esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition
#### 2.2.1. Shoreline Surveys  
Shoreline surveys were conducted using Sokkia GRX1 and GSX2 Global Navigation Satellite System receivers (Sokkia Topcon, Atsugi, Japan), as shown in Table 2. The shoreline was surveyed at two sandbars formed by sedimentation. To obtain accurate results, a triangulation network was formed using nearby tidal benchmarks and adjusted accordingly. The survey was conducted at low and high tides based on tidal forecasts. The shoreline was measured every 10-15 m, and Figure 2 shows the surveying process. The results of the shoreline survey were processed using the Universal Transverse Mercator coordinate system, and the heights were based on Korean National Geoid 14. In addition, the data obtained through the field survey were compared with the data surveyed by period using the AutoCAD 2022 program, which is effective for creating 2D drawings or 3D object files by sketching/drawing and designing them with graphics software.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Month** & **Period** \\ \hline
2018 & October & 26–11 \\ \hline
2019 & April & 18–22 \\  & October & 18–22 \\ \hline
2020 & April & 7–12 \\  & October & 16–21 \\ \hline
2021 & April & 8–13 \\  & October & 20–25 \\ \hline
2022 & April & 13–19 \\  & October & 26–31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survey dates for this study.  
Figure 2: Acquisition of shoreline data using GPS surveying.
## 3 Results  
### Spatio-Temporal Characteristics of DE and GD  
#### 3.1.1 Comprehensive Evaluation of DE and GD Level  
The comprehensive assignment method was utilized to measure the levels of both the DE and GD during 2013-2021. ArcGIS was then used to map the spatial distribution characteristics in 2013, 2017, and 2021 (see Figure 1 for details, where the grey areas denote the unaccounted regions). The results showed that China's provincial DE development levels ranged from 0.015 to 0.754, while the GD levels spanned from 0.026 to 0.671, indicating substantial disparities among provinces. The DE development level showed an upward trend, while the spatial pattern of GD remained relatively stable. Provinces with higher development levels were concentrated in the eastern coastal area, favored by more favorable geographical conditions. In contrast, provinces situated in the western region with poor location conditions, like Xinjiang and Qinghai, exhibited lower development levels.
## 3 Results
### Spatial and Temporal Dimensions of Reviewed Papers  
The review process revealed 199 articles from 46 countries (Figure 2), with the United States having the most studies (n = 38; being primarily related to management, production, species composition, and degradation) followed closely by China (n = 36). Asia had a higher proportion of relevant papers (i.e., 24.6%) than other continents due to the publications from China; Australia (n = 16), South Africa (n = 12), Argentina (n = 8), Canada (n = 7), New Zealand (n = 5), Germany (n = 5), Sweden (n = 4), Uruguay (n = 4), Brazil (n = 4), and France (n = 4) had relevant publications.  
\begin{table}
\begin{tabular}{l l} \hline
**Search Categories** & **Search Strings/Synonyms/Terms** \\ \hline \multirow{2}{*}{Pasture Management traits} & quality, fertilizer, manure, irrigation, nutrient, \\  & management, “soil condition”, “water”, “moving” \\ \hline \multirow{2}{*}{Pasture Production} & quantity, height”, sward, biomass, production, \\  & productivity, yield*, growth, “growth rate” \\ \hline \multirow{2}{*}{Pasture Composition} & species, botanical*, classification, \\  & decline, “grazing intensity”, “grazing pressure”, \\ \multirow{2}{*}{Pasture Degradation} & “overgrazing”, “carrying capacity”, “stocking rate”, \\  & “stocking density”, “land use”, “fractional cover” \\ \hline Vegetation & Grassland*, rangeland*, pasture *, graz* \\ \hline Remote Sensing & “Earth observation”, UAS or UAV, drone, satellite*, \\ \hline Remote Sensing Adoption & “end-user*, adoption*, technology \\ \hline \end{tabular}
\end{table}
Table 1: Search phrases used to refine papers reviewed.  
The number of remote sensing studies was low in 1991, and has increased exponentially. Studies in earlier years focused on management and production, while the proportion of studies on pasture degradation, productivity, and management increased significantly in later years. A lack of publicly available satellites and UAS may have caused fewer studies in the early 1990s. Figure 3 shows the temporal pattern of studies when grouped by management traits, pasture production, species classification and degradation.  
Figure 3: Temporal (annual) pattern of studies reviewed by their topics of coverage. Bars indicate the number of studies published each year.  
Figure 2: Number of studies from each country and across continents reviewed. Note: the blue and orange colours represent the ratio of the number of studies (blue) compared to the total number of studies (orange).
# Influence of Rice Variety and Freezing on Flour Properties
## 3 Results and Discussion  
### Effect of Rice Freezing on the Grinding Process  
The proximate composition of Basmati and Arborio rice varieties is presented in Table 1. It can be observed that the Basmati variety had significantly higher protein content and significantly lower crude fiber and starch contents compared to the Arborio variety, while the results obtained for fat and ash contents of the two varieties are very close. The performance indicators of the grinding process of Basmati and Arborio commercial rice varieties are presented in Table 2.  
Analyzing the particle size distribution results (Table 2), it can be seen that milling the two varieties of raw rice resulted in similar percentages of particles that exceed 630 \(\upmu\)m, while for the particles of 400-630 \(\upmu\)m, 160-315 \(\upmu\)m, and less than 160 \(\upmu\)m the differences are significant. Thus, higher percentages of particles with size ranging from 400 to 630 \(\upmu\)m (33.3%), and lower than 160 \(\upmu\)m (11.6%) were obtained by grinding the Basmati variety, while when grinding the Arborio variety, most of the particles (29.7%) were in the 160 to 315 \(\upmu\)m range. Moreover, in case of the Arborio variety, the amounts of particles between 400 and 630 \(\upmu\)m and those between 315 and 160 \(\upmu\)m are close: 27.9% and 29.7%, respectively. Even in these conditions, the whole products resulted by milling the two commercial rice varieties have close fineness modules: 3.25 and 3.26 in case Basmati and Arborio rice varieties, respectively. The flows samples resulted after sieving process were composed of the particles with sizes that do not exceed 315 \(\upmu\)m, and the fineness modules were 1.89 and 2.11 for flours from Basmati and Arborio varieties, respectively. It can be noticed that the difference between fineness modules of the two flours samples (about 12%) is lower compared to the whole grinding products, \(\sim\)29%. These results highlight the influence of the variety on the grinding behavior; the Arborio variety is an Italian short-grain rice while Basmati is an Italian long-grain rice. In particular, the structure of the endosperm, which varies with rice variety [24], greatly influences the grinding behavior of the rice. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Basmati** & **Arborio** \\ \hline Protein, \% & 8.72 \(\pm\) 0.07 \({}^{\text{a}}\) & 6.74 \(\pm\) 0.05 \({}^{\text{b}}\) \\ Fat, \% & 1.23 \(\pm\) 0.04 \({}^{\text{a}}\) & 1.26 \(\pm\) 0.04 \({}^{\text{a}}\) \\ Crude fiber, \% & 1.26 \(\pm\) 0.06 \({}^{\text{b}}\) & 1.83 \(\pm\) 0.06 \({}^{\text{a}}\) \\ Ash, \% & 0.41 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.38 \(\pm\) 0.02 \({}^{\text{a}}\) \\ Starch, \% & 76.19 \(\pm\) 0.17 \({}^{\text{b}}\) & 77.60 \(\pm\) 0.09 \({}^{\text{a}}\) \\ \hline \end{tabular} Data expressed as mean \(\pm\) standard deviation (\(n=3\)). Mean values on the same row which do not share a superscript letter are significantly different at \(p<0.05\).  
\end{table} 
Table 1: Proximate compositions (% d.w.) of Basmati and Arborio commercial rice varieties.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 5 Design Guidelines  
From the analysis presented in Section 4, we have extracted several recommendations for the circuit design of radenv-ADCs. We have distributed these advises along the following simplified 5-step design flow:  
Step 1--Analysis of the target application and the available solutions  
Step 2--Selection of the manufacturing technology  
Step 3--Selection of the ADC architecture  
Step 4--Circuit design  
Step 5--Design validation  
For a better understanding, before describing each step in Sections 5.2-5.6, we have summarized in Section 5.1 the overall radiation hardening strategy across the design flow.
#### Contributions  
The core contributions of the proposed communication framework are as follows:  
* The proposed framework integrates multiple bio-inspired metaheuristic algorithms, such as GA, FA, LFA, BFA, and BPSO, with optimization and hybridization of EHO. This hybridization optimizes the scheduling of underwater nodes, resulting in significant energy savings and improved network efficiency.
* The framework is designed to enhance the Packet Delivery Ratio (PDR) by addressing the limitations of existing routing protocols such as RDBF, EEPDBR, UDB, 3 DUL, UL, and RSMB, respectively. Through the strategic use of scheduled beacon nodes and optimized routing processes, the proposed scheme outperforms the prior protocols.
* Results from extensive simulations indicate that the scheme performs well even with an increased number of nodes (up to 700), maintaining stable performance with minimal localization errors and optimized resource utilization. These results validate the scheme's effectiveness in managing network traffic, improving communication efficiency, and ensuring reliable data delivery in large-scale underwater networks.  
These contributions collectively address the core challenges of underwater communication, providing a highly efficient and reliable solution for managing the unique characteristics of UASNs. The proposed scheme demonstrates potential for real-world applications, particularly in scenarios where energy efficiency, accurate localization, and optimized routing for data transmission are critical.
## 3 Results
### Antibiofouling Activity of CPDs/PU and C\({}_{60}\)/PU  
It is very important to observe the formation, reduction or even eradication of biofilms of different bacteria strains. In this research, it was very interesting to observe the antibiofouling activity of the CPD/PU and C\({}_{60}\)/PU composite films against bacteria strains present in hospitals: _P. aeruginosa_, _S. aureus_ and _E. coli_. Figure 6a-c shows the antibiofouling activity of both types of testing composite films against three bacteria: _P. aeruginosa_, _S. aureus_ and _E. coli_.  
Figure 6: Antibiofouling effect of CPD/PU and C\({}_{60}\)/PU composite films on _P. aeruginosa_ (**a**), _S. aureus_ (**b**) and _E. coli_ (**c**) bacterial biofilms with and without blue light irradiation for 1 h. Absorbance is an indicator of the quantity of biofilm formed.  
The results indicated the following: biofilm formation on both the CPDs/PU and C\({}_{60}\)/PU composite films was strain dependent, and differences were observed between blue-light-irradiated and nonirradiated samples. Under blue light irradiation, P. aeruginosa biofilm was reduced by 97% on the CPD/PU composite films and by only 20% on the C\({}_{60}\)/PU composite films; see Figure 6a. The CPD/PU composite films had potent antibio-fouling activity against _P. aeruginosa_ with blue light irradiation. Under the same conditions, _S. aureus_ biofilm was reduced by 95% on the CPD/PU composite film and by 65% on the C\({}_{60}\)/PU composite film; see Figure 6b. These composite films showed antibiofilm activity against this bacterium without blue light irradiation: 94% of bacteria biofilm was reduced on the CPD/PU composite films and 75% of bacteria biofilm on the C\({}_{60}\)/PU composite films, respectively. In the case of _E. coli_, CPDs/PU composite films reduced 50% of the bacteria biofilm, whereas the C\({}_{60}\)/PU composite films reduced 45% of the bacteria biofilm under blue light irradiation; see Figure 6c. Both composite films showed better antibiofouling activity against _S. aureus_ and _E. coli_ under blue light at 470 nm.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results
### OFR Surface Area Time Series
During the first occasion, PlanetScope and Basemap indicated surface area (~9.5 ha) 95% greater than Planet Fusion (0.5 ha); on the second occasion, a contrasting scenario in which Planet Fusion surface area (12.25 ha) was 86% higher than that of PlanetScope and Basemap (~2 ha). These results shed light on the importance of assessing the OFR environmental conditions and how they impact the OFR surface area time series before employing these datasets to monitor surface area changes.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 2 Spatiotemporal Fusion Approaches
### Fsdaf  
In FSDAF [8], the input data include one pair of coarse and fine resolution images acquired at \(t_{1}\) and one coarse-resolution image at \(t_{2}\) (Figure 1). The output is a predicted fine-resolution image at \(t_{2}\). In this study, the coarse-resolution image is Planet image while the fine-resolution image is WV image. FSDAF includes six main steps: (1) classify the fine-resolution image at \(t_{1}\) by an unsupervised classifier, Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) [19]; (2) estimate the temporal change of each class in the coarse-resolution image from \(t_{1}\) to \(t_{2}\); (3) predict the fine-resolution image at \(t_{2}\) using the class-level temporal change (refereed as temporal prediction)and calculate residuals at each coarse pixel. This temporal prediction would be accurate if no abrupt land cover change occurs; (4) predict the fine-resolution image from the coarse image at \(t_{2}\) with a Thin Plate Spline (TPS) interpolator. This TPS prediction should be more accurate than the prediction in the previous step if land cover change occurs; (5) distribute the residuals in step 3 based on TPS prediction to update the temporal prediction; and (6) further smooth the temporal prediction to get the final prediction of the fine-resolution image at \(t_{2}\) using the similar strategy of STARFM. The number of classes used in Step 2 is based on the output of ISODATA. In ISODATA, users need to set the minimum and maximum number of classes based on their prior knowledge of the image, and then ISODATA will find the optimal number of classes. In Step 6, FSDAF smooths the temporal prediction using the weighted average of the same-class pixels in a neighborhood. The weight is determined by spatial distance. More details can be found in the paper introducing FSDAF [8].
# Entropy Optimization in MHD Nanofluid Flow over an Exponential Stretching Sheet
## 3 Entropy Generation Analysis  
The principal contributors to irreversibilities in the fluid include the heat transfer through thermal radiation, viscous dissipation, magnetic field, and mass transfer, respectively.  
The volumetric rate of the entropy generation rate is written as (see [21; 22])  
\[S_{gen}^{\prime\prime\prime}=\underbrace{\frac{1}{T_{\infty}^{2 }}\left[k_{f}+\frac{16\sigma^{*}T_{\infty}^{3}}{3k^{*}}\right]\left(\frac{ \partial T}{\partial y}\right)^{2}}_{\text{heat transfer}}+\underbrace{ \frac{\mu}{T_{\infty}}\left(\frac{\partial u}{\partial y}\right)^{2}}_{\text{ viscous dissipation}}+\underbrace{\frac{\sigma B_{0}^{2}}{T_{\infty}}u^{2}}_{\text{ magnetic field}}+\] \[\underbrace{\frac{RD}{C_{\infty}}\left[\left(\frac{\partial C}{ \partial x}\right)^{2}+\left(\frac{\partial C}{\partial y}\right)^{2}\right] +\frac{RD}{T_{\infty}}\left[\left(\frac{\partial T}{\partial x}\right)\left( \frac{\partial C}{\partial x}\right)+\left(\frac{\partial T}{\partial y} \right)\left(\frac{\partial C}{\partial y}\right)\right]}_{\text{mass transfer}}. \tag{12}\]  
We define \(S_{0}^{\prime\prime\prime}\) as the dimensionless entropy generation rate--see [12; 23]--such that  
\[S_{0}^{\prime\prime\prime}=\frac{k_{f}\Big{(}T_{w}-T_{\infty} \Big{)}^{2}}{T_{\infty}^{2}x^{2}}. \tag{13}\]The rate of entropy generation can be expressed as  
\[N_{G}(\eta,\xi)=\frac{S_{gen}^{\prime\prime\prime}}{S_{0}^{\prime \prime\prime}}=Re\big{(}1+Nr\big{)}\theta^{\prime 2}+\frac{ReBr}{\Omega}\xi f^{\prime 2}+ \Sigma\Big{(}\frac{\chi}{\Omega}\Big{)}^{2}\Bigg{[}\bigg{(}Re+\frac{\eta^{2}}{4 }\bigg{)}h^{\prime 2}-\eta\xi h^{\prime}\frac{\partial h}{\partial\xi}+\] \[\xi^{2}\bigg{(}\frac{\partial h}{\partial\xi}\bigg{)}^{2}\Bigg{]}+ \Sigma\frac{\chi}{\Omega}\Bigg{[}\bigg{(}Re+\frac{\eta^{2}}{4}\bigg{)}\theta^{ \prime}h^{\prime}+\xi^{2}\frac{\partial\theta}{\partial\xi}\frac{\partial h}{ \partial\xi}-\frac{\eta\xi}{2}\bigg{(}\theta^{\prime}\frac{\partial h}{\partial \xi}+h^{\prime}\frac{\partial\theta}{\partial\xi}\bigg{)}\Bigg{]}+\frac{ReBr}{ \Omega}e^{-\beta_{1}\theta}{f^{\prime\prime}}^{2}. \tag{14}\]  
The parameters appearing in Equation (14) are defined as  
\[Re=\frac{U_{\infty}(x)x}{\
u_{\infty}},\ \ Br=\frac{\mu_{ \infty}U_{\infty}^{2}}{k_{f}\Delta T},\ \ \Omega=\frac{\Delta T}{T_{\infty}}=\frac{T_{w}-T_{ \infty}}{T_{\infty}},\] \[\Sigma=\frac{C_{\infty}RD}{k_{f}},\ \ \chi=\frac{C_{w}-C_{\infty}}{C_{ \infty}}, \tag{15}\]  
where \(Re\) is the local Reynolds number, \(Br\) is the Brinkman number, \(\Omega\) is the temperature difference, \(\Sigma\) is the dimensionless parameter, \(\chi\) and \(R\) are the diffusion parameter and universal gas constant, respectively, and \(D\) is the mass diffusion.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
###### Abstract  
The impact of alternative distribution channels, such as bank Automated Teller Machines (ATMs), on the financial industry is growing due to technological advancements. Investing in ideal locations is critical for new ATM companies. Due to the many factors to be evaluated, this study addresses the problem of determining the best location for ATMs to be deployed in Istanbul districts by utilizing the multi-criteria decision-making framework. Furthermore, the advantages of fuzzy logic are used to convert expert opinions into mathematical expressions and incorporate them into decision-making processes. For the first time in the literature, a model has been proposed for ATM location selection, integrating clustering and the interval-valued Fermatean fuzzy analytic hierarchy process (IVFF-AHP). With the proposed methodology, the districts of Istanbul are first clustered to find the risky ones. Then, the most suitable alternative location in this district is determined using IVFF-AHP. After deciding the ATM locations with IVFF-AHP, in the last step, a Double Deep Q-Network Reinforcement Learning model is used to optimize the Cash in Transit (CIT) vehicle route. The study results reveal that the proposed approach provides stable, efficient, and adaptive routing for real-world CIT operations.  
location selection; clustering; automatic teller machines; Fermatean fuzzy sets; routing optimization; deep reinforcement learning Article
## 3 Results
### Optical Emission Spectroscopy  
The oxygen flow rate dependences of the spectral radiation intensities of selected strong representative emission lines--Al\({}^{+}\) (394.36 nm), Ar (706.19 nm), Ar\({}^{+}\) (480.67 nm), Ar\({}^{2+}\) (434.81 nm), O (777.14 nm), and O\({}_{2}\)\({}^{+}\) (602.61 nm)--are shown in Figure 7. Note that the data have an error of \(\sim\)20 a. u., corresponding to the noise level of the dark signal recorded by the spectrometer.  
The spectral intensities of plasma species emission are presented in log scale for a clearer understanding of the dependencies. Figure 7a shows the dependences plotted with increasing reactive gas flow in the forward direction, while Figure 7b shows the dependences plotted with decreasing reactive gas flow in the reverse direction.  
Figure 6: Fluxes of positively and negatively charged ions in the transition region vs. oxygen flow rate.  
We observe a decrease in the radiation intensity for the metal ions (Al\({}^{+}\)) with a corresponding increase in the neutral oxygen atoms (O) and oxygen ions (O\({}_{2}\)\({}^{+}\)) measured at different oxygen flows. These changes were observed in the transition zone near the oxygen flow value 1.2 sccm. A slight increase in the radiation intensity after transition can be observed for neutral argon (Ar) atoms and Ar\({}^{+}\) ions.
# Improving Automated Machine-Learning Systems through Green AI
## 4 Materials and Methods
### Hyperparameter Optimization, Pipelines, and Machine-Learning Algorithms  
Two hyperparameter optimization algorithms were defined: Bayesian optimization (see Algorithm 2) and random search (see Algorithm 3). These selections were made based on their widespread use within the AutoML community and their potential to improve efficiency and sustainability. Grip search was not included as it is computationally very costly and, therefore, has a greater impact on the carbon footprint. Several data preprocessing pipelines and a taxonomy of machine-learning algorithms were designed,  
Figure 3: This class diagram shows the main classes and relationships within a sustainable-focused AutoML system, including the AutoML classifier, data processing pipelines, and a variety of machine-learning algorithms.  
covering different approaches and strategies (see Table 1). This allowed exploring a variety of configurations and modeling options, contributing to a comprehensive assessment of sustainability and efficiency.  
```
1:Let \(f\) be the objective function to optimize
2:Define the search space \(\mathcal{X}\)
3:Initialize the set \(\mathcal{D}=\{(\mathbf{x}_{1},f(\mathbf{x}_{1})),\ldots,(\mathbf{x}_{n_{r}},f( \mathbf{x}_{n}))\}\)
4:while stop criterion not met do
5: Fit GP: \(f\sim\mathcal{GP}(m(\mathbf{x}),k(\mathbf{x},\mathbf{x}^{\prime}))\)
6: Predict with GP: \(p(f|\mathbf{x},\mathcal{D})\sim\mathcal{N}(\mu(\mathbf{x}),\sigma^{2}(\mathbf{ x}))\)
7: Choose \(\mathbf{x}_{t}=\underset{\mathbf{x}}{\operatorname{argmax}}\,\alpha(\mathbf{x}| \mathcal{D})\)
8: Evaluate \(f(\mathbf{x}_{t})\)
9: Update \(\mathcal{D}\leftarrow\mathcal{D}\cup(\mathbf{x}_{t},f(\mathbf{x}_{t}))\)
10: Update GP model
11:endwhile
12:return Best x found
```  
**Algorithm 2** Bayesian Optimization.  
```
1:\(\mathcal{X}\leftarrow\) hyperparameter search space
2:\(f(\mathbf{x})\leftarrow\) loss function to minimize
3:\(n_{iter}\leftarrow\) number of iterations
4:for\(i=1\) to \(n_{iter}\)do
5:\(\mathbf{x}_{i}\sim\mathcal{X}\)
6:\(s_{i}\leftarrow\operatorname{CV}(\mathbf{x}_{i})\)
7:endfor
8:\(\mathbf{x}_{best}\leftarrow\underset{\mathbf{x}_{i}}{\operatorname{argmax}}\,s_{i}\)
9:return\(\mathbf{x}_{best}\)
```  
**Algorithm 3** Random Hyperparameter Search.