esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms
### Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citizen Science project** & **Website** & \begin{tabular}{c} **Water quality parameter** \\ **measured** \\ \end{tabular} & **Area** \\ \hline \multirow{2}{*}{Bloomin’Algae} & [[https://www.ceh.ac.uk/](https://www.ceh.ac.uk/)]([https://www.ceh.ac.uk/](https://www.ceh.ac.uk/)) & \begin{tabular}{c} Harmful Algal Blooms \\ (Cyanobacteria) \\ \end{tabular} & UK \\ \hline \multirow{2}{*}{BloomWatch} & [[https://cyanos.org/bloomwatch/](https://cyanos.org/bloomwatch/)]([https://cyanos.org/bloomwatch/](https://cyanos.org/bloomwatch/)) & \begin{tabular}{c} Harmful Algal Blooms \\ (Cyanobacteria) \\ \end{tabular} & USA \\ \hline \multirow{2}{*}{CyanoTracker} & [[http://www.cyanotracker](http://www.cyanotracker)]([http://www.cyanotracker](http://www.cyanotracker)). & \begin{tabular}{c} Harmful Algal Blooms \\ (Cyanobacteria) \\ \end{tabular} & USA \\ \hline \multirow{2}{*}{FreshWaterWatch} & https://freshwaterwatch. & \
\begin{tabular}{c} Water Quality (Nitrate, \\ Phosphate, Clarity) \\ \end{tabular} & Global \\ \hline \multirow{2}{*}{Secchi Dip In} & http: & Water Clarity & Global \\  & //www.secchidipin.org/ &
\begin{tabular}{c} Water Clarity \\ \end{tabular} & Global \\ \hline \multirow{2}{*}{Secchi Disk} & [[http://www.secchidisk.org/](http://www.secchidisk.org/)]([http://www.secchidisk.org/](http://www.secchidisk.org/)) & Water Clarity & Global \\ \hline \multirow{2}{*}{The Sneaker Index} & [PERSON] et al. [105] & Water Clarity & Chesapeake Bay (USA) \\ \hline \multirow{2}{*}{EyeOnWater} & https: & \multirow{2}{*}{Water Color and Clarity} & Global, \\  & //www.eyeonwater.org/ & & Australia \\ \hline \multirow{2}{*}{HydroColor} & [[http://misclab.umeo](http://misclab.umeo)]([http://misclab.umeo](http://misclab.umeo)). & \multirow{2}{*}{Remote-Sensing Reflectance} & Global \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of citizen science projects focused on water quality. from the water-sediment interface, as well as submerged plant communities, macrophytes, and more variation in local meteorology, result in a far more dynamic environment. There is also reduced availability of data to characterize lake or coastal ecosystems using only a few sampling points, often only a single site per lake, which might not be representative for the entire system [127; 128]. Models that can integrate discrete, continuous, remote sensing, and citizen science data more readily may achieve desired forecasting systems for short- and long-term management of ecological, hydrological, and climatological impacts. The current challenge is the timely delivery of model result information to stakeholders for decision making. This is mainly achieved through online operational information portals [110; 121]. Online operational portals aim at providing real-time monitoring and short-term information, which can be used on a variety of scales and problems for a variety of stakeholder interests.
## 3 Results and Discussions
### Case Study-2, 1 HP Induction Motor  
To test the FRA technique's capability to detect SC faults of induction motors of various sizes, FRA measurements are conducted on a 1 HP induction motor with the same types of faults investigated above. The FRA measurements of this case study are shown in Figure 6. As can be seen in Figure 6, all faults affect the FRA signature in a similar trend to the previous case study. For instance, Figure 6a shows a slight increase in the magnitude in the low and mid-frequency ranges when a turn-to-turn SC fault is staged on the stator winding. Similarly, the coil-to-coil SC fault increases the magnitude of the FRA signature in the low and mid-frequency ranges, as shown in Figure 6b. The turn-to-ground and coil-to-ground faults, as, respectively, shown in Figure 6c,d generally have similar effects on the signature as in the previous case study. Unlike the neutral-to-ground SC fault in the 3 HP motor, the  
Figure 5: U–V terminals FRA measured responses for the 3 HP induction motor (**a**) turn–to–turn SC (**b**) coil–to–coil SC (**c**) turn–to–ground SC (**d**) coil–to–ground SC (**e**) neutral–to–ground SC (**f**) phase–to–phase SC.  
FRA signature of the 1 HP motor shown in Figure 5e does not reveal any impact of the neutral-to-ground fault on the IM FRA signature. The phase-to-phase SC fault shown in Figure 6f has a substantial influence on the FRA trace at the low and mid frequencies.
## 5 Diagnosis Performance Analysis and Comparative Performance Evaluation
### Performance Comparisons of Other Methods  
To evaluate the performance of the proposed method, this section provides a comparative analysis with existing multilevel fault diagnosis approaches, as detailed in Table 5. Although [15; 33] can achieve multi-switch fault diagnosis for T-type three-level inverters, they exhibit critical limitations. Specifically, Ref. [15] employs a multimodal deep residual network for fault identification. This data-driven approach demands substantial training datasets and computational resources, with experimental measurements indicating an average diagnosis latency of 21 ms. In contrast, as demonstrated in Figure 15, the proposed method achieves faster fault detection, with a maximum detection time not exceeding 15 ms.  
Concerning signal requirements, the proposed method requires only three-phase current signals for diagnosis. This contrasts sharply with [19; 23; 33], which necessitate additional signals such as switching states, pole voltages, and inductance values, thereby increasing diagnostic complexity. Notably, several existing methods depend on voltage signals for fault identification. For instance, Refs. [18; 23; 33] require voltage signal acquisition, typically implying the need for extra voltage sensors and higher hardware costs.  
Figure 15: Diagnosis time of single- and double-switch OC faults.  
Comparatively, Refs. [31; 32] adopt observer-based methods for multilevel inverter fault diagnosis. While these methods perform well for single-switch faults, they are unable to effectively identify multiple-switch faults. Compared to these approaches, the proposed method not only enables multi-switch fault diagnosis with strong robustness but also demonstrates significant hardware and computational advantages. These are achieved by eliminating the need for additional sensors and reducing calculation cost.
## 4 Results and Discussion  
### Validity of the Numerical Model  
The measured result was compared with the test data to illustrate the validity of the FEM model. The temperature variation during the freezing point in C7 and C5 is shown in Figure 5.  
As shown in Figure 5, the simulation results agreed well with the test data. It can be seen that the temperature decreased faster in the early stage of the freezing development but was slower in the later stages of the freezing development. The temperature field model, established based on the finite element method, could accurately describe the temperature development trend and values at the observation points during freezing.
## 3 Results and Discussion
### Anodic Dissolution Mechanism
The addition of EG into imidazolium-based IL one results in a shift in the oxidation onset potential of metallic copper by approximately 300 mV, as presented in Figure 2. Similarly, a quasi-passivation profile is also observed showing that passivation is probably associated to the chloride concentration. As the concentration of chloride declines due to addition of EG, shifting in the onset potential occurs. It is noteworthy that the chloride ion caused a higher current response. The effect of anion concentration on copper dissolution in choline chloride-based IL was investigated as shown in Figure 3. In the study the concentration of chloride in electrolyte (ChCl:EG 1:2) was manipulated by diluting the electrolyte with EG. As the molar ratio altered from 1:2 to 1:3 and 1:4, the passivation potential was changed to less positive potential values. This was predictable, as in such cases, there is less chloride available to the electrode's surface, so it is more difficult to produce CuCl\({}_{4}\)\({}^{2-}\) and more likely forms CuCl\({}_{2}\). Figure 2: Linear sweep voltammetry (LSVs) of copper discs in choline chloride-based IL (ChCl:EG 1:2, solid line), and the other three shifted, LSV-overlapped responses of copper discs in 1-butyl-3-methylimidazolium chloride (C4 mimCl):EG with ratios of 1:2, 1:3, and 1:4, represented by dashes, dots, and solid lines, respectively, at 5 mV\(\cdot\)s\({}^{-1}\) and 20 \({}^{\circ}\)C.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics  
One of the main limitations of conventional processes like thermal processing is the impact on food quality aspects, including the taste, flavor, color, as well as nutritional and bioactive components. Being a non-thermal process, PL processing is expected to have no harmful impacts on the food quality while making it decontaminated. The potentiality of PL applications for food processing is thus affected by how it affects their color, taste, nutrients and other properties of food products. Various researches have been conducted to understand the effects of PL on food quality parameters. The effects of PL processing on various food quality attributes are discussed in following subsections.
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 3 Experimental Validation
### Case Study on Incipient Bearing Fault Diagnosis
The spectrum generated using the SK-ES method reveals the FCF of the bearing's outer race fault \(f_{o}\), which is shown in Figure 13b. However, it also contains multiple obvious interfering frequency components and random noise. The spectrum generated using the IES method includes multiple higher-order harmonics associated with the FCF of the bearing's outer race fault \(f_{o}\), as shown in Figure 13c. However, it still contains significant interfering frequency components and random noise, which may compromise the accuracy of the fault diagnosis. Compared to the SK-ES method and IES method, the proposed DIES method exhibits superior performance, as illustrated in Figure 13d. It can be clearly observed that the proposed DIES method effectively highlights the FCF \(f_{o}\) and its harmonics corresponding to the bearing's outer race fault. Additionally, the spectrum processed by the DIES is devoid of noise and interfering frequency components, making it significantly less intrusive. To further validate the effectiveness of the proposed DIES method, an effective denoising technique, ITD-SVD, was applied to the same raw signal presented in Figure 12. The results are illustrated in Figure 14. It is obvious that ITD-SVD is capable of detecting the bearing outer race FCF \(f_{o}\) and its second harmonic \(2\times f_{o}\). However, the performance of ITD-SVD is not as effective as DIES in identifying these features. It failed to extract the higher-order harmonics associated with the outer race fault frequency. Furthermore, the results exhibit several noticeable interfering frequency components that could hinder the bearing fault diagnosis. Figure 13: Incipient bearing fault case: **(a)** the FFT spectrum of the original signal, **(b)** the spectrum generated using the SK-ES method, **(c)** the spectrum generated using the IES method, **(d)** the spectrum generated using the proposed DIES method.
## 2 Theoretical Background
### Optimization Algorithms
#### 2.2.1 Particle Swarm Optimization (PSO)  
Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique inspired by the social behavior of flocking birds and schooling fish. Developed by [PERSON] and [PERSON], PSO aims to find optimal solutions to problems by iteratively updating the positions of a swarm of particles in a multidimensional search space [40; 41]. The algorithm begins by initializing the swarm with random positions and velocities within the search space, where the current position of particle \(i\) in dimension \(j\) is denoted by \(x_{ij}\) and the velocity is denoted by \(v_{ij}\). A fitness function is then defined to evaluate the quality of each particle's position. Each particle's velocity is then updated based on its previous velocity, its personal best position, and the best position found by any particle in the swarm (the global best) as in Equation (1) [42; 43].  
\[v_{ij}(t+1)=\omega v_{ij}(t)+c_{1}r_{1}(p_{ij}-x_{ij}(t))+c_{2}r_{2}(\mathbf{G} -x_{ij}(t)) \tag{1}\]  
where  
* \(v_{ij}(t+1)\) is the velocity of the particle \(i\) in dimension \(j\) at iteration \((t+1)\).
* \(\omega\) is the inertia weight controlling the impact of the previous velocity.
* \(c_{1}\) and \(c_{2}\) are acceleration coefficients controlling the impact of personal and global best.
* \(r_{1}\) and \(r_{2}\) are random values between 0 and 1.
* \(p_{ij}\) is the best position of the particle \(i\) in dimension \(j\).
* \(\mathbf{G}\) is the position of the best solution, and it is updated iteratively when there is a better solution.
* \(x_{ij}(t)\) is the current position of particle \(i\) in dimension \(j\) at iteration \((t)\).  
Updating each particle's position based on its velocity is defined as follows: \(x_{ij}(t+1)=x_{ij}(t)+v_{ij}(t+1)\). The fitness function is used to evaluate the fitness of each particle's new position. The personal best position is then updated based on the improvement of each particle's fitness function value. The global best position is updated if any particle finds a better position than the current global best. The previous steps are repeated until a stopping criterion (e.g., reaching the maximum number of iterations) is met [44; 45].
## 4 Results and Discussion
### The Results of the ADI Prediction Models
#### 4.1.3 The Evaluation Parameters of the Cascaded ML and DL Classifier-Based Prediction Model  
The evaluation parameters of the cascaded ML and DL classifier-based prediction models are given in Table 18. The results are given for both Scenarios One and Scenario Two. The performance of the cascaded machine learning (ML) and deep learning (DL) classifier-based prediction models across two scenarios reveals a nuanced variation in accuracy, precision, and other evaluation metrics. In Scenario One, Classifier 2 achieved the highest accuracy at 0.69, alongside a precision of 0.60 and an F1 score of 0.54. Although Classifier 5 and the LSTM model showed comparable accuracy values (0.68 and 0.70, respectively), their F1 scores were significantly lower at 0.20, indicating a reduced balance between precision and recall. Most classifiers demonstrated moderate precision and recall, with MSE values ranging between 0.14 and 0.45, suggesting room for optimization in model generalization.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Scenario One**} & \multicolumn{4}{c}{**Scenario Two**} \\ \hline
**Classifier** & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline
**Convolutional neural network classifier** & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 17: The evaluation parameters of the ML and DL classifier-based prediction models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Scenario One**} & \multicolumn{4}{c}{**Scenario Two**} \\ \hline
**Classifier** & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline
**Convolutional neural network classifier** & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 18: The evaluation parameters of the ML and DL cascaded -based prediction models.  
In Scenario Two, the overall model performance generally improved. Classifier 2 once again stood out, increasing its test accuracy to 0.72 and achieving the highest precision (0.75) and a solid F1 score of 0.63, coupled with a relatively low MSE of 0.34. Classifier 1 and Classifier 3 also saw improvements in both accuracy and F1 scores, while LSTM showed a slight drop in accuracy to 0.66 but a marked increase in its F1 score to 0.40, indicating better precision-recall trade-off under localized data. Notably, Classifiers 4 and 5 maintained low F1 scores (0.05 and 0.20, respectively), despite consistent precision values, suggesting challenges in achieving effective recall.  
Overall, the results demonstrate that the cascaded models benefit from scenario-specific tuning, with classifiers like Classifier 2 showing strong adaptability. Localized training data, as in Scenario Two, appears to support better predictive consistency for several models.
## 1 Introduction
In this study, five metaheuristic global optimization methods were applied to solve five representative optimization problems in the dimensional synthesis of path-generating four-bar linkages, in which two of these methods include a widely used intelligent algorithm and evolutionary algorithm (i.e., PSO and DE methods), and three of these methods are improved PSO- and DE-based methods (i.e., hybrid PSO (HPSO) [33], ensemble of parameters and mutation strategies in differential evolution (EPSDE) [95], and linearly EPSDE (L-EPSDE) method). The HPSO, EPSDE, and L-EPSDE methods would be verified to be suitable for application in middle or high dimensional problems. In this study, one hundred trials of optimization design were investigated to solve each case of the five representative synthesis problems. The accuracy and efficiency (effectiveness) of these five metaheuristic optimization methods were compared; moreover, they were also compared to those of previously reported algorithms in the literature. The comparative results revealed that the accuracy and efficiency of the adaptive improved methods, EPSDE and L-EPSDE, are superior to numerous previously reported methods, and that they are suitable for application in middle or high dimensional problems, and that DE with golden ratio (DE-gr) also exhibited a similar outperforming characteristic as EPSDE and L-EPSDE in middle dimensional problems. In addition, HPSOexhibited improved accuracy and efficiency compared to the original PSO, and is suitable for application in middle or high dimensional problems. The remainder of this paper is organized as follows: Section 2 presents the formulation of the synthesis problem of a path-generating four-bar linkage. Section 3 briefly describes the five metaheuristic global optimization algorithms employed in this study. Section 4 presents the application of the five metaheuristic optimization algorithms for solving five representative problems of a four-bar path generator and the comparative study of the performance of the algorithms to those of previously reported algorithms. Section 5 briefly discusses the overall optimal results of the five metaheuristic optimization methods. Lastly, the conclusion is drawn in Section 6.
###### Abstract  
Enhancing performance of downlink MU systems is an attractive and important research for future wireless systems. The non-orthogonal multiple access (NOMA) method was proposed to improve the performance of MU systems. In order to further improve the outage probability (OP) and ergodic capacity (EC) of downlink NOMA MU systems, we propose the combination of precoding and NOMA methods, and then the OP and EC of MU systems with our novel method are derived in scenarios of perfect and imperfect successive interference cancellation (SIC) scheme. Moreover, the closed-form expression of OP and EC for both scenarios is theoretically derived and compared with Monte Carlo simulations. The results show that, the analysis method is accurate, and the proposed combining precoding and NOMA can further enhance the performance of MU systems in comparing with the original orthogonal multiple access method.  
non orthogonal multiple access; successive interference cancellation; close-form expression; outage probability; ergodic capacity; combination of precoding and NOMA; perfect and imperfect SIC +
Footnote †: journal: Applied Sciences  
_Applied_  
_Acticle_
# A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems
## 5 Metaheuristic and Heuristic Algorithms in This Study
### Proposed SA Algorithm  
A SA algorithm is a simple and effective optimization metaheuristic algorithm to solve optimization problems. The basic information for starting the SA algorithm is as follows.  
The initial temperature (\(T_{0}\)) in which the algorithm starts to work must be set up at the beginning of the algorithm, and the Boltzmann probability function generates the value of 1. In other words, at the beginning of the algorithm, the answers of the worst neighbor will be accepted with a probability of 1. They will decline to zero gradually [81]. The temperature reduction rate (\(\alpha\)) is considered in the following equation [82].  
\[T_{h}=\alpha\times T_{h-1}\;\;h>2,0<\alpha<1 \tag{17}\]  
where \(T_{h}\) and \(\alpha\) signify the temperature in the repetition of h and the rate of decrease in temperature, respectively.  
In this study, the initial temperature is considered \(70\;^{\circ}\)C and the temperature decrease rate is 0.98.  
How to display the answer applied in this paper is in the form of a matrix with a row and \(m\times n\) column. The elements of this matrix are members of the set \(1,2,3,\ldots,m\times n\); each indicating a scheduling operation.  
Based on the recommended answer description, each problem can have \((m\times n)!\) answer modes; the SA algorithm will reach an optimized answer by searching the neighborhood based on initial response. To search for neighbors, we need operators to obtain the final solution by modifying the answers.  
In this algorithm, to accelerate the search for finding a better answer, three operators of swap, reversion, and insertion are employed to select the neighborhood.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### GPS-Based Systems Integration with Other Sensory Data
#### 4.1.1 GPS Integration with Barcode  
It includes integration of the barcode and GPS technology for material identification and localization respectively. In this technique, various items are tagged by barcodes and their real-time locations are sent to a central database through Wi-Fi as scanned by a barcode reader equipped with GPS. For instance, in [52] an automated system for material tracking based on the integrated barcode and GPS technology was developed in which the location of materials is determined with three-meter accuracy. In fact, automated and real-time identification and localization of materials make the retrieval of these elements much easier which results in a better material tracking and management system.  
The advantage of this tracking technology over other technologies includes relatively low cost, high level of standardization, and scalability for employment in various projects. Barcode labels have been widely used for many applications in industry with defined protocols to be attached to various materials for many years with relatively low cost (see Table 3). However, the reader should be very close to the barcodes which is not practical on many occasions for tracking items on site. Besides, the barcode system cannot differentiate between the items from the same type which decreases the level of detail provided for the identification process [52].