esa-sceva/satcom-chunk-collection huggingface dataset:
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results
### Pressure Variation Model (PVM)  
[PERSON] et al. (2020) proposed PV models correlating motion parameters with pressure variations (PVs) observed during the movement of a robotic fish. According to [PERSON]'s discussion ([PERSON], 2018), the hydrodynamic PVs across the body surface of the robotic fish can be approximated by the unsteady Bernoulli equation, assuming an irrotational flow and the absence of a boundary layer. Here, the linearized velocity potential is related to the robot fish's body with the translational velocity \(U\), the pitch angle \(\theta\), and body sway angular velocity \(\omega\), forming the expression of the PV model. Then, the dynamic pressure, \(P_{d}\), can be expressed as shown in Equation (1), and the coefficients for each motion are derived through linear regression analysis using experimental data.  
\[P_{d}=C_{1}U^{2}+C_{2}\theta^{2}+C_{3}\omega^{2}+C_{4}U\theta+C_{5}U\omega+C_{6 }\theta\omega+C_{7} \tag{1}\]  
However, this model has limitations. It requires the prior identification of the body's movements and using coefficients specific to each motion, making it challenging to apply to multiple movements. So, in this study, the PVM was designed to utilize identical coefficients for the entire motion range, and it was structured to encompass both velocity estimation algorithms and state estimation algorithms.
## 4 Proposed System Evaluation  
In the proposed downlink NOMA system, the transmitter and the receiver should have complete knowledge of the pilot sequences (\(x_{i}\)). The total number of subcarriers and the NOMA system parameters used in the simulations are based upon the long-term evolution (LTE) physical layer standards. The subcarrier spacing should be larger than 10 kHz to reduce performance degradation due to phase noise. Additionally, the subcarrier spacing should lie between 9 and 17 kHz in order to support users up to velocities of 350 km/h. Consequently, a subcarrier spacing of 15 kHz was chosen, which is similar to the case of LTE systems. Moreover, reducing the subcarrier spacing increases the spectral efficiency, as a larger number of data symbols is available for a certain bandwidth. Smaller subcarrier spacing correspondingly guarantees that the fading on every subcarrier is non-selective or nearly flat [31]. All the system parameters used are summarized in Table 1.  
The proposed system was evaluated by means of BER, outage probability, and user sum rates. The upcoming sections illustrate how the outage probability and sum rates were evaluated.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 3 Background
### Self-Organizing Maps  
SOM are an unsupervised type of Artificial Neural Network (ANN) [53]. It uses an unsupervised learning model to create a map of different groups [54]. As illustrated in Figure 1, SOM consist of two main layers: the input layer where inputs or neurons are inserted, and the output layer, also called a competitive layer, where groups of similar inputs are formed. The outputs of the SOM are generated by multiplying the inputs with SOM weights. These weights are randomly initiated when training a new network, and then at each iteration, the weights are updated in accordance to Equation (1). Correspondingly, Equation (2) is used to update the neighborhood function \(\theta(t)\) in which the SOM network topology is defined.  
\[w_{ij}(t+1)=w_{ij}(t)+\alpha(t)\times\theta(t)\cdot(x(t)-w_{ij}(t)) \tag{1}\]\(w_{ij}\): the weights. * \(t\): the current iteration. * \(\alpha(t)\): the learning rate. * \(\theta(t)\): the neighborhood function. * \(x(t)\): the input \[\theta(t)=exp\bigg{(}-\frac{distance\ from\ BMU^{2}}{2\alpha(t)^{2}}\bigg{)}\] (2)
* BMU: The Best Matching Unit reflecting the closest weight for the input instance.  
There are two crucial attributes behind the effectiveness of the SOM algorithm. The first attribute is its capability to diminish the input space by moving similar inputs close to eventually form clusters of outputs. The second attribute is forming topological ordering which is based on the location of the neurons in the SOM grid. This ordering is correlated to the input space features [38].
## 3 Results
### Burial Prediction Accuracy  
The average of all iterative validation models, each reserving a different 30% of the training data for validation, resulted in prediction accuracy scores of 92.7% (87%-98%) and 55.8% (36%-72%) for intact and buried stream segments, respectively, with a mean model accuracy of 83.1% (Table 2). Accuracy scores for the full model, utilizing all available data for both training and accuracy, were 88% and 71% for intact and buried segments, respectively, with a similar overall accuracy of 83%. A plot of the receiver operating characteristic identified a burial probability threshold of 0.35 as the optimum level that minimizes false positive predictions while maximizing true positive predictions.
## 1 Introduction
In recent years, the rapid advancement of computational technologies has further transformed the landscape of optimization in the food industry. The integration of artificial neural networks (ANNs) represents one of the most significant innovations in this field. ANNs, inspired by the structure and functioning of the human brain, are highly effective at modeling complex systems, detecting non-linear patterns, and providing accurate predictions even in scenarios involving extensive datasets and numerous variables. When combined with established optimization techniques such as RSM or evolutionary algorithms, ANNs create powerful hybrid systems capable of addressing modern optimization challenges. These hybrid approaches have demonstrated remarkable success in enhancing both product quality and process efficiency, solidifying ANNs as essential tools in the optimization of food processes [3; 5]. Additionally, the integration of Industry 4.0 technologies, including the Internet of Things (IoT), big data analytics, and machine learning, is enabling real-time optimization and adaptive process control, setting the stage for the next generation of food production systems. While previous reviews have examined specific optimization techniques within the food industry, they often focus on individual methodologies and lack an integrative perspective. This review aims to provide a comprehensive analysis of the evolution and application of optimization methodologies in the food industry, emphasizing their role in addressing current challenges. It evaluates the transition from traditional approaches to advanced multi-objective optimization techniques and their integration with cutting-edge technologies such as artificial neural networks and Industry 4.0 tools. By providing a detailed understanding of the diverse optimization methods available--ranging from statistical techniques like response surface methodology to more complex frameworks such as neural networks and multi-objective optimization--this article offers valuable insights into their advantages, limitations, and specific applications. It sheds light on how these methodologies are employed to address various industry challenges, such as improving process efficiency, enhancing product quality, reducing resource consumption, and promoting sustainable practices. Furthermore, the article highlights practical examples and success stories, showcasing the tangible benefits of implementing optimization techniques in areas such as product development, process improvement, and extraction technologies. By emphasizing their potential to enhance resource efficiency, improve product quality, and achieve sustainability goals, this review contributes to the growing body of knowledge aimed at driving innovation and progress in the food industry.
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 3 Data Batch Algorithm
### Dynamic Cost Scheduling Algorithm for Data Batch Processing (DCSDBP)  
Usually, given the current business practice dealing with such techniques, certain market-based rules govern the implementation of these scheduling processes. Therefore, the following assumptions underpinning the proposed algorithm are applied:  
* The service-providing company lease a fixed number of data processors.
* Reserving processors is not allowed at the beginning or during the batch process scheduling. A processor is immediately acquired once a decision is made to rent it.
* The characteristics of data files that will be processed as batches such as size and priorities are specified at the beginning of the scheduling process.
* Hardware and software costs are incurred for processing data.
* The hardware cost is a fixed amount.
* Software cost will have fixed and variable cost amounts; the variable cost is charged based on usage per unit time.
* Additional processors can be rented anytime during the execution; however, costs will be higher, 1.25 times, than the currently leased processors' hardware and software fixed cost. Also, a software variable cost is charged per unit time of usage. These assumptions are derived from practices in the field.
* Additional processors are rented only if there is a chance of not meeting the SLA.
* When an additional processor is acquired, it will be accounted for from the time it is acquired until the end of the batch window.
* Whenever an additional processor is rented at any time unit \(T\), the endorsed fixed hardware and software costs will be calculated from the time of renting until the end of the batch process.
* Each leased or additionally rented processor executes a single task at a time.
* Different tasks can be processed in parallel. Parallel processing can occur only if there is no restriction due to priorities and predecessors relations provided in advance.
* A single data file containing multiple tasks can be multi-processed on multiple processors at the same time if it is allowed by the tasks predefined predecessors relations and the hardware and software resources are available.
* The iteration clock time unit is estimated by the time it takes to process the smallest size unit of the data file.
* The total DBP time is a multiple of the iteration unit time. Files are allocated to processors until a predetermined SLA time is reached. A penalty cost is imposed on each delay unit of time if the SLA is exceeded. The total penalty cost is calculated by multiplying the time delay by the penalty cost per unit time.
* At the beginning of each time unit \(T\), files are allocated and resources are captured. However, at the end of the time period \(T\), all resources are freed and ready for the next time period \(T\).
# Autonomous Penetration Testing Based on Improved Deep Q-Network
## 3 Methodology
### Integration and Decoupling
We use the average of the host \(Q\) value and the operation \(Q\) value to calculate the overall loss:  
\[L(\psi,\theta_{I},\theta_{F})=\lambda\mathbb{E}_{(s,h,o,r,s^{\prime})\sim D}[y- \frac{1}{2}(Q(s,h,\varepsilon;\psi_{h})+Q(s,o,\varepsilon;\psi_{o}))]^{2}+(1- \delta)L_{I}+\delta L_{F}, \tag{14}\]  
where \(\psi_{h},\psi_{o}\) are the parameters of the host stream and operation stream, \(\psi=\{\psi_{h},\psi_{o}\}\) is the common network parameters and  
\[y=r+\frac{\gamma}{2}\bigg{[}\sigma_{h}log\sum_{h^{\prime}}exp(\frac{1}{ \sigma_{h}}Q(s^{\prime},h^{\prime},\varepsilon^{\prime};\psi_{h}{}^{-}))+ \sigma_{o}log\sum_{\sigma^{\prime}}exp(\frac{1}{\sigma_{o}}Q(s^{\prime},o^{ \prime},\varepsilon^{\prime};\psi_{o}{}^{-}))\bigg{]}. \tag{15}\]  
The algorithm of NDSPI-DQN and its decoupling version are presented in Algorithm 1. ```
Initialize the replay memory \(D\), the set of random variables \(\xi\)  Initialize the \(Q\) network with weights \(\psi\), the target \(Q\) network with weights \(\psi^{-}\)  Initialize the ICM with weights \(\theta_{I},\theta_{F}\)  Initialize the environment \(env\), the boolean Decoupling: False for NDSPI-DQN and True for decoupling NDSPI-DQN for episode = 1, M do  Reset the environment state \(s_{0}\sim env\) for step = 1, T do  Set \(s_{t}\gets s_{0}\)  Sample the noisy network \(\varepsilon\sim\xi\) if Decoupling then  Select \(h_{t}\gets softmax_{h}(\frac{1}{\sigma_{h}}Q(s_{t},h,\varepsilon;\psi_{h}))\), \(o_{t}\gets softmax_{o}(\frac{1}{\sigma_{o}}Q(s_{t},o,\varepsilon;\psi_{o}))\)  Execute action \(a_{t}=<h_{t},o_{t}>\), obtain the external reward \(r^{\prime}_{t}\) and next state \(s_{t+1}\)  Store the transition \((s_{t},h_{t},o_{t},r^{\prime}_{t},s_{t+1})\) in \(D\), update the priority of transitions  Sample a minibatch of transitions \((s_{j},h_{j},o,r^{\prime}_{j},s_{j+1})\) from \(D\) with probability using Equation (9) else  Select action \(a_{t}\gets softmax_{a}(\frac{1}{\sigma}Q(s_{t},a,\varepsilon;\psi))\)  Execute action \(a_{t}\), obtain the external reward \(r^{\prime}_{t}\) and observer next state \(s_{t+1}\)  Store the transition \((s_{t},a_{t},r^{\prime}_{t},s_{t+1})\) in \(D\) and update the priority of transitions  Sample a minibatch of transitions \((s_{j},a_{j},r^{\prime}_{j},s_{j+1})\) from \(D\) with probability using Equation (9) endif  Sample the noisy network \(\varepsilon^{\prime}\sim\xi\)  Calculate the loss functions \(L_{I},L_{F}\) of ICM and the internal reward \(r^{i}_{j}\) if episode terminate at step \(j+1\) then  Set \(y_{j}=r^{e}_{j}+r^{i}_{j}\) else if Decoupling then  Set \(y_{j}\) using Equation (15)  Do a gradient step with the overall loss using Equation (14) with respect to \(\psi\) else  Set \(y_{j}\) using Equation (12)  Do a gradient step with the overall loss using Equation (13) with respect to \(\psi\) endif endif  Every \(C\) steps update the target \(Q\) network \(\psi^{-}\leftarrow\psi\) \(s_{t}\gets s_{t+1}\) endfor endfor
```  
**Algorithm 1**NDSPI-DQN.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Other Types of Exploration Techniques
#### 2.4.4 Infrared Thermography  
Infrared thermography (IRT) is a technique that detects hidden dike defects by sensing the temperature anomalies of infrared thermal radiation on the surface of earth and rock dams. IRT uses the difference in the infrared thermal radiation coming from different areas of an object to convert invisible thermal radiation into an image. This technology uses images to determine the location of structural defects and can be used as a non-contact structural inspection method [82]. There are two main types of IRT: active and passive. Active IRT requires external heat source excitation to be applied to the inspected object, whereas the imaging in passive IRT is done under natural conditions. As it is difficult to artificially apply thermal excitation to actual earth and rock-fill dam projects characterized by large volumes and long distances, passive IRT is often used in detecting hidden dangers in earth and rock embankments. Compared with conventional point or line temperature measurement techniques, IRT obtains thermal results in a rectangular plane and thus can perform digital image analysis, which is not possible in other detection methods.  
[PERSON] et al. [83] conducted IRT monitoring experiments on the earth dam failure process on Randall Creek, Taiwan, and concluded that the dam surface with significant changes in the radiation temperature could be used as a potential damage zone for earth and rock dams. [PERSON] et al. [84] verified the feasibility of IRT leakage sensing for earth and rock dams under ambient temperature conditions using an earth dam model test. [PERSON] et al. [85] conducted indoor model tests on concentrated leakage and random leakage of mortar specimens under different water temperature conditions to verify the feasibility of IRT leakage sensing under water heating conditions. [PERSON] et al. [86] conducted an indoor test on the thermal imaging of concentrated leakage in homogeneous soil dams under various head effects. The feasibility of applying IRT to detect leakage in earth and rock dams under heat source excitation conditions could be verified.  
As a noncontact nondestructive detection method [87], IRT has the advantages of high mobility, high efficiency, and large coverage. It can be adapted to the hidden danger detection and flood emergency inspection of earth and rock dams. However, there are some limitations. IRT, as a surface detection technology, cannot detect the internal hidden features of earth and rock dams. The detection effect is evident only when the influence of leakage reaches the surface. The image resolution is low, the image information details are less, and the image interpretation is difficult under complex geological conditions, which requires combining with other methods for detection.
# 60 Years of Studies into the Initiation of Chromosome Replication in Bacteria
## 3 The DnaA Protein  
### Domain Traits  
The _dnaA_ gene codes for a monomeric protein of 52 kD comprise four domains [30; 31] (Table 1). In addition to several deletion mutants, 16 temperature-sensitive mutants have been isolated and their mutated sites identified; 10 of these mutants have double mutations [32]. Among the nine mutants of the domain IIIa, all seven mutated at the ATP-binding site had a second mutation in other domains as if the first mutation was lethal and suppressed by the second.  
Domain I is important for the interaction of DnaA with other proteins, such as DnaB [33] or Dps [34]. Domain II is thought to be a linker domain within which no mutation has been reported. That said, the deletion of Domain II suppresses the phenotype of the _seqA_, _objE_ double mutant but without affecting its growth [35]. Domain IIIa has the ATP-binding site, which plays an important role in its interaction with _oriC_ (see below). Two proteins, DnaA5 and DnaA46, with mutations in Domain IIIa have been shown to be deficient in ATP-binding [36]. Moreover, this domain serves as an anchor for the opened DUE (duplex-unwinding-element) of _oriC_[37]. Domain IIIb, which can be deleted without a pronounced effect on growth, allows DnaA to bind to the membrane [38]. Two other interesting characteristics of Domain IIIb are its cAMP-binding site [39] and the existence of an ATPase-deficient mutation (_R344A_) [40]. Curiously, cold-sensitive _dnaA_ mutants are all mapped in Domain IIIb [41]. One interpretation would be that this domain allows the protein to respond to the physical state of the membrane, which depends on temperature. Domain IV serves for sequence-specific binding to DNA [42].
## 4 Case Study and Discussion
### Position and Orientation Deviation Computation of Mechanical Product
#### 4.2.2 Position and Orientation Deviations at the Beginning of the Sliding Stroke  
The initial assembly position is determined when the PDCSs of the static guide and sliding table are set to coincide with each other because the assembly position in the direction of the X-axis is required to be the sliding stroke. Next, the surface deviations of each mating plane are input, such that determining contact points can begin.  
Over the process of determining contact points, the three contact points on the support planes of PPA and PPB are determined through the one-step-adjusting-based strategy. Next, if computing PODs of assembly state A, the two contact points on the guide plane of PPA are determined through the strategies of progressively approaching the position and orientation and recursion of the contact points. Otherwise, the two contact points on the guide plane of PPB should be determined.  
The five contact points and PODs corresponding to assembly state A and B were determined, as shown in Figure 13 and Table 5, respectively.  
Figure 12: Sliding workbench. (**a**) A real sliding workbench, (**b**) The sliding workbench model of this case study.  
Figure 13: Determined contact points of assembly state A and B.
# Verification Methodology for Simulation Models of the Synchronous Generator on Transients Analysis
###### Abstract  
During transients that occur in an electric network, large currents can flow and large electromagnetic torques can be developed in electric generators. Accurate calculation of currents and magnetic fields during transients is an important element in the optimal design of generators and network parts, as well as mechanical parts of machines and other torque transmission parts. This paper describes the modeling of a sudden three-phase short-circuit on a synchronous generator using the finite element method (FEM) and the dynamic model. The model for simulations that use the FEM was built in the MagNet software package, and the dynamic model is embedded in the MATLAB/Simulink software package. The dynamic simulation model of a part of a network with two identical generators, represented by equivalent parameters, was developed. The results obtained after the simulation of a sudden three-phase fault in the generators by both methods are presented, including three-phase voltages, three-phase currents, machine speeds, excitation voltages, and mechanical power. In particular, the short-circuit current in the phase with the highest peak value was analyzed to determine the accuracy of the equivalent parameters used in the dynamic model. Finally, the results of these two calculation methods are compared, and recommendations are presented for the application of different modeling methods.  
field calculation method; finite element method; generator modeling; simulation model; three-phase fault +
Footnote †: journal: Applied Sciences