esa-sceva/satcom-chunk-collection huggingface dataset:
# _Vallisneria spiralis_ Promotes P and Fe Retention via Radial Oxygen Loss in Contaminated Sediments
## 3 Results and Discussion
### Oxygen Profiles and Diffusive O\({}_{2}\) Uptake in Bare and Vegetated Sediments  
After the 3-week conditioning period, O\({}_{2}\) microprofiles were carried out in microcosms with sediment bare (S) and in sediment with _V. spiralis_ (S + V) (n = 6 per treatment) revealed significantly different O\({}_{2}\) penetration depth (OPD) in the two treatments (\(t\)-test, \(t\) = -14.025, DF = 10, \(p\) < 0.001) (Figure 2). OPD averaged 3.0 \(\pm\) 0.1 mm in bare sediments and 4.4 \(\pm\) 0.2 mm in sediments with the macrophyte. The diffusive O\({}_{2}\) consumption in bare and vegetated sediments was determined per unit area by integrating the O\({}_{2}\) consumption rates per unit volume of sediment over the OPD [48]. The combination of higher concentration gradients but lower OPD in treatment S and of lower concentration gradient but higher OPD in treatment S + V resulted in not statistically different DOUs, averaging 898 \(\pm\) 197 and 816 \(\pm\) 212 umd O\({}_{2}\) m\({}^{-2}\) h\({}^{-1}\) in S and S + V, respectively (\(t\)-test, \(t\) = 1.46, DF = 10, \(p\) = 0.174) (Figure 2). Oxygen profiles were limited to the upper layer of sediment in the microcosms and did not investigate the rhizosphere. A previous study has demonstrated via planar optodes that _V. spiralis_ releases O\({}_{2}\) from the roots to the sediments and creates micro oxic zones adjacent to the roots, both during the day and during the night [37]. The profiles carried out in this experiment revealed higher O\({}_{2}\) penetration depth in the sediment with _V. spiralis_. Such difference is likely due to the subsurface oxidation of reduced solutes by the plant ROL, decreasing the request of electron acceptors and the redox gradients along the sediment profile, as during microprofiling bottom water O\({}_{2}\) concentration was comparable in S and S + V. Different studies in the literature have addressed the role of aquatic plants in modulating sediment redox and diffusive O\({}_{2}\) uptake, reporting contrasting results that depend on different rates of ROL by emergent and submersed plants [56; 57]. Results from this study suggest that _V. spiralis_ increases O\({}_{2}\) penetration in sediments but does not affect DOU.
# Commuted PD Controller for Nonlinear Systems: Glucose-Insulin Regulatory Case
## 4 Discussion of Results  
A closed-loop artificial pancreas would measure glucose blood concentration, determine the expected time course following insulin administration, and dispense the optimal dose [36; 37; 38; 39]. Our proposal seeks to do so by designing a commuted linear control able to keep the blood sugar concentration in a healthy region in terms of reducing the error between the actual glucose concentration and the desired one. To compare our strategy, we consider the reference [11] due to its similarity. As was remarked in the introduction, there are many results on the insulin-glucose regulation system, and we choose the Bergman model because is commonly used for diabetic systems. As was presented in Section 3, our approach shows better performance, surely due to two main reasons: (a) the glucose level alerts when hypoglycemia occurs (not considered in [11] or [16], for instance); (b) and the error between the glucose level and the desired setting is reduced. In fact, despite this, our control approach can be considered as an applied mathematics study, where its application is easy to understand. Additionally, the proportional factor in our control design tries to reduce the error signal, but the parameter \(k_{1}\) in (8) must be tuned carefully. But once it is set, the controller must be able to face different patients' biological uncertainties. On the other hand, the derivative factor of the controller regulates the rate of the changing error signal, which is mainly governed by parameter \(k_{2}\) in (8). That is, it governs the future value of glucose concentration in terms of the present measurement (anticipatory control). Furthermore, if a white noise is present in the blood glucose measurement, our control strategy maintains healthy behavior, but the rise time is increased (see Figure 10). Only for the case of persistent hypoglycemia (Patient 2) the controller does not accomplish its purpose. In this case, the strategy must be different, maybe by considering a saturation effect on the insulin administration to avoid hypoglycemic episodes.
## 3 Results and Discussion
### Chemical Characterization of Feedstock and Processed Solids
#### 3.2.1 Sugar Release

Table 2: Sugar yields in the hydrolysates after HTP, alkaline and enzymatic treatments of the solid residues. Figure 2: (**a**) Yield of acetic acid (g/L) detected in the hydrolysates after HTP, alkaline and enzymatic treatments of the solid residues; ns: not statistically significant; ** Correlation is significant at the 0.05 level (2-tailed); (**b**) Yield of furfural and HMF (mg/L) detected in the hydrolysates after HTP treatment. highest severity factor (3.53) (3.80 \(\pm\) 0.2 g/L and 0.59 \(\pm\) 0.04 g/L, respectively) after the alkaline treatment method. [PERSON] et al. observed that xylose and arabinee were the main sugars extracted after alkaline treatment of wheat straw (0.047 g-0.056 g xylose/g WS) [58]. [PERSON] et al. [59] applied an alkaline treatment (4% KOH, 1% \(w/v\) NaBH\({}_{4}\)) to extract xylan from different agricultural wastes (wheat straw, tobacco stalk, cotton stalk and sunflower stalk) and observed that WS had the highest amount of arabinee and a more arabinylan structure compared to the other biomasses. Enzymatic hydrolysis was performed with two different enzymes for xylan and cellulose hydrolysis using Xylanase from _T. viride_ and commercial cellulases (Celic(r) CTec2) in different concentrations. Alkaline enzymatic treatment was also applied to the residues to improve the accessibility of the enzymes, since lignin is soluble in highly alkaline media [57]. With regards to the enzymatic hydrolysis with xylanase, xylose release was highest in the alkaline hydrolys of the residue treated at 180 \({}^{\circ}\)C, showing a quantity of 4.76 \(\pm\) 0.35 g/L after 48 h of enzymatic hydrolysis at a pH of 4.9. When combining enzymatic with alkaline treatment, the xylose release almost doubled, showing a release of 9.47 \(\pm\) 0.71 g/L in the hydrolysate of the residue treated at 180 \({}^{\circ}\)C. Glucose release was highest when the residues were treated with the enzymatic mix Cellic(r)CTec2, which contains aggressive cellulases, \(\beta\)-glucosidase and hemicellulases. Statistically significant differences were observed regarding conversion of cellulose to glucose after the different enzymatic treatments; the maximum glucose yield after 48 h of enzymatic hydrolysis with Cellic(r)CTec2 was found to be 10.72 \(\pm\) 0.23 g/L at the autohydrolysis condition of 140 \({}^{\circ}\)C, which means about 19% of cellulose in the solid residue. [PERSON] et al. used the same enzymatic cocktail to pretreat waste wheat straw (180 \({}^{\circ}\)C, 40 min) to recover soluble sugars (xylose, glucose) and obtained a recovery of maximum 33.4 g glucose and 6.1 g xylose after applying a washing treatment to remove the ash [18]. The authors also suggested that the ash in the residues conducted to reduced enzymatic hydrolysis efficiency; thus, the prewashing step was necessary. [PERSON] et al. reported that alkaline treatment (1%) im
## 7 Positioning Performance of LEO NR Signal
### Positioning Performance with Error Components  
The previous simulation experiment only considered receiver thermal noise, but actual LEO NR systems necessitate comprehensive error modeling. These error sources include (1) the clock error of receiver and satellite; and (2) the propagation delay uncertainties induced by ionospheric and tropospheric effects. These errors are empirically configured as follows [78]:  
* Receiver clock phase offset: 0.1 m;
* Receiver clock frequency drift: 0.01 m/s;
* Satellite clock phase offset STD: 0.5 m;
* Ionospheric error STD: 0.1 m;
* Tropospheric error STD: 0.2 m.  
Over the simulation duration, these error components are stable and can be viewed as constant. For the \(i\)-th satellite, its satellite clock offset \(\delta t_{\text{sat},i}\), ionospheric delay \(I_{i}\), and tropospheric delay \(T_{i}\) can be modeled as an equivalent time-delay error \(\delta t_{eq,i}\), e.g.,:  
\[\delta t_{eq,i}=\delta t_{\text{sat},i}+I_{i}+T_{i} \tag{39}\]  
Moreover, the satellite-receiver geometry impacts the ionosphere and troposphere delay. In the simulation, the six satellites' equivalent range delays \(c\delta t_{eq,i}\) are 0.238, 0.506, \(-\)0.747, 0.258, \(-\)0.466, and \(-\)0.062 m, respectively. Figure 17 and Table 6 demonstrate the effect of different error components on the positioning error. The experiment sets four error combinations: (1) white noise only; (2) white noise adds receiver clock offset; (3) composition of white noise, clock error, and ionospheric delay; and (4) composition of white noise, clock error, and ionospheric and tropospheric delays. The comparison results reveal that (1) the first and second configuration have identical positioning accuracies, indicating that the clock error has negligible positioning accuracy degradation due to receiver clock estimation in the state vector; and (2) the third and fourth configurations generate substantial error output, resulting in a notable EKF positioning bias. When comparing the results of the third and fourth configurations, it is evident that incorporating tropospheric effects exacerbates the positioning outcomes. Since the receiver is unable to estimate these errors, alternative error-elimination techniques should be adopted to eliminate them.  
Figure 16: Position and velocity error comparison: ZPC and CON receiver.  
It is obvious that the simulation is essential for the LEO NR navigation system. The simulation can estimate positioning performance and analyze the influence of error components. The simulation method is cost-effective and repeatable in the LEO navigation system design.
## 3 The Feature Selection Approach of Composite Faults
### Selection of Important Composite Fault Features  
Fault feature selection is to determine the relative importance of features in affecting the fault diagnosis results. When the feature set and its internal causal relationships are abstracted into a network, fault feature selection becomes an issue of nodes' centrality. Features contribute differently to the representation of composite fault information and the diagnosis results. Quantifying the importance of features in composite fault diagnosis is essential to the feature selection of composite faults. In the complex network, nodes with high centrality play a critical role.  
In this section, we propose to use nodes' centrality indicators to quantify the importance of composite fault features. Specifically, N top-ranked important features are selected in order of node importance (according to one of the centrality indicators), at first. Then, the diagnostic accuracy is obtained by calculating the proportion of sample faults correctly diagnosed in different cases (each centrality index of different numbers of features). The results are finally getting the number of features corresponding to the time when the accuracy reaches the peak.  
Taking the DC index as an example, the algorithm first calculates the DC value of each node in the CFN network through Equation (1). Secondly, the 20-dimensional features within the feature set are ranked in ascending order according to the DC value. Thirdly, one feature representative sample with the top ranking is selected to perform the composite fault type recognition task to get the classification accuracy. Then, continue to increase the number of features until we get the accuracy of all the feature representative samples when performing the composite fault type recognition task. Finally, comparing all the accuracy results, the number of features corresponding to the largest accuracy value is the number of features included in the feature set selected according to the DC index.  
In the causal feature network, the centrality indicators show the features' importance in the network. Specifically, degree centrality shows the number of causal relationships of a feature, which indicates the feature's range of influence. Closeness centrality represents the average minimum distance from a feature to all other features, thus eliminating the interference of special relationships. Betweenness centrality describes a node's function as a bridge by representing the shortest transmission path through the specific feature and between two other random features. PageRank centrality shows a feature's position in the network while considering the position of other features which have causal relationships with the specific feature.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM  
In the era of advanced communication characterized by high speed and high bandwidth network topologies, the need for EM systems to offer reconfigurability, compactness, directivity, and energy efficiency has become a necessity. Furthermore, the detection and identification of remote objects and faulty components in systems as well as pattern recognition of radiating signals in electrical systems are the latest trends in EM. On the other hand, the optimization of EM systems for superior performance given the constraints has been a recurrent challenge. ML-based approaches have the potential to address such complex and multifaceted challenges. The EM scientific community has leveraged ML for various applications. In the next few subsections, a few of the applications of ML in EM will be discussed.
## 2 Material and Methods
### Comparison of Measurement Results between Test Equipment
#### 3.3.1 Measurement Using UTM Equipment  
Evaluation was conducted using the UTM equipment at a temperature of 20 \({}^{\circ}\)C maintained inside a temperature chamber for over one hour, with an extension rate of 100 mm/min. The results of the measurements are presented in Table 4. Representative graphs and peel-out patterns for each specimen, drawn automatically by the dedicated program during the UTM measurement, are shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen Types** & **A-1** & **A-2** & **B-1** & **B-2** & **C-1** & **C-2** \\ \hline Peel-out Strength & 3.0 & 3.5 & 2.4 & 2.6 & 3.2 & 1.6 \\ \hline \end{tabular}
\end{table}
Table 4: Peel-out measurement results using UTM equipment (unit: N/mm).  
Figure 17: UTM equipment measurement peeling load-displacement graph—experimental condition 20 \({}^{\circ}\)C; (**a**) load-displacement result for A-1 and A-2 specimen, (**b**) load-displacement result for B-1 and B-2 specimen, (**c**) load-displacement result for C-1 and C-2 specimen.  
As shown in Figure 17, characteristic graphs for the peel-out strength measurement process at 20 \({}^{\circ}\)C under constant temperature conditions were observed for each material. Among the graphs obtained from measuring a total of 10 specimens, one representative graph was selected from those in which the graphs of at least two or more specimens overlapped.  
The rubberized asphalt-based materials, A-1 and A-2, as well as the butyl rubber-based materials, B-1 and B-2, exhibited similar graph shapes. On the other hand, the adhesive synthetic rubber-based material, C-1, showed a resistance load against peel-out that was more than twice as high as that of C-2. This comparison of the representative graphs provides insights into the distinctive behavior and performance of each material during the peel-out testing process.  
The difference here was that the rubberized asphalt and pressure-sensitive synthetic rubber series maintained a linear peeling pattern during the peeling process, with the polymer adhesive layer appearing in a narrow bonding area. On the other hand, the butyl rubber series maintained the bonding and elongation state of the polymer adhesive layer across the entire surface, not in a linear fashion, and Figure 18 confirms that this elongated polymer adhesive layer remained under tension.  
When examining the surface condition of the polymer adhesive layer after peeling in other specimens, it can be observed that the sheet exhibited a wrinkled appearance and formed a blister-like shape due to air entrainment during the bonding process. However, in the case of C-2, the surface of the polymer adhesive layer after peeling showed the presence of air bubbles. This can be attributed to the insufficient continuity of the air layer that was entrapped during the production process, resulting in a lack of stress transfer in the polymer adhesive layer during the cohesive resistance process against the applied tensile force during peeling.
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 2 Related Work
### Improved Routing Protocols Based on DL  
DL predicts the future state of a network by training with sample data to avoid connection failures and congestion, making it suitable for studying complex network routing protocols [26].  
A distributed routing algorithm based on local geolocation information has been proposed in the literature [27], which constructed a dataset using historical flight data to train Deep Neural Networks (DNN), considering end-to-end delay, network capacity, and path lifetime in the optimization process. Simulation results show that this DL-assisted routing algorithm outperforms existing location-based protocols in these metrics, though its computational complexity and power consumption increase due to the Pareto frontier problem in DL. Study [28] utilized the learning ability of neural networks and the explanatory ability of fuzzy logic to find routing next hops, optimize quality of service (QoS) metrics, and improve network performance. The algorithm considers distance, movement trends, and queuing delays but not energy consumption. Experiments based on real historical flight data demonstrate significant improvements in end-to-end delay and transmission rate.
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
## 3 Results and Discussion
### Distribution of Surfactin in Flax Tissues
#### 3.2.1 Detection of Surfactin in Root Extracts  
By comparing the data with the NMR spectrum of surfactin standard, a signal corresponding to surfactin was detected in surfactin-treated root extracts, analyzed by 1D \({}^{1}\)H NMR, which was a doublet at 0.85 ppm (J = 6.7 Hz). It was not present in the spectra of control root extracts (Figure 3A). Considering the 2D NMR spectrum, a correlation signal of 0.85 ppm/25 ppm was also observed on the \({}^{1}\)H/\({}^{13}\)C HSQC spectrum of the surfactin-treated root extracts but was absent on the \({}^{1}\)H/\({}^{13}\)C HSQC spectrum of the control root extracts.  
The comparison of LC-MS chromatograms of root extracts obtained from the control or treated flax plants reveals three additional peaks with a retention time of around 6.3 min in the treated conditions. By comparing with the surfactin standard, these three peaks corresponding to [M-H]\({}^{-}\) ions could be assigned to surfactin A (6.27 min, 1006.6414 \(m/z\)), surfactin B (6.35 min, 1020.6557 \(m/z\)), and surfactin C (6.43 min, 1034.6714 \(m/z\)) (Figure 3B,C). The relative area of these three peaks showed that surfactin B was the main form of the surfactin standard (more than 40%), followed by surfactin C (less than 40%) and surfactin A (less than 20%). The structure of these molecules of surfactin is presented in Table 1. The three surfactin forms had a similar amino acid basic structure, whereas the fatty acid chain length of surfactin A, B, and C was 13, 14, and 15 carbon atoms, respectively. These signals were removed from the dataset to perform multivariate data analysis. The quantitative analysis of surfactin in root extracts revealed that around 60% and 80% of the surfactin added in the culture medium was found in the root extracts 56 h and 10 days after the treatment, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surfactin Type** & & **ID Reference** \\
**Molecular Formula** & **Chemical Structure** & **ChemSpider** \\
**Monoisotopic Mass** & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different types of surfactin detected in negative mode ionization on treated root extracts analyzed by LC-MS.  
In parallel to the effect of surfactin at the point of application, i.e., in the roots, the effect of surfactin far from the point of application, i.e., in the aerial parts of the flax, was studied.
# Advanced Thermal Management for High-Power ICs: Optimizing Heatsink and Airflow Design
## 4 Results
### Firmed Copper  
In the second configuration, the objective was to increase the surface area exposed to free convection by incorporating fins on the copper and aluminum heatsinks. One of the most effective ways to enhance heat transfer is by increasing the surface area, as described by heat transfer relations [23]. This strategy is commonly applied in systems like radiators, operator coils, and condensers. Additionally, introducing fins on the heatsink helps increase turbulence in the airflow, which further improves cooling efficiency [24]. The heat transfer mechanisms of conduction, radiation, and natural convection were simulated again with this modified geometry, as shown in Figure 6.  
The boundary conditions applied to the conduction mechanism, including the heat source, material properties, and IC configuration, remained consistent with the first case. After applying these conditions, the second case--incorporating both the board cooling system and heatsink under free convection--was simulated. The temperature distribution and isothermal levels are illustrated in Figure 7.  
Figure 5: First geometry: (**a**) temperature contour and; (**b**) isothermal levels in Kelvin.  
As seen in Figure 7, the maximum temperature (T_max = 185 \({}^{\circ}\)C) occurred on the IC surface, while the minimum temperature of the entire geometry was T_min = 141 \({}^{\circ}\)C. As expected, the heat flux was directed toward the heatsink. However, adding fins to the aluminum frame did not have a substantial impact on cooling performance. Therefore, further changes in the cooling approach must be considered. Although the addition of fins increased heat transfer, the improvement remains limited, indicating that additional optimizations are necessary to achieve the high heat transfer coefficients required for effective cooling.