esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Strength and Stiffness of the Frame
### The Strength of Welded Joints at Intervals  
In general, the ultimate tensile strength of welding material could be evaluated by:  
\[\sigma_{t}=\frac{F}{A}=\frac{F}{a\times b} \tag{6}\]  
where \(F\) is the maximum value of force during monotonic tensile test, \(A\) is a cross-sectional area, \(a\) and \(b\) are width and thickness of specimen, respectively.  
\(\left[\underline{\sigma_{t}},\overline{\sigma_{t}}\right]\) is employed to describe interval allowable tensile strength of welded frame, and could be estimated by:  
\[\left[\underline{\sigma_{t}},\overline{\sigma_{t}}\right]=\frac{\left[ \underline{F},\overline{F}\right]}{\left[\underline{a},\overline{a}\right] \times\left[\underline{b},\overline{b}\right]} \tag{7}\]  
where \(\left[\underline{F},\overline{F}\right]\) stands for interval maximum load, \(\left[\underline{a},\overline{a}\right]\) is interval width of specimen, and \(\left[\underline{b},\overline{b}\right]\) is interval thickness of specimen. According to Equation (7) and the blind number algorithms, the ultimate tensile strength of welded joints could be obtained if maximum loading, width and thickness of specimen at considered intervals are known.  
Five monotonic tensile tests were conducted on specimens of welded joint. The results are listed in Table 1. The flat dog-bone shaped specimen was designed to characterize the mechanical properties of butt joint [1; 2; 4; 19]. Then, the ultimate tensile strength in each test was calculated by Equation (6), Table 1[19].  
Based on the above five parameters, the initial interval of tensile strength, width and thickness of specimen and maximum loading could be defined as \([471.42,762.32]\) MPa, \([5.89,6.22]\) mm, \([5.81,6.10]\) mm and \([16.66,27.12]\) kN. They were considered as blind numbers. According to Equation (7) and operation rule of blind number, the total 125 probable values of tensile strength and their corresponding credibility values were obtained, and some of them were listed in Table 2[19]. Then, the interval tensile strength of welding material was determined by \([439.11,792.52]\) MPa.
# Improvement on Flux Weakening Control Strategy for Electric Vehicle Applications
## 2 PM Vector Control Equations  
Equations (1) and (2) describe respectively the dynamic voltage model and the steady-state one in a rotor d-q reference frame:  
\[\begin{cases}v_{sd}=R_{s}i_{sd}+L_{sd}\frac{d_{id}}{dt}-\omega_{e}L_{sq}i_{sq} \\ v_{sq}=R_{s}i_{sq}+L_{sd}\frac{d_{id}}{dt}+\omega_{e}\Delta_{PM}+\omega_{e}L_{ sd}i_{sd}\end{cases} \tag{1}\]  
\[\begin{cases}v_{sd}=R_{s}i_{sd}-\omega_{e}L_{sq}i_{sq}\\ v_{sq}=R_{s}i_{sq}+\omega_{e}\Lambda_{PM}+\omega_{e}L_{sd}i_{sd}\end{cases} \tag{2}\]  
where \(i_{sd},i_{sq},v_{sd},v_{sq},\omega_{e}\) are the stator current and voltage components and the electrical angular speed, \(\Lambda_{PM}\) is the PM flux, \(R_{s}\) is the stator resistance, \(L_{sd}\) and \(L_{sq}\) are d- and q-axis inductances.  
In the case of surface PM the rotor has no saliency and the \(L_{sd}\) and \(L_{sq}\) inductances are equal.  
The terms \(-\omega_{e}L_{sq}i_{sq}\) and \(+\omega_{e}L_{sd}i_{sd}\) are the motional back EMF that can be compensated to decouple axes.  
The torque is expressed in Equation (3) where the amplitude invariant form of [PERSON]'s transformation was chosen:  
\[T=\frac{3}{2}p\big{[}\Lambda_{PM}i_{sq}+(L_{sq}-L_{sd})i_{sd}i_{sq}\big{]} \tag{3}\]  
where \(p\) is the pole pair number.  
In the constant torque region, where the motor operates in the MTPA mode, limits are due the current limit circle see Equation (4) only. On the contrary in the constant power region they rely on both current and voltage limits Equations (4) and (5). The voltage limit circle becomes an ellipse in the \(i_{sd}-i_{sq}\) plane, due to the rotor saliency, see Equation (6) where \(\omega_{m}\) is the rotor mechanical speed [22].  
\[i_{sd}^{2}+i_{sq}^{2}\leq i_{Lim}^{2} \tag{4}\]  
\[v_{sd}^{2}+v_{sq}^{2}\leq v_{Lim}^{2} \tag{5}\]  
\[L_{sd}^{2}\bigg{(}i_{Lim}+\frac{\Lambda_{PM}}{L_{sd}}\bigg{)}^{2}+L_{sq}^{2}i _{sq}^{2}\leq\bigg{(}\frac{V_{Lim}}{p\omega_{m}}\bigg{)}^{2} \tag{6}\]  
The maximum current depends on the inverter, while is the battery voltage and state of charge that limit the BUS voltage. It is to be noted that for a given machine, the voltage suitable for current regulators depends on machine mechanical speed \(\omega_{m}\) as well since both the axes of the ellipse grow shorter as the mechanical speed increases. Equation (7) describes the intersection of the axis of the voltage ellipse.  
\[i_{ch}=-\frac{\Lambda_{PM}}{L_{sd}} \tag{7}\]The purpose of the motor control is to calculate the voltage at machine terminals that satisfies torque setpoint despite the mechanical speed.
## 5 Results and Discussion  
### Data Normalization and Parameter Setting  
#### 5.1.1 Data Normalization  
In statistical modeling, the model may be unevenly scaled across various dimensions. Differences in the scaling of dimensions can lead to calculation results being influenced more by features with larger dimensions, causing a disparity between the optimal solution and the original data. Therefore, to ensure the reliability of the results, the original monitoring data are normalized to the same dimension to eliminate dimensional differences.  
The commonly used data standardization methods are min-max standardization and Z-score standardization. Z-score normalization is the most commonly used normalization method in SPSS, also known as standard deviation normalization. This method calculates the mean and standard deviation of the original data in order to normalize it. The processed data conform to the standard normal distribution, with a mean value of 0 and a standard deviation of 1. The transformation function is as follows.  
\[x^{*}=\frac{(x-\mu)}{\sigma} \tag{19}\]  
where \(\mu\) represents the sample mean, and \(\sigma\) represents the sample standard deviation.  
The Z-score normalization method is suitable for cases where the maximum and minimum values of attribute A are unknown, or when there are outliers beyond the range of values. The dust concentration data are disorganized and fluctuate significantly, with more outliers present. The normalized data preserve the valuable information in the outliers, reducing the algorithm's sensitivity to them. Compared with the min-max method, this method is less affected by outliers. Therefore, this paper utilizes the Z-score method to normalize the data. The normalized change steps are as follows.  
1. The mathematical expectation \(X_{i}\) and standard deviation \(S_{i}\) of each input and output variable are calculated, respectively.
2. Normalization: \[Z_{ij}=\frac{\left(X_{ij}-X_{i}\right)}{S_{i}}\] (20) where \(Z_{ij}\) is the normalized variable value; \(X_{ij}\) is the actual variable value;
3. Adjust the sign in front of the inverse variable.  
The normalized variable value fluctuates around 0. A value greater than 0 indicates a level higher than the average, while a value less than 0 indicates a level lower than the average.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 2 Background and Motivation
### Previous Work on Comparing Methods  
Numerous studies have been conducted in the field of computer vision for object detection and localization, exploring various techniques and their applications in different domains. However, research specifically focused on the comparative analyses of these algorithms for depalletizing system implementation under resource constraints is relatively limited. The applications related to vision-based depalletizing reveal an abundance of scientific literature; however, the number of methods covered by each study may be restricted and is sometimes out of date. For instance, pioneering work was conducted in object detection with SIFT-based clustering [2] to pick and place objects; while this is presented as a single technique, it is highly flexible for different objects. In another work, [PERSON] and [PERSON] [3] propose two feature selection algorithms Haar-like feature selection and Local Binary Patterns (LPB) for the detection of a single object and multiple objects in the same scene and for both standard platforms and embedded systems. [PERSON] and [PERSON] [32] presented the performance of various object recognition approaches in a comparative analysis of SIFT, SURF, and ORB feature descriptors and multiple combinations of these feature descriptors. This experimental work was conducted using a public dataset, namely Caltech-101.  
In other research [5], the development and comparison of two distinct approaches, a machine learning strategy based on Support Vector Machines (SVMs) [33] and a deep learning approach employing the YOLO (You Only Look Once) [34] network, for small target detection is analysed. Due to the restrictions in the region of interest (ROI) selection, the SVM-based method performs better in terms of computing resources and time consumption but suffers from accuracy and robustness problems in some cases, such as occlusion, illumination fluctuations, and tilting. Conversely, the YOLO network-based deep learning approach has better accuracy but has trouble in reaching real-time performance, particularly on onboard computers that have weight and power limitations.  
Recent changes in the YOLO network architecture made it possible to run the system on low computing power edge devices, although the issue of detection latency may not be suitable for real-time operation. Furthermore, the training of deep learning algorithms requires a large amount of data, and the process of creating a dataset and manually labelling it takes a long time. As a result, object detection techniques should be properly chosen based on the application and design requirements, and adjustments must be made as needed. For these reasons, deep learning approaches are not considered in this work.
## 3 Methodology
### Obtaining the Local ET,\(LE_{loc}\)
#### 3.1.3 Calculation of \(R_{n}\) and \(G\)  
Net radiation, \(R_{n}\), can be derived by Equation (11); soil heat flux, \(G\), can be estimated by an empirical equation (Equation (12)) that is used in some previous studies ([PERSON] et al., 2017).  
\[R_{n}=\left(1-\alpha\right)S_{0}+R_{ld}-\varepsilon_{s}\sigma T_{0}{}^{4} \tag{11}\]  
\[G\approx 0.3\left(1-0.9f_{v}\right)R_{n} \tag{12}\]  
where \(\alpha\) is the surface albedo, \(S_{0}\) (W/m\({}^{2}\)) is the downward shortwave radiation, \(R_{ld}\) (W/m\({}^{2}\)) is the downward longwave radiation, \(\varepsilon_{s}\) is the surface emissivity, \(\sigma\) (5.67 \(\times\) 10\({}^{-8}\) W\(\cdot\)m\({}^{-2}\).\({}^{\circ}\)K\({}^{-4}\)) is the Stefan-Boltzmann constant, and \(f_{v}\) is the fraction of vegetated surface.
# Key Action Mechanisms of Intentional Mistuning
## 5 Main Results and Conclusions  
The application of the AMM methodology allows to understand the way of using intentional mistuning to reduce the vibration amplification and the sensitivity to the unavoidable underlying random mistuning, and the limitations of this strategy. The  
Figure 16: Frequency sweep and (inset) blade displacement at the maximum (black line corresponds to the tuned amplitude), \(Aim=10\%\). Rotor B, \([1,0,-1,\ldots,1,0,-1]\) pattern, \(\xi=10^{-3}\).  
Figure 15: Maximum vibration amplitude for increasing intentional mistuning amplitude for 50 random mistuning patterns. Rotor B, \([1,0,-1,\ldots,1,0,-1]\) pattern, \(\xi=10^{-3}\).  
effective attenuation of the random mistuning effects using intentional mistuning is seen to depend on the relative value of three small effects: elastic coupling of the modal family, blade damping, and the random mistuning amplitude:  
* Blades are coupled through the elastic forces. The magnitude of this coupling is measured by the influence coefficients (\(a_{k}\)) associated with the frequency deviations of the modal family (\(\Delta\omega_{k}\));
* The effect of the intentional mistuning is to separate in frequency the different types of blades, reducing the coupling between them;
* In order to attenuate the effect of random mistuning, the intentional mistuning pattern should first separate in frequency those blades that show a high elastic coupling, and then, the damping of the modal family, \(\xi\), should be much larger than the remaining influence coefficients;
* The AMM analysis gives also a very simple expression for the limit value of the maximum vibration amplitude that the blades can experience when the intentional mistuning is effective in uncoupling them: \[|x_{j}|_{max}=1\] (the tuned vibration amplitude).
* The analysis in [10] considered the case of a rotor with an even number of blades and coupling only with the adjacent blades (only one elastic influence coefficient \(a_{1}\)). In this situation, the alternate mistuning was always effective in reducing the amplification and the sensitivity to random mistuning, independently of the magnitude of the damping.  
The authors contributed equally to this work. Both authors have read and agreed to the published version of the manuscript.  
Spanish Ministerio de Economia, Industria y Competitividad.  
Not applicable.  
The authors wish to thank [PERSON] and [PERSON] for providing us with the FEM models of the blisks. This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad under grant DPI2017-84700-R.  
The authors declare no conflict of interest.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 6 Conclusions and Future Works  
In this paper, we propose the use of consecutive drops for quick and accurate detection of inside packet drop attackers. We design a new trust model that probabilistically penalizes attackers based on the consecutive drops and build a hybrid trust model that adaptively uses the popular beta trust model and our new trust model. Extensive simulation results demonstrate that our hybrid trust model outperforms the beta trust model under various packet drop attacks in terms of detection speed, detection accuracy, routing reliability, and energy efficiency.  
In our future work, we plan to study the following. First, we will study the distinct characteristics of colluding packet drop attackers and design defense mechanisms based on our hybrid trust model to defend against colluding packet drop attackers. Second, we will study applying the game theoretic approach for advancing our hybrid trust model to better defend against inside packet drop attacks. Third, we will study devising a new, sophisticated packet drop attack method avoiding the state-of-the-art defense methods, including our trust model. Finally, we will study an inside packet drop attack problem in a WSN adopting information fusion where intermediate nodes modify received packets, and thus existing trust mechanisms with the watchdog mechanism may not work because they cannot tell if a forwarded packet matches an observed packet.  
Conceptualization, Y.C.; methodology, Y.C. and G.Q.; software, Y.C.; validation, Y.C. and G.Q.; formal analysis, Y.C. and G.Q.; investigation, Y.C.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and G.Q.; visualization, Y.C.; supervision, G.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank OPNET Technologies, Inc. for providing us with the OPNET Wireless Modeler to validate our ideas based on extensive simulations based on it.  
The authors declare no conflict of interest.  
Figure 18: The number of packet drops at various error rates when the error period is set to be 1200 s.
## 3 Methodology
### Feature Extraction
#### 3.3.4 Grey-Level Co-Occurrence Matrix (GLCM)  
The GLCM is a powerful feature descriptor that evaluates the spatial link between two pixels to assess the textural qualities of an image. Because the spacing and orientation between pixels are altered, the simultaneous presence of pixel pairs can be determined. There are 14 types of features presented in the GLCM. Here, some of the textural features are derived [38]. Successful description is based on the textural information gained from the GLCM.  
The instantaneous occurrence probability of two pixels in the GLCM is stated as \(P(i,j,\delta,\theta)\). It contains the pixel with, respectively, grey-level \(i\) and \(j\) as \(f(x,y)\) and \((x+\Delta x,y+\Delta y)\); \(\theta\) is defined as declination, and \(\delta\) is stated as distance. The statistical formulation is stated in Equation (9):  
\[P(i,j,\delta,\theta)=\left\{\begin{matrix}|[(x,y),(x+\Delta x,y+\Delta y)]|f(x,y)=i,\\ (x,y),\quad f(x+\Delta x,y+\Delta y)=j;x=0,1,\ldots,N_{x}-1;\\ y=0,1,\ldots,N_{y}-1\end{matrix}\right\} \tag{9}\]  
where \(i,j=0,1,\ldots L-1\); image pixel coordinates are represented as \(x\)and\(y\); the grey-level image is stated as \(L\); numbers of columns and rows are stated as \(N_{x}\)and\(N_{y}\). As a result, the appropriate feature selection is performed with IDOA, which decreases the model's operating performance and computational efficiency.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models  
Vehicular communication, a burgeoning field in automotive technology, facilitates inter-vehicle communication through innovative methods such as sidelink communication. This direct vehicle-to-vehicle communication paradigm operates independently of centralized infrastructure, employing sensing-based semi-persistent scheduling (SB-SPS) for efficient resource allocation and management. SB-SPS enables vehicles to designate communication resources for specific time and frequency intervals while allowing for dynamic adjustments in transmission power and modulation and coding scheme (MCS). These adaptive capabilities empower vehicles to optimize their communication strategies in response to varying environmental conditions, such as increasing transmission power during traffic congestion to maintain reliable communication or modifying the MCS to maximize throughput for large data transfers. The inherent flexibility of this system enables vehicles to navigate diverse communication scenarios, ultimately achieving optimal performance in a wide range of operational contexts [19].
# A Greedy Pursuit Hierarchical Iteration Algorithm
## 3 Greedy Pursuit Hierarchical Iterative Parameter Estimation Algorithm
Define \(l\) and collect sampled data \(\{u_{i}(d),y(d)\colon d=1,2,\ldots,L\}\) to form \(\boldsymbol{Y}\). 2. To initialize external iteration: let \(k=1\), \(\hat{\boldsymbol{\theta}}=\boldsymbol{1}_{N}/p_{0}\), \(p_{0}=10^{6}\), \(\hat{w}_{0}(d),\hat{e}_{0}(d)\) be a random number, and give allowable error \(\epsilon\) and \(\varepsilon\). 3. Form \(\hat{\boldsymbol{\varphi}}_{k,n}(d)\) by Equation (20), and \(\hat{\boldsymbol{\varphi}}_{k}(d)\) by \[\hat{\boldsymbol{\varphi}}_{k}(d)=[\boldsymbol{\varphi}_{\mathrm{s}}^{ \mathrm{T}}(d),\hat{\boldsymbol{\varphi}}_{k,n}^{\mathrm{T}}(d)]^{\mathrm{T}}.\] (28) Using Equation (29), update \(\hat{\boldsymbol{\Phi}}_{k}\), and using Equation (12) based on \(\hat{\boldsymbol{\Phi}}_{k}\), construct \(\hat{\boldsymbol{T}}\). Then, construct \(\hat{\boldsymbol{\Psi}}_{k}\) by using \(\hat{\boldsymbol{\Phi}}_{k}\) and \(\hat{T}\). 4. Begin the internal iteration. Let \(q=1\), \(\boldsymbol{r}_{k,0}=\boldsymbol{Y}\), and \(\Lambda_{0}=\varnothing\), \(\hat{\boldsymbol{\Psi}}_{k,\Lambda_{0}}=\varnothing\). 1. Find \(\lambda_{q}\) by Equation (24). 2. Update \(\Lambda_{q}\) and \(\hat{\boldsymbol{\Psi}}_{k,\Lambda_{q}}\) by Equation (25). 3. Compute the parameter estimate \(\hat{\boldsymbol{\vartheta}}_{k,\Lambda_{q}}\) by Equation (27). Update residual \(\boldsymbol{r}_{q}\) by Equation (23). 4. If \(\|\hat{\boldsymbol{\vartheta}}_{k,\Lambda_{q}}-\hat{\boldsymbol{\vartheta}}_{k,\Lambda_{q-1}}\|\leqslant\epsilon\), obtain \(\hat{\boldsymbol{\vartheta}}_{k,\Lambda_{q}}\); otherwise, increase \(q\) by \(1\) and go to step (a). 5. Recover parameter estimate \(\hat{\boldsymbol{\theta}}_{k}\) by \[\hat{\boldsymbol{\vartheta}}_{k,\Lambda_{q}}\in\mathbb{R}^{p}\overset{\Lambda _{q}}{\Longrightarrow}\hat{\boldsymbol{\vartheta}}_{k}\in\mathbb{R}^{N}, \hat{\boldsymbol{\theta}}_{k}=\hat{T}\hat{\boldsymbol{\vartheta}}_{k}.\] (29) Update noise estimates \(\hat{w}_{k}(d-j),\hat{e}_{k}(d-j)\) by Equations (21) and (22), and update the noise estimation vectors \(\hat{\boldsymbol{\varphi}}_{k,n}(d),\hat{\boldsymbol{\varphi}}_{k}(d)\) using Equations (20) and (29), \(\hat{\boldsymbol{\Phi}}_{k}\) and \(\hat{\boldsymbol{\Psi}}_{k}\) by \[\hat{\boldsymbol{\Phi}}_{k}=[\hat{\boldsymbol{\varphi}}_{k}(1),\hat{ \boldsymbol{\varphi}}_{k}(2),\cdots,\hat{\boldsymbol{\varphi}}_{k}(L)]^{ \mathrm{T}},\hat{\boldsymbol{\Psi}}_{k}=\hat{\boldsymbol{\Phi}}_{k}\hat{T}.\] (30)
6. If \(\|\hat{\boldsymbol{\theta}}_{k}-\hat{\boldsymbol{\theta}}_{k-1}\|\leqslant\epsilon\), complete the iteration stage and receive the final estimate \(\hat{\boldsymbol{\theta}}\); otherwise, let \(k=k+1\) and turn to step 4. The recovered parameter vector \(\hat{\boldsymbol{\theta}}\) has \(r+1\) zero blocks. With prior knowledge \(l\) and orders \(n_{a},n_{bi},n_{c},n_{f}\), time-delay estimates will be received by  
\[ \begin{array}{l}\hat{\tau}_{1}=n_{1},\\ \hat{\tau}_{i}=n_{j}-(l-\hat{\tau}_{i-1}-n_{bj}),\end{array}  \tag{31}\]  
where \(n_{j}(j=2,3,\cdots,r)\) is the number of zero items in each zero block.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 4 Data Acquisition Tool--IT System Design
### Maintainability Analysis Module
#### 4.2.2 Task Scheduling Optimization  
The system optimizes the scheduling of maintenance tasks by minimizing downtime \(T\), defined as the total time required to complete all tasks \(\{T_{1}\),\(T_{2}\),...,\(T_{m}\)}.  
* Objective Function: \[\min_{\sigma}T=\sum_{i=1}^{m}\sigma(T_{i})\cdot E[D_{i}]\] (7) where \(\sigma(T_{i})\) represents the sequence or priority of task \(T_{i}\).
* Constraints: Tasks dependent on preceding tasks: \[\sigma(T_{i})>\sigma(T_{j})\ if\ T_{i}\ depends\ on\ T_{j}.\] (8) Availability of resources (technicians, tools, etc.).