esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Seismic Data Processing  
The seismic data time processing was performed in two separate phases: Phase I included more than 1100 km of legacy seismic data of various campaigns and various acquisition parameters such as Vibroseis or Dynamite source, with relatively low active folds (24-60) and maximum offsets (up to 5.5 km). Phase II, on the other hand, concentrated on the newly acquired 400 km of seismic data of the IO17 Campaign, with much improved acquisition parameters such as a higher load/depth Dynamite source, greater active fold (167 CMP/1002 Channels) and a maximum offset of 10 km.  
For Phase I--legacy lines, processing started from the raw datasets with geometry and underwent coherent and anomalous noise attenuation. Using an in-house structural smoothing algorithm, the 2D shots from the acquisition were sorted in a way that the shots resembled a 3D volume in space (shot stations becoming inline numbers and receiver stations becoming crossline numbers). This further enhanced the coherency of deeper events and allowed for better structural model building. For Phase II, the processing also started with raw data for the initial model building, and later, in more advanced stages, were replaced with the preprocessed dataset from the Repsol/CGG Madrid processing center. This dataset underwent the same pseudo 3D structural smoothing sequence as the Phase I datasets.  
As far as the seismic data depth processing was concerned, velocity model building was performed by using commercial software suites, along with Repsol's proprietary algorithms. A data-driven workflow for migration velocity estimation was implemented, starting with near surface modeling (NSM) using first break arrival times, including long offsets. Diving wave tomography provided a model that was consistent with surface geology and available well velocities. Flooding models were created based on the deepest NSM velocities as permitted by the maximum depth of penetration, which allowed us to image the top of carbonate sections. Images corresponding to the flood models were used to provide interpretation for guiding deeper model building in an iterative workflow using a structural tensor algorithm which incorporated the measured structural dips at the surface. Such dips acquired as part of the acquisition campaign were instrumental for controlling the interpretation and, consequently, the velocity model. The final velocity model allowed us to improve the seismic imaging around several structures developed by salt tectonics that are outcropping in the area.
## 4 Orthogonal Experiment Analysis
### Influence of Roughness  
The roughness of the bushing surface had a significant influence on the average Reynolds equation coefficient. It is recommended that the connecting rod bushing surface roughness not exceed 0.63, in accordance with the applicable standards. Meanwhile, to consider the influence of extreme working conditions, the range of bushing surface roughness was set to 0.1\(\sim\)0.9. Different bushing surface roughnesses were evaluated for their influence on asperity contact percentage, MOFT, film thickness ratios, and friction power consumption.  
Figure 14 illustrates the trend of asperity contact percentage variation in one cycle under different bushing roughnesses, and Figure 15 shows the trend of the MOFT and film thickness ratio change in one cycle under different roughness conditions. As the roughness increased, the asperity contact ratio and average oil film thickness increased, whereas MOFT did not change significantly. The increase in roughness led to a slight increase in the average film thickness ratio; however, the minimum film thickness ratio showed a decreasing trend. When viewed in conjunction with the minimum oil film thickness ratio analysis, it can be seen that an increase in roughness raises the rough contact area at the maximum pressure crank angle but improves the flow of lubricant at other times. Because of the wedge effect, the average value of MOFT increased, but the increment in the film thickness ratio was slight, and the overall lubrication state changed slightly.  
Figure 14: Asperity contact percentage in different bushing roughnesses.  
Figure 13: (**a**) Hydrodynamics lubrication friction power at different clearances. (**b**) Asperity contact friction power at different clearances.  
Figure 16 shows the variation in the friction power in one cycle under different bushing roughness conditions. Based on the comparison, it can be concluded that as the roughness increases, the MOFT increases, as does its bearing capacity. As the roughness increased, the asperity contact percentage increased, whereas the film thickness ratio remained almost the same, resulting in an increase in the power associated with the asperity friction generated by the piston pin bushing during spring compression.  
From the above analysis, it is evident that increasing the roughness within a specific range can improve the oil film carrying capacity but will not significantly improve the lubrication condition of the small-end bearing and, at the same time, will increase the friction power generated by the asperity contact. Accordingly, a small-end bushing should be designed with a suitable surface roughness based on the engine load and design requirements.
# _electronics_
## 1 Introduction
Based on traditional NDN caching for content file segmentation, coding operations are used to remove data transmission correlation, while clustering is carried out based on the geographical location of satellites to simplify the management of satellite networks and reduce the satellite downlink consumption when the user is covered by multiple satellite nodes. Then, according to the different content popularity types, and to minimize the backhaul link load and the link content acquisition delay as the optimization goal, the satellite nodes are placed with different numbers of coded packages, and the optimization problem is solved through the multi-colony ant colony optimization algorithms [18], then an efficient content retrieval and cooperative distribution mechanism is achieved. The remainder of the paper is organized as follows. Section 2 presents the satellite networks model; we discuss the satellite network model in this paper and the process of clustering satellite nodes. Section 3 presents the coded package naming lookup and cache placement. In Section 4, we discuss the coordinated distribution of the coded packages. In Section 5, the simulation results and discussions are provided and the conclusions are finally drawn in Section 6.
## 3 The Proposed Method and Scenarios
### Initial Authentication Phase
#### 3.4.3 Vehicle and RSU Communication Stage  
Upon reception of attribute messages and the service ticket, the system will progress to steps (g) and (h) of Figure 6. The vehicles will utilize \(KT_{GSse}\) to decipher the \(AT_{TGSV}\), subsequently obtaining \(K_{Sse}\). Subsequently, a fresh \(AU_{VIS}\) will be created by the vehicle, encompassing \(ID_{p}\) and \(TS_{VIS}\), which will then be encrypted using \(K_{Sse}\). The vehicle will then transmit both the ST and the authentication message to the server. In this scenario, the RSU represents the desired service for the vehicles. The messages sent from the vehicle to the RSU are detailed below:  
\[ST= K_{Ss}[(ST||ID_{v}||ID_{S}||TS_{TGSV}||\quad IP_{p}||LT_{ST}||K_{Sse})]. \tag{9}\] \[AU_{VIS}=K_{Sse}[ID_{v}||TS_{VIS}]. \tag{10}\]  
\[AU_{Sm}=K_{Sse}[ID_{v}||TS_{SV}]. \tag{11}\]  
The RSU will perform a similar procedure involving the \(TGS\). Subsequently, the RSU will decipher the \(ST\) utilizing its \(K_{Sse}\), thereby acquiring authorization from the \(K_{Sse}\). This authorization will be utilized to decrypt \(AU_{VIS}\). Upon completion, the RSU will generate its own \(AU_{Sm}\) comprising \(ID_{RSU}\) and the \(TS_{SV}\), as demonstrated by Equation (11).  
Subsequently, the \(AU_{Sm}\) will be delivered to the user and subsequently decrypted by the vehicle utilizing \(K_{Sse}\). The vehicle will then proceed to verify whether or not the service name included in the authentication corresponds to its designated service recipient. To mitigate potential replay assaults, the vehicle will also validate the timeliness of the authentication by inspecting \(TS_{SV}\). Furthermore, the vehicle will maintain its cache mechanism. After the mutual authentication procedure between the user and the service, the vehicle will retain a protected version of the service ticket in its cache for future reference.
# Direct Superbubble Detection
## Theory
### Putting It All together  
Theorem 2.2.: _Algorithm 2 correctly identifies the superbubbles of a digraph \(G\) in linear time._  
Proof.: Theorem 2.2 ensures that for every digraph \(G\), there is a set \(R\) of quasi-legitimate roots such that, given \(R\), the algorithm Superbubble# identifies all superbubbles of \(G\) in linear time. Every vertex in \(V(G)\) is reachable from a source or a cycle in \(G\). By Lemma 2.2, all sources are legitimate roots. Lemma 2.2 shows that a set of cycles can be constructed in linear time from which all vertices of \(G\) can be reached by DFS. Algorithm 1 identifies a quasi-legitimate root in a cycle (Lemma 2.2). As discussed in the text following Lemma 2.2, the effort for this step is again linear in size of \(G\). Algorithm 2 therefore correctly identifies the superbubbles of a digraph \(G\) and does so in \(O(|E|+|V|)\) time.
```
0: Digraph \(G\) \(R\leftarrow\) all sources in \(G\)  generate a random DFS-forest \(\hat{F}\)  find set \(W\) of \(\prec\)-maximal vertices with a back edge in \(\hat{F}\)  generate set \(\mathfrak{C}\) of cycles from \(W\) with \(\hat{F}\) for all cycles \(C_{k}\in\mathfrak{C}\)do  run get \(-\operatorname{\texttt{root}}(C_{k},G)\) to identify quasi-legitimate root \(r_{k}\)  add \(r_{k}\) to \(R\)  generate DFS-forest \(F\) with root set \(R\)  run Superbubble\(\#\) on \(F\)
```  
**Algorithm 2** Identification of all superbubbles in an arbitrary digraph \(G\).
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 5 Solution Method  
### Particle Swarm Optimization (PSO)  
PSO, which was first introduced in [29; 30], is a population-based stochastic optimization tool. It was originally developed via the simulation of the simplified social models. PSO has been found to be simple, robust, and computationally efficient in solving optimization problems.  
Suppose that there are \(n\) particles, denoted by \(\{\mathbf{X}_{i}\}\,i=1,\,2,\cdots,\,n\}\), in a population. Each \(\mathbf{X}_{i}\) represents a potential solution to the problem. Let \(x\) and \(v\) be the position and velocity of the particle coordinates in an \(m\)-dimensional search space, in which \(m\) denotes the number of optimized parameters. Thus, the \(i\)th particle \(\mathbf{X}_{i}\) and its velocity \(\mathbf{V}_{i}\) can be expressed as \(\mathbf{X}_{i}=[x_{1i},\,x_{i2},\cdots,\,x_{im}]^{T}\) and \(\mathbf{V}_{i}=[v_{1i},\,v_{2i},\cdots,\,v_{im}]^{T}\), which are two real-valued vectors. In a PSO algorithm, particles move via the feasible solution space. For the search process, each particle in the swarm updates its position according to the inertia weight \(\omega\), the personal best _pbest_, and the global best _gbest_. At time step \(t\), the velocity and position of the \(i\)th particle corresponding to the \(d\)th dimension are modified by  
\[v_{id}^{t+1}=\omega v_{id}^{t}+c_{1}r_{1}\big{(}pbest_{id}^{t}-x_{id}^{t}\big{)} +c_{2}r_{2}\big{(}gbest_{d}^{t}-x_{id}^{t}\big{)} \tag{18}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1} \tag{19}\]  
Notice that \(c_{1}\) and \(c_{2}\) in Equation (18) denote learning coefficients, and \(r_{1}\) and \(r_{2}\) denote random values from uniform distribution in the range of \([0,1]\). To ensure the convergence of the PSO algorithm, a constriction factor proposed in [35] is added in Equation (18) yielding  
\[v_{id}^{t+1}=\psi\big{[}v_{id}^{t}+c_{1}r_{1}\big{(}pbest_{id}^{t}-x_{id}^{t} \big{)}+c_{2}r_{2}\big{(}gbest_{d}^{t}-x_{id}^{t}\big{)}\big{]} \tag{20}\]  
with  
\[\psi=2\Big{/}\Big{(}\Big{|}2-c-\sqrt{c^{2}-4c}\Big{|}\Big{)} \tag{21}\]  
where \(c=c_{1}+c_{2}\) and \(c>4\). In general, both \(c_{1}\) and \(c_{2}\) are set to \(2.05\), resulting in \(c=4.1\) and \(\psi=0.729\). More details on the comparison of inertia weight and constriction factors in particle swarm optimization can be found in [36].
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
###### Abstract  
With the increasing adoption of the Internet-of-Things (IoT), the wireless sensors network (WSN), as an underlying application of IoT, has attracted increasing attention. Topology, the working structure used to observe WSN, is the most instinctive form in troubleshooting and has great significance to WSN management and safety. To this end, it is imperative to recover WSN topology for the purpose of network management and non-cooperative network detection. Traditional network topology recovery mainly relies on the monitoring modules installed in nodes, or an extra network attached. However, these two approaches have several limitations, such as high energy consumption for monitoring nodes, time synchronization problems, reuse failure, limitation to specific targeted networks and high cost. In this paper, we present a new approach to recover the topology of WSN that adopts location-based routing protocols, based on movable platforms. Our observation is that the network topology is consistent with the node routing, as the nodes choose the next hop according to the geological position of neighbor nodes. Hence, we calculate the cost parameters of choosing routing nodes for the targeted network according to the partial connection of the nodes. Based on those cost parameters, we can determine the topology of the whole network. More specifically, by collecting the geological position and data packets of the nodes from movable platforms, we are able to infer the topology of the WSN according to the recovered partial connection of nodes. Our approach can be easily adopted to many scenarios, especially for non-cooperative large-scale networks. The evaluation of 30 simulations shows that the accuracy of recovery is above 90%.  
topology recovery; movable platform; location-based routing protocol 1
## 2 Development History of Traffic Prediction
### Statistical Methods  
Statistical methods play pivotal roles in traffic prediction, representing a major paradigm of data-driven methodologies. Statistical methods, exemplified by time series analysis encompassing HA models ([PERSON] et al., 2016), Exponential Smoothing models (ES) ([PERSON] et al., 2016), ARIMA models ([PERSON] et al., 2016), Vector Auto Regression (VAR) models ([PERSON] et al., 2016), etc., rely on the statistical attributes of historical data to anticipate forthcoming traffic. By capturing the temporal dependencies and seasonal patterns inherent in traffic data, these methodologies furnish a robust groundwork for short-term and mid-term traffic forecasting.  
In time series analysis, the ARIMA model and its variants represent mature methodologies rooted in classical statistics and have been widely applied in traffic prediction tasks ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017). [PERSON] et al. (2018) were the first to use the ARIMA model for traffic prediction problems. Since then, many scholars have made improvements in this area. For example, [PERSON] et al. (2017) proposed seasonal ARIMA to predict traffic flow, considering the periodicity of traffic data. The success of these models lies in their ability to capture trends and  
Figure 1: A taxonomy of traffic prediction methods and applications.  
seasonal patterns in time series data, thereby providing accurate predictions for domains, such as traffic volume and accident rates. However, despite the efficacy of the ARIMA model and its variants in addressing linear time series forecasting problems, they exhibit certain limitations, mainly when dealing with complex issues, like traffic prediction. Firstly, ARIMA models assume data has linear relationships and fail to capture non-linear patterns in traffic flow data. Secondly, ARIMA models are limited to a small set of features (lags of the time series itself) and struggle to integrate external factors affecting traffic, such as weather. Additionally, these models are computationally unsuitable for handling large or high-dimensional datasets and require manual tuning of parameters. ARIMA models are sensitive to missing data and noise, necessitating preprocessing and imputation to address gaps in the time series. Their capability for real-time prediction is limited, especially when the models need frequent retraining.
## 2 Lung Lobe Segmentation Algorithm
### Lung Fissure Surface Fitting and Lung Lobe Segmentation  
As shown in Figure 3, there may be an incomplete lung fissure after classification, which results in the failure to segment adjacent lobes directly. To overcome it, we used the method named multistage B-spline fitting [17]. On the basis of the existing lung fissure information, we made up the missing lung fissure and extended it to the lung boundary to form a complete surface that could segment adjacent lung lobes. This method can take into account both the calculation efficiency and the fitting accuracy. The specific mathematical principle is as follows.  
The fitting function \(f\) can be constructed by using the known lung fissure coordinate \((x,\ y,\ z)\). The component \((x,\ y)\) is defined as the independent variable, while the component \(z\) is the function value, then the missing points' coordinates can be obtained by the fitting function \(f(x,\ y)\).  
However, it is difficult to find a balance point between the shape smoothing and fitting accuracy in the B-spline fitting algorithm. The multistage B-spline fitting algorithm [17] can effectively overcome this problem by using top-down hierarchical control grid functions. Firstly, the coarse grid control  
Figure 3: Results of pulmonary fissure classification: (**a**) original pulmonary fissure point cloud, and (**b**) clustered pulmonary fissure point cloud.  
function was used to get a rough estimate, and then the finer control grid function was iteratively used to fit the previous estimation error, finally giving a more accurate and smooth fitting function.  
To verify the surface fitting effect, the incomplete right lung oblique fissure and horizontal fissure were taken as the fitting objects, their fitting results are shown in Figure 4. It can be seen that the fitting surface was smooth without deviation, could effectively fill the missing pulmonary fissure and ensure that it was consistent with the existing pulmonary fissure.  
Through the above steps, the complete oblique fissure and horizontal fissure were fitted, and the lung could be easily separated into independent lung lobe units by using these surfaces. Since the right lung contains three lobes, we divided it in two steps. Firstly, the oblique fissure surface was used to segment the right lung into upper and lower parts, where the lower half was the lower lobe. Then we used the horizontal fissure surface to segment the upper half into upper lobe and middle lobe. The segmentation results are shown in Figure 5.
# Review on Application of Quaternary Ammonium Salts for Gas Hydrate Inhibition
## 2 Flow Assurance Issues Related to Natural Gas  
Flow assurance is a term used to evaluate the effects of fluid hydrocarbon solids such as hydrate, wax, and asphaltene; and their potential to disrupt production due to deposition in the pipeline system [18; 19]. Deep water environment that exists at low temperatures and high pressures provides the ideal condition for solid deposits such as hydrates to form, with the risk of reducing the hydrocarbon production. Essentially, in petroleum production operations, flow assurance activities ensure operational and economical sustainability of hydrocarbon streams from the reservoir to the surface [20]. Hydrates are often regarded as one of the most serious and challenging problems in flow assurance, since the formation rate of gas hydrates are relatively more rapid than other solid deposits, such as asphaltenes, scale and wax [21]. Hydrates which are formed inside the subsea flowlines affect the production of hydrocarbon significantly, since the formation creates blockage which seriously affect the development and production safety of deep water gas fields [22; 23; 24; 25]. Gas-dominated wells are prone to hydrate blockage since the system cool more rapidly compared to the oil-dominated wells, which are typically insulated by design to sustain its high temperature in the flowlines prior to arrival to the surface [26]. Furthermore, when the formed hydrate completely restricts the pipelines, the pressure contained inside the pipelines will increase sharply and eventually causes a serious pipeline safety accident [27; 28]. In order to ensure undisturbed flow of hydrocarbon transportation to the surface, up to 8% of the total estimated cost, which is equivalent to more than USD200 million is spent annually on gas hydrate inhibition techniques, as reported by [29].
## 3 Results and Discussion
### Metal Ion Detection
On the other hand, more hydroxyl groups deprotonate, and an electrostatic interaction occurs between the metal cations and hydroxyl groups at values above PZC, which has a positive effect on the complexation of metal ions by TiO\({}_{2}\). Increasing the light exposure time results in increased stripping currents, as shown in Figure 4C,D. Consequently, the sensitivity of the electrodes is increased due to a larger accumulation of photoelectrons, which facilitates the deposition of a larger amount of reduced ions onto the electrode surfaces. The response of the Ti/TiO\({}_{2}\)-NT and Ti/TiO\({}_{2}\)-SR electrodes irradiated with light at wavelengths of 389 nm and 426 nm for the simultaneous detection of Pb(II), Cu(II), and Hg (II) ions at different concentrations was evaluated under optimized conditions (Figure 5A-D). The peak currents increase with the concentration of metal ions, and their linear dependence on the concentration is shown in Figures S3 and S4 (see Supplementary Materials). The linear dependence equation of the calibration curve, the linear range, and the detection limit based on a signal-to-noise ratio of 3 are shown in Table 1 for both electrodes. The calibration curves were plotted using the mean value of triplicate results for each standard solution. The standard deviation of the results was situated in the range of 3% to 5%. As expected, the Ti/TiO\({}_{2}\)-NT electrode only showed a response to the Pb and Cu ions at the two wavelengths used, while the Ti/TiO\({}_{2}\)-SR electrode only showed a response to all the metal ions considered when irradiated with light of a wavelength of 426 nm. However, irradiating the electrodes with light at 426 nm results in the best response for both electrodes. The obtained electrodes demonstrated better performance when compared to earlier findings which used photo-assisted accumulation of metal ions (Table 2). Figure 3: **(A)** A schematic of the reaction mechanism during light irradiation; DPV curves recorded on (**B**) Ti/TiO\({}_{2}\)-NT and (**C**) Ti/TiO\({}_{2}\)-SR electrodes at different wavelengths of irradiation light. The photo-accumulation of metal ions was performed in 4 \(\times\) 10\({}^{-6}\) M Zn(II), Cd(II), Pb(II), Cu(II), and Hg(II) for 30 min. Figure 4: DPV curves recorded at (**A,C**) Ti/TiO\({}_{2}\)-NT and (**B,D**) Ti/TiO\({}_{2}\)-SR electrodes at different (**A,B**) pH values of accumulation solution and (**C,D**) irradiation time. Figure 5: DPV curves at Ti/TiO\({}_{2}\)-NT (**A,B**) and Ti/TiO\({}_{2}\)-SR (**C,D**) electrodes for different concentrations of Pb(II), Cu(II), and Hg(II) ions under irradiation with light at wavelengths of 389 nm (**A,B**) and 426 nm (**C,D**).