esa-sceva/satcom-chunk-collection huggingface dataset:
as nucleation and crystal growth [6; 7; 8]. Nucleation occurs when the temperature of the crude oil decreases to the wax appearance temperature (WAT). The wax molecules form clusters, causing a cloudy appearance--prompting the term \"cloud point\", which refers to WAT value. Progressively, the paraffin wax molecules attach and detach until they reach a critical size cluster in order to be stable. These clusters are better known as nuclei, and the formation of these nuclei is defined as nucleation. Homogeneous nucleation takes place in the absence of contaminant or nucleating materials [6]. Meanwhile, heterogeneous nucleation occurs in the presence of nucleating material(s) throughout the liquid. The crystal growth process then begins after the nuclei has stabilized as further molecules attach in a plate-like or lamellar structure. In general, the precipitation of wax can cause an increment in crude oil viscosity, although the adhesion of wax to the pipe wall does not occur [9].  
The correlation between the crude oil properties and rheology, including the deposition as well as the precipitation of wax during flow, has been extensively studied by a group of researchers [10; 11; 12; 13]. The developed correlation is applied to predict the onset precipitation and deposition of wax in the pipeline. The standard technique used to investigate wax precipitation is by using a cold finger system, flow loop, or viscometer apparatus [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Nonetheless, the precise modelling of deposition along the pipeline walls is challenging. This is because the deposition of wax is a function of thermodynamic variables, hydrodynamic flow, heat and mass transfer, as well as solid-solid and surface-solid interactions. Modelling wax deposition in the presence of multiphase flow is even more complex to derive manually, as numerous variables need to be considered. In addition, a few wax deposition models have been put forward by researchers over the past few years in order to describe wax precipitation using computer simulation software such as OLGA(tm) from Schlumberger, LedaFlow from Kongsberg and FloWax from KBC [30]. A brief explanation of the model utilized by the software is illustrated in Table 1.  
Hence, this review paper aims to discuss the current understanding of the wax deposition mechanism in pipelines. The mechanisms for wax deposition include molecular diffusion, Soret diffusion, Brownian diffusion, gravity settling, shear dispersion, and shear stripping, which will be further discussed in this paper. In addition, the factors affecting wax deposition in multiphase flow, especially in the presence of gas-oil and oil-water flow, respectively, are also discussed. Further, this review covers the latest chemical inhibitors in the literature and the factors that affect the efficiency of chemical inhibitors in developing a sustainable source of knowledge in flow assurance.
## 4 Rationale for Each TACE Technique  
### Rationale for Bland Embolization and DEB-TACE  
Various kinds of microspheres are available. Experimental studies show that smaller-sized microspheres induce more necrosis of the hepatic parenchyma. Doxorubicin-loaded DEBs can also cause a coagulative necrosis of the adjacent liver parenchyma; therefore, smaller-sized DEBs induce more marked necrosis of the hepatic parenchyma than the same-sized bland beads or larger-sized DEBs [29]. For tumor tissues, smaller DEBs are likely to occlude distal tumor vessels, resulting in more localized chemotherapeutic delivery and a good imaging response [30], although the risk of unexpected complications, such as biliary necrosis and systemic embolization, may increase [28; 31; 32]. However, one of the important technical problems of particle embolization is that microspheres cannot embolize the tumor drainage. Therefore, the reversed flow from the surrounding hepatic sinusoids and portal venules into the peripheral tumor portion following arterial blockage cannot be blocked. Micro-arterial communications may also be unblockable. As a result, tumor tissues supplied not only by both arterial and portal blood but also by collateral blood including portal blood may survive mainly at the periphery (Figure 3) [33; 34]. In DEB-TACE, it is expected that eluted chemotherapeutics may necrotize the surviving tumor cells; however, it is uncertain how the chemotherapeutics reach the viable tumors.  
In addition, DEB-TACE damages the hepatic artery more severely than cTACE (per branch, \(p<0.001\) and per patient, \(p=0.005\)) [35] and the incidences of arteriportal shunt formation are also significantly higher following DEB-TACE than cTACE (48.7 vs. 8.1%, respectively, \(p<0.001\)) [36].  
Figure 3: Schematic representation of rationale for superselective cTACE and DEB-TACE/bland embolization. Reproduced with permission from Ref. [33]. An Open Access article (CC BY 4.0). Abbreviations: W-D, well-differentiated; PBP, peribiliary vascular plexus, PV, portal vein.
## 4 Results and Discussion  
### Performance of the Bias Correction Model  
The multi-layer perceptron (MLP) bias correction model was trained using data from months with intensive irrigation activity (July through November). Once trained, the model's predicted error was reapplied across the full synthetic discharge series, effectively integrating the learned corrections into the HEC-HMS output. This produced a complete, bias-corrected streamflow time series, which was then used in both the training and validation phases of the deep learning model.  
The bias correction demonstrated strong improvements across multiple performance metrics. Specifically, on the calibration dataset, the Nash-Sutcliffe Efficiency (NSE) increased from an initial 0.52 (raw HEC-HMS output) to 0.84 after correction, the Kling-Gupta Efficiency (KGE) improved from 0.65 to 0.90, and the R-squared value rose from 0.56 to 0.88. The validation dataset results similarly indicated substantial performance gains (NSE = 0.82, KGE = 0.88, R-squared = 0.85).  
The full time series (including calibration and validation periods combined) demonstrated robust performance (NSE = 0.83, KGE = 0.89, R-squared = 0.86), indicating that the bias-corrected synthetic discharge closely resembles the observed discharge patterns, effectively capturing human irrigation impacts (Figure 8). These results confirm that the corrected synthetic discharge provides a reliable and realistic input dataset for subsequent deep learning modeling.
## 4 Prototype Piezoelectric Floor Analysis  
A comparison of the different piezoelectric platforms in the market has been presented by [PERSON] et al. [8]. The firms, Waynergy Floor, Sustainable Energy Floor (SEF), and Pavegen Tiles have built their piezoelectric platforms for specific purposes and installed them in public places such as train stations, airports, shopping centers, offices, etc. These piezoelectric platforms have high power generation yields, i.e., reaching 10 W per step, with the cost of Waynergy Floor platforms 400E per unit, Sustainable Energy Floor platforms around 800E, and Pavegen Tiles platforms around 1800E [8].  
Although the performance of the prototype presented in this work is lower than those platforms, the price of the materials used for its construction does not exceed 30E, being a low-cost device, but the business study has not been considered yet.  
In addition, the power generation per step is higher than other commercial piezoelectric platforms, for example, Sound Power, which generates, PZT ceramic, Parquet PVDF layers, Drum Harvesters Piezo buzzer Piezoelectric Ceramic, and Power Leap PZT [8]. The power generated by the platforms, in this paper, for a person weighing 85 kg, reached 43.3 mW per step, which was more than some commercial platforms. This platform was analyzed to compare the different performances using different piezoelectric devices, and it has not been considered for any commercial use.  
Figure 8: Output voltage generated with steps of people with different weight and positions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight (kg)** & **Voltage (V)** & **Current (mA)** & **Power (mW)** \\ \hline
55 & 4 & 7.1 & 28 \\
70 & 4.2 & 8.47 & 35.6 \\
85 & 4.5 & 9.62 & 43.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Voltage, current, and power obtained for the football of people with different weights.  
Table 6 shows the main commercial piezoelectric floors together with the platform presented in this paper. The low cost of this platform could be an option for certain applications where low energy consumption is required, for example, powering circuits, LED lighting, transmitting signals via Bluetooth, IoT, etc. We also observed that its good performance is comparable with other similar commercial solutions due to the choice of PZTs disks and their arrangement on the platform.  
The electrical energy that a piezoelectric floor generates is lower as compared with other energy sources, but it could be enough for applications that require low consumption, for example, feeding circuits, smart homes, communications, and, IoT [14; 30].
## 2 Network Model  
Since nodes in DUANs always have different positions over time due to the drifted movement by sea waves, in this paper, we consider a time varying network model to represent DUANs which is denoted as \(G^{k}=\{V^{k},L^{k}\}\). \(V^{k}=\{\Phi^{k},\Gamma^{k}\}\) is the collection of all anchor nodes \(\Phi^{k}\) and all ordinary nodes \(\Gamma^{k}\) at time \(k\). \(L^{k}=\{(i,j)|i,j\in V^{k}\}\) is the set of communication links between each node in the network. In DUANs, if \(d_{ij}\leq R_{c}\), node \(i\), and node \(j\) can communicate with each other, where \(d_{ij}\) is the distance between node \(i\) and \(j\), \(R_{c}\) is the communication range. In this paper, we assume that all nodes in the network can communicate with each other.
## 3 Results
### TEP Components  
Based on the results of the temporal and spatial correlation analysis, we select as the TEP of interest the average waveform of the nine central channels FC1, FCz, FC2, C1, Cz, C2, CP1, CPz, and CP2, with this set of electrodes constituting our ROI. Figure 5 shows the TEP for each subject (records from dataset B) and the grand average across all subjects in panels Figure 5a,b, respectively. In Figure 5a, weessimily between the TEPs, but this is mostly with regards to the broader morphology of the TEPs rather than the finer details. It is evident that there is significant variability in the latencies and amplitudes of the components. In Figure 5b, we present the grand average TEP, along with the components that showed the most consistency across subjects, specifically P60, N75, P90, N110 and P150. The existence of these components is also verified in records from dataset A (see Figure S1). Other components, such as the P30 and N45, or components after 200 ms, were not consistently observed across subjects, due to the lower signal-to-noise ratio, or had very large variability in their latencies, so we excluded them from further analysis. We note that for each subject, the components were detected independently and are not consistent across them with respect to their latency, but the notation is kept the same for all subjects for the sake of simplicity.  
Figure 4: Topographic plots for the average across all subjects E-field, and CEA(25,80) and CEA(25,275), as well as indicative CEA values for two pre-TMS windows.  
Figure 5: (a) ROI-averaged TEPs for the ROI of nine central channels and for individual subject, and (b) the average across subjects TEP, along with identified components.
# A Novel Dual-Rotor Ultrasonic Motor for Underwater Propulsion
## 4 Experimental Results and Discussions
The mechanical characteristics of the prototype motor were measured and results are shown in Figure 11. It can be seen that the stalling torque was 3 mN-m, the no-load speed was 110 r/min, and the maximum output power was about 7.5 mW corresponding to the output torque of 1.2 mN-m and rotating speed of 60 r/min. Both rotors were tested respectively and there were no obvious differences in mechanical characteristics between the two rotors. Compared to our previous inertial-type ultrasonic motors designed for actuating underwater robots [24], the output torque for individual rotor increased 2.4 times. Measured results show that the prototype motor had a good performance at a driving frequency of around 63.2 kHz. Finally, we fabricated a cylindrical underwater robot model according to the conceptual design shown in Figure 1 to test the propulsion behavior of the prototype motor. The prototype motor was installed to the end part of the robot model and one pair of thrusters with reversed turning directions were respectively mounted to each rotating shaft end of the motor. Counter weights were placed at the bottom face of the robot model's middle part for balancing the attitude during propulsion. Figure 11: Mechanical characteristics of the prototype motor. Figure 5.0 Relationship between the no-load rotating speed and the pre-pressure. The appearance size of the robot model was \(\mathcal{O}80\) mm \(\times\) 210 mm and the mass was 295 g. The underwater propulsion experiment was conducted and the propelling process was captured, as shown in Figure 5.1 It was observed that the immersed robot model, propelled by the dual-rotor ultrasonic motor, moved smoothly from the right side to the left side with the displacement of 520 mm in 20 s. Variance of the propulsion velocity was measured and plotted as the black points shown in Figure 5.2 For comparison, simulated results based on the methods in our prior study [24] were also plotted as the red line shown in Figure 5.3 The inset shows the ambient flow distribution around the robot model. It was observed from the tested results that the underwater robot experienced an accelerating state in the first 6 s, then moved at a relatively steady velocity of 30 mm/s for 9 s, and then decelerated gradually to 25 mm/s for the last 3 s. Simulated results showed a faster acceleration process (\(\sim\)4 s) and higher steady state velocity (\(\sim\)36 mm/s), which may have been caused by the simplification of the robot model geometry. In addition, hysteresis effects of piezoelectric materials not considered during the simulation may have also suppressed the propulsion velocity when the robot model operated for a long time. Nonetheless, experimental results basically agreed with the calculated propulsion process, proving that the proposed dual-rotor ultrasonic motor could effectively actuate the underwater robot model.
## 2 Material and Methods
### The Initial Weights for Modelling Process  
As a multiple-criteria decision analysis (MCDM) method, the step-wise weight assessment ratio analysis (SWARA) algorithm was firstly introduced for assessing the correlation between factors by computing a set of weights for each factor. There are two strategies for the SWARA algorithm. The first strategy is to analyse the different situations and each layer of influential factors (criteria) is prioritized based on the requirements and aims. The other strategy is experts' knowledge, which is of great importance to the prioritization of each layer of the influencing factors [105]. For clarification, the SWARA algorithm is summarized in two steps:  
**Step 1:** According to the relationship between the influencing factors, a decision-making model based on experts' judgement is developed and then the criteria are prioritized and sorted in descending order.  
**Step 2:** Compute weights for each factor.  
First, each expert judges the prioritization of each criterion for each influencing factor. Then, the comparative importance of the average value (CIAV) is given by [106]:  
\[IAV=\frac{\sum_{i}^{N}D_{i}}{N} \tag{1}\]  
where \(N\) is the number of experts and \(D_{i}\) denotes the offered ranks by the experts for each factor.  
Figure 4: Flowchart of the proposed framework.  
Then, the coefficient \(A_{i}\) is defined as follows:  
\[A_{i}=\left\{\begin{array}{cc}1&j=1\\ CIAV+1&j>1\end{array}\right. \tag{2}\]  
where \(j\) indicate the number of the factors.  
The re-calculated weight \(D_{j}\) is computed as follows:  
\[D_{j}=\frac{CIAV-1}{A_{j}} \tag{3}\]  
Finally, the relative weights of the criteria are represented as follows:  
\[W_{j}=\frac{D_{j}}{\sum_{j=1}^{m}D_{j}} \tag{4}\]  
where \(W_{j}\) denotes the relative weight of the \(j\)th criterion and \(m\) represents the total of the criteria.
## 1 Introduction  
Recent advancements in Information and Communication Technology (ICT), encompassing big data, cloud computing, mobile technologies, and multimedia, have prompted a growing need for enhanced service management, an increased user bandwidth, and improved Internet accessibility. To address these evolving demands, Software-Defined Networking (SDN) has emerged as a promising solution. A comprehensive overview of SDN, including its essential features, is provided by [1, 2]. This summary underscores two key characteristics of SDN: the programmability of the control plane and the separation of control and data planes. However, it is emphasized that these aspects, as elaborated in the subsequent discussion, are not entirely novel in the realm of network architecture.  
In the domain of SDN, its distinctive feature lies in providing programmability through the clear separation of the control and data planes. This approach fundamentally transformsthe way network devices are programmed, offering a user-friendly alternative to the intricacies of active networking. SDN advocates for the separation of control and data planes within the network's architectural framework, enabling the autonomous management of network control on the control plane without disrupting the data flow. This configuration facilitates the extraction of network intelligence from switching devices, relocating it to controllers. Simultaneously, it empowers external software to manage switching devices without necessitating embedded intelligence.  
The decoupling of the control plane from the data plane not only establishes a simplified, programmable environment but also provides increased flexibility for external software to shape the network's behavior. This transformation signifies a pivotal shift in network management and control, streamlining the process and creating opportunities for a more adaptable and responsive network infrastructure in the context of SDN.
## 5 Experiments
### Experimental Results
#### 5.2.1 Landmark Use Case  
The most important metric that presents the quality of the solutions generated by BAG-DSM is the distance, i.e., the number of attribute changes required to achieve the final solution starting from the current state of the current alternative (as defined in Equation (3)). Figure 8 presents the distance required to achieve the final solution with the real-life model. On the \(x\) axis are the 23 randomly generated starting alternatives. The boxplots were generated using the results from the 10 repetitions for each experiment. Several observations can be seen from this figure: (i) the boxplots for BAG-DSM are always just a horizontal line, meaning that BAG-DSM was stable and it always found solutions with the same quality, i.e., same distance; (ii) the results of BAG-DSM are equally good as the results of the baseline method, and in the majority of the cases even better; (iii) in all of the cases, the final solution found by BAG-DSM was at a distance shorter than 5, i.e., only 5 (or fewer) attributes out of 26 need to be changed to reach the required optimal alternative.  
For each input starting alternative, the method outputs a set of optimal alternatives that have an equal distance to the starting alternative with respect to the objective function (defined in Equation (5)). The bigger the solution set is, the more options to choose from for the user. Figure 9 presents the size of the solution sets for each of the 23 alternatives. It can be seen that BAG-DSM found larger solution sets than the baseline in most of the cases.  
Figure 8: Boxplots for the distances (shorter is better) between the current alternative and the optimal alternative found by BAG-DSM (Bayesian) vs. baseline for the Landmark model.  
Figure 9: Boxplots for the size of the solution set (bigger is better) generated by BAG-DSM vs. baseline for the Landmark model.  
Figure 9 shows that the final output of the algorithm can consist of thousands of different alternatives. However, from a user perspective, only a few alternatives should be enough. For that reason, the time required to generate the first solution is a relevant metric to consider. Figure 10 presents the time (in seconds) required to generate the first solution for each of the 23 alternatives. From the results, it can be seen that BAG-DSM was faster than the baseline in all of the cases, with a difference of close to 500 s on average. One more important consideration is that the standard deviation of the boxplots for BAG-DSM is much smaller than the baseline's standard deviation, which means that BAG-DSM is also stable with respect to the time required to generate the first solution.  
BAG-DSM's dominance has been confirmed with statistical tests of significance. Considering both performance measures (time to first solution and number of solutions), BAG-DSM stochastically dominates the baseline method (\(p\)-value close to 0)--for more details, please check Supplementary Materials, Section S2.2.
## 3 Materials and Methods
### Performance under Light Conditions  
#### 3.5.1 Effect of Photocatalysis on Filtration Performance  
In order to understand the effect of photocatalysis of bare TiO\({}_{2}\) and polydopamine-modified TiO\({}_{2}\) on the filtration performance, the membranes modified by TiO\({}_{2}\) using both binding methods were tested before and after visible light illumination. The experiments were performed in the transparent plastic dead-end cell. After each modification, the pure water permeability and salt rejections were tested. Following this, these membranes were illuminated using a 60-W bulb for 24 h, and the filtration performance was then tested again. The distance between the membrane surface and the bulb was 45 cm; at this distance, the heating of the light could be ignored, which was indicated by a thermometer located beside the dead-end cell.