esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 5 Discussion  
This section is not mandatory, but can be added to the manuscript if the discussion is unusually long or complex.  
An improved method for attitude determination, with low-cost receivers in single-frequency single-epoch, was proposed in this paper. The method improves the existing CLAMBDA method with geometric constraints between the baselines. The degrees of freedom of the attitude angle are reduced by the fixed ambiguity, and thus the searching is performed in another degree of freedom. The final solution is obtained by globally minimizing the objective function. We propose a series of verification methods based on geometric constraints at the same time.  
Compared with the CLAMBDA method, the approved method could obtain more robust and reliable solutions, which uses the information of the other baseline to obtain float solutions by searching in the attitude domain, and obtains fixed solutions by minimizing the ambiguity objective function. Compared with the MCLAMBDA method that uses the full geometric constraints, the approved method uses a Kalman filter model, which does not take into account the common satellites between the multiple baselines, and the computational burden is also acceptable.  
Further work will mainly focus on the integrity monitoring. For low-cost ground navigation applications, GNSS as external input calibration information needs to ensure high reliability. In this article, we still have not achieved a completely reliable output, which will be a further explored direction. Besides, dynamic experiments should be further tested with a more theoretical method, such as Monte Carlo simulations of the radiopropagation conditions, which could give us more comprehensive and reliable test results.  
Figure 8: Dynamic experiment. (**a**) trajectory expressed in horizontal East–North coordinates as a function of time. The purple and red star lines are baseline one case, while the yellow and purple star lines are baseline two case. (**b**)baseline solutions expressed in horizontal East–North plane, with blue line for rover 1 and red line for rover 2. The unit of the x-axis and y-axis is meters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LAMBDA** & **CLAMBDA** & **MCLAMBDA** & **CLAMBDA-Search** \\ \hline Taiyuan & 4.8 (4.8) & 69.4 (67.0) & 76.87 (76.87) & 77.7/07.4) \\ Heping Square & 49.7 (49.1) & 91.7 (89.3) & 88.97 (88.97) & **92.5(92.3)** \\ Kaifa Roudabout & 97.2 (97.1) & 99.2 (98.9) & 96.47 (96.47) & **99.4(99.4)** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Euler success rate (correctly fixed) [%].
# Effect of Different Control Strategies on the Heat Transfer Mechanism of Helical Energy Piles
## 2 Methods
### Comparison of the Effect of Energy Pile Type on Heat Transfer  
In the following data processing procedure, the actual spatial coordinates \(x\), \(y\), \(z\), the diameter of the spiral tube (\(d\)), the radius (\(r_{s}\)), and the pitch (\(P\)) are nondimensionalized using Equations (1) and (2).  
\[(X,Y,Z)=(x/d,y/d,z/d) \tag{1}\]  
\[SP=P/d,SR=Sd=r_{s}/d \tag{2}\]  
where \(X\), \(Y\), \(Z\) are dimensionless coordinates; \(SP\), \(SR\), \(Sd\) denote the dimensionless dimensions of \(P\), \(r_{s}\), and \(d\), respectively.  
Three types of energy piles were compared: a U-shaped energy pile with diameter of 0.65 m(\(r_{b}\) = 0.65 m), an SGHEs-S, and an SGHEs-P (Figure 2) Under the same inlet mass flow, it is found that SGHEs-P-type energy pile has the best heat transfer effect. From 0.5 h to 12 h, the heat transfer of SGHEs-P pile is 120% and 63% higher than that of U-type energy pile and SGHEs-S pile, respectively. From 12 h to 1000 h, the heat transfer of SGHEs-P pile is 45% and 20% higher than that of U-type energy pile and SGHEs-S pile, respectively.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model
### Network Infrastructure
#### 3.1.3 Buffers Sizing  
Each \(CH_{j}\) must also store, in the worst case, the entire set of data messages generated from all messages streams of its child nodes, including from its connected mobile nodes. That is, there is a following buffer constraint:  
\[B_{j}\geq\sum_{i\in S_{below}^{j}}S_{n},\quad\text{for }1\leq n\leq N_{nodes} \tag{4}\]  
where \(B_{j}\) is the MAC buffer size of the \(CH_{j}\), and \(n\) corresponds to the number of generated messages by \(S_{below}^{j}\) message streams located in the child nodes during one BI.  
Figure 3: Bottom-up cluster active period scheduling.  
Consider that the MN moves toward the sink and back. Assuming that the entire mobility area is covered by the CHs and MN is associated to CH4, for instance, the worst case would occur when MN reaches the CH4 threshold (\(th_{4}\)), requests an association to CH3, changes its direction and returns. However, in the same way as above-mentioned, the handover procedure must be completed before MN reaches the next threshold, \(th_{3}\) in this case. Therefore, in the proposed scenario, the direction of the node does not interfere in the association process.
# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 3 Deep Learning Based MQA Approaches
### Recurrent Neural-Network-Based Models
#### 3.3.2. Gated Recurrent Unit (GRU)  
GRU, introduced in 2014, is another variant of RNN, similar to LSTM [69]. GRU aims to solve the problem of vanishing gradient that comes with a classical RNN. GRU uses two gates (update gate and reset gate) to solve the vanishing gradient problem of a classical RNN. The reset gate governs the combination of new input and previous computations. This gate is used to decide how much of the past information to forget. The update gate determines what information is retained from past computations. GRU is defined as a simplified LSTM model, which is computationally more efficient compared to both LSTM and normal RNNs.  
He, et al. [70] used a bidirectional GRU model to encode the \(n\)-gram feature representations, which contains a forward GRU and a backward GRU. Equations (14)-(17) give more details about the model in [70].  
\[\mathbf{r}_{j}=\sigma\big{(}W_{r}\cdot C_{j}+U_{r}\cdot\mathbf{h}_{j-1}+ \mathbf{b}_{r}\big{)} \tag{14}\]  
\[\mathbf{z}_{j}=\sigma\big{(}W_{z}\cdot C_{j}+U_{z}\cdot\mathbf{h}_{j-1}+ \mathbf{b}_{z}\big{)} \tag{15}\]  
\[\mathbf{\hat{h}}_{j}=\tanh\big{(}W_{h}\cdot C_{j}+\mathbf{r}_{j}\odot\big{(}U _{h}\cdot\mathbf{h}_{j-1}\big{)}+\mathbf{b}_{h}\big{)} \tag{16}\]  
\[\mathbf{h}_{j}=\big{(}1-\mathbf{z}_{j}\big{)}\odot\mathbf{h}_{j-1}+\mathbf{z }_{j}\odot\mathbf{\hat{h}}_{j} \tag{17}\]  
where \(\sigma\) is the sigmoid function, \(\odot\) stands for the element-wise multiplication, \(C_{j}\) is the current n-gram feature representation, \(\mathbf{h}_{j-1}\) and \(\mathbf{h}_{j}\) are the previous and the candidate hidden state, respectively, and \(\mathbf{h}_{j}\in\mathbb{R}^{d}\) is the current hidden state.  
The final \(j\)-th hidden state can be obtained by concatenating the \(j\)-th forward and backward hidden state: \(\mathbf{h}_{j}=\left[\overrightarrow{\mathbf{h}_{j}}^{T},\overleftarrow{ \mathbf{h}_{j}}^{T}\right]\), which contains the dependencies of the preceding and following n-gram features.  
In addition, [PERSON], et al. [71] discussed the application of deep learning models to the QA task. In their project, RNN-based baselines were described with more attention to the state-of-the-art, end-to-end memory networks which are fast to train and provide better results on various QA tasks. As shown in Figure 12, the GRU model is trained using Keras. The model proposed in [71] generates separate representations for the query and each sentence of the passage using a GRU cell. They combine the representation of the query with the representation of each sentence by adding the two vectors. Afterwards, the combined vector is projected to a dense layer \(D\in\mathbb{R}^{V}\). Finally, the output of the model is obtained by taking a Softball over the dense layer \(D\).
## 4 Datasets  
In order to evaluate our proposed method, we tested our method on four datasets: the Cracktree200 dataset [25], the CFD dataset [26], the Crack500 dataset [27] and the OAD_CRACK dataset. Detailed descriptions of these datasets are provided as follows.  
The Cracktree200 dataset: the Cracktree200 dataset is mainly collected on road pavement. This dataset is a visible light dataset containing various kinds of cracks in complex interference environments such as shadow, occlusion, low contrast, noise and other forms of interference. It contains 206 crack images of size 800 \(\times\) 600. For the Cracktree200 dataset, 80% of the images are used for training and 20% of the images are used for testing.  
The CFD dataset: The CFD dataset consists of 118 images of the size 480 \(\times\) 320 pixels. Each image has manually labeled crack contours. The device used to acquire the images is an iPhone5 with a focus of 4 mm, aperture of f/2.4 and exposure time of 1/135 s. For the CFD dataset, 80% of the images are used for training and 20% of the images are used for testing.  
Figure 6: Comparison of the ReLU and Swish, (**a**) is the ReLU activation function and (**b**) is the Swish activation function.  
The Crack500 dataset: The Crack500 dataset is a pavement crack dataset including 3368 images captured by a cell phone on the main road of Temple University, which has the size of 1440 \(\times\) 2560 or 2560 \(\times\) 1440. This dataset was divided into a training dataset and testing dataset by the author. There are 1896 images in the training dataset and 1124 images in the testing dataset.  
The OAD_CRACK dataset: Since there are too few crack images in the public datasets above, we collected a dataset by ourselves, named the OAD_CRACK dataset. This dataset has 5000 images with a 1920\(\times\) 1080-pixel resolution. All images were taken by us with Huawei P30. Some sample images of the OAD_CRACK dataset are shown in Figure 7:  
In this dataset, crack images were taken in Shenzhen, and ae divided into four classes: linear crack, circular crack, void and background. All images are manually annotated by the EISeg (an image segmentation labeling tool provided by Baidu) and labeled as 0, 1, 2, 3 respectively. For the purpose of preventing the over-fitting problem during the training process, these images were transformed with light and shadow to create more varied training data. Through this transformation, the whole dataset was extended into 30,000 images. Finally, we divided the whole dataset into two parts: 70% of the images were used for training and 30% of the images were used for testing.
###### Abstract  
In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.  
Keywords:lithium-ion batteries; simplified electrochemical model; state of charge estimator; extended kalman filter +
Footnote †: journal: Applied Physics Letters
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### Towards Microsecond Real-Time XPCS at Storage-Ring Sources
#### 4.1.1 Dynamical Heterogeneities via Two-Times Correlations
Stress-induced microcollapses were found to couple with cooperative rearrangements on larger scales, as indicated by dynamical heterogeneities at small \(q\). These were captured in the \"slower\" horizontal direction, while aging dominates the temporal fluctuations in the vertical direction. As the gel ages, the distribution of the stress points homogenizes and the anisotropy of the dynamics reduces. [PERSON] et al. investigated the stress relaxation in colloidal glasses [32]. They used a special sample preparation that leads to colloidal glasses with an directional built-in stress field. The stress-induced dynamics were investigated as a function of direction from the two-dimensional speckle patterns, see Figure 7 showing a two-time correlation function in horizontal direction together with the corresponding \(\chi\) results. The maximum of \(\chi\) is found at \(q_{\text{max}}\) corresponding to the \(S(q)\) maximum and thus to the interparticle distance. In this study, the stress-induced dynamics were interpreted as a collection of mobile regions with approximately 20 particle diameters corresponding to about 2 \(\upmu\)m that move cooperatively with an overall stationary but locally ballistic dynamics. This study showed that a glass may consist of a collection of mobile regions on the mesoscale. These rearrange intermittently and heterogeneously with the residual stresses acting as driving force. Furthermore, the glass remains trapped in an overall out-of-equilibrium condition, which evolves only very slowly on the time scale of the experiment. Figure 7: (**A**) Two-time correlation matrix \(C(q_{\text{max}},t_{1},t_{2})\) computed at the peak of \(S(q)\) in horizontal direction. Fluctuations are clearly visible as a sign of heterogeneous dynamics. (**B**) \(\chi(q,t)\) (here named \(\chi_{4}\)) and the corresponding normalized intensity autocorrelation function at \(q_{\text{max}}\). (**C**) Peak value of \(\chi(q,t)\) compared to \(S(q)\). Both functions show a maximum at \(q_{\text{max}}\). Multiplying the maximum of \(\chi(q,t)\) and the total number of particles in the scattering volume an estimate of the number of particles participating in a correlated ballistic motion is found to \(10^{4}\). Figure from [32] reproduced under CC BY-NC 4.0. [PERSON] and co-workers combined XPCS with in situ rheometry on soft colloidal glasses [117]. The microscopic process of stress relaxation was found to be associated with slow convective motion antiparallel to the preceding strain. This motion persist for long times and is accompanied by intermittent motion in the perpendicular direction. Similarities to other studies suggest that this stress relaxation may be generalized to all amorphous solids.
## 3 Results
### GC-MS Analysis Reveals the Isolation of Luped from C. nutans Roots  
Through maceration and recrystallization methods, 1.138 g of the white amorphous compound was successfully isolated from an initial 80 g of freeze-dried _C. nutans_ roots. The extraction yield was therefore 1.42% (1.138 g/80 g \(\times\) 100%), or 14.2 mg/g freeze-dried root. The white amorphous compound was then re-dissolved with HPLC-grade ethyl acetate and analyzed with GC-MS. The profile was compared with standard lupeol and isolated compound spiked with standard lupeol (Figure 3). From Figure 3A, we observed that only one single main peak was shown in the profile at a retention time of 28.769 min for the isolated white amorphous compound. This isolated compound also showed almost the same retention time with the purchased authentic reference standard, lupeol, which has an Rt of 28.789 min (Figure 3B). When the electron ionization-mass spectrum (EI-MS) of the single peak from Figure 3A was compared to the EI-MS of standard lupeol, they were identical (Supplemental Figure S1A). When this isolated white amorphous compound was spiked with an equal amount of standard lupeol and loaded into GC-MS, only one single peak was detected in the profile with a retention time of 28.799 min (Figure 3C). The EI-MS also overlapped well (Supplementary Figure S1A). Lupeol has a molecular weight of 426.729 g/mol and a molecular formula of C\({}_{30}\)H\({}_{50}\)O. The EI-MS of this single peak showed the presence of the parent ion (M+) at \(m/z\) 426, which corresponds to the molecular formula of C\({}_{30}\)H\({}_{50}\)O and most probably represented lupeol. The EI-MS also revealed the presence of fragment ions at \(m/z\) 189 and 218, which were the main characteristic of a pentacyclic triterpenoid [10]. Another key feature of pentacyclic triterpenoid is the isopropenyl fragment at \(m/z\) 411 [M-CH\({}_{3}\)]\({}^{+}\)[11], which was also detected (Supplemental Figure S1A). Overall, the identical fragmentation patterns of EI-MS of the isolated compound with the fragmentation patterns of EI-MS of standard lupeol suggested that the isolated compound from _C. nutans_ roots was lupeol.
## 1 Introduction
### Advantages of Using CR in WSNs
#### Avoiding Attacks
\begin{table}
\begin{tabular}{l|l|l|l} \hline \hline
**Factor** & _Ad Hoc_ CRNs** & **WSNs** & **CR-WSNs** \\ \hline Mobility & Often (MANET) & Less mobile or stationary & Less mobile or stationary \\ \hline Routing & All-to-all & Broadcast/Fcho from/to sink & Broadcast/Fcho from/to sink \\ \hline Multichannel & Required & Possible & Required \\ \hline CCC requirement & Mostly Required (except some exceptions) [17] & Not really & Mostly Required (except some exceptions) \\ \hline In-network & Supposed to deliver bits from one end to the & Expected to provide information on the other & Expected to provide information on the \\ processing & other & end, but not necessarily original bits & other end, but not necessarily original bits \\ \hline Scalability & Not many (10s to 100s of nodes) & Very large (10s to 1,000s) & Very large (10s to 1000s) \\ \hline \multirow{9}{*}{QoS interpretation} & Receipt rate, & Energy consumption, & Energy consumption \\  & Dissemination, & Redundancy Efficiency, & Redundancy Efficiency, \\  & Speed, & Latency, Scalability, & Latency, Scalability, Robustness \\  & Spectrum utilization, & Robustness & Throughput/Delay \\  & Interference to PUs & - & Event detection/reporting probability \\  & & - & Event classification error, detection delay \\  & & - & Probability of missing a periodic report \\  & & - & Approximation accuracy \\  & & - & Tracking accuracy \\  & & - & Spectrum utilization \\ \hline \multirow{9}{*}{Research direction} & Many areas are still to explore & Although, there is always room for improvement, & Research is still in infancy \\  & Currently focus of research is predominantly & most of the areas are explored and now research & Almost all area are still to explore \\  & directed towards & focus on & Currently focus of research is \\  & \(-\) Game theoretic approaches & - & QoS, & predominantly directed towards \\  & for spectrum utilization & - & reliability, & - Game theoretic approaches for spectrum utilization \\  & \(-\) Predictions for the PUs arrival & - & performance enhancement & - Predictions for the PUs arrival \\  & \(-\) Energy efficient routing and MAC & - & trust and security & - Energy efficient routing and MAC protocols \\  & \(-\) protocols & - & _etc._ & - Development of middleware architectures \\  & \(-\) Development of middleware architectures & - & Distributed aggregation applications \\  & \(-\) Distributed aggregation applications & - & Design of cross-layer algorithms \\  & \(-\) Design of cross-layer algorithms & & for improved power efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 The Proposed Channel Estimation Methods
### Denoising Method 2  
Moving average (MA) is one of the most commonly used denoising methods in digital signal processing, capable of reducing white noise while maintaining the stability of step response to some extent. The working principle of the moving average filter is to take the average of multiple points in the input sequence as the corresponding point in the output sequence, expressed as  
\[y(n)=\frac{1}{D}\sum_{i=0}^{D-1}x(n-i) \tag{23}\]  
where \(x(n)\), \(y(n)\) are the input and output sequences, respectively, and \(D\) represents the moving average step length. Adding an MA filter after the IPNLMS algorithm convergence, the updating Equation (17) can be expressed as  
\[\hat{\mathbf{\Lambda}}^{\ddagger}(n)=\frac{1}{D}\sum_{i=0}^{D-1}(\hat{\mathbf{\Lambda} }^{\ddagger}(n-i)) \tag{24}\]  
Furthermore, to enhance the convergence efficiency of the moving average, we also consider the exponential moving average (EMA) filter when denoising. When computing EMA, the weight in the window tends to exponentially decrease with the length of the filter. The input-output relation of EMA filter is given by  
\[y(n)=\frac{\sum_{i=0}^{D-1}\gamma^{i}x(n-i)}{\sum_{i=0}^{D-1}\gamma^{i}} \tag{25}\]  
where \(\gamma\) is called the weight factor, usually a value close to 1. It is clear that \(\gamma\) is a parameter to control the real-time performance of the moving average, since it shows a positive relation with past cumulative value and a negative relation with the current sampler.  
Adding an EMA filter after the IPNLMS algorithm convergence, the updating Equation (17) can be expressed as  
\[\hat{\mathbf{\Lambda}}^{\ddagger}(n)=\frac{\sum_{i=0}^{D-1}\gamma^{i}(\hat{\mathbf{\Lambda} }^{\ddagger}(n-i))}{\sum_{i=0}^{D-1}\gamma^{i}} \tag{26}\]