esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Correlation Analysis of Landslide and Conditioning Factors Using the CF Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditioning Factors** & **Classes** & **Percentage of Landslide** & **Percentage of Domain** & **CF** \\ \hline \multirow{8}{*}{Elevation (m)} & 933–1000 & 2.17 & 1.14 & 0.476 \\  & 1000–1100 & 22.28 & 13.38 & 0.400 \\  & 1100–1200 & 33.15 & 28.22 & 0.149 \\  & 1200–1300 & 29.35 & 31.06 & \(-\)0.055 \\  & 1300–1400 & 9.24 & 20.45 & \(-\)0.548 \\  & 1400–1500 & 3.26 & 5.57 & \(-\)0.415 \\  & 1500–1574 & 0.54 & 0.18 & 0.661 \\ \hline \multirow{8}{*}{Slope (\({}^{\circ}\))} & \(<\)10 & 0.00 & 10.44 & \(-\)1.000 \\  & 10–20 & 30.43 & 26.09 & 0.143 \\  & 20–30 & 36.96 & 35.14 & 0.049 \\  & 30–40 & 26.09 & 23.90 & 0.084 \\  & 40–50 & 6.52 & 4.41 & 0.324 \\  & \(>\)50 & 0.00 & 0.02 & \(-\)1.000 \\ \hline \multirow{8}{*}{Aspect (\({}^{\circ}\))} & F (\(-\)1) & 0.00 & 0.05 & \(-\)1.000 \\  & N (0–22.5;337.5–360) & 7.61 & 9.25 & \(-\)0.177 \\  & NE (22.5–67.5) & 6.52 & 13.16 & \(-\)0.504 \\  & E (67.5-112.5) & 17.39 & 16.34 & 0.060 \\  & SE (112.5–157.5) & 13.59 & 11.26 & 0.171 \\  & S (157.5-202.5) & 14.67 & 10.14 & 0.309 \\  & SW (202.5–247.5) & 16.85 & 12.77 & 0.242 \\  & W (247.5–292.5) & 17.93 & 15.44 & 0.139 \\  & NW (292.5–337.5) & 5.43 & 11.59 & \(-\)0.531 \\ \hline \multirow{8}{*}{Plan curvature (100/m)} & (\(-\)9.24)–(\(-\)1.79) & 2.72 & 5.38 & \(-\)0.495 \\  & (\(-\)1.79)–(\(-\)0.54) & 15.76 & 17.98 & \(-\)0.124 \\  & (\(-\)0.54)–0.38 & 45.11 & 42.08 & 0.067 \\  & 0.38–144 & 25.54 & 26.34 & \(-\)0.030 \\  & 1.44–7.56 & 10.87 & 8.22 & 0.244 \\ \hline \multirow{8}{*}{Profile curvature (100/m)} & (\(-\)7.29)–(\(-\)1.65) & 7.07 & 8.08 & \(-\)0.126 \\  & (\(-\)1.65)–(\(-\)0.46) & 15.76 & 24.10 & \(-\)0.346 \\ \cline{1-1}  & (\(-\)0.46)–0.58 & 41.85 & 39.33 & 0.060 \\ \cline{1-1}  & 0.58–1.97 & 29.35 & 21.23 & 0.277 \\ \cline{1-1}  & 1.97–9.45 & 5.98 & 7.26 & \(-\)0.177 \\ \hline \multirow{8}{*}{STI} & 0–10 & 44.02 & 48.27 & \(-\)0.088 \\ \cline{1-1}  & 10–20 & 33.70 & 30.96 & 0.081 \\ \cline{1-1}  & 20–30 & 15.22 & 11.21 & 0.263 \\ \cline{1-1}  & 30–40 & 3.26 & 4.22 & \(-\)0.227 \\ \cline{1-1}  & \(>\)40 & 3.80 & 5.34 & \(-\)0.287 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship between landslides and conditioning factors using the certainty factor (CF) method.
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides
### Template-Assisted Solution-Phase Synthesis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials** & **Approach** & \(\sigma\) **(S cm\({}^{-1}\))** & **S** & \(\kappa\) **(uV K\({}^{-1}\))** & **ZT\({}_{\text{max}}\)** & **T (K)** & **Ref.** \\ \hline Bi\({}_{2}\)Te\({}_{3}\) nanostrings & HT + HP & 5.1 & \(-\)172 & 0.29 & 0.016 & 298 & [51] \\ Y-doped Bi\({}_{2}\)Te\({}_{3}\) & HT + HP & 1.180 & \(-\)150 & 0.85 & 1.23 & 410 & [65] \\ nanopowders & HT + PS & 370 & \(-\)152 & 0.5 & 0.7 & 453 & [56] \\ La-doped Bi\({}_{2}\)Te\({}_{3}\) & HT + HP & 62 & \(-\)133 & 0.92 & 0.58 & 480 & [64] \\ nanofowders & & & & & & & \\ Ce-doped Bi\({}_{2}\)Te\({}_{3}\) & HT + HP & 770 & \(-\)146 & 0.5 & 1.29 & 398 & [62] \\ nanosheets & HT + SPS & 1000 & 101 & 1.2 & 0.62 & 773 & [67] \\ Cu\({}_{2}\)Se nanoplates & HT + HP & 250 & 215 & 0.8 & 1.1 & 773 & [61] \\ nanosheets & HT + HP & 180 & \(-\)135 & 0.35 & 0.43 & 473 & [53] \\ Bi\({}_{2}\)Te\({}_{3}\) & ST + SPS & 480 & \(-\)198 & 0.72 & 1.23 & 480 & [57] \\ Cu\({}_{2}\)Se nanoplates & ST + SPS & 100 & 290 & 0.3 & 1.82 & 850 & [63] \\ Sb-doped SnSe & ST + SPS & 39.4 & 247 & 0.17 & 1.1 & 773 & [60] \\ micropolates & & & & & & & \\ SnSe nanobelts & ST + HP & 22.2 & 350 & 0.25 & 0.83 & 803 & [55] \\ Bi\({}_{2}\)Te\({}_{3}\) nanowires & TA + HT & 13.3 & \(-\)74 & 0.05 & 0.005 & 300 & [68] \\ Bi\({}_{2}\)Te\({}_{3}\) nanowires & TA + SPS & 425 & \(-\)250 & 1.0 & 0.96 & 380 & [69] \\ Cu\({}_{2}\)Se nanoparticles & CRP + SPS & 3000 & 37.5 & 0.75 & 0.3 & 673 & [70] \\ SnSe nanoparticles & CRP + SPS & 30 & \(-\)305 & 0.5 & 0.4 & 573 & [70] \\ Cu\({}_{2}\)Se nanopowders & CRP + HT & 333 & 140 & 0.75 & 1.35 & 900 & [71] \\ Bi\({}_{2}\)Te\({}_{3}\) nanowires & ED + HT & 2000 & \(-\)70 & 0.75 & 0.9 & 300 & [72] \\ Bi\({}_{2}\)Te\({}_{3}\) films & ED & 666 \({}^{\text{a}}\) & \(-\)58 \({}^{\text{a}}\) & 1.2 \({}^{\text{a}}\) & 0.056 \({}^{\text{a}}\) & \\ Bi\({}_{2}\)Te\({}_{3}\) films & ED & 3200 \({}^{\text{b}}\) & \(-\)50 \({}^{\text{b}}\) & 2.4 \({}^{\text{b}}\) & 0.104 \({}^{\text{b}}\) & 300 & [73] \\ S-doped SnSe nanosheets & LE & 4.76 & 560 & 0.4 & 0.12 & 310 & [44] \\ \hline \hline \end{tabular}  
* In-plane thermoelectric properties of electrodeposited films. \({}^{\text{b}}\) Out-of-plane thermoelectric properties of electrodeposited films. Note: HT\({}^{-}\) hydrothermal method, HP\({}^{-}\)hot press, PS\({}^{-}\)pressure sintering, SPS\({}^{-}\)spark plasma sintering, ST\({}^{-}\)solvothermal method, TA\({}^{-}\)template-assisted solution-phase synthesis, CRP\({}^{-}\)chemical reduction\({}^{-}\)precipitation method, ED\({}^{-}\)electrodeposition method, LE\({}^{-}\)liquid exfoliation method.  
\end{table} 
Table 1: Summary of the thermoelectric properties of nanostructured metal chalcogenides synthesized by various solution-based methods.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 4 Discussion
Therefore, the strength increase of mortar (13.27%-15.45%) caused by drying is not as pronounced as that of cement paste (23.91%-25.73%), as shown in Figure 4.  
Concrete scale is composed of mortar, coarse aggregate, and ITZ between mortar and coarse aggregate. The introduction of coarse aggregate with higher stiffness not only results in the smaller deformation of concrete than mortar and cement paste in drying process (Figure 6c), but also results in the more complicated ITZ with a higher volume content. Similarly, when subjected to compression loading, failure of concrete results from the propagation, and coalescence of microcracks, which depends to a large extent on the performance of ITZ. For dry concrete, the harmful effect to strengthdue to the weakened bonding between coarse aggregate and mortar is greater than mortar. Therefore, the saturation effect on compressive strength of concrete is less pronounced (4.23%-5.26%) than that of mortar (13.27%-15.45%), as shown in Figure 4.  
From above analysis, it can be seen that the drying effect on compressive strength is different at three scales. On one hand, the denser microstructure and further cement hydration caused by drying promote the strength increase. On the other hand, the porosity increase and the coarsened pore structure, as well as the weakened bonding between paste and aggregate, would cause strength reduction. The drying effect on compressive strength of concrete is the result of competition between the strengthening effect and the weakening effect. From cement paste to mortar and concrete, the drying-strengthening effect on strength decreases with the decrease of paste content. The authors analyzed some experimental results from other researchers and found that the difference in compressive strength between dry and saturated concrete at different scales was closely related to the paste content. Results from the literature [4; 9; 12; 24; 31; 32; 33], as well as our test data, are plotted in Figure 10. It is evident that the drying effect on compressive strength decreases significantly with the decrease of paste content. Figure 10: Relative compressive strength versus mass fraction of paste. (\(\div\) represents the data from molecular simulation of C–S–H. C–S–H accounts for 60%–70% of hydration products, here it takes the middle value of 65%. So preparing a unit mass of C–S–H requires 1.54 unit mass of cement paste.) Figure 9: Microcrack growth under compression loading.
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
## 5 Future Research Lines and Applications  
This study demonstrates the potential of AM for designing and producing high-performance monopulse antennas with complex internal geometries, especially in the mm-Wave band. By applying AM techniques, particularly LPBF, to the development of antenna systems, a range of new research directions and applications can be pursued. This  
Figure 9: Near-field measurements at 57 mm of difference pattern horn array prototype: (**a**) amplitude and (**b**) phase at 34 GHz, (**c**) amplitude and (**d**) phase at 35 GHz, and (**e**) amplitude and (**f**) phase at 36 GHz.  
section outlines potential areas for future exploration and improvement, focusing on the capability of AM to facilitate complex waveguide-based designs, produce high-precision difference patterns for monopulse applications, and create scalable, modular antenna arrays suitable for advanced applications.
## 5 Conclusions  
In this paper, a novel scheme is proposed for reducing the communication BER by mitigating the tailing effect in LED light sources under OOK modulation. The tailing phenomenon, which is a significant issue affecting the performance of UV links, leading to signal fluctuations at the receiver and thereby increasing the system BER, especially evident during high-speed communication, is considered. The impact of path loss and tailing effects of the light source on the performance of UV communication systems is analyzed, and the probability density functions (PDFs) of signals sent and received (\(\lambda_{\text{s}}\) and \(\lambda_{\text{b}}\)) are derived. Utilizing these derivations, an analysis of the error phenomena caused by tailing is conducted, and a UV communication scheme is proposed based on adjusting the duty cycle to reduce the BER.  
Closed-form expressions for the signal transmission BER are first derived based on a single-scattering model, along with expressions for the Poisson distribution based on the probability of UV photons. Simulation and experimental analyses of the proposed UV system are then conducted based on these expressions. Simulation and experimental results demonstrate that for higher communication rates, the tailing effect significantly impacts the BER, whereas for lower rates, the BER remains relatively insensitive. This is because the tailing effect is determined by the LED response characteristics and manifests as a fixed value over time, with higher communication rates acting on individual symbols for shorter durations, thus resulting in greater tailing effect influence. Further analysis is conducted to determine the optimal duty cycle settings for communication rates sensitive to the tailing effect, concluding that for rates of 150 k, the optimal duty cycle setting is 48%; for 200 k, it is 47%; and for 300 k, it is 45%. For higher communication rates, adopting lower duty cycle settings can notably reduce the BER.  
In conclusion, the UV communication scheme proposed in this paper, based on altering the signal duty cycle, holds significant importance in reducing the BER and optimizing system performance. Through experimental and simulation analyses, the effectiveness of this scheme is validated, and practical recommendations are provided for different communication rates, offering valuable insights and guidance for the design and optimization of UV communication systems.  
Conceptualization, Y.J. and Y.Z.; methodology, Y.J. and Y.Z.; software, Y.J., Y.Z. and L.K.; validation, Y.J., Y.Z. and R.L.; formal analysis, L.K. and R.L.; writing--original draft preparation, Y.Z.; writing--review and editing, J.N. and Y.J.; project administration, J.L., Y.J. and Y.Z. made equal contributions to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 62231005.  
Not applicable.  
The data are contained within the article.  
The authors declare no conflicts of interest.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
### Inputs of the PV System  
The input to the PV system is the part of the global horizontal radiation that is incident on the panels and is assumed to be converted into electrical energy. This radiation is called global incident radiation \(G_{i}\), and is measured in kWh/m\({}^{2}\). The global incident radiation depends on the global horizontal radiation \(G_{h}\), which is composed of two components, according to [9], as follows:  
\[G_{h}\ =\ D_{h}\ +\ B_{h}\]  
where \(B_{h}\) and \(D_{h}\) are the diffuse and direct beam horizontal radiations in kWh/m\({}^{2}\). The direct beam horizontal irradiation \(B_{h}\) is the radiation that reaches the Earth and that comes from a beam traveling directly from the sun to the Earth without any atmospheric scattering [9]. Conversely, the diffuse horizontal radiation \(D_{h}\) is the sun radiation that hasbeen scattered by particles and molecules of the atmosphere but finally reaches the Earth's surface [9]. These variables are meteorological data available in the databases.  
The global horizontal incident radiation \(G_{i}\) is defined as the amount of global solar radiation incident on the collector plane and is related to the field orientation parameters \(B_{h}\) and \(D_{h}\) by the following relation:  
\[G_{i}\ =\ \frac{cos(\theta_{i})}{cos(\theta_{Z})}B_{h}\ +\ \frac{(1\ +\ \cos( \beta))}{2}D_{h} \tag{1}\]  
where the angle of incidence \(\theta_{i}\) and \(\theta_{Z}\) are evaluated geometrically by the direction of the plane to the sun (declination, latitude, hour angle) and the field orientation (\(\beta,\ \alpha_{z}\)) of the used PV plane.  
The effective global radiation incident on the collector (\(G_{eff}\)) is related to \(G_{i}\) after correction for optical losses due to shades and soiling. Thus, it is considered as an input instead of \(G_{i}\), to take the optical losses into consideration.
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
## 2 Related Work  
### Anomaly Detection Based on Traditional Methods  
The future of intelligent cars must be toward the direction of unmanned information and network connection. The in-vehicle information must be the combination of the multi-node convergence of multiple sensors. However, in-vehicle information security is the barrier for the normal running of self-driving vehicles. Therefore, the establishment of an in-vehicle information anomaly detection system is extremely urgent. In the literature [8], a novel entropy-based attack detection method for in-vehicle networks is presented, which utilized entropy to describe a measure for the uncertainty of a collection of data items. The collected CAN bus messages are pre-treated, and then the information entropy calculation and relative distance measurement are compared to the baseline sample library established in the calibration stage to detect whether there are abnormalities. If there are abnormalities, the alarm will sound. If not, the CAN bus network will not be threatened by attacks during this period. A structured approach [9] is introduced to detect anomaly messages for in-vehicle networks, which allows the recognition of attacks during the operation of the vehicle without causing false positives. A further study [10] proposed an actual attack model using a malicious smartphone app in the connected car environment and demonstrated it through practical experiments, and at the same time designed a security protocol that could be applied to the car environment. Some further studies [11; 12; 13] present a set of broadcast authentication protocols, which can utilize the simplest one-way functions that are computationally efficient while authentication does not depend on disclosure delays as in the case of protocols based on one-way chains and time synchronization. A new intrusion detection algorithm is presented in [14], which is designed to identify malicious CAN messages injected by attackers in the modern vehicle CAN bus. This algorithm can recognize the anomalies in the message sequence of the CAN bus and has the characteristics of small memory and small computation. It is suitable for current bus ECUs. Aiming at the popularization of external interfaces of the vehicle control network, such as the (On-Board Diagnostic) OBD port and auxiliary media port, the technology of vehicle-to-vehicle/vehicle-to-vehicle infrastructure will be realized in the near future. The increasing attack area of vehicles exposes the control network to life-threatening attacks. A method [15] is proposed to detect the abnormal flow pattern by detecting the abnormal refresh rate of some commands, so as to meet the need of protecting the CAN bus.
# A Cognitive Diagnosis Model Using Convolutional Neural Networks to Predict Student Scores
## 1 Introduction
This means the training questions selected by each student are inconsistent, resulting in the model's dependence on the trained questions, that is, the model has a good predictive effect on the trained questions (known questions) and a poor predictive effect on the untrained questions (unknown questions). The reality is often that the results of some questions (such as the student's usual quizzes) are known and the results of other questions (such as the student's final exam questions) are predicted. In the face of this situation, the performance of previous cognitive diagnostic models will be reduced due to their lack of training for unknown questions. To solve the above problems, this paper proposes a cognitive diagnosis model based on convolutional neural networks. First of all, in the selection of questions, inspired by [PERSON] [8], the questions are divided into usual quizzes \(E_{1}\) and final exam questions \(E_{2}\), whose correlation matrix with knowledge points is represented by \(q_{1}\) and \(q_{2}\), respectively. Students are awarded their usual grades \(R_{usual}\) by answering the usual quizzes. The model combines two multi-layer (general and special) one-dimensional convolutional neural networks to model the process from students' usual grades \(R_{usual}\) to students' mastery of knowledge points \(\alpha\) to students' answers to the final exam questions \(R\). The process is shown in Figure 2. At the same time, an attention mechanism further improves the prediction accuracy of the model. The main work is as follows:  
1. Two multi-layer (general and special) one-dimensional convolutional neural networks are combined, that is, the convolutional kernel of the special one-dimensional convolutional neural network is generated by the ordinary one-dimensional convolutional neural network, so as to model the cognitive diagnosis process of students and the prediction process of final exam results. 2. The attention mechanism is used to further improve the prediction accuracy of the model by considering the influence of the intra-level relationships among students, questions and knowledge points on students' answer results. 3. On the public datasets FrcSub [5], Math1 [5] and Math2 [5] and the self-built BOIT dataset, the proposed CNNCD model and some classical cognitive diagnosis models are compared and analyzed through experiments, and the three indexes of Accuracy [9], Root Mean Square Error (RMSE [10]) and Mean Absolute Error (MAE [10]) are evaluated, respectively. The experimental results show that the proposed model is superior to the comparison models, which indicates the feasibility and effectiveness of the proposed model. Figure 1: The previous cognitive diagnosis process diagram. Figure 2: Cognitive diagnosis process diagram in this paper.
## 4 Experiments
### Qualitative Evaluation
#### 4.5.2 Qualitative Evaluation of Public Dataset UNIV  
This section presents the visual representation of the model's prediction outcomes in real-world settings. Figure 9 illustrates the selection of five sample study scenarios. They cover the complex interaction behaviors of human beings in real life.  
Scenario (a) is a person following two people in front of him, and the predicted results of our model are consistent with the true values, demonstrating our model's ability to perceive pedestrian interactions. Scenario (b) is a pedestrian walking opposite to four other people--for this situation, where a collision may be traveling, the model can sense the pedestrian interaction, so the output of the model is consistent with the real situation.  
In Scenario (c), two pedestrians are seen strolling side by side. The model is capable of capturing the relative locations of these two individuals and making precise predictions about their future positions. Scenario (d) is a group of people walking relative to each other, and our model also shows strong performance in the face of this highly collision-prone situation. Scenario (e) involves a cluster of five individuals congregating in a certain direction. By detecting the convergence of pedestrians from that direction, the model effectively forecasts the future paths of each individual pedestrian inside the group.  
The above experiments show that our model exhibits strong predictive performance in both simulation datasets and real-world datasets.  
Figure 8: The model’s trajectory prediction results in various scenarios are as follows: scenario (**a**) represents one person following two people; scenario (**b**) represents one person meeting a group of pedestrians; scenario (**c**) represents three people walking in parallel; scenario (**d**) represents a group of pedestrians moving in the opposite direction; scenario (**e**) represents a group of pedestrians gathering in the same direction; and scenario (**f**) represents a situation where four people are walking side by side, two towards the gate and two towards the exit.
## 3 Measurement Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **IEEE JSSC 2021** & **IEEE ISSCC** & **App.Sci 2020 [8]** & **IEEE ICMSP** & **IEEE SSC 2019** & **IEEE NEWCAS** & **This Work** \\  & [4] & **2021 [5]** & **App.Sci 2020 [8]** & **2021 [11]** & **[30]** & **2022 [31]** & **This Work** \\ \hline Process (\(\mu\)m) & 0.18 & 0.18 & 0.18 & 0.18 & 0.18 & 0.028 & 0.18 \\ \hline Supply voltage (V) & 1.2 & 1.2 & 1.8 & 1.8 & 5 & 1.8 & 1.8 \\ \hline Current & & & & & & & \\ consumption (\(\mu\)A) & 20.6 & 23.1 & 10 & 1.7 & 6.2 & 2.5 & 13.2 \\ \hline \multirow{2}{*}{\begin{tabular}{c} CMI reduction \\ structure \\ \end{tabular} } & Single & Single & Feedback current & & & & Window \\  & CMCP & CMCP & & generator & & & - \\ \hline Tolerance to CMI (V) & 15 & 30 & - & & (respectively) & 2.8 & \
\begin{tabular}{c} CND-VDD \\ (respectively) \\ \end{tabular} & 20 \\ \hline Input referred noise & 1.67 & 5.05 & 2.68 & 2.96 & 3.7 & 10 & 0.59 \\ (\(\mu\)Voss) & (1–100 Hz) & (1–100 Hz) & (1–100 Hz) & (1–10-25 Hz) & (0.5–100 Hz) & (450–9.2 kHz) & (1–100 Hz) \\ \hline Bandwidth (kHz) & 10 & 15 & 0.107 & 0.250 & 20 & 8.75 & 2.7 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} CMRR (dB) \\ \end{tabular} } & 66 & 68 & 105 & N/A & 66 & 78 & 51.4 \\  & (Systematic) & (Systematic) & 105 & N/A & 66 & 78 & (1–10 Hz) \\ \hline NEF 1 & 29.1 & 93.3 & 31.4 & 9.38 & 8.43 & 6.34 & 8.27 \\ \hline \hline \end{tabular}  
* noise efficiency factor (NEF) = V\({}_{\rm{in}}\)/(2–1\({}_{\rm{tot}}\)/\(\pi\)-V\({}_{\rm{T}}\)-4 kT-BW).  
\end{table} 
Table 1: Performance summary between the proposed readout circuit and previous studies. Figure 19: ECG signal acquisition result. (**a**) CMCP disabled; (**b**) CMCP enabled.
## 2 Background
### Failure Mode and Effect Analysis  
FMEA is an inductive analysis method, which is used in the early development process to identify the potential failure modes and their causes of all components in the system [34; 35]. FMEA calculates the effects of the identified failure modes through measurement of the severity, occurrence and detection probability. The analysis starts with the lowest level components and goes on to the effect of the failures on the whole system. At last, countermeasures will be proposed to reduce or minimize the negative effects of the identified failure modes. The steps in usual FMEA are shown in Figure 2.