esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 3 Description of the System Under Study
### Dynamic Model of the Scrobot Robot
\begin{array}{c}C_{21}=\Big{(}\Big{(}\frac{\mathrm{m}\mathrm{sin}(2\uptheta_ {2})\mathrm{l}_{\mathrm{c}2}{}^{2}}{2}\Big{)}+\mathrm{m}_{3}(\mathrm{l}_{ \mathrm{c}3}\mathrm{cos}(\uptheta_{2}+\uptheta_{3})+\mathrm{l}_{2}\mathrm{ cos}(\uptheta_{2}))(\mathrm{l}_{\mathrm{c}3}\mathrm{sin}(\uptheta_{2}+\uptheta_{3})+ \mathrm{l}_{2}\mathrm{sin}(\uptheta_{2}))\Big{)}\mathrm{d}\uptheta_{1}{}^{2} \\ \qquad\qquad\qquad-\mathrm{l}_{2}\mathrm{l}_{\mathrm{c}3}\mathrm{m}_{3} \mathrm{sin}(\uptheta_{3})\mathrm{d}\uptheta_{3}{}^{2}-2\mathrm{d}\mathrm{d} _{2}\mathrm{l}_{2}\mathrm{l}_{3}\mathrm{m}_{3}\mathrm{sin}(\uptheta_{3}) \mathrm{d}\uptheta_{3}\end{array}  \tag{32}\]  
\[C_{31}=\mathrm{l}_{\mathrm{c}3}\mathrm{m}_{3}\mathrm{sin}(\uptheta_{2}+\uptheta _{3})(\mathrm{l}_{\mathrm{c}3}\mathrm{cos}(\uptheta_{2}+\uptheta_{3})+ \mathrm{l}_{2}\mathrm{cos}(\uptheta_{2}))\mathrm{d}\uptheta_{1}{}^{2}+\mathrm{ l}_{2}\mathrm{l}_{3}\mathrm{m}_{3}\mathrm{sin}(\uptheta_{3})\mathrm{d}\uptheta_{2}{}^{2} \tag{33}\]  
\[G=\left[ \begin{array}{c}G_{11}\\ G_{21}\\ G_{31}\end{array} \right] \tag{34}\]  
\[\mathrm{G}_{11}=0 \tag{35}\]  
\[\mathrm{G}_{21}=\mathrm{g}_{x}(\mathrm{m}_{3}(\mathrm{l}_{\mathrm{c}3}\mathrm{ cos}(\uptheta_{2}+\uptheta_{3})+\mathrm{l}_{2}\mathrm{cos}(\uptheta_{2}))+ \mathrm{l}_{2}\mathrm{m}_{2}\mathrm{cos}(\uptheta_{2})) \tag{36}\]  
\[G_{31}=\mathrm{g}_{x}\mathrm{l}_{\mathrm{c}3}\mathrm{m}_{3}\mathrm{cos}( \uptheta_{2}+\uptheta_{3}) \tag{37}\]  
\[F=\left[ \begin{array}{ccc}F_{11}&F_{21}&F_{31}\end{array} \right]^{T} \tag{38}\]  
where  
\(\mathrm{m}_{1}\), \(\mathrm{m}_{2}\), and \(\mathrm{m}_{3}\): represent the masses of links one, two, and three, respectively. \(\mathrm{I}_{1 zz}\), \(\mathrm{I}_{2 zz}\), and \(\mathrm{I}_{3 zz}\): represent the moments of inertia of the first, second, and third links with respect to the first \(z\)-axis of their respective joints to facilitate their modeling [24]. Nonlinear control in a robot uses the dynamic equation described in (19) [1,2], where  
\(\mathrm{\tau}\): vector of torques and forces with dimension \((nx1)\). \(M\): inertia matrix with dimension \((nxn)\). \(\mathrm{C}\): vector of Coriolis and centrifugal forces with dimension \((nx1)\). \(G\): gravity vector with dimension \((nx1)\). \(F\): friction vector with dimension \((nx1)\). \(\mathrm{\tau}_{d}\): externals torques and forces with dimension \((nx1)\)_in_[N\(\cdot\)m]. \(n\): number of DoF. \(q\): represents the position term. \(\dot{q}\): represents the velocity term. \(\ddot{q}\): represents the acceleration term. Next, Table 3 presents the Scorbot model with some of its real technical specifications for modeling.
# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 1 Introduction
### Internet of Things (IoT)  
The Fourth Industrial Revolution is pushing towards intelligent organizations, where communication and access to the information within an organization will not only take place between people. Machines, on behalf of humans, will seek to communicate with other machines and collect the necessary data. All this communication will take place via the Internet, called the Internet of Things, whose architecture is technically based on data communication tools. All objects (human or machine) defined in the Cyber-Physical system will use the Internet of Things to communicate [5].  
The Internet of Things (IoT) can be characterized as the outcome of the radical evolution of the Internet connected to a network of interconnected objects that create an intelligent environment. The IoT is a system of interconnected computing devices that are equipped with unique identifiers and the ability to transport data within a network without the need for human-to-human or human-to-computer interaction, according to the technical definition. The devices can either be mechanical or digital. The basic idea of IoT is to take all physical devices around the world and connect them with the internet [21].  
IoT enables the connection of people and objects anytime, anywhere, with anything and anyone by the exploitation of any path or network. The designation for IoT application in industrial organizations is called IIoT and refers to integrating IoT into the industry by connecting sensors, control systems, machines, devices, Big Data analysis and security. By utilizing IIoT, industrial organizations are able to become intelligent based on optimizing processes and resources and creating autonomous systems [20].  
IoT can be described as a network comprising several connected nodes that depend on sensory, transmission and information-processing technology that communicate with each other as smart components to achieve a specific goal using the Internet as a communication medium with no time and space constraints [22].  
Figure 3: Model-controlled production process vs. data-driven production process (Own processing [17]).  
The Internet of Things extends the application of technologies such as RFID to tools, technologies and components in manufacturing processes, enabling communication and information sharing between them. RFID is a wireless technology that enables the automatic identification of objects at a distance [23]. Every physical object can become intelligent thanks to IoT, and the data obtained from intelligent objects can assist organizations in optimizing processes at all levels of the organization [24].  
IoT strategically promotes the use and effective integration of sensors into manufacturing processes to collect real-time data, thereby contributing to data-driven manufacturing [16].
## 3 Inclusion of Temperature in the DNN Model-Based Method
### Tests  
After the training and validation process, an unbiased evaluation of the model performance was carried out. The absolute maximum error is approximately 0.5%, so if this method was used to detect ITFs, faults with a severity greater than 0.5% could be detected. In other words, because 0.5% is the smallest severe detectable fault, the tripping limit should never be fixed beneath this value.  
Table 1 shows the evaluation results of the DNN network for the machine in healthy conditions, including the value of the mathematical metrics used to evaluate its accuracy: the mean average error (MAE) and the root mean square error (RMSE).  
The errors obtained through DNN prediction are significantly beneath the errors obtained in previous papers where the temperature was not considered as an input in the model [16]. An MAE of 0.1% shows that the trained DNN has a fairly high overall estimation accuracy, in terms of overall closeness to null error values, while an RMSE of 0.06 implies a fair mean value of prediction dispersion with respect to the actual values, as this metric penalizes outliers or large deviations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Set of Data** & **MAE [\%]** & **RMSE [mA]** \\ \hline Tests under healthy conditions & 0.1 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results obtained from the DNN predictions under healthy conditions.  
Figure 6: DNN training process through backpropagation.
# Robustness of Deep Learning Models for Vision Tasks
## 3 Adversarial Attacks and Defenses
### Adversarial Attacks
#### 3.1.2 FGSM Attack  
[PERSON] et al. [114] proposed the simplest and fastest method for constructing adversarial examples. To reduce the classification confidence and increase the confusion between classes, the fast gradient sign method (FGSM) attack adds perturbations and linearizes the loss function in the gradient direction as follows:  
\[x^{\prime}=x+\varepsilon\cdot\text{sign}(\
abla_{X}J(x,\ y)) \tag{3}\]  
where \(x^{\prime}\) represents an adversarial example from the given input \(x,\varepsilon\) is a randomly selected initial hyperparameter, sign, \((\cdot)\) denotes the signum function, \(y\) represents the true label corresponding to \(x\), \(J(\cdot)\) represents the cost function for training the neural network model, and \(\
abla_{X}(\cdot)\) is the gradient of \(x\). The FGSM attack employs analytical computation to calculate the gradient, whereas the L-BFGS attack involves numerical optimization; thus, the FGSM attack finds a solution significantly faster. This implies that the FGSM attack cannot produce a perceptual minimal difference between \(x\) and \(x^{\prime}\) owing to \(\varepsilon\). Once an appropriate value of \(\varepsilon\) is found empirically, an imperceptible adversarial sample can be obtained by applying the values around it. In experiments involving the MNIST dataset, a shallow softmax classifier had an error rate of 99.9% owing to an FGSM attack.  
The authors argued that the output of the neural network model is vulnerable to adversarial examples when it is excessively biased toward linear processing, and the generalization of adversarial examples across different models can be explained by adversarial perturbations being highly aligned with the weight vectors of a model and different models learning similar functions when trained to perform the same task. An adversarial example and perturbation generated by an FGSM attack are shown in Figure 17.  
[PERSON] et al. [120] improved the effectiveness of FGSM attacks through a more refined iterative optimization procedure to find the optimized solution. In their method, which is called the Basic Iterative Method (BIM), in each iteration, an FGSM calculation is carried out with a small step size to revise the adversarial sample and restrict the updated adversarial example within a valid range for \(T\) iterations.  
\[x^{\prime}_{t+1}=clip(x^{\prime}_{t}+\alpha\cdot\text{sign}\big{(}\
abla_{X}J \big{(}x^{\prime}_{t},\ y\big{)}\big{)} \tag{4}\]  
Here, \(\alpha T=\epsilon\) and \(\alpha\) denotes the size of the disturbance in each iteration.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 2 General Background
### Approaches to Deal with Class Imbalance Problems  
To deal with the class imbalance problem, three approaches are commonly used: data level, algorithm level, and cost-sensitive [15, 31, 34].  
1. Data level: The objective of this approach is to create a balanced training dataset by preprocessing the data through artificial manipulation. There are three solutions to data sampling: over-sampling, under-sampling, and hybrid-sampling. 1. Over-sampling: New objects are generated for the minority class. The basic version of this solution is the random oversampling of the objects belonging to the minority class. However, the main drawback is that it can lead to overfitting [35; 36]. Under-sampling: Objects are removed from the majority class. The goal is to have the same number of objects in each class. The basic solution of this method is random under-sampling. The disadvantage of using this method is that it can exclude a significant amount of the original data. Hybrid-sampling: A combination of over-sampling and under-sampling. This approach generates objects for the minority class while it eliminates objects from the majority class.
2. Algorithm level: The aim of this type of approach is a specific modification of the classifier. This approach is not flexible for different classification problems because it focuses on a specific classifier with a specific type of database. Nevertheless, the results could lead to good classification results for a particular problem. This type of solution can also combine strengths of different solutions as the NeuroFuzzy Model [37], which combines a fuzzy system trained as a neural network [31].
3. Cost-sensitive: The objective is to create a cost matrix that is built with different misclassification costs. The misclassified objects of the minority class have a higher misclassification cost than the misclassified objects of the majority class. One of the main disadvantages is the cost-sensitive problem, which appears because the cost of misclassification is different for each of the classes. Therefore, this type of problem cannot be compared against non-cost-sensitive problems [38].
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 1 Introduction
To overcome the shortcomings of the DCAR-type schemes, we propose a constrained coding-aware routing (CCAR) mechanism, which is more energy-efficient and suitable for wireless ad-hoc networks. The CCAR mechanism aims to find a more appropriate route to maximize the coding benefits instead of the coding opportunities. We also modify the routing discovery process to ensure the balance of the network load. Moreover, the proposed scheme cannot only save more energy, but can also encode the flows with different rates. The contributions of this paper can be summarized as follows:  
Figure 1: A simple example of the network coding (NC) idea. 1. To reduce network congestion and balance network load, we propose the constrained coding conditions (CCC) modified from PCC, which allows some neighbor nodes of the destination nodes to assist in decoding. 2. To save energy and be more adaptive to the constrained coding conditions, the RREQ and RREP messages are revised. Moreover, we combine and simplify the routing discovery and coding processes. 3. We use the length of the output queue as an indicator of the current status of the coding nodes and propose a novel coding benefit function to reduce the negative effects of rate diversity. The results show that our scheme achieves better network performance with respect to network throughput, delay, and energy consumption. The remainder of the paper is organized as follows. Section 2 reviews the related work concerning the coding-aware routing mechanism. Section 3 presents the routing ideas, procedures, and implementation issues of CCAR. Section 4 evaluates the performance of the CCAR scheme through the simulation results. Conclusions are drawn in Section 5.
## 3 Results and Discussion  
### Analysis of Variance  
The test for normality is necessary before conducting analysis of variance (ANOVA), since large scale error may occur if the data set is not following normal distribution. The normality test was conducted by Anderson-Darling test in Minitab 17, where normal distribution was indicated when the \(p\)-value was larger than 0.05 [36]. From Figure 2 below, the \(p\)-values were both larger than 0.05 for the S/N conversion of micro-hardness and wear volume in the Anderson-Darling test. Thus, normal distributions for both responses were verified, and ANOVA was conducted hereafter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Run**} & **Micro-Hardness** & \begin{tabular}{c} **S/N of** \\ **(HRC)** \\ \end{tabular} & \begin{tabular}{c} **Wear Volume** \\ **Micro-Hardness** \\ **(dB)** \\ \end{tabular} & \
\begin{tabular}{c} **S/N of** \\ **(mm\({}^{\text{3}}\))** \\ \end{tabular} &
\begin{tabular}{c} **S/N of Wear** \\ **Volume (dB)** \\ \end{tabular} \\ \hline
1 & 19.0 & 25.575 & 0.0042296 & 47.474 \\
2 & 24.9 & 27.924 & 0.0034816 & 49.164 \\
3 & 39.1 & 31.844 & 0.0020500 & 53.765 \\
4 & 50.6 & 34.083 & 0.0016755 & 55.517 \\
5 & 40.0 & 32.041 & 0.0009220 & 60.705 \\
6 & 66.3 & 36.430 & 0.0003904 & 68.170 \\
7 & 37.1 & 31.387 & 0.0020868 & 53.610 \\
8 & 47.4 & 33.516 & 0.0003091 & 70.198 \\
9 & 18.0 & 25.105 & 0.0032789 & 49.685 \\
10 & 45.7 & 33.198 & 0.0008109 & 61.821 \\
11 & 25.6 & 28.165 & 0.0028956 & 50.765 \\
12 & 17.4 & 24.811 & 0.0032958 & 49.641 \\
13 & 47.8 & 33.589 & 0.0003133 & 70.081 \\
14 & 18.2 & 25.201 & 0.0054664 & 45.246 \\
15 & 57.9 & 35.254 & 0.0004863 & 66.262 \\
16 & 28.0 & 28.943 & 0.0039519 & 48.064 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Signal to noise ratio (S/N) conversion results for micro-hardness and wear volume.
# Multi-Objective Optimization of Three-Stage Turbomachine
## 3 Optimization Problem  
Engineering design problems often involve multiple objective functions and constraints, making them complex optimization challenges. Solving these challenges necessitates advanced techniques from operations research, such as mathematical programming, stochastic processes, and statistical methods [33]. The mathematical model of rotordynamics operates within predefined constraints. Consequently, optimization techniques, especially mathematical programming, are well suited for addressing these problems in operational research. Beyond traditional methods, modern optimization approaches offer significant diversity. These include metaheuristic algorithms, neural network-driven techniques [33], and fuzzy systems-based optimization [34].  
Metaheuristic algorithms, which form the foundation of the optimization methods examined in this study, generally reach an optimal point through exploration and exploitation, two fundamental processes. However, the mathematical expression of these steps varies depending on the inspiration behind each algorithm. According to the no free lunch (NFL) theorem, optimization algorithms demonstrate varied performances depending on the type of problem [35]. For instance, while some algorithms perform above average for certain types of problems, another algorithm may yield better results for different problem types. Therefore, in the context of rotordynamic optimization, understanding the specific nature of the fitness function is essential for accurately evaluating algorithm performance.  
In the context of rotordynamic design optimization, the primary goals are to ensure that natural frequencies remain outside the operational range and to minimize the weight of the rotor structure [36; 37]. Additionally, adjustments in bearing parameters enable control over rotordynamics, bearing loads, and disk deflections [38; 39]. These rotordynamic problems are categorized based on their specific objective functions and design complexities.  
This study evaluates the performance of the TMM by applying nine different optimization algorithms. This approach allows for a thorough assessment of how effectively these algorithms address the multi-objective, multivariable rotordynamic optimization problem and the stability of the TMM solution.
## 1 Introduction
In particular, they are able to: (i) produce indol-3-acetic acid (IAA) implementing root development and its architecture to increase the surface area available for nutrient uptake [15]; (ii) produce 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, which is able to reduce the level of plant ethylene and enhancing plant tolerance to abiotic stresses [16]; (iii) produce siderophores--secondary metabolites able to scavenge iron from environmental stocks forming soluble Fe\({}^{3+}\) complexes that are actively taken up via specific receptors [17]; (iv) mineralize or solubilize some nutrients, such as phosphate-compounds. Moreover, PGPR can suppress the growth and activity of plant pathogens by producing antibiotics and degrading enzymes [18]. In the literature are reported many mechanisms to counteract salt stress in plants by PGPR. In particular, they reduce the detrimental effects of high ethylene levels on plants, such as chlorosis, abscission and senescence [19]. Additionally, under salinity stress, NaCl leads a rapid K\({}^{+}\) loss from the cellular cytosol of the plant, whereas PGPR maintains a greater amount of K\({}^{+}\) in order to preserve a higher K\({}^{+}\)-Na\({}^{+}\) ratio and avoid osmotic imbalance [1]. Furthermore, PGPR contributes to the reduction of reactive oxygen species (ROS) induced by NaCl in the plant cells, increasing the levels of ROS-scavenging enzyme [20]. Therefore, the use of PGPR in agriculture is a sustainable and biological approach that is able to ameliorate the soil's fertility and quality [21]. The effects of salt on PGP features remain little known, and few studies focusing on NaCl's effect on IAA production have been reported for fungi and bacteria. Specifically, negative effects were detected on IAA by increasing the NaCl concentration in _Bacillus_ and _Pseudomonas_ species, and in some enterobacteria [22; 23]. The aim of our study was to characterize halophilic/halotolerant PGPR strains with biotechnological potential in the recovery of salinized agricultural soil and to expand the knowledge on salt's role in the regulation of bacterial PGP activities. In this regard, we characterized physiologically, genetically, and biochemically, novel PGPR isolated in an our previous work [24] from the rhizosphere of _Zea mays_ L. and _Chenopodium quinoa_ Wild., exposed to high concentrations of NaCl.
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## 4 Conclusions  
In this paper, the explosive shock wave propagation and dynamic behaviors of a coal mine mobile refuge chamber under the gas explosion were simulated and analyzed by using the explicit nonlinear dynamic finite element program ANSYS/LS-DYNA and the ALE numerical calculation method. The numerical model included an underground mine tunnel, a coal mine mobile refuge chamber, and the gas/air mixture. The following conclusions were reached:  
The phenomenon of interaction between the chamber and shockwave was pronounced. The propagation of shock waves and the affection to the chamber could be successfully simulated by ALE fluid-structure interaction method. The shockwave is reflected by the chamber and the reflected wave pressure value on the front surface of the chamber is about twice higher than the incident one. The peak pressure on the side and upper surface gradually reduces along the distance and the value which measured close to the front ranged between 0.15 and 0.19 MPa is almost 1/3 of that in the front surface.  
Based on the simulation results, there are two failure modes of the refuge chamber; one is the unrecoverable extrusion large deformation of the front door, the other is the front door detaching from the bulkhead. The front door is the first component that bears the blast shockwave, and it is the most easily damaged part. The deformation is effectively controlled by increasing the thickness of the door and stiffeners. When welding is weak, the upper portion of the front door may detach from the bulkhead, and the refuge chamber becomes invalid. Therefore, in order to ensure the refuge chamber safety, some measures such as increasing the thickness of the door and stiffeners and strengthening the welding between the door and the bulkhead are suggested.  
The authors thank the financial support of the National Natural Science Foundation of China (Grant No. 51108141) and Natural Science Foundation of Heilongjiang Province of China (Grant No. QC2011C064) for carrying out this research.  
[PERSON] and [PERSON] conceived and designed the simulation research; [PERSON] set up the simulation models; [PERSON] and [PERSON] the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 3 Triple Modular Redundancy in FSMs
### Model 2--Triplicated Transition Block \(\delta\) and State Register DFF  
Model 2 corresponds to a situation where TMR is implemented on the transition block \(\delta\) together with state register DFF--Figure 10.  
1. As before, if an error occurs in the output block \(\lambda\), the output will be corrupted.
2. If an error is injected into one of the three transition block \(\delta\), the appropriate state register will latch the wrong value and this FSM will be in the wrong state. However, the voter at the output of the state registers will verify this and fix it in the next clock.
3. If the error is in the state register DFF, two of the three transition blocks will still operate correctly, which will cause the third FSM to return to the correct cycle as well. TMR can be implemented independently on the output block.  
Figure 10: Model 2—transition block \(\delta\) and state register DFF is triplicated.  
Figure 9: Model 1—state register is triplicated.