutions, which result from their excellent permeability and high surface area, the monodispersemicrospheres with porous structures have been widely used in chromatographic columns as packing materials [22; 23; 24; 25; 26; 27]. For example, [PERSON] et al. [28] investigated the effect of the monomer/seed latex ratio for the chromatographic performance of monodisperse porous particles produced by a modified seeded polymerization method. [PERSON] et al. [29; 30; 31; 32] explored the impact of different surface chemistries of monodisperse macroporous particles on the chromatographic behavior.  
In this paper, we synthesized monodisperse porous P(S-DVB) microspheres by utilizing the activated swelling technique and investigated the effect of the porogen ratio, temperature and crosslinking agents. The porous P(S-DVB) microparticles were further sulfonated with concentrated sulfuric acid and subsequently modified with diazoresin(DR)via electrostatic self-assembly and UV (ultraviolet) radiation. These porous microspheres were applied as column packing material in mix-mode HPLC analysis of benzene analogs, organic acids and fullerenes.
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 3 Methodology
### BIGRU Model Algorithm  
The GRU is an improvement of the recurrent neural network (RNN), which solves the problems of gradient explosion and gradient disappearance [28]. Building a BiGRU semantic analysis model requires the input of historical data into both forward GRUs and reverse GRUs to obtain maximum contextual information. The GRU has no cell state and uses gates to regulate the flow of information inside the unit, improving on the LSTM; the gates are reduced to two, namely, the update gate and the reset gate (\(\mathrm{z_{t}}\) and \(\mathrm{r_{t}}\)). BiGRU network structure diagram is shown in Figure 5.  
Figure 4: Overall architecture diagram of the model.  
Figure 5: BiGRU network structure diagram.  
The reset gate indicates the degree of information ignored at the previous time step. A smaller value indicates that more information is ignored.  
\[r_{t}=\sigma(w_{r}x_{t}+U_{t}h_{t-1}+b_{r}) \tag{1}\]  
The update gate controls how much information is retained from the previous moment to the current state and how much information is updated from the candidate state. The higher the value, the more data it remembers.  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}) \tag{2}\]  
\[h_{t}=(1-z_{t})\widetilde{h}_{t}+z_{t}*h_{t-1} \tag{3}\]  
\[\widetilde{h}_{t}=\tanh\bigl{(}W_{\widetilde{h}}x_{t}+U_{\widetilde{h}}(h_{t- 1},r_{t})+b_{\widetilde{h}}\bigr{)} \tag{4}\]  
In the model, \(x\) represents the input, \(h_{t}\) is the current hidden state, and \(\widetilde{h}_{t}\) is the previous hidden state. \(W\) is the weight matrix, \(b\) is the bias matrix, \(\sigma\) and \(\tanh\) are activation functions, and \(U\) represents the randomly initialized attention matrix.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 3 Simulation
### Sequential Computation and Parallel Computation  
Multilayer MPC is a distributed control method, so there are multiple topologies. The simplest two are sequential and parallel. These two calculation processes are shown in Figure 11. Of course, multi-threaded computing hardware is the basis of parallel computation.  
From a theoretical point of view, sequential computation and parallel computation only have an effect on real-time performance but have no effect on control accuracy. In a trajectory figure similar to Figure 4, it is difficult to observe the difference between the two topologies. So, in this group of simulations, we will compare the control inputs and states.  
Figures 12-16 show the longitudinal velocity, articulated angle, articulated angle speed, displacement error, and heading error, respectively. In theory, the variables of the two topologies should be identical. In the simulation, there were some deviations on a small scale. This phenomenon is caused by factors such as the sampling interval in the simulation.  
Figure 11: Two calculation processes of multilayer MPC.  
Figure 12: The longitudinal velocity of the comparison between calculation processes.  
Figure 14: Articulated angle speed of the comparison between calculation processes.  
Figure 13: The articulated angle of the comparison between calculation processes.  
Figure 15: Displacement error of the comparison between calculation processes.  
The real-time performance of parallel computation is obviously better than that of sequential computation, as shown in Figure 17. The maximum computation time for parallel computation was 0.0090 s, which was 30.77% smaller than that of sequential computation. The average computation time for parallel computation was 0.0044 s, which was 48.84% less than that of the sequential computation.
## 2 Failure Rate Modeling in Ice Disaster Scenarios
### Failure Rate Modeling  
The highly dynamic spatial and temporal characteristics of ice disasters have profound impacts on power systems. Specifically, transmission line failures are mainly attributed to the combined effects of ice loads and wind loads. Ice loads exert vertical pressure due to ice and snow accumulation on conductors. To accurately assess failure rates, we employ differential equations to compute the rate of ice accretion based on the [PERSON] model [24; 25] and establish an ice thickness model. In addition, the failure probabilities of both conductors and transmission towers are derived. The failure rate modeling consists of two main components: conductor failures and tower collapse. The specific calculation process is described below.  
The failure rate of conductors is determined by considering the combined effects of wind and ice loads, as follows:  
\[P_{\text{tension}}=\begin{cases}0,&L_{\text{total}}\leq a_{i}\\ \exp\Bigl{(}\psi f\Bigl{(}\frac{L_{\text{total}}-a_{i}}{b_{i}-a_{i}}\Bigr{)} \Bigr{)}-1,&a_{i}<L_{\text{total}}<b_{i}\\ 1,&L_{\text{total}}\geq b_{i}\end{cases} \tag{1}\]  
where \(a_{i}\) and \(b_{i}\) represent the lower and upper limits of the wind-ice gravity load, \(L_{\text{total}}\) is the combined load, and \(\psi_{f}\) is the probability coefficient of line disconnection. The failure rate of the tower considers the total load borne by the tower, as follows:  
\[P_{f}=\begin{cases}0,&T_{IW}\leq a_{W}\\ \exp\Bigl{(}\psi_{w}\Bigl{(}\frac{T_{IW}-a_{W}}{b_{W}-a_{W}}\Bigr{)}\Bigr{)}-1,&a_{W}<T_{IW}<b_{W}\\ 1,&T_{IW}\geq b_{W}\end{cases} \tag{2}\]  
where \(a_{W}\) and \(b_{W}\) represent the upper and lower limits of the total load, respectively, \(T_{IW}\) is the total load, and \(\psi_{W}\) is the collapse probability coefficient. Thus, the unified failure probability \(P_{ij}\) of the line segment \(ij\) is obtained as  
\[P_{ij}=1-\prod_{i=1}^{z}(1-P_{\text{tension}})\prod_{j=1}^{z}\Bigl{(}1-P_{f} \Bigr{)}. \tag{3}\]
# Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel
## 1 Introduction
### Objective of Study  
This study developed a deep learning model that achieves high prediction accuracy for the heat transfer coefficient of micro-fins, including post-dryout, using a relatively small amount of data. The proposed approach involves conducting pre-training based on data from mini-channels, which are highly relevant to the heat transfer coefficient of micro-fins and fine-tuning the pre-trained model using the target data. Furthermore, we added dimensionless numbers assumed to significantly influence heat transfer coefficients calculated from experimental data to the training dataset. By applying fine-tuning, the model can learn the features (weights) of the data based on a related dataset before training on the target dataset, thereby improving learning accuracy, particularly when the target dataset is limited. In natural language processing, fine-tuning using relevant data is commonly employed, but its application to predicting heat transfer coefficients is limited. Furthermore, we propose a novel approach that utilizes mini-channel data to predict the heat transfer coefficients of micro-fins, combining physical mechanisms with DNN/GPR/Fine-tuning.  
This approach allows for predictions based on a small amount of data for new refrigerants or missing operating conditions, leading to significant reductions in time costs. Additionally, due to the limited experimental data available for post-dryout heat transfer coefficients in previous studies, post-dryout data was included in the evaluation as part of the prediction target.
# Current Context and Research Trends in Linear DC-DC Converters
## 6 LDO's Recent Research Trends
### Improving the Efficiency  
Many researchers focus on designing control circuits for LDOs with minimum power consumption. Control circuits consist of high-frequency processors that continuously monitor the regulated voltage of the LDO and generate feedback according to the regulated voltage. Currently, researchers are focused on using approaches such as time-coding architectures [54] to drive LDO control circuits. Time-driven controllers operate at every clock edge. If the LDO output varies after a locking edge, the controller captures the deviation at its next cycle. As shown in Figure 8c, the authors proposed an architecture consisting of analog-to-digital converter with a refresh circuit, proportional-integral controller, and a power transistor. By using time-coding architectures, LDO control circuits can be operated as event-driven controllers instead of continuously operating. As a result, consumption of quiescent current has been significantly reduced and the efficiency has increased to 93.6% [54].  
Figure 8: (**a**) Proposed structure of LDO 1 [53]; (**b**) proposed structure of LDO 2 [53]; and (**c**) LDO architecture with the proposed event-driven PI control systems [54]. Copyright 2022 IEEE.
## 4 Results and Discussion
### Saturated Hydraulic Conductivity (\(K_{\mathrm{sat}}\))  
Figure 7 shows the K\({}_{\mathrm{sat}}\) values for soil amendment with macro-biochar and nano-biochar. In addition, the K\({}_{\mathrm{sat}}\) value decreased more with biochar application. Thus, with the addition of biochar, the average value decreased by 85.35% and 98.98% for macro and nano-biochar, respectively. The position of the amended layer of biochar caused a positive effect on the saturated hydraulic conductivity compared with the control soil (Figure 7). K\({}_{\mathrm{sat}}\) in the unamended soil showed the highest value of saturated hydraulic conductivity (0.6 cm min\({}^{-1}\)). K\({}_{\mathrm{sat}}\) in the 5-10 cm amended layer of the macro and nano-biochar decreased to less than 0.1 cm min\({}^{-1}\) compared with the control. Similar findings were reported in previous studies [10; 11; 17]. As reported by [PERSON] et al. [44], hydraulic conductivity is controlled by organic matter, number and distribution of macrospores, soil texture, and the tension of the soil water. Therefore, the additive biochar may be able to modify the sand soil structure and reduce the sizes of soil pores, particularly macro/large pores [32; 45].  
Figure 6: Effect of application of macro-biochar and nano-biochar on cumulative infiltration (**A**) and infiltration rate (**B**).