esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurements and Results
### Infrared Light Source  
(1) The rising phase affected from thermal radiation produced by infrared light source:  
After the sensing device turned on, the operating temperature of the infrared light source will gradually increase. However, the light source produced less thermal radiation in the beginning (the light intensity was weak), which might cause the sensor have more chances to receive less amount of thermal radiation. That is, the circuit would feedback control the light source to increase the heating duty (Ratio = \(\frac{m}{N}\uparrow\)) and higher frequently to maintain the average amount of thermal radiation.  
(2) The falling phase affected from thermal radiation produced by infrared light source:  
As the measurement time increases, the operating temperature of the infrared light source is getting higher than in beginning. To fix the heating duty of light source as constant, the light source will generate more thermal radiation, which means the sensor will receive higher amount of the thermal radiation. That will cause the circuit feedback control the light source to decrease the heating duty (Ratio \(\downarrow\)) till the light intensity is saturated.
# A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
## 4 Conclusions  
In this paper, we first conducted droplet experiments on glycerol aqueous solutions. Then we analysed the kinetic process of liquid neck contraction during droplet falling and discussed the strong correlation between viscosity and the contraction process. Subsequently, based on the experimentally demonstrated strong correlation between viscosity and the liquid neck contraction process, the relationship was established using a machine learning model with liquid neck contraction data as the dataset.  
In the first part of the Section 3, the droplet neck contraction curves of the dropping process of five aqueous glycerol solutions with different viscosities were analysed and compared with other research results. The results show that viscosity has a great influence on the droplet neck contraction process. This was manifested by the change in the contraction curve. Based on the correlation between the droplet neck contraction process and viscosity, three machine learning models were used for modelling. After extracting the liquid neck contraction data through a self-programmed procedure, they were dimensionless and the factors related to specific scales were removed to be more general and universal. Three machine learning models, neural network, multiple linear regression and random forest, were used to train the model using the liquid neck contraction data and predict the viscosity of the fluid based on the contraction data. The results show that the three models using fluid neck contraction data are more effective, with random forest having the lowest correlation coefficient \(R^{2}\) of 0.95, while multiple linear regression gives more accurate predictions with a correlation coefficient \(R^{2}\) of 0.98, and multiple linear regression has the smallest \(MAE\) and \(RMSE\) among the three models, at 12.85 and 16.95.  
Conceptualisation, Z.Q.; methodology, F.W.; formal analysis, F.W. and S.T.; investigation, S.L.; resources, Z.Q.; data curation, Z.Q. and F.W.; writing--original draft preparation, S.L.; writing--review and editing, Z.Q., F.W. and S.T.; supervision, Z.Q.; project administration, F.W.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation of China (Grant No. 12172094) and Guangxi Bagui Scholars Project (No. 2019A02).  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 The Proposed Model
### Hyperparameter Tuning
#### 3.4.2 Local Search Process  
Step 4-1. If \(im=0\), then \(im\) represents the count of sub-population to compare to the number of sub-populations. If \(in=0\), then \(in\) indicates the count of local search progress for comparing to Lmax. The local search algorithm is the comprehensive procedure of step 4 of the global search technique.  
Step 4-2. \(Xw\) and \(Xb\) described in the \(in\)-\(th\) populations. Afterward \(D_{S}\) is evaluated based on Equation (10), the worst solution is upgraded in Equation (11) to increase the location of the worst frogs in the sub-population. Once the upgrade fitness number of the worst frog is superior to the existing fitness values, \(Xw^{\prime}\) is utilized for replacing \(Xw\), if the updated fitness value is inferior to the current fitness value, the global optima \(Xg\) is used to replace \(Xb\). Furthermore, the local search procedure is accomplished based on Equations (10) and (11), once the upgrade fitness number of the worst frog is superior to the existing one, then \(Xw^{\prime}\) is utilized for replacing \(Xw\). Or then, a solution is arbitrarily produced for replacing \(Xw\) to the worst frog. The fitness value of the upgraded sub-population is ordered in descending sequence. Set \(in=in+1\) and reiterate these steps until \(in=Ls\).  
Step 4-3. Consider \(im=im+1\), and skip to steps 4-2 until \(im=m\).  
Step 4-4. The global data exchange is implemented if \(im=m\).  
The SFLO algorithm makes a derivation of a fitness function (FF) for acquiring enhanced classifier outcomes. It sets a positive value for indicating superior performance of the candidate solutions. In this paper, the reduction in the classifier error rate was taken as the FF, as presented below in Equation (13). The optimum solution comprises the least fault rate whereas the worst one gains a higher fault rate.  
\[\begin{split} fitness(x_{i})&=Classifier\ Error\ Rate(x_{i})\\ &=\frac{number\ of\ misclassified\ samples}{Total\ number\ of\ samples}*100\end{split} \tag{13}\]
## 4 Cloud Application Management Patterns
### Move/Update Management with Downtime Pattern  
**Icon:** the icon of the _move/update management with downtime pattern_ is depicted in Figure 11.  
**Driving Question:**_How can an application component be moved to a different environment or updated to a new software, middleware, or hardware version, if downtime is acceptable?_  
**Context**: a _stateless application component_, which has no internal state but, relies on external data and is part of a _composite application_. This component may be unavailable for the timeframe in which it is updated/moved.  
**Challenges**: during the transition it has to be ensured that application components are idle prior to their deprovisioning. Further, accesses either have to be stopped on the application level or have to be queued during the downtime.  
**Solution**: as depicted in Figure 12, ensure that no new requests are sent to the component to be moved or updated. Then, provision a new component instance with the new version (update) or in the new runtime environment (move). Send requests to the new component when it becomes available and deprovision the old one once it has finished processing requests.  
**Result**: the provisioning of the new component and the deprovisioning of the old component can partly be parallelized. Problems may arise if the time required for the migration/update is hard to  
Figure 12: Abstract Management Flow of the Move/Update Management with Downtime Pattern.  
Figure 11: Icon of the Move/Update Management with Downtime Pattern.  
estimate or depends on external factors, such as the behavior of the used cloud provider. This is due to the fact that the acceptable downtime is often fixed, for example, it could be limited to night hours.  
**Variations**: if the stateless component does not rely on external data, concurrently provisioning both versions of the component is likely to be possible ensuring a very timely switchover.  
**Relations to other patterns**: a migration/update can also be realized without downtime as described by the _migration/update management without downtime pattern_. The managed components are often accessed via messaging queues [19].  
**Know Uses**: Peecho [40], offering printing as a service, is based completely on Amazon AWS. An overview how it handles updates with minimal downtime is given by [41]. [PERSON] [42] also automated this management flow based on Amazon AWS [43].
## 4 Verification of Simulations and Experiments  
### Simulation Results  
In this study, the electromagnetic analyses of the no-load and load characteristics of PMSM were performed to compare the characteristics of the initial and the optimal models. In general, in the case of an electric motor, magnetic properties occur almost linearly except for the electrical part of the end-winding along the stacking length in the axial direction. To evaluate the validity of the analytical model and the optimization design procedure presented in the previous section, the characteristics of PMSM were predicted with two-dimensional FE analysis based on the electromagnetic field simulation software JMAG-Designer (v18.0, JSOL Corporation, Tokyo, Japan). To ensure analytical accuracy, model geometries used high-quality meshes with 35,100 elements and 22,300 nodes.
# Ising-Based Kernel Clustering +
## 3 Ising-Based Kernel Clustering
### QUBO Matrix Generation by Matrix-Matrix Calculations
\[H_{constraint} = \mathbf{q}((-\mathbf{\Lambda}\otimes\mathbf{I_{K}})+(2\mathbf{\Lambda}\otimes\mathbf{ U_{K}}))\mathbf{q}^{T} \tag{19}\] \[= \mathbf{q}(\mathbf{\Lambda}\otimes(2\mathbf{U_{K}}-\mathbf{I_{K}}))\mathbf{q}^{T}.\]  
Equation (14) is expressed as the sum of the matrix-matrix product forms in Equations (16) and (19), as in Equation (20). \[H = H_{objective}+H_{constraint} \tag{20}\] \[= \mathbf{q}((-\mathbf{G_{diag}}-2\mathbf{G_{triu}})\otimes\mathbf{I_{K}}+\mathbf{ \Lambda}\otimes(2\mathbf{U_{K}}-\mathbf{I_{K}}))\mathbf{q}^{T}.\]  
Recent numerical libraries are equipped with necessary functions for calculating the QUBO matrix \(((-\mathbf{G_{diag}}-2\mathbf{G_{triu}})\otimes\mathbf{I_{K}}+\mathbf{\Lambda}\otimes(2\mathbf{U_{ K}}-\mathbf{I_{K}}))\). Therefore, the method of generating the QUBO matrix for kernel clustering by matrix-matrix calculations is effective to accelerate the preprocess for annealing methods. The computational complexity of the proposed Ising-based kernel clustering is estimated to be \(\mathcal{O}(N^{2}K^{2})\) at most. The complexity is mainly occupied by the process of solving the QUBO problem. When \(NK\) spins are given, SA takes the computational time of \(\mathcal{O}(N^{2}K^{2})\). Conversely, QA takes the computational time of \(\mathcal{O}(NK)\). This paper uses the SA to solve the QUBO problem because the recent QA cannot yet solve the large problem at high computational precision. The kernel clustering by solving the QA is one of our main future directions. The maximum preprocess times for annealing are estimated to be \(\mathcal{O}(N^{2})\), \(\mathcal{O}(N^{2})\), and \(\mathcal{O}(N^{2}K)\) for calculating the Gaussian kernel matrix (Equation (9)), the Gram matrix (Equation (7)), and the QUBO matrix (Equation (20)), respectively. Additionally, the preprocess is formulated in the matrix-matrix calculation to utilize the numerical libraries. Since the numerical libraries accelerate the preprocess, the actual computational time is reduced more. Therefore, the proposed kernel clustering has a similar high computational complexity of \(\mathcal{O}(N^{2}K^{2})\) as the conventional Euclidean-based Ising clustering. The proposed method can expand the system availability even without increasing the computational complexity. Conversely, Ising-based clustering methods have more computational complexity than state-of-the-art approximated clustering methods. The approximated clustering methods obtain at most the computational complexity of \(\mathcal{O}(NK)\) at the expense of finding a locally optimum solution. While the quasi-optimal clustering methods do not guarantee to provide the globally optimum solution, the Ising-based clustering methods have the potential to find a ground state. When the ground state of the clustering is required, the proposed method shows an ideal clustering one by leveraging the nature of Ising machines.
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Fusion Module
#### 3.6.2 Weighted Design Based on Global Gradient  
Traditional methods primarily assign fusion weights based on the local gradient magnitude in multi-exposure image fusion. While this approach effectively preserves the detailed features of high-contrast regions, it may overlook the exposure quality of low-gradient areas. An innovative weight assignment strategy based on cumulative histogram analysis evaluates exposure quality by analyzing the distribution characteristics of pixel intensities. Specifically, the differential properties of the cumulative histogram can reveal pixel distribution patterns under varying exposure conditions. In underexposed images, the cumulative histogram displays a steep upward trend in the low-intensity interval, indicating a significant presence of saturated pixels in this region. Conversely, in moderately exposed images, the cumulative histogram exhibits a more gradual increase, reflecting a more uniform distribution of pixel values. When a pixel is situated in an interval with a lower rate of change in the cumulative histogram, it suggests that the intensity value is statistically rare, thus identifying it as a quality exposure region. Consequently, these observations were encoded as weights:  
\[w_{2,n}(x)=\frac{\text{Grad}_{n}(L_{n}(x))^{-1}}{\sum_{n=1}^{N}\text{Grad}_{n}(L _{n}(x))^{-1}+\epsilon} \tag{26}\]  
To prevent the denominator from becoming zero, a small positive value, denoted as \(\epsilon\), is added to it. \(\text{Grad}_{n}(\cdot)\) denotes the gradient of the cumulative histogram, which is called the global gradient. In the field of image processing, the global gradient characterizes the gradient features of similar regions across a large area of the image, in contrast to the local gradient, which only reflects variations in the vicinity of a specific pixel. This global analysis method captures the structural features and spatial distribution patternsof an image more comprehensively. During the weight calculation process, local gradient weights must be effectively combined with global gradient weights, and their coordination is achieved through normalization. The final weight calculation formula can be expressed as follows:  
\[W_{n}(x)=\frac{w_{1,n}(x)\times w_{2,n}(x)}{\sum_{n=1}^{N}w_{1,n}(x)\times w_{2, n}(x)+\epsilon} \tag{27}\]  
\(W_{n}(x)\) denotes the normalization result of the two weights for each image. Therefore, the final fusion result of the model is shown in Equation (28):  
\[F(x)=W_{1}(x)L_{1}(x)+W_{2}(x)L_{2}(x) \tag{28}\]
## 3 Trends in Operational USVs
### Sea States  
The maximum sea states tolerated by the USVs reveal that these vessels predominantly tolerate slight-moderate wave conditions, with sea states within 4 and 5 being the most frequently encountered (Figure 10). The highest occurrence is observed at sea state 5, which corresponds to moderate conditions, indicating that many USVs are designed to function reliably in environments with wave heights up to 3 m. This aligns with typical maritime operational requirements, where stability and control must be maintained even under challenging conditions. The noticeable decrease in frequency for sea states above 6 suggests that most USVs are not commonly deployed in rough or extreme sea conditions, likely due to limitations in their hull design, propulsion capabilities, and onboard stability control. However, the presence of data points at higher sea states (9 and beyond) indicates that some specialized USVs may be capable of enduring extreme conditions, possibly for military, research, or offshore applications.
## 3 Physics-Based Particle Simulation Approach
### Molecular Mapping and Coarse-Graining  
Water, oil, and emulsifier molecules were mapped to coarse-grained DPD beads. Each water bead (W) represented three water molecules (54 g/mol). The emulsifiers were modeled based on graphene-like polyaromatic cores (PACs) with variable side chains:* AS1: PAC with no side chains;
* AS2: PAC with 4 aliphatic + 2 heteroatom side chains;
* AS3: AS2 with 2 added perpendicular aliphatic chains.  
Four oil types were modeled:
* Oil1-4: Alkanes;
* Oil5-7: Amides;
* Oil8-10: Ethers;
* Oil11-14: Aromatics.  
For the simulation of the emulsification process, a 3D periodic model was used to construct the emulsion system. The oil, emulsifier, and water molecules were packed into a simulation cell with dimensions 100 \(\times\) 100 \(\times\) 100 \(R^{3}\), creating a mixture with a water-to-(emulsifier plus oil) volume ratio of either 7:3 or 8:2. The emulsifier concentration was fixed at a 1:1 mass ratio relative to oil, as concentration variation was beyond the scope of this study. All molecules were randomly distributed within the simulation box to represent the pre-equilibrium state typically observed in real emulsification processes, such as high-shear mixing. This approach is widely adopted in DPD studies, as the system naturally evolves toward thermodynamic equilibrium over the course of the simulation. Each model was labeled ASxOy, where ASx (x = 1-3) denotes the emulsifier type and Oy (y = 1-14) indicates the oil type. To study interfacial behavior, an additional slab configuration was used, consisting of two oil slabs (each 25 \(R_{c}\) thick) separated by a central water slab (50 \(R_{c}\) thick), with 20% emulsifier added to the oil slabs to ensure sub-monolayer coverage. The IFT was calculated using Equation (7) [40]:  
\[\text{IFT}=\left\langle L_{x}(p_{xx}-\frac{p_{z\text{=}}+p_{yy}}{2})\right\rangle \tag{7}\]  
where \(L_{x}\) is the cell length along the \(x\) direction (normal to the interface) and \(p_{xx}\), \(p_{yy}\), and \(p_{z\text{=}}\) are the pressures in the x, y, and z directions, respectively. Each simulation was conducted in NVT ensemble at 65 \({}^{\circ}\)C for 1,000,000 steps with a time step of 0.005 \(t_{c}\) (approx. 15 fs), followed by geometry optimization.  
The molecular mapping and composition of the coarse-grained beads for water, oils, and emulsifiers are illustrated in Figure 2, which visually summarizes the core structures and corresponding bead representations used in the DPD simulation.  
Figure 2: Coarse-grained beads and molecules and the molecules.
# Coastal Sargassum Level Estimation from Smartphone Pictures
###### Abstract  
Since 2011, significant and atypical arrival of two species of surface dwelling algae, _Sargassum natans_ and _Sargassum Fuitans_, have been detected in the Mexican Caribbean. This massive accumulation of algae has had a great environmental and economic impact. Most works addressing this topic use high-resolution satellite imagery which is expensive or may be time delayed. We propose to estimate the amount of Sargassum based on ground-level smartphone photographs that, unlike previous approaches, is much less expensive and can be implemented to make predictions almost in real time. Another contribution of this work is the creation of a Sargassum images dataset with more than one thousand examples collected from public forums such as Facebook or Instagram, labeled into 5 categories of Sargassum level (none, low, mild, plenty, and excessive), a relevant difference with respect to previous works, which only detect the presence or not of Sargassum in a image. Several state-of-the-art convolutional networks: AlexNet, GoogleNet, VGG, and ResNet, were tested using this dataset. The VGG network trained under fine-tuning showed the best performance. The results of the carried out experiments show that convolutional neuronal networks are adequate for providing an estimate of the Sargassum level only from smartphone cameras images.  
Sargassum; classification; deep learning; environmental monitoring +
Footnote †: journal: 26 August 2022  
Accepted: 28 September 2022  
Published: 5 October 2022
## 3 Results
The coulombic efficiency is calculated based on Equation (7) [63]:  
\[\eta=\frac{t_{d}}{t_{c}}, \tag{7}\]  
where, \(\eta\) (%) is the coulombic efficiency, \(t_{\rm d}\) (s) is the discharge time, and \(t_{\rm c}\) (s) is the charge time. As recorded in Figure 8, the deduced capacitance retentions gradually decreased at first, and after approximately 1000 cycles remained nearly constant. Impressively, even after 2000 cycles, the Ni\({}_{0.5}\)Co\({}_{0.5}\)(OH)\({}_{2}\) electrode still reached 80.2% of its initial specific capacitance and its high coulombic efficiency exceeded 90%. The capacitance loss after long-term cycling is inevitable, which is likely attributed to several reasons, (e.g., the presence of irreversible redox reactions, damage to the electrode,  
Figure 6: (**a**) Specific capacitances as a function of current densities and (**b**) Ragone plots of the Ni\({}_{1-x}\)Co\({}_{x}\)(OH)\({}_{2}\) samples. or impurities in the electrolyte) [65]. The remarkable durability performance of the Ni\({}_{0.5}\)Co\({}_{0.5}\)(OH)\({}_{2}\) electrode is pivotal for potential commercial application. In contrast, the cycling stability of the Ni\({}_{0.3}\)Co\({}_{0.7}\)(OH)\({}_{2}\) electrode is poor (capacitance retention of 62.1% after 2000 cycles), possibly since partial nanowires were not attached to the nickel foam tightly enough, giving rise to low material utilization for capacitance.
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 5 Implementation of Switch Architecture in Matlab/SimEvents  
Matlab/SimEvents is one of the most significant software in the engineering area. Developing a switch architecture that can cope with the GOOSE protocol requirements in SimEvents tool is not an easy task and not many studies have been done in this particular area. Therefore, choosing the proper blocks to construct the proposed switch was an essential task in this work. The switch design consists of three important parts: transmitter, queuing and processing, and receiver. The transmitter part is a set of events generated by \"Time-Based Entity Generator\" block in SimEvents. These events were generated in a specific time depending on the case study requirements; in this paper, the generation time of the events set to 4 ms based on the RUGGEDCOM switch [23]. The events were forwarded from the transmitter to the processor for processing. The processor (CPU) is represented by the \"Single Server\" block in SimEvents. When the Single Server block is busy processing the incoming events and the transmitter keeps sending more events, the extra incoming events are stored in the FIFO (First Input First Output) buffer for a finite period of time until it gets permission from the CPU to release them and send the packets to the processor and then to the receiver. The receiver part of the switch is modeled using the \"Entity Sink\" block in SimEvents. Figure 6 shows the proposed switch by using SimEvents software. The scenario of the drop packets happens when the number of events that come from the input generators exceeds the overall capacity of the processor. Such an occasion reduces the reliability of the system; therefore, calculating the number of drop packets is crucial in this study and it is formulated as follows:  
No. of drop packets = Total number of inputs events \(-\) Buffer capacity \(-\) Service rate of CPU  
Figure 5: Proposed switch architecture design.