esa-sceva/satcom-chunk-collection huggingface dataset:
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 3 Construction of MultiWOZ-API
### Data Analysis  
We perform the analysis on the annotated MultiWOZ-API. As shown in Table 1, there are 2877 single-domain dialogues dialogues, including 13,254 turns, with an average of 4.61 turns per dialogue. There are 7025 multi-domain dialogues, including 56,536 turns, with an average of 8.05 turns per dialogue. In total, there are 9902 dialogues, including 69,790 turns, with an average of 7.05 turns per dialogue.  
We then calculate the number of turns annotated with APIs and the scale of the turns of each kind of API in all dialogue turns. As shown in Table 1, about 57.25% of the dialogues interact with the database, and the scale in multi-domain dialogues is slightly higher than that in single-domain dialogues. Among the three kinds of APIs, we _find_ that APIs account for a large proportion, followed by _book_ APIs, while _get_attr_ APIs have the smallest share.  
Some turns may call a range from 1 to 4 APIs. We calculate the number of APIs in each turn. As shown in Table 1, about 95.05% of the dialogue turns only call 1 API, about 4.92% involve 2 APIs, and about 0.03% call 3 or more APIs. In the single-domain dialogues, each turn calls at most 2 APIs. While in the multi-domain dialogues, each turn calls at most  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Single** & **Multi** & **Total** \\ \hline dial\_num & 2877 & 7025 & 9902 \\ turn\_num & 13,254 & 56,536 & 69,790 \\ average\_turn & 4.61 & 8.05 & 7.05 \\ \hline all\_API & 57.25 & 63.13 & 62.01 \\ API\_turn & find & 35.16 & 39.97 & 48.22 \\ ratio (\%) & book & 15.61 & 15.08 & 18.74 \\  & get\_attr & 9.41 & 11.2 & 13.4 \\ \hline API\_num & 1 & 94.89 & 95.09 & 95.05 \\ ratio (\%) & 2 & 5.11 & 4.88 & 4.92 \\  & \(>\)=3 & 0 & 0.04 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: API call statistics in single domain, multi-domain, and the total data.  
4 APIs. The complexity of the APIs of each turn in multi-domain dialogues is higher than in single-domain dialogues.
## 4 Results and Discussion
### U, \(\theta\) and \(\phi\) Profiles to Check Accuracy of Numerical Data
Moreover, the maximum amplitude of fluctuation in heat transfer and current density was obtained at both the \(\pi/2\) and \(\pi\) location for maximum \(\mathrm{Pr}\) with prominent variation, see Figure 6b,c. The influence of \(\lambda\) for transient \(\tau_{s}\), oscillatory \(\tau_{t}\) rate and \(\tau_{m}\) plots is shown in Figure 7a-c with reduced gravity and magnetohydrodynamic effects along two prominent locations of the non-conducting circular shape. The lower oscillating performance in \(\tau_{s}\) was noticed for each choice of \(\lambda=1.0,\ 3.0\) and \(5.0\) at both stations \(\pi/2\) in Figure 7a. The larger amplitude of oscillating coefficients of \(\tau_{t}\) and \(\tau_{m}\) was concluded with a prominent variation for each \(\lambda\) in Figure 7b,c in the presence of reduced gravity. Table 1 presents the validated numerical data for the skin-friction coefficient for the accuracy of results by comparing with existing results available in [PERSON] et al. [29] and [PERSON] et al. [28] by applying the implicit finite difference approach, for diverse values of the magnetic Prandtl parameter \(\gamma\) for \(\xi=0.8\), at the leading edge \(\alpha=0.0\). The comparison of skin-friction coefficients by using the local non-similarity method for magneticforce \(S=0.1\), free stream velocity gradient \(n=0.0\) and local transpiration parameter \(\xi=0.0\) through a magnetized wedge in [29] but [28] compared the numerical values of skinfriction with the finite difference method for the magnetic force parameter \(\xi=0.1\) at the leading edge \(\alpha=0.0\) with more accurate results along the magnetized cone. In the present work, the numerical results were compared with both [28; 29] for magnetic force parameter \(\xi=0.8\) at the leading edge \(\alpha=0.0\) with accurate results along the non-conducting horizontal circular cylinder. In Table 2, the comparison between [PERSON] [30] and the present analysis of the numerical values of skinfriction for three values of magnetic-force number has been computed. [PERSON] [30] computed the numerical values by using the Karman-Pohlhausen technique but in the present work, the analysis was performed by applying the finite-difference method \(\xi=0.1,\ 0.2,\ 0.3\) at the leading edge \(\alpha=0.0\). \begin{table}
\begin{tabular}{c c c c} \hline \(\gamma\) & **[PERSON] et al. [29]** & **[PERSON] et al. [28]** & **Present Analysis** \\ \hline \(1\) & 0.3148 & 0.3122 & 0.3040 \\ \(10\) & 0.3151 & 0.3137 & 0.3106 \\ \(100\) & 0.3156 & 0.3149 & 0.3118 \\ \hline \end{tabular}
\end{table} 
Table 1: The numerical results of skinfriction for various choices of magnetic Prandtl parameter \(\gamma=1.0\), \(10.0\), \(100.0\), for \(\xi=0.8\) at the leading edge \(\alpha=0.0\).
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### Energy Balance Based on the RPL
#### 3.1.3 Multi-Path Energy Consumption  
If we use \(\alpha_{x,y}\) as the traffic ratio of node \(x\) sent to its parent node \(y\), then the bottleneck node's traffic is the traffic ratio that the node sends to three parent nodes individually, multiplying the traffic ratio that they retransmit to bottleneck nodes, and then adding them. More generally, the expression should be:  
\[\alpha_{a,c}=\sum_{b\in Parents(a)}\alpha_{a,b}\times\alpha_{b,c} \tag{6}\]  
We analyze the bottleneck nodes' energy consumption based on multi-path distribution strategy in this section. As we know, nodes will send packets to several parent nodes through links of different quality, namely, nodes will transmit traffic through several paths, and hence, the energy that will be consumed will depend on the traffic sent to the each parent node. First, we elaborate how to calculate the bottleneck nodes' remaining energy consumption under a multi-path environment.  
In Figure 5, node A is the sending node, its parent nodes are B1, B2 and B3, and the bottleneck nodes are C1 and C2. For node C1, it receives 100% of the traffic of B1 and 20% of B2. The real traffic of C1 is 30% + 50% \(\times\) 20%. Similarly, the real traffic of C2 is 20% + 50% \(\times\) 80%.  
\[Tat(a,c)=\sum_{b\in Parents(a)}\alpha_{a,b}\times\frac{Tat(a,b)}{PRR(a,b)}\times \alpha_{b,c}\times\frac{Tat(b,c)}{PRR(b,c)} \tag{7}\]  
Thus, in terms of bottleneck node C1, Equations (1) and (3) will be:  
\[E_{rec}(c_{1})=Tat(a,c_{1})\times\frac{E_{r}}{D_{r}} \tag{8}\]  
\[E_{tat}(c_{1})=\sum_{d\in Parents(c_{1})}\frac{\alpha_{c_{1},d}\times Tat(c_{1},d)}{PRR(c_{1},d)}\times\frac{E_{t}}{D_{d}} \tag{9}\]  
In this case, the energy consumption of bottleneck node C1 is:  
\[\begin{array}{cc}E_{rec}(c_{1})+E_{cmp}(c_{1})+E_{tat}(c_{1})=&Tat(a,c_{1}) \times\frac{E_{r}}{D_{r}}+N_{task}(Buffer\_Use(c_{1}))\times E_{c}+\\ &\sum_{d\in Parents(c_{1})}\frac{\alpha_{c_{1},d}\times Tat(c_{1},d)}{PRR(c_{1}, d)}\times\frac{E_{t}}{D_{d}}\end{array} \tag{10}\]  
The parameter in the above equation can be obtained before transmission, except for the distribution traffic ratio, thus node A can assess the impact of the survival time of the bottleneck node.  
Each node can calculate the transmission ratio of arriving at bottleneck nodes in a recursion manner. Thus the real traffic that the bottleneck nodes obtain is multi-path traffic.  
Figure 5: Multi-path energy consumption.
## Methodologies for the Extraction of Phenolic Compounds from Environmental Samples: New Approaches  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Chemistry, Faculty of Marine Sciences, University of Las Palmas de Gran Canaria, 35017, Las Palmas de Gran Canaria, Spain; E-mails: [EMAIL_ADDRESS] (C-M. S.), [EMAIL_ADDRESS] (Z-S. F.), [EMAIL_ADDRESS] (M-T. P.)  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]; Tel. +34928452915; Fax: +34928452922.  
_Received: 26 November 2008; in revised form: 24 December 2008 / Accepted: 4 January 2009 / Published: 9 January 2009_  
**Abstract:** Phenolic derivatives are among the most important contaminants present in the environment. These compounds are used in several industrial processes to manufacture chemicals such as pesticides, explosives, drugs and dyes. They also are used in the bleaching process of paper manufacturing. Apart from these sources, phenolic compounds have substantial applications in agriculture as herbicides, insecticides and fungicides. However, phenolic compounds are not only generated by human activity, but they are also formed naturally, e.g., during the decomposition of leaves or wood. As a result of these applications, they are found in soils and sediments and this often leads to wastewater and ground water contamination. Owing to their high toxicity and persistence in the environment, both, the US Environmental Protection Agency (EPA) and the European Union have included some of them in their lists of priority pollutants. Current standard methods of phenolic compounds analysis in water samples are based on liquid-liquid extraction (LLE) while Soxhlet extraction is the most used technique for isolating phenols from solid matrices. However, these techniques require extensive cleanup procedures that are time-intensive and involve expensive and hazardous organic solvents, which are undesirable for health and disposal reasons. In the last years, the use of news methodologies such as solid-phase extraction (SPE) and solid-phase microextraction (SPME) have increased for the extraction of phenolic compounds from liquid samples. Inthe case of solid samples, microwave assisted extraction (MAE) is demonstrated to be an efficient technique for the extraction of these compounds. In this work we review the developed methods in the extraction and determination of phenolic derivatives in different types of environmental matrices such as water, sediments and soils. Moreover, we present the new approach in the use of micellar media coupled with SPME process for the extraction of phenolic compounds. The advantages of micellar media over conventional extractants are reduction of organic solvent, low cost, easy handling and shorter time procedures.  
**Keywords:** Phenolic compounds; Solid-phase extraction; Solid-phase microextraction; Microwave assisted extraction; Micellar medium.
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 1 Introduction
As a result, this paper is devoted to the CNN-based CSS for the possible performance improvement. This paper considers the scenario where the received energy vector of each node is sent to the FC and a two-dimensional matrix is formed at the FC. Then, the covariance matrix of this two-dimensional matrix is obtained and the mean value of the covariance matrix is removed. After this, the updated covariance matrix is input to the CNN and the decision result is obtained. Finally, the FC feeds back the decision result to each local node. The main work and contributions of this paper are as follows:  
1. A novel two-dimensional dataset of the received signal is established for CSS and the signal-to-noise ratio (SNR) of the dataset varies from \(-8\) dB to \(5\) dB with an SNR step of \(1\) dB. 2. The classical LeNet network, AlexNet network and VGG-16 network are trained on the proposed dataset. The corresponding false alarm probability and detection probability of the three networks are analyzed and compared. 3. The AND rule-based CSS, the OR rule-based SS and the MV-based CSS are compared with the proposed CNN-based CSS scheme. The experimental results validate the effectiveness of the proposed scheme. The rest of this paper is organized as follows: Section 2 gives the related work of this paper. In Section 3, the sensing scenario and sensing model are discussed. The main contribution of this paper is shown in Section 4. Simulation experiments and result analysis are presented in Section 5. Finally, Section 6 concludes this paper.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### KLF11-Mody (mody 7)  
Krueppel-like factor 11 (KLF11)-MODY occurs as a result of heterozygous _KLF11_ gene mutations. The _KLF11_ gene encodes for a transcriptional factor from the KLF/Sp1 family found in all human tissues [40; 76]. _KLF11_ regulates the expression of free radical scavengers, such as catalase and superoxide dismutase (SOD), both of which are essential for pancreatic \(\beta\)-cell function (SOD) [40; 65; 77]. Heterozygous mutations in the _KLF11_ gene eventually result in beta cell dysfunction and insulin secretion impairment [76]. _KLF11_-MODY is a type of diabetes that appears early in life and is treated with either OADs or insulin [34; 50].
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 3 System Calibration
### Laser Plane Calibration  
Laser plane calibration determines the normal direction of the line laser plane in the camera coordinate system. Inspired by [21], the calibration method for laser plane calibration is developed. The principle of the method is first to calculate several laser lines in the camera coordinate system by plane intersection as shown in Figure 5, and then the normal direction of the laser plane can be determined by the several laser lines. The concrete calibration procedure is as follows.  
Actually, the calibration images used in this step are also the images used in single camera calibration. For the \(i\)-th calibration image, the line laser illuminated on the plane pattern is regarded as a line in the image, which can be expressed as  
\[a_{i}u+b_{i}v+c_{i}=0 \tag{6}\]  
Combining Equations (5) and (6), as it is shown in Figure 5, the first plane across the laser line can be expressed as Equation (7) in the camera coordinate system.  
\[a_{i}m\cdot x_{c}+b_{i}m\cdot y_{c}+a_{i}u_{0}+b_{i}v_{0}+c_{i}=0 \tag{7}\]  
Figure 5: Two planes across the laser line.  
The unit normal vector of the plane in Equation (7) is \(\left(a_{i}/\sqrt{a_{i}^{2}+b_{i}^{2}},b_{i}/\sqrt{a_{i}^{2}+b_{i}^{2}},0\right)^{T}\). Meanwhile, the calibration board is the second plane across the laser line. The unit normal vector of the plane in the camera coordinate system is \(\left(r_{13-i},r_{23-i},r_{33-i}\right)^{T}\), which is the third column of the corresponding rotation matrix \(R_{i}\) achieved in single camera calibration. Therefore, the unit vector of the laser line on the \(i^{th}\) calibration image can be obtained by the unit normal vectors of the above two planes.  
\[n_{li}=\left(\frac{a_{i}}{\sqrt{a_{i}^{2}+b_{i}^{2}}},\frac{b_{i}}{\sqrt{a_{i} ^{2}+b_{i}^{2}}},0\right)^{T}\times\left(r_{13-i},r_{23-i},r_{33-i}\right)^{T}. \tag{8}\]  
With Equation (8), a unit vector is determined for every calibration image in the camera coordinate system. Meanwhile, the corresponding laser lines of the unit vectors are located in the laser plane, and the normal vector of the laser plane can be recovered with at least two unit vectors ideally which are not parallel. In practice, more direction vectors are placed to overcome the impact of noise. The unit normal vector of the laser plane \(n_{p}\) can be obtained by minimizing the following formula with the Singular Value Decomposition (SVD) method.  
\[\min\sum_{i=1}^{m}\|n_{li}^{T}n_{p}\|^{2},\text{ subject to }\big{|}n_{p}\big{|}=1. \tag{9}\]
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 4 Simulation Experiment
### Experimental Results
The drain current \(I_{D.N}\) of devices with gate lengths ranging from 90 nm to 40 nm also increases by approximately three orders of magnitude due to the influence of gate tunneling current, as indicated in Figure 7.  
In Figure 12, the NMOSFET is biased at gate state \(V_{GS.N}=V_{DD}\) and \(V_{DS.N}=0.02V_{DD}\), and is operating in the linear bias region. The simulation results show that, for all devices with oxide thickness ranging from 0.7 nm to 1.3 nm, the circular-point line representing \(I_{G.N}\) and the rectangular-point line representing \(I_{D.N}\) exhibit inverse variations, consistent with the model (Equation (38)). The two dashed lines in Figure 12 represent the theoretical results obtained from the model. Notably, for gate lengths ranging from 90 nm to 53 nm, the gate current \(I_{G.N}\) is significantly smaller than the drain current \(I_{D.N}\). For the 45 nm gate length device, the values of \(I_{G.N}\) and \(I_{D.N}\) are approximately equal, while for the 40 nm gate length device, \(I_{G.N}\) is smaller than \(I_{D.N}\). These experimental results align with the derived model and support the conclusions drawn from Equation (38). Figures 9, 10, 13 and 14 show the experimental results for the PMOSFET corresponding to the two gate states of the NMOSFET, which are similar to the NMOSFET results and are not further elaborated in the article.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Database
#### 2.1.4 Satellite Data Processing  
Both radar backscattering coefficients and _NDVI_ time series are identified at each station. A temporal linear interpolation of _NDVI_ data is proposed to estimate the _NDVI_ at each radar acquisition date. In this averaging, a filter is applied to the optical pixels to only consider data between 0.15 and 0.8 of _NDVI_ to avoid urban areas and water covers with low _NDVI_ or strong _NDVI_ corresponding mainly to dense forests.  
The radar signal is averaged over a radius of 500 m around each station. For a given station, if more than 50% of the Sentinel-1 pixels are excluded, the processing of radar data is not considered for the analyzed data.
# Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions
## 2 Preliminaries
### System Model  
The system linear model can be modeled as  
\[\mathbf{Z}=\mathbf{A}\mathbf{x}, \tag{1}\]  
where \(\mathbf{A}\) is a \(Q\times U\) matrix, \(\mathbf{Z}\) is a \(Q\times 1\) received vector, and \(\mathbf{x}\) is a \(U\times 1\) unknown vector. We assume that \(U<Q\) and the matrix and vectors are real-valued. In addition, \(K\) (\(K<U\)) elements of vector \(\mathbf{x}\) are non-zero. For example, \(\mathbf{x}\) is a sparse vector, and the index set of non-zero values is not given. There are many applications to solve problems with sparse true solutions. The multi-path communication channels, concerning the uncertainty delay interval, several multi-path components can be very small. The maximum likelihood (ML) estimation is usually applied to solve normal equations [21], to achieve the sparse true solutions \(\mathbf{x}_{0}\) in sparse multi-path channels. Multi-user detection can be considered to be another example when the number of active users \(K\) is less than the expected number of users \(U\)[22]. Whether the matrix \(\mathbf{R}=\mathbf{A}^{H}\mathbf{A}\) can be pre-computed determines the different computational complexity of sparse solution estimators. Calculation of the matrix \(\mathbf{R}\) is complex for the algorithm. The pre-computed matrix \(\mathbf{R}\) leads to the low complexity of the algorithm. In this work, we assume that matrix \(\mathbf{R}\) is known. The vector of the signal is calculated as:  
\[\mathbf{x}=\mathbf{R}^{-1}\boldsymbol{\beta}, \tag{2}\]  
where \(\boldsymbol{\beta}=\mathbf{A}^{T}\mathbf{Z}\). It costs \(\mathcal{O}(U^{3})\) complexity for computing the matrix inversion directly. The matrix inversion computing becomes prohibitively high when the system size \(U\) increases. To avoid such a high computational complexity, we can solve the normal Equation (1) by minimizing the quadratic function:  
\[J(\mathbf{x})=\|\boldsymbol{\beta}-\mathbf{R}\mathbf{x}\|_{2}^{2} \tag{3}\]  
To solve (3), an iterative descent search method can be applied as a possible choice. The descent search method updates the solution vector \(\mathbf{\hat{x}}^{(i)}\) via \(\mathbf{\hat{x}}^{(i)}=\mathbf{\hat{x}}^{(i-1)}+\sigma^{(i)}\boldsymbol{ \Lambda}^{(i)}\) at each iteration \(i\), where \(\boldsymbol{\Lambda}^{(i)}\) is the update direction of the solution. The direction is designed to be non-orthogonal to the residual vector \(\boldsymbol{\Gamma}\) (\(\boldsymbol{\Gamma}^{(i)}=\boldsymbol{\beta}-\mathbf{R}\mathbf{\hat{x}}^{(i)}\)). When current \(\mathbf{\hat{x}}^{(i)}\) is not the exact point, i.e., \(J(\mathbf{\hat{x}}^{(i+1)})<J(\mathbf{\hat{x}}^{(i)})\), it shows that the descent search method converges to the original problem solution with descent objective values in the convex area.
###### Abstract  
This paper presents the Cell Transformer (CeT), which utilizes high-definition (HD) map data to predict future traffic states at signalized intersections, thereby aiding trajectory planning for autonomous vehicles. CeT employs discretized lane segments to emulate the cell transmission model, creating a cell space to forecast vehicle counts across all segments based on historical traffic data. CeT enhances prediction accuracy by distinguishing between different vehicle types by incorporating vehicle-type attributes into vehicle-state representations through multi-head attention. In this framework, cells are modeled as nodes in a directed graph, with dynamic connections representing variations in signal phases, thereby embedding spatial relationships and signal information within dynamic graphs. Temporal embeddings derived from time attributes are integrated with these graphs to generate comprehensive spatial-temporal representations. Utilizing an encoder-decoder architecture, CeT captures dependencies and correlations from past cell states to predict future traffic conditions. Validation using real traffic data from pNEUMA demonstrates that CeT significantly outperforms baseline models in two-phase signalized intersection scenarios, achieving reductions of 11.47% in Mean Absolute Error (MAE), 13.48% in Root Mean Square Error (RMSE), and an increase of 4.36% in Accuracy (ACC). In four-phase signalized intersection scenarios, CeT shows even greater effectiveness, with improvements of 13.36% in MAE, 12.93% in RMSE, and 4.78% in ACC. These results underscore CeT's superior predictive capabilities and highlight the contributions of its core components.  
c cell transmission model; data-driven; high-definition map; signalized intersection; traffic prediction; Transformer 2025
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Determining LUE GPP's Response Time to Water Availability  
To screen out vegetation productivity's response time to water availability, a monthly GPP series from 1982 to 2015 was correlated with the multiple timescale SPEI (1- to 24-months). For the i-th month in a year, Spearman's correlation analysis was performed between the monthly GPP and SPEI series at different timescales as follows  
\[R_{j}^{i}=corr\Big{(}GPP^{i},SPEI_{j}^{i}\Big{)}\ i=1,\ 2\,\ldots,12,\ j=1,\ 2, \ldots,24, \tag{8}\]  
in which R is the correlation coefficient and a timescale j denotes the cumulative water deficit or surplus from j-1 months before to the current month. Finally, a timescale able to maximize the GPP-SPEI association, is retained as the vegetation productivity response time (VPRT) to water variations for the i-th month.  
\[VPRT^{i}=max\Big{\{}R_{j}^{i}\Big{\}}\ j=1,\ 2,\ \ldots,\ 24. \tag{9}\]