esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 4 Case Study: FDI System for Oil Pipeline
### FDI System for Oil Pipeline
Table 3 summarizes all sets of FMSO sets found for complete isolation, and the local faults that each local diagnoser detects and isolates are highlighted:  
Then, from the design of the local diagnosers presented in Table 3, it follows that, according to the sensitivity of faults, both subsystems \(\Sigma_{1}\) and \(\Sigma_{2}\) require two FMSO sets each to detect and isolate their faults. In the case of subsystems \(\Sigma_{3}\) and \(\Sigma_{4}\), they have three FMSO sets each. Next, applying Definitions 6 and 7, an ARR is calculated first and then a residual generator for each of these 10 FMSO sets of Table 3.  
Finally, the residual generators, which receive only measured variables as inputs, are designed and programmed in a programmable automation controller (PAC) using the structured text language. For the implementation, a PAC is assigned for each local diagnoser as shown in the architecture of the proposed FDI system in Figure 7.  
Ultimately, to validate the implementation of the designed distributed FDI system, all the faults of the set of faults of interest are emulated: faults \(f_{h_{2}}\), \(f_{h_{3}}\) and \(f_{h_{L}}\) related to pressure sensors, faults \(f_{Q_{1}}\) and \(f_{Q_{L}}\) related to flow sensors and faults \(f_{i}(i=1-3)\) related to pipeline segments. Then, in Figure 8, the response of the system to emulations of all the faults simultaneously is presented, and it can be seen that the system detects them for all these signals through the response of the residuals that differ from zero during the duration of the fault. In the same way, isolation is demonstrated by different fault signatures. The results shown in Figure 8 demonstrate that all faults can be detected and that a different fault signature is generated for all faults except for faults \(f_{3}\) and \(f_{Q_{L}}\) that cannot be isolated from each other. Each column of this figure corresponds to each fault with simulation occurring, and each row corresponds to the sensibility to each residual generator \(r_{i}\) for each fault with emulation occurring. Figure 7: Distributed FDI System proposed.
## 4 Experiments
### Ablation Experiments
#### 4.3.3 Performance Comparison with Surrogate Model and without Surrogate Model  
To verify the effectiveness of the surrogate model, we removed the RF surrogate model in RFEPPO and compared it with our method. In this experiment, we performed comparisons on nine tabular datasets. Table 7 shows the accuracy and search time of the two sets of experiments. Each method was run five times and trained 200 episodes each time.  
As can be seen from Table 7, the time spent is reduced after adding the surrogate model. This indicates that the predicted accuracy of the surrogate model is used to update the agent during the search, making part of the process without actually training XGBoost, which ultimately reduced the time. Additionally, seven of the nine datasets also show an increase in accuracy after adding the surrogate model. Thus, this demonstrates that the inclusion of the surrogate model speeds up the search without affecting the accuracy of the test set.
## 3 Experimental Verification of the SJB
### Load Operation Test (On/Off Control)  
A driving test was conducted for each headlight load to evaluate the characteristics of the load. The voltage drop was measured based on the current output by the SJB, the length of the wire, and the power consumed by the headlights. A schematic of the driving test for each headlight is shown in Figure 13. All messages were sent via the CAN communications equipment shown in the figure.  
A headlight in a vehicle has a large inrush current at startup, the consequence of which is that the headlight may burn out or its lifespan may be shortened. It also places significant electrical burden on the SJB that supplies the power. Therefore, it is important to characterize the inrush current during the initial ramp-up period via load control tests.  
The current was monitored to measure the transient inrush current and steady state operating current. The measured current characteristic of the H/LP High LH output is plotted in Figure 14 in  
Figure 12: Test environment.  
Figure 13: Schematic of the setup used for the driving test for each headlight load.  
which the inrush current extended from 0 to 160 ms (denoted A in the figure). The root-mean-square (rms) value of the operating current was measured from 500 ms to 1 s after the initial drive start.  
The results of the operational test for each headlight are listed in Table 4. When the headlight was turned on, a voltage drop was detected as the resistance of the wire harness differed when supplying each headlight. Therefore, there were situations in which the brightness of the headlights on the left and the right differed, which would adversely affect the life of the headlight. The benefit of PWM control is that it allows voltage fluctuations to be managed while still providing stable power to the headlights.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 2 Related Work
### Ipbtl-Rdhei
#### 2.2.1 Pixel Labeling and Data Hiding  
As mentioned, the first row and the first column of the cover image are selected as the reference pixels. Then, the prediction errors of the remaining pixels are calculated. After obtaining the prediction error array, the whole image is encrypted with the image encryption key and each non-reference pixel is labeled according to its prediction error. Finally, the secret information is encrypted with the data-hiding key and embedded into the predefined embeddable space.
## 3 Results and Discussion
### Optimization of Phosphorus Adsorption in Fixed-Bed Column
This may be because lower flow rates led to higher residence times of P in the column. Consequently, longer contact between the wastewater and the adsorbent is achieved, helping to reach equilibrium before the ions move out of the column [34]. The previous results discussed from the design of experiments aimed at determining the optimal conditions for operating a lab-scale column using a few experiments and concluding which factor affects the process the most. These optimal conditions were used to scale up the process. The response parameters considered were _FSB_ and \(\Delta t_{ads}\), and the software JMP Statistical Discovery was used to assess the significance of the effects of \(h\) and \(Q\) on these parameters at a 95% confidence level. The response variables _FSB_ and \(\Delta t_{ads}\) were influenced by both factors \(h\) and \(Q\), with _p_-values of 0.0002 and 0.0013, respectively (for _FSB_), and 0.0173 and 0.0021, respectively (for _AL\({}_{ads}\)_). The quadratic terms did not significantly affect the behavior of the response variables. For the _FSB_, the flow rates have a greater influence on the data, while \(\Delta t_{ads}\) is more dependent on the bed height. Thus, the predictive models for _FSB_ and \(\Delta t_{ads}\) without considering the quadratic terms, are shown in Equations (12) and (13)  
\[FSB=0.253-0.1255\ Q^{\prime}+0.0728\ h^{\prime} \tag{12}\]  
\[\Delta t_{ads}=121.32-17.592\ Q^{\prime}+31.908\ h^{\prime} \tag{13}\]  
where \(Q^{\prime}\) and \(h^{\prime}\) are the normalized variables. These predictive models will allow the determination of _FSB_ and \(\Delta t_{ads}\) values for other initial operating conditions within the range studied in the present work (20 < \(h\) < 40 cm and 5 < \(Q\) < 10 mL/min). Figure 3 plots the experimental versus predicted values for _FSB_ and \(\Delta t_{ads}\), where it can be concluded that the former parameter is better predicted by the model (R2 = 0.985). Figure 3: Experimental versus predicted (**a**) _FSB_ and (**b**) \(\Delta t_{ads}\). According to Table 4, the experimental and predicted results were similar, with relative errors below 10% in most cases. This indicates that the developed models are appropriate for determining the values of these two variables when experimental work cannot be carried out. Two additional experiments were conducted (runs 10 and 11) to validate the models. The operating conditions were as follows: (i) run 10: \(h\) = 25 cm, \(Q\) = 8.5 mL/min, and (ii) run 1: \(h\) = 35 cm, \(Q\) = 6.5 mL/min. This set of experiments was aimed at determining the optimal operating conditions for the scale-up of the system. Considering the maximization of \(FSB\) and minimization of the time between exhaustion and breakthrough (\(\Delta t_{ads}\)), the analytical optimal solutions given by the software were found when \(h\) = 30 cm and \(Q\) = 5 mL/min. These conditions will form the basis for proceeding with the experiments and scaling up the process.
## 4 Experimental Tests
### Test Results
Since the ground resolution of visible light real-time image is significantly higher than the reference image, we can save the computation without losing the position precision by decreasing the resolution of real-time image properly. From the statistics of Table 6, it can be noted that the proposed coarse matching algorithm can realize an average position update rate of 12.6 Hz (79.1 ms) for infrared image, and an average position update rate of 12.1 Hz (82.6 ms) for visible light. The positioning and heading obtained from the image matching are finally used to enhance the MEMS INS in order to improve the autonomous navigation performance of UAV according to the approach proposed in Section 3. Herein, the positioning and heading results of CMM and those of HOP are separately used to aid the MEMS INS, whose raw sensors data are collected during the flying tests. The CMM enhanced MEMS INS positioning errors are shown in Figure 10, and the comparative HOP enhanced MEMS INS positioning errors are shown in Figure 11. By comparing Figure 10; Figure 11, it can be seen that both the positioning accuracy and the positioning stability are improved dramatically. It can be noted that there exists some error \"jumps\" in Figure 11 during the HOP enhanced MEMS INS process. This phenomenon arises from the fact that the HOP imaging matching approach has a low CPR, which affects the integration of HOP and MEMS INS. The statistics of the enhanced MEMS INS \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Image** & **Referenced** & **Average Time Cost** & **Average Time Cost** \\
**Type** & **Resolution** & **Image** & **of Coarse Matching** & **of Fine Alignment** \\ \hline Infrared & 640 \(\times\) 480 & 1024 \(\times\) 768 & 79.1 ms & 2451.1 ms \\ Visible light & 512 \(\times\) 512 & 1024 \(\times\) 768 & 82.6 ms & 2454.4 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time-consuming statistics of CMM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flight No. ** & **CMM CPR** & **CMM Positioning** & **HOP CPR** & **HOP Positioning** \\  & & **RMS Error** & & **RMS Error** \\ \hline Flight 1 & 99.8\% & 3.777 m & 95.5\% & 18.986 m \\ Flight 2 & 99.2\% & 4.683 m & 97.3\% & 23.659 m \\ Flight 3 & 99.6\% & 4.416 m & 55.8\% & 22.867 m \\ Flight 4 & 99.2\% & 4.149 m & 63.7\% & 22.005 m \\ Flight 5 & 99.1\% & 6.142 m & 62.3\% & 19.215 m \\ Flight 6 & 99.5\% & 6.021 m & 67.5\% & 24.612 m \\ Flight 7 & 9
# A Strain Hardening and Softening Constitutive Model for Hard Brittle Rocks
## 3 Mechanical Behaviour of Brittle Hard Rock
### Strength Parameter Evolution Model
The fitting coefficients of each parameter are shown in Table 4, and their fitting curves are shown in Figure 6. The fitting correlation coefficients of each parameter are all greater than 0.9, which indicates that the confining pressure function Equation (25) can satisfactorily describe the relationship between parameters \(\varepsilon_{1,p}^{ps}\), \(\xi_{c}\) and \(\xi_{\varphi}\) with various confining pressures. According to Equations (21)-(25), an overall fit for granite and diabase is created to illustrate the strength parameter evolution model that considers both axial plastic strain and confining pressure simultaneously. As shown in Figure 7a-d, the results of the overall fit using the strength parameter evolution model are in good agreement with the individual fit results using the experimental data. With increasing confining pressure, the strength parameters first increase and then decrease, accurately describing the initial, peak and residual strength values. The strength parameters at the peak points gradually shift to the right, and the model curve tends to increase in width, which means that the failure mode of hard brittle rock transitions from brittle failure to plastic failure under high confining pressures. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{6}{c}{**Granite**} & \multicolumn{6}{c}{**Diabase**} \\ \cline{2-11}  & \(\mathbf{P_{0}}\) & \(\mathbf{A_{1}}\) & \(\mathbf{A_{2}}\) & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{P_{0}}\) & \(\mathbf{A_{1}}\) & \(\mathbf{A_{2}}\) & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{R^{2}}\) \\ \hline \(\varepsilon_{1,p}^{ps}\) & 1.510 & 0.075 & 1.445 & 0.565 & 321.094 & 0.99 & 0.114 & 0.119 & 0 & 13.422 & - & 1.00 \\ \(\xi_{c}\) & 6.790 & 0.198 & 6.414 & 1.027 & 874.929 & 0.92 & 0.285 & 0.227 & 0 & 8.947 & - & 0.99 \\ \(\xi_{\varphi}\) & 7.075 & 0.203 & 6.709 & 1.006 & 914.635 & 0.91 & 0.313 & 0.237 & 0 & 10.757 & - & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fitting coefficient of parameters \(\varepsilon_{1,p}^{ps}\), \(\xi_{c}\) and \(\xi_{\varphi}\) of two hard brittle rocks with the confining pressure function. Figure 6: Fitting curves of parameters for two hard brittle rocks at different confining pressures: (a) \(\varepsilon_{1,p}^{ps}\) (b) \(\xi_{c}\), (c) \(\xi_{\varphi}\).
## 2 Methodology
### Machine Learning Models
#### 2.3.2 Tree-Based Algorithms  
Beyond these parametric models, several tree-based algorithms were explored, including decision trees, random forests, gradient boosting, and extreme gradient boosting.  
Decision trees are a type of algorithm that predicts the target variable by learning simple decision rules inferred from the data features. The prediction of a given input is made by traversing the tree from the root node to a leaf node, where each internal node represents a decision based on a feature.  
Random forests are an ensemble learning method that builds multiple decision trees and merges them to get a more accurate and stable prediction. Each tree in the forest is trained on a bootstrapped subset of the data, and the final prediction is made by averaging the predictions of all trees in the ensemble.  
\[\hat{y}=\frac{1}{T}\sum_{t=1}^{T}\hat{y}^{t}\, \tag{3}\]  
where \(T\) is the number of trees and \(\hat{y}^{t}\) is the prediction from the \(t^{th}\) tree.  
Gradient boosting (GB) in an ensemble technique that builds models sequentially, with each new model attempting to correct the errors of the previous ones. The models are added until no further significant improvements can be made. It minimizes the differentiable loss function \(L(y,\ \hat{y})\) using gradient descent.  
(d) Extreme Gradient Boosting (XGBoost)  
XGBoost is an optimized implementation of GB that incorporates regularization techniques to prevent overfitting and enhance performance. It also includes features for handling missing data and parallel computation, making it a robust and efficient algorithm.  
For these models, hyperparameter tuning was crucial. Methods such as GridSearchCV with cross-validation were employed to determine the optimal hyperparameters, focusingon parameters like the maximum tree depth, minimum number of samples required to split a node, and minimum number of samples required to be at a leaf node.
## 4 Graph Filters for Library Recommendations
### Revisiting Graph Filters for Collaborative Library Recommendations  
We base our approach on collaborative filtering paradigms that run graph filters in bipartite graphs to find nodes relevant to ones of interest. Graph filters, especially personalized PageRank, were at some point a popular collaborative filtering tool [22], but this direction has in large part been abandoned in favor of the added accuracy offered by representation learning approaches such as GNNs. Other graph-mining tasks, however, have recently seen a resurgence of graph filters as equivalence to those is now understood as a primary contributor towards the efficacy of many GNN architectures [31; 32; 35; 36]. Therefore, given that GNNs already boast a high predictive quality for library recommendations, we search for filters that are not lagging significantly behind with respect to predictive performance, while also satisfying our no-learning requirement.  
To reconcile the opposite trends of collaborative filtering having abandoned graph filters and the latter being revisited by state-of-the-art research from other domains, we theorize that widely adopted graph filters are missing crucial assumptions that more sophisticated collaborative filtering mechanisms do not; these assumptions may not be as important in other predictive tasks, but are crucial for recommendation systems. In the next subsection, we identify lack of memory as one such assumption when emulating human-driven library recommendation.  
We consider a general formulation of graph filters \(F(\hat{A})\) that can take any functional form dependent on adjacency matrix normalizations \(\hat{A}\) such as those described in Section 2. To recommend libraries for query projects with these, they need to parse project inputs. However, singleton data samples can lead to non-informed graph mining due to a lack of pairwise structural relations to mine. Thus, we employ the neighborhood inflation heuristic of [PERSON] et al. [37] to expand the search terms by including the immediate neighborhood of projects, i.e., their known dependent libraries, as query-able information to be included within graph signals. This kind of information was already sent to graph inference systems following the deployment of Figure 1 so that dependencies between the query project and at least one library are added to the dependency graph. Hence, there are no additional communication or computational costs associated with following this practice.  
We hereby consider query graph signal \(h_{0}\) with elements  
\[h_{0}[v]=\{1\text{ if }u=v\text{ or }v\text{ is a dependency of }u,0\text{ otherwise}\} \tag{8}\]  
where \(u\) are the projects for which we provide library recommendations. Given these query signals, we pass them through filters of choice to obtain their structural proximity of all graph nodes \(h=F(\hat{A})\). Finally, our methodology focuses on the proximity scores \(h[v]\) of libraries \(v\), where higher scores are structurally \"closer\" to target projects and thus indicate preferred recommendations.
## 2 Materials and Methods
### Adsorption Isotherm  
Adsorption isotherms of Cu(II) and Zn(II) on the extracted EPS were studied by varying the EPS concentrations from 0 to 100 mg L\({}^{-1}\) (pH = 7.0) while keeping Cu(II) and Zn(II) concentrations constant at 25 mg L\({}^{-1}\). Other process parameters were similar as the above. The adsorption of Cu(II) and Zn(II) by EPS was tested using the linear Langmuir isotherm model represented by the equation given below:  
\[\frac{\mathrm{C_{e}}}{\mathrm{q}}=\ \frac{\mathrm{C_{e}}}{\mathrm{q_{m}}}+\ \frac{1}{ \mathrm{q_{m}}\,\mathrm{b}} \tag{2}\]  
where, q represents the uptake of metal by EPS (mg g\({}^{-1}\)) calculated by the formula given below (Equation (3)), C\({}_{\mathrm{e}}\) is the metal ion concentration at equilibrium (mg L\({}^{-1}\)), q\({}_{\mathrm{m}}\) (mg g\({}^{-1}\)) and b (L mg\({}^{-1}\)) are the Langmuir constants, which determines the maximum metal adsorption capacity and the affinity between the biosorbent and the metal ion, respectively. The constants are determined from the linear plot of C\({}_{\mathrm{e}}\)/q versus C\({}_{\mathrm{e}}\).  
\[\mathrm{q}=\frac{\mathrm{V}\ (\mathrm{C_{i}}-\mathrm{C_{f}})}{\mathrm{W}} \tag{3}\]  
where V is the volume of EPS solution (L), C\({}_{\mathrm{i}}\) and C\({}_{\mathrm{f}}\) are the initial and final metal ion concentrations (mg L\({}^{-1}\)), respectively. W is the dry weight of EPS (g).  
Furthermore, a dimensionless constant known as separation factor (RL) was calculated in order to test the thermodynamic favorability of adsorption using the following equation:  
\[\mathrm{RL}=\ \frac{1}{(1+\mathrm{bC_{i}})} \tag{4}\]  
where b is the Langmuir isotherm constant, and C\({}_{\mathrm{i}}\) is the initial metal ion concentration (mg L\({}^{-1}\)). The RL parameter explains the isotherm as follows: RL \(>\) 1 means unfavorable; RL = 1 means linear; 0 \(<\) RL \(<\) 1 means favorable; and RL = 0 means irreversible adsorption [28; 29].  
The percentage removal potential of metal ions by EPS was calculated using the following equation [27]:  
\[\mathrm{Removal\ potential}(\%)=\frac{\mathrm{Initial\ metal\ concentration\ -\ Final\ metal\ concentration}}{\mathrm{Initial\ metal\ concentration}}\times 100 \tag{5}\]  
The adsorption of Cu(II) and Zn(II) with EPS was also studied using the Freundlich isotherm model which assumes a heterogeneous adsorption surface and active sites with different energies. The isotherm is represented by the equation given below:  
\[\mathrm{Log\ q}=\mathrm{log\ K_{f}}+\mathrm{n}^{-1}\ (\mathrm{log\ C_{e}}) \tag{6}\]  
where q and C\({}_{\mathrm{e}}\) are same as described above. K\({}_{\mathrm{f}}\) is the Freundlich constant which indicates the adsorption capacity (L g\({}^{-1}\)), and n represents the exponent known as adsorption intensity. The constants K\({}_{\mathrm{f}}\) (L g\({}^{-1}\)) and n can be determined from the linear plot of log q versus log C\({}_{\mathrm{e}}\)[28; 29].
# The Role of Infrastructural and Psychological Factors in Sustainable Transportation Mode Choices
## 2 Methods
### Survey Content
#### 2.2.2 Infrastructural Variables  
Further analyses are based on two infrastructural variables: walking travel time in minutes from each residence to the nearest train station and the nearest city center. To calculate walking travel time to the destinations, the address information and the associated coordinates of residential locations were used. These coordinates served as starting points for subsequent calculations using QGIS (Quantum Geographic Information System, version 3.28.13-Firenze) in conjunction with the ORS (OpenRouteService, version 1.10.0) tool. The walking time calculation within the ORS tool was based on an assumed walking speed of 5 km/h. The city's centers were also digitized as polygons in QGIS according to the city's central concept [42]. The center accessibility of the studied city revolves around a primary main center and three district centers. The demarcation of the centers includes the functionally dense population of retail and service facilities. The centroids of the polygons were used as destination points for subsequent calculations.  
The predictor of PT captures the continuous nature of travel times rather than categorizing them as acceptable or unacceptable. The granularity allows for statistical analysis, as highlighted by [43], facilitating deeper insights into the relationship between accessibility and urban factors. Moreover, as noted by [44], continuous measures enhance our ability to identify specific areas needing intervention, leading to more effective PT planning and policymaking. Figure 1 illustrates the spatial distribution of participants, railway stations, and the city center, providing an overview of their relative locations within the study area.