esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.6 Sensitivity Analysis
The analysis of the standard deviation evolution across the four scenarios highlights the following key observations:  
Figure 20: Standard deviation of scores for a variation range of \(-10\leq x\leq 10\) percent. * SD-TOPSIS is the most stable algorithm, as its standard deviation remains the lowest in all four scenarios, meaning that it is less affected by small changes in criteria values. * Entropy-TOPSIS is the most sensitive, exhibiting a continuous increase in standard deviation, which indicates a high variability in scores in response to small changes in criteria. * Importance-TOPSIS falls in between SD-TOPSIS and Entropy-TOPSIS, with moderate variations in standard deviation, meaning intermediate stability. While SD-TOPSIS has been successfully applied in terrestrial cellular networks [10], our results clearly demonstrate its suitability for handover management in LEO satellite networks. Our results confirm that for small \(x\%\) variations in criteria values within the ranges \(-5\leq x\leq 5\) and \(-10\leq x\leq 10\), SD-TOPSIS stands out as a better handover management algorithm, outperforming its counterparts Entropy-TOPSIS and Importance-TOPSIS previously presented in [7] as efficient TOPSIS-based handover management algorithms. Our study reveals that SD-TOPSIS is more stable and effective in the dynamic environment of LEO satellite networks. This stability, combined with its ability to manage the complexities and variability inherent in LEO satellite communication systems, makes SD-TOPSIS a suitable choice for handover management in dynamic LEO satellite-based networks.
## 4 Simulation Results
### Simulation Parameters  
To investigate the performance of the proposed approach, we consider OTFS modulation with \(M=16\) and \(N=16\) with subcarrier spacing of \(\Delta f=25\) kHz and a carrier frequency \(f_{c}=5.1\) GHz. Thus, delay and Doppler resolutions are \(\tau_{\text{res}}=2.5\)\(\upmu\)s, and \(\
u_{\text{res}}=1.5625\) kHz. As in [11], the parameters for running channel estimation algorithms are \(P_{\text{max}}=15,\ s_{\text{max}}=10,\ \epsilon_{\text{r}}=10^{-10},\ \epsilon_{\text{r}}=10^{-2},\ m_{\tau}=10,\ n_{\
u}=10\). Finally, as discussed above, the convergence tolerance parameter for the stopping criterion is set according to the noise variance and OTFS frame parameters.
## 6 Simulations and Results
### Complexity Analysis  
Here, we analyze the computational complexity of CEVTI. Since the main computing burden of CEVTI is the matrix inverse operation, we can only consider Equations (38)-(40). Since a \(N\times N\) matrix takes \(N^{3}\) calculations to calculate its inverse, we can see that Equation (38) takes \(\mathcal{O}(N^{3})\) calculations, and Equations (39) and (40) takes \(\mathcal{O}(L_{p}^{3}+(L-L_{p})^{3})\) calculations. Therefore, the total complexity of CEVTI is \(\mathcal{O}(N^{3}+L_{p}^{3}+(L-L_{p})^{3})\).
## 4 Discussion  
The results of the GPP and NPP are generally comparable to the existing observational evidence and model outputs from forests under similar climate conditions (Table 1). This re-enforces the model's performance and ability to properly simulate PFs' productivity, in addition to the previous evaluation and validation of the model performed over various Chinese forests. Meanwhile, the values of GPP and NPP from our study are lower than those reported for tropical forests and for shrub-dominant ecosystems such as Caatinga and Savanna. The NEP varies significantly between different studies from various forest ecosystems. The NEP values obtained in this study are relatively low compared to those reported in the literature. From a modeling perspective, one important source of uncertainty in estimating the NEP is the quantification of an ecosystem's heterotrophic respiration. In the existing BEPS model, a CENTURY-derived scheme is applied to simulate soil organic C dynamics, in which the soil microbial decomposition processes are simplified through conceptualizing different soil C pools with different intrinsic decomposition rates [53]. Updating and incorporating a new soil biogeochemical model, for example, an explicit consideration of soil microbial behavior and a corresponding parameterization, will better capture the underlying mechanisms of heterotrophic respiration and will hence benefit the model's application to forests at larger geographical extensions [54]. Figure 4: Climate responses and the corresponding critical period, i.e., the periods with the maximum correlations of the GPP, NPP, and NEP of plantation forests during 1981–2019. The percentages presented are over the study region. The left, middle, and right sub-plots in each panel show the proportion of area, the partial correlation coefficients, and the corresponding critical period with the positive or negative responses of the plantation forests, respectively.
## 4 Simulation Results
### Validation of the Local Linear Models  
In order to obtain the best identification results, random signals are implemented to the MEA-based CO\({}_{2}\) capture plant in the identification experiment of each local linear model. Considering the slow dynamics of the CO\({}_{2}\) capture process, the sampling time was chosen as 30 s. Taking the identification of 80% capture rate operating point model (in Group I) as an example, the input sequences for lean MEA flow rate and steam flow rate are plotted in Figure 8 and the identification results are presented in Figure 9. It can be seen that outputs of the local model are in good agreement with the plant. The fitness values of all the local models in Group I and Group II are plotted in Figure 10. The fitness value of the model is defined as:  
\[FitnessValue=100\times(1-\frac{\sqrt{\sum_{k=1}^{N}(y_{k}-\overline{y_{k}}^{2})} }{\sqrt{\sum_{k=1}^{N}(y_{k}-\overline{y})^{2}}} \tag{18}\]  
where \(y_{k}\) and \(\overline{y}_{k}\) are the model outputs and plant measurements at time \(k\), respectively, \(N\) is the number of validation data points, and \(\overline{y}\) is the average of \(y_{k}\).  
Figure 8: Identification signals for the operating point of 80% capture rate (in Group I).  
Figure 9: Validation results for the operating point of 80% capture rate (solid lines: model output; dashed lines: measurements from the process).  
As seen in Figure 10, the local linear models in Group I and Group II have very high fitness, which indicates that the local linear models can precisely predict the output of the CO\({}_{2}\) capture process around the selected operating point. Thus, the local linear models are qualified for the nonlinearity analysis and the establishment of the multi-linear model. Meanwhile, it can be seen that the model of the 95% capture rate in Group I has a slightly lower fitness value. This is because the process nonlinearity around 95% CO\({}_{2}\) capture rate is very strong and reduces the prediction accuracy of the single linear model.
## 2 Analysis of Resistance Characteristics of Towed Tunnel Ship Navigation  
### Analysis of the Characteristics of Ship Navigation in Towed Navigation Tunnel  
The resistance of a ship during navigation refers to the force acting on the hull that opposes the ship's motion. Based on the conditions during navigation, the total resistance of a ship during navigation consists of four parts: wave-making resistance, vortex resistance, viscous pressure resistance, and frictional resistance. The remaining resistance includes wave-making resistance and vortex resistance, but the latter is relatively small. Therefore, the remaining resistance can be considered as wave-making resistance, and thus the total resistance of a ship during navigation can also be considered as consisting of two parts: wave-making resistance and viscous resistance:  
\[R_{t}=R_{w}+R_{v} \tag{1}\]  
\[R_{v}=R_{f}+R_{pv} \tag{2}\]  
The symbols in the formula represent: \(R_{t}\) represents the total resistance of the ship's navigation (kN); \(R_{w}\) represents the wave-making resistance (kN); \(R_{v}\) represents the viscous resistance (kN); \(R_{f}\) represents the frictional resistance (kN); \(R_{pv}\) represents the viscous pressure resistance (kN).  
Pressure resistance includes viscous pressure resistance and wave-making resistance, while frictional resistance and viscous pressure resistance make up viscous resistance. Therefore, in Equations (1) and (2), the total resistance of a ship is the sum of wave resistance and viscous resistance. If the above equation is represented graphically, the relationship between the classification of ship navigation resistance is shown in Figure 1.  
Figure 1: Composition of total resistance in ship navigation.  
For ships with different speeds, the proportion of each resistance component in the total resistance is different. For low-speed ships, wave-making resistance accounts for a relatively small proportion, friction resistance accounts for about 70% to 80%, and viscous pressure resistance accounts for more than 10%. For high-speed ships, the proportion of wave-making resistance will increase to about 50%, and the energy generated by it and the angle of its diffusion are determined by the Froude number Fr of the water depth. Friction resistance accounts for about 50%, and viscous pressure resistance is only about 5%.  
In a towed navigation tunnel, the resistance of ship navigation is affected by factors such as the cross-sectional coefficient n, Froude number Fr, channel depth-to-draft ratio h/T, speed, and water flow velocity. Under the same navigation requirements, the width of the towed navigation tunnel is smaller than the general restricted channel, and the total resistance of the ship will vary greatly due to the superposition of various factors, resulting in the towing resistance of the ship being much greater than that of the general inland river channel.
## 3 Numerical and Experimental Verification
### Experimental Verification
For specimens E200C100, E300C100 and E100C200, the position of the receiver at which the transferred distance of the ultrasonic pulse through the anchor started to converge was relatively larger than the specimens' height, which provided a lesser number for  
Figure 11: Results of the experimental verification: (**a**) E100C100; (**b**) E200C100; (**c**) E300C100; and (**d**) E100C200. calculating the average. Moreover, the adoption of a relatively smaller regularization factor increased the error. In addition, concrete may deteriorate and shrink due to chemical reaction and moisture movement, and cracks may occur inside as well as on the surface, which can greatly attenuate the propagation velocity of ultrasonic P-waves [43; 44]. These factors are also considered to have an effect on the error of the estimated results. Nevertheless, the error remains at an acceptable level considering that the specimens were offered an environment similar to that of the anchor bolt in a real bridge and the presence of fabrication error in the specimens.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods
### Experimental Procedure
#### 2.2.2 Temporal Measurement Setup  
As can be seen in Figure 6, the measurement system designed to capture the time variations includes a Tektronix 5054 oscilloscope. This kind of equipment is characterized by a high real-time measurement accuracy with a sample rate of 20 Giga samples/s, and a resolution of 8 bits per caliber. A sampling frequency equal to 2.5 Ms/s is adopted. The oscilloscope is connected to the LV-network through the capacitive coupler described in Figure 5. A high impedance voltage probe (reference: P5200A, Tektronix, Beaverton, OR, USA) is used to synchronize the temporal noise measurement with  
Figure 4: Spectral setup measurement protocol.  
Figure 5: Electrical schematic of the NB-PLC capacitive coupler.  
the 50 Hz mains frequency. For each measurement, the acquisition of the set of data lasts 20 ms to analyze the noise amplitude in greater detail.
## 4 Near-Surface Defect Detection and Identification  
Figure 3 depicts the proposed method's flowchart. Firstly, the signal undergoes SST to obtain precise time-frequency distribution results. The method then proceeds in two parts--the signal detection section and the signal identification section. During the signal detection section, the algorithm analyses blind zone areas in the low-frequency band of the time-frequency results, and determines the location of defects based on the maximum value of the SST transformation results. In the signal identification section, the trained  
Figure 1: Ultrasonic signals of five types of defects (blue dashed line represents the blind zone signal, and red dashed line represents the defect signal).  
Figure 2: Ultrasonic signals before and after data enhancement.  
defect identification model analyzes the time-frequency map without requiring manual feature extraction. The model outputs the presence of defects and identifies their type based on the trained parameters.
## 6 Conclusions  
This study compared the cooling performances of constant vertical spacing and gradient vertical spacing designs with different vertical spacing values at five flow rates. The simulation results showed that increasing the vertical spacing values of the constant vertical spacing design was ineffective in reducing either T\({}_{\text{max}}\) or \(\Delta\)T. Smaller spacing (30 mm) was always beneficial, reducing both T\({}_{\text{max}}\) and \(\Delta\)T of constant spacing designs at different flow rates. The gradient vertical spacing designs had a worse thermal performance at lower flow rates (10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s and 21.12 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s), especially since the temperature uniformity was unsatisfactory. However, with the increase of flow rate (52.8 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s), the gradient vertical spacing design with larger gradient vertical spacings (44/33/26.4 mm) exhibited superb temperature uniformity (\(\Delta\)T = 2.6 K) while the gradient vertical spacing design with smaller gradient vertical spacings (40/30/24 mm) suppressed the T\({}_{\text{max}}\) under 301.9 K, showing the potential of the gradient vertical spacing designs to enhance the upper limit of the system cooling capacities. For the \(\Delta\)P, the gradient vertical spacing designs always show higher values than the constant vertical spacing ones when the inlet and outlet sizes are equal. Besides, the larger vertical spacing could adversely increase the total volume of the air-cooling BTMS by 3.75% (Design 2 and 6) and 7.5% (Design 3 and 7) compared to the basic design (Design 1, 4, and 5). A reasonable trade-off between cooling performance, energy consumption, and volume should always be made according to the requirements of the EV battery pack.  
Conceptualization, G.Z., X.W. and M.N.; methodology, G.Z., X.W. and M.N.; supervision, X.W. and M.N.; software, G.Z.; validation, G.Z.; formal analysis, G.Z.; investigation, G.Z., X.W. and M.N.; resources, X.W.; data curation, G.Z.; writing--original draft preparation, G.Z.; writing--review and editing, X.W. and M.N.; project administration, X.W.; funding acquisition, X.W. and M.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Research Council, grant number LP170100879.  
Not applicable.  
The authors declare no conflict of interest.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 4 Time-Varying Characteristics of DCB and OSB on BDS Satellites  
More detailed analyses were conducted with DCB and OSB products from Jun 2020 to Jun 2021 that were provided by the CAS. In the following sections, the long-term and short-term stability of BDS DCBs and OSBs are comprehensively analyzed from the perspectives of the mean, STD, and monthly STD. As an absolute bias, OSB can be converted into the corresponding DCB. The differences between DCBs and combined OSBs are also compared.
## 3 Methodology  
The data were acquired from the Directorate General Concession Petroleum (DGPC), Ministry of Petroleum, Islamabad Pakistan. The seismic data were composed of a high-resolution 3D post-stack seismic volume that covered an area of approximately 25 km\({}^{2}\) of the Sawan gas field. The seismic grid contained an inline that ranged from 643 to 867, and the crossline ranged from 893 to 1031. A total of 225 inlines and 139 crosslines were used. The length of the inline was 3459.09, and the interval of the inlines was 25.03, whereas the length of the crossline was 5606.15, and the interval of the crosslines was 25.07. The sample value format was the International Business Machines (IBM) floating point. The sample interval was 4 ms, and the number of traces per sample was 1125.  
The workflow employed for the current study incorporated the analysis of data conditioning, evaluation, and selection of seismic attributes, neural network computation, and artificial ant-tracking (Figure 2). In the first step, the data were conditioned, a process that incorporated seismic conditioning. The seismic conditioning included geostatistical and structural filtering to enhance and sharpen the structural features of the seismic data. In the second step, multiple structural attributes were analyzed to execute the selection process. The seismic attributes, which were extracting the best possible results of discontinuities, were shortlisted. In the third process, the shortlisted seismic attributes were tested and trained for the neural network analysis. In the fourth and last step of the study, the optimum attributes were selected and conditioned to process the ACO algorithm. The ACO was applied to evaluate the fracture network parameters.  
Figure 1: (**a**) Regional geological map showing the structural settings, tectonics, and local multiple sedimentary basins of Pakistan. The Sawan gas field is located near Jacobabad-Khairpur Highs towards the junction of the Middle and Lower Indus Basin (highlighted by blue squares); (**b**) generalized stratigraphy of the study area (lower Indus Basin).
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 6 Software Packages for Scenario Decomposition
### DSP: Decompositions for Structured Programming
#### 6.2.2 Computational Results for DSP in Comparison with PySP  
We test the dual decomposition algorithm on the SSLP, SSLRP, and DCAP libraries. Each subproblem is solved with CPLEX (12.7.0) mixed-integer linear solver. The interior point method proposed by the authors [49] is used to solve the Lagrangian master problem, which is solved with CPLEX as well. Benders-like cuts are not used because the implementation of Benders cuts in DSP only works with SCIP.  
In Figures 4 and 5, we evaluate the best feasible solution (the upper bound) obtained by PySP, and the upper and lower bounds obtained by DSP. For each instance, we include three different gaps. The upper and lower bounds from Ref. [61] are used to evaluate the bounds from PySP and DSP. Note that the bounds from literature are close to the global optimality of each instance. The first column for each instance in both Figures 4 and 5 is the gap between the upper bound from PySP (\(PySP_{ub}\)) and the lower bound from the literature (\(Literature_{lb}\)). The second column represents the gap between the upper bound from DSP (\(DSP_{ub}\)) and the lower bound from the literature (\(Literature_{lb}\)). The third column represents the gap between the upper bound from literature (\(Literature_{ub}\)) and the lower bound from DSP (\(DSP_{lb}\)).  
For the SSLP and SSLRP instances shown in Figure 4, although PySP can converge within the time and iteration limit, the best feasible solution obtained from PySP (\(PySP_{ub}\)) may not be optimal. There are about 1% gaps for some of the SSLP instances (see the first column of each instance in Figure 4). DSP can solve more instances to optimality than PySP (see the second column of each instance in Figure 4). The lower bounds obtained by DSP are also quite tight, usually less than 0.01% (see the third column of each instance in Figure 4). Note that the literature values for SSLRPFF5_50_100(1), SSLRPFF5_50_100(2), and SSLRPFF5_50_100(3) do not match the values from our experiment. Therefore, we try to solve the deterministic equivalent of these instances to obtain bounds. The literature bounds of SSLRPFF5_50_100(3) come from solving the deterministic equivalent. The gaps of SSLRPFF5_50_100(1) and SSLRPFF5_50_100(2) are omitted since the corresponding deterministic equivalent cannot be solved within 3 h.  
For the DCAP instances where we have mixed-integer first-stage variables, the best feasible solutions from PySP (\(PySP_{ub}\)) and DSP (\(DSP_{ub}\)) are quite far from optimality. The gaps of the first two columns are around 10%. On the other hand, the lower bounds obtained from DSP (\(DSP_{lb}\)) are tight. The gaps between (\(Literature_{ub}\)) and (\(DSP_{lb}\)) are around 0.1%. Therefore, in order to improve the relative optimality gap of DSP, the focus should be on designing more advanced heuristics to obtain better feasible solutions.
## 2 Materials and Methods
### Measurements and Calculations  
To determine the effect of density on the yield of _D. lufifrons_ and _B. vulgaris_ chard in aquaponic culture, three stocking densities 10, 30, and 50 fish m\({}^{-3}\) (equivalent to 0.21, 0.68, and 1.04 kg m\({}^{-3}\) T1, T2, T3, respectively) were evaluated in triplicate and a fixed plant density of 20 plants m\({}^{-2}\) was considered. The total length (cm) of the fish was recorded using a digital Vernier caliper, measuring from the tip of the snout to the most distal part of the tail. The total weight (g) was obtained using an electronic balance (0.001 g precision; Ohaus Scout(r), Ohaus Corp., Parsippany, NJ, USA). At the beginning of the experiment, the fish had an average weight of 21.65 \(\pm\) 9.41 g and a length of 10.73 \(\pm\) 0.13 cm. They were fed once a day with Growfish(r) (Malta Texo de Mexico S.A. de C.V., Guadalajara, Mexico) brand commercial tilapia feed (35% protein and 5.5% lipids) at 2% of total biomass. Evaporated water was replenished daily, and the pond bottoms were cleaned weekly with the aid of a siphon. After 120 days of feeding (time considered as the system maturation period), the chards were transplanted to the EU with an average total weight of 1.26 \(\pm\) 0.04 g. The concentration of total ammoniacal nitrogen (TAN), nitrites (NO\({}^{2-}\)), and nitrates (NO\({}^{3-}\)) were evaluated once a week during the last three weeks before concluding the experiment. At the end of the bioassay, the total weight of chard and root were recorded separately. The dry weight of leaves and root was determined by dehydrating the material in a convection oven (Novatech(r) Avante Technology, SA de CV, Tlaquepaque, Mexico) at a temperature of 70 \({}^{\circ}\)C for three days, in order to obtain the root-shoot ratio. The weight (g) and total length (cm) (of all fish), daily weight gain DWG (g fish\({}^{-1}\) day\({}^{-1}\)), net yield NY (g m\({}^{-3}\)), feed conversion ratio (FCR), and fish survival (%) were also determined.  
\[\text{DWG}=(\text{final weight}-\text{initial weight})/\text{no. of fish}/\text{time (d)} \tag{1}\]  
\[\text{NY}=(\text{biomass m}^{-3}-\text{initial biomass m}^{-3})/\text{time (d)} \tag{2}\]  
\[\text{FCR}=\text{total dry feed weight}/(\text{final fish biomass}-\text{initial fish biomass}) \tag{3}\]