esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### In-Tube SPME-UHPLC-MS/MS Configuration  
Two columns were connected by means of a six-port valve, as shown in Figure 6. The monolithic column (first dimension) was connected to the quaternary pump (QSM), and the analytical column (second dimension) was connected to the binary pump (BSM). In the first step, the six-port valve was set in position 1, which allowed the columns to be conditioned with the initial mobile phase composition (Table 7). Then, 10 \(\mu\)L of the sample solution was injected into the system. Water was used as mobile phase to percolate the sample solution through the monolithic capillary at a flow rate of 100 \(\mu\)L min\({}^{-1}\). In this step, analytes were pre-concentrated, and macromolecules from plasma samples were eluted for waste. After 2.0 min, the six-port valve was set to position 2. The target drugs from the monolithic capillary were eluted to the analytical column using the mobile phase (BSM pump), consisted of (A) 10 mmol L\({}^{-1}\) ammonium acetate (with 0.1% formic acid) and (B) acetonitrile (80:20 \(v\)/\(v\)), at a flow rate of 100 \(\mu\)L min\({}^{-1}\). At 6.50 min, the six-port valve was set to position 1. Using the same mobile phase, from 6.51 to 13.00 min, the chromatographic separation occurred at 300 \(\mu\)L min\({}^{-1}\). From 7.0 to 16.5 min, acetonitrile was percolated through the monolithic capillary for clean-up. After this time, both columns were conditioned with the initial mobile phase composition for the following sample injection (Table 7). Different mobile phases were evaluated to establish the highest analyte sorption and the best analyte resolution within the minimum analysis time.  
Figure 6: Scheme of in-tube SPME-UHPLC-MS/MS system. **(1)** Sample clean-up and sorption of the analytes, and **(2)** elution of the analytes from the 1 st to 2 nd column. QSM: quaternary pump, BSM: binary pump, MS: mass spectrometry, 1 st column: monolithic capillary, 2 nd column: analytical column.
## 1 Introduction
[2] conducted a CFD analysis to investigate the combustion characteristics of heavy fuel oil (HFO) and a palm-based bioliquid (BL) in a 100 MW\({}_{\text{e}}\)-capacity boiler. They reported that the BL-firing case exhibited lower radiation and more uniform temperature in the combustion zone when compared with the HFO-firing case. Also, a significant reduction in NO\({}_{x}\) and SO\({}_{x}\) emissions was achieved owing to the inherently low sulphur and nitrogen content. More recently, [PERSON] et al. [21] performed a CFD analysis on BL co-firing with HFO in a 400 MW\({}_{\text{e}}\) power plant with a wall-firing boiler. They used a BL blend of palm oil, its residue, and animal fat at a 20% co-firing ratio and reported that lower soot formation decreased radiation on the furnace wall in the BL co-firing. The development of renewable bio-energy has recently attracted a lot of attention, where especially, biowaste conversion to energy has been of great interest [22; 23; 24; 25; 26] due to the depletion of fossil fuels and the associated negative environmental impacts. A large amount of a byproduct called spent coffee grounds is generated after brewing coffee. Spent coffee grounds are the primary waste product generated by thermal water extraction from roasted coffee beans [27]. In Taiwan, coffee consumption is gradually growing. Currently, the Taiwanese population consumes 2.85 billion cups of coffee (which generate about 34,200 tons of coffee ground residue) every year. It is estimated that 14,275 kL of bio-oil can be derived from the above coffee ground residue per year. Coffee grounds are expected to be an essential energy source due to consumption growth and the fact that this product has a higher heating value than woody biomass [28]. The use of FPBO to completely replace fossil fuels has some limitations since it has negative properties including higher water and oxygen contents, higher viscosity, poor volatility, and a low heating value. However, a low blend ratio of bio-oil to substitute for petroleum-derived oil has advantages. For instance, it can be easily combusted in existing industrial boilers and furnaces without modifications. Moreover, biofuels like biodiesel, ethanol and butanol are made from agricultural crops, leading to food security issues and an increase in energy costs. On the other hand, bio-oil derived from fast pyrolysis of non-edible feedstocks (e.g., coffee bean residue) does not cause the problem of food security and an increase in energy prices. Thus, a promising solution is the partial substitution of conventional diesel fuel with bio-oil, rather than completely replacing it. However, there is still limited amount of data in the literature regarding the combustion characteristics of co-firing FPBO and diesel for application in furnaces. This study is aimed at investigating the combustion characteristics and pollutant emissions of co-combustion bio-oil (produced from the fast pyrolysis process of coffee bean residue) and diesel at a co-firing ratio of 5 vol % FPBO in an oil-fired furnace.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 5 Numerical Experiments
### Real-Time Precise Point Positioning Experiment  
In order to verify the availability of the proposed clock model in the real-time precise point positioning (RTPPP), eight MGEX stations distributed around the Asia-Pacific region are selected and processed. The distribution of the selected MGEX stations is shown in Figure 14. The sampling interval of observations is 30 s and the observing time is 24 h. The ISU and GBU ultra-rapid predicting orbit products are selected to match the corresponding clock offset products. The main processing strategies of RTPPP experiment are as follows: First, the real-time orbit predictions of ISU-P and GBU-P are selected. Second, the clock predictions from ISU-P and GBU-P products and the proposed model are chosen separately. Third, the kinematic RTPPP results of eight stations which use different clock prediction products are computed. The positioning residuals of NNOR and PNGM stations are given in Figures 15 and 16. The position accuracies of eight stations are achieved and shown in Table 4.  
Figure 16: The PTPPP residuals series of PNGM station shown at **(a)** using the GBU-P clock offset; **(b)** using the GBU modified model clock offset; **(c)** using the ISU-P clock offset; and **(d)** using the ISU modified model clock offset.  
Figure 15: The PTPPP residuals series of NNOR station shown at **(a)** using the GBU-P clock offset; **(b)** using the GBU modified model clock offset; **(c)** using the ISU-P clock offset; and **(d)** using the ISU modified model clock offset.
## 1 Introduction
**Definition 3**: A cellular type grid denoted by \(Z\) and having a size \(r\times c\), where \(c\) and \(r\) are greater than or equal to \(2\), is derived from a bi-dimensional grid, \(A\), which has a similar size. This is done by augmenting the group of edges using diagonal connections that extend from left to right. Specifically, each of the vertices of \(Z\), represented by \(a=(n,m)\), is linked to another vertex, represented by \(b=(n-1,m-1)\) and \(d=(n+1,m+1)\). As a result, the vertex now possesses a degree of \(6\), excluding the vertices at the border regions. **Definition 4**: A coloring algorithm, \(L(h,k)\), labels integers that are greater than \(0\) to the vertex \(U\) in a manner that allows neighboring nodes to be color-labeled and to be separated by at least a distance \(h\); the same applies to the vertices (distance \(2\)), which are also color-labeled and separated by at least a distance \(k\). This coloring algorithm reduces the differences between the most and the least used colors, i.e., the span \(oh\), \(k(U)\). The smallest span in other labeling functions is represented by \(\lambda\) h, \(k(U)\). The rest of this paper is organized as follows. An overview of relevant literature regarding the resolution of channel allocation problems is presented in Section 2. Section 3 describes the methodology. Section 4 describes the experimental and evaluation procedures. Sections 5 and 6 give conclusions and discuss possible future research on the subject, respectively.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 3 ESGM Model
### BERT Encoding Module  
BERT is a pretrained masked language model consisting of multiple identical bidirectional Transformer encoders [31]. It has been widely applied in various downstream tasks, including information extraction and machine translation. BERT typically divides the input sentence into a segment embedding, token embedding, and position embedding; however, for the discussion of sentence-level entity and relation extraction in this paper, segment embedding is not relevant. The encoding layer is used to obtain the encoded vectors for each token in the input sentence. The algorithm execution procedure of the encoding layer for the input sentence is illustrated in Equations (2) and (3):  
\[H_{0}=S\cdot W_{s}+W_{p} \tag{2}\]  
\[H_{\alpha}=Encoder(H_{\alpha-1}),\alpha\in[0,N] \tag{3}\]  
where \(S\) represents the one-hot vector matrix for each token in the input sentence. A one-hot matrix is a common vector representation method used to convert discrete categories or labels into vector form. It is a sparse representation in which each category is represented as an independent vector and each vector has only one element equal to 1 at its corresponding category index, with all other elements being 0. Here, \(W_{s}\) represents the token embedding matrix for each token and \(W_{p}\) represents the position embedding matrix for each token. The variable \(H_{\alpha}\) represents the hidden layer vector, where \(\alpha\) represents the \(\alpha\)th layer of the Transformer. The term \(Encoder()\) refers to the transformer encoder in the BERT module, which encodes the input vectors; \(N\) represents the number of Transformer blocks, which in this paper we set to 12. Through these operations, a sentence representation is obtained that incorporates both semantic and positional information, providing a foundation for the subsequent entity and relation extraction tasks.
## 4 Renewable Energy Capacity Configuration Method
### Wind and PV Capacity Configuration Calculation  
After calculating the maximum regulation capacity of the cascade small hydropower stations and the expected output power of wind and PV, it can be seen that the wind and PV capacity of the cascade small hydropower generation system can be configured after the transformation:  
The wind and PV control power \(P_{b}\) is determined by the difference between the expected value \(P_{c}\) and the measured value \(P_{w}\):  
\[P_{b}=P_{c}-P_{w} \tag{9}\]  
Perform the DFT on \(P_{b}\) to obtain the corresponding frequency domain signal. According to Parseval's Theorem, the total energy of the signal in the frequency domain is equal to its total energy in the time domain [35]. Based on this theorem, the system's regulation capacity for smoothing wind and PV power can be calculated as follows:  
\[P(f_{1},f_{2})=\sqrt{\sum_{f_{1}}^{f_{2}}\lvert Y(f)\rvert^{2}} \tag{10}\]  
where \(P(f_{1},f_{2})\) is the energy storage demand value in \([f_{1},f_{2}]\), kW.  
The total configurable wind and PV capacity \(S^{\prime}\) of cascade hydropower stations with the maximum regulation capacity of M is  
\[S^{\prime}\leq M/\eta \tag{11}\]where \(\eta\) is the proportion of the regulation capacity of the system to smooth the wind and PV power in the total installed capacity. Assuming the wind and PV with capacity of \(S\), then \(\eta\) is  
\[\eta=P(f_{1},f_{2})/S \tag{12}\]  
The specific flow of the capacity configuration of the cascade small hydropower-pumped storage-wind-PV complementary system is shown in Figure 2.  
Figure 2: Capacity configuration flow chart of cascade small hydropower-pumped storage–wind–PV complementary system.
# Semiconductor Laser Linewidth Theory Revisited
## Appendix A Dispersion Operator  
The dispersion operator \(\widehat{\mathcal{D}}(z,t)\) in the coupled-wave equations (2) models the frequency dependence of the gain and associated refractive index. The approach followed here is based on approximating the material gain spectrum locally around the gain peak by a Lorentzian, see Figure 11, and supplementing the time-domain coupled-wave equations (2) with additional dynamical equations for auxiliary polarizations [31; 36; 37]  
\[\frac{\partial}{\partial t}P(z,t)=(i\omega_{\mathcal{D}}(z)-\gamma_{\mathcal{D }}(z))P(z,t)+\gamma_{\mathcal{D}}(z)\Psi(z,t), \tag{11}\]  
where \(\omega_{\mathcal{D}}\) is the detuning between the peak gain frequency \(\omega_{p}\) and the reference frequency \(\omega_{0}\), \(\gamma_{\mathcal{D}}\) is the half width at half maximum (HWHM) of the gain curve and  
\[P(z,t)=\begin{bmatrix}P^{+}(z,t)\\ P^{-}(z,t)\end{bmatrix}.\]  
The corresponding dispersion operator in (2) reads  
\[\widehat{\mathcal{D}}(z,t)\Psi(z,t)=-\frac{i}{2}g_{\mathcal{D}}(z)(\Psi(z,t)- P(z,t)), \tag{12}\]  
where \(g_{\mathcal{D}}\) describes the difference between the maximum amplitude gain \(g(z,N,\|\Psi\|^{2})\), see (3c), and the gain at the reference frequency. The parameters \(\omega_{\mathcal{D}}\), \(\gamma_{\mathcal{D}}\), and \(g_{\mathcal{D}}\) are obtained from a fit to a microscopically computed gain spectrum or experimental data.  
A Fourier transformation of (12) yields, together with the frequency domain solution of the polarization Equation (11), an expression for the dispersion factor (complex Lorentzian)  
\[\beta_{\mathcal{D}}(z,\Omega)=\frac{g_{\mathcal{D}}(z)}{2}\frac{\Omega-\omega _{\mathcal{D}}(z)}{i(\Omega-\omega_{\mathcal{D}}(z))+\gamma_{\mathcal{D}}(z)}. \tag{13}\]  
The associated effective gain spectrum entering the rate of stimulated emission (67) reads  
\[\begin{split} g_{\text{eff}}(z,N,\|\Psi\|^{2},\Omega)& =g(z,N,\|\Psi\|^{2})+2\operatorname{Im}(\beta_{\mathcal{D}}(z, \Omega))\\ &=g(z,N,\|\Psi\|^{2})-g_{\mathcal{D}}\frac{\left(\Omega-\omega_{ \mathcal{D}}(z)\right)^{2}}{\left(\Omega-\omega_{\mathcal{D}}(z)\right)^{2}+ \gamma_{\mathcal{D}}^{2}(z)},\end{split} \tag{14}\]  
which is maximal at \(\Omega=\omega_{\mathcal{D}}\), i.e., if the laser operates at the peak gain frequency.  
An alternative representation of (A2) is obtained with the help of the Green's function of the polarization Equation (A1) as  
\[\widehat{D}(z,t)\Psi(z,t)=-\frac{i}{2}g_{\mathcal{D}}(z)\bigg{(}\Psi(z,t)-\gamma _{\mathcal{D}}(z)\int_{0}^{t}\mathrm{e}^{(i\omega_{\mathcal{D}}(z)-\gamma_{ \mathcal{D}}(z))\tau}\Psi(z,t-\tau)\,d\tau\bigg{)}, \tag{10}\]  
where dispersion is induced via the convolution of the memory kernel with the time-delayed field amplitude (we omitted the quickly decaying initial value term here).
# Fault Classification and Localization Scheme for Power Distribution Network
## 6 Results and Discussion
To verify the unknown fault distance, the single phase to the ground instance is used, and the results are depicted in Figure 9. It can be noted from Figure 9 that the scheme is able to estimate the fault position with a minimum error of 0.0010. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Fault Type** & \multicolumn{2}{c}{**45 km**} & \multicolumn{2}{c}{**90 km**} \\ \hline  & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) \\ \cline{2-6} LG & 45.0 & 44.9 & 45.0 & 89.9 & 90.0 & 90.0 \\ LL & 45.0 & 45.0 & 45.0 & 90.0 & 90.0 & 90.0 \\ LLG & 44.9 & 45.0 & 44.9 & 90.0 & 90.0 & 90.0 \\ LLL & 45.0 & 45.0 & 43.9 & 90.0 & 90.0 & 90.0 \\ \hline \end{tabular}
\end{table} 
Table 10: Fault estimation at 45 and 90 km with a 10 \(\Omega\) fault impedance. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Class** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **ROC** \\ \hline AG & 0.991 & 0.000 & 0.960 & 1.000 & 0.991 & 0.970 \\ BG & 0.920 & 0.000 & 0.991 & 0.990 & 0.990 & 0.966 \\ CG & 0.933 & 0.000 & 0.990 & 0.980 & 0.995 & 0.985 \\ AB & 0.985 & 0.000 & 0.961 & 0.985 & 0.977 & 0.990 \\ AC & 0.968 & 0.003 & 0.950 & 0.930 & 0.988 & 0.900 \\ BC & 0.987 & 0.000 & 0.910 & 0.975 & 0.970 & 0.988 \\ ABG & 0.980 & 0.014 & 0.960 & 0.957 & 0.966 & 0.955 \\ ACG & 0.981 & 0.010 & 0.988 & 0.911 & 0.960 & 0.975 \\ BCG & 0.971 & 0.035 & 0.991 & 0.980 & 0.991 & 0.961 \\ ABC & 0.981 & 0.043 & 0.950 & 0.990 & 0.955 & 0.992 \\ Avg & 0.969 & 0.011 & 0.965 & 0.970 & 0.978 & 0.968 \\ \hline \end{tabular}
\end{table} 
Table 8: SVM classification with two cycles post-fault. \begin{table}
\begin{tabular}{c c c c c} \hline
**Fault Type** & **No. of Instances** & **No. of Instances** & **Accuracy (\%)** \\  & **Correctly Classified** & **Incorrectly Classified** & **Accuracy (\%)** \\ \hline LG & 25,400 & 25,400 & 0.000 & 100 \\ LL & 25,400 & 25,250 & 150.0 & 99.4 \\ LLG & 25,400 & 25,085 & 315.0 & 98.8 \\ LLL & 25,400 & 25,357 & 43.00 & 99.8 \\ Total & 101,600 & 101,092 & 508.0 & 99.5 \\ \hline \end{tabular}
\end{table} 
Table 10: Fault estimation at 45 and 90 km with a 10 \(\Omega\) fault impedance. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Fault Type** & \multicolumn{2}{c}{**110 km**} & \multicolumn{2}{c}{**140 km**} \\ \hline  & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) \\ \cline{2-6} LG & 110.0 & 110.0 & 110.0 & 139.9 & 135.0 & 140.0 \\ LL & 109.8 & 109.0 & 110.0 & 140.0 & 138.0 & 139.0 \\ LLG & 109.5 & 110.0 & 109.9 & 140.0 & 140.0 & 140.0 \\ LLL & 110.0 & 110.0 & 110.0 & 140.0 & 140.0 & 140.0 \\ \hline \end{tabular}
\end{table} 
Table 11: Fault estimation at 110 and 90 km with a 20 \(\Omega\) fault impedance. Figure 8: Fault estimation training sample using SVR. Figure 9: Fault estimation testing sample using SVR.
## 1 Introduction
### Contributions  
This work tries to answer these research questions from the development of a novel multi-objective approach where the robots, when selecting their targets, are always considering travelling costs and the opportunity cost of keeping connected or regaining connectivity. A simple yet useful model for the signal strength and attenuation effects provide the robots with connectivity awareness. Thereupon, connectivity level measurements and path costs are considered together into a _task utility_ function for finding solutions with a right balance between the benefit of visiting the closer targets and the usefulness of keeping the team connectivity level as high as possible.  
For the sake of robustness, a decentralised approach is followed. Robots make decisions asynchronously addressing coordination implicitly through localisation and mapping data exchanging. The human operator is asked to use his application-field expertise to play a part in the task assessment process.  
The main contributions of this proposal can be summarised as follows.  
Ease to Deploy and Flexibility  
In order to establish the task selection criterion, the human operator only needs to choose the extra distance he is willing to ask the robots to travel in order to keep or enlarge the connectivity level of the fleet. From this criterion and through formal analysis, the weights of these potentially conflicting objectives are derived. This way the robots can deal with communication constraints auto-adjusting the weights of each objective in a more intuitive manner. Furthermore, by eliminating the need for training stages the system is more adaptable to different environments.  
Good Performance  
Asynchronism is taken as a natural way of avoiding waiting times to make a decision as well as decreasing the number of robots that are simultaneously making a decision. Since the task allocation computation strongly depends on the number of robots under consideration, asynchronism also makes optimal decisions can be linearly computable most of the time. As a consequence, robots can compute optimal tasks-to-robots distributions in a short time, achieving high levels of dispersion efficiently. Besides, regarding reconnections, the proposal consists of a rendezvous policy where the locations of the selected tasks become the meeting points themselves, avoiding deviations from the planned paths. Compared with others, the proposed approaches are capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.
## 5 Conclusions  
For the studied sites in Portugal (Douro, Dao and Alentejo) and their environmental conditions, at berry maturity, high temperatures tend to decrease berry weight, titratable acidity, anthocyanins and TPI, and increase pH and potential alcohol. Concerning precipitation, its influence was largely dependent on location and variety. Although these outcomes are in line with previous findings and were thereby expected, this study is unprecedented for Portugal, as it is based on a large systematised and comprehensive database of grape berry maturity parameters. After identifying the most important climatic variables, the development of a model to be used for both operational short-to-medium range prediction and climate change impact assessments should be envisioned in future research. The grape maturation database that was prepared in the context of the present study still contains crucial information that can be further exploited to increase the level of knowledge about the influence of climatic variability on grape composition. In particular, it would be interesting: (i) to search correlations between climatic variables and grape quality parameters not studied in this work, such as malic acid, and to the varietal aromatic potential; (ii) to extend the study to other regions and varieties; and (iii) to examine the interaction of soil/weather/morphology of the territory.  
Conceptualization and methodology, all authors; software, C.C.; validation, C.C.; formal analysis, C.C.; investigation, all authors; resources, C.C. and A.G.; data curation, C.C., N.F., M.T. and A.G.; writing--original draft preparation, C.C. and J.A.S. writing--review and editing, all authors; visualization, C.C.; supervision, H.G., A.G. and J.A.S.; project administration, N.F. and A.G.; funding acquisition, J.A.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Clim4 Vis project--\"Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach\", funded by European Union's Horizon 2020 Research and Innovation Programme, under grant agreement no. 810176.  
This work was supported by the \"Contraro-Programa\" UIDB/04050/2020 and UIDB/04033/2020 funded by national funds through the FCT L.P. The work was also supported by FCT and European Funds (FEDER/POCI/COMPETE2020) through the research projects MitiVineDrought (PTDC/BIA-FBT/30341/2017 and POCI-01-0145-FEDER-030341). The study was also supported by FCT--Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
## 1 Introduction
The impacts of the activation states of the PIN diodes and the gap length on the antenna's overall performance are also analyzed. In Section 3, we present both simulated and measured results, focusing on the antenna's reflection coefficient and radiation properties. The performance of our proposed antenna is compared with previously published works from recent years. The conclusions are presented in Section 4.