esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 6 Conclusions  
This article proposes a new method to study the critical derailment speed of a vehicle, which is the progressive trial method, analyzing the wheel-rail contact state when operating  
Figure 14: Variation of vertical force at critical derailment speed.  
Figure 15: Variation of derailment coefficient at critical derailment speed.  
at the critical speed of derailment calculated by the progressive trial method, studying the dynamics of the index change law during derailment. The conclusions are as follows:  
* Compared with the root locus method, both the progressive trial method and the limit cycle method take into account the nonlinear factors in the vehicle system, but the calculated critical speed is lower. Compared with the limit cycle method, the progressive trial method is more suitable for calculating the safe speed of trains running on small radius curves.
* The critical derailment speed is positively correlated with the curve radius, and its growth trend slows down with the increase in the curve radius. The smaller the radius of the curve, the greater the influence of this factor on the critical speed.
* When that the wheel-rail attack angle exceeds 0.06 rad, the vehicle is in a critical state of instability. Attention should be paid in time to avoid derailment accidents.
* On curved lines of different radii, when the train is derailed at the corresponding critical derailment speed, the maximum derailment coefficient is less than 0.8, but the derailment phenomenon still occurs. It indicates that the single derailment coefficient standard is not enough to measure whether the train derails or not.  
Conceptualization, R.Y. and H.M.; methodology, J.Z.; software, R.Y.; validation, R.Y. and H.M.; formal analysis, R.Y. and H.M.; investigation, Y.N.; resources, H.M.; data curation, R.Y.; writing--original draft preparation, R.Y.; writing--review and editing, H.M. and Y.N.; visualization, Y.N.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China (grant number 51775031), R&D Program of Beijing Municipal Education Commission (grant number KM202210016003), Young Teachers' Research Ability Enhancement Program Project of Beijing University of Civil Engineering and Architecture (grant number X21059).  
Not applicable.  
The authors declare no conflict of interest.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## Materials and Methods
### Theoretical Basis of the High-Spatial-Resolution Land/Water Mapping Capability
The greatest number of samples occurs near an incidence angle of \(\sim\)30\({}^{\circ}\) and \(\overline{r_{R}}\) = 600 km. An incidence angle of \(\sim\)30\({}^{\circ}\) corresponds to the angle of maximum gain in the receive antenna pattern. Figure 1: Histogram of propagation distances: (**a**) \(\overline{r_{R}}\) (from specular point to CYGNSS receiver); (**b**) \(\overline{r_{T}}\) (from GPS transmitter to specular point) for a full day of samples on 9 June 2017. The strength of the received signal scattered from coherent (smooth) surface features is more than 1000-times stronger than that received from incoherent (rough) surfaces at all measurement geometries, with a value of 1,600 (32 dB) at the most common incidence angle. This enhanced sensitivity explains why small, smooth water bodies occupying a very small fraction of the total incoherent scattering area can make such a dominant contribution to the net received signal. It also explains why very small inland water bodies can be so finely resolved by the GNSS-R bistatic radar measurements. For example, a small inland water body that fills only 1% of the first Fresnel zone (-20 dB beam filling) would still produce an 11-17 dB higher contribution to the total received signal strength than would the surrounding incoherently scattered signal from rough land surfaces.
## 9 Non-Symmetric Section
### Axial Symmetry  
Finally, take the case of axially symmetric channel sections A-C, considered earlier. Because G and E both lie on the centroidal \(x\)-axis of symmetry, a single trans-moment refers to the shift in \(M_{y}\) from centroidal axis \(y\) to shear centre axis \(y_{E}\), i.e., through a perpendicular distance, \(E_{x}=e_{x}+X^{\prime}\). There is no shift in \(M_{x}\) along the centroidal axis. Therefore, from Equation (30b,c),  
\[\sigma_{z}=M_{y}\ E_{x}/I_{Ey}\ \mathrm{where}\ I_{Ey}=I_{y}+AE_{x}{}^{2}\]  
Applied to the symmetrical channel section in Figure 1a, for example, with \(F_{x}=100\ \mathrm{N}\), the uniform axial stress that arises from the trans-moment is found:  
\[E_{x}=e_{x}+X^{\prime}=3a/8+a/4=5\mathrm{a}/8\]  
Figure 21: Elastic bending stresses under four transverse load combinations under (a-1), (b-2) etc.  
\[I_{Ey}=(3.28\times 10^{-3})d^{4}+(2 dt)(5a/8)^{2}\] \[I_{Ey}/d^{4}=(3.28\times 10^{-3})+2(t/d)(5/8)^{2}(a/d)^{2}\] \[=(3.28\times 10^{-3})+2(1/16)(5/8)^{2}(1/2)^{2}=0.01548\] \[\therefore I_{Ey}=0.01548d^{4}=6446.2\text{ mm}^{4}\] \[\sigma_{z}=(M_{y}\ E_{x})/I_{Ey}=(F\times 300)(5/8\times 12.7)/6446.2 =36.9\text{ MPa}\] (34a) which is distributed to each median point in proportion to its \[x\] -co-ordinate by two methods: (i) From Equation ( 34a ), the stress adjustments to be applied to Table 12 are as follows: \[\sigma_{z1}=\sigma_{z7}=(a-X^{\prime})\sigma_{z}/(e_{x}+X^{\prime})\] \[=(a-a/4)\sigma_{z}/(3a/8+a/4)=6\sigma_{z}/5=6\times 36.9/5=+44.28 \text{ MPa}\] \[\sigma_{z2}=\sigma_{z6}=0\] \[\sigma_{z3}=\sigma_{z4}=\sigma_{z5}=-X^{\prime}\ \sigma_{z}/(e_{x}+X^{\prime})\] \[=-(a/4)\sigma_{z}/(3a/8+a/4)=-2\sigma_{z}/5=-14.76\text{ MPa}\] (ii) In another interpretation of the trans-moment's conversion to bending stress, the moment arm is increased from \[L\] to \[L+E_{x}\], giving \[M_{y}=F_{x}(L+E_{x})\] : \[\sigma_{z}=(M_{y}\ E_{x})/I_{Ey}=F_{x}(L+E_{x})x_{i}/I_{Ey} \tag{34b}\]  
Here, \(M_{y}\) is referred to axis \(y_{E}\) where the parallel axis theorem has been applied to give \(I_{Ey}\) and where \(x_{i}\) represents the \(x\)-co-ordinates for median points \(i=1\), \(2\), \(3\ldots 7\). Applying Equation (34b) to symmetrical channel section A yields the following: \[\sigma_{z1}=\sigma_{z7}=100\times(300+5a/8)(a-a/4)/6446.2\] \[=100\times 307.94\times 9.525/6446.2=+45.5\text{ MPa}\] \[\sigma_{z2}=\sigma_{z6}=0\] \[\sigma_{z3}=\sigma_{z4}=\sigma_{z5}=100\times(300+5a/8)(-a/4)/6446.2\] \[=100\times 307.94\times(-3.175)/6446.2=-15.17\text{ MPa}\] Numerically, with these adjustments, method (ii) in reasonable agreement with the method (i).
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 3 Statistical Models for Forecasting Traffic
### Seasonal ARIMA Model  
The seasonal autoregressive integrated moving average (SARIMA) model is an extension of the ARIMA model that explicitly supports univariate time series data with a seasonal component [45; 46; 47]. The model comprises three hyperparameters that define the autoregressive (AR), integrated (I), and moving average (MA) for the non-seasonal component of the time series, as well as an additional hyperparameter for the seasonal period (S). The model is denoted as \(\text{SARIMA}(p,d,q)(P,D,Q)_{m}\), where the first three parameters \((p,d,q)\) refer to the non-seasonal part of the model (ARIMA), whereas the last parameters \((P,D,Q)_{m}\) represent the seasonal part. Specifically, \(p\) is the order (number of time lags) of the non-seasonal AR part, \(d\) is the degree of differencing (the number of times the data have had past values subtracted) for the non-seasonal part, \(q\) is the order of the non-seasonal MA part, \(P\) is the order of the seasonal AR part, \(D\) is the degree of differencing for the seasonal part, \(Q\) is the order of the seasonal MA part, and \(m\) is the number of periods in each season.  
Given a time series of data \(x_{i}\), \(i=0,\ldots,t-1\), in order to calculate the forecast value of \(y_{t}=x_{t}\) at time \(t\), the SARIMA model is used and written as  
\[\left(1-\sum_{i=1}^{p}\phi_{i}L^{i}\right)\left(1-\sum_{i=1}^{p} \Phi_{i}L^{im}\right)(1-L)^{d}(1-L^{m})^{D}x_{t}\\ =\left(1+\sum_{i=1}^{q}\theta_{i}L^{i}\right)\left(1+\sum_{i=1}^ {Q}\Theta_{i}L^{im}\right)\epsilon_{t},\]  
where \(Lx_{t}=x_{t-1}\) and \(L^{i}x_{t}=x_{t-i}\) represent the lag operator, \(\epsilon_{t}\) are the error terms, \(\phi_{i}\) are the parameters of the non-seasonal AR part of the model, \(\theta_{i}\) are the parameters of the non-seasonal MA part, \(\Phi_{i}\) are the parameters of the seasonal AR part, \(\Theta_{i}\) are the parameters of the seasonal MA part.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 2 The Navigation Filter
### Observation Model
#### 2.2.1 Doppler Shift Model
\begin{array}{rl}\Delta\hat{\rho}^{k}\cong\Big{(}\frac{(\mathbf{v}-\mathbf{v}^{k}) ^{T}}{\|\mathbf{r}-\mathbf{r}^{k}\|}-\mathbf{u}^{k}\mathbf{T}\Big{(}\mathbf{v}-\mathbf{v}^{k}\Big{)} \frac{\mathbf{u}^{k}\mathbf{T}}{\|\mathbf{r}-\mathbf{r}^{k}\|}\Big{)}\delta\mathbf{r}+\mathbf{u}^{k} \mathbf{T}\delta\mathbf{v}+\\ \frac{1}{c}\bigg{(}\frac{\Big{(}\mathbf{u}^{k}\mathbf{T}^{k}\Big{)}\mathbf{u}^{k}-\hat{\mathbf{r }}^{k}\Big{)}^{T}-\mathbf{r}^{k}}{\|\mathbf{r}-\mathbf{r}^{k}\|}\Big{(}\mathbf{v}-\mathbf{v}^{k} \Big{)}-\mathbf{u}^{k}\mathbf{T}\hat{\mathbf{v}}^{k}\bigg{)}c\delta t+\delta\Big{(}c\dot {\delta t}\Big{)}+\eta_{\dot{\rho}}\end{array}  \tag{15}\]  
Finally, the range rate error of (15) can be written in the following form:  
\[\Delta\hat{\rho}^{k}\cong\mathbf{h}_{r}\,\delta\mathbf{r}+\mathbf{h}_{v}\,\delta\mathbf{v}+h_{ c\delta t}\,c\delta t+\delta\Big{(}c\dot{\delta t}\Big{)}+\eta_{\dot{\rho}} \tag{16}\]  
where the coefficients \(\mathbf{h}_{r}\), \(\mathbf{h}_{v}\) and \(h_{c\delta t}\) can be easily deduced by identification between (15) and (16).
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 2 Physical Model and Correction Method  
### THAAD-like Seeker Model  
As the aircraft's speed increases, the aerodynamic heat of the optical window also in-creases. Elevated temperatures can lead to two issues: firstly, they can result in significant thermal radiation noise, which affects the normal functioning of the detection system; secondly, they may cause cracks in the optical window due to thermal stress. Therefore, when designing a hypersonic infrared imaging seeker, it is essential to consider various design factors, such as the imaging guidance system, aero-optical effects, head-heat protection, infrared window and aerodynamic resistance. These design requirements should be combined effectively. To mitigate the pneumatic heating's impact on the optical window and reduce challenges in refrigeration system design, a cooling method utilizing side recess windows is employed to lower the temperature of the optical window.  
The seeker's optical window retains a concave structure, similar to that of the THAAD model, as shown in Figure 1, with a recessed height of \(\delta\) = 4 mm, due to airflow passage, and a slope length of 5.7 mm connecting its outer surface to the seeker wall. The dimensions for the optical window itself are 200 mm in length and 60 mm in width. Additional dimensions for other parts of the window model are illustrated in Figure 1 below. Positioned on top of the missile body, this optical window is located on its leeward side when flying at an angle of direct attack. Sapphire is selected as the optical window material, and its physical properties are shown in Table 1 below.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 2 Construction of On-Orbit Reconstruction Indicators for a Giant Satellite Swarm  
Fault reconfiguration refers to the process in which the satellite swarm maintains a healthy state by adjusting the positions of healthy satellites to replace faulty satellites or by isolating faulty satellites to limit their influence on the swarm. In essence, reconfigurationinvolves a change in the satellite swarm topology; for example, position replacement results in a configuration change, and fault isolation results in a change in the information path. In some scenarios, the latter is also an independent satellite swarm fault isolation problem. The core issue of reconstruction is how to select reconstruction indicators. Starting from actual engineering, this paper studies the reconstruction of the swarm configuration. It constructs mathematical models for properties such as the satellite swarm health state, the total energy consumption of reconstruction, and the balance of fuel consumption, which lays the foundation for the selection of reconstruction indicators.
## 6 Complexity Analysis  
In this section, we present the time complexity of the proposed heuristic LNR-RCTD algorithm. The overall complexity analysis is divided into three parts to maintain uniformity with our proposed heuristic. First, we analyze the time complexity of line 1 and 2 of the algorithm 1 as shown below. The time complexity of line 1 and 2 is \(\sum_{i=1}^{an}1=\alpha n\) and \(\sum_{j=1}^{N}1=m\), Respectively. Where \(\alpha n\) represent the total number of network flows and \(N\) is the average path length. Thus, the complexity of this portion is as follows:  
\[\sum\
olimits_{i=1}^{an}\sum\
olimits_{j=1}^{N}c_{0}=c_{0}.\alpha n.N \tag{23}\]  
Second, we analyze the time complexity of the \(LINK\)-\(OPTIMIZATION\) algorithm. Its time complexity is mainly based on line 2, 3, and 4 while the remaining portion of Algorithm 2 is comprised of a few subroutines that have almost constant time and do not contribute much to the overall complexity. Thus, the time complexity of Algorithm 2 is as follows:  
\[\sum\limits_{k=1}^{z}NM_{paths}\sum\limits_{l=1}^{M_{paths}}C=z.NM_{paths}. CM_{paths}=CNz(M_{paths})^{2} \tag{24}\]  
where \(z\) is the average degree of the node and \(NM_{paths}\) is the average path length multiplied by the total number of paths. Hence, \(NM_{paths}\) is the time complexity of path finding and is calculated in APAC [52]. The loop at line 4 runs approximately \(M_{paths}\) times and \(C\) is a constant representing the time complexity of the portions comprised of subroutines. Finally, we analyzed the time complexity of the \(TOPOLOGY\)-\(OPTIMIZATION\) algorithm. Its time complexity is mainly based on line1, 4, and 9. Thus, the time complexity of Algorithm 3 is as follows:  
\[\sum\limits_{e_{t}}^{E_{extra}}c_{1}+\sum\limits_{v}^{V}c_{2}+\sum\limits_{v _{t}}^{V_{extra}}c_{3}=c_{1}E_{extra}+c_{2}v+c_{3}V_{extra} \tag{25}\]  
Equation (26) is the result of combing Equations (23)-(25):  
\[TC=(c_{0}.\alpha n.N).CNz(M_{paths})^{2}+(E_{extra}+v+V_{extra}) \tag{26}\]where \(TC\) is the time complexity of LNR-RCTD. Since, we are calculating the Big-Oh time complexity. Therefore, we eliminate the least significant factors and only select the most significant factors those mainly contribute in the time complexity of LNR-RCTD. Thus, finally we get:  
\[TC=O\big{(}\ C.\ z\alpha n.\ N^{2}M_{paths}{}^{2}\big{)} \tag{27}\]  
Equation (27) shows that the time complexity of the proposed heuristic algorithm is polynomial time.  
```
0: A set of extra links \(F_{extra}\) and set of vertices V
0: A connected sub-graph \(G\)'\((V\)',\(E\)'). Begin
1:for each \(e_{extra}\)\(\in\)\(F_{extra}\)do
2: Remove \((e_{extra})\)
3:endfor
4:for each \(v_{j}\)\(\in\)\(V\)do
5:if (Isolated ( \(v_{j}\) ) = \(\mathrm{True}\)) then
6:\(V_{extra}\)\(\leftarrow\)\(v_{j}\)
7:endif
8:endfor
9:for each \(v_{extra}\)\(\in\)\(V_{extra}\)do
10: Remove ( \(v_{extra}\))
11:endfor
```  
**Algorithm 3**TOPOLOGY-OPTIMIIZATION
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Power Allocation Complexity  
The amount of transmit power provided to a user influences their throughput. Because power-domain user multiplexing is the foundation of NOMA, the power allocation also impacts the possible capacity of other users. If we want the highest potential throughput in NOMA, we need to resort to a brute-force search of all possible user pairings with dynamic power allocation. Unfortunately, performing such a thorough search takes a lot of processing power. Thus, future research works will need to pay attention to this research area.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Summary of Shared Challenges for Electric Propulsion Technology Development
#### 4.4.2 Technologies Lower on The S-Curve  
While in theory many of the technologies shown in Figures 3, 8, and 13 could meet the expanding need of the space industry, nearly all of them remain low on their s-curve at some power level and, thus, not ready for flight. There are several shared challenges that would need to be addressed to raise the maturity of these technologies. We summarize these as follows:  
* Scaling to low power incurs increasing ionization cost;
* Optimal performance is anticipated above practical or economical power levels for laboratory development (typically \(\gg\)100 kW);
* Incomplete theoretical understanding of performance or lifetime.  
Following the historical precedent set by Hall thrusters and gridded ion thrusters, the development cycle to bring these systems into flight-readiness could take decades. This is assuming that none of the known technical challenges for these systems prove to be logistically insurmountable in the foreseeable future. For example, it is still an open question if there are fundamental limitations to the packing density of electrosprays or for the theoretical upper limit of performance for PITs. Additionally, there must be sufficient \"pull\" from mission planners to maintain research interest in these technologies; if no missions are suited to massively-scaled electrospray arrays, the development of this technology will stagnate, shallowing the s-curve. With that said, the growing and immediate need for low- or high-power propulsion solutions means that there may not be time to follow more traditional development cycles. There is a vested interest, then, in being able to increase the slope of the s-curve for these technologies. To resolve the issues slowing the development of \"low s-curve\"technologies, we make the broad recommendations below, and in the following section we discuss specific strategies to this end.  
* Establish the theoretical bounds in performance and identify any fundamental limitations;
* Develop techniques for optimization that more rapidly allow the theoretical performance to be realized experimentally.
# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 5 Road Tracking System  
The road detection result is received from the road detection system described in Section 3, and can be represented simply as a bounding box at time \(t\):  
\[\mathbf{z}_{t}=\left[u_{t},v_{t},w_{t}^{img},h_{t}^{img}\right]^{T} \tag{12}\]  
We assume that the ground is relatively flat, ignoring any in-plane rotation of the cylindrical object. For a pinhole camera with focal length \(f\) and principal point (\(c_{x}\), \(c_{y}\)), we can write  
\[\begin{array}{l}u_{t}=\left(fx_{t}^{cam}+c_{x}\right)/z_{t}^{cam}\\ v_{t}=\left(fy_{t}^{cam}+c_{y}\right)/z_{t}^{cam}\\ w_{t}^{img}=fw_{0}/z_{t}^{cam}\\ h_{t}^{img}=fh_{0}/z_{t}^{cam}\end{array} \tag{13}\]  
in which (\(h_{0}\), \(w_{0}\)) is the assumed road target's height and width.  
The rigid transformation of the center of the road target into the camera coordinate systems is homogeneously represented as  
\[x_{t}^{cam}=\left[\begin{array}{c}x_{t}^{cam}\\ y_{t}^{cam}\\ z_{t}^{cam}\\ 1\end{array}\right]=T_{t}^{W/C}\left[\begin{array}{c}x_{t}\\ y_{t}\\ z_{t}\\ 1\end{array}\right] \tag{14}\]  
in which \((x_{t},y_{t},z_{t})\) is the road target's position in the world coordinate and the transformation \(T_{t}^{W/C}\)  
in which \(\mathbf{x}_{t}^{\prime}=(x_{t}^{\prime},y_{t},z_{t}^{\prime})\) and \(R_{t}^{T}\) represent the orientation of the robot in the world coordinate frame, which can be obtained through the odometer and inertial measurement unit.  
Hence, given the bounding box \(\mathbf{z}_{0}\) of road detection, the initial position \([x_{0}^{cam},y_{0}^{cam},z_{0}^{cam},1]^{T}\) of the initial road target in the camera coordinate frame can be obtained by using (13). In (13), \(x_{0}^{cam}\) and \(y_{0}^{cam}\) can be calculated given \(u_{0}\) if \(z_{0}^{cam}\) is known. \(z_{0}^{cam}\) can be obtained from \(w_{0}^{img}\) or \(h_{0}^{img}\), such as \(z_{0}^{cam}=fh_{0}/h_{0}^{img}\). Then, the road target position at time 0 is mapped to the world coordinate frame by  
\[\left[\begin{array}{c}x_{0}\\ y_{0}\\ z_{0}\\ 1\end{array}\right]=(T^{R/C}T_{0}^{W/C})^{-1}\left[\begin{array}{c}x_{0}^{ cam}\\ y_{0}^{cam}\\ z_{0}^{cam}\\ 1\end{array}\right] \tag{17}\]  
For the given road detection bounding box \(\mathbf{z}_{t}\) at time \(t\), the corresponding road target position \((x_{t},y_{t},z_{t})\) in the world coordinate frame can be calculated using (13) and (14) frame by frame.  
After the road target position in the world coordinate is obtained, we treat it as the next tracking waypoint of the robot. The regular waypoint tracking controller can be integrated into the system to calculate the desired control commands, such as pure pursuit, MPC, and DP.
## 2 Integrated and Decentralised Urban Water Management: A Resilient Approach
### Rainwater  
Alternative options to conventional drainage, such as rainwater harvesting and infiltration, with decentralised detention, not only reduce the demand for drinking water and water discharge but also maintain the natural water cycle [51]. The use of rainwater covers a variety of applications, from toilet flushing and washing in clothes washers to general cleaning and watering of green areas, as well as other uses that do not require drinking water (e.g., cooling towers, fire networks, air conditioning in buildings) [52], in urban communities and public spaces such as streets, parks, golf courses, and car parks [51].  
However, it is crucial to consider several factors that affect both the quality and quantity of the water collected. These include the local rainfall pattern, collection area, surface drainage materials and characteristics, the sizing and material of pipework systems, pollution levels in the collection area, and the risk of contamination of the system [51].  
To prevent the deterioration of water quality in the storage reservoir, it is recommended to install devices such as grids and screens to remove unwanted solids, which should be retained and/or diverted. It is also recommended that the first waters are not used, and it is preferable to install a device in the system to divert the initial runoff (first flush), preferably automatically [52; 53]. If this is not sufficient to meet minimum quality standards, additional treatment using physical and chemical solutions may be necessary [53].  
Rainwater supply: to determine the volume of rainwater to be used, expression (3) is used [51; 52; 53]:  
\[Y_{R}=A\times P\times C\times\eta \tag{3}\]  
where  
\begin{tabular}{l l} \(Y_{R}\) & is the rainwater yield/supply per time interval, expressed in litres (L); \\  & is the horizontal projection of the collection area/surface, expressed in \\ \(A\) & square metres (m\({}^{2}\)); do not consider surfaces that are in contact with \\  & polluting sources; \\  & is the total rainfall height for a given time interval, expressed in millimetres \\ \(P\) & (mm); consider rainfall studies with historical rainfall series corresponding \\  & to periods of no less than 10 years; \\ \(C\) & is the surface runoff coefficient (ratio between the volume collected and the \\  & total volume of precipitation); \\ \(\eta\) & is the hydraulic efficiency coefficient of the filtration. \\ \end{tabular}  
Rainwater demand: Table 4 shows indicative consumption values by type of device or use, which can be taken into account when sizing systems. These values were estimated based on devices classified as efficient [52].