esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodological Description of Economic Viability
#### 2.3.1 Net Present Value (NPV)  
NPV, also called \"capital value\", consists of updating the casw-flow \"Ct\" (the difference between income and expenses for each period analyzed.) of the project for the different periods to an estimated discount interest rate \"i\" (discount rate or calculating interest). Assuming, as usual, the same interest rate, \"i\", for all periods, N, generally equal to each other and equal to the calendar year. We considered the first time period, the first year of amortization, as year 1. So \"t\" takes values between 1 and N. We consider the time period t = 0 is related to the investment during the design and construction phase of the gasifier. The NPV considers this discount rate \"i\" (calculated as WACC--weighted average cost of capital) during the useful life of the project, giving the annual cash flows at current values. NPV can be equated as [63]:  
\[\rm{NPV}(i,N)=-K+\sum_{t=1}^{N}\frac{Ct}{(1+i)^{t}} \tag{1}\]  
where \"i\" is the financial discount rate, \"Ct\" is the annual cash flow (Income minus expenses) each year, and \"N\" is the total number of years. The time period \(t\) = 0 is related to the investment during the design and construction phase of the gasifier.  
The NPV represents the net profit generated by the project, obtained by financial equivalence at time zero. If the NPV is greater than zero, the project is viable for that interest rate. It is, therefore, a necessary condition although it does not have to be sufficient.  
The WACC is calculated based on the cost of capital and the cost of debt (borrowed capital), which allow for determining the discount rate applicable to cash flows to calculate the NPV. The WACC is given by [64]:  
\[\rm{WACC}=\left(\frac{E}{E+D}\right)K_{e}+\left(\frac{D}{E+D}\right)K_{d}(1-T) \tag{2}\]  
where \"E\" is the market value of the equity, \"D\" is the market value of the debt, \"K\({}_{e}\)\" is the cost of equity, \"K\({}_{d}\)\" is the cost of debt and \"T\" is the marginal tax rate.
## 5 Test Results
This bias is considered in the HPLs by replacing the \(\bar{m}_{DD,k}\) in Equation (49) with \(\bar{m}_{DD,k}+m_{c,k}\), where \(m_{c,k}\) is a vector containing 0.25 \(\times\)\(\lambda_{j}\) in range for the DD phase observations, when the rover elevation angle to satellite \(s\) (\(s\
eq\) 1) is lower than the elevation angle threshold \(\bar{\theta}^{s}\). For other observations, the entry in \(m_{c,k}\) amounts to zero. The number of the phase observations suffering from the bias \(m_{c,k}\) increases with the increase in \(\bar{\theta}^{s}\). Taking the time period T1 as an example, Figure 9a shows the change of the mean and maximum HPLs when changing the elevation angle threshold \(\bar{\theta}^{s}\). Note that the ambiguity-fixed solutions based on the dataset used in the suburban scenario are still considered fixed. Starting from an elevation mask of 10 degrees, the mean HPL (the blue line) has increased by about 41% when increasing \(\bar{\theta}^{s}\) to 30 degrees. It can also be observed that the maximal HPL is still below the HAL of 0.5 m when \(\bar{\theta}^{s}\) is set to 30 degrees, which means that the availability is so far not influenced by the bias. Note that the change of the availability is only considered to be influenced by the change of the HPLs here, and the possible reduction of the epochs achieving FAR is not further investigated in this section. Since the HALs for different road transport applications are not clearly set yet, in particular the very challenging autonomous driving, for applications with lower HALs, the bias \(m_{c,k}\) could play a larger role in the achievable availabilities. In Figure 9b, the change of the availabilities is illustrated for different values of HALs selected from 0.2 m down to 0.1 m. In Figure 9b it can be observed that the availabilities with HAL of 0.2 m are similar to those with a HAL of 0.5 m, i.e., above 95%. Dramatic decrease of the availabilities occurs for applications with a higher accuracy requirement in the horizontal direction, i.e., with a HAL of 0.1 m (the blue line). In such a case, the availability is below 50% with \(\bar{\theta}^{s}\) higher than 21 degrees. This suggests that even for ambiguity-fixed solutions for  
Figure 8: Ambiguity-fixed horizontal positioning errors (HPEs) and horizontal protection levels (HPLs) during T1 for: (**a**) the open-sky scenario and (**b**) the suburban scenario. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **RMS of Fixed HPEs** & **Mean HPLs** & **Epochs with FAR** & **Mean Fix-Rate for PAR** & **Availability** \\ \hline Open-sky scenario & 0.014/0.009 & 0.062/0.060 & 99.98\%/100\% & 92.8\%/- & 100\%/100\% \\ \hline Suburban scenario & 0.022/0.017 & 0.093/0.088 & 94.50\%/99.96\% & 90.6\%/91.0\% & 100\%/100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of the ambiguity-fixed HPEs and the HPLs for the time periods T1 and T2. The values for T1 and T2 are separated by ’/’. short-baseline RTK under quiet atmosphere conditions, a clear environment with limited multipath effects might be required for applications with HAL as low as 0.1 m.
## 3 Experimental Evaluation  
We experimentally verified the practical effectiveness of shift-peristrophic multiplexing. The composition of the experimental system is shown in Figure 5. The reference beam became a spherical wave when using an objective lens and is emitted into the medium. After the spatial light modulator (SLM) modulates the signal beam and the hologram's spatial band is limited with a low-pass filter (LPF), the beam is emitted into the medium. The bandwidth of the LPF is about twice the size of the Nyquist \(D\), and \(D\) is determined by:  
\[D=\frac{f\lambda}{\Delta} \tag{3}\]  
where \(f\) is the focal length of the lens for the signal beam, \(\lambda\) is the wavelength, and \(\Delta\) is the pixel size of SLM. The over-sampling ratio of the data page is equal to three when the charge coupled device (CCD)  
Figure 4: The recording method to the disk-shaped medium.  
Figure 3: Recording method shift-peristrophic multiplexing.  
signal is detected. Stepping motors are used for the shift and rotation of the medium. The minimum motor steps are 1 \(\mu\)m for the shift and 0.005\({}^{\circ}\) for the rotation. Repeat accuracies are \(\pm\)0.5 \(\mu\)m for the shift and \(\pm\)0.02\({}^{\circ}\) for the rotation, respectively. We used the radical polymerization photopolymer as a recording medium. The photopolymer film thickness was 1.5 mm. The dynamic range of the medium indicated by \(M\#\) is \(M\#\approx 50\). Detailed experimental conditions are shown in Table 1.
# Crosslinked Bifunctional Particles for the Removal of Bilirubin in Hyperbilirubin Cases
## 3 Results
### BSA Immobilization  
In order to link albumin to PEGMA [25] and/or GMA brushes, it is necessary to control the pH to prevent protein denaturation due to their sensitivity to pH changes. Therefore, physiological pH conditions (pH = 7.4) were employed, as proteins are highly sensitive towards pH changes [26].  
The quantity of BSA immobilized onto the particles was determined through UV spectroscopy, by calculating the difference between the initial and final BSA concentration in PBS solution. The kinetics of albumin immobilization onto non-wetted St-DVB based particles was carried out (Figure 3). The BSA immobilized in each case was 1.8 mg BSA/g particles for non-functionalized particles, 1.3 mg BSA/g particles for functionalized particles with GMA and PEGMA, and 0.53 mg BSA/g particles for PEGMA functionalized particles. In the case of GMA functionalization, no BSA was immobilized onto the particles.  
As previously reported by the research group [27], the wetting of the particles based on St-MMA in ethyl lactate implied a clear improvement in the albumin immobilization. For that reason, the albumin retention of these St-DVB particles will be also studied after prewetting in EL. The particles were pre-wetted with ethyl lactate before the immobilization process, as it facilitates access to the linking sites. The time evolution of BSA concentration is illustrated in Figure 4.  
As is shown in Figure 4, the particles without functionalization represented the best support for albumin immobilization, with an average value of 2 mg BSA/g particles. PEGMA functionalization showed a similar performance, with 1.9 mg BSA/g particles while GMA and GMA-PEGMA functionalization presented values of only 1.5 and 1.25 mg BSA/g particles, respectively. The reduction of immobilized BSA in the functionalization with GMA can be explained due to its capacity as a slight crosslinker [27; 28] close to the well-known use of divinylbenzene as a crosslinker [29]. The ability of GMA to act as a mild crosslinker can prevent the swelling effect, but it may also restrict access to certain binding sites. This leads to the formation of a highly crosslinked, rigid 3D structure that can impede the entry of large biomolecules like albumin into the internal active sites, ultimately decreasing the capacity for albumin immobilization on the particles.  
Figure 3: Kinetics of BSA immobilization in St-DVB non-pre-wetted particles. Wp = 2.5 g; T = 25 \({}^{\circ}\)C; t = 72 h and Fr = 8 mL/min.
## 3 Results and Discussion
### Air Quality in Haizhu in 2015
#### Air Quality during Pollution Episodes  
Table 2 summarizes the pollution episodes in Haizhu district in 2015. Shayuan station was the only station where heavy pollution episodes were observed in both winter and summer, and the two pollution episodes were also the typical pollution (PM\({}_{2.5}\) and NO\({}_{2}\) pollution in winter, O\({}_{3}\) pollution in summer). Therefore, these two typical pollution episodes in Shayuan will be analysed in detail below.  
(1) Heavy pollution episode in winter  
A winter heavy pollution episode in Haizhu district occurred during 14-28 January 2015 (Table S4 shows the statistical data). In this pollution episode, the air quality was light pollution in the beginning (15-19 January), then the pollution level peaked during 20-21 January. It finally decreased to light pollution again in the third period (22-27 January). On 28 January, the air quality was good again. This episode was a process in which the air quality decreased gradually and then increased gradually. During this episode, the wind speed was in the range of 1.1-2.9 m/s, the RH was in the range of 52-85%, and there was no precipitation. The temperature was in the range of 10.3-18.0 \({}^{\circ}\)C, and the surface pressure was in the range of 1009.1-1016.1 hPa. The low wind speed during this winter episode (\(\sim\)1.5 m/s) indicated that the atmosphere was stable, and as reported in previous studies, low or calm wind speed generally leads to high pollution [67; 68], because stable atmospheric condition favours the accumulation of pollutants. There is another possibility that the existence of temperature inversion layer could also lead to the accumulation of the air pollutants (e.g., [PERSON] et al., 2006 [69]).  
(2) Heavy pollution episode in summer  
A summer heavy pollution episode in Haizhu district happened during 3-8 August 2015 (Table S5). The whole episode covered only six days, including the two days of light pollution (4 and 7 August), one day of moderate pollution (5 August), and one day of heavy pollution (6 August). The pollution occurred and disappeared within a short time, but it exhibited high intensity. In this episode, the wind speed was in the range of 1.1-4.4 m/s, the RH was between 65 and 76%, and there was no precipitation. In the first period (3-6 August 2015), the wind speed was in the range of 1.1-1.4 m/s. There was no precipitation nor cloud. The temperature was in the range of 27.6-30.3 \({}^{\circ}\)C, and the surface pressure was in the range of 991.7-1002.4 hPa. The hourly temperatures were between 23.4 and 35.6 \({}^{\circ}\)C. Sunny weather, low wind, and high temperatures would create favourable conditions for O\({}_{3}\) production and accumulation [10]. Then, the wind speed increased to 3.9-4.4 m/s during 7-8 August 2015. Strong wind transported the air pollutants out of Haizhu, improving the air quality in Haizhu to light pollution. The temperature inversion layer might also enhance the accumulation of the air pollutants (e.g., [PERSON] et al., 2006 [69]).
# Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre
## 2 Materials and the Mixture Method  
### Characteristics of CNF Used  
There are many types of CNFs in terms of raw materials and manufacturing processes, and the ones used in this study were two typical commercially available CNFs (TEMPO-CNF and ACC-CNF). Two types of ACC-CNFs with different raw materials were used, which are referred to as ACC-CNF-S and ACC-CNF-B. The tests used these three types of CNFs and two states of TEMPO-CNF: a gel containing 2% CNF and a powder containing90% CNF. The CNFs used are listed in Table 1. CNF is generally supplied in gel form mixed with water, although when mixed with soil to produce cement-treated soil, the soil includes a large quantity of water. The addition of water, which is included in the cement slurry, and CNF gel to the soil causes the soil to soften, and more cement is required to solidify the soft ground. Powdered CNF was also used in this study to investigate whether it might be possible to mix it with soil.  
The production methods for the CNFs are different, and their degrees of fibrillation are also different. TEMPO-CNF is produced from wood pulp by a chemical defibration technique called TEMPO-mediated oxidation [18]. It is characterised by a very fine and uniform fibre width, which is as thin as approximately 3 nm. On the other hand, ACC-CNF is produced from bleached pulps of softwood or bamboo by a physical defibration technique called the aqueous counter collision (ACC) method [19]. CNF derived from soft woods is referred to as ACC-CNF-S, and that derived from bamboo is called ACC-CNF-B. These CNFs are characterised by their low chemical environmental impact as they are fibrillated using only water and raw materials, their amphiphilicity, and the concise control of the fibre width due to the gradual refinement of the fibre by repeated processing. The fibre width ranges from a few nanometres to several millimetres, depending on the number of treatments.
# The Suitability of Short Rotation Coppice Crops for Phytoremediation of Urban Soils
## 3 Results and Discussion
### Soil PTE Concentrations  
The total and the bioavailable concentrations of PTE in the studied soil before and after the field trial are reported in Table 4.  
Although two years is a limited time for a phytoremediation process, the bioavailable concentrations of the two metals preferentially uptaken by plants (Cu and Zn) decreased for all species. The decrease for Zn was statistically significant for all clones at the end of the experiment and, on average, _Poplar_ clones reduced Zn by 28%, _Salix_ by 36%, and _Robinia_ 26%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Total concentrations (mg/kg)**} \\ \cline{3-8}  & & **Cd** & **Co** & **Cr** & **Cu** & **Ni** & **Pb** & **Zn** \\ \hline
**P** & Before & 1.7 (0.08) & 27.9 (1.1) & 195 (15) & 96 (32) & 185 (3) & 203 (25) & 421 (80) \\  & After & 0.4 (0.16) & 27.3 (0.5) & 229 (19) & 88 (8) & 198 (16) & 2017 (70) &  
Contrarily, available Pb concentrations seem to increase during the trial (although not statistically significant).  
The total PTE concentrations did not change during the trial, consistent with literature results, where a decrease has been rarely described in short term experiments [17].  
Possible release of PTE in soil-porewater and groundwater was monitored. The soil solution was basic, with an average pH of 8.0; PTE concentrations were below the detection limits of the instruments during the whole of the trial.  
In the groundwater, CrVI and Ni contents were higher than the limits defined by Italian law for green areas before and during the experiment (reported in Supplementary Materials, Table S6). These high concentrations could probably be attributed to the past industrial use of this area, leading to a relict contamination. However, considering the analysis on interstitial water and groundwater, a leaching process induced by the phytoremediation can be excluded.
# Location-Aware Point-to-Point RPL in Indoor IR-UWB Networks
## 3 UWB-Based Cooperative Multi-Hop Self Localization  
In this section, the fully-distributed Bayesian cooperative multi-hop positioning method is proposed to support the location-aided routing in UWB-based WPANs. To attach a location error field to the routing message and announce the statistical characteristics of the location error to neighbors, each node must be able to estimate its own location error characteristics. We use the two-dimensional network scenario and assume that the exact initial locations of the anchor nodes are known. We consider that \(N\) wireless nodes are ad-hoc deployed in the area of \(A(m^{2})\). The \(N\) wireless nodes are composed of \(N_{A}\) anchor nodes and \(N_{N}\) non-anchor nodes, satisfying \(N_{A}\ll N_{N}\). All anchor nodes are static, and there are \(N_{M}\) non-anchor mobile nodes in the network. The set of all anchor nodes and all non-anchor nodes in the network are denoted as \(\Omega_{a}=\left\{\eta_{j}\mid j\in\{1,\cdots,N_{a}\}\right\}\) and \(\Omega_{n}=\left\{\eta_{j}\mid j\in\{N_{a}+1,\cdots,N\}\right\}\), respectively. The set of mobile non-anchor nodes is denoted by \(\Omega_{m}=\left\{\eta_{j}\mid j\in\{N-N_{m}+1,\cdots,N\}\right\}\).  
Figure 1 gives the overview of the proposed UWB-based cooperative multi-hop positioning system. The positioning module combines the Bounding-box (B-box) algorithm with utilizing UWB-based ranging measurements and particle filter-based inertial navigation. Each node observes B-box information of neighboring nodes through UWB Transceiver. B-box information includes the  
location state of adjacent nodes and the measured distance information to the adjacent node. The B-box state of a node is initialized or updated through the B-box algorithm [23]. The particle filter-based inertial navigation system tracks the movements of mobile nodes. Each mobile node maintains a set of particles in the particle filtering process. The particle weighting module adjusts the weight of the particles according to the surrounding B-box information. The mobile tracking module has rotation tracking, step detection, and step length estimation procedures. The location of each particle is updated by using the inertial sensor output sequences. Whenever the B-box state of each node is updated, the particle weighting module removes particles which are located outside the B-box area. The B-box state of the mobile node is determined by the smallest rectangle area which contains all the survived particles.
## 4 Joint Decoding-Complexity and Rate Control
### Determining the Model Parameters and Trade-Off Factors
#### 4.2.1 Determining QP  
Once the CTU-level decoding-complexity and bit allocations are made, the QP selection is first performed using a similar MSE based approach. In this case, MSE of the co-located CTU, \(\mathrm{MSE}_{k,j,\mathrm{co}}^{\mathrm{CTU}}\), is now compared with \(\widetilde{\mathrm{MSE}}_{k,j}^{\mathrm{CTU}}\) for all QPs to obtain a QP \(\tilde{q_{0}}\) such that  
\[\min_{\tilde{q_{0}}}\,\left|\widetilde{\mathrm{MSE}}_{k,j}^{ \mathrm{CTU}}(q)-\mathrm{MSE}_{k,j,\mathrm{co}}^{\mathrm{CTU}}\right|. \tag{14}\]  
In this case, MSE of the \(k\)th CTU in the \(j\)th frame of the GOP is estimated for each QP \(q\) using  
\[\widetilde{\mathrm{MSE}}_{k,j}^{\mathrm{CTU}}(q) = a_{k,j}(q)\times\left\{\frac{R_{\mathrm{T}}^{\mathrm{CTU}}(k,j) }{N}\right\}^{b_{k,j}(q)}+ \tag{15}\] \[c_{k,j}(q)\times\left\{\frac{C_{\mathrm{T}}^{\mathrm{CTU}}(k,j) }{N}\right\}^{d_{k,j}(q)},\]  
where, \(N\) is the total number of pixels in the CTU and \(R_{\mathrm{T}}^{\mathrm{CTU}}\), and \(C_{\mathrm{T}}^{\mathrm{CTU}}\) are the bit and decoding-complexity levels allocated for the CTU, respectively.
## 3 Results and Discussion
### Design of the Conventional TCED Process with PG as New Proposed Extractive-Solvent
#### 3.1.4 Design of the Solvent Regeneration Column (SRC)  
The next step in process simulation is the SRC design. The B2 flow of the EDC is fed to a distillation column (noted as SRC). PG is recovered in the SRC as the bottom product. After cooling, the PG is pumped to the top of the EDC on stage 5. The distillate product of the SRC is noted as D3 and contains water and traces of IPA. The entire IPA lost in B2 will be found as a contaminant in D3. According to [PERSON] and [PERSON] [31], the best practice is to feed D3, along with the B1, to a sewage plant. Therefore, we will consider this, as explained in Section 3.2.  
For the solvent regeneration column design, we followed the same procedure as for the preconcentration column, but as specifications the purity of PG in the bottoms and purity of water in the top were used. Initially, the target of the SRC simulations was established at a maximum of 5 ppm water in B3 and a maximum of 2 ppm PG in D3. Likewise, the proper  
Figure 8: The influence of RR2 on the water concentrations in the IPA product and EDC reboiler’s duty (E/D1 = 3.4/1, NT2 = 30, NFE = 5, NF2 = 20).  
Figure 7: The influence of NF2 on the water concentrations in the IPA product and EDC reboiler’s duty (E/D1 = 3.4/1, NT2 = 30, NFE = 5, RR2 = 0.9).  
duty of QR3 was evaluated according to these targets. The first goal of the SRC simulations was to establish the optimal operating conditions for continuous PG regeneration. For this purpose, was evaluated the effect of the number of stages (noted as NT3) in the range of 13 to 17 on the IPA and PG concentrations in D3, considering the following stated operating conditions: the feed tray (noted as NF3) at 7 and the reflux molar ratio (noted as RR3) at 1.5. The SRC operated at 101.3 kPa in the condenser, and the column pressure drop was 5 kPa.  
The increase of the NT3 leads to a decrease in the PG concentration in D3 and a decrease in the QR3 duty. In this investigation step, the optimal NT3 was found to be 15. The D3 with 0.1022 mol.% IPA in water and 2.52 mol.% PG is not suitable to be mixed with the pure water from the PC column. The QR3 was 2530 kW. In order to obtain a higher purity of the D3 water stream and prevent any PG losses, several simulations were performed to manipulate the RR3 in the range of 1.5 to 1.9. The increase of the RR3 values leads to a drastic decrease in the PG concentration in D3, from 2.52 mol.% to 2 ppm. So, we could ascertain that the SRC with NT3 = 15, NF3 = 7, and RR3 = 1.9 was an adequate design to achieve the goals, as mentioned earlier. The B3 stream consisting of the recovered PG with only 5 ppm water was recycled to the EDC after cooling from 464.78 K to 348 K.