esa-sceva/satcom-chunk-collection huggingface dataset:
# Soliton Content of Fiber-Optic Light Pulses
## 4 Methods to Verify Soliton Content  
The inverse scattering technique allows one to find solutions to integrable nonlinear wave equations. However, in practice, one quite often is faced with the reversed task of determining the soliton content of a given signal. The task is relatively straightforward when the signal consists of isolated bell-shaped pulses. One can check whether the power profile approximates a \(\mathrm{sech}^{2}(t)\) shape and whether the chirp is close to zero. Then, duration and peak power can be checked with Equation (4). This way, an \(N=1\) soliton can be directly identified, and for pulses with \(N\
eq 1\), the local value of \(N\) can be found. However, the applicability of this direct method is limited to simple cases. It is of no use for waveforms, as they may be found in more complex situations like soliton gas or optical supercontinuum; see Section 4.1.3.  
We will therefore briefly describe more advanced techniques. The direct scattering method is often considered as the benchmark for the determination of soliton parameters. However, it has its limitations, chief among which is that it relies on the integrability of the system. An alternative is soliton radiation beat analysis, which does not have that same restriction, but the price to be paid for that is that (i) more input data are required and that (ii) in complex cases, the obtained pattern may get too complicated to interpret.
## 3 Results  
### Detection of Body Fluid-Specific Markers Using SPRi  
Body fluid-specific markers were included as control, to confirm that the extraction of the body fluids from the glass slides was conducted successfully. PSA and semenogelin-1, which are known proteins that are highly abundant in semen traces, could be detectedin ten out of ten fresh samples and ten out of ten fresh samples, respectively (Figure 2A). However, the number of samples in which semenogelin-1 could be detected decreased over time to seven out of ten samples after 21 days. For urine, three samples were excluded as these samples did not pass our inclusion criteria, which are described Section 2.3.2, \"SPRi data processing\". The body fluid-specific markers osteopontin and THP for urine could be measured in at least five out of seven urine samples for all time points (Figure 2B). Amylase, a highly abundant enzyme in saliva, could be detected in nine out of nine samples (Figure 2C), for all time intervals. Samples from one specific donor did not pass our inclusion criteria at all time intervals and were therefore excluded. The total number of donors in which non-specific body fluid biomarkers could be detected was high, but expected since these biomarkers are present in multiple body fluids. PSA, for instance, is found in low concentrations in female urine, while amylase is present in urine and semen [24,25]. In urine and semen, amylase could be measured in two out of four and one out of seven, respectively. Additionally, because seminal fluid and urine travel through the same tract, traces of osteopontin and THP could be present in semen. Body fluids are complex samples, thus effects from matrix constituents or interfering agents to antibody sites could influence the response signals. To assess background levels, we used coupling buffer as negative control. Isotype controls were also included to assess non-specific binding of antibodies, ensuring that observed signals were specific to the target biomarkers. Future studies should analyze the specificity of the antibodies by including control samples with known concentrations of the biomarkers. Additionally, we performed replicate measurements to ensure reproducibility and consistency in our findings.
## 1 Introduction
It consists of deciding which flows should be transmitted through which paths in each instant to guarantee that none of the flows miss their deadlines and that the energy demand is kept to a minimum. There are different kinds of topologies for DCN [4]. In this paper, the fat-tree [13] is used as it is the most accepted since it provides full connectivity among any pair of servers. It is based on three switching levels where each switch provides the same amount of input ports as output ports guaranteeing that any flow arriving into the switch will have an output link available. In Figure 1, a simple network with eight servers is shown. Contribution: In this paper, we introduce an Integer Linear Programming (ILP) model to optimize the scheduling of real-time flows with energy aware considerations. The model  
Figure 1: An eight-server fat-tree network. can be solved with well-known tools, such as CPLEX. However, as it is an NP-hard problem, its exact solution is not possible when several servers are included in the DCN. In order to obtain a valid schedule, the problem is solved using a heuristic approach in three steps designed and implemented by the authors. In the first step, the time scheduling is solved; in the second step, path allocation is performed; and finally, in the third stage, flows that were not scheduled in the first two steps are allocated. Experimental results show that the DCN can be scheduled in a short amount of time allowing for a dynamic operation of the system. Organization: The rest of the paper is organized in the following way. In Section 2, previous works are discussed. After that, Section 3 introduces the real-time problem, and general scheduling considerations are provided. Section 4 presents the ILP model, and it is explained. The following section, 5, describes the proposed heuristics and the algorithms used. In Section 6, the different approaches are evaluated, and the results are compared. A brief discussion of the results is made in Section 7, and finally, in Section 8, conclusions are drawn, and future work lines are described.
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Visible- and Near-Infrared-Based Methods  
Visible- and near-infrared measurements can be used (i) to detect changes in vegetation greenness, health, and water content represented by empirical vegetation indices, (ii) to retrieve land surface temperature, and (iii) to model evapotranspiration. All these types of information potentially facilitate irrigation mapping and monitoring and indeed, many different strategies have been developed to do so, as will be described in the following sections.
## 2 Navigation and Localization
### Acoustic Navigation  
Compared with other signals, such radio and electromagnetic, acoustic-based signals propagates better in water and can reach considerable distances. This allows the use of acoustic transponders to navigate an AUV. Some of the navigation methods based on acoustic signals are the Sound Navigation and Ranging (SONAR), and acoustic ranging.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Product Name**} & **Heading** & **Pitch and Roll** & **Data Rate** & **Depth Rated** \\  & & **Accuracy/** & **Accuracy/** & **Resolution** & **(Hz)** & **(m)** \\ \hline Impact Subsea & ISM3D [31] & \(\pm 0.5^{\circ}\)/\(0.1^{\circ}\) & \(\pm 0.07^{\circ}\)/\(0.01^{\circ}\) & 250 & 1000–6000 \\ \hline Seascape & Seascape & \(\pm 0.5^{\circ}\)/\(0.01^{\circ}\) & \(\pm 0.5^{\circ}\)/\(0.01^{\circ}\) & 400 & 750 \\ \hline Inertial Labs & AHRS-10P [33] & \(\pm 0.6^{\circ}\)/\(0.01^{\circ}\) & \(\pm 0.08^{\circ}\)/\(0.01^{\circ}\) & 200 & 600 \\ \hline SBG Systems & Ellipse2-N [34] & \(\pm 1.0^{\circ}\)/- & \(\pm 0.1^{\circ}\)/- & 200 & - \\ \hline TMI-Orion & DSPRH [35] & \(\pm 0.5^{\circ}\)/\(0.1^{\circ}\) & \(\pm 0.5^{\circ}\)/\(0.1^{\circ}\) & 100 & 500–2000 \\ \hline VectorNav & VN-100 [36] & \(\pm 2.0^{\circ}\)/\(0.05^{\circ}\) & \(\pm 1.0^{\circ}\)/\(0.05^{\circ}\) & 400 & - \\ \hline XSENS & MTi-600 [37] & \(\pm 1.0^{\circ}\)/- & \(\pm 0.2^{\circ}\)/- & 400 & - \\ \hline \end{tabular}
\end{table}
Table 1: Commercial Inertial Measurement Unit (IMU)-Attitude and Heading Reference System (AHRS) systems  
Figure 3: Vertical deflection.
## 1 Introduction
### Limitations of Standardised Models  
For optimum fire safety level of premises protected by FES-gaseous systems, it is vital to select the right quality and quantity of the fire-extinguishing gas, which will extinguish the fire immediately after being released to space and will prevent fire recurrence. The fire-fighting process effectiveness depends  
Figure 1: Three stages of the release of extinguishing gas into the compartment. (**A**) Flow field in the compartment dominated by fire growth and smoke buoyancy, prior to release, (**B**) release of the fire extinguishing gas, (**C**) stabilized flow field in the compartment filled with extinguishing gas after the discharge. Stage (**C**) is modelled in this study.  
on some factors, including the fire-extinguishing gas retention in the premises [20]. The time is usually identified analytically, according to selected models of the gas flow from the premises. A review of current standards concerning FES-gaseous systems is given in work [11; 21], where we identified the following analytical models:  
* Model with a sharp interface between extinguishing gas and air, presented in NFPA2001 [22];
* Model with wide interface between the gas and air, presented in EN15004 and ISO14520-1 [23; 24];
* Model of continuous mixing of extinguishing gas with air, at a constant rate within the volume--valid for areas in which the air is continuously moving, e.g., through air-conditioners [22; 23; 24].  
Verification of the applied models suggests discrepancies between the measured and identified values [25]. The assumptions for the applied models do not work for gases whose density is similar to the density of air, under specific environmental conditions. Standard models assume a different profile of the fire-extinguishing gas distribution in a room (gas stratification) than for gases with a density similar to the density of air (natural gas mixing). Moreover, standard models do not take into consideration the impact of atmospheric conditions on the value of air density inside and outside the protected premise, as well as on the fire-extinguishing gas density. The acquired parameter values are identified for normal conditions. Standard models assume a constant and equal value of the pressure inside and outside the protected space. Another limitation observed for standard models is a lack of possibility to cover the impact of phenomena occurring alongside with the gas outflow from the cylinders or gas flow through the room (turbulence, temperature drop, different physical form) and not covering the impact of internal sources of heat, including the impact of fire. Another simplification in standard models is neglecting or simplifying the architectural characteristics of the room (equipment inside, the shape of the compartment, the presence of architectural barriers, etc.). Finally, standard models do not fully account for the impact of the sources of flow inside the protected space--protected machinery, fans, air-conditioners, etc. The devices cause a turbulent movement, which can disturb the natural flow after the gas has been discharged.
## 3 Results and Discussion
### Oil Absorption Capacity and Continuous Oil/Water Separation Using the GN\({}_{4}\)/PSt Composites  
As shown in Figure 6a,b, both light (petroleum ether) and heavy (chloroform) oils were rapidly absorbed into the GN\({}_{4}\)/PSt composite by capillary forces, suggesting the prepared composites should show good performance in water/oil separation experiments. The saturated oil absorption capacity of the GN\({}_{4}\)/PSt foams for various types of organic solvents (Figure 6c) and ranged from 27.44 to 56.9 g/g. These results suggested that the prepared composites had a high absorption capacity for various organic solvents, and the variations in the different oil absorption capacities were due to differences in the densities and viscosities of the absorbed organic solvents [58]. The reusability of materials is also a key factor in the actual treatment of oily wastewater. The reusability of GN\({}_{4}\)/PSt foam was examined by absorption-centrifugation. After the oil absorption reached saturation, the oil in the GN\({}_{4}\)/PSt foam was removed by centrifugation at 6000 rpm for 2 min, while the foam was regenerated for the next absorption/centrifugation cycle without further treatment (Figure S3a). The absorption/centrifugation cycle test was repeated 10 times and the results are shown in Figure S3b. The porous material exhibited stable reuse performance with almost no change in oil absorption capacity, and approximately 84% of petroleum ether was removed by each centrifugation. It is speculated that the residual petroleum ether was  
Figure 5: (**a**) WCA and (**b**) OCA of GN\({}_{4}\)/PSt, (**c**) photographs of water (dyed with brilliant green and methyl orange) and oil (dyed with oil red O) droplets on the surface of GN\({}_{4}\)/PSt, (**d**) dynamic adhesion behavior of water droplets on GN\({}_{4}\)/PSt surface, (**e**) the rolling angle, (**f**) water droplets falling on the surface of GN\({}_{4}\)/PSt, (**g**) water droplets on a random surface of GN\({}_{4}\)/PSt, (**h**) water impact resistance of GN\({}_{4}\)/PSt.  
stably adsorbed and retained in the micropores by van der Waals and capillary forces. In addition, the GN\({}_{4}\)/PSt sample continuously absorb oil pumped through the foam, which is a necessary feature of materials used in large-scale oily wastewater treatment. As shown in Figure 6d and Video S1, both light oil (petroleum ether) and heavy oil (chloroform) were quickly and continuously separated from water without leaving any residual red oil in the water phase. As shown in Figure 6e, the GN\({}_{4}\)/PSt foams maintained a high separation efficiency for various organic solvents in water (above 99.6%), further highlighting that the GN\({}_{4}\)/PSt composite material has great application potential for efficient, large-scale and continuous treatment of oily wastewater.
## 3 Results
### Content Changes Based on Conditions  
The correlation between the inflammation-related factors and major active components was shown in Figure 6. A negative correlation can be observed for the relationship between the two variables, in which one variable increases as the other decreases. The greater the correlation between X and Y, the closer the value of the Pearson's correlation coefficient (r\({}^{2}\)) is to 1. Nitrite levels were negatively correlated with \(\alpha\)-amyrin (r\({}^{2}\) = 0.572, \(p\) = 0.018) and \(\beta\)-amyrin (r\({}^{2}\) = 0.585, \(p\) = 0.016) with statistical significance. Furthermore, iNOS mRNA expression was negatively correlated with \(\alpha\)-amyrin (r\({}^{2}\) = 0.500, \(p\) = 0.033) and \(\beta\)-amyrin (r\({}^{2}\) = 0.491, \(p\) = 0.035). COX-2 mRNA expression showed a negative correlation with \(\alpha\)-amyrin (r\({}^{2}\) = 0.523, \(p\) = 0.028) and \(\beta\)-amyrin (r\({}^{2}\) = 0.519, \(p\) = 0.028). No other significant correlation was found (Figure S1 and Table S1). Both nitrite production and iNOS mRNA expression showed similar results of a decreased relationship with \(\alpha\)-amyrin and \(\beta\)-amyrin. All relationships had more than a 50% negative correlation, except for that between \(\beta\)-amyrin and iNOS mRNA expression. It was revealed that as \(\alpha\)-amyrin or \(\beta\)-amyrin contents increased, the contents or expression of inflammatory mediators diminished.  
Figure 6: Relationship between amyrin and inflammation regulators. Correlations are presented as Pearson’s correlation coefficient (\(r^{2}\)). The correlation between a-amyrin and nitrite (**a**), and the relative mRNA expression levels of inducible nitric oxide synthase (NOS) (b) and cyclooxygenase-2 (COX-2) (\(\epsilon\)). The correlation between \(\beta\)-amyrin and nitrite (**d**), and the relative mRNA expression levels of iNOS (**e**) and COX-2 (\(\beta\)).
## 8 Conclusions  
Using experimental data collected in South Korea, this study has demonstrated that the relationship between plasma intensity and the unused gas rate (1-Ui) in semiconductor processes varies by gas type and intensity level, revealing unique trends across three plasma groups. In Group 1 (plasma intensities below 100), a weak relationship was observed for most gases, indicating that lower intensities minimally affect 1-Ui.  
Figure 13: Violin plot of 1-Ui by plasma and gas.  
In Group 2 (moderate intensities of 500 to 700), an inverse relationship emerged, where increases in plasma intensity moderately decreased 1-Ui for gases such as CH2F2 and CHF3, suggesting that moderate intensities are more effective in reducing unused rates.  
In Group 3 (high intensities above 1000), non-linear behavior was evident. There were significant reductions in 1-Ui for gases such as CF4 and C4F6. These results together show that high plasma intensities significantly impact 1-Ui, but accurately capturing the complex effects requires non-linear models. This distinction among groups highlights that while moderate intensities can reduce unused rates predictably, optimizing plasma conditions at higher intensities requires a nuanced, non-linear approach. Tailoring intensity settings by gas type and leveraging advanced models for Group 3 intensities can enhance efficiency, minimizing the unused gas ratio.  
This study highlights the potential for industrial application by identifying optimal plasma intensity settings tailored to specific gases, such as CF4 and C4F6, to maximize efficiency while reducing waste. Furthermore, by optimizing gas utilization rates, the findings contribute to enhancing energy efficiency, lowering operational costs, and minimizing environmental impacts in semiconductor manufacturing.  
All authors contributed to the research presented in this work. Their contributions are presented as follows. Conceptualization and writing--original draft, investigation, and formal analysis, D.K.M.; writing--original draft, investigation, and formal analysis, J.W.; data curation and validation, J.K.; resources and funding acquisition, B.-J.L.; project administration and supervision, E.-C.J.; writing--review and editing and supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Environment Industry &Technology Institute (KEITI) through \"Climate Change R&D Project for New Climate Regime\", funded by Korea Ministry of Environment (MOE) (2022003560008).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Korea Quality Assurance (KQA). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.1 Aodv  
AODV is a routing protocol designed for mobile ad hoc networks. It is a reactive protocol, and routes are created only when a node wants to send a packet. It uses traditional routing tables, one entry per destination, and sequence numbers to determine up-to-date routing information. AODV store the routes (source-destination) while the source requires us to send information while the connectivity between nodes is active.  
The principal stage of this protocol is route discovery, which works by the source node broadcasting route request messages (RREQ) to the other nodes to find the destination node. Route reply messages (RREP) are sent back to the RREQ source in unicast communication. The full path is formed storing information in intermediate nodes along the route in local routing tables. It also uses error messages (RERR) to notify when a communication break occurs. In this case, a new route-discovery process should begin.\"Hello\" messages are permanently used for detecting and monitoring connectivity with neighbors [26].
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 3 Results
### NFC Potentiostat Performance
#### 3.2.1 Cyclic Voltammetry
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Cumercial Potentiostat**} & \multicolumn{6}{c}{**NFC Potentiostat**} \\ \cline{2-13}  & \multicolumn{2}{c}{**0.1 mM**} & \multicolumn{2}{c}{**0.5 mM**} & \multicolumn{2}{c}{**1 mM**} & \multicolumn{2}{c}{**0.1 mM**} & \multicolumn{2}{c}{**0.5 mM**} & \multicolumn{2}{c}{**1 mM**} \\ \cline{2-13}  & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**\% RSD**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**AVG**} \\ \cline{2-13}  & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} & \multicolumn{2}{c}{\(\pm\) SD} \\ \hline \multirow{3}{*}{I\({}_{\text{pc}}\)} & \(-1.62\) & \multirow{3}{*}{\(-1.50\)} & \(-8.54\) & \(-2.77\) & \(-16.33\) & \(-2.82\) & \(-1.48\) & \(-0.97\) & \(-7.76\) & \(-0.46\) & \(-14.82\) & \multirow{3}{*}{\(-3.85\)} \\  & \(\pm\) 0.02 & & \(\pm\) 0.24 & & \(\pm\) 0.46 & & \(\pm\) 0.02 & & \(\pm\) 0.04 & & \(\pm\) 0.57 & \(-3.85\) \\ \cline{1-1} \cline{3-13}  & \(1.65\) \(\pm\) & \multirow{3}{*}{2.00} & \(8.40\) & \(1.85\) & \(16.18\) & \(2.86\) & \(1.47\) \(\pm\) & \multirow{3}{*}{2.19} & \(7.62\) \(\pm\) & \multirow{3}{*}{0.85} & \(14.93\) & \multirow{3}{*}{0.73} \\ \cline{1-1} \cline{3-13}  & \(0.03\) & & \(0.16\) & & \(\pm\) 0.46 & & \(0.03\) & & & \(0.06\) & & \(\pm\) 0.11 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average (AVG), standard deviation (SD) and relative standard deviation (RSD) of cathodic and anodic peak currents using the commercial potentiostat and the NFC potentiostat. Figure 3: Cyclic voltammograms of (**a**) 0.1 mM, (**b**) 0.5 mM and (**c**) 1 mM potassium ferricamide containing 0.1 M KCl using the NFC potentiostat (red) and a commercial potentiostat, CHI 611E, (black) sweeping the potential between \(-\)300 mV and 600 mV, at a scan rate of 100 mV/s. where _ip_ is the peak current (A), n is the number of electrons involved in the redox reaction, \(F\) is the Faraday's constant (96,485 C/mol), \(A\) is the surface area of the working electrode (cm\({}^{2}\)), _C_\({}^{0}\) is the concentration of the redox species (mol/cm\({}^{3}\)), \(D\) is the diffusion coefficient of the redox species (cm\({}^{2}\)/s), \(R\) is the universal gas constant (8.314 J/mol K), \(T\) is the absolute temperature (K) and \(v\) is the scan rate (V/s).