esa-sceva/satcom-chunk-collection huggingface dataset:
# Electricity Generation in LCA of Electric Vehicles: A Review  
[PERSON]  
1 Mat4 En2--Materials for Energy and Environment, Dipartimento di Chimica, Materiali e Ingegneria Chimica  
[PERSON]/  
Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; [EMAIL_ADDRESS]  
Mohiligus and Automotive Technology Research Centre, Department of Electric Engineering and Energy Technology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (J.V.M.)  
[PERSON]  
22  
[PERSON]  
1 Mat4 En2--Materials for Energy and Environment, Dipartimento di Chimica, Materiali e Ingegneria Chimica  
[PERSON]/  
Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; [EMAIL_ADDRESS]  
[PERSON]  
23  
2  
The main causes of divergence in the literature, which make it difficult to compare studies are identified as follows by [PERSON] et al. [1]: Different system boundaries, different level of detail and quality in the datasets, different lifetimes, different vehicles' typologies and masses, battery technologies, vehicles performances, and then the electricity mix.  
[PERSON] et al. [2] present an exhaustive analysis, and performs various meta-analyses from the findings of LCAs. They also widen the discussion to impact categories like resource depletion and toxicity, while the [PERSON]'s focus remained on climate change.  
Both reviews identified electricity production as the most impactful phase when it comes to climate change, and agreed on the need to find consensus on the appropriate electricity mix.  
Since these two seminal reviews have been published, a variety of papers appeared in the literature, paving the way to new and interesting discussions as well as diverging results and methods.  
A significant change in the way to account for electricity in the use phase of electric vehicles has occurred in the last few years: from an overgeneralisation of the inventory, using generic datasets (from EcoInvent, Emissions & Generation Resource Integrated Database [eGRID], Greenhouse gases Regulated Emissions and Energy use in Transportation [GREET], etc.... ), the trend in the most recent LCAs has been a high detail of temporal and spatial variability, following the work by [PERSON] et al. [3].  
A blossoming of different methods to account for the \"correct\" electricity mix has led to lots of different, and sometime conflicting, results.  
Lack of consensus in LCI data selection and lack of clear goal definitions are still the key factors to explain the difficult path of providing policy makers with robust and clear results.  
This review analyses the methodological choices made by the scientific papers when assessing EVs. Since its relevance has been proved, a special focus is placed on electricity generation. The aim is to find a trend in accounting for the electricity production method, thus helping users navigate the conflicting results found in the literature, providing guidelines for future EVs studies and informing policy makers on the right method to use when assessing political choices.
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 5 Experimental Verification
### Baseline Optimization Method Verification
We can conclude from the results shown in Figures 13 and 14 that the NDFT and CS algorithms were better able to distinguish those three simulated targets when \(q=1.0\), \(e_{\max}=10\%\). This verifies the system had stable and good imaging performance in the height direction when the baseline satisfied a weak-deviation uniform distribution. Furthermore, the system had superior  
Figure 14: Results of the CS algorithm. Figure 13: Results of the NDFT algorithm. height-orientation performance when the baseline of an SMS-TomoSAR system met the allowable maximum baseline perturbation. In other words, the allowable maximum baseline perturbation can provide a reference satellite baseline control deviation for the design or spatial position control of SMS-TomoSAR systems. For example, when the experiment set \(S_{span}=500\) m, \(MN=7\) with the NDFT imaging algorithm, a maximum baseline perturbation provided optimal height-orientation performance for SMS-TomoSAR systems.
## 6 A Case Study  
In order to demonstrate the applicability of proposed scheme, we choose the case study proposed by [PERSON] et al. [28]. They studied the promotion criteria of university professors and used multiple regression analysis to choose the factors that play important roles in the professors' promotion while making decisions at the university level and build a more reasonable method for rewarding and evaluating university professors. Actually, they were inspired by the fact that there is an unfair way under which professors get a promotion from assistant to associate and associate to full professor. They said that a professor's promotion runs through an irrational and chaotic way and a more justifiable approach could be designed to improve the decision ranking process. In this section, we depict our proposed methodology by evaluating the university faculty in Pakistan adapted from [29].  
The problem is formulated as follows: Let \(X=\{x_{1},x_{2},x_{3},x_{4}\}\) be a set of four faculty professors (alternatives) to be evaluated using the IFNs. Suppose that we use three criteria \(c_{1}=\) teaching, \(c_{2}=\) research and \(c_{3}=\) service to evaluate the faculty professors for tenure and promotion. Based on these criteria, a committee of four experts \(m_{1}\), \(m_{2}\) and \(m_{3}\) and \(m_{4}\) provides their opinions in Tables 1-4, respectively, in the form of IFNs.  
_TOPSIS Method for the Illustrative Example_  
We employ the proposed TOPSIS method designed in Section 5 to handle the above problem as follows:  
Step 1: Based on the evaluation matrices \(R_{k}(k=1,2,3,4)\), we compute the overall evaluation matrix \(R=[pd_{ij}]_{4\times 3}\) given in the Table 5.  
Step 2: The score and deviation values of the PDHFEs in the overall evaluation matrix R are computed according to Equations (10) and (11) and displayed in Table 6.  
Step 3: Since all criteria are benefit criteria, following Definitions 17 and 18, the positive ideal solution is  
\[R^{+}=\left\{pd_{31},pd_{12},pd_{13}\right\}\]  
and the negative ideal solution is  
\[R^{-}=\left\{pd_{11},pd_{42},pd_{23}\right\}.\]  
Step 4: According to Definition 19, the separation of each alternative from the PIS are \(s_{1}^{+}=0.03383,s_{2}^{+}=0.03676,s_{3}^{+}=0.00958,s_{4}^{+}=0.04389\), and according to Definition 20, the separation of each alternative from the NIS are \(s_{1}^{-}=-.02778,s_{2}^{-}=-.02826,s_{3}^{-}=-.05028,s_{4}^{-}=0.02694\).  
Step 5: Compute the closeness coefficient \(cl(x_{i});i=1,2,3,4:cl(x_{1})=0.45090,cl(x_{2})=0.43463\), \(cl(x_{3})=0.83996\), \(cl(x_{4})=0.38035\).  
Step 6: The ranking result of all professors on their performance is  
\[x_{3}>x_{1}>x_{2}>x_{4}.\]  
Therefore, professor \(x_{3}\) achievement is the top of the range.  
Comparative Analysis of the Proposed Decision-Making Scheme with Existing Ones on the Basis of Case Study  
In what follows, the detailed comparative analysis is presented with the traditional approach and Ref. [22] approach to illustrate the essentiality of our proposed scheme.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Communication Decision-Making Parameter
#### 3.2.3 Time Domain Decision-Making Parameters  
The decision-making parameter of adaptive time hopping is dwell time. The determination method is as follows: choose transmission in time slot with no jamming; choose silence in time with jamming.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Failure Maintenance Decision
#### 2.4.5 Anti-Interference Problems  
The anti-interference ability is embodied in the stability index of the communication system under electromagnetic compatibility, lightning impulse, strong magnetism, vibration, and high temperature shock, which is a comprehensive and complex system problem. In order to improve the anti-interference ability of the communication system, the communication cable and the power cable shall be laid vertically through pipes. For the fault handling of the field bus network, the problem of system interference can be considered when all factors are considered, and the fault cannot be handled. Therefore, in addition to taking effective measures in power supply, grounding, shielding, signal isolation, and other aspects to ensure the card quality and communication parameters are correct, the communication system should also give full consideration to electromagnetic compatibility (such as smelting area), vibration (various driving), and high temperature factors. In addition, the more devices that are connected to the bus and the faster the communication speed is, the heavier the system load is and the worse the anti-interference ability of the system is.
## 2 Materials and Methods
### The Exact (Mathematical) Method for SSTPC2L
**_Since the bisector of the angle between \(d_{1}\) and \(d_{2}\) is parallel to \(Oy\) axis, it is not difficult to see that:_  
\[\text{b}_{\text{i}}\ =\ y_{\text{i}1}-\text{a}_{\text{i}1}x_{\text{i}1} \tag{12}\]1  
In order to obtain the solution \(a_{min}\) of the Equation (20), a numerical method [14] can be used such as bisection method [15], or tangent method. Since \(f^{\prime}(a_{L})<0\) and \(f^{\prime}(a_{R})>0=>\ f^{\prime}(a_{L}).f^{\prime}(a_{R})<0\), the bisection method is suitable to be applied on the interval:  
\[\text{b}_{\text{i}}\ =\ y_{\text{i}1}-\text{a}_{\text{i}1}x_{\text{i}1} \tag{12}\]2  
After \(a_{min}\) is calculated, the points \(P_{i}\) (\(i=1\), \(2\)) are obtained using (13) and (14) and the distance between these two points is:  
\[\text{b}_{\text{i}}\ =\ y_{\text{i}1}-\text{a}_{\text{i}1}x_{\text{i}1} \tag{12}\]3  
In order to go back to the initial system of coordinates, the inverse initial transformations must be applied in reverse order to the points \(P_{1}\) and \(P_{2}\), i.e.,:  
1. Translation with \((-x_{Q},-y_{Q})\)
2. Rotation with \(\sin(u)\) and \(\cos(u)\) (were calculated in (8))
3. Rotation with \(\sin(v)=\sin(v)\) and \(\cos(\cdot v)=\cos(v)\) (calculated in (9))Translation with (xI, yI). The following Algorithm 3 (denoted A2 SSTPC2L) is obtained to solve SSTPC2L:  
```
A2 SSTPC2L: Input: Q and the points of the closest two level curves to Q Output: The slope at point Q Apply AIPS to find A11, A12, A21, A22; Transform the points A11, A12, A21, A22 and Q so that the bisector of the angle between the lines d1 and d2 is parallel to Oy axis and the point Q is in the origin O; Find the solution amin of the equation \(\mathrm{f^{\prime}(a)}~{}=~{}0\) using bisection method on the interval \((a_{L},a_{R})\) (see Equations (19) and (20)); Calculate the coordinates of the points P1 (i = 1,2) using (13) and (14), where a = amin; Apply the inverse initial transformations in reverse order to the points P1 and P2; Calculate the slope at point Q using (1). ```  
**Algorithm 3** Solving SSTPC2L  
We implemented the above algorithm in Visual C++ and in the bisection method we set the error err = 0.0001 for finding the value amin. In Figure 6, a graphical output of our program is presented.
## 4 The SEME-TDHM Algorithm
### Encryption Process  
Capitalizing on the remarkable chaotic properties exhibited by the proposed chaotic map, this section introduces the SEME-TDHM algorithm grounded in the 2D hyperchaotic map. This algorithm encompasses preprocessing, scrambling, and diffusion processes, as visually depicted in Figure 8. The specific encryption procedure is detailed as follows: _A1_, _A2_, and _A3_ represent the pixel values of the RGB channels, respectively. Most encryption algorithms that use hash functions directly input image values to generate hash values, increasing the risk of the initial values being cracked. Preprocessing the image before hashing effectively mitigates this issue. Furthermore, preprocessing disrupts the pixels across the three channels at an early stage, which enhances the randomness of the image pixels obtained through subsequent encryption operations. Figure 7: 0–1 test result. * The R, G, and B channel pixel values are preprocessed as shown in Figure 8 and finally the result is decomposed into three \(M\times N\) pixel matrices, each corresponding to the \(A1\), \(A2\), and \(A3\) of the original image;
* The pixel values of the preprocessed plaintext image are used as input to the SHA\(-\)512 algorithm to generate a 128-bit string key \(P\). According to Equation (6), \(P\) is divided into sixteen 8-bit strings, which are then converted into sixteen decimal subkeys. They will be used for the generation of initial values for scramble and diffusion, respectively, as shown in Figure 8;  
\[p(i)=hex2 dec(P(8\times i-7:8\times i)),i=1\cdots 16 \tag{6}\]  
Figure 8: Diagram of the encryption process. * Single-channel random scrambling: A chaotic sequence is used to generate the selection sequence _T1(i)_ and the corresponding shift sequence _Q(i)_, which was used for the first pixel block _A1_ to perform a row and column random shift as in Equation (7): \[\left\{ \begin{array}{l}A1(T1(i)_{:})^{\text{\emph{shift right by Q(i) units}}}\\ A1(:,T1(i)-M)^{\text{\emph{shift upward by Q(i) units}}}\end{array} A1(T1(i),:),T1(i)<M+1, \right.\] (7) where \(T1(i)\in[1,M+N]\), \(Q(i)\in[0,M]\);
* Full channel random scrambling: In the previous step, _P1_ for \(M\times 3N\) was obtained. In this step, the chaotic system is used to generate the index sequences _s1_, _s2_ and the offset sequences _uu1_, _uu2_. The rows and columns of _P1_ are sorted according to Algorithm 1, with the row and column index sequences randomly shifted before each sort.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
###### Abstract  
Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.  
e 2024 4 150-174. [[https://doi.org/10.3390/network4020008](https://doi.org/10.3390/network4020008)]([https://doi.org/10.3390/network4020008](https://doi.org/10.3390/network4020008))
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 2 Dynamic Model of the PSVS  
The hydraulic schematic diagram of PSVS is shown in Figure 1. And the structural schematic diagram of the pressure servo valve is shown in Figure 2. The parameters in Figure 2 are described in the Table 1.  
Figure 1: The hydraulic schematic diagram of PSVS.  
Figure 2: The structural schematic diagram of the pressure servo valve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Unit** \\ \hline \(\Delta i\) & Input current & mA \\ \(K_{t}\) & Electromagnetic moment coefficient & Nm/rad \\ \(K_{m}\) & Magnetic torque spring stiffness & Nm/rad \\ \(\psi\) & Jet pipe rotation angle & rad \\ \(J_{a}\) & Rotational inertia of jet pipe & kg\(\cdot\)m\({}^{2}\) \\ \(B_{a}\) & Damping coefficient of jet pipe & Nm/rad/s \\ \(K_{a}\) & Stiffness of jet pipe & Nm/rad \\ \(K_{S}\) & Stiffness of the feedback rod & N/m \\ \(r\) & Distance from feedback rod rotation center to nozzle & m \\ \(k_{q}\) & Linearized flow gain coefficient of jet nozzle & m\({}^{2}\)/s \\ \(k_{c}\) & Linearized flow pressure coefficient of jet nozzle & m\({}^{3}\)/sPa \\ \(x_{s}\) & Displacement of jet nozzle & m \\ \(x_{c}\) & Displacement of the second stage spool & m \\ \(p_{c}\) & Buildup pressure & Pa \\ \(p_{s}\) & Oil supply pressure & Pa \\ \(p_{h}\) & Backpressure of the system & Pa \\ \(\rho\) & Oil density & kg/m\({}^{3}\) \\ \(\beta\) & Volumetric modulus of elasticity of oil. & Pa \\ \(V_{cv}\) & Volume of the control cavity & m\({}^{3}\) \\ \(A_{c}\) & Area of feedback valve spool & m\({}^{2}\) \\ \(K_{c}\) & Feedback spring stiffness & N/m \\ \(m_{c}\) & Mass of the feedback spool & kg \\ \(B_{c}\) & Damping coefficient of the feedback spool & Nm/rad/s \\ \(A_{o}\) & Effective area of buildup pressure & m\({}^{2}\) \\ \(p_{b}\) & Output pressure & Pa \\ \(A_{b}\) & Effective area of output pressure & m\({}^{2}\) \\ \(A_{h}\) & Effective area of backpressure & m\({}^{2}\) \\ \(K_{f}\) & Steady-state flow force stiffness of main valve spool & N/m \\ \(x_{v}\) & Displacement of power stage valve spool & m \\ \(x_{v0}\) & Initial compression of power stage spring & m \\ \(M_{o}\) & Mass of power stage valve spool & kg \\ \(B_{v}\) & Damping coefficient of power stage valve spool & Nm/rad/s \\ \(K\) & Spring stiffness of power stage valve spool & N/m \\ \(C_{d}\) & Flow coefficient of main valve spool & - \\ \(W\) & Area gradient of the main valve spool & m \\ \(Q_{L}\) & Load flow & m\({}^{3}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters.
## 4 Experiments and Results
### Node Distribution  
Figure 15 shows the evolution in time of parameters related to node distribution. Figure 15a presents the average distance that nodes move in time in all presented simulations. Only living nodes are taken into account to calculate this average. It can be observed that mean distances tend to evolve similarly for non-hierarchical network deployment, although they are initially larger in an environment with obstacles. Nodes move on average 38 m for deployment if there are no obstacles and 44 m for the proposed obstacle layout. Then, when nodes start to die, all remaining nodes adjust their positions, and hence, the mean distance grows steadily. After 100 days, the mean distance is over 100 m for non-hierarchical simulations. Distances also evolve similarly for both hierarchical simulations. After initial deployment, the mean distance is equal to 34 m and 45 m for the environment without and with obstacles. Since nodes last longer in these simulations, they stay in position until Day 250. After that, when nodes start to die, living nodes relocate themselves. In this case, the mean distance grows more slowly, probably because most variations affect only limited areas where L1 nodes died. Nevertheless, the final nodes also reach a mean distance close to 100 m after 70% of the nodes are dead. Figure 15b shows network uniformity. As expected, uniformity is much better in geographic routing simulations, where all nodes have the same roles and, hence, are affected by the same forces. It is interesting to note that when nodes start to die, uniformity punctually grows up to 0.75 for these simulations. This probably happens because after some nodes are dead, the network can extend to its fullest with respect to the simulation area and the allowed communication range (see Equation (4)). Soon afterwards, the number of nodes starts to decrease, and there are not enough nodes to extend over the full simulation area. At this point, uniformity decreases very quickly. In hierarchical routing simulations, uniformity is initially lower, as expected. This is typical for deployment algorithms where nodes tend to conform structures. Nevertheless, like other parameters, including coverage, uniformity decreases much slower in these cases.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 2 Common Frequency Domain Methods
### The Influence of Material Properties  
In general, fatigue life curve is used to describe the fatigue performance of materials in engineering, and its power function formula is:  
\[S^{m}\cdot N=C \tag{25}\]  
In Equation (25), m and C are material constants. According to Equation (8), when calculating the random fatigue damage in the frequency domain method, the S-N curve of the material also has an important influence. The type of material also affects the accuracy of the random fatigue life in the frequency domain method.  
Steel and aluminum alloy are three commonly used materials in engineering. Among them, steel is the most widely used material in most engineering fields, and aluminum alloy is a non-ferrous metal material commonly used in engineering. The functional relationship between the stress amplitude and fatigue life is selected in the form of a power function and three parameters. In order to ensure the  
Figure 4: Band-limited white noise simulation spectrum.  
Figure 3: Multiple peak simulation spectrum.  
reliability of the results, this paper selects one of the three material types for analysis. The slope of the S-N curve affects the calculation accuracy of the frequency domain method, and the slope value of steel and aluminum alloys does not change much, spring steel with a larger slope is selected for analysis. Spring steel is specifically used to make springs and elastic Components of steel. In order to ensure the reliability of the results, in this paper, Q460D steel, LY12 aluminum alloy, and GB/T4657-89 carbon spring steel wire are selected for analysis. The parameters of the three materials are shown in Table 1[37] to verify the applicability of different frequency-domain methods to different S-N curve models. S\({}_{u}\) in the table is the ultimate tensile strength.