esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 3 Scenario  
A scenario was created to represent leakage fault conditions in pipeline systems. Based on this scenario, the data collection process was carried out using the experimental dataset described above. Water was used as the fluid in the experimental dataset. Figure 2 shows the graph and the location of the leakage for the leakage scenario. The graph nodes (A0, A2, A3, A4, A6, A7, A8, A9) represent the pressure-measurement points in the experimental dataset. Pressure measurements were taken at eight different points. In the leakage scenario, the experimental dataset included four pipelines shown in green in Figure 2. Two different pipe diameters, 1 mm and 3 mm, were used in the transmission lines for the leakage scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes** & **Feature-1** & \(\ldots\) & **Feature-339** & **System State** \\ \hline s1n\_A0\_1 & 1 & \(\ldots\) & 1 & Normal \\ s1n\_A2\_1 & 0 & \(\ldots\) & 0 & Normal \\ s1o\_A2\_1 & 0 & \(\ldots\) & 1 & Risky \\ s1a\_A2\_1 & 0 & \(\ldots\) & 0 & Leakage \\ \hline \hline \end{tabular}
\end{table}
Table 4: Node list.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Target** & **Edge Weight** \\ \hline s3n\_A0\_1 & s3n\_A3\_1 & 1 \\ s3n\_A3\_2 & s3n\_A8\_2 & 1 \\ \hline s3o\_A8\_3 & s3o\_A9\_3 & 2 \\ s3o\_A7\_4 & s3o\_A9\_4 & 2 \\ \hline s3a\_A8\_15 & s3a\_A9\_15 & 3 \\ s3a\_A7\_15 & s3a\_A8\_15 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Edge list.  
The conditions of the created scenario are as follows:  
1. The scenario was conducted at room temperature.
2. Before data collection, the air in the pipes was purged after operating the experimental setup.
3. The system was allowed to reach a stable state, and the pipes were completely filled with water before data collection.
4. The pressure sensors were calibrated.
5. Considering the structure of water distribution networks, pipes with different diameters and heights were selected for the scenario. The location of the leakage point was chosen so that measurements could be taken both before and after the leakage point using sensors.  
The graph structure depicted in Figure 3 represents the pressure sensors and PVC water pipes in our experimental setup. The pressure sensors are the graph nodes, while the water pipes carry the edge attributes. The graph is directed, as there is a flow direction within the pipelines.  
Figure 2: A scenario diagram was created for measuring the pipeline’s pressure and flow rate.  
The expressions A0, A2, A3, A4, A6, A7, A8, and A9 depicted in Figure 3 represent the pressure sensors present in the experimental dataset given in Figure 1. When the pipeline network in the experimental dataset is represented as a graph structure, the points A0, A2, A3, A4, A6, A7, A8, and A9 serve as the nodes of the graph structure. These node points are recorded by measuring the pressure values over time to create a dataset.
## 2 Materials and Methods
### Measurement Points and Fitted Free-Form Surfaces  
The measurement points for generating the measurement paths were all set on the ideal surface in the original CAD model. The number of measurement points is related to the accuracy of the surface fitting. The locations of the measurement points were determined based on the slopes of the points on the curves along the \(u\)-direction. We added measurement points at locations where the change of the slope of two adjacent measurement points was significant, and removed the measurement points at locations where the change of the slope of two adjacent measurement points was little. Figure 5 shows the measurement points on Surface one. Seven curves (not shown) in the \(u\)-direction were used, and ten measurement points are distributed along each curve. From this figure, we can see that there were more measurement points on the top-left of the surface because that was the place where the slopes of the points on the curves changed rapidly.  
In this study, we used the functions in NX Open API to perform both the curve fitting and the surface fitting. After obtaining the compensation points, we used the points on the same \(v\) coordinate to perform curve fitting along the \(u\)-direction, and the points on the same \(u\) coordinate to perform curve fitting along the \(v\)-direction. The two groups of curves were then used for surface fitting. The order of the fitted curve was determined using the criteria that the average of the distances from the compensation points to the fitted curve must be less than a threshold, which was set at 1 \(\upmu\)m in the study. It was found that we could use the third-degree curves to achieve this.  
NX includes at least five surface fitting methods: Studio Surface, Fit Surface, Through Curves, Through Curve Mesh, and UFun. To evaluate the methods, we designed two experiments: One was to use the five methods with a set of the compensation points for each surface design to fit surfaces, and find the average of the distances, \(d_{1}\), from the compensation points to the fitted surface. The other was to arbitrarily pick up 1000 points from the surfaces of the reconstructed CAD models, and find the average of the distance, \(d_{2}\), of these points to the fitted surface used for the reconstruction of the CAD models. The experimental results are listed in Table 1. It can be seen that the Through Curves Mesh method had the best results for Surface two and Surface three. For Surface one, the Through Curve Mesh performed second to UFun. However, UFun did not perform well for Surface two and Surface three. Therefore, in the subsequent study, we used Through Curve Mesh for surface fitting.  
Figure 5: Illustration of the measurement points on Surface one.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Simulation Setup  
A random distribution of nodes within the designated area was used to form the network using the random waypoint mobility model. The connection probability was used to determine connectivity between nodes. Performance metrics were recorded while simulating each routing protocol for the specified circumstances.
## 4 Control Algorithms
### Control System for the Attitude Dynamics  
Concerning the attitude stabilization, it should be remarked that it represents a problem of particular importance for spacecraft, since it is fundamental for enforcing precision and also for guidance, as short propulsive maneuvers must be executed with extremely accurate alignment [28]. A second order sliding mode (two-sliding mode) algorithm, known as super-twisting (STW) [27], is considered for the attitude tracking. The STW algorithm designs a continuous control law, which steers to zero in finite time both the sliding output and its first time derivative. This continuous controller is suitable for the actuation system related to the attitude dynamics (reaction wheels). Moreover, since the STW algorithm contains a term that is obtained as the integral of a discontinuous component, the chattering is strongly attenuated.  
The input \(u_{\omega}=M_{\text{RW}}\in\mathbb{R}^{3}\) is defined in accordance to the STW algorithm, [27], as follows  
\[u_{\omega}=-\lambda|\sigma_{\omega}|^{\tfrac{1}{2}}\text{sgn}( \sigma_{\omega})+v_{\omega}\,, \tag{20}\] \[\dot{v}_{\omega}=\begin{cases}-u_{\omega}&\text{if}\,|u_{\omega} |>U_{M}\,,\\ -\alpha\text{sgn}(\sigma_{\omega})&\text{if}\,|u_{\omega}|\leq U_{M}\,,\end{cases}\]  
where the control parameters \(\lambda\), \(\alpha\), and \(U_{M}\) have to be chosen as specified in [27] and, for this specific case study, in [25].  
The sliding output for the super-twisting controller is defined as  
\[\sigma_{\omega}=\omega_{B}+C_{\omega}\delta q_{13} \tag{21}\]  
with \(C_{\omega}\in\mathbb{R}^{3,3}\) a positive definite matrix. The vector \(\delta q_{13}\) is evaluated starting from the desired attitude vector, evaluated as in Equation (17)  
\[\delta q_{13}=\Sigma^{T}(q_{d})q \tag{22}\]  
where \(\Sigma(q_{d})\in\mathbb{R}^{3,4}\) is defined from the matrix \(\Sigma(q)\) (Equation (9)) including \(q_{d}\) definition.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 2 Swarm Health State Model  
### Model of the Health State  
The health status of a swarm of microsatellites is time-varying, and for this reason, the health degree (\(H\)) is used to quantify its health. Swarm health (\(H_{ Swarm}\)) is defined as the degree of resistance to the destruction of the constituent elements and swarm topology and the degree of the ability to meet mission requirements within a continuous-time. Based on the above analysis, the swarm can be divided into three parts: single satellite, intersatellite communication link, and swarm effectiveness, but the swarm topology is complex and intertwined, and each part affects the others. Therefore, the health of a microsatellite swarm can be expressed in a triad as  
\[H_{ swarm}=\Theta(H_{sat},H_{link},E_{ swarm}) \tag{1}\]  
where \(H_{sat}\) is the total health of all individual stars in the swarm system, \(H_{link}\) is the total health of the intersatellite communication links, and \(E_{ Swarm}\) is the swarm effectiveness, which indicates the degree of mission accomplishment. \(\Theta\) indicates that the health of the swarm system is determined by a mathematical relationship between the three parameters.
# Optimization of the Position and Stiffness of Passive Walking Assistance Devices
## 3 Results
### Optimal Location of the Passive Walking Assistance  
An optimization problem to find the placement of spring system to reduce total positive energy is given in Equation (3). The stiffness of the spring was kept constant during the first step. In addition to the placement of the spring, a design variable, \(d_{s}\), was used to define the free length, \(l_{s}\), as shown in Equation (4). The distance between the two  
Figure 3: The definition of \(u\)–\(v\) coordinate system to determine the position of passive walking assistance device.  
points of the spring at the initial contact (\(t=0\)) was denoted as \(l_{initial}\). Thus, the free length of the spring was determined within the interval of 90-110% of \(l_{initial}\) by the design variable, \(d_{s}\). The position of the walking assistance device was defined by using \(x_{1}\), \(x_{2}\), \(y_{1}\), and \(y_{2}\) as shown in Figure 3. Then, the optimization problem was implemented in MATLAB(r) (The Mathworks, Natick, MA, USA) and solved by using the \"Optimization Toolbox\".  
\[\begin{array}{c}\text{min}f=\sum\limits_{i=1}^{n_{joints}}\left(\frac{\int_ {0}^{T}\max\left(0,P_{(i)}\right)\text{dt}}{T}\right)\\ \text{subject to }0<x_{1}<1\\ -5<y_{1}<5\\ 0<x_{2}<1\\ -5<y_{2}<5\\ -1<d_{s}<1\end{array} \tag{3}\]  
\[l_{s}=l_{initial}\times\left(1+0.1\times d_{s}\right) \tag{4}\]  
The optimal placement of the walking assistance system for a given stiffness was found around the hip joint. The effect of the walking assistance system on hip joint moment and power is given in Figure 4. The total positive joint energy was reduced by 8.8% when the walking assistance device was applied to both legs. The second positive power region of the hip joint (55-70% of gait cycle) diminished to almost zero. The negative power region of the hip joint (20-55% of the gait cycle) increased and became almost zero as well. The negative joint moment around 20-70% of the gait cycle almost vanished. In addition to the hip joint, the average moment and total positive power of the pelvis-trunk joint were also reduced. However, the joint moment and the power of knee and ankle joints were not significantly affected.
## 5 Securing ITS Applications: A Practical Case Study
### ETSI Security Layer: TS 103 097
#### 5.3.2 Certificate Format  
ETSI TS 103 097 [24] proposes a new certificate format that specifies how to encode the different information required by each type of certificate, _i.e._, root certificate, Authority Authorization (AA) certificate, _etc._, as described in the ETSI ITS authority hierarchy [93].  
```
Struct{ uint8 version; SignerInfosigner_info; SubjectInfosubject_info; SubjectAttributesubject_attributes<var>; ValidityRestrictionvalidity_restrictions<var>; Signaturesignature;} Certificate;
```  
Listing 2: ETSI ITS certificate structure.  
As shown in Listing 2, an ETSI ITS certificate starts with a one-octet certificate version field that shall be set to two for conformance with the latest standard specifications [24]. The information of the certificate's signer is then given by the signer_info field, which can be of type self (0), certificate_digest_with_sha256 (1), certificate (2), certificate_chain (3) or certificate_digest_with_other_algorithm (4). Next, the certificate's subject name and type (e.g., root_ca, crl_signer, enrollment_credential, authorization_ticket, _etc._) are given by the subject_info field; whereas the certificate's subject attributes are given by the variable-length vector subject_attributes, such as verification_key (0), encryption_key (1), assurance_level (2), its_aid_list (32), _etc_.  
The certificate's validity information is then encoded by the variable-length vector validity_restrictions, whose elements can include the time_end (0), time_start_and_end (1), time_start_and_duration (2) and region (3). It should be noted that the elements of the subject_attributes and validity_restrictions fields should be encoded in ascending numerical order of their respective types. Finally, the signature field holds the certificate's signature, which is signed by the certificate authority (CA). This signature is calculated based of the preceding certificate fields.
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 3 Approach
### Finite Element Modeling  
All the considered composite steel girder bridges in this investigation are modeled and analyzed within the elastic range by the ANSYS Version 14 software [15]. Over the past few decades, researchers working in the field of structural analysis of slab-on-girder bridges have used various approaches in their modeling that employ different types of finite elements and connectivity [4; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. In this study, the girders are modeled with four-node shell elements for both flanges and the web (SHELL181). The material for all girders is steel which has a 200 GPa modulus of elasticity and 0.3 Poisson ratio. The uncracked concrete deck slab is modeled using multiple layers of eight-node solid elements (SOLID185) to account for the strain variation within these elements through the thickness. The deck slab material is concrete with a 25 GPa modulus of elasticity, which corresponds to approximately 30 MPa compressive strength, and 0.2 Poisson ratio. Diaphrags in the form of cross-bracing are modeled using two-node beam elements (BEAM188). The diaphragm material is steel with the same modulus of elasticity and Poisson ratio as the girders.  
All the girders in the considered bridges are simply supported; they are simulated by a pin at one end and a roller at the opposite end. This means that the longitudinal translation of each girder is restrained at the pin location and unrestrained at the roller location. Live load is represented by the truck component of the AASHTO HL-93 live load, which is a common practice since neglecting the lane load component leads to conservative live load distribution results. Note that the truck is composed of three axles with 1.8 m gauge width, of which the front axle weighs 35 kN while the middle and rear axles each weigh 145 kN, with the distance between the front and middle axles being 4.3 m and between the middle and rear axles ranging between 4.3 and 9 m. The finite element model in this paper was verified by [PERSON] [27] using the results of the laboratory test on a full-scale bridge conducted by [PERSON] at the University of Texas [28] and the field testing of the Creek Relief bridge in Texas carried out by [PERSON] [29]. Figure 2 provides a summary of the type of finite elements used to model the composite steel bridge girder.
## 2 Related Work
Their work provided further analysis of the reconfiguration-related performance of MTD but lacked discussion about the security of MTD. As mentioned in Section 4.1, ACD techniques are always described with categories: Shuffle, Diversity, and Redundancy. When evaluating their effectiveness, most literature focuses on one or two categories. As another comprehensive assessment work, [PERSON] et al. [7] proposed a hierarchical attack representation model to assess the effectiveness of MTD. Such a model contains an attack graph in the upper layer and attack trees in the lower layer. Besides, the model's scalability was improved with \"importance measure\". However, they assess the effectiveness of Shuffle, Diversity, and Redundancy one by one, rather than adopting them in combinations. Existing studies have contributed to the optimization of ACDs in different aspects, but there is still a lack of comprehensive analysis of an integrated system combining various ACD technologies.
# CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
## 5 Case Study and Experimental Results
### Performance Evaluation
#### 5.3.1 Resource Usage  
The performance of the proposed platform is evaluated from CPU usage, Memory Usage and disk I/O performance, as shown in Table 2. There are two types of container: the continuous execution tasks, e.g., data processors and _modelets_, and intermittent execution tasks, e.g., _trainers_. The data are collected in two different scenes: case with _trainers_ off and case with a _trainer_ on.  
As shown in Table 2, no matter whether a trainer is on, the resources used by _modelets_ and data processors would not change, as these services do not stop during the operation of the platform and their resource consumption is rather limited. The data processors contain Kafka(r), Zookeeper(r), etc. and the total CPU usage is about 17.24% (100% for one core full usage and this server has about 32 vCores) and about 2825 MB memory as the real-time database is implemented by Redis(r), which is a memory database. The message-caching technique of Kafka is also adopted. Those values can be further reduced with lighter configuration, e.g., shortening the data expiry time. The four _modelet_ containers total about 369 MB of memory. Their memory usage highly depends on the complexity of the models. In comparison, the _trainers_ use more resources; the data shown are is collected when only one of the _trainers_ is under execution. However, due to the _orchestrator_' schedules, their peak resource consumption is limited to about 12 GB memory and disk i/o is also within a reasonable range. These results show that this platform is lightweight and can support many _trainers_ with limited resources.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Num** & **CPU (\%)** & **Memory (MB)** & **Disk (KB/s)** \\ \hline Data processors & 10 & 17.24/17.24 & 2824/2825 & 76.6/76.6 \\ Trainers & 4 & 2.12/160.4 & 46.72/ 11059 & 0/176.2 \\ Modelet & 4 & 18.17/18.17 & 369.4/369.8 & 24.2/ 24.2 \\ Orchestrator & 1 & 6.3/5.9 & 51.6/48.7 & 17/15.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The resource consumption of key components (the four trainers are off/one of four trainers is on).
## 3 Results and Discussion
### Performance Comparison between BBD-RSM and ANN Models  
Numerous researchers have shown that design within response surface methodology (BBD-RSM) and artificial neural network (ANN) modeling for sensitivity analysis is effective [41, 44, 58]. This work shows forecast stability coefficients with varying water levels using BBD-RSM and ANN. To assess their predictions' accuracy, the study compared predicted and actual data and calculated absolute relative error (ARE).  
Figure 9 shows the comparison between the data from the ANN and BBD-RSM models and the actual data to determine model accuracy. Scatter plots comparing each value to the number of runs showed that model-predicted values matched actual values. This comparison study found that the BBD-RSM model aligned data better than the ANN model. Thus, the BBD-RSM model slightly outperformed the ANN model in data representation.  
Figure 8: ANN model FWL response graph of predicted and actual values for (**a**) training, (**b**) validation, (**c**) testing, and (**d**) optimization profiler.  
Figure 9: Comparative evaluation between ANN and BBD-RSM models predictions values with the actual values for (**a**) RWL and (**b**) FWL response.  
The study also found that BBD-RSM models predicted responses marginally better. However, both the BBD-RSM and ANN models accurately predicted numerical findings and validated them across diverse data combinations utilized to investigate water level changes, whether rising or falling. The appropriateness of the BBD-RSM and ANN-based models was further examined using \(R^{2}\), \(MRE\), and \(RMSE\) (Table 6). The significant relationship between these indicators shows the models can imitate real-world outcomes. However, the time lag between surface water and groundwater should be further investigated to validate the results of this study.