esa-sceva/satcom-chunk-collection huggingface dataset:
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 2 GNSS-RTK Technology
### Position Computation  
Most of GNSS receivers, such as those included in mobile phones, cars, smartwatches, etc., solve the position computation by aligning pseudo-random signals (code-phase), one from the satellite and another from the receiver [2]. The time delay between the two signals is the time that the signal needs to travel from the satellite to the receiver, and knowing that it travels at the speed of light, then it is possible to compute the distance between satellite and receiver. Repeating this operation with at least four satellites [12], the receiver position can be defined in 3D (latitude, longitude, and height). The code-phase method can ideally get an accuracy of 3-5 m, which is defined by the frequency at which the bits of the pseudo-random signal are sent (approx. 1 MHz) combined with an accuracy of the electronics of around 1-2% [13].  
RTK technology employs a similar concept, but instead of aligning the content of the signal, it tries to synchronize the carrier signal (ignoring its content) with a signal generated by a local oscillator in the receiver (carrier-phase). The difficulty in the carrier-phase method lies in knowing the total number of cycles between the satellite and the receiver signals (see Figure 1). To overcome these carrier-phaseambiguities, the code-phase method is firstly employed to find an approximate estimation, and then, the carrier-phase method is used for a more accurate solution. This way, the problem becomes simpler, although it does not have a simple solution [14].  
Some GNSS receivers can receive signals in more than one transmit frequency band. They have the important advantage of simplifying the computation to solve the phase difference ambiguities of the carrier signal, which results in a larger position resolution speed, as shown in Figure 2.  
The ambiguity resolution methods are complex; for detailed explanations and several resolution methods, the reader is referred to [15; 16; 17] and references therein.
# Resource Allocation for Relay-Based OFDMA Power Line Communication System
## 2 System Model and Problem Formulation
### Problem Formulation  
In this section, we derive the optimal resource allocation problem in the relay-based OFDM PLC system. The maximum information rate is regarded as the object function, and both the transmit power constraint and interference limitation between the subcarriers should be guaranteed. Then the problem can be formulated as  
\[\begin{split}\underset{P_{s_{nk},P_{n,k}}}{\max}& R=\sum_{k=1}^{K}\sum_{n=1}^{N}R_{n,k},\\ \text{s.t.}&\sum_{k=1}^{K}\sum_{n\in\Omega_{T_{k}}} P_{s_{nk}}\leq P_{S},\\ &\sum_{k=1}^{K}\sum_{n,m\in\Omega_{T_{k}}}\eta_{n}P_{s_{nk}}\leq I _{tol},\\ & P_{s_{nk}}\geq 0,\qquad\forall\ k,\ n,\\ &\Omega_{T_{1}}\bigcup\cdots\bigcup\Omega_{T_{K}}\subseteq\Omega _{T},\\ &\Omega_{T_{i}}\bigcup\Omega_{T_{j}}=\emptyset,\ \ \forall i\
eq j,\ i,j\in\{1,\cdots,K\},\end{split} \tag{10}\]  
where \(P_{s}\) denotes the source node's average transmit power, \(I_{tol}\) denotes the average interference power, \(\Omega_{T_{k}}\) represents the subcarrier set for the \(k\)-th user, \(\Omega_{T}\) represents the set of all subcarriers. In (10), constraints 1 and 2 represent the transmission power constraint of the source of the PLC system and the interference power constraint between subcarriers, respectively, constraint 3 represents the non-negative property of the power value, constraints 4 and 5 indicate that the subcarrier sets are disjointed. It obvious that problem (10) belongs to mixed integer nonlinear programming (MINLP), which usually leads to a \(\mathcal{NP}\)-hard problem. Since the information rate \(R_{n,k}\) can be explicitly expressed as in (7), a general mathematical form for simplicity can be expressed as  
\[\begin{split}\underset{x_{n},\Omega_{T_{k}}}{\max}& f(\mathbf{x})=\sum_{k=1}^{K}\sum_{n=1}^{N}\log_{2}\left(1+ \frac{x_{n}}{b_{n}x_{n}+c_{n}}\right),\\ \text{s.t.}&\sum_{k=1}^{K}\sum_{n\in\Omega_{T_{k}}} x_{n}\leq P_{S},\\ &\sum_{k=1}^{K}\sum_{n,m\in\Omega_{T_{k}}}\eta_{n}x_{n}\leq I_{ tol},\\ & x_{n}\geq 0,\qquad\forall\ k,\ n,\\ &\Omega_{T_{1}}\bigcup\cdots\bigcup\Omega_{T_{K}}\subseteq\Omega _{T},\\ &\Omega_{T_{i}}\bigcup\Omega_{T_{j}}=\emptyset,\ \ \forall i\
eq j,\ i,j\in\{1,\cdots,K\},\end{split} \tag{11}\]  
where \(x_{n}\) denotes the transmit power on the \(n\)-th subcarrier, \(b_{n}\) and \(c_{n}\) are constants determined by (7), which can be given by  
\[b_{n}=\frac{\sigma^{2}}{h_{n,k}^{2}P_{n,k}},\qquad c_{n}=\frac{\sigma^{2}}{h_{ 1}^{2}}\left(1+\frac{\sigma^{2}}{h_{n,k}^{2}P_{n,k}}\right). \tag{12}\]
## 3 Results
### Force Characteristics Results of Segment  
#### 3.3.1 The Influence of the Tunnel Burial Depth on the Segment Forces  
The burial depth of the tunnel not only impacts the initial support but also influences the segment through the lining. By modifying the burial depth H of the tunnel to 24, 26, 28, 30, 32, 34, and 36 m while keeping the other simulation parameters of the soil layer constant, the evolution of the bending moment and axial force in the segment was obtained, as illustrated in Table 7 and Figure 7a. From the analysis, it can be observed that the variation trend of the internal force variation in the segment is similar to that of the initial support when the burial depth of the tunnel changes. However, compared to the initial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Support Thickness (m)** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
0.25 & 100.33 & 106.89 & 899.61 \\
0.27 & 127.63 & 135.73 & 940.32 \\
0.29 & 149.36 & 158.61 & 974.12 \\
0.3 & 176.14 & 186.81 & 1000.38 \\
0.31 & 210.74 & 223.25 & 1022.65 \\
0.33 & 239.76 & 253.73 & 1045.32 \\
0.35 & 272.91 & 288.53 & 1055.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial support calculation results under different initial support thickness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\(\mathbf{\gamma\) (kN/m3)** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
18 & 119.38 & 127.04 & 782.54 \\
19 & 134.61 & 143.14 & 884.30 \\
20 & 140.66 & 149.46 & 924.05 \\
21 & 149.08 & 158.30 & 977.33 \\
22 & 161.26 & 171.15 & 1059.54 \\
23 & 166.78 & 176.91 & 1091.48 \\
24 & 173.45 & 183.91 & 1137.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial support calculation results under different soil bulk density.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\(\mathbf{\mu}\)** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
0.15 & 290.76 & 304.28 & 997.32 \\
0.20 & 269.23 & 282.26 & 993.02 \\
0.25 & 234.05 & 245.98 & 990.53 \\
0.30 & 188.46 & 198.85 & 987.59 \\
0.35 & 150.86 & 160.21 & 991.22 \\
0.40 & 103.41 & 111.18 & 997.02 \\
0.45 & 52.47 & 57.09 & 1009.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial support calculation results under different soil poisson’s ratio.  
support, the increase in the tunnel's burial depth had a more substantial effect on the growth trend of the internal forces in the segment. This indicates that the range of burial depth should be appropriate in this project to balance the fluctuation effects on the segment and the initial support.  
Figure 7: Segment stress variation curve. (**a**) Influence curve of tunnel depth on segment stress; (**b**) influence curve of soil unit weight on segment stress; (**c**) influence curve of soil Poisson’s ratio on segment stress; (**d**) influence curve of lining thickness on segment stress; and (**e**) influence curve of backfill thickness on segment stress.
## 4 Results and Discussion
### Evaluation of CMIP5 Global Climate Models Used in the Analysis
#### 4.1.1 Precipitation  
An analytical exploration into the diverse climate models for precipitation offers an intriguing narrative, as summarized in Table 2. These models, evaluated against several statistical measures, display a range of outcomes that echo the multifaceted reality of precipitation forecasting.  
Examining the Mean Bias Error (MBE), it becomes evident that models such as ACCESS1.3 and CMCC-CESM exhibit a stark divergence, with MBE values of 24.894 and 19.867, respectively. This difference underscores the inherent disparities in the model's forecast tendencies, from potential overestimations to underestimations. The Mean Absolute Error (MAE) metric brings forth another layer of discrepancy. The HadCM3 model, with an MAE value of 117.517, contrasts significantly with the MPI-ESM-P model which demonstrates a lower MAE value of 70.709. This difference represents the diverse magnitude of absolute differences between the models' predictions and actual observations. The Root Mean Square Error (RMSE) values further delineate the differences in the model's ability to predict precipitation accurately. Models like GFDL-CM3 and HadCM3 showcase RMSE values of 196.425 and 247.197, respectively, reflecting varying levels of prediction accuracy. Nash-Sutcliffe Efficiency (NSE) values bring a more discerning evaluation, as models like HadCM3 venture into negative territories (\(-\)0.351), demonstrating a level of prediction accuracy that falls below the mean of the observed data. The correlation coefficients present an aspect of association between model predictions and observed data. A positive correlation, as seen in the GFDL-CM3 model (0.421), indicates a closer agreement with the observations, as opposed to a lower correlation coefficient in the HadCM3 model (0.231).  
In conclusion, the performance of these diverse climate models for precipitation varies significantly, underlining the complexity of precipitation modeling. These variances, as revealed through the different statistical metrics, contribute to our comprehensive understanding of the climate model performance for precipitation forecasting.
## 3 Simulation Results and Discussion
### Validation of Soil-Pile Interaction  
The interaction between the soil particles and the pile was verified by comparing the development of the soil plug length in numerical simulations with the results from the model pile test reported by [PERSON] et al. (2019). This comparison covered the entire process (Stages 1 to 12) of pile installation as shown in Figure 7, representing the trend of soil plug length against penetration depth.  
In the early stage of driving, reaching a penetration depth of 162 mm, the pile is driven in a mode of fully coring penetration. This implies that the plugging effect has not yet been achieved at this stage in the DEM model. However, with further penetration, the plug length plot starts to gradually deviate from the fully coring line and follows the trend observed in the experimental results. The gradual change in soil plug length is visualized in Figure 8 with respect to the successive simulation steps of impact driving.  
Figure 8: Soil plug length comparison between the numerical simulation and experimental result ([PERSON] et al., 2019).  
Figure 7: Progressive state of the model by pile penetration.  
The plugging effect in the DEM model was confirmed to match that observed in the experimental case, validating the soil-pile interaction in the DEM model. At the completion of installation, the soil plug inside the open-ended pile measured 320.0 mm, compared with the experimental measurement of 350.7 mm.
## 3 Synthesis of Amine-Functionalized Silica/Organosilica Membranes
### Post-Treatment of Mesoporous Silica Membranes  
Mesoporous silica membranes with pore sizes ranging from 2-50 nm are ineffective for CO\({}_{2}\) separation. The meso-pores of these membranes could be minished, however, by the post-grafting or impregnation of amine-containing agents to impart high separation performance. Post-impregnation methods involve the loading of a large quantity of amines dissolved in a solvent inside the meso-pores. However, the loaded amines tend to conglomerate, and/or leak out from the pores, particularly under pressurized gas flow [31]. On the other hand, during the post-grafting process, the amine groups from the functionalized agents could be covalently attached to the pore/membrane surface, and a certain proportion of Si atoms could be replaced by aminosilane groups. This approach roughly maintains the structure of the substrate, and the grafted amine groups remain stable [31]. Numerous studies have described amine-functionalized silica-based membranes [11; 12; 13; 32; 33]. Table 2 lists the synthesis conditions and membrane performance for reported amine-functionalized silica-based membranes fabricated via post-grafting or impregnation. Similar to those produced via sol-gel routes, reports of these membranes show CO\({}_{2}\) separation properties that are even more scattered with either low permeance or low CO\({}_{2}\)/N\({}_{2}\) selectivity. This phenomenon probably can be attributed to the relatively larger pore size of the mesoporous support as well as to the excessively strong affinity to CO\({}_{2}\) of these unhindered amines (APTES etc. ), which can limit the CO\({}_{2}\) transport rate. With the respect to formation of dense membranes via post-grafting, these membranes tended to show relatively high CO\({}_{2}\)/N\({}_{2}\) selectivity due to the higher resistance to N\({}_{2}\) permeation. On the contrary, this formation of loose membranes may result in higher CO\({}_{2}\) permeance but lower CO\({}_{2}\)/N\({}_{2}\) selectivity. Note that for practical process designs of membrane separation applications from an economic perspective, increases in membrane permeance are more important than increases in selectivity in order to enhance competitiveness for CO\({}_{2}\) capture of flue gas [34]. In addition, amine-containing facilitated membranes are expected to simultaneously promote both CO\({}_{2}\) permeance and CO\({}_{2}\)/gas selectivity via reversible reactions/interactions. However, this is not always true in the case of amine-functionalized silica-based membranes. Given that the membrane performance can be generally governed by parameters such as the materials used for the selective layer, the geometry and performance of the support, and the preparation technique adopted, systematic studies are needed to address unexpected and somewhat disappointing performances.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 2 Methodology
### Clock-Constrained Precise Orbit Determination  
When the clock prediction model is determined, we can generate predicted clocks to constrain the RPPP during maneuvering. The processing scheme of the clock-constrained RPPP POD strategy for maneuvering satellites is summarized and illustrated in Figure 2. As shown in this figure, the proposed algorithm consists of three steps: the dynamic POD, clock modelling and prediction, and clock-constrained RPPP POD. In step 1, the traditional dynamic POD method is employed to jointly determine the precise orbits of GPS and BDS satellites without the observations of maneuvering satellites during and after the maneuvering. Therefore, the receiver positions, receiver clocks, zenith tropospheric delays (ZTDs), ambiguities and inter-system biases (ISBs) are unaffected by maneuvers and can be precisely determined. In step 2, the clock prediction model for the maneuvering satellites is determined based on the historical clock estimates in step 1. Then, the clock model is extrapolated to the maneuvering and post-maneuvering epochs to generate the predicted clocks. Finally, the RPPP method is employed to determine orbit of the maneuvering satellite based on its observations during and after maneuvering, in which the unknown parameters are fixed, rather than estimated, to the corresponding estimates from step 1 and the predicted clocks are used as fictitious observations to constrain the satellite clock parameters. If there are no cycle slips, the ambiguities of phase observations are corrected for with the latest estimates in step 1. Otherwise, new ambiguity parameters will be introduced and estimated. The weights of the predicted clocks are computed from the weighting function (4). Therefore, only satellite positions, satellite clocks and newly introduced ambiguities need to be estimated in the clock-constrained RPPP processing.
## 3 Aircraft Observation
### Analysis of On-Board Observations  
Data have been collected from the aircraft under investigation. A time series of aircraft altitude, derived vertical winds and EDR (based on the algorithm of [13]) can be found in Figure 12. From Figure 12a, it is shown that the aircraft aborted landing shortly after 13:36 UTC. There had been fast fluctuations in the altitude around 13:34 UTC, 13:37 UTC and 13:41 UTC, which suggested an encounter with significant turbulence, which could be examined objectively with the calculated EDR. From Figure 12b, the aircraft encountered three short periods of significant turbulence (EDR equals or exceed 0.45), namely, around 13:34 UTC, 13:37 UTC and 13:41 UTC. Only the first and the third episodes would be considered in this paper. The peak EDR was generally in excess of 0.3 m\({}^{2/3}\)s\({}^{-1}\), which is basically consistent with radar observations, and a detailed comparison is discussed below. Moreover, for the third episode, the EDR once reached around 0.8 m\({}^{2/3}\)s\({}^{-1}\), which is very severe turbulence, and this could be expected inside a radar echo with reflectivity in excess of 53 dBZ.  
For the vertical velocity shown in Figure 12c, in the first episode, it was generally a downward motion, and the negative vertical velocity reached a magnitude of around 10 m/s. For the third episode, the air motion was more turbulent, and the vertical velocity was both positive and negative. The downward motion reached around 12 m/s and the upward motion reached around 14 m/s. This is consistent with the much higher EDR in this episode.  
Figure 11: As shown from the HKO’s Windshear and Turbulence Warning System (WTWS), a microburst alert of \(-\)30 knots (filled red ellipse) was generated from the BP TDWR near the location of the TDWR at 13:42 UTC on 30 April 2024.  
Figure 12: Time series of (**a**) aircraft altitude, (**b**) calculated vertical wind speed and (**c**) eddy dissipation rate using data collected from the aircraft encountering microburst on 30 April 2024. Two insets showing the spatial locations of the aircraft at around 13:36 UTC and 13:41 UTC on 30 April 2024 are shown in panel (**a**) with the red arrows pointing out roughly the corresponding aircraft altitude.
## 3 Case Study
### Data Preprocessing
#### 3.2.2 Noise Reduction  
The EPB tunneling parameters are recorded by the automatic acquisition system. The working environment of the acquisition system is a moving environment with strong vibration and electromagnetic interference, sometimes causing the recording of data beyond the reasonable range. [PERSON] et al. [19] and [PERSON] et al. [52] indicated that noise could interfere with the training process and reduce the generalization ability of the model. Common methods used to eliminate noise information include Fourier transform, low-pass filtering, and wavelet transform, among which low-pass filtering can block or attenuate high-frequency signals to maximize the effect of noise reduction [53,54]. Thereby, the low-pass filter Butterworth that blocks or attenuates the high-frequency signal to maximize the denoising effect was used to reduce the data noise. The expression of the Butterworth filter is as follows [55,56]:  
\[\left|H(\omega)\right|^{2}=\frac{1}{1+(\omega)^{2n}}, \tag{12}\]  
Figure 7: Data preprocessing of earth pressure balance (EPB) shield machines parameters (99 + 764): (**a**) raw data for one day; (**b**) a drilling cycle; (**c**) noise reduction.  
where \(\left|H(\omega)\right|^{2}\) is the square magnitude of the filter; \(n\) is the order of the filter; \(\omega\) is the unit angular cutoff frequency. The characteristic of the Butterworth filter is that the frequency response curve in the pass band is a maximally flat approximation because its response magnitude monotonically decays as the frequency increases [56]. As the order of the Butterworth filter increases, the degree of noise reduction increase. To achieve a good data noise reduction effect while avoiding information loss, \(n\) and \(\omega\) were taken as 2 and 0.2 through the trial-and-error method. The noise reduction effect of the advance rate is shown in Figure 7c.
## Results and Discussion
### Cell Viability Test  
For _E. coli_, we tested bacterial growth at different sensor concentrations. No significant cytostatic or cytotoxicity was observed with a constant cell density (\(5\times 10^{5}\) cfu/mL), while different sensor concentrations ranging from 8-120 ug/mL, corresponding to PtTFPP concentrations of 0.9 to 6.8 uM (Figure 10). It should be noted that when these micelles are used to measure the oxygen concentration, the content of PtTFPP is usually 2.5 uM, which is much lower than 6.8 uM.  
Figure 10: Growth curves of _E. coli_ with OD\({}_{600}\) of 0.06 under different concentrations of micelles  
Figure 9: Oxygen consumptions of Romas cells at different cell densities in the emission model. Concentration of PtTFPP in medium for these measurements: 3 ug/mL.  
**M1-M3** with or without oil sealing. (**A,C,E**): Representative absorbance changes at 600 nm with _E. coli_ of **M1-M3** without oil sealing. (**B,D,F**): Representative absorbance changes at 600 nm with _E. coli_ of **M1-M3** with oil sealing. (**A,B**) are for **M1**; (**C,D**) are for **M2**; (**E,F**) are for **M3**.  
We use the methyl thiazolyl tetrazolium (MTT) method to measure the biocompatibility of the sensors to Ramos cells. In this experiment, cells were assayed for cell activity after incubation with different concentrations of **M2** (from 35 \(\upmu\)g/mL to 280 \(\upmu\)g/mL) for 24 hours in a 37 \({}^{\circ}\)C cell culture incubator. It was found that 90% cell viability was observed after 24 hours of incubation with **M2** at a concentration of 280 \(\upmu\)g/mL (eight times higher than that of the normal concentration), indicating the superior biocompatibility of this nano-oxygen sensor (Figure 11). This result is compared with similar probes by [PERSON] et al. (higher than 80%) and [PERSON] (higher than 88% with three times higher than normal concentration), suggesting that no obvious cell toxicity is observed [39; 48]. It should be pointed out that these micelles did not have cell permeability during the eight-hour experimental period in which we performed cell respiration testing. Therefore, the sensor can be utilized for extracellular applications.