esa-sceva/satcom-chunk-collection huggingface dataset:
# Immobilized TiO\({}_{2}\)-Polyethylene Glycol: Effects of Aeration and pH of Methylene Blue Dye
## 3 Result and Discussion
### FT-IR and XPS Analysis of Immobilized TiO\({}_{2}\) and TiO\({}_{2}\)/PEG
The high photoactivity efficiency rate of immobilized TiO\({}_{2}\)/PEG can first be attributed to the synergistic effect of adsorption and photocatalysis [27]. Results revealed that the enlarged surface area was dominant in the TiO\({}_{2}\)/PEG film and was found to be twice as large as the latter. The washings of immobilized TiO\({}_{2}\)/PEG film improved the photocatalytic ability in degrading the MB under NL illumination. Through the photooxidation process in washing, the PEG became oxidized and created more pores and active sites on the TiO\({}_{2}\) surface. Thus, the photoactivity efficiency rate for immobilized TiO\({}_{2}\)/PEG was found to be 0.065 min\({}^{-1}\), and 0.055 min\({}^{-1}\) for immobilized TiO\({}_{2}\). It is obvious that the k value for immobilized TiO\({}_{2}\)/PEG is 1.2 times higher than the immobilized TiO\({}_{2}\) sample. In addition, the decolorization for immobilized TiO\({}_{2}\) under VL irradiation was nearly a straight line, as presented in Figure 3b. Through the pseudo first-order equation, the photoactivity efficiency rate of immobilized TiO\({}_{2}\)/PEG was found to be 0.012 min\({}^{-1}\). This result is in contrast to immobilized TiO\({}_{2}\) photocatalyst, where it was inactive under VL irradiation due to its large band gap energy. From Figure 3b, the dye remaining for immobilized TiO\({}_{2}\)/PEG was colourless in only 180 min of irradiation. Compared to immobilized TiO\({}_{2}\), the remaining MB was found to be 0.75 in the same  
Figure 3: Immobilized TiO\({}_{2}\) and TiO\({}_{2}\)/PEG sample for MB photodegradation under: (**a**) normal light irradiation and (**b**) visible light irradiation at MB\({}_{0}\) = 12 ppm, pH 11, 28 \({}^{\circ}\)C.  
conditions. Thus, the immobilized TiO\({}_{2}\)/PEG sample performed better photodegradation as compared to immobilized TiO\({}_{2}\) under VL spectrum. This is mainly because of the presence of C=O and C-O bonds that contributed to the VL response ability in immobilized TiO\({}_{2}\)/PEG.
# Launch Bar Dynamics Character Analysis of Carrier-Based Aircraft Catapult Launch
## 2 Mathematical Model of Catapult Launch
### Modeling Assumptions  
In order to research the basic law of motion for a launch bar during the process of a catapult launch, this article made the following assumptions:  
1. The thermodynamic process of the steam catapult is an adiabatic process; the steam in the catapult is not affected by its environment.
2. The accumulator, cylinder, and piston are rigid bodies, neglecting any changes in shape caused by temperature and pressure.
3. The thermodynamic processes of the accumulator and cylinder are quasi-static processes.
4. The steam has no friction with the pipe wall during the progress from accumulator to cylinder.
5. The fuselage of the carrier-based aircraft is a rigid body.
6. The deck motion and airflow interference are neglected.
7. The carrier-based aircraft has no yaw angle during the process of catapult launch.
8. The effects of asymmetric factors are not considered during the process of catapult launch.
9. The nose gear is vertical with the flight deck surface of the carrier during the process of catapult launch.
10. The elevator angle is fixed during the whole process of catapult launch.
11. The force and damper model of the nose gear adopts the classical two-mass spring-damper model which divides the aircraft into two parts: the elastic support mass [36] and the inelastic support mass.
## 1 Introduction
Even though recent technologies in preventive treatment, which are combined with artificial intelligence (AI), have been developing rapidly, additional research is needed to provide a result that can represent a cross-section of the population, and the privacy and security of the data used should be supplemented, which can reveal the users' medical history details [20]. This study aims to propose a human body simulation that predicts and visualizes the individual's 3D body change based on 2D images taken by a portable device (e.g., a smart phone). Notice that 3D body models provide more accurate volume information and body shape than conventional 2D body images, so they can be used to precisely calculate health indicators such as BMI. The main part of the proposed simulation consists of two major subprocesses in the anthropometric measurement module: (1) A semantic segmentation process [21; 22; 23], which segments the human image from 2D imagery taken by a smart phone, and (2) an image-based reconstruction process [24; 25], which constructs the 3D body model. Experiments were conducted to obtain optimal conditions in the aforementioned subprocesses to solve limitations, such as the expensive equipment requirement in 3D scanner-based anthropometry and direct anthropometry, while deriving reliable anthropometric variables. According to the experiments, the \(p\)-value of the paired \(t\)-test conducted between the measurement given by the proposed simulation and the actual measurement is 0.3483, so the proposed simulation can accurately estimate the human body in the 3D environment (see Section 4 for more detail). As a result, the proposed simulation enables not only the reconstruction of an accurate 3D body model from 2D images, but also the visualization of bodily changes in accordance with a prescribed exercise plan through the 3D body model. This will contribute to eliminating inequalities for people who are not provided with expensive healthcare services. The contributions of this study are as follows. This study presents a specific method for preprocessing anthropometric measurements to better utilize the existing 3D body modeling techniques, and designs a selection module for precise 3D body part implementation from 2D images. In addition, it is significant that a module for predicting body changes according to a given exercise plan is developed and implemented as a single simulation program. The organization of the paper as follows: Section 2 briefly introduces existing studies associated with 3D modeling of the human body and image segmentation, while Section 3 introduces a three-phase simulation-based modeling approach for the visualization and prediction of a 3D human body. Section 4 demonstrates the proposed simulation with experimental analysis and test results, while Section 5 concludes the paper and indicates future work associated with this study.
## 2 Materials and Methods
### Research Methodology
#### 2.2.4 ESV Driver Model Construction  
In order to ensure the scientific validity of the study, the geographically weighted regression (GWR) model was invoked to assess the effect of the drivers on the ESV in the study area at the spatial level, which was modeled as follows:  
\[y_{i}=\beta_{0}(U_{i},V_{i})+\sum_{k=1}^{n}\beta_{k}(U_{i},V_{i})x_{k}(U_{i},V _{i})+\varepsilon_{i} \tag{5}\]  
\[\beta_{k}(U_{i},V_{i})=\left[X^{T}W(U_{i},V_{i})X\right]^{-1}X^{T}W(U_{i},V_{ i})y \tag{6}\]  
where \(\beta_{0}\) (\(U_{i}\), \(V_{i}\)) is the geographically weighted regression intercept of the spatial position of (\(U_{i}\), \(V_{i}\)), \(\beta_{k}\) (\(U_{i}\), \(V_{i}\)) is the weighted regression coefficient of the independent variable (driver) of the \(k\)th at the (\(U_{i}\), \(V_{i}\)) spatial location, \(x_{k}\) (\(U_{i}\), \(V_{i}\)) is the value of the \(k\)th independent variable (driver) at the (\(U_{i}\), \(V_{i}\)) spatial location, and \(\varepsilon_{i}\) is the algorithmic residuals. \(X^{T}\) is the transpose of the independent variables (drivers), and \(W\) (\(U_{i}\), \(V_{i}\)) is the distance weight matrix.  
The GWR model applies the weighted least squares (WLSs) method to estimate the parameters of each observation, and the Gaussian function is generally used to construct the weighting function when weighting. The basic idea of a Gaussian function is to represent the relationship between the weights and distance by selecting a continuous, monotonically decreasing function. The specific formula is as follows:  
\[W_{ij}=\epsilon sp\left[-\left(d_{ij}/h\right)^{2}\right] \tag{7}\]  
where \(h\) is the bandwidth of the AIC criterion, and the larger the bandwidth, the slower the weights decay with increasing distance, and conversely, the faster the weights decay. \(d_{ij}\) is the direct spatial distance between sample point \(i\) and sample point \(j\).  
A total of 21 factors relating to natural systems and socio-economic elements were selected as the ecosystem service driving the variables of the Guangdong section of the Xijiang River Basin; the ecosystem of the Guangdong section of the Xijiang River Basin in 2020 in the nearest state was selected as the object of analysis to evaluate and analyze the driving factors; and, finally, the simulation results were spatially visualized in ArcGIS10.7 software to analyze the effects of the driving factors on the ESV in the study area.
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 2 Methodology
### Development of the Health Degradation Trends
#### 2.2.2 Health Assessment Indicator  
Principal component analysis can effectively reduce the dimensionality of the data matrix [14]. It performs an orthogonal transformation to convert a set of observations, for instance, the correlated statistical features extracted from the raw vibration signals into a set of uncorrelated principal components [14]. Since the first principal component has the largest variance, it was taken as an index for evaluating the given datasets. After the extraction of 18 statistical features from the valid signals, they were reconstructed into a new feature matrix. Further details regarding this transformation can be seen in the previous work by the corresponding authors in [14; 16]. The new feature matrix was sent to a designed algorithm that was the combination of principal component analysis and a moving average method. The algorithm constructed the HDTs of 18 statistical features (9 in time domain and 9 in frequency domain), as shown in Figures S2 and S3 (Supplementary Materials). It was observed that the first principal component for channel 2 datasets was the time domain feature \"mean\". On the contrary, for channel 4, it was noticed to be the time domain feature \"STD\". Therefore, the health degradation tracking indexes that were formed by the time domain features mean and STD were considered the required HDTs for the _RUL_ estimation process. The constructed HDTs are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Pass Score** & **Valid** & **Invalid** \\ \hline
1. RMS (Time domain) & 1 & & \\ \hline
2. Kurtosis (Time domain) & 1 & & \\ \hline
3. Percentage mean difference (time domain) & 1 & & \\ \hline
4. Kurtosis (Frequency domain) & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The selected criteria for separating valid and invalid data sets.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Region Formation  
The entire forest area consists of \"M\" nodes that sense various parameters such as temperature, humidity, soil moisture, oxygen level, carbon dioxide level, rainfall, etc. The area is divided into several regions based on the energy resource of the aggregator node. An aggregator node is placed for every region, which is responsible for aggregating the sensed data based on its type. The aggregator node act as a collector for each split region. All the sensor nodes lie at leaf level in the DODAG structure. The aggregator nodes act as an intermediate parent, which collects group aggregate using compressed sensing and forwards the data towards the root of DODAG. The selection of the parent node is carried out with the aggregator constraint as ETX and power level. Figure 1 shows the network representation of random nodes and aggregator nodes.
## 2 Related Works
### Vessel Trajectory Prediction  
Vessel trajectory prediction methods based on deep learning are developing rapidly. Most deep learning models adopt RNN structures. For example, some models are based on LSTM [18] or gate recurrent unit (GRU) [4]. Ref. [19] proposes an LSTM model for vessel trajectory prediction combined with the sequence prediction method. Furthermore, ref. [20] explores BiLSTM, which can enhance more relevance between historical and future time-series data compared to single LSTM and improve the accuracy. To focus on the different sections of hidden features of the BiLSTM network, ref. [21] introduces the attention mechanism into the task. In the work of [22], researchers train a single LSTM model for each type of vessel since the type of a vessel may influence its movement characteristics. Using automatic identification system (AIS) data, ref. [23] investigates a novel approach based on variational LSTM to predict the trajectory of a vessel. Ref. [24] selects a bi-directional gate recurrent unit (BiGRU) network with less trainable parameters for estimation. Others are trying to utilize the Seq2 Seq frameworks to solve the task. To address the problem of trajectory prediction with uncertainty quantification, ref. [25] proposes an attention-based recurrent encoder-decoder model. Ref. [26] presents a method based on the Seq2 Seq model which uses a spatial grid for trajectory prediction. In the study of [27], a neural Seq2 Seq model is proposed, which is based on the LSTM encoder-decoder architecture to capture long-term temporal dependencies of AIS data effectively. Ref. [28] incorporates both spatial and temporal attention mechanisms and proposes a novel attention-based LSTM encoder-decoder method on the structure of [27]. Ref. [29] proposes a Seq2 Seq framework based on GRU for short-term prediction. The researchers in [30] introduce a recurrent encoder-decoder model to address the problem of the presence of complex mobility patterns. Ref. [31] proposes a novel vessel trajectory prediction model, which is based on the integrated model of LSTM auto-encode, attention mechanism, and bi-directional LSTM (AABiL) structure. The researchers in [32] introduce generative adversarial networks [33] into trajectory prediction, where they use an LSTM Seq2 Seq model as the generator and a naive LSTM as the discriminator. Ref. [34] formulates a transformer-based model for vessel trajectory prediction which embeds the inputs into higher-dimensional vectors.  
We are committed to a long-term prediction model. The current short-term prediction models focus on the status of ships in a short period, and the time granularity is less than 20 min. The long-term prediction model is used for long-term prediction, which is more helpful for collision detection and risk warning in practice.
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 3 The Path Planning Algorithm Based on PPR
### Prior Path Reuse in the PPR Algorithm
```
Input: \(C_{plant}\), \(C_{obstacle}\), \(C_{prir}\), \(x_{start}\), \(x_{end}\), \(S\), \(N\), \(T_{prir}\), \(T_{exploring}\) Result: A path \(\Gamma\) with the minimum cost from \(x_{start}\) to \(x_{end}\)\(T_{exploring}\):init); // Initialize the exploring tree \(T_{exploring}\)\(\Psi\).init(); // Initialize the prior path set \(\Psi\) for \(n=1\) to \(N\) do //Sample N times { \(x_{rand}\)\(\leftarrow\)Sample(\(C_{plant}\)); //Generate a random node \(x_{rand}\) in the configuration space \(C_{plant}\)\(x_{near}\)\(\leftarrow\)Near(\(x_{rand}\), \(T_{exploring}\)); //Discover the nearest node \(x_{near}\) near \(x_{rand}\) on \(T_{exploring}\)\(x_{new}\)\(\leftarrow\)Steer(\(x_{rand}\), \(x_{near}\), \(S\)); //Generate a new node \(x_{new}\) between \(x_{rand}\) and \(x_{near}\) with step \(S\) if CollisionFree(\(C_{obstacle}\), \(x_{new}\)) then //Determine whether \(x_{new}\) is in the obstacle space \(C_{obstacle}\) { \(X_{near}\)\(\leftarrow\)Near (\(x_{new}\), \(T_{exploring}\)); //Create a set \(X_{near}\) with the nodes on \(T_{exploring}\) near \(x_{new}\)\(x_{min}\)\(\leftarrow\)SelectParentNode(\(X_{near}\), \(x_{near}\), \(x_{new}\)); //Select the minimum cost parent node of \(x_{new}\)\(T_{exploring}\).AddEdge(Edge(\(x_{min}\), \(x_{new}\))); //Add the new branch Edge(\(x_{min}\), \(x_{new}\)) to \(T_{exploring}\)\(T_{exploring}\).Rewire(); //Rewire the local part of the exploring tree near \(x_{new}\) for lower cost if InPriorSpace(\(C_{prir}\), \(x_{new}\)) then //Determine whether \(x_{new}\) is in the prior space \(C_{prior}\) { \(x_{connect}\), \(p_{connect}\)\(\leftarrow\)ConnectNode(\(T_{exploring}\), \(T_{prior}\), \(x_{new}\)); //Obtain the connection nodes \(P_{connect}\), \(x_{connect}\) \(T_{exploring}\)-AddEdge(\(x_{connect}\), \(p_{connect}\)); //Add the new branch Edge(\(x_{connect}\), \(p_{connect}\)) to \(T_{exploring}\)\(\leftarrow\)PathBacktrack(T\({}_{exploring}\), \(T_{prior}\), \(x_{connect}\), \(p_{connect}\)); //Obtain a path \(\Gamma_{i}\) by path backtracking \(\Psi\).AddPath(\(\Gamma_{i}\)); //Add \(\Gamma_{i}\) to \(\Psi\) } } \(\Gamma_{min}\)\(\leftarrow\)MinCostPath(\(\Psi\)); //Obtain the minimum cost path \(\Gamma_{min}\) return \(\Gamma_{min}\); //Retrieve \(\Gamma_{min}\) as the final path \(\Gamma\)
```  
**Algorithm 1** PPR algorithm  
The PPR method greatly reduces the number of samplings by reusing the prior path, which is helpful to improve the efficiency of APP.
## 3 System Model
### Link Quality
#### 3.2.4 ETX Metric  
In addition to the influence of the wireless communication channel characteristics, bursty link anomalies or malicious link attacks by other devices could also lead to packet loss. Therefore, relying only on the SNR to measure link quality was not feasible. As a result, we used the expected transmission count (ETX) as another link quality evaluation metric to assist routing decisions, which was critical to decrease packet retransmission and loss on poor links. The ETX measured the number of expected packet transmissions that could be successfully received at the receiver node. We counted the successful probability of receiving hello packets by both end nodes of the bidirectional link. The ETX of the link between node \(i\) and node \(j\) was expressed by the following:  
\[ETX_{i,j}=\frac{1}{d_{f}^{i,j}\times d_{r}^{i,j}} \tag{7}\]  
where \(d_{f}^{i,j}\) is the successful delivery probability of node \(j\) receiving a hello packet from node \(i\) during the last \(w\) seconds, and \(d_{r}^{i,j}\) is the successful delivery probability of node \(i\) receiving a hello packet from node \(j\) during the last \(w\) seconds.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Storage Methods of the BuildingTree
#### 3.3.3 Storage of BuildingTree in MongoDB  
The multiple representation data are stored in three collections in the NoSQL database (similar to tables in the relational database):  
BuildingTree: The BuildingTree collection contains information on the multiple representation data.  
CLOD(CSG): This collection stores the CSG attribute data of the building parts, such as type, centroid, orientation, etc.  
LOD3 ES: This collection stores the 3D exterior shell representation of a building or a generalized building group.  
The reason for separating the two latter collections in this study is performance. The querying of the data is mainly implemented on the Geo collection which supports a 2D geospatial index. It should be noted that the NoSQL database does not support joint operations between collections. It is therefore vital that the CLOD(CSG) table contains all the relevant information used in the data search. In other words, semantic information on the building is stored in the Geo table; 3D geometry data is stored in the 3D table (a structure used to store the 3D geometry data) and its dynamic visualization structure is stored in the BuildingTree.  
Querying on 3D information is implemented by a map-reduce method. Since the map-reduce method makes full use of parallel computing, it is suitable for the large volume of 3D data.
## 3 Fault-Tolerant Control Design for the Attitude System of HSV
### Fault-Tolerant Control Design for Fast Loop of HSV
#### 3.2.1 Fault-Tolerant Controller without Exact Knowledge of Inertial Matrix under Nominal Condition  
As for the crucial role of the inertial matrix J on the controller design, once it is unexpectedly moved, as variation (\(\Delta J\)) can present great challenges to controllers that rely on the inverse matrix of the system's inertial matrix due to the potential irreversibility of (\(J^{*}+\Delta J\)), as shown in (8), such as sliding mode control [13], robust adaptive control [27], etc. In order to cope with this question, an adaptive feedback fault-tolerant controller that does not need the exact knowledge of \((J^{*}+\Delta J)^{-1}\) is developed in this work to handle this question.  
Before the fault-tolerant controller design, the angular velocity error \(\tilde{\omega}\) is introduced as  
\[\tilde{\omega}=\omega-\omega_{c} \tag{41}\]  
where \(\omega_{c}\) is the desired angular velocity. Furthermore, combined with Assumption 4 and (7), taking the derivative of \(\tilde{\omega}\) yields [23]  
\[\tilde{\omega}=-J^{-1}(\tilde{\omega}+\omega_{c})^{\times}J(\tilde{\omega}+ \omega_{c})+J^{-1}\tau-\dot{\omega}_{c}-J^{-1}f(\tilde{\rho})+J^{-1}d_{f}+J^{-1 }\Delta\tau \tag{42}\]  
Considering (41) and (42), by assuming that system uncertainty \(f(\tilde{\rho})\), external disturbance \(d_{f}\) and eccentric moment \(\Delta\tau\) are all known, the nominal controller \(\tau\) without the knowledge of \(J\) is developed to achieve that \(\tilde{\omega}\) is bounded and the other signals are bounded. The details are conveyed in the following three steps.  
* Step 1, a linear operator \(L:R^{3}\to R^{3\times 6}\) acting on \(a=[a_{1},a_{2},a_{3}]^{T}\) is introduced by \[L(a)=\left[\begin{array}{cccccc}a_{1}&0&0&0&a_{3}&a_{2}\\ 0&a_{2}&0&a_{3}&0&a_{1}\\ 0&0&a_{3}&a_{2}&a_{1}&0\end{array}\right]\] (43) Then, based on (8), an intermediate vector \(\Xi=[J_{xx},J_{yy},J_{zz},-J_{yz},-J_{xz},-J_{xy}]^{T}\) is introduced to obtain the following transformation. \[Ja=L(a)\Xi\] (44)
* Step 2, based on (44), the derivative of \(\tilde{\omega}\) seen in (42) can be rewritten as \[\hat{\omega}=-J^{-1}F(\tilde{\omega},\omega_{c},\dot{\omega}_{c})\Xi+J^{-1} \tau-J^{-1}f(\tilde{\rho})+J^{-1}d_{f}+J^{-1}\Delta\tau\] (45) where \(F:R^{3}\times R^{3}\times R^{3}\to R^{3\times 6}\) is defined by \[F(\tilde{\omega},\omega_{c},\dot{\omega}_{c})=(\tilde{\omega}+\omega_{c})^{ \times}L(\tilde{\omega}+\omega_{c})+L(\dot{\omega}_{c})\] (46)
* Step 3, combined with Assumption 2, an adaptive feedback control law and its adaptive law are synthesized as \[\tau=-K\hat{\omega}+F(\tilde{\omega},\omega_{c},\dot{\omega}_{c})\hat{\Xi}+f( \tilde{\rho})-d_{f}-\Delta\tau,\hat{\Xi}=Q(-F^{T}(\tilde{\omega},\omega_{c}, \dot{\omega}_{c})\tilde{\omega}-k_{\hat{\Xi}}\hat{\Xi})\] (47) where \(Q\in R^{6\times 6}\) is a designed as positive definite symmetric matric, and \(k_{\hat{\Xi}}\) represents the designed parameter, satisfying \(k_{\hat{\Xi}}>0\).