esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.10 Packet Forwarding Process  
Whenever a packet reaches a UAV, the UAV first checks for the destination address. If this UAV is the destination, it sends an ACK to the immediately previous custodian, and if it is not the destination, the UAV checks the packet's hop count. If the hop count is more than the number of UAVs participating in the mission, the UAV discards the packet. Then, the UAV checks its one- and two-hop neighbor list for the destination. If the destination is available on that list, it forwards the packet to the destination. If the neighbor is not found in the one- and two-hop neighbor list, the UAV calculates the \(FC\) value for itself and the  
Figure 10: Simplified 2D example of \(FC\) calculation: (**a**) sample mission scenario and (**b**) sample \(FC\) calculation method.  
one- and two-hop neighbor pairs. Then, it forwards the packet to the one-hop neighbor that belongs to the pair with the lowest \(FC\) value. If the UAV has the lowest \(FC\) value, it waits for the \(P_{interval}\) time and starts the forwarding process again. Algorithm 1 summarizes the packet forwarding process.  
```
Upon reception of a packet or upon expiry of \(P_{interval}\): ifdestination_address == my_address then send ACK return endif ifcurrent_hop_count > hop_count_max then discard packet return endif for all one-hop-neighbor \(i\)do ifdestination_address == one_hop_neighbor_i send packet to one_hop_neighbor_i return endif for all two-hop-neighbor-via-one-hop-neighbor-\(i\)do ifdestination_address == two_hop_neighbor_i send packet to one_hop_neighbor_i return endif endfor endfor calculateFC\({}_{p}\). //* calculating FC for myself*/ for all one-hop-neighbor \(i\)do calculateFC\({}_{i}\) for all two-hop-neighbor-via-one-hop-neighbor-\(i\)do calculateFC\({}_{ij}\) calculateFC\({}_{p}\). //* calculating FC from for two-hop-neighbor \(j\) and the corresponding one-hop-neighbor \(i\)*/ endfor endfor temp_FC = 0 for all FC \(p\)do iftemp_FC < FC\({}_{p}\)then temp_FC = FC\({}_{p}\)  nest_custodian = address_of_FC\({}_{p}\) endif endfor ifmy_address == address_of_FC\({}_{p}\)then queue packet until\(P_{interval}\) else send packet to address_of_FC\({}_{p}\) endif
```  
**Algorithm 1** The packet forwarding mechanism in LECAR
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter
### Routing Evaluation Parameter
#### 2.2.2 Single-Hop Transmission Ratio  
Because of limited battery capacity, sensor nodes will eventually be exhausted. Therefore, when selecting the next hop, it should be clear how data transmission affects node energy. Generally, the energy consumption of a node includes three parts: perception, data processing, and communication. Of those, communication consumes much of the energy, more than the sum of perception and data processing consumption. Hence, this paper only considers the communication energy consumption. At the same time, because the energy consumption of data receiving is far less than that of transmitting, it can be neglected. Therefore, reducing the energy consumption of transmission between nodes is the key to slow down the energy attenuation speed and extend survival time. Thus, combining the residual energy of node \(i\) and the transmission energy by single-hop, this paper defines the single-hop transmission ratio \(EC^{ij}\), which is expressed as follows:  
\[EC^{ij}=E_{i}\times\frac{e_{R}-e_{ij}}{e_{R}}=E_{i}\times\left(1-\frac{e_{ij}} {e_{R}}\right) \tag{12}\]  
where \(e_{ij}\) is the energy consumption of single-hop between node \(i\) and \(j\). \(e_{R}\) is the energy consumption by transmitted to the maximum range. The communication energy consumption model used in this paper comes from [35; 36]. Assuming that the communication distance threshold of sensor nodes is \(d_{o}\), when \(d\) is less than \(d_{o}\), the free-space channel model is adopted. In contrast, the multichannel attenuation model will be taken. Then, the energy consumption for transmitting \(l\)_bits_ data is as follows:  
\[e_{l}(l,d)=\begin{cases}IE_{\text{bas}}+IE_{fs}d^{2},&d<d_{o}\\ IE_{\text{bas}}+IE_{mp}d^{4},&d\geq d_{o}\end{cases} \tag{13}\]  
The energy of receiving \(l\)_bits_ data is as follows:  
\[e_{r}(l)=IE_{\text{bas}} \tag{14}\]  
where \(E_{fs}\) and \(E_{mp}\) are the amplification factor of the node radio signal amplifier. \(E_{\text{bas}}\) represents energy consumption per one bit of the control circuit in the node.
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 2 Fundamentals of Fourier Basis Pursuit
### Fourier Basis Pursuit
Furthermore, in contrast to the FIR and IIR filters typically employed in digital signal processing, the FBPBPF method, rooted in the FBP spectrum, accurately identifies and segregates coupled signals comprised of two or more quasi-periodic signals from noise, while maintaining high boundary extraction accuracy. The periodic term in the satellite clock bias is characterized as a quasi-periodic signal. In applications such as clock bias prediction and performance assessment of spaceborne atomic clocks, which often involve clock bias signals of limited length or those mixed with multiple quasi-periodic terms of similar frequencies, simulated and measured data can prove that the FBPBPF method is highly effective for extracting periodic terms.
# Corner Centrality of Nodes in Multilayer Networks: A Case Study in the Network Analysis of Keywords
## 2 Mathematical Model for the Corner Centrality of Nodes in a Multilayer Network
```
1:for every node \(u\) in \(L_{*}\), do
2:for every layer \(L\) in G, do
3:if\(u\) is in \(L\), then
4: obtain \(I_{L}(u)\) by using Equation (1) \(\rhd\) by using intralinks within L
5:else, if\(u\) is not in \(L\), then
6: obtain \(I_{L}(u)\) by using Equation (2) \(\rhd\) by using interlinks between \(L_{*}\) and \(L\) layers. 7:endif
8:endfor
9:endfor
10:for every layer \(L\) in G, do
11: Normalize \(I_{L}\)
12:endfor
13:for every node \(u\) in \(L_{*}\), do
14: obtain the matrix \(M(u)\) by using Equation (9). \(\rhd\)with only two layers by using Equation (10)
15: obtain the score of the node \(S_{L_{*}}(u)\rhd\)by using Equation (12)
16:endfor
17: Obtain the rank of every node in \(L_{*}\) by sorting \(S_{L*}\)
```  
**Algorithm 1**corner centrality. Let \(\mathcal{G}=\{L_{1},L_{2},\cdots,L_{M}\}\), a multilayer network with \(M\) layers. Let \(L_{*}\) be the target layer from \(\mathcal{G}\). With a multiplex network having every layer with the same set of nodes, without interlinks, the corner centrality value for every node is defined as the minimum value of corner centrality obtained across the layers. For example, in Figure 1, MLN2 obtains \(S_{L_{1}}\) and \(S_{L_{2}}\) for the node \(a\), and the final score will be the minimum between \(S_{L_{1}}\) and \(S_{L_{2}}\). The computational time of the algorithm would be:  
1. If the number of nodes \(n\) in every layer \(L\) is bigger than the number of layers \(M\), the computational time is \(O\big{(}n^{2}\times M\big{)}\). Usually, \(n\gg M\) and \(O\big{(}n^{2}\times M\big{)}\approx O\big{(}n^{2}\big{)}\). 2. If the number of nodes \(n\) in every layer \(L\) is lower than the number of layers \(M\), the computational time is \(O\big{(}n\times M^{2.373}\big{)}\). A more detailed analysis of the computational efficiency is described in Appendix A. Additionally, Appendix A describes the memory needs of the algorithm proposed.
# A DEM Study on Bearing Behavior of Floating Geosynthetic-Encased Stone Column in Deep Soft Clays
## 3 Results and Discussion
### Stresses and Deformation of the FGESC
#### 3.1.1 Vertical and Radial Stresses of FGESC and Underlying Soil  
Figure 7 shows the vertical stress distribution along the column and the underlying soil at different settlements. When the settlement was 9 mm, the vertical stress distribution of the column was relatively uniform. With the increase in the settlement, the vertical stress in the column top area gradually increased. When the settlement reached 36 mm, the vertical stress in the column was maximum. The maximum value was 88.3 kPa, which occurred at a height of approximately 400 mm. There was a significant reduction in the vertical stress in the unreinforced section of the column. The vertical stress in the underlying soil increased slightly with settlement. This meant that the column could transfer the upper load to the underlying stratum.  
Figure 8 shows the radial stress distribution along the column and the underlying soil at different settlements. When the settlement was 9 mm, the radial stress in the encased  
Figure 6: Load–settlement curves from DEM model and laboratory test.  
Figure 7: Vertical stresses of the FGESC and underlying soil.  
section at the top of the column was high. As the settlement increased, the radial stress in the encased section of the column top gradually increased. When the settlement reached 36 mm, the radial stress in the column was maximum. The maximum value was 44.8 kPa. The radial stress in the unreinforced section of the column was reduced significantly. The radial stress in the underlying soil increased slightly with settlement.  
From the above, it can be seen that the vertical and radial stresses of the column are mainly concentrated in the encased section at the top of the column. The vertical and radial stresses in the unreinforced section at the bottom of the column and in the underlying soil increased slightly with the increase in the settlement.
## 5 Conclusions
\begin{tabular}{l l} \(H_{ice,c}\) & Heat produced by the centralized ICE (kWh) \\ \(H_{ngt}\) & Heat produced by the MGT (kWh) \\ \(H_{net}\) & Thermal energy transferred through the pipeline (kWh) \\ \(H_{net,c}\) & Thermal energy transferred through the pipeline of the central DHN (kWh) \\ \(H_{net,lim}\) & Size limits of the pipelines (kWh) \\ \(H_{stp}\) & Solar panel thermal production \\ \(H_{stp,c}\) & Centralized solar field thermal production \\ \(H_{hs}\) & Thermal energy storage input (kWh) \\ \(H_{hs,c}\) & Thermal energy storage input (kWh) \\ \(j\) & Generic component \\ \(k\) & Generic site/user \\ \(Kf_{ice}\) & ICE Performance curve linearization coefficient \\ \(Kf_{ice,c}\) & Centralized ICE Performance curve linearization coefficient \\ \(Kf_{ice}\) & ICE Performance curve linearization coefficient \\ \(Kf_{ice,c}\) & Central ICE performance curve linearization coefficient \\ \(K_{hp}\) & HP Performance curve linearization coefficient \\ \(K_{bs,hs}\) & Percentage thermal loss coefficient \\ \(K_{pv}\) & Unitary PV production \\ \(K_{stp}\) & Unitary solar thermal production \\ \(l_{p}\) & Length of the pipeline (m) \\ \(m\) & Generic month \\ \(\mathbf{O}_{boi,c}\) & Central BOI operation (binary) \\ \(\mathbf{O}_{hp,c}\) & HP cold operation (binary) \\ \(\mathbf{O}_{hp,h}\) & HP heat operation (binary) \\ \(\mathbf{O}_{ice}\) & ICE operation (binary) \\ \(\mathbf{O}_{icen,c}\) & Centralized ICE operation (binary) \\ \(p_{t}\) & Pipeline thermal loss per unit length (km\({}^{-1}\)) \\ \(p_{t,c}\) & Pipeline thermal loss per unit length of the central DHN pipeline (km\({}^{-1}\)) \\ \(\mathbf{Q}_{p}\) & Heat transferred by a DHCN pipeline (kWh) \\ \(\mathbf{Q}_{ts}\) & Thermal energy stored in a thermal storage (kWh) \\ \(s\) & Generic week \\ \(\mathbf{S}_{boi}\) & BOI size (kW) \\ \(S_{boi,c}\) & Central BOI size (kW) \\ \(S_{bc,lim,c}\) & Central BOI size limits (kW) \\ \(S_{cc}\) & CC size (kW) \\ \(\mathbf{S}_{C,net}\) & Size of the cooling pipeline (kW) \\ \(\mathbf{S}_{cs}\) & Cooling storage size (kWh) \\ \(\mathbf{S}_{H,net}\) & Size of the thermal pipeline (kW) \\ \(\mathbf{S}_{H,net,c}\) & Size of the central DHN pipeline (kW) \\ \(S_{hp,lim}\) & HP operation limits (kW) \\ \(S_{ice,c}\) & Centralized ICE size \\ \(S_{icc,lim,c}\) & Centralized ICE size limits (kW) \\ \(S_{rcp}\) & Size of the PV panels equipment \\ \(\mathbf{S}_{stp}\) & Size of the solar equipment \\ \(\mathbf{S}_{stp,c}\) & Size of the central solar field \\ \(\mathbf{S}_{hs}\) & Thermal storage size (kWh) \\ \(\mathbf{S}_{ts,c}\) & Central thermal storage size (kWh) \\ \(u\), \(v\) & Generic unit \\ \(v_{p}\) & Velocity of the medium inside the pipeline (m/s) \\ \(\mathbf{V}_{ts}\) & Thermal storage volume (m\({}^{3}\)) \\ \(wgt\) & Time interval weight \\ \(\mathbf{X}_{abs}\) & ABS existence (binary) \\ \(\mathbf{X}_{boi,c}\) & Central BOI existence (binary) \\ \(\mathbf{X}_{cp}\) & Existence of the cooling pipeline (binary) \\ \(\mathbf{X}_{hp}\) & HP existence (binary) \\ \end{tabular}
## 4 Results  
### Wave Tank Experiments  
#### 4.1.1 Modulation of Surfactant Concentration Due to IW  
The first experimental set was with the density difference between the lower and upper layers of 0.07 g/cm\({}^{3}\), and the estimated IW phase velocity was 23 cm/s. Amplitudes of IWs were about 5 mm. Figure 6 illustrates OLE concentrations over IW crests, troughs, forward and rear slopes. The relative variations in surface concentration related to the mean OLE concentrations were small, about 5% to 10%, which is comparable or less than the measurement error. However, analyzing several concentration profiles, one could conclude that the maximum surfactant concentration was located over IW troughs.  
In the second experimental set, the density difference between the lower salt layer and the fresh upper layer was very small, about 0.005 g/cm\({}^{3}\); therefore, the phase velocity of long IWs was comparable with the wind drift velocity on the water surface, i.e., the IW phase velocity was 7 cm/s, for an IW length of 175 cm and IW period of 25 s. This IW phase velocity is smaller than the minimum group velocity of GCW; therefore, no resonance condition between IW and GCW is fulfilled. However, we characterized this case as \"quasi-resonance\" in terms of a resonance between IW and a film moving due to wind drift. Figure 7 demonstrates the measured surfactant concentration variations over IW profiles at different dropping rates. At a wind speed of 2 m/s, surfactant concentration maxima were observed mostly over IW troughs (phase \(\pi\)); at a wind speed of 2.5 m/s, the maximum surface concentration was observed mostly over the rear slopes of IW. Sampling only in four points on the IW profile did not allow us to study the process of the maximum concentration shift from the trough to the rear slope with better resolution in the IW phase. We should emphasize that, in one case out of five, the concentration maximum was over IW troughs at wind speeds of 2.5 m/s, and over rear IW slopes at wind speeds of 2 m/s, although we did register strong variations of OLE concentration which exceeded the error of measurement. The relative variations of surface concentration normalized by their means are presented in Figure 8 for two case studies.  
Figure 7: Same as in Figure 6, but for quasi-resonance cases. Curves/dots correspond to different average surfactant concentrations and IW amplitudes. Left panel: black—0.6 mg/m\({}^{2}\), IW amplitude of 3 mm; blue—0.8 mg/m\({}^{2}\), and 5 mm; violet—0.4 mg/m\({}^{2}\), and 5 mm; green—0.3 mg/m\({}^{2}\), and 3 mm; Right panel: black and blue—0.8 mg/m\({}^{2}\), IW amplitude of 5 mm; violet and green—0.3 mg/m\({}^{2}\), and amplitude of IW—3 mm. Period and wavelength are 25 s and 175 cm, respectively.
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 7 Factors Influencing Mycoremediation
### pH  
pH controls the bioavailability of harmful pollutants to achieve remediation [228]. The solubility, precipitation, and bioavailability of individual toxic chemicals or components are based on their intrinsic toxicity under certain reactive conditions [229]. The bio removal of the violet-coloured (methyl violet 10B or hexamethyl pararosaniline chloride) textile dye by _Aspergillus niger_ was 62.3%, 91.4%, 64.0%, and 92.4%, respectively, after a 24-h incubation period at pH 9, 8, 3, and 2 [230]. Studies have shown that pH is the most critical element in the fungal discolourisation of several azo dyes (Table 2). _Aspergillus foetidus_ demonstrated the capacity to decolourise more than 99% of the reactive black dye five at pH 2-3; this ability was insignificant between pH 3.0 and 5.5 [231]. _R. arrhizus_ and _A. versicolor_ showed the maximum discolourisation of remazol blue (RB) (89.4%) at a pH of 6.0 [232]. 50% biodegradation of PAHs, such as pyrene, phenanthrene, fluoranthene, and anthracene, was optimal at soil pH 7.5 [223]. Furthermore, _Penicillium_ species were dominant in soils with acidic pH, while _Aspergillus_ populations were abundant in alkaline environments.
## 3 Results
### Calibration and Validation of the Model  
This study selected seven research sites where the typical vegetation types were densely distributed (Figure 1c; 918, 78, 64, 701, 764, 945, and 276 grids). Because these seven research sites corresponded to the most densely distributed vegetation types and had the best validation data quality, they were selected. Based on the Biome-BGC-DE parameter optimization method, the uncertain parameters of the typical vegetation types in the YRB were optimized. Notably, wheat and maize used site number 276 together.  
The curve graphs of the monthly simulated and observed GPP values for the typical vegetation types in the YRB (Figure 2) can be summarized into the following results: After parameter calibration, the GPP simulation curve of each typical vegetation type was very close to the GPP observation points, which not only reflected the GPP change trend but also accurately simulated the change in monthly GPP values. The parameter optimization with the differential evolution algorithm resulted in strong modeling performance for the various vegetation types.  
The scatter plots of the monthly simulated and observed GPP values for the typical vegetation types in the YRB (Figure 3) can be summarized into the following results: Through the parameter optimization with the differential evolution algorithm, the R\({}^{2}\) values between the simulated GPP and the observed GPP at the seven optimal sites were above 0.9, and the RMSE values were below 40 g C m\({}^{-2}\). One crop a year and alpine  
Figure 2: The curve graphs of monthly simulated and observed GPP values for typical vegetation types in the YRB.  
meadows had better simulation performance, of which the R\({}^{2}\) and RMSE values were 0.96 and 0.94 and 16.19 g C m\({}^{-2}\) and 18.13 g C m\({}^{-2}\), respectively. For temperate grasses, shrubs, evergreen needle leaf forests, and deciduous broad leaf forests, the R\({}^{2}\) values were 0.94, 0.94, 0.96, and 0.94, respectively, and the RMSE values were 24.33 g C m\({}^{-2}\), 29.20 g C m\({}^{-2}\), 27.88 g C m\({}^{-2}\), and 32.09 g C m\({}^{-2}\), respectively. In this study, winter wheat and summer maize were simulated separately and then superposed due to the lack of crop rotation for the Biome-BGC model. Hence, the simulation performance of two crops a year was slightly poor, with an R\({}^{2}\) value of 0.90 and RMSE value of 38.15 g C m\({}^{-2}\).
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 3 Materials and Methods
### Calculation of ACS Parameter
Therefore, points closer to the center cannot be fully taken into account because any incorrectness in estimating the coordinates of these keypoints overwhelmingly affects the result. There is also a problem when rotating images: it causes modifications to the values of the points. In the case of moving images by an integer number of points, there are no such problems because these points remain the same, but they are only placed in different coordinates. Rotating images requires pixel interpolations. Therefore, rotating them by a minor angle may cause image-quality deterioration on the level of individual pixels without a significant gain caused by the improvement in calibration. For this reason, the ACS module does not rotate images when the angle \(\theta\) is below some value. In the experiments presented in this paper, this value was set to \(\pi/600\).
## 2 Experimental Program and Setup
### Test Setup and Loading Protocol
Figure 5b shows the schematic diagram of curvature calculation at each cross-section of bridge pier through Equation (1) [34]. \[\varphi=\frac{\theta}{H}=\frac{\Delta h}{BH}=\frac{|h_{1}|+|h_{2}|}{BH} \tag{1}\]  
In which, \(\varphi\) is the average curvature of pier; \(\theta\) is the degree of rotation; \(\Delta h\) is the relative displacement measured from two transducers, i.e., \(|h_{1}|+|h_{2}|\); \(B\) is the distance between two transducers; and \(H\) is the height of the cross-section locates at. To study the reinforcement bar behavior in the plastic hinge region of the pier, strains in the spiral and longitudinal reinforcement are measured using strain gauges, as depicted in Figure 6. The typical locations of strain gauge sets (10 for longitudinal bars and 6 for stirrups) were installed at 0, 12, and 24 cm from the bottom. Figure 4: Reversed cyclic lateral loading protocol in the study. Figure 5: (**a**) Setup of curvature measurement rod and cable-extension transducer at the plastic hinge; (**b**) Schematic diagram of curvature calculation. Figure 6: Setup of strain measurement at the plastic hinge region of bridge pier.