esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis
###### Abstract  
Failure mode and effect analysis (FMEA) is a structured technique for identifying risks that may occur during a given stage of a system's life cycle. However, the use of the risk priority number (RPN) in traditional FMEA results in difficulties with regard to quantification of the degree of risk in the hierarchical failure structure. This study proposes the use of a hierarchical time-dependent FMEA approach to overcome the limitations encountered during the implementation of traditional FMEA approaches. In place of the RPN, a probabilistic loss model is developed under a hierarchical structure considering the elapsed time from the failure-cause (FC) to the system failure. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each FC can be evaluated. As a result of the practical application of the time-dependent probabilistic model through the numerical example, we could reasonably evaluate the risk from the cause of failure in the hierarchical structure in terms of economic loss.  
Keywords:FMEA; HTD-FMEA; hierarchical structure; time-dependent probabilistic loss model; expected loss; risk evaluation +
Footnote †: journal: Applied Sciences
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 6 Conclusions  
Rainwater infiltration into open fractures in porous rock is investigated by means of discrete fracture and matrix (DFM) simulation, using a hybrid finite element-finite volume method with lower dimensional fracture representations. Fracture nodes with pressure and saturation degrees of freedom are separated from the rock matrix by \"split boundaries.\" Complex fracture-matrix interface conditions and fluid transfer are modelled using a specific nonlinear solver embedded into an operator-splitting IMPES approach. This permits simulation of spontaneous imbibition of rainwater into the rock matrix as well as capillary sealing. The new scheme also resolves the movement of water and air in the fractures, which is demonstrated with two- and three-dimensional models with variable fracture aperture. As expected, the results indicate that spontaneous imbibition retards the wetting front significantly. However, this effect depends on rainfall intensity, fracture aperture, and rock matrix water saturation. Matrix imbibition retards the wetting front in fractures only up to a critical fracture aperture, above which the wetting front velocity matches analytic solutions for saturated fracture flow. In all cases, front velocity is constant unless the fluid supply runs out. Multiple interacting fractures have yet to be investigated. Regarding rainfall events in semi arid regions, vertically continuous fractures are shown to rapidly conduct water far below the earth's surface, suggesting that the saturated-zone aquifers below can get recharged by these events. Fracture aperture variation within individual fractures destabilises wetting fronts. Thus, even without consideration of complex wetting front physics like pinning, sheet flow can give way to fingering as narrow, water-saturated down-flow zones alternate with air-filled up-flow regions within the fracture.  
Conceptualization, L.K.T. and S.K.M.; Formal analysis, L.K.T.; Methodology, L.K.T. and S.K.M.; Software, L.K.T. and S.K.M.; Supervision, S.K.M.; Validation, L.K.T. and S.K.M.; Visualization, L.K.T.; Writing-original draft, and S.K.M.; Writing, review and editing, L.K.T. and S.K.M. All authors have read and agreed to the published version of the manuscript.  
Research funded by Melbourne School of Engineering, University of Melbourne (Melbourne Research Scholarship).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 3 SD-BROV: An SDX-Based BGP Route Origin Validation
### Building Concepts
#### 3.1.2 RPKI Cache/Validator  
Since RPKI-based origin validation relies on the widespread facilities [3], the global RPKI is still in an early stage of deployment. Currently, there is no permanent, single root trust anchor. Several initial tests and deployments have been completed by the RIRs. To validate the origin ASes of BGP announcements, the validation mechanism should contain the following three basic elements:  
* ROA information entries must come from RPKI database.
* Trusted route entries are determined by applied ROAs.
* RPKI cache server (RPKI Validator) has to stitch to RPKI database and router.  
For supporting ROV, the cache server/validator has to query all of the distributed RPKI databases to collect all of the ROAs, trying to validate each entry's signature. To support validation, it replies to each router's request and forwards latest ROAs. In addition, routers do not have to worry about decrypting the certificates of RPKI signature because this part will be handled by cache server/validator initially.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 3 Milling Experimental Validation
### Experimental Setup  
As shown in Figure 4, the robotic milling system is composed of a self-designed robot, a spindle, and a cutting tool. The robot is structured with Y-series harmonic reducers, offering six degrees of freedom (6-DoF). The robot exhibits a positioning accuracy of 0.12 mm and a positioning repeatability of 0.018 mm. The spindle operates at a maximum rotational speed of 40,000 r/min. Figure 4 illustrates the selection of points for the milling experiment, and the coordinates of each reference point are detailed in Table 1. During the milling process, the robot performs the milling operation at different redundancy angles at each point in the \(y\)-axis direction. The machining parameters are listed in Table 2.  
Figure 4: Experimental setup.  
In this section, a carbide flat-end milling cutter with integral carbide is used, and wear is neglected. The cutter has a diameter of 6 mm, 3 teeth, a helix angle of 45\({}^{\circ}\), and a clamped tool overhang of 25 mm. The workpiece is made of aluminum alloy 7075-T6, with dimensions of 100 mm \(\times\) 80 mm \(\times\) 60 mm. The cutting force coefficients obtained after calibration are \(K_{rc}=1240.8\) N/mm\({}^{2}\), \(K_{tc}=1661.8\) N/mm\({}^{2}\).  
The robot is tested through 15 sets of impact modal tests, each performed under 15 different postures within the machining plane to capture the frequency response distribution characteristics. Figure 4 illustrates the position distribution and numbering. The impact tests utilize the WX-HM03 hammer with a sensitivity of 0.0814 mV/N, along with a three-axis accelerometer (PCB LW22652), featuring a sensitivity of 10.16 mV/m/s\({}^{2}\). These instruments are integrated into the DHDAS dynamic signal acquisition system, with an IPEE input mode for both the hammer and accelerometer. The test results are obtained using a multi-point response single-point force measurement method. Additionally, for monitoring the milling process, a three-axis accelerometer (PCB LW29042) with a sensitivity of 10.23 mV/m/s\({}^{2}\) is attached to the spindle.
# VulPathsFinder: A Static Method for Finding Vulnerable Paths in PHP Applications Based on CPG
## 5 Evaluation
### Summary of Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**\#Projects**} & \multicolumn{6}{c}{**VulPathsFinder**} & \multicolumn{6}{c}{**OOPivy**} & \multicolumn{6}{c}{**RIPS**} & \multicolumn{6}{c}{**Jeon-Pipher**} \\ \cline{3-19}  & & _TP_ & _PP_ & \(P\) & \(R\) & \(F\) & \(T\) & _TP_ & _PP_ & \(P\) & \(R\) & \(F\) & \(T\) & _TP_ & _PP_ & \(R\) & \(F\) & \(T\) & _TP_ & _PP_ & \(P\) & \(R\) & \(F\) & \(T\) & _TP_ & _PP_ & \(P\) & \(R\) & \(F\) & \(T\) \\ \hline \multirow{9}{*}{**Non-MVC**} & SQL Injection & 4 & 4 & 3 & 0.56 & 1.00 & 0.72 & 138 & 0 & 0 & 0.00 & 0.00 & - & 3 & 0 & 0 & 0.00 & - & 30 & 4 & 3 & 0.57 & 1.00 & 0.73 & 201 \\  & Command Injection & 1 & 1 & 2 & 0.33 & 1.00 & 0.50 & 17 & 0 & 0 & 0.00 & 0.00 & - & 1 & 0 & 0.00 & 0.00 & - & 23 & 1 & 2 & 0.33 & 1.00 & 0.50 & 38 \\  & Cross-Regit & 1 & 1 & 3 & 0.25 & 1.00 & 0.40 & 24 & 0 & 0 & 0.00 & 0.00 & - & 2 & 0 & 0.00 & 0.00 & - & 38 & 1 & 3 & 0.25 & 1.00 & 0.40 & 50 \\  & Fire Read & 1 & 1 & 1 & 0 & 1.00 & 1.00 & 1.00 & 51 & - & - & - & 1 & 0 & 0 & 0.00 & 0.00 & - & 67 & 1 & 0 & 1.00 & 1.00 & 1.00 & 59 \\  & Fire Upload & 1 & 1 & 0 & 1.00 & 1.00 & 1.00 & 50 & - & - & - & - & 1 & 0 & 0 & 0.00 & 0.00 & - & 65 & 1 & 0 & 1.00 & 1.00 & 1.00 & 58 \\  & Code Execution & 2 & 2 & 0 & 1.00 & 1.00 & 1.00 & 29 & 0 & 0 & 0.00 & 0.00 & - & 3 & 0 & 0 & 0.00 & 0.00 & - & 25 & 2 & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 53 \\ \hline \multirow{9}{*}{**MVC-Based**} & SQL Injection & 4 & 4 & 1 & 0.80 & 1.00 & 0.89 & 19 & 0 & 0 & 0.00 & 0.00 & - & 3 & 0 & 0 & 0.00 & 0.00 & - & 42 & 1 & 0.67 & 0.50 & 1.00 & 172 \\  & Command Injection & 1 & 1 & 1 & 0.30 & 1.00 & 0.67 & 19 & 0 & 0 & 0.00 & 0.00 & - & 1 & 0 & 0 & 0.00 & - & 36 & 0 & 0 & 0.00 & 0.00 & - & 34 \\  & Cross-Regit & 1 & 1 & 1 & 0.30 & 1.00 & 0.67 & 23 & 0 & 0 & 0.00 & 0.00 & - & 2 & 0 & 0 & 0.00 & 0.00 & - & 47 & 1 & 1 & 0.50 & 1.00 & 0.67 & 39 \\  & Fire Read & 1 & 1 & 0 & 1.00 & 1.00 & 1.00 & 51 & - & - & - & - & 1 & 0 & 0 & 0.00 & - & 73 & 0 & 0 & 0.00 & - & 38 \\  & Fire Upload & 1 & 1 & 1 & 0 & 1.00 & 1.00 & 52 & - & - & - & - & - & 1 & 0 & 0 & 0.00 & 0.00 & - & 69 & 0 & 0 & 0 & 0.00 & - & 37 \\  & Code Execution & 2 & 2 & 0 & 1.00 & 1.00 & 1.00 & 30 & 0 & 0 & 0.00 & 0.00 & - & 2 & 0 & 0 & 0.00 & 0.00 & - & 39 & 0 & 0 & 0.00 & 0.00 & - & 27 \\ \hline \multicolumn{9}{c}{**Geometric Mean**} & 0.76 & 1.00 & 0.86 & 40.36 & - & - & 0.00 & 0.00 & - & 1.57 & - & - & 0.00 & 0.00 & - & 42.99 & - & - & 0.58 & 0.71 & 0.94 & 57.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results of vulnerability. T denotes the running time (s); _TF_, _PP_, \(P\), \(R\), and \(F\) are the same as described in Section 5.2; \(a\) dash means the tool does not support the detection of the vulnerability.
## 2 Design Process Using Lumped-Parameter Method (LPM)
### Optimizing the Design Process by a Genetic Algorithm and Requirement for Machine Design  
The performance of the motor was calculated through magnetic-circuit analysis of the LPM, while the optimized candidate model could be obtained through the optimization model selection process in Figure 5. First, an initial design shape variable and its allowable range were set, and values within the set range were randomly selected to generate first-generation candidate models. Second, the probability of realizing the shape of the generated candidate model was determined, and motor performance was calculated through the magnetic-circuit analysis of the LPM on the basis of design variables; third, the objective function value of Equation (21) was calculated. As a result of the first-generation model having the minimal objective function value calculated, the next-generation models were created by recombining the design variable values, and the above process was repeated to derive an optimized candidate model. To evaluate the fault-tolerance of the machine, the terms of power factor and torque ripple were not included in the objective function Equation (21).  
\[O.F=\sqrt{k_{1}(1/Efficiency)^{2}+k_{2}(Material\ cost/Initial\ cost)^{2}},\ k_{1}+k_{2}=1 \tag{21}\]  
Equation (21) is composed of efficiency and cost terms, and \(k_{1}\) and \(k_{2}\) were set to 0.6 and 0.4 as weights for each term, respectively. These weights were calculated as ideal for deriving the optimized shape in which the objective function value was the minimum with respect to the calculated efficiency, with the cost value of  
Figure 4: Calculation process for D-axis inductance in the case of a nonlinear MEC.  
Figure 5: Flowchart describing the optimization process using LPM.  
denominator in the equation. The material cost included in the objective function was set as a per-unit value (p.u/kg) relative to the unit cost per weight of each material constituting the motor. The total cost was then calculated by multiplying weights by unit cost per weight for each material calculated through LPM, where the unit cost per weight was set as iron to be 1, copper to be 6, and PM to be 60.  
To minimize the possibilities of faults in the motor and high manufacturing cost, a concentrated winding armature and a PMa-SynRM rotor structure were adopted. Table 1 shows the design conditions of the down-scaled 3 kW PMa-SynRM proposed in this paper. Here, an optimal design was calculated based on minimum output torque of 16 Nm, efficiency of 95% at a base speed of 1800 r/min, and rated current of 10 Arms.
## 2 Synchronization Design and Dynamic Chaos-Based Random Key Generators
### Chaos Synchronization Design
Therefore, the state responses of the controlled slave Henon map (2) can fully track the random chaotic behavior of master Henon map (1) in the sliding manifold (s(\(k\)) = 0). After discussing the synchronization under the condition of s(\(k)=0\), the sliding mode controller \(u(k)\) is given as follows:  
\[u(k)=-[\alpha e_{1}(k)+(\beta-x_{2}(k)-y_{2}(k))e_{2}(k)-0.1e_{3}(k)]+\delta s (k) \tag{6}\]  
Figure 1: Strange attractors of the master Henon map. where \(|\delta|<1\). If the sliding mode controller is given as Equation (6), the following equation can be obtained:  
\[ \begin{array}{ll}s(k+1)&=\alpha e_{1}(k)+(\beta-x_{2}(k)-y_{2}(k))e_{2}(k)-0.1e _{3}(k)+u(k)\\ &=\delta s(k)\end{array}  \tag{7}\]  
According to Equation (7), \(s(k+1)=\delta s(k)\). Because \(|\delta|<1\) was specified, the trajectory could be forced to hit and operate in the sliding manifold (\(s(k)=0\)). In addition, the state behavior of the master-slave Henon maps would be synchronized and would have the same chaotic dynamics as in Figure 1.  
Next, the simulation tool of MATLAB was used to examine the control design discussed above. In this simulation, the initial conditions were selected as \(x(0)=\left[ \begin{array}{rrrr}1.5&-0.3&0.4\end{array} \right]^{T}\) and \(y(0)=\left[ \begin{array}{rrrr}-0.3&-1.1&0.8\end{array} \right]^{T}\). \(\alpha=0.1\) and \(\beta=0.3\) were selected, such that the eigenvalues of \(A\) were \(\lambda_{1}=0.6\), \(\lambda_{2}=-0.5\), and \(\delta=0.5\). Therefore, the sliding surface could be constructed as follows:  
\[s(k)=e_{1}(k)+0.1e_{2}(k)+0.3e_{3}(k) \tag{8}\]  
According to Equation (6), the controller was designed as follows:  
\[u(k)=-[0.1e_{1}(k)+(0.3-x_{2}(k)-y_{2}(k))e_{2}(k)-0.1e_{3}(k)]+0.5s(k) \tag{9}\]  
Figures 2-4 show the simulation results. Figure 2 shows the error state with the proposed control input (Equation (9)). Figure 3 shows the state responses of the controlled systems. The responses of the switching function of \(s(k)\) and the sliding mode controller \(u(k)\) were, respectively, given in Figure 4. By observing the simulation results, it was revealed that the trajectory of the controlled systems could be forced to the sliding manifold of \(s(k)=0\); the error state responses between the master-slave chaotic system converged to zero, as expected; and the controlled master-slave system reached exact synchronization. Figure 3: Time responses of controlled states. Figure 2: Time responses of error states.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Measurement of RDS, SDS, RS, and TS Contents  
The moisture percentage was determined using the oven-drying method (AOAC 940.26) with a laboratory dryer (SML 30/250, Zalmed, Warszawa, Poland). Rapidly digestible starch (RDS), slowly digestible starch (SDS), resistant starch (RS), and total starch (TS) contents were determined using commercial kits (Megazyme International Ireland, Bray Business Park, Bray, Co., Wicklow, Ireland) with the application of AOAC standards [38; 39]. In summary, the samples were incubated in a shaking water bath (MEMMERT WNE 14, Schwabach, Germany) with a mixture of pancreatic \(\alpha\)-amylase and amyloglucidase (PAA (40 KU/g)/AMG (17 KU/g)) in maleate buffer, pH 6.0, at 37 \({}^{\circ}\)C for up to 4 h with continual stirring to hydrolyse the starch into \(D\)-glucose. A total of 1.0 mL of aliquots of the reaction solution was removed at 20, 120, 180, and 240 min. To terminate the reaction, the aliquots were transferred to 20 mL of 50 mM acetic acid. These solutions were thoroughly mixed, and 0.1 mL aliquots were incubated with 0.1 mL of AMG (100 U/mL) to hydrolyse the remaining traces of maltose to glucose, which were measured with glucose oxidase/peroxidase reagent (GOPOD) on a spectrophotometer (SPECORD 40, Analytik Jena GmbH, Jena, Germany) at 510 nm. To measure RS, a 4 mL aliquot was removed from the stirring solution after 240 min, added to 4 mL of aqueous ethanol (50% \(v/v\)), and thoroughly mixed. The samples were centrifuged (MPW MED. INSTRUMENTS 351e, Warszawa, Poland), and the pellet was washed with aqueous ethanol to remove free glucose and then suspended in sodium hydroxide to dissolve RS. The solution was neutralised, starch was hydrolys to glucose with AMG, and the glucose was measured with a GOPOD reagent. All values were calculated based on dry matter (g-100 g\({}^{-1}\) d.m.).
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 3 Proposed Method
### Running the CSP Algorithm on a Set of Cluster Heads  
In this section, the CSP algorithm runs on a set of cluster heads. First of all, the network is divided into two distinct clusters. Then, each cluster is divided into smaller clusters. This process will be continued since the number of clusters \((k)\) is obtained.  
In each phase of the CSP algorithm, this algorithm selects the cluster head that has the farthest distance from other cluster heads. This selection ensures the coverage of all of the nodes in the network [26]. Furthermore, for the same load distribution on the cluster head, the clustering technique in [29] is used. Based on this technique, each cluster is decomposed until the number of achieved cluster heads is approximately equal. Therefore, the four steps of this algorithm are described as follows:1. In cluster head sets, two nodes are selected with the farthest distance from each other.
2. Based on the distance, all remaining nodes (other members of cluster heads and nodes) from two selected cluster heads are located in a closer cluster head.
3. The two cluster head sets are the same when they have approximately the same number of nodes. As a result, two sub-clusters are formed.
4. According to Step 3, cluster head sets are divided into smaller sets.  
After categorizing sensor nodes and selecting the cluster heads, one of the most important issues that should be taken into consideration is to establish the best connection between cluster heads for transferring data to the base station. Sending data to base stations consumes much energy, whereas, in normal conditions, the nearest cluster head is always selected to transfer data. Thus, as shown in Figure 4, the energy load can be divided by the random selection of the cluster head and the minimum spanning tree [30]. Finally, the base station determines a scheduled program for other nodes to send the data to their cluster head.
## 2 System Design
### Data Releasing Module
#### 2.5.1 Text Message Releasing  
The water-level data can be obtained by text message and the modality of obtaining data can be controlled in the RWMS. It is very practical where the Internet is inaccessible. The real-time data can be obtained and the modality of obtained data can be modified conveniently by the user by sending a pre-set text message to the mobile phone number bound to the GPRS/GSM module. Users can also receive data, set the time interval of data transfer and cancel the mobile phone number bound and so on by sending messages. Any spam messages can be blocked in the ZKOS by defining the password with a short message.
# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Data Mining
The purpose of the application is to help hospitals to decide on the patient priority order when the system is submerged and avoid delays in dispensing to patients the needed care. COVID-19 has had a lot of interest from the medical informatics community since its emergence. Deep learning algorithms are widely used as in [29] where they are combined with CT images to screen for COVID-19. The authors proposed a deep learning algorithm framework to extract specific graphical features from CT images and propose a clinical diagnosis in front of the pathogenic test. The ultimate objective is saving critical time for disease control. To estimate and predict mortality caused by COVID-19 in real time, a new methodology is reported in [29]. This methodology consists of the Patient Information Based Algorithm (PIBA), using available data gathered during an epidemic. To evaluate the methodology, the authors used preliminary data from China, the algorithm was successfully applied to estimate the death rate and predict the death numbers in the Korean population. According to the authors the algorithm can be used to estimate the death rate of a new infectious disease in real-time and to predict future deaths. Another interesting work proposed an automatic detection system as a fast alternative diagnosis option to prevent the spread of COVID-19 [30]. The approach consists of using three different convolutional neural network-based models (ResNet50, InceptionV3, and InceptionResNetV2) for the detection of a coronavirus pneumonia infected patient using chest X-ray radiographs. Among the results of this work is that chest X-ray images are a very good tool for the detection of COVID-19.
## 6 Analysis and Discussion of the Results
#### 6.1.2 CO Level  
Figure 7 shows that the BEB fire was found to cause higher CO levels downstream of the burning bus compared to the ICEB, in both cases of ventilation investigated.  
Figure 6: Spatial distributions downstream of the fire related to the HF concentration at the height of breathing along the escape route toward Portal \(B\) for both BEB and ICEB fires, in both cases of ventilation investigated.  
With reference to the natural ventilation, Figure 7 shows that during the BEB fire, the CO levels slightly exceed the threshold value of 1200 ppm [44] at a distance between 150 m and 175 m downstream of the fire center, achieving a peak value of approximately 1240 ppm at about 170 m away from the fire center. The CO concentrations produced by the burning ICEB never reach 1200 ppm, with a maximum value of about 1050 ppm detected at approximately 200 m downstream of the fire center. From Figure 7, it is also possible to observe that the last user exiting the tunnel through Portal \(B\) may encounter CO concentrations above the tenability limit only during the burning of the BEB, which might contribute to the worsening of their health conditions.  
Regarding the case of the mechanically ventilated tunnel, Figure 7 shows how the fire involving both BEB and ICEB leads to CO concentrations above 1200 ppm only in the vicinity of the burning vehicle, with a peak value, measured at about 20 m away from the fire center, that was found to be much higher for the combustion of the BEB rather than the ICEB (i.e., approximatively 2615 ppm against about 1990 ppm). Figure 7 also shows how the last user evacuating toward Portal \(B\) is affected by CO levels below the acceptability limit during the fire of both the BEB and the ICEB.