esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Comparison and Discussion
#### 4.2.2 ManhattanGrid Results  
The graphs below illustrate the impact of the number of nodes on Delay, jitter, PDR, PLR, Throughput, and Lost Packets. It can be observed that MCW_OLSR shows improvements compared to OLSR, DSDV, and SR_OLSR, particularly in networks with a larger number of nodes. These results demonstrate the effectiveness of MCW_OLSR in dense networks.  
In Figure 9, the time taken for data packets to travel from source to destination is examined. The results indicate that MCW_OLSR offers a lower delay compared to OLSR, DSDV, and SR_OLSR. This improvement is due to enhanced node stability, security, and longevity, which ensures stable connections with direct neighbors.  
Jitter is decreased due to the multicriteria-weighted MPR scheme. MCW_OLSR exhibits lower jitter compared to OLSR, DSDV, and SR_OLSR, as shown in Figure 10. This improvement confirms the advantage of the proposed technique in dense and mobile environments.  
Figure 8: Throughput comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 7: Lost Packets comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Compared to OLSR, DSDV, and SR_OLSR, the MCW_OLSR protocol demonstrates a lower PLR value, as presented in Figure 11. This is due to the enhanced link stability and node reliability, ensuring successful packet transmission.  
Figure 11: PLR comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 10: Mean Jitter comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 9: Mean Delay comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 12 compares the packets successfully broadcasted by all protocols. MCW_OLSR achieves better packet distribution compared to OLSR, DSDV, and SR_OLSR. This shows that MCW_OLSR performs well in terms of PDR.  
Figure 13 shows the comparison of lost packets. The number of lost packets in MCW_OLSR is negligible compared to OLSR, DSDV, and SR_OLSR. The stability of the links and reachability play a key role in avoiding frequent broken links in mobile environments.  
Figure 14 presents the impact of the number of nodes on throughput. MCW_OLSR, by optimizing the selection of MPRs, shows a marked improvement in throughput compared to OLSR, DSDV, and SR_OLSR.  
Figure 12: PDR comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 13: Lost Packets comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.
# Soliton Content of Fiber-Optic Light Pulses
## 5 Conclusions  
Solitons in optical fibers are pulses of light that maintain a balance between linear and nonlinear distortions. Real-world systems are rarely, if ever, of the integrable kind. It is therefore appropriate to apply the word'soliton' also to pulses for which the balance is maintained at least approximately; to do so is now common usage. However, one occasionally sees research papers in which the word is used in a very loose sense, with no effort being made to actually check whether the balance is fulfilled.  
In this review, we outline the concept of fiber-optic solitons, with a particular view toward methods of verification of the solitonic nature of a given pulse, or group of pulses, or a similar structure. Soliton content has been discussed in the context of structures like soliton molecules, soliton gas and crystal, Akhmediev breather, optical supercontinuum and rogue waves. The simplest method is to check pulse parameters with Equation (4), which directly expresses an equilibrium condition. Unfortunately, this straightforward test is only applicable in very simple situations when there are clean, well-isolated pulses, and it does not fully quantify the soliton content. Grown from inverse scattering theory, the first method to analytically find the soliton solution of the NLSE is direct scattering transform. It basically tests whether the structure under test contains pulses that hang together as entities even when they collide, the property that gave solitons their 'particle-like' name [95]. An alternative method is soliton radiation beat analysis, which basically tracks the nonlinear phase rotation, which is the signature of solitons. We discuss some issues of practicality and accuracy and compare the ranges of applicability of both DST and SRBA.  
There is no single method that covers all cases; indeed, in some relevant cases, no available method is fully satisfactory. In the complex evolution of supercontinuum, for example, the method is often adapted ad hoc to the situation, for want of alternatives. In some interesting cases, soliton content analysis has not been pursued yet, to our knowledge. Dissipative solitons are subject to yet another condition beyond Equation (4), but should be accessible for SRBA. This article is meant to provide the reader with a survey to what is currently known, to foster future research.  
[PERSON], [PERSON] and [PERSON] contributed equally to this manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} cw & continuous wave \\ DM & dispersion-management \\ DST & direct scattering transform \\ IST & inverse scattering transform \\ NLSE & nonlinear Schrodinger equation \\ SRBA & soliton radiation beat analysis \\ \end{tabular}
## 3 Design of Isl Network for IoT
### Network Routing  
In the cases where multi-hop communication is required to connect particular nodes, flooding techniques provide an option for unicast connections, as well as for broadcasting. However, particularly for large distances, flooding algorithms perform worse, compared to routing ones, due to duplicated packages and higher bandwidth load; hence, they are not considered further in this paper.  
A routing protocol is called proactive when the routing information (destination and routes) is stored in the memory and the routing table is updated periodically throughout the network. Constantly updating the routing tables due to topology changes when no traffic is being transmitted may be considered wasteful when there are limited resources. In comparison, in a reactive routing protocol, new routing information must be identified each time in order to transmit a new message, which generates extra overhead. As the topology of a satellite network is predictable, no overhead typical of reactive routing is necessary. However, the routing tables must be dynamically updated as topology changes are identified.  
A memoryless distributed algorithm based on unicast geographic routing is, thus, recommended. No control information (e.g., routing tables) needs to be exchanged between nodes, and no central controller is required. Let \(G=(V,E)\) be an Euclidean three-dimensional surface consisting of a finite number of vertices \(n\). The task of a geographic unicast routing algorithm \(\mathcal{A}\) is to find an optimal path from any source \(s\) to any defined destination \(t\), while complying with the following conditions:  
* All nodes \(v\in V\) know their own geographic positions and the geographic position of all other nodes \(w\in V\backslash v\).
* There is no control information which needs to be stored in the node (i.e., it is a memoryless algorithm).
* Nodes are not allowed to change any information addressed to another node.
* Nodes are not allowed to maintain any information addressed to another node, except for temporary storage before forwarding.  
[PERSON]'s algorithm fulfills these requirements [20]. In addition, Dijkstra delivers an optimal solution and has a finite running time, and was thus selected as an optimal algorithm for meshed satellite networks. It performs well in every discussed network topology with connected graph representations (see Figure 7). Each node's position information can be updated, by ISL or TM/TC, into the onboard orbit prediction algorithm.  
Figure 7: Satellite network topologies: (**a**) Star; (**b**) fully connected; (**c**) line; and (**d**) mesh.  
[PERSON]'s algorithm scans all vertices with distances smaller than \(dist(s,t)\) and is, thus, slower for larger graphs. With a high number of nodes, goal-oriented techniques, such as the A* search or Arclag, can accelerate the point-to-point computations significantly, as proposed in [21]. If backwards search is required within complex graphs to increase robustness, the SHARC algorithm provides a fast and robust unidirectional routing method [22].
## 4 Discussion  
### Illumination Conditions and Vegetation Indices  
The IC model provided a detailed view of the effect of sun-sensor geometry and topography at a pixel level. Our research reveals that frequently shadowed areas experience more variation in IC than sunlit areas. The increase in IC values with the improvement of sun-sensor geometry has been reported in other studies, but usually comparing two images at different times or several images across a season in the same year [7; 12; 41]. The effect of terrain in the correlations between EVI-IC and NDVI-IC has important implications when interpreting VI, especially EVI, which showed higher sensitivity. Because EVI-IC correlations were positive in shadowed areas and neutral to negative in sunlit areas, it can be interpreted that the areas with different IC experienced \"greening\" or \"browning\" when in reality there was no change in the vegetation conditions. The explanation for this would be that sunlit areas keep high IC values with low variation due to more constant sun-sensor geometry conditions. This would have a low effect on the variation of EVI and NDVI which correspond to neutral or even negative correlation values. However, shadowed areas experience a higher range of IC values which would have a higher effect on EVI and NDVI values, showing positive correlations. The strong effect observed in EVI in this irregular terrain corroborates the need for removing the topographic effect in the reflectance data before calculating this index [7]. Therefore, the results using EVI for modeling phenology patterns, vegetation functioning or GPP would be biased if the IC effect is omitted, in accordance with research supporting this effect [3; 17; 40; 42]. In relation to the research that claims the effect of IC on VI, most of these studies used coarser resolution than Landsat and did not employ a DEM to simulate IC. In addition, most of them were carried out in the Amazon region in relatively flat terrain and yet IC effects were patent [12; 14; 16; 17]. We agree with previous studies that found drastic forest changes comparing various images in irregular terrain [10] and we suggest that using IC is critical to understanding these patterns. Although using a DEM with the same scale as the satellite data improved the results of our interpretation, caution is required because of known issues in SRTM data such as shadows that can bias the results, further investigation is needed [23]. Despite this, the spatial resolution of the images used, allowed more detailed and better interpretations of changes in IC than sensors with coarser spatial resolution such as MODIS or methodologies relying upon angle information of the image [12; 14; 16; 17].  
VI are also sensitive to vegetation structure factors such as LAI or canopy shadow which very likely have an effect on the variations observed, but lower resolutions than those used in this study would be required, especially for EVI [40]. Factors such as the background influence of soil and leaf litter or LAI are probably responsible for quickly saturating NDVI [43], hiding the effect of IC variation on this index.
## 1 Introduction  
Previously developed methods for the synthesis of various heat-resistant polyheteroarylenes (PHA) arose due to the needs of modern technology. As a rule, these are polymers with rigid chains containing stable heterocycles such as polyimides, polybenzimidazoles, and polyoxadiazoles and exhibiting high thermal stability, fire and chemical resistance, and hydrolytic and radiation resistance [1; 2; 3]. Their synthesis is carried out in solid phase, in phenolic or amide solvents at high temperatures during a long period of time [1; 2; 3]. It should be noted that polymaphthoylenebenzimidazoles (PNBIs) are distinguished among PHAs by their unique properties, although this type of PHA is the least studied [4; 5] due to the complexity of its synthesis and processing.  
Modification of the chemical structure of PNBI by the introduction of sulfo groups is of considerable interest in terms of the creation of new materials with valuable properties [6]. The well-known two-stage method for the preparation of sulfonated PNBI (SPNBI) is very laborious. They are obtained by a polymer-analogous reaction of PNBI sulfonation with oleum-sulfuric acid mixture [6]. Therefore, the development of a more technologically advanced synthesis of SPNBI is of undoubted scientific and applied interest.  
SPNBI can be considered as potentially effective precursors of heat-resistant polymeric ionomers. Another promising application of SPNBIs is their use as a sensitive layer for multisensor systems. The main idea of this approach is to use an array of sensors with weak selectivity and slightly different responses to various analytes. The array of sensors and computer processing of the \"fingerprint\" type response make it possible to detect various substances in a gas and water environment. The development of a multisensor system including a set of sensors, or biosensors, for the recognition of hazardous substances, food products, and drugs is a promising scientific and technical task [7; 8; 9].  
In this work, we propose to use SPNBI and ionomers based on them as coatings for quartz crystal microbalance (QCM) sensors. There are a lot of various applications of QCM sensors in the literature [10; 11; 12], but they are mainly related to the recognition of substances in the gas phase and there are only few examples of their use for the analysis of liquids [13; 14]. One of the reasons is the low adhesion of coatings to the surface of sensors and the solubility of polymer coatings of QCM sensors in liquid media.  
Thus, the aim of this work was to develop an alternative method for the one-stage synthesis of PNBI with attached functional sulfo groups and to create ionomers based on them by replacing hydrogen in sulfo groups with metal ions, as well as to study their applicability as a sensitive layer for multisensor systems.
## 2 Materials and Methods
### Ion Interaction Calculations  
A simplified ion interaction model was implemented in MATLAB (Version 2017a), starting with 10 \(\times\) 10 \(\times\) 10 iron (Fe) atoms arranged in a cube at constant initial distances to derive the forces between the Fe atoms, assuming no contamination. A simple Lennard-Jones pair potential was used to describe the atom interactions [11]. It is known that, by using the simple Lennard-Jones potential, metals are typically not completely described, and many body interactions need to be included in the calculations to derive highly accurate results [53]. However, in order to gain knowledge about trends and simple interactions, the simple model can give first indications with little calculation effort. No electron density impacts of neighboring atoms or external fields were used. Comparably large time steps were applied, which leads to reduced accuracy but rapid convergence, since atoms arrange to a comfortable position quite quickly. In combination with the initial atom arrangement based on the thermal expansion at the fixed and constant calculation temperature, the system shows sufficient convergence after only a few calculation steps. The deviation between time steps of the atoms is \(\sim\)0.1% for all tested temperatures [51]. However, the simplifications using a comparably small number of atoms and a simple pair potential allow  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Deviation** & **Ref./Origin** \\ \hline Frequency / in Hz & measured & \(\pm\)10 & Pixel resolution \\ \hline Wavelength \(\lambda\) in m & measured & \(\pm\)2.00 \(\times\) 10\({}^{-6}\) & Pixel resolution \\ \hline Density \(\rho\) in kg/m\({}^{3}\) & 7.874 & \(\pm\)0.063 & [52] \\ \hline Gravity constant \(g\) in m/s\({}^{2}\) & 9.81 & \(\pm\)1.26 \(\times\) 10\({}^{-3}\) &
\begin{tabular}{c} Committee on Data for Science \\ and Technology (CODATA) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assumed symmetrical deviations for the parameters in Equation (2), including varying parameters.  
a rapid calculation of the interaction forces between the atoms and the general macroscopic trends can be derived.  
Using the mechanical approach to derive surface tension [54], normal and tangential components of the pressure in the ion layers were extracted. The direct neighbor interactions were calculated for all Fe atoms and the resulting forces initiated the atom movements before the next calculation step [51]. It was seen that, already after three iterations, a quasi-static positioning had been arranged [51]. For the surface tension calculations, the results after seven steps were taken. Similar to in [49], the surface tension value of the system was derived, averaging atom forces within the atom layers and a certain thickness. Evaluation lines were defined to determine the tension between the combined ions of the two regions in one layer on each side of the line in both x- and y-direction. The surface tension was determined from averaged force values from the first and second layer (Figure 2).
## 3 Results
### Biological Pretreatment Performance
#### 3.1.1. FOG Removal  
The results of this study illustrated that the supplementation of 20 mL of the Eco-flushTM for every 20 L of PSW highly influenced FOG removal. Using Eco-flushTM as a FOG reduction agent was indicative of the bacterial consortia present in it, which aided in the biological degradation of the pollutants presents in the effluent. Since the raw PSW was first aerated through sparging, which supplies dissolved oxygen to the wastewater and the microorganisms, this resulted in the proliferation of the bacterial community that subsequently degraded the organic material and pollutants in the wastewater. Basically, the microbes decoupled bonds in the organic contaminants and further coagulated less soluble matter found in the raw PSW. At an average FOG removal efficiency of 80 \(\pm\) 6.3% (Table 1), the results depicted a pre-treatment unit that performed exceptionally well under the above set design and operational conditions. This study proved to perform well as compared to another study whereby an evaluation of a biosurfactant, obtained from cultures grown  
Figure 5: Boxplots of the investigated parameters during the pre-treatment stage.  
Figure 6: Performance of the pre-treatment system after outlier replacement.  
on cassava wastewater were used in the pre-treatment of PSW, yielding an oil and grease removal efficiency of 70% [16].
###### Abstract
Among the myriad regression algorithms, Elastic Net regression, Polynomial regression, Stepwise regression, Ridge regression, Lasso regression, and Linear regression hold particular relevance for predicting nutrient concentration by considering the co-existence and composition of multiple nutrients. These algorithms offer a structured, data-driven approach to unravel the complexities of rice nutrition, providing accurate predictions and contributing to the standardization of nutrient management practices. Moreover, they play a crucial role in fostering sustainable and environmentally friendly rice cultivation practices. The singular nutrient prediction method offers advantages in two distinct scenarios. Firstly, it proves beneficial when a farmer or scientist intends to simulate the concentration value of a specific nutrient, already possessing knowledge of the concentration of other nutrient components. Secondly, this approach becomes valuable if the sensor for a particular nutrient malfunctions. In such cases, the digital twin system promptly alerts the user regarding the sensor breakdown and provides a predictive value while awaiting sensor replacement. Regardless of the scenario, the digital twin system ensures user awareness when the detected nutrient concentration surpasses the recommended range. Furthermore, the system recommends nutrient recovery interventions. The nutrient composition prediction approach serves as a comprehensive intervention preparation tool by informing the farmer or scientist about the anticipated nutrient concentration. The projected value, in turn, aids the digital twin system in suggesting the appropriate amount of nutrient recovery, aligning with best practices. This paper unfolds in five sections. The Section 2 underscores the significance of predicting rice essential nutrients and elucidates the challenges in this domain, along with the role of Linear and Polynomial Regression algorithms in addressing these issues. In Section 3, the dataset is thoroughly described, highlighting its key attributes. The subsequent step involves data pre-processing using Min-Max Normalization to ensure uniformity. Following this, the methodology branches into two main aspects: (1) Single-nutrient concentration prediction (Section 3.3.1), and (2) Nutrient composition concentration prediction (Section 3.3.2), offering a comprehensive approach to understanding and forecasting nutrient concentrations in rice. Section 4 presents the experimental results and their comprehensive analysis. Finally, Section 5 of the paper concludes by summarizing the findings and proposing potential avenues for future research.
## 2 Experimental Section
### Electrospray Mass Spectrometry-Time of Flight (ESI-TOF) Intact Mass Analysis  
Purified proteins (0.5-1 mg/mL) were diluted 1:50 in 0.1% (v/v) formic acid (50 \(\mu\)L final volume), in round bottomed 96-well microtiter plates (Agilent part number 5042-1385). It is important not to exceed this amount of protein, as column overloading reduces performance and leads to sample cross-contamination. When analyzing nickel eluates from small-scale test expression, 5 \(\mu\)L of each sample was transferred using a hand held 8-channel 10 \(\mu\)L automatic pipette to wells containing 45 \(\mu\)L 0.1% (v/v) formic acid. Reversed-phase chromatography was performed in-line prior to mass spectrometry using an Agilent 1100 HPLC system (Agilent Technologies Inc., Palo Alto, CA, USA). 50 \(\mu\)L was injected from each well onto a 2.1 mm \(\times\) 12.5 mm Zorbax 5\(\mu\)m 300 SB-C3 guard column housed in a column oven set at 40 \({}^{\circ}\)C. The solvent system used consisted of 0.1% (v/v) formic acid in LC-MS grade water (Millipore, solvent A) and 0.1% (v/v) formic acid in methanol (LC-MS grade, Chromasolv, solvent B). Chromatography was performed as follows: Initial conditions were 90% A and 10% B and a flow rate of 1.0 mL/min. After 15 s at 10% B, a linear gradient from 10% B to 80% B was applied over 45 s, followed by 80% B to 95% B over 3 s. Elution continued isocartically at 95% B for 1 min 12 s followed by equilibration at initial conditions for a further 45 s. Protein intact mass was determined using an MSD-TOF electrospray ionization orthogonal time-of-flight mass spectrometer (Agilent Technologies Inc., Palo Alto, CA, USA). The instrument was configured with the standard ESI source and operated in positive ion mode. The ion source was operated with the capillary voltage at 4000 V, nebulizer pressure at 60 psig, drying gas at 350 \({}^{\circ}\)C and drying gas flow rate at 12 L/min. The instrument ion optic voltages were as follows: Fragmentor 250 V, skimmer 60 V and octopole RF 250 V.