esa-sceva/satcom-chunk-collection huggingface dataset:
# Threat Modelling and Beyond-Novel Approaches to Cyber Secure the Smart Energy System
## 7 Risk Analysis Using Formal Methods
### Formal Methods Overview
#### 7.1.4 Comparison of Probabilistic Model Checkers  
A comparison of the described model checkers' capabilities, and their technical details are given in Table 6. A comparison of features is presented in Table 7.  
An extensive comparison between the tools UPPAAL and PRISM is given in [80]. The authors used timed automata and a benchmark study for the modeling and verification in both tools. Another comparison of several tools for probabilistic model checking is given in [70]. This study also includes the tools PRISM and STORM. The authors state that STORM is clearly the most versatile tool. STORM covered all formalism considered in the most of competition. The authors conclude that PRISM and STORM tools support the widest range of properties, comparing to wide variety of available tools. Moreover, they have a wide range of algorithms implemented. On the other hand, the authors state that the PRISM is the most commonly used tool in research, due to its extensive online documentation, the graphical user interface and since it is independent on the platform used.  
Considering all discussed aspects in this section, the PRISM tool is selected for the following experiments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **PRISM** & **UPPAAL** & **STORM** \\ \hline Operating System & Windows, Linux, Mac & Windows, Linux, Mac & Linux (min Debian 9, \\  & & & Ubuntu 16.10 ), Mac (min \\ Last Version & version 4.7 (March 2021) & official release 4.0.15 (Nov. 2019) & version 1.6.3 (Nov. 2020) \\ Licence & GNU GPL 2 & free for non-commercial & GNU GPL 3 \\  & & applications in academia & \\ Type of input & PRISM language & XTA and XML & PRISM, JANI, GSPNs, \\ Simulator & yes & yes & no \\ GUI & ✓ & ✓ & ✗ \\ Case Studies & ✓ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of probabilistic model checking tools.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **PRISM** & **UPPAAL** & **STORM** \\ \hline Statistical model checking & ✓ & ✓ & ✗ \\ Probabilistic model checking & ✓ & ✗ & ✓ \\ DTMC & ✓ & ✗ & ✓ \\ CTMC & ✓ & ✗ & ✓ \\ MDP & ✓ & ✗ & ✓ \\ MA & ✗ & ✗ & ✓ \\ PA & ✓ & ✗ & ✗ \\ PTA & ✓ & ✗ & ✗ \\ priced TA & ✗ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Probabilistic model checkers’ feature comparison.
## 2 Seismic Resilience Evaluation Framework
### Direct Seismic Loss Analysis  
By using the above-proposed fragility functions, the probabilities of exceeding the particular damage states can be calculated. Subsequently, the direct seismic losses (\(S_{loss}\)s) of tunnels owing to structural damage after earthquake events can be evaluated using the following equation [27]:  
\[S_{loss}=R_{c}\cdot L\cdot\sum_{j=1}^{n}\big{(}R_{j}\cdot P\big{[}DS_{j}|IM \big{]}\big{)}, \tag{3}\]  
where \(S_{loss}\) is the average estimated cost of repairing the tunnel, given the specific level of earthquake intensity measure, _IM_; \(R_{c}\) stands for the cost of tunnel replacement per meter; \(L\) is the length of the examined tunnels (\(L\) is set as 1 m in this study); the index \(j\) stands for the \(j\)th damage state (\(DS_{j}\)), and the index \(n\) represents the total number of damage states; \(R_{j}\) represents the repair ratio, which is the fraction of \(R_{c}\) necessary to repair the \(j\)th damage state; \(P[DS_{j}|IM]\) represents the probability that the tunnel is located at each damage state \(j\) and seismic intensity level (_IM_), as presented in the following equations based on the fragility functions:  
\[P\big{[}DS_{j}|IM\big{]}=P\big{[}ds>DS_{j+1}|IM\big{]}-P\big{[}ds>DS_{j}|IM \big{]},\text{ when }j=1,2,3, \tag{4}\]  
\[P\big{[}DS_{j}|IM\big{]}=P\big{[}ds>DS_{j}|IM\big{]},\text{when }j=4. \tag{5}\]  
More specifically, the replacement cost of the examined tunnel per meter (\(R_{c}\)) is assumed to be 1,000,000 RMB/m in this study, which is a preliminary estimate of the direct costs based on the construction cost of tunnels in China. The repair ratios (\(R_{j}\)) utilized in this work were put forward by [PERSON] et al. [28] for tunnels, as shown in Table 3. For instance, a minor damage state of the tunnel indicates that the repair cost is equal to 10% of the total replacement cost. An example of using fragility functions under a specific seismic _IM_ to calculate the direct seismic loss of tunnels is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Damage State (_ds_)** & **Range of Damage Index (_DI_)** & **Central Value of _DI_** \\ \hline \(ds_{0}\): none & \(M_{sd}/M_{Rd}\leq 1.0\) & - \\ \(ds_{1}\): minor & \(1.0<M_{sd}/M_{Rd}\leq 1.5\) & 1.25 \\ \(ds_{2}\): moderate & \(1.5<M_{sd}/M_{Rd}\leq 2.5\) & 2.00 \\ \(ds_{3}\): extensive & \(2.5<M_{sd}/M_{Rd}\leq 3.5\) & 3.00 \\ \(ds_{4}\): complete & \(M_{sd}/M_{Rd}\geq 3.5\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on damage states and corresponding damage indexes [26].
## 3 Methodology
### Failure Classification Using Machine Learning Techniques (Step 3)  
Given its effectiveness, as pointed out by researchers in [33], we developed a Random Forest (RF) model to classify the failure type. RF is a machine learning technique that creates an ensemble of decision trees during training and then makes predictions based on decisions from those trees. For classification, each tree \"votes\" a class for a specific input [6,10,27,33].  
Figure 2 illustrates the process of RF training, which considers the most important input variables, defined by the feature selection technique, to produce the output indicating the type of failure after each tree votes for one of the six possible failure classes. The model training was conducted with 50, 100, and 200 estimators (trees), separating 70% of the data for training and 30% for testing. We used accuracy, precision, and recall metrics to evaluate the model, as indicated in [11,26,27,28].  
Figure 2: Failure classification using Random Forest.  
Afterward, the Support Vector Machine (SVM) technique was employed. Its objective is to maximize the margin between different planes. SVM seeks to find a hyperplane that separates instances of different classes in a feature space. A hyperplane is the decision surface that maximizes the margin (support vectors) between opposing class examples [3; 10; 20].  
Figure 3 illustrates the architecture of the SVM model for failure classification, which comprises input, hidden, and output layers. In the hidden layer, the linear kernel functions, regularization equal to 1, and gamma equal to scale were used.  
Finally, the Multilayer Perceptron Neural Network (MLP) technique was used to classify machine failures. This sophisticated architecture includes an input layer for data reception, hidden layers for applying non-linear transformations via activation functions, and an output layer for generating predictions. Each neuron connection has a weight, and the output layer's function depends on the task, which can be sigmoid for binary classification or SoftMax for multiclass classification. The training process involves forward propagation for network output calculation, a loss function for output comparison, and backpropagation for weight and bias adjustment. Optimization algorithms iterate this process to minimize loss [7; 26; 34].  
Figure 4 illustrates the MLP neural network's architecture used in this research. It comprises three layers: input, hidden, and output. The number of neurons in the hidden layer (\(n_{h}\)) was determined using the method developed by [34], described by Equation (1), in which \(n_{i}\) and \(n_{o}\) are, respectively, the number of input and output variables.  
\[n_{h}=n_{o}\bigg{(}\sqrt{\frac{n_{i}}{n_{o}}}\bigg{)}^{2} \tag{1}\]  
Figure 4: Failure classification using MLP.  
Figure 3: Failure classification using SVM.  
Additionally, the parameters adopted include the SoftMax activation function and the Adam solver, which are most used in the literature, as indicated by [34]. The training was conducted with 100 epochs using 70% of the data. The remaining 30% was used for testing.
## 4 Experimental Verification  
To validate the practical effectiveness of the proposed method and assess its applicability in real-world scenarios, this study evaluates the accuracy and reliability of the method by comparing the experimental results with the simulation data. The consistency of trends between the experimental and simulation data serves as a critical measure of the method's performance in replicating actual conditions. A force-thermal loading experiment was conducted on a multi-stage telescopic wing to verify the method. The study compared the dynamic deflection curves and driving force variations under force-thermal loads with their corresponding simulation results. Error metrics were calculated to quantitatively analyze the differences. Figure 33 illustrates the experimental setup for the force-thermal loading system. The primary components include a prototype of the multi-stage telescopic wing, the force-thermal loading unit, ceramic heating elements, and a laser displacement sensor.
# One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
###### Abstract  
Cross-linked esterified starch (CES) was prepared using a one-step method, where maize starch was selected as the raw material, sodium trimetraphosphosphate as the cross-linking agent, and acetic anhydride as the esterifying agent, respectively. A response surface experiment was systematically conducted for analyzing the correlation of the experimental variables (cross-linked temperature, pH, reaction time, sodium trimetraphosphate and acetic anhydride dosage) and properties of the product (peak and final viscosity). The Brabender viscosity, freeze-thaw stability, shearing resistance, and acid tolerance of the cross-linked acetylated dual modified starch were studied under different conditions of crosslinking degree and acetyl content. Meanwhile, the granular structure and morphology of the modified starch were analyzed. The results indicated that: after cross-linked acetylated dual modification, the starch had a distinct birefringence and granular structure, along with the creation of new carbonyl groups. The low degree of crosslinking and high acetyl contents were beneficial to the viscosity, which was significantly increased at both low and high temperatures. Moreover, the freeze-thaw stability of CES was elevated sharply after five cycles. In addition, CES displayed increased shear and acid tolerance compared to the original wavy maize, and their lowest differences between wavy maize and CES were only 0.62% and 0.59%, respectively. In summary, a novel method for starch modification was provided, and the synthesized CES was suggested to have exceptional performance for the food industry.  
maze starch; cross-linked esterified starch; freeze-thaw stability; shearing tolerance +
Footnote †: journal:  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
Received: 22 March 2022  
Accepted: 14 April 2022  
Published: 18 April 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**
# Decision Framework for Predictive Maintenance Method Selection
## 4 Framework for the Maintenance Method Selection  
To offer decision support for practitioners, a predictive maintenance method selection framework is proposed in this section. This framework is constructed from the five elements that have been previously presented in Section 3. The proposed framework is shown in Figure 5 as a decision diagram. The choice for a specific maintenance method can be made via two starting points: (_i_) _decision pull_, based on the wish to achieve a certain ambition level (element 2A), or (_ii_) _technology push_, based on available and (possibly) useful data (element 2B).  
Ideally, the selection starts via the _decision pull_ starting point. This gives the company the opportunity to select the optimal maintenance technique (i.e., a selection that is not directly limited by the available data). After the ambition level has been determined, the company checks whether there is a match with the available data, as follows from element 2B. When there is a match between the available data and the ambition level, the framework directly indicates the associated maintenance method, based on Figure 4 (element 2C). The only thing remaining then is that a business case should be made (element 3). When a positive business case can be made, the selected approach can be conducted.  
However, in practice, there is quite often a mismatch between the ambition level and the available data. The framework then guides the user to a feasible combination. When the required data for the selected ambition level are not available, the first check is whether it is possible to start collecting these data. If so, the business case for the scenario associated with this ambition level should be checked to verify whether collecting this additional data (and the associated costs) is worth the effort. If not, the availability of other data is checked, starting from the lowest level (at the top of the diagram). Typically, other data appear to be available, but this mostly implies that the ambition level should be lowered.  
Figure 5: Proposed preventive maintenance approach selection framework.  
When starting from the _technology push_ point, the available data lead to the choice of the maintenance approach. Therefore, the first step after receiving advice for the associated maintenance approach is to examine the business case. If this check is negative, it should again be evaluated whether improvements can be made with a lower or different type of data. In that case, the associated ambition level will also be lower again.  
Note that starting from a technology push, led by the available data, is a common approach in many companies. However, it is somewhat dangerous since the definition of the ambition level is neglected. This could lead to a positive business case for the selected data type and maintenance approach, but the company could discover at some stage that this approach does not perform as expected. Explicitly defining the ambition level beforehand and incorporating that in the selection process, as is performed in the framework, could prevent such a situation.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Signal Preprocessing  
#### 4.1.1 Empirical Mode Decomposition (EMD)  
In this study, Empirical Mode Decomposition (EMD) is employed for denoising. Typically, measurement noise is mainly present in high-frequency components, while low-frequency fault characteristic spectra are still adequate for fault diagnosis. The reconstructed signal removes interference while retaining the key features required for fault diagnosis, as shown in Figure 2. The steps are as follows:  
Figure 1: Framework of the fault diagnosis method based on edge computing: BPFI: Ball Pass Frequency of the Inner Race; BPFO: Ball Pass Frequency of the Outer Race; FTF: Fundamental Train Frequency; BSF: Ball Spin Frequency.  
Figure 2: Empirical Mode Decomposition (EMD).  
Step 1: Decompose the original signal into multiple Intrinsic Mode Functions (IMFs).  
Step 2: Select certain IMFs for signal reconstruction. Typically, the first few IMFs contain high-frequency noise components, which can be discarded or processed. The selection of IMFs is based on frequency component analysis, usually choosing IMFs that contain low-frequency signals for reconstruction.  
Step 3: Reconstruct the signal using the selected IMFs. By combining the chosen IMFs in their original time sequence with appropriate weighting, a denoised signal is obtained.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Autonomous 5G services
#### 4.4.3 Drones  
Drones can take on various tasks and bring new efficiency to a wide range of industries and enterprises. Drones can be used as flying cameras, for delivery purposes, for public safety, for disaster monitoring, for infra-structure facility management, in the military and agricultural fields, etc. With the high reliability and low latency of 5G, these tasks can be carried out by a group of unmanned vehicles (UAVs) communicating and adjusting their behavior through real-time data inputs and data sharing. For military purposes, drones can collaborate with manned aerial vehicles with or ground forces to enhance fighting efficiency. The establishment of mobile surveillance networks using drones can also be considered. To monitor disasters, inspect the status of infrastructure facilities and rescue people in emergencies, drones may communicate and share real-time data with each other, thus increasing the speed and effectiveness of search and rescue missions. Besides, drones may play an important role in intelligent transportation networks. Drones may monitor and deliver transportation status in urban areas or highways through 5G networks. Currently, there are no specific standards for communication links for drones. Hence, 900-MHz systems are used for them as an alternative, and 2.4 GHz or 5 GHz Wi-Fi is used for data transmission for videos. Thus, there exist some serious problems, such as limitations in communication range, ensuring reliability and network latency. Although the volume of data used to control drones and the data regarding their control status is not very big, the low latency and high reliability of such networks must be secured with 5G technology. In addition, 5G transmission and network technologies with high energy efficiency may be used to improve the operation time of drones. Another example of drone services with  
Figure 10: Remote surgery services with robots.  
5G connectivity is the Internet by drones [28]. Drones will help spread the Internet to areas that lack reliable connectivity. Such technology is already being tested, and connectivity through 5G will make it more reliable.
## 3 Lightweight Cryptography for Smart Objects
### Symmetric-Key LWC Algorithms
#### 3.1.1 Tiny Encryption Algorithm (TEA)  
The Tiny Encryption Algorithm (TEA) is a block cipher renowned for its simplicity of description and implementation, typically a few lines of code [25]. TEA operates on two 32-bit unsigned integers (could be derived from a 64-bit data block) and uses a 128-bit key. TEA relies only on arithmetic operations on 32-bit words and uses only addition, XORing, and shifts. TEA uses a large number of iterations, rather than a complicated program, in order to avoid preset tables and long setup times. The main design goal of TEA is to define a simple and short cipher that does not rely on preset tables or pre-computations, thus leading to a smaller footprint.  
TEA was later revised in order to fix some weaknesses found in the original algorithm, such as the problem of equivalent keys, which reduced the actual key size from 128 to 126 bits. The redesign of TEA, named XTEA (Extended TEA) [26], fixes this problem by changing the key schedule. XTEA also requires two fewer additions, thus resulting in a slightly faster algorithm. Other modifications of the TEA algorithm have been presented, such as XXTEA, Block TEA, Speed TEA, and Tiny XTEA.  
As the TEA family uses exclusively very simple operations (addition, XORing, shifts), and has a very small code size, it is an ideal candidate as a cryptographic algorithm to implement security mechanisms in smart objects and wireless sensors.
## 3 Results and Discussion
### Created VR Application for Rehabilitation
#### 3.1.1 Basis of the Application  
In the first step, the patient selects an object on the right or left side (Figure 3a). The choice of object determines the side from which he or she will grasp objects. For patients who will use the game to increase motor skills, the choice of side is less important than for patients with USN syndrome, as side selection is not linked to hand selection, which is performed before the game by taking the appropriate controller. The importance of side selection is due to the fact that on the selected side there are objects to be grasped and on the opposite side there are boxes into which the object must be moved. It is suggested that the patient has objects to grasp on the non-neglected side and boxes on the neglected side. The grasped object must then be transferred by the patient to a box on the other side. This arrangement allows the patient to grasp the object (Figure 3b) in the area they can see well and move it to the skipped area. Because the target box (Figure 3c) is significantly larger than the target item, the patient's movement may be less precise than when grasping, which is intended to motivate the patient to exercise and turn toward the neglected side. The use of virtual reality and the transfer of exercise to a different, more engaging environment engages the patient and increases their motivation to exercise. The increase in motivation with using virtual reality is confirmed by studies [50; 51].
## 7 Conclusions  
The robustness measure of a construction schedule is an effective and indispensable tool to evaluate the capability of robust scheduling. However, current research mainly pays pay to a single robustness criterion. Few studies consider both the solution robustness and quality robustness while neglecting the bounded rationality of subjective weights and the inherent importance and nonlinear intercriteria correlations of objective weights in the composite criterion. In view of these limitations, a construction schedule robustness measure based on improved prospect theory and the Copula-CRITIC method is proposed in this paper. An underground power station in China was used as a case study to verify its advantages. The main contributions of this paper are listed as follows:  
1. With the establishment of a composite robustness criterion containing solution robustness criteria of start time deviation and structural deviation, and quality robustness criterion of completion probability, construction schedule robustness can be measured in terms of both project execution and project result.  
Figure 12: The optimized schedules with the objectives of \(T\)-\(r\) using two sets of weighting methods: (a) \(r_{s}\), \(r_{p}\), and \(r_{c}\) of the optimized schedules; (b) \(r_{s}\) and \(r_{p}\) of the optimized schedules; (c) \(r_{s}\), and \(r_{c}\) of the optimized schedules; (d) \(r_{p}\) and \(r_{c}\) of the optimized schedules.  
2. The subjective weights considering bounded rationality are assigned using prospect theory improved by an interval distance formula, the objective weights taking both inherent importance and nonlinear intercriteria correlations into account are assigned using the Copula-CRITIC method, and the subjective and objective weights are combined using an information-entropy-based evidence reasoning method.
3. A real underground power station was used as a case study. The results of the proposed criterion and methods, one with a single robustness criterion of either solution robustness or quality robustness, and one with a composite robustness criterion using a traditional weighting method were compared to verify the consistency, representativeness, and advantages of the proposed composite robust criterion and weighting methods.  
Conceptualization, X.W. and M.Z.; methodology, M.Z.; software, M.Z.; validation, M.Z., and J.Y.; investigation, M.Z. and L.X.; data curation, L.X. and S.Y.; writing--original draft preparation, M.Z.; writing--review and editing, J.Y. and S.Y. 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, grant number 51679165; the Yalong River Joint Funds of the National Natural Science Foundation of China, grant number U1965207; and the National Natural Science Foundation of China, grant number 51839007.  
The authors declare no conflict of interest.