esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparative Methods and Related Settings  
In the experiments, to evaluate the performance of the proposed MSGATN, we selected five related CD approaches for comparison with our MSGATN. All methods are described as follows:  
1. PCA_K-means [19]: This approach is one of the popular unsupervised CD methods, which adopts principal component analysis (PCA) and k-means clustering to acquire binary change map. In this method, two parameters (\(h\) and \(s\)) should be set. For the Guangzhou City dataset, \(h\) and \(s\) are set to 9 and 3, respectively. For the Hongqi Canal dataset, \(h\) and \(s\) are set to 5 and 3, respectively.
2. ASEA [66]: It is a state-of-the-art method that exploits the contextual information around a pixel to improve detection accuracy. This method requires no parameter setting.
3. GaborPCANet [46]: This was proposed in [46]. It utilizes PCA filters as convolution kernels to obtain representative neighborhood features. In this approach, a parameter, patch size, is set to 5 for both experimental datasets.
4. DBN [49]: This is a superpixel-based method, which can acquire a better detection result by difference representation learning. For our experimental datasets, the parameter patch size is fixed to 5 in this method.
5. CWNN [47]: It devises a convolutional-wavelet neural network in SAR images. In the experiments, the parameter patch size is fixed to 7 for our datasets.
6. Proposed MSGATN: In our MSGATN, the number of superpixels is a hyperparameter. Specifically, in our method, we selected six scales of superpixel segmentation, which, respectively, include 1000, 2000, 3000, 4000, 5000, and 6000 superpixels, to train our MSGATN in a Siamese manner. For both experimental results, the results of 6000 superpixels are chosen to be compared with other methods.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 5 Conclusions  
The current work aimed to study the performance of the Pecora-Carroll synchronization technique to achieve synchronization between the analog and discrete chaos oscillators. The two key parameters of the chaotic synchronization, synchronization noise immunity and synchronization speed, were investigated. The noise immunity study demonstrated the quality of synchronization at various noise signal power levels present during the synchronization, while the synchronization speed demonstrates how quickly the analog-discrete synchronization is achieved, along with how quickly the two systems diverge when the synchronization signal is no longer applied.  
The discrete chaos oscillator was implemented as the discrete solution of the chaos oscillator circuit; MATLAB was used to simulate the discrete model. The analog chaos oscillator was implemented in PCB, but the study also used an LTspice model of the oscillator to compare the behavior of analog-discrete and discrete-analog synchronization using a real prototype and its model in the commonly commercial software such as LTspice. The work also performed two studies on different chaos oscillators--RC chaos oscillator and Vilnius chaos oscillator--to compare the analog-discrete and discrete-analog chaotic synchronization performance of different chaos oscillators. The acquired synchronization results were in favor of the RC chaos oscillator.  
The hybrid analog-discrete approach of implementing chaos oscillators opens new possible communication schemes for IoT applications. The analog implementation of chaos oscillators can benefit the simpler sensor node (SN) integration, while the discrete implementation can be used on the gateway. The acquired results, developed models and prototypes will serve as the foundation for further developing the hybrid chaos-based communication system. This future development in this direction will be aided by the knowledge and the competencies gained in the previous works [32; 33; 34] when developing analog chaos oscillator-based communication systems.  
Conceptualization, R.B. and D.K.; Data curation, D.C. and D.K.; Methodology, R.B.; Project administration, A.L.; Resources, F.C.; Software, R.B.; Supervision, D.K.; Validation, D.C.; Writing--original draft, R.B.; Writing--review and editing, F.C. and D.K. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the European Social Fund within the Project No. 8.2.2.0/20/1/008 <<Strengthening of PhD students and academic personnel of Riga Technical University and BA School of Business and Finance in the strategic fields of specialization>> of the Specific  
Figure 23: Vilnius chaos oscillator analog-discrete synchronization and desynchronization times of the \(x\) (**a**) and \(z\) (**b**) state variables and discrete-analog synchronization and desynchronization times of the \(x\) (**c**) and \(z\) (**d**).
## 5 Emerging Trends in FSO Communication
### Incorporation of Emerging Technologies  
The landscape of wireless communication is rapidly evolving, fueled by the increasing need for higher data rates, improved reliability, and seamless connectivity. As mentioned in the preceding section, FSO communication, known for its high bandwidth and security, is being integrated with other advanced technologies such as RF, mmWave, and THz systems [2; 93]. This section discusses these emerging trends in detail, highlighting how these integrations can enhance communication systems' overall performance and capabilities.
## 3 Results
### Global Hemodynamics  
Assessment of global hemodynamic parameters is shown in Figure 2 and Table 2. Volunteers showed higher hemodynamics values than patients at peak systole and e-wave, except for helicity density. Remarkably, hemodynamic parameters in complete responder DCM patients remained low compared to volunteers. We found statistical differences between HV and DCM patients: non- and complete responders at peak systole and e-wave in velocity, vorticity, viscous dissipation, energy loss, and kinetic energy. In all cases, \(p\)-values were lower or equal to 0.005. There were no statistical differences in the parameters at end-diastole. Furthermore, we did not find statistical differences between DCM groups. In addition, ROC curves showed that previous parameters discriminated between HV and DCM patients (Figure S1).  
The total computational time used to process the data, once the multi-slice b-SSFP was segmented and registered, varied between 30-40 s for one cardiac phase, using a standard computer (3.4 GHz Intel(r) Core i7 TM, 16 GB RAM).  
Hierarchical cluster analysis (Figure 3) provides an alternative method for reliable identification of correlation between ejection fraction and hemodynamics parameters from 4D-flow MRI. According to their similarities, they are classified into two clusters identified at peak-systole, e-wave, and end-diastole. At peak-systole and e-wave, cluster 1 (black): helicity density; and cluster 2 (red): ejection fraction, energy loss, vorticity, viscous dissipation, velocity, and kinetic energy. Finally, at end-diastole, cluster 1 (red): helicity density and ejection fraction, and cluster 2 (red): energy loss, viscous dissipation, vorticity, velocity, and kinetic energy. This means that ejection fraction correlates with all parameters except helicity density at peak systole and e-wave.
## 4 Data Processing and Features
### Reinforcement Learning  
Reinforcement learning (RL) is a branch of machine learning that looks at how smart agents should act in an environment to obtain the most rewards over time. The setting in which the agent learns and makes decisions is referred to as the environment. In response to the activities taken by the agent, the environment will either deliver rewards or transition to a new state. Therefore, in the learning method known as reinforcement, do not instruct an agent on how it should carry out a certain task; rather, we give the agent incentives, either positive or negative, depending on the agent's behavior [21; 23; 31; 40; 44; 63; 72]. Figure 8 shows the basic concept of reinforcement learning. The following are the fundamental components of a reinforcement learning problem:  
* Environment: The external environment in which the agent has interactions.
* State: Current circumstances involving the agent.
* Reward: Feedback signal in the form of numbers from the environment.
* Policy: A method for mapping the state of the agent to its actions. A policy is what determines the course of action to take in any particular condition.
* Value: The future reward, that an agent would be eligible to earn if they act while in a certain condition.  
The various states of the environment were represented in two distinct ways, both of which were offered. To determine the most effective methods of dynamic trading, the Q-learning algorithm of reinforcement learning was used during the training of the trading agent in this study. Conducted tests using actual data taken from the Indian and American stock markets and used both of the models that were offered. In terms of profitability, the suggested models demonstrated superior performance to both the Buy-and-Hold and Decision-Tree-based trading strategies [40; 58].
# Contingent Task and Motion Planning under Uncertainty for Human-Robot Interactions
## Related Work
### Task and Motion Planning without Uncertainty  
Recently, much study has been centered to solve robotics manipulation tasks by combining task and motion planning problems with no consideration on uncertainty. It is assumed that the initial state of the environment is perfectly known, and actions are deterministic, i.e., state of planning is only changed by the selected action. There is a huge number of task planners being able to solve manipulation problems under perfect information [7].  
In principle, two methods of combining task and motion planning have been explored: interleaved or simultaneously. Several studies call first task planning, and then motion planning to determine whether a plan is feasible or not such as [8; 9; 10; 11; 12]. In the case of failure, geometric constraints are identified and reported to task planning and the procedure continues. This might be costly as a number of times the process could be repeated in order to find a geometrically feasible plan.  
On the other hand, other approaches enable task planning to incorporate geometric reasoning within the task planning process [13; 14; 15; 16; 17; 18]. Hence, in this case, task planning results in a feasible manipulation plan. In this line, we recently proposed a heuristic-based task and motion planner [19] to deal with constrained table-top problems for bi-manual robots by offering different type of geometric reasoners that can be used in heuristic computation or when an action is selected. Our previous approach does not consider any uncertainty, human actions, and reasoning about mobile manipulation problems which are the subjects of this paper.  
The way of integrating task and motion planning information in the current proposal is based on the simultaneous approach in order to generate feasible plans, and is an extension of [19] that copes with mobile manipulators, uncertainty, and to consider collaborative tasks with human operators.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 3 Results and Discussion
### Adsorption Kinetics at Different Temperatures
#### 3.1.4 Adsorption Stability  
In Table 6, the results for the desorption of ZSFB composite fiber in clean water are listed. We can see that the desorption rate increased with the increase of the adsorption mass (as phosphorus), but the overall desorption rate was very low (\(<1\%\)), which indicates that the composite fiber had strong adsorption to phosphorus and can be applied to remove phosphorus in wastewater and surface water. According to the previous analysis, the adsorption of phosphorus by the composite fiber was mainly chemical adsorption, that is, the metal ion in the composite fiber combined with the phosphate ion in the water to form a phosphate precipitate. Since the solubility of the phosphate precipitate was low, the desorption rate of the composite fiber was also low.  
Table 7 shows the desorption of ZSFB composite fiber in hot water. The results show that the desorption rate increased with the increase of the adsorbed amount of the composite fiber, but the overall desorption rate was still low (\(<1.1\%\)). Compared with the previous section, the desorption rate of the composite fiber in hot water was higher than that in clean water, which was due to the increase in the solubility of the phosphate precipitate as the temperature increased [27].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adsorption (mg P)** & **Desorption (mg P)** & **Desorption Rate (\%)** \\ \hline
0.414 & 0.0005 & 0.121 \\
0.812 & 0.0013 & 0.160 \\
1.946 & 0.0067 & 0.344 \\
2.897 & 0.0169 & 0.583 \\
3.444 & 0.0247 & 0.717 \\
3.650 & 0.0273 & 0.748 \\
3.751 & 0.0286 & 0.762 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Desorption of ZSFB composite fiber in clean water.  
Figure 8: Effect of anion on phosphorus removal efficiency of ZSFB composite fiber.  
Table 8 shows the desorption of ZSFB composite fiber in a sulfuric acid solution. The results show that the desorption rate increased with the increase of the sulfuric acid concentration. When the sulfuric acid concentration was 0.5 mol/L, the desorption rate reached 99%, which can be regarded as complete desorption. This was caused by sulfuric acid reacting with the calcium phosphate to form calcium sulfate precipitates and phosphoric acid [28]. At lower concentrations, the phosphate in the composite fiber did not completely react with the sulfuric acid. As the concentration of sulfuric acid increased, the phosphate and sulfuric acid reacted more thoroughly until a complete reaction. Since the desorption rate of the composite fiber in the sulfuric acid solution was high, the saturated composite fiber could be regenerated by the sulfuric acid solution.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.4 Scenario 3: Rectifier and Capacitor Bank  
In this scenario, a capacitor bank is added to the six-pulse rectifier, disabling linear loads. The goal is to maintain a \(PF\) above the desired limits (\(PF>0.95\)) while allowing a broad range of firing angles (\(\alpha\)).  
* \(Q_{cb}\) varies from 50.1 kVAr to 60.7 kVAr. Two configurations are considered: non-detuned and detuned capacitor banks.
- Non-detuned capacitor bank (Figure 11a):
- Voltage waveforms are heavily distorted with indistinct notches, indicating excitation of the network's natural frequencies.
- Total line currents are also significantly distorted.
- Increasing \(Q_{cb}\) within the range slightly smooths the waveforms.  
Figure 11: Impact of the capacitor bank \(Q_{cb}\) on voltage notch waveforms (**top**), input line currents of the rectifier (**middle**), and total line currents of the LV installation (**bottom**). (**a**) Non-detuned capacitor bank and (**b**) detuned capacitor bank.  
Figure 10: Impact of \(S_{ll}=P_{ll}+jQ_{ll}\), with a fixed \(PF_{ll}=0.9\), on voltage notch waveforms (**top**), input line currents of the rectifier (**middle**), and total line currents of the LV installation (**bottom**).  
- Notches are clear and distortion-free, as natural frequencies are not excited.
- \(Q_{cb}\) variation within the range shows no significant impact.
- Provides fundamental voltage support and slightly reduces notch depth.
- Commutation oscillations are filtered by the detuning reactor.  
The analysis shows that detuned capacitor banks effectively mitigate voltage distortions and maintain clear voltage notches, providing a more stable and efficient operation compared to non-detuned capacitor banks.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 1 Introduction
Using the Kalman filtering framework, we can effectively make up for the defect of fewer neural network training samples. At the same time, we can modify the Kalman filtering results using LSTM network, which can reduce the estimation deviations, be closer to the actual position of the target, and obtain an ideal result.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 2 Materials and Methods
### Reference Wind Speed Products
#### 2.2.2 Global Reanalysis Wind Speed Model  
Near-coincident matchups were used between CYGNSS observations and ERA5. ERA5 wind speed has been found to have an uncertainty of 1.45 m/s for off-shore winds [28] and an uncertainty of 1.73 m/s globally [29].  
The matchup method used with ERA5 initializes a regular grid interpolator object using the modeled data at their associated latitude, longitude, and time. The interpolator takes CYGNSS L2 data dimensioned by latitude, longitude, and time as input and uses trilinear interpolation (nearest neighbor) to estimate ERA5 values at each CYGNSS three-dimensional point. Histograms of the spatial and temporal separation between matchup samples are shown in Figure 3. The mean (\(\pm\)standard deviation) of the distribution of separations is 17.2 km (\(\pm\)15.8 km) and 30.3 min (\(\pm\)16.8 min) for distance and time, respectively. The matchup method for ASCAT-B and GMI (described in Section 2.2.3) starts with all CYGNSS and ERA5 samples within the same hour, then finds the closest matchups to CYGNSS points in lat/lon using a nearest neighbor BallTree algorithm with a Euclidean metric. Only the closest observations in space and within a given time are considered.  
Figure 3: Histograms of (**a**) spatial and (**b**) temporal separation between matchups of CYGNSS and ERA5 wind speed samples. The mean separations are 17.2 km and 30.3 min.  
Figure 2: Locations of the colocated tropical buoys used for intercomparison.
## 2 Materials and Methods
### Analysis of Gene Characteristics for Methanogens  
The counts and activity of methanogenic microorganisms were determined by Real-Time PCR (qPCR). This method was used to determine the prevalence of two methanogen families of the order _Methanosactinales_: _Methanosactinaceae_ and _Methanosactinaceae_, both responsible for CH\({}_{4}\) production. The group-specific methanogenic primers have been used [23]. A meaningful share of other genera in the domain _Archaea_, including _Methanobacteriales_, _Methanococcales_, and _Methanomicrobiales_, were eliminated in a preliminary analysis (data not shown). The activity of methanogenic microorganisms was evaluated by estimating the concentration of the gene encoding methyl-coenzyme M reductase (_mcrA_), which catalyzes methanogenesis, the last step of AD. Standard curves were plotted before gene quantification based on serial dilutions of samples with known copy numbers of the examined genes. Amplicons were cloned from positive controls in vector pCR2.1-TOPO (Invitrogen, Waltham, MA, USA). The abundance of genes characteristic for methanogenic _Archaea_ (_mcrA_ gene, _Methanosactinaceae_- and _Methanosactinaceae_-specific genes) during the AD of SS and CS was determined with the LightCycler(r) instrument (Roche Diagnostics, Mannheim, Germany) with LightCycler (r) software (version 1.5.0). The concentrations were expressed by the copy number in 1 g of digestate (g\({}_{\text{D}}\)\({}^{-1}\)). All qPCR reactions were performed according to the methodology described in our previous study [20]. Reaction conditions and primer sequences [23,24] are presented in Table S1 in the Supplementary Materials.
# Jet Noise in Airframe Integration and Shielding
## 4 Numerical Simulation of the Shielding Effect  
To demonstrate the computational capabilities of the current state of the art, we consider the case of simulating the effects of shielding on jet noise. In this case, a density-based compressible solver is employed with the advantage of total variation diminishing (TVD) scheme to simulate the flow field of a supersonic, ideally expanded heated jet exhausting from a 2:1 aspect ratio nozzle. A hybrid LES--URANS approach is used here to simulate the turbulence fluctuations of the flow and avoid  
Figure 20: Snapshots of the instantaneous pressure fluctuations in a \(M=2.1\) round jet for four different cases of inflow excitation with excitation amplitude of \(\varepsilon\). (**a**) Single frequency \(\varepsilon=0.04\). (**b**) Single frequency \(\varepsilon=0.001\). (**c**) Bi-modal frequency \(\varepsilon=0.04\). (**d**) Random frequency (Ref. [58,71]).  
computationally expensive LES simulations to model the near-wall boundary layer. Instead, URANS with the \(k-\omega\) SST turbulence model is used near the walls. The FWH surface integral approach is used to predict the far-field acoustics.  
_Geometry:_ The geometry of the convergent-divergent (C-D) nozzle is obtained from experimental studies on the rectangular (\(12.95\) mm \(\times\) 25.91 mm) supersonic jet by [PERSON] et al. [35]. The equivalent diameter of the nozzle exit is \(D=~{}20.65\) mm. Figure 21 shows a planar cut of the 2:1 aspect ratio rectangular nozzle and the location of far-field microphone probes. In addition, the reflected and shielded side of the domain are illustrated. The C-D nozzle has the area ratio of 1.18 with a design Mach number of 1.5, which corresponds to a nozzle pressure ratio (NPR) of 3.67. The nozzle temperature ratio (TR) is chosen such that it resembles the set-up defined by [PERSON] et al. [35] as \((TR=T_{o}/T_{a}=3.0)\), where \(T_{o}\) is the total temperature of the jet and \(T_{a}\) is the ambient temperature. The nozzle has sharp-edged throat. In order to simulate the shielding effect and the flow field of the supersonic jet exhausting over airframe surfaces scenario, a 12.7 mm flat plate is placed parallel the jet axis and aligned with the nozzle's major axis, and it extends up to \(x/D=30\) downstream of the jet axis and \(z/D=10.5\) in the major axis. This configuration is illustrated in Figure 21 and is explained in detail by [PERSON] et al. [35]. For the simulations carried out here, the flat plate is located such that the top surface of the plate is at the same elevation as the inner walls of the nozzle (\(h/D=0\)).
## 3 Results and Discussion
### Statistical Analysis of FTIR Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Descriptive Statistics** & & & & & \\ \hline  & **N Analysis** & **N Missing** & **Mean** & **Standard Deviation** & **SE of Mean** \\ \hline PLA 3045 & 5 & 0 & 0.2085 & 0.44265 & 0.19796 \\ PLA 3145 & 5 & 0 & 0.20927 & 0.44212 & 0.19772 \\ PLA 1379 & 5 & 0 & 0.98746 & 0.00955 & 0.00427 \\ PLA 1458 & 5 & 0 & 0.98145 & 0.0264 & 0.01102 \\ \hline \multicolumn{5}{l}{One Way ANOVA} \\ \hline Overall ANOVA & & & & & \\ \hline  & DF & Sum of Squares & Mean Square & F Value & Prob-F \\ \hline Model & 3 & 3.00764 & 1.00255 & 10.22729 & 5.3002\(\times 10^{-4}\) \\ Error & 16 & 1.56843 & 0.09803 & & \\ Total & 19 & 4.57697 & & & \\ \hline \multicolumn{5}{l}{Null Hypothesis: The means of all levels are equal} \\ Alternative Hypothesis: The means of one or more levels are different. & & & \\ At the 0.05 level, the population means are significantly different. & & & \\ Fit Statistics & & & & \\ \hline \multicolumn{5}{l}{R-Square} & Coeff Var & Root MSE & Data Mean \\ \hline
0.6725 & 0.52473 & 0.31309 & 0.59667 \\ \hline \end{tabular}
\end{table} 
Table 5: One-way ANOVA for PHB samples under UV radiation at 0, 2, 5, 24, and 144 h.  
Figure 10: Bar Chart.