esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Works  
This paper presents a methodology for obtaining simple and sufficiently accurate simplified models of the push-pull converter from its full non-ideal equivalent model.  
Unlike the works reviewed in the scientific literature, which present simplified models for specific applications (without general validity), this paper proposes a methodology for obtaining simplified models based on the development of a sensitivity analysis that allows identifying the effect that each non-ideality of the actual (physical) converter has on the response of the simplified model. To obtain these simplified models, the absolute percentage errors of the dynamic response with respect to the reference model behaviour are analysed, considering for their definition those non-idealities whose effect (error) is perceptible in practice.  
The performance of the simplified models was evaluated and validated by comparison with respect to the dynamic and steady-state response of the full non-ideal (reference) model, showing absolute percentage errors in all cases below 5%, as well as a dynamic response very similar to that of the reference model.  
In view of the analysis carried out, it is concluded that the use of the proposed methodology, based on the use of a sensitivity analysis, allows defining, quantifying and assessing the effect of each non-ideality of the actual (physical) converter to obtain simple and very accurate simplified models of the push-pull converter.  
Figure 9: Time response of the simplified model (Simp) with respect to the reference model (Ref) and the ideal model (Ideal) for cases 1 (**a**) and 4 (**b**) for buck and boost topologies, respectively.  
Future work from this research focuses on the application of the simplified models obtained for the design of model-based controllers with the aim of improving the performance of the controller-converter set.  
Methodology, F.J.V.; Software, F.S.; Formal analysis, J.M.A.; Investigation, F.J.V., J.M.A. and F.S.; Writing--original draft, F.J.V. and F.S.; Writing--review & editing, J.M.A.; Supervision, J.M.A. All authors have read and agreed to the published version of the manuscript.  
This work is a contribution of the PID2020-116616 RB-C31 Project supported by the Spanish Ministry of Economy and Competitiveness, by the P20_00730 Project supported by Regional Andalusian Government under the European Union Regional Development Fund. Funding for open access charge: Universidad de Huelva/CBUA.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare
## 3 Results  
### Fitting Infiltration Parameter Infexp  
To select the best fitting _infexp_ value, the results of all depths equipped with sensors were evaluated. Figure 2 shows for four depths per site the r\({}^{2}\) of the correlation between measured and modeled soil water content for various infexp values. The results indicate by variations between _infexp_ values, that the influence of _infexp_ varied between the sites--in site 1202, it was highest. The r\({}^{2}\) exhibited a high variation between _infexp_ values at the lowest measurement depths (Figure 2, right column), In contrast, in site 1302, _infexp_ had nearly no influence. There, the r\({}^{2}\) varied less between _infexp_ values.  
After optimizing the infiltration parameter _infexp_, we assigned the following _infexp_ values to the sites 1101: 0.4; 1202: 0.8; 1203: 0.6; 1204: 0.8; 1205: 0.2; 1302: 0.2; and 1303: 1.0. The lower the _infexp_ value is, the more water is fed to the upper soil layers, while an _infexp_ value of 1.0 distributes the water evenly within the soil column up to maximum infiltration depths.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in the Encryption Aspect
#### 3.3.3 Primitives
Another paper by [PERSON] et al. [143] proposes a dynamic encryption algorithm so that the cipher image is always different even if the same key and plain image are used. One-Time Key [PERSON] et al. [144] proposed an image encryption algorithm that uses a one-time-key to generate chaotic maps using the hash of the plaintext image. The algorithm employs a novel concept known as a rotor machine, and through simulation, the results showed that the algorithm possesses strong cryptographic properties. Secret Sharing Multiple Secret Sharing (MSS) in the context of image encryption is where k plaintext images are required to create k cipher-text images, and those same k cipher-text images are required to obtain even just one plaintext image [145]. [PERSON] et al. [145] proposed an MSS algorithm for images that addresses common shortcomings.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 7 Conclusions  
Designing a MAC protocol for underwater networks with acoustic communication is affected by many factors, the most important of which are long propagation delay, low bandwidth, and energy constraints. In this paper, we have proposed an efficient reservation-based distributed MAC protocol (ED-MAC) for underwater sensor networks. ED-MAC uses a duty cycle mechanism that can reduce energy consumption as well as improve throughput and fairness by handling the traffic contention effectively. The spatial-temporal uncertainty as well as the hidden node problem have also been addressed. Using an extensive simulation study, the performance of ED-MAC has been compared against those of two other protocols from the same category recently reported in the literature. Simulation results have shown that ED-MAC performs well in terms of the successful delivery ratio, throughput, energy consumption, and fairness index with varying offered traffic and number of nodes. In terms of the network scalability, ED-MAC highly outperforms those two other compared protocols. As future work, we plan to improve the energy efficiency and reliability by applying the knowledge of depth (layer) at each node. In this way, we are able to vertically and horizontally prevent any possibility of collisions between sensors while the scheduling phase is not based on the deep message exchange and the concept of timer. We also plan to consider the variable traffic load during the operational phase. In this case, the whole setup process is not required to be repeated.  
[PERSON], [PERSON], and [PERSON] developed the algorithm and analysed the results. [PERSON] carried out the computer simulations and wrote the manuscript. The simulation model was developed by [PERSON] and [PERSON], [PERSON] and [PERSON] assisted with the integrity of the research and also to revise the manuscript.  
**Acknowledgments:** This research was conducted as part of a PhD, and was supported by Saudi Arabia King Abdullah Scholarships Program.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Methodology
### Fuzzy Image Classification
#### 3.2.5 Contextual Check  
Having applied all the above mentioned rules to the image, still some unclassified segments might remain. Unclassified features are tested to see if they resemble specific classes. For example, since strict rules are applied for road detection, some parts of roads remain unclassified. Therefore, within unclassified objects those which are in the neighborhood of roads and have spectral similarity to roads are classified as Road. Also, some parts of the buildings might be remained unclassified. A similar rule is applied to them for a proper classification. The fuzzy rules used for further building or road classification are:  
If **spectral similarity to road** is _high_ and **relative border to road** is _medium_ or **relative border to road** is _high_, then segment is **Road**.  
If **spectral similarity to building** is _high_ and **relative border to building** is _medium_ or **relative border to building** is _high_, then segment is **Building**.  
Other than the objects mentioned above, there are some other image objects which are not of interest of this study. For example, vehicles are irrelevant to this project, even though they might be the topic of other studies. Thus, using contextual information vehicles are removed. Courts and roundabouts must be assigned to road classification as well. In the following some of the contextual features with the related fuzzy rule are mentioned.  
**Vehicle removing:** Considering the spatial resolution of the used satellite images, which is around 50 cm, and also the size of a typical vehicle which is approximately 1.5 m \(\times\) 2.5 m, vehicles will be a rectangular shaped objects in satellite images with area around 15 pixels. The largest size vehicle that is counted in this study is a truck with size 2.5 m \(\times\) 10 m. Therefore, objects which have an area between 15 to 100 pixels are probable to be vehicles. In addition, vehicles can be found in roads. Therefore, they must be connected to a road segment, which means their relative border with roads is greater than 50%.  
Therefore the associated fuzzy rule is:  
If **Area** is _small_ and **Rectangular fit** is _high_ and **relative border to road** is _high_, then segment is **Vehicle**.  
And since we do not deal with vehicles in this study, we assign it to road class.  
**Courts and Roundabouts:** The segments of the image relating to courts and roundabouts fail to be classified as roads because of their round shapes or spectral reflectance different of that of roads. In this study, the unclassified segments are searched for circular or semi-circular features which are in neighborhood of the roads (covering roundabouts) or they have similar spectral reflectance to roads (covering courts). Here, these features are also classified as roads. The related rule is:  
If **Elliptic Fit** is _high_ and **relative border to road** is _high_, then segment is **Road**.  
If **Elliptic Fit** is _high_ and **spectral similarity to road** is _high_, then segment is **Road**.
## 1 Introduction
The influence of sample plot size on the accuracy of AGB and Corg estimation models has been well explored in ecosystems with densevegetation, such as forests and mangroves [22; 31; 32; 33]. These studies demonstrate that larger plots contribute to more accurate predictions. However, similar studies in wetlands are rare and non-existent in herbaceous marshes. The absence of research on optimal plot size for remote monitoring and predictive models in wetlands impacts the consistency of predictions with alike vegetation. Therefore, this study jointly examines the research gaps on the influence of different input data treatments and plot sizes on the accuracy of remote AGB and Corg estimation in wetlands using an ML model. This research aims to enhance remote estimation methods for AGB and Corg, expanding their application to wetland ecosystems, especially herbaceous wetlands. Therefore, this study aimed to answer (a) whether different sampling treatments and plot sizes affect the performance of a predictive model of AGB and Corg based on remote sensing and ML and the importance of the spectral variables used, and (b) which sensors and spectral variables obtained the most accurate estimate and the greatest predictive importance, respectively. We aimed to explore these research gaps, with a special interest in improving RF models for the prediction and spatialization of AGB and Corg in wetlands. To this end, we utilized data from Sentinel-2A and PlanetScope sensors and field-collected data in a palustrine wetland in southern Brazil. The results, though based on species-specific models, provide a foundation for enhancing predictive ML models in wetlands. They offer crucial insights for AGB and Corg prediction models, with the potential to drive standardization in the collection and processing of input data for predictive models with the aim of ensuring consistent predictions in inventories and monitoring. In addition, this research contributes to the understanding of these ecological processes in wetlands, especially in light of the difficulties of access and collection.
## Appendix A Information on Geometry Dimensions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Geometry** & **Parameter** & **Value** & **Geometry** & **Parameter** & **Value** \\ \hline Rectangular prism & \(h_{\text{rp}}\) & 7.00 & LPA & \(r_{\text{cyc}}\) & 8.60 \\ \(L_{\text{rp}}\) & 29.00 & half-cylinder & \(L_{\text{cyc}}\) & 30.00 \\ \(W_{\text{rp}}\) & 15.00 & & \(W_{\text{cyc}}\) & 19.86 \\ \hline Hollow rectangular & \(h_{\text{hrp}}\) & 17.80 & & \(r^{\prime}_{\text{cy}}\) & 19.00 \\ rectangular & \(L_{\text{hrp}}\) & 32.00 & WPA inverted & \(L^{\prime}_{\text{cyl}}\) & 28.80 \\ prism & \(W_{\text{hrp}}\) & 18.00 & & \(W^{\prime}_{\text{cyl}}\) & 18.00 \\ \hline  & \(h_{\text{hy}}\) & 1.44 & & \(r^{\prime}_{\text{cyh}}\) & 3.00 \\ Cylindrical hemisphere & \(r_{\text{hy}}\) & 8.40 & WPA hollow inverted & \(L^{\prime}_{\text{cyh}}\) & 28.77 \\  & & & & \(W^{\prime}_{\text{cyh}}\) & 15.00 \\  & & & & \(t^{\prime}_{\text{cyh}}\) & 1.00 \\ \hline  & \(r_{\text{rp}}\) & 22.70 & WPA & \(r^{\prime}_{\text{cyc}}\) & 6.90 \\ Concave hemisphere & & & & \(L^{\prime}_{\text{cyc}}\) & 28.80 \\  & & & & \(W^{\prime}_{\text{cyc}}\) & 18.00 \\ \hline  & \(r_{\text{gh}}\) & 16.00 & & \(h_{\text{p}}\) & 26.50 \\ Hollow concave hemisphere & & & LPA inverted & \(L_{\text{bs}}\) & 28.20 \\  & & & trapezoidal prism & \(W_{\text{bs}}\) & 28.28 \\  & & & prism & \(L_{\text{bip}}\) & 14.00 \\  & & & & \(W_{\text{bip}}\) & 15.21 \\ \hline  & \(r_{\text{spc}}\) & 22.97 & & \(h_{\text{ph}}\) & 21.30 \\  & & & LPA inverted & \(L_{\text{bsh}}\) & 28.00 \\ Hemisphere & & & trapezoidal & \(L_{\text{bib}}\) & 14.00 \\  & & & prism & \(W_{\text{bip}}\) & 15.21 \\  & & & & \(h_{\text{ph}}\) & 21.30 \\  & & & & \(L_{\text{bsh}}\) & 28.00 \\ Hemisphere & & & & \(W_{\text{bsh}}\) & 28.28 \\  & & & trapezoidal prism & \(L_{\text{bib}}\) & 14.00 \\  & & & prism & \(W_{\text{bib}}\) & 15.21 \\  & & & & \(t_{\text{ph}}\) & 4.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The glycerin-made resonator: The dimensions (in mm) offering the best MLM in the numerical simulations.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 4 Evaluation
### Memory and Computational Complexity Analysis  
The proposed curriculum learning requires additional computation and memory space. The parameters to be computed are  
* Dominance factors: \(\gamma_{1}(t,f)\) and \(\gamma_{2}(t,f)\) in Equations (13) and (14).
* Loss function weights: \(w(t,f)\) in Equation (15).
* Loss function adjustment: \(J_{w}\) in Equation (16).  
To calculate \(\gamma_{1}\) in Equation (13), it requires three square operations, an addition, and a division. \(\gamma_{2}=1-\gamma_{1}\) requires an additional subtraction. If the unit time for basic floating-point operations is identical, the additional number of calculations is 6 for each time-frequency bin, thus making the total amount of operations for \(T\) frames and \(F\) frequency bins to be \(6 TF\). Weight calculation requires two multiplications and an addition, and loss function weighting requires a multiplication, so it totally costs 4 unit times for each time and frequency bin. The total amount of loss function update is \(4 TF\). In total, the proposed curriculum learning approach requires \(10 TF\) of unit computation time for each iteration in training of the networks. These additive computation only updates the final loss function, so it does not depend on the network architecture. The stacked RNN has three hidden layers of 1024 output units, and they should be computed for each sample, \(\mathbf{x}_{t}\). The number of weights in the stacked RNN is roughly defined by input dimension \(\times 1024^{2}\times\) output dimension \(\times 2\), so the additional computation is negligible in training. As shown in Table 1, the U-Net also requires large number of weights but additional computation (10 units per input) slightly affects the computation time.  
In terms of memory resources, the proposed learning method requires memory spaces to store variables \(\gamma_{1}\) and \(w\) in Equations (13) and (15). Total number is \(2 TF\), and each variable is stored as 4-byte float data. The number of frames refers to the batch size in batch gradient learning, so \(T=128\) in our experiments. \(F\) is the number of frequency bins of the spectrogram, which is a half of the STFT analysis size, so \(F=1024/2=512\). Therefore, the required memory space for a single batch is \(2 TF\times 4\) bytes = \(2\times 128\times 512\times 4=512\) kilobytes. We used NVIDIA GTX 1080 GPU with 8 gigabytes memory, so the additional memory space can be disregarded in training models. Because the network architecture is the same, there is no difference in computation and the number of model parameters, and a testing procedure of the proposed model is same as that of the baseline model. Although the training time varies with the model configuration, 10,000 training steps were usually conducted within an hour.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Solar Still Distillation (SSD)  
Solar Still Distillation (SSD) can be realized by using a blackened tank containing saline, water and air [28; 79]. The device is covered with inclined glass. In this way the solar radiation enters the system, increasing the temperature and facilitating the evaporation of freshwater. The internal humidity condenses on the surface of the glass, as this part has the lower temperature. The condensate is collected, obtaining freshwater [80]. A possible solution is depicted in Figure 30[23; 24]. The condensate is characterized by being high quality, with a daily production about 2-3 L/m\({}^{2}\). Consequently, this system can be used only in small applications.
# Therapeutic Applications of Halloysite
## 4 Recent Trends in HNT Research  
### HNTs as a Drug Carrier  
As mentioned previously, HNTs have been used for various biomedical areas because of unique properties such as hemostatic properties. However, one of the most critical applications of HNT is drug delivery systems, and it is used as a drug carrier. For instance, in 2020, [PERSON] et al. [75] used HNTs to fabricate smart hydrogels with H\({}_{2}\)O\({}_{2}\)--responsive release properties. The researchers first prepared drug loaded HNTs and then, they characterized the composite hydrogels by FTIR, TGA, XPS, XRD, and TEM. The smart hydrogel was prepared with polyvinyl alcohol and released the drugs under a pathological concentration ([H\({}_{2}\)O\({}_{2}\)] = 200 \(\upmu\)m) because of degradation of the hydrogel in the presence of H\({}_{2}\)O\({}_{2}\) (Figure 3). Core-shell gel-based chitosan fabricated by a dropping method and HNTs because of hollow cavity used as a carrier [76]. In this study, [PERSON] et al. [76] utilized alginate as a coating layer (shell). In the hybrid gel beads, HNTs showed proper dispersion within the beads, and because of the core-shell structure of gel, the release of the drug was controllable.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 2 Related Work
Clustering robots is feasible considering the slow movement of robots. Multi-connectivity management for V2X was proposed in [20]. To increase the reliability of message delivery, multi-path delivery was considered. The combined utilization of dedicated short-range communications (DSRC) and cellular V2X was examined in [21] for the fast and reliable dissemination of traffic information. The proposed method allowed each vehicle to transmit its message through two communication modules for both speed and reliability. However, few studies have explicitly examined the functionality of sensing-information extraction from vehicles for extended sensing-perception at the edge server, along with sensing information sharing among vehicles. Furthermore, few studies have explicitly addressed the cooperation mechanism among vehicles for the efficient extraction of sensing information in the V2X network, which suffers from the information constraints of nearby vehicles and lacks the cooperation structure among vehicles.
## 4 Model Solution
### The Upper-Level Algorithm for FJSSP
#### 4.2.4 Multi-Parent Crossover Operator  
The performance of GA depends on the crossover operator to a large extent, which determines its global search capability. In traditional GA, the most common crossover operator uses two parents to produce offspring. Recently, multi-parent crossovers have attracted the attention of more and more researchers, and many studies have indicated the high performance of multi-parent crossover in  
Figure 7: A chromosome example for the scheduling scheme.  
some numerical optimization problems [37; 38; 39]. In the process of generating offspring, it is generally believed that multi-parent crossover is likely to synthesize more information of the parent individuals to obtain better search efficiency and higher quality solutions. Inspired by this crossover operation, to improve the global search efficiency of the algorithm and enhance the diversity of solutions, the multi-parent crossover is integrated with the improved precedence crossover (IPOX) [36; 40] and multi-point preservative crossover (MPX) [41; 42], and named MIPOX and MMPX, respectively.  
The processes of the two designed crossover operators are described in Algorithms 1 and 2 separately and two examples are shown in Figures 8 and 9, respectively.  
```
Input: Three parent operation sequence chromosomes Output: Two offspring operation sequence chromosomes
1: Randomly divide the set of job numbers \(\{1,\ 2,\ \ldots,\ n\}\) into two nonempty exclusive subsets \(J1\) and \(J2\);
2: Copy those numbers in \(J2\) from parent 1 to offspring 1 and from parent 3 to offspring 2, preserving their order;
3: Copy those numbers in \(J1\) from parent 2 to offspring 2 and from parent 3 to offspring 1, preserving their order.
```  
**Algorithm 1** The procedure of MIPOX  
```
Input: Three parent machine assignment chromosomes Output: Two offspring machine assignment chromosomes
1: Generate a random set Rand0_1, which consists of integer 0 and 1, and has the same length as machine assignment chromosomes;
2: If Rand0_1 = 0, machine assignment number copies directly from Parent 1 to Offspring 1 and from Parent 2 to Offspring 2;
3: If Rand0_1 = 1, machine assignment number copies randomly from Parent 2 and Parent 3 to Offspring 1 and from Parent 1 and Parent 3 to Offspring 2.
```  
**Algorithm 2** The procedure of MMPX  
Figure 8: An example for MIPOX.