esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes
### Biochar Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feedstock**} & **Pyrolysis** & **Char Yield (\%)** & **Surface Area** & **Pore Volume** & **Ash Content** & **Reference** \\  & **Temperature (\({}^{\circ}\)C)** & **Char Yield (\%)** & **m\({}^{2}\)/g** & **cm\({}^{3}\)/g** & **(\%)** & **Reference** \\ \hline \hline Sewage sludge & 750 & - & 60.7 & & - & [94] \\ \hline Sewage sludge & 400 & 76.1 & 23.7 & & - & [95] \\ \hline Sewage sludge & 600 & - & 92.3 & & - & [96] \\ \hline Sewage sludge & 500 & - & 25.4 & 0.056 & 74.2 & [97] \\ \hline Sewage sludge & 900 & - & 67.6 & 0.099 & 88.1 & [97] \\ \hline \hline Palm oil mill & 400 & 54.2 & 47.7 & 0.007 & 44.8 & [98] \\ sludge & 800 & - & 193.1 & 0.065 & 59.5 & [99] \\ \hline \hline Pine needles & 400 & 30 & 112.4 & 0.044 & 2.3 & [83] \\ \cline{2-6}  & 700 & 14 & 490.8 & 0.186 & 2.2 & \\ \hline Pine needles & 700 & 25 & 390 & 0.12 & 18.7 & [73] \\ \hline Used tea leaves & 350–550 & - & 8.1 & 0.012 & - & [100] \\ \hline _Ponderos pine_ & 500 & 28.4 & 196 & & 2.1 & \\ _Ponderos pine_ & 700 & 22.0 & 347 & & 1.7 & [101] \\ \cline{2-6}  & Tall fescue straw & 700 & 28.8 & 139 & & 19.3 & \\ \hline _Quercus lobata_ & & & 225 & & 3.7 & \\  wood & & & 77 & & \\ _Pinus taeda_ wood & & & (N\({}_{2}\)) & & 1.1 & [89] \\ \cline{2-6}  & 650 & & 528 & & \\ _Tripsacum_ & & & 643 & & 15.9 & \\ _foridanum_ grass & & & 427 & & \\  & & & (CO\({}_{2}\)) & & & \\ \hline \hline  & 800 & 12.5 \(\pm\) 0.2 & 70.2 & 0.003 & & \\ \cline{2-6}  & 1200 & 10.0 \(\pm\) 0.7 & 110.2 & 0.047 & - & [86] \\ \cline{2-6}  & 1600 & 8.3 \(\pm\) 0.4 & 48.7 & 0.040 & & \\  & 2000 & 8.3 \(\pm\) 0.5 & 22.2 & 0.032 & & \\ \hline \hline Poplar wood & 600 & - & 411 & 0.182 & 4.7 & [87] \\ \hline \hline Durian wood & & & & & \\ _(Durio ziehlinus)_ & 550 & 24.6 & 221 & 0.008 & 20.8 & [102] \\ \hline \hline _Paulowenia elongata_ & - & - & 310 & & 4.1 & [103] \\ \cline{2-6}  & wood & & & & \\ \hline \hline
\begin{tabular}{c} _Pinewood sawdust_ \\ _(Pinus radiata)_ \\ \end{tabular} & 800 & 11.6 & 738.0 & 0.244 & 1.9 & [104] \\ \hline Oak bark & 450 & 22.8 & 1.9 & 1.060 & 11.3 & [105] \\ \hline Corn stover & 450 & 15 & 12 & & 58.0 & [106] \\ \hline Corn stover & 500 & 17 & 3.1 & & 32.4 & [107] \\ \hline Soybean stover & 700 & 21.6 & 420.3 & 0.190 & 17.2 & [108] \\ \hline Cotton stalk & 500 & & 1.5 & 0.007 & 2.7 & [81] \\ \hline Duckweed & 500 & 44 & 12 & 0.014 & 9.5 & [109] \\ \hline Rice husk & 500 & - & 34.4 & 0.028 & 42.2 & [110] \\ \hline Rice husk & 550 & - & 181 & & - & [111] \\ \hline Rice straw & 400 & - & 4.4 & 0.015 & 40.7 & \\ \cline{2-6}  & 700 & - & 161.2 & 0.086 & 52.5 & [112] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pyrolysis temperature, char yield, surface properties, and ash content of different biochars.
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 4 Numerical Results
### Evaluation Achievements  
The results obtained have shown that the IHON mechanism can support FH and BH traffic multiplexing on the same wavelength channel while guaranteeing time transparency for FH. BH packet transmission probability increases with the payload length \(L_{\text{F}}\) of the CPRI frame but results are quite limited with option 6 if no segmentation is applied. Further comparisons show, instead, that the throughput, normalized to line rate, can reach almost the full residual capacity utilization with segmentation, at the cost of some additional overhead, especially for option 6, which requires more segments to fill the smaller gaps.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 4 BFISAR Image Reconstruction Algorithm
### Basic Operations  
First, based on the object's geometry and kinematics, and the DVB-T waveform, a BFISAR signal \(S(k,p)\) reflected from the 3-D object space is created using expression (12). Second, the demodulation is executed by the multiplication of \(S(k,p)\) with the complex-conjugated waveform \(\exp[-j2\pi.(f_{0}+\Delta f)k.\Delta T]\), i.e.,  
\[\begin{array}{c}\hat{S}(k,p)=S(k,p).\exp[-j2\pi.(f_{0}+k.\Delta f)k\Delta T] \\ =\sum\limits_{i,j\hat{k}}\text{rect}\Big{(}\frac{t-t_{ijk}}{T_{U}}\Big{)}a_{ ijk}.\exp\Big{\{}-j2\pi.\Big{[}k.\Delta f.\hat{t}+(f_{0}+k.\Delta f).t_{ijk} \Big{]}\Big{\}}.\end{array} \tag{28}\]  
After demodulation of the BFISAR signal, a range compression is performed by applying an inverse Fourier transform:  
\[\widetilde{S}(\hat{k},p)=K^{-1}\sum_{k=1}^{K}\hat{S}(k,p).\exp\left(j2\pi \frac{k\hat{k}}{K}\right). \tag{29}\]  
The range-compressed BFSAR signal is azimuth-compressed by applying an inverse Fourier transform:  
\[a(\hat{k},\hat{p})=N^{-1}\sum_{p=1}^{N}\widetilde{S}(\hat{k},p).\exp\left(j2 \pi\frac{p\hat{p}}{N}\right). \tag{30}\]  
In case the obtained image is blurred, a higher-order phase correction is applied. An image contrast function or an entropy function is used to evaluate the quality of the image. The higher-order phase correction is performed by multiplication of the BFISAR signal with a phase correction function \(\exp[-j\Phi(k,p)]\), i.e.,  
\[\hat{S}_{c}(k,p)=\hat{S}(k,p).\exp[-j\Phi(k,p)]. \tag{31}\]  
The image reconstruction procedure is repeated with the phase-corrected signal. The coefficients of higher-order terms in the Taylor expansion of \(\Phi(k,p)\) are calculated iteratively by minimizing the image cost function that evaluates the image quality. The procedure lasts until global minimum of the image cost function is achieved. The main stages of the image reconstruction algorithm are illustrated by the simulation experimental results.
## 3 Optimal Control Problem
### Analysis of the Optimal Control Problem  
[PERSON]'s key idea was to use adjoint functions to attach the state dynamics to the objective functional. This converts the problem into minimizing the Hamiltonian and generates the adjoint differential equations and final-time transversality conditions. In this paper, we use a generalization of [PERSON]'s maximum principle to systems of delay differential equations (developed by [27]).  
Letting \(f\) denote the integrand of the objective functional (9) and \(g_{1},\ldots,g_{5}\) be the right-hand sides of System (8), the Hamiltonian for our optimal control problem is:  
\[H=f+\lambda_{1}g_{1}+\lambda_{2}g_{2}+\lambda_{3}g_{3}+\lambda_{4}g_{4}+ \lambda_{5}g_{5} \tag{10}\]  
where the adjoint functions \(\lambda_{i}\) correspond to the states \(x_{i}\), for \(i\in\{1,\ldots,5\}\).  
From the Hamiltonian, we derive the adjoint equations. For \(i=1\), 3 and 5, we have:  
\[\frac{d\lambda_{i}}{dt}=-\frac{\partial H}{\partial x_{i}} \tag{11}\]  
on \([0,T]\). For \(i=2\) and 4, on the interval \([0,T-\tau]\) we have  
\[\begin{split}\frac{d\lambda_{2}}{dt}&=-\frac{ \partial H}{\partial x_{2}}-\left.\frac{\partial H}{\partial z_{2}}\right|_{t +\tau}\\ \frac{d\lambda_{4}}{dt}&=-\frac{\partial H}{ \partial x_{4}}-\left.\frac{\partial H}{\partial z_{4}}\right|_{t+\tau}\end{split} \tag{12}\]  
where \(z_{i}(t)=x_{i}(t-\tau)\) represent the delayed state variables, while on the interval \([T-\tau,T]\), we have:  
\[\begin{split}\frac{d\lambda_{2}}{dt}&=-\frac{ \partial H}{\partial x_{2}}\\ \frac{d\lambda_{4}}{dt}&=-\frac{\partial H}{ \partial x_{4}}\end{split} \tag{13}\]  
just as in [11].  
The equations containing delayed state variables produce adjoint equations with a \"forward\" delay, due to the opposite time orientation of the adjoint differential equation. The adjoint equations are subject to the final condition that \(\lambda_{3}(T)=c_{5}\), \(\lambda_{i}(T)=0\) and \(i\in\{1,2,4,5\}\). On the interior of the control set, minimizing the Hamiltonian gives:  
\[\frac{\partial H}{\partial u_{i}}=0\]  
at \((u_{1}^{*},u_{2}^{*})\), and then, the optimal control pair becomes:  
\[\begin{split} u_{1}^{*}&=\frac{-c_{3}+\beta x_{1}x _{3}(-\lambda_{1}+\lambda_{2}){+}\lambda_{3}(1-f)b_{2}\pi x_{2}}{2\epsilon_{1} }\\ u_{2}^{*}&=\frac{-c_{4}+\lambda_{2}a_{1}\mu x_{2}x _{4}+\lambda_{3}fa_{2}\pi x_{2}-\lambda_{4}da_{3}x_{4}}{2\epsilon_{2}}\end{split} \tag{14}\]  
Using the bounds on the controls, we obtain the following characterization of the optimal control:  
\[\begin{split} u_{1}^{*}&=\min\left\{M_{1},\max \left\{0,\frac{-c_{3}+\beta x_{1}x_{3}(-\lambda_{1}+\lambda_{2}){+}\lambda_{3 }(1-f)b_{2}\pi x_{2}}{2\epsilon_{1}}\right\}\right\}\\ u_{2}^{*}&=\min\left\{M_{2},\max\left\{\frac{-c_{4}+ \lambda_{2}a_{1}\mu x_{2}x_{4}+\lambda_{3}fa_{2}\pi x_{2}-\lambda_{4}da_{3}x_{ 4}}{2\epsilon_{2}}\right\}\right\}\end{split} \tag{15}\]
## 3 Results and Discussion
### Case Study 1: Dispute Between Supplier and Customer  
Suppose the maximum flash point value allowed for jet fuel in Brazil is assumed to be 40.0 \({}^{\circ}\)C, based on real data from the Brazilian oil and gas industry provided in Table 1. Is it possible to commercialize this product?  
Table 1 contains the flash points and their respective expanded uncertainties for the supplier and customer for this evaluation.  
This case study is a typical dispute scenario within the oil and gas industry. The supplier's results indicate that the product meets the specifications, while the customer's results suggest that it does not.  
Mathematical model: \(m_{1}^{c}=m_{2}^{c}\)  
Reconciled value: From Equations (17) and (19), applying the DR technique, it is noticed that the flash point measured by Tag Closed Cup Tester is clearly beyond the specified limits; thus, the reconciled value, \(m^{c}=37.5\)\({}^{\circ}\)C, and its minimized uncertainty, \(U_{m^{c}}=2.0\)\({}^{\circ}\)C, that is, 35.5 \({}^{\circ}\)C to 39.5 \({}^{\circ}\)C, fall below the allowable maximum limit, which is 40.0 \({}^{\circ}\)C.  
\[\begin{array}{l}m^{c}=\frac{\frac{36.0}{5.2}+\frac{40.5}{3.4}}{2.5\%+\frac{ 1}{3.4\%}}=37.5\)\({}^{\circ}\)C\\ U_{m^{c}}=\sqrt{\frac{1}{2.5\%+\frac{1}{3.5\%}}}=2.0\)\({}^{\circ}\)C \\ \end{array}\]  
In this specific case, it was noticed that the significant advantage of utilizing this approach has a negative impact on the arithmetic average and expanded uncertainty, as it is widely employed today. Considering the supplier result as 36.0 \({}^{\circ}\)C \(\pm\) 2.5 \({}^{\circ}\)C and the customer result as 40.5 \({}^{\circ}\)C \(\pm\) 3.5 \({}^{\circ}\)C, the arithmetic average value is 38.2 \({}^{\circ}\)C, and its expanded uncertainty is 2.2 \({}^{\circ}\)C (\(\sqrt{\left(2.5/2\right)^{2}+\left(3.5/2\right)^{2}}\)), resulting in a range of 36.0 \({}^{\circ}\)C to 40.4 \({}^{\circ}\)C.  
According to these conventional statistics, which did not consider the weight of the measurement uncertainty, the product fell within the specification. However, the uncertainty band partially overlapped with the specification limit, placing it outside the specification. This discrepancy led to a dispute between the parties involved, as depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Flash Point** & **Expanded Uncertainty** & **Specification** \\ \hline Supplier & 36.0 \({}^{\circ}\)C & 2.5 \({}^{\circ}\)C & In \\ Customer & 40.5 \({}^{\circ}\)C & 3.5 \({}^{\circ}\)C & Out \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dispute between supplier and customer.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Volcanoes
The fluctuations of the magnetic field components are studied by wavelet analysis, the corresponding scalograms for one crossing path, together with an illustrative map, are shown in Figure 1. The statistical distributions of the fluctuations for all crossings, i.e., individual pre- and post-event segments, are shown in Figure 2. These are statistics for time slices, similar to the global wavelet spectrum, but with the magnitude of the wavelet transform (nT) instead of the squared values. For _Swarm_ A/C VFM the variations in the X- and Y-component (horizontal plane) after the event are increased (higher red bar), the Z-component is nearly constant. In the _Swarm_ B VFM measurements only the Y-component is found to increase after the event. Figure 1: _Cont._  
Figure 1: (**a**): Magnetic field variation \(\Delta B_{z}\) (nT) time series from _Swarm_ A VFM (50 Hz) and scalogram for a post-crossing with a time lag of +3 h of the 28 August 2014 volcanic eruption event (Holuhraun 1), Baröarbunga, Iceland. The so-called “cone of influence” (red line in the spectra) divides the wavelet spectrum in two parts, in the inner (large) area edge effects according to the chosen parameters are negligible. (**b**): Coarse map of Iceland with the illustration of the Baröarbunga volcano (yellow star), the Holuhraun lava field (yellow diamond), and the footprints of _Swarm_ A (7 traces, red colour), _Swarm_ B (4 traces, black), and _Swarm_ C (5 traces, white) for the investigated eruption time span 28 August 2014 \(\pm\) 10 days within the spatial domain given in a box size (footprint area) of \(\pm\) 1.5 deg in latitude and longitude centred around Baröarbunga. The ingress points are marked with larger symbols, i.e., north-south crossings or vice versa are readily identifiable. (**c**): Oblique view of a _Swarm_ A VFM single trace scalogram based on \(\Delta B_{x}\) (nT) time series variations with noticeable fluctuations (after wavelet analysis) in the higher frequency range (smaller scales), i.e., attenuations and enhancements of magnetic field fluctuations possibly given by AGW interaction with the F-region plasma. (**d**): like in (c), 3D scalogram view for the \(\Delta B_{y}\) (nT) component. Figure 2: _Cont._
## 4 Experimental Results and Discussion
### Experimental Environment in Case 2
The purpose of this experiment was to find out the correlation between Taiwan's rainfall and electricity use per capita, and the temperature and electricity use per capita based on statistical correlation. In the use case AP, flight information in the airport with the standard csv data file was collected from an airport in New York and the purpose of this experiment was to calculate the frequency in statistics about how many times the departure flights get to the next airport each week. The final use case TA examined the data concerning traffic violation/accidents reported in USA and this experiment aimed to calculate the frequency in statistics regarding how often traffic violations/accidents occur every month.
## 3 Design and Development of the Measuring System
### Software Design and Data Algorithm
#### 3.3.3 Data Collection and Storage Module  
The calibrated system could be used to measure the spacing between two parallel metal surfaces at multi-points. The metal surface must have the same material as the calibration platform and must also meet other requirements like surface roughness, area, and measuring range. A similar platform has been manufactured for experimental tests, which is shown in Figure 11. In the measuring process, the eddy current sensor array gets the displacements, and RS485 communication is employed. The upper computer analyzes the collected data according to the standard Modbus protocol. The distance \(h\) between each group of sensor probes is known based on the calibration process. Measure the distance from the sensor probe to the metal plate on one side as \(h_{1}\), and the distance from the other sensor probe to the surface on the other side as \(h_{2}\). Finally, the distance between the plates at the measured point can be obtained as \(H=h+h_{1}+h_{2}\).
## 1 Introduction
In general, SMA and classification methods are complex and time-consuming, and their accuracy is easily influenced by the quality of the selected samples, whereas index methods involve no additional parameters and are more easily applied [11]. Extraction methods of impervious surface distribution from Landsat imagery (Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)) have often been validated using imagery obtained in summer, when the vegetation is lush and often distinguished from impervious surfaces easily [7; 8; 9; 10]. However, remote sensing images are not always available in every area in summer, such as the east of China where summer is often rainy. Besides, the methods themselves have poor abilities to mask bare soil [10; 11]. The quality of Landsat 8 Operational Land Imager (OLI) imagery is better than that of Landsat 5 TM and Landsat 7 ETM+ imagery, but the existing index methods for the extraction of impervious surface distribution may lose applicability because of the differences in the sensor, wavelength range, and image quantisation bit [22]. In this study, a ratio-based impervious surface index (RISI) was proposed to simplify the extraction process and mask permeable surfaces such as bare soil. The index was validated by three typical images covering urban areas of China in winter and summer and the results were compared with those of five other typical methods for the extraction of impervious surface distribution, namely, the NDBI, IBI, NDISI, NDII, and CBI.
## 5 Limitations  
This study has several limitations that could influence the interpretation and generalizability of the findings:  
1. **Small sample size:** This study included only 15 participants, limiting the statistical power and generalizability of the results. While the sample aimed to represent a diverse population of cochlear implant (CI) users, a larger cohort would provide more robust insights into user behavior and adjustment practices.
2. **Participant criteria:** Only bilateral and bimodal CI users were included, excluding unilateral users. This limitation restricts the findings to specific user groups and leaves the applicability of the self-adjustment app for unilateral CI users unexplored.
3. **Technological proficiency bias:** This study required participants to have prior experience using smartphones, ensuring they could navigate the app. This criterion may have excluded less technologically adept individuals, potentially skewing the results. Future research should examine whether users with limited smartphone experience achieve similar outcomes or face additional challenges.
4. **Range of parameter adjustments:** The objective data showed that the range of possible parameter adjustments was sufficient for meaningful modifications. This finding aligns with [PERSON] et al. [9].
5. **Environmental contextual data:** This study collected extensive data on daily hearing situations but did not analyze the specific factors influencing participants' preferences for user interface types or app usage in diverse environments. This gap limits the understanding of context-driven adjustment behaviors.
6. **Participant user ratings:** The user rating data only reflected saved adjustments. Settings discarded by participants were not included, potentially overlooking instances where the app failed to meet user expectations. This omission could introduce bias by emphasizing positive outcomes over neutral or negative experiences.  
Addressing these limitations in future studies could enhance the understanding of self-adjustment practices and broaden the applicability of the findings. Future work might involve larger and more diverse participant groups, longitudinal studies with technologically inexperienced users, and detailed investigations into situational factors affecting app usage.
## 7 Proposed System Analysis  
Two analytical schemes are established to compute metrics for describing the primary cases concerning future studies focusing on optimisation and compensation. Figure 6 depicts the characteristics of daily demand for the suggested hybrid microgrid system. The curve is obtained by computing total hourly demand specific to every load defined by the model, which includes the critical load components, namely commercial and industrial. Load modelling was based on fixed impedance; therefore, the nominal power and actual power used differ slightly because of the voltage drop  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Variables** & **Values** \\ \hline Proportional gain & \(\mathcal{E}_{p}\) & 0.827 \\ Resonant gain & \(\mathcal{E}_{\varepsilon}\) & 234.02 \\ B0 coefficient & \(\mathcal{O}_{0}\) & \(3.14\times 10^{-4}\) \\ B1 coefficient & \(\mathcal{O}_{1}\) & \(-3.14\times 10^{-4}\) \\ B2 coefficient & \(\mathcal{O}_{2}\) & 0 \\ A0 coefficient & \(\omega_{0}\) & 1 \\ A1 coefficient & \(\omega_{1}\) & \(-1.99\) \\ A2 coefficient & \(\omega_{2}\) & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Designed digital Proportional-Resonant (PR) controller [34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Symbols** \\ \hline Inductance of Inverter & 10 mH & \(L_{inv}\) \\ Resistance of Inverter & 0.5 m\(\Omega\) & \(R_{inv}\) \\ Voltage of DC link & 450 V & \(V_{dc}\) \\ Current sensor gain & 0.1 A & \(H_{i}\) \\ Power of inverter & 1500 W & \(P_{inv}\) \\ Sampling angle frequency & \(1.88\times 10^{5}\) Rad/s & \(\omega_{a}=2\pi f_{a}\) \\ Sampling period & 34 \(\mu\)s & \(T_{a}=1/f_{a}\) \\ Grid Peak voltage & 180 V & \(V_{p}\) \\ Angular frequency of grid & 377 Rad/s & \(\omega_{g}=2\pi f_{g}\) \\ Inductance of grid & 100 \(\mu\)H & \(L_{g}\) \\ Resistor of grid & 0.1 m\(\Omega\) & \(R_{g}\) \\ Resonant angle frequency & 377 Rad/s & \(\omega_{r}=2\pi f_{r}\) \\ Resonant bandwidth & 1.5 Hz & \(B_{s}\) \\ Bandwidth of Resonant angular & 10 rad/s & \(B_{s}=2\pi B_{s}\) \\ Resonant gains & \(\mathcal{E}_{r}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Case study parameter value.  
created in the system. As illustrated in Figure 6, the system's minimum demand is only 30% of the maximum demand. Active and reactive power demand in the system is assumed to have a similar relationship. The minimum demand situation must be used to ascertain the capacity and placement of the compensation mechanism. Interacting compensating components may cause an overvoltage scenario in the system. Referring to Figure 6, it can be observed that there is maximum demand at 11:00, which is also the time at which solar radiation, and, therefore, solar power production is also maximised. The increased power demand witnessed at this time is caused by the combination of industrial and domestic loads.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 4 Discussion
### Glonass
* The number of tracked satellites of GLONASS is similar to that provided by BeiDou and only behind the performance of GPS. Moreover, the quantity of tracked satellites is less affected by attenuation compared to BeiDou and Galileo.
* GLONASS is the second best GNSS in terms of SNR under optimal conditions, being overcome only by BeiDou. However, its signal power is the one most affected by attenuation compared to the other three GNSSs.
* GLONASS provides the lowest TTFF of all GNSSs until 4 dB of attenuation, but above that level, only GPS achieves better results. However, this fast TTFF is obtained at the expense of a high horizontal error, which also degrades remarkably when attenuation is applied.