esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Predictive Control Methods
### Optimization Problem and Establishment of Performance INDEX Function
#### 2.3.1 Gradient Establishment Based on Error Constraint  
In order to ensure that the output tracking error decreases with time, gradient descent is introduced into the constraint function, and we make the error function in the constraint condition change along the gradient descent direction. As a constraint condition, it is necessary to find the constraint range of the error function so that the system output tracking error can be reduced quickly, the convergence speed can be accelerated, and the set value can be tracked as soon as possible. In order to ensure that the tracking error value of the system decreases as quickly as possible until it reaches zero, we establish constraint conditions, and the tracking error function value changes along the gradient descent direction as follows:  
\[m\|e(k)\|\leq\|e(k+1)\|\leq n\|e(k)\|,\;0<m<n<1\]  
It is necessary to determine the values of \(m\) and \(n\). We set the error tracking performance index function as:  
\[J_{e}=\frac{1}{2}e(k)^{2} \tag{16}\]  
\[\Delta y(k+1)=-\alpha\frac{\partial}{\partial e(k)}J_{e} \tag{17}\]  
\[\Delta y(k+1)=\Delta x(k+1) \tag{18}\]  
\[\frac{\partial}{\partial e(k)}J_{e}=e(k) \tag{19}\]  
\[-\alpha\frac{\partial}{\partial e(k)}J_{e}=-\alpha e(k) \tag{20}\]  
\[\Delta x(k+1)=-\alpha e(k) \tag{21}\]  
According to (19) and (8), we will get,  
\[\begin{gathered} e(k+1)=(1-\alpha_{1})e(k)\\ e(k+2)=(1-\alpha_{1})(1-\alpha_{2})e(k)\\ e(k+3)=(1-\alpha_{1})(1-\alpha_{2})(1-\alpha_{3})e(k)\\ e(k+n)=(1-\alpha_{1})(1-\alpha_{2})\dots\dots(1-\alpha_{n})e(k) \end{gathered} \tag{22}\]
## 3 Results and Discussion  
### Fractionated Condensation of the Model Solution of Red Wine  
A model red wine solution was used under defined upstream operating conditions to originate a vapour that mimics the headspace of wine fermentation. The aromas were  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**C**} & \multicolumn{2}{c}{**Range of**} & \multirow{2}{*}{**Reference**} \\  & & & & & **Temperature (\({}^{\circ}\)C)** & \\ \hline Water & 5.40 & 1838.68 & \(-31.74\) & \(-015\) to 29.85 & [23] \\ Ethanol & 5.25 & 1598.67 & \(-46.42\) & \(-0.15\) to 78.55 & [23] \\ Ethyl acetate & 4.23 & 1245.70 & \(-55.19\) & - & [23] \\ Ethyl hexanoate & 15.99 & 3118.28 & \(-106.76\) & - & [19] \\ Isoamyl acetate & 16.50 & 2871.68 & \(-110.92\) & - & [19] \\
4-Ethyl phenol & 7.62 & 1955.30 & 195.46 & 99.76 to 244.80 & [23] \\
4-Ethyl guaiacol & 7.90 & 2203.80 & 234.22 & 85.27 to 233.09 & [24] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antoine law constants for each compound studied.  
found in a range of concentrations in the feed vessel in the model solution (0.6-50.0 ppm). The vapour formed inside the feed vessel was the starting point of the model developed in this work. To understand how effective the removal of off-flavours can be, modelling of the fractionated condensation process at atmospheric pressure was performed.  
To achieve a reduction in the ethyl phenol content from the desirable aroma concentrate, the aroma fractionation aimed at obtaining two different condensates, where an optimal condensation temperature (in the first condenser) allowed the separation of valuable aromas from off-flavours. The mathematical model was used to simulate the percentage of each compound, \(i\), recovered in the first condenser, %_Condensi1_, for different values of \(T_{1\_condens}\) (\"C]), which are presented in Figure 5.  
The optimal temperature suggested by the simulation was \(-\)5 \({}^{\circ}\)C. With this condition, the model estimated a recovery of more than 87% of 4-EG, 22% of 4-EP, 16% of ethyl acetate, and a residual (%_Condensi1_ < 4%) of ethyl hexanoate and isoamyl acetate. These results represent a good recovery of the target esters in the 2 nd condenser and a good reduction in the ethyl phenol content, more expressive in the case of 4-EG.  
Due to the lower energy costs involved, fractionated condensation allows for aroma recovery under more economical conditions. However, this process does not allow for proper off-flavour fractionation, leaving 78% of 4-EP, the most important off-flavour, in the 2 nd condenser.
## 2 Multilayer Plate Concept  
The plate is considered to be built by a number of layers, without slipping between them. Each layer is considered to be homogeneous and isotropic.  
This concept of multilayer plate and its parameters are illustrated in Figure 4. For a plate, made up of a finite number of layers, each layer being considered homogeneous and isotropic, the bending rigidity \(D=D^{*}\) is [24]:  
\[D^{*}=\frac{A\cdot C-B^{2}}{A} \tag{2}\]  
where  
\[A=\sum_{j}\frac{E_{j}}{1-v_{j}^{2}}\cdot\left(z_{j}-z_{j-1}\right) \tag{3}\]  
\[B=\sum_{j}\frac{E_{j}}{1-v_{j}^{2}}\frac{z_{j}^{2}-z_{j-1}^{2}}{2} \tag{4}\]\[C=\sum_{j}\frac{E_{j}}{1-v_{j}^{2}}\cdot\frac{z_{j}^{3}-z_{j-1}^{3}}{3} \tag{5}\]  
The index \(j\), in the relations (3) to (5) refers to the \(j\)th layer, like in Figure 4. So, the governing differential equation (Sophie-Germain) of the multilayered plates is [24,25]:  
\[\
abla^{2}\
abla^{2}w(x,y)=p_{z}(x,y)/D^{*} \tag{6}\]  
For a circular plane plate, with a constant distributed load \(p_{z}\) the relation (3) becomes [24,25]:  
\[\
abla^{2}\
abla^{2}w(r)=p_{z}/D^{*} \tag{7}\]  
The methodology for solving FGPs, based on the multilayer plate concept, involves replacing the continuous variation of the material properties (Figure 3), with a step variation (Figure 5), which would correspond to a set of 20 homogeneous and isotropic layers, each of them having different properties.  
Looking at the curves in Figure 5, we find that the step variation is not a constant one. This is higher or lower depending on the power coefficient value, which also influences the sign and value of the curvature.  
Figure 4: The reference system, the lines “\(i\)” and layers “\(j\)” that define the multilayer plate.  
Figure 5: The step variation, compared to the continuous variation at the layer level, for two values of power coefficient \(k\).
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 1 Introduction  
With the increased use of social media and share of multimedia over communication networks, image encryption has become a critical research topic in information security, where researchers aim to protect the integrity and confidentiality of shared image data. Since multimedia data tend to have high amounts of redundancy (due to the inherent correlation between neighboring pixels), a specialized and robust image encryption algorithm is essential. As secure, widely used, and recommended encryption methods, such as the advanced encryption standard (AES), Twofish, RSA, etc., are not suitable for real-time multimedia data encryption (due to high computational requirements and slow encryption speeds), researchers have proposed various encryption techniques designed specifically for image encryption.  
In this paper, several proposed state-of-the-art image encryption algorithm techniques are reviewed with an emphasis on their strengths and weaknesses. This paper classifies image encryption algorithms into seven different approaches based on the techniques used. These approaches are traditional ciphers, chaotic systems, DNA encoding, neural networks, compressive sensing, frequency domain, and meaningful sensing. Additionally, we discuss security, quality, and efficiency evaluation metrics, such as the correlation coefficient, histogram analysis, entropy, mean square error (MSE), the NIST SP 800-22 Test [1], etc. This paper provides a brief introduction to image encryption algorithms for researchers and practitioners starting in this field and aids them in understanding the current state of image encryption algorithms. Our main contributions in this article are as follows:  
* Classification of image encryption into seven approaches based on the techniques used in the algorithms.
* A thorough review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption.  
* Calculation of the upper and lower bounds for each of the evaluation metrics for image encryption.  
The rest of this paper is organized as follows: Section 2 discusses the general concept of image encryption algorithms and provides a detailed review of different approaches to image encryption algorithms. Section 3 presents the metrics used by researchers to evaluate image encryption algorithms. Section 4 discusses the advantages, disadvantages and the applications of different image encryption algorithms. Finally, Section 5 concludes this work.
### Related Works  
Several studies have reported on benign and/or malignant classification of pulmonary nodules using image analysis [6; 7; 8; 9; 10; 11; 12]. [PERSON] et al. developed a scheme for the automated classification of the lung nodule CT images using the linear discriminant analysis (LDA) or support vector machines (SVMs) with stepwise feature selection [6]. The evaluation of 256 CT scans in that study revealed that the area under the receiver operating characteristic (AUROC) curve was 0.857. [PERSON] et al. introduced a method that used an ensemble of artificial neural networks (ANNs) for classifying pulmonary nodules [7]. The leave-one-out cross-validation of that method yielded the AUROC of 0.915. [PERSON] et al. developed a semi-automated scheme for classifying benign and malignant pulmonary nodules by integrating the PET and CT imaging results [8].  
Recently, approaches combining non-imaging medical data, such as electronic medical records (EMRs), for medical analysis have been reported [13; 14; 15; 16; 17]. Studies combining heterogeneous types of medical data are described below.  
[PERSON] et al. proposed an automated classification method for Parkinson's disease and controls using magnetic resonance (MR) images and EMR information [13]. [PERSON] et al. introduced MR images, PET images, apolipoprotein E (APOE), and cerebrospinal fluid (CSF) information for predicting mild dementia outcomes in patients who were likely to develop Alzheimer's disease [14]. [PERSON] et al. developed a method for depression diagnosis; the method combines MR images with demographics and cognitive ability scores [15]. Regarding lung cancer, [PERSON] developed an automated detection scheme using CT images, body mass index (BMI), and smoking-related information [16]. Furthermore, [PERSON] et al. reported an automated classification scheme for lung cancer types by combining liquid-based cytological (LBC) images and EMR information [17].  
Personalized analysis schemes can improve the accuracy of clinical decision support systems. However, to the best of our knowledge, an automated classification method for pulmonary nodules combining PET/CT images with patient information has not been developed yet.
## 1 Introduction
The Mei-Yu front is a quasi-stationary front stretching for thousands of kilometers, extending eastward from southwestern China to the central Pacific through the Japanese archipelago [39; 40]. It is one of the most significant rainfall systems affecting the hydrological cycle in the East Asian monsoon region. Rainstorm associated with Mei-Yu (also called \"Baiu\" in Japan) is one of the most disastrous weathers in East Asia [41; 42; 43; 44; 45]. Research has shown that, over the last few decades, Mei-Yu frontal precipitation in the Yangtze-Huaihe region has been increasing, both in heavy rainfall frequency and in total amount [46; 47; 48; 49]. In addition, with the accelerated urbanization and industrialization of the Yangtze River Basin, atmospheric pollution has been on the rise in recent years [50; 51; 52; 53; 54]. Measurements have shown that the atmospheric pollution directly contributes to the enhanced concentrations of aerosols in the Yangtze River Basin [55; 56; 57]. Have changes in the concentration of aerosols increased or decreased Mei-Yu frontal precipitation? Through what physical processes do aerosols affect Mei-Yu precipitation? These scientific questions have not been studied or revealed yet. To explore the microphysical and dynamical mechanisms of aerosol impacts on Mei-Yu precipitation, numerical experiments based on mesoscale Weather Research Forecasting (WRF) model [58] are implemented. The research could be useful in gaining a better grasp of the effects on weather and climate changes by anthropogenic pollution, and with possible application to reducing the significant uncertainties in weather and climate prediction resulting from such a lack of adequate knowledge. The rest of this paper proceeds as follows. Section 2 describes the numerical model and the experimental design. Results of the numerical experiments are presented and discussed in Section 3. Section 4 summarizes the physics mechanisms. Finally, conclusions are given in Section 5.
## 4 Discussion
### Spt  
SPT is a destructive test but has clear advantages over UTT. SPT requires a small volume of material, with samples obtained by simple straight cut machining, and exhibits a strong potential for high-throughput automation. As a result, there is tremendous potential for cost and effort savings compared to UTT, which requires larger material sample volumes. As shown in Table 2, a calculation of the machining and testing cost for SPT, using the experimental setup from this study, showed the cost of one SPT to be approximately 17% of the cost to machine and test one UTT sample.  
A calculation of the time savings for SPT vs UTT showed similar results. These cost and time savings can be greatly increased through the improvement of the SPT fixture design for automated high-throughput SPT.  
Because of the use of the Swift equation for the characterization of the strain hardening response of the material, it is possible to estimate the values of yield strength and UTS from the best-fit values of the plasticity parameters, \(K\), \(\varepsilon_{0}\), and \(n\). Yield strength may be predicted by using a plastic strain of 0.002 in Equation (14). [PERSON] et al. [47] determined that UTS value can be estimated by applying the Considere criterion on the material constitutive response defined by the Swift equation.  
The values of yield strength and UTS recovered from SPT measurements are compared against the corresponding UTT values in Figure 9.  
The table summarizes the percent variation between the average SPT estimated yield strength and UTS values and the corresponding values measured in UTT.  
This figure shows that the variation between the average SPT predicted yield strength and UTT yield strength for each sample was no greater than 5%. It also shows that the variation between the average SPT estimated UTS and the corresponding measurement from UTT was no greater than 6% for all of the samples tested in this work.  
Build parameters and post-build processing parameters often vary between AM builds and even within a single build. This study was aimed at evaluating these variations in both HIP and non-HIP samples. In the present work, HIPed samples 4 and 8 showed higher average UTS and yield strengths compared to the non-HIPed samples. This result was seen in both the standard UTT measurements as well as the SPT measurements. Therefore, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Processing Cost and Time** & **SPT** & **Tensile** \\ \hline \hline Machining Cost for 18 Samples (\$) & 112.5 & 562 \\ Machining Time for 18 Samples (hours) & 1.25 & 7.5 \\ Cost per Test (\$) & \textless{}2 & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cost comparison between SPT and UTT.  
results obtained in this work demonstrated the ability of SPT to produce reliable estimates of both yield strength and UTS, which are typically the most important properties used in optimizing the performance of the final product.
## 2 System Model
The anycast methodology helps forward the sensor data to the next hop nodes. The nodes in each region forward the collected data of each event to the DON. Thus, efficient bandwidth utilization and bidirectional end-to-end reliability are the focus to maintain the smooth transfer of data from a region's nodes to the DON because they are mandatory in WSNs [16, 17, 18]. End-to-end reliability is acknowledged when the data from each event are reported to the DON. The DON of each region forwards the collected data from the nodes in regions applying exterior communication to the base station using the IP network. The DON uses the purely scheduled based approach of time division multiple access (TDMA). We assume that the transmission range of the mobile and static sensor nodes is the same as that of the high-power sensor nodes. Therefore, each sensor node is enabled to communicate within a 1-hop neighborhood. Each sensor node collects the data from its region only. There are two types of communication: one is between sensor nodes and the DON, and the other is from DON to DON in the case of a greater distance of the DON from the base station. Therefore, TDMA is combined with frequency division and multiple access (FDMA) to enable concurrent communication among the sensor nodes [19]. The frequency channels are recycled throughout the simulation process in the entire WSN so as to minimize the required number of frequency channels. Consequently, the sensor nodes and DON can communicate self-reliantly without snooping each other. We have considered the industrial, scientific and medical (ISM) bandwidth for the WSN. The entire bandwidth is 2.4 GHz and is initially distributed into two portions with a ratio of 1:6. The larger 2.0 GHz portion is assigned to the nodes of the region, and the smaller 0.4 GHz portion is allocated to the DONs that use the FDMA technique for communication. The bandwidth is distributed among the sensor nodes such that each neighboring sensor node uses a different frequency band. The PT routing paradigm is used for data routing. PT involves two important features: the packet generation rate, the pheromone sensitivity, which helps determine the predicted amount of pheromone, and each link's capacity. The packet generation rate helps determine how many packets could be generated. As such, this feature provides advanced information regarding the flow of traffic that helps avoid congestion over the network. In addition, pheromone sensitivity predicts the amount of traffic on each link and helps in forwarding the data to each link according to the capacity, which maintains load balancing. If this system model is deployed in a realistic environment, it could provide both operational stability and a swift recovery for critical scenarios. It would also guarantee that the disaster recovery process could be accurately monitored and communicated to all staff, clearly recognizing all of the essential roles and their responsibilities using WSNs.
## 1 Introduction
Moreover, defining clear research targets that facilitate the creation of links between different software components is also a key aspect of addressing these challenges. The study also includes solutions for the identification of compatible software releases to establish connections between different platforms, ensuring compatibility at the interface level, facilitating communication between the systems, and providing essential hardware tools. These procedurescollectively aim to improve compatibility between competitive suppliers by establishing links between their virtual commissioning programs. This article is based on the realization of the VC approach using a combination of the recent software releases, ABB RobotStudio and Simatic Robot Integrator, combined with an OPC communication method. The novelty of this research consists of the following:  
* The virtual conception of teach-pendant control for the SRI via parameterization of the graphic model in the form of a screen from the HMI interface;
* Establishing the communication and parameterization in the SRI interface of code control from the TIA Portal with geometric models simulated in Siemens MCD;
* Verification and testing of SRI integration with Factory IO and the insertion of an environment virtual function, different from Siemens MCD;
* Conception and testing of a connection bridge with the help of external tools to achieve further communication between ABB RobotStudio and SRI;
* Establishing the logical graph scheme of second-level control and converting it into ladder code using mathematical methods;
* Developing a mathematical model for the evaluation of the volumetric accuracy of double-portal-type RI, for inspection applications such as ultrasonic defectoscopy, and model NDT. This work is structured as follows: Section 2 encompasses a critical literature review from the perspective of available hardware and software solutions. A case study and a comparison between different offline simulation environments are included with virtual commissioning functions. The aim is to highlight the capabilities of each actual software employed in VC, the limitations, and how these limitations can be overcome. A synthesis of the research methodology is presented in Section 3, as well as the main steps of the approach. The results of the original virtual commissioning approach are discussed step by step, as well as the implementation procedures and control sequences. Finally, because at present in most popular unified interfaces, a double-portal robot architecture is missing, a mathematical algorithm was included in Section 4, to evaluate the volumetric accuracy of a double-portal robot and to illustrate the efficiency of the developed functionalities for a detailed examination of the effectiveness of the commissioning strategy. The article ends with Section 5, encompassing the conclusion, limitations of the VC approach, and perspectives for future work.