esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mapping Aboveground Carbon Content in Saltmarsh Using ArborCam  
The simple linear regression model between the ArborCam NDVI (using bands four and seven) and the aboveground carbon derived from the 41 herbs, grasses, and sedges quadrats provided a better goodness-of-fit (R\({}^{2}\) = 0.62) than when all 47 quadrats were included (R\({}^{2}\) = 0.26; Figure 4). The six outlier quadrats dominated by the rush species _J. krawsii_ had a relatively high carbon content (>600 g C m\({}^{-2}\)) with correspondingly low NDVI values. However, a simple linear regression model using just the six _J. krawsii_ quadrats had a very good fit (R\({}^{2}\) = 0.79).  
The LOOCV analysis of the NDVI-calibrated carbon model for the herbs, grasses, and sedges group indicated that there was a relatively large uncertainty for predicted carbon content values (MAE = \(\pm\)49.13 g C m\({}^{-2}\); RMSE = \(\pm\)78.16 g C m\({}^{-2}\)). The spatial distribution of the aboveground carbon content across the study site (Figure 5) derived from the calibrated ArborCam NDVI image showed that higher predicted carbon content occurred around the edges of the saltmarsh before the transition into _Casuarina_ forest. The aboveground carbon content was particularly concentrated at the northernmost part of the saltmarsh, where Cararma Inlet reached furthest inland. The estimated mean aboveground carbon content across the 124 ha saltmarsh study area was 1.32 Mg C ha\({}^{-1}\).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & **Band 1** & **Band 2** & **Band 3** & **Band 4** & **Band 5** & **Band 6** \\ \hline Band 2 & \(-\)0.19 & & & & & \\ Band 3 & \(-\)0.23 & \(-\)0.27 & & & & \\ Band 4 & \(-\)0.46 ** & \(-\)0.54 ** & \(-\)0.57 ** & & & \\ Band 5 & \(-\)0.46 ** & \(-\)0.53 ** & \(-\)0.54 ** & \(-\)0.10 & & \\ Band 6 & 0.38 * & 0.53 ** & 0.64 ** & 0.79 ** & 0.79 ** & \\ Band 7 & 0.54 ** & 0.64 ** & 0.70 ** & 0.79 ** & 0.79 ** & 0.76 ** \\ \hline \end{tabular}
\end{table}
Table 4: Correlation (r) between aboveground carbon content of saltmarsh herbs, grasses, and sedges quadrats with each normalized-difference (ND) ArborCam image (* \(p\) < 0.05; ** \(p\) < 0.01).  
\begin{table}
\begin{tabular}{l l l l l l} \hline  & **Band 1** & **Band 2** & **Band 3** & **Band 4** & **Band 5** & **Band 6** \\ \hline Band 2 & \(-\)0.27 & & & & & \\ Band 3 & \(-\)0.19 & \(-\)0.11 & & & & \\ Band 4 & \(-\)0.13 & \(-\)0.08 & \(-\)0.02 & & & \\ Band 5 & 0.05 & 0.17 & 0.25 & 0.83 * &Figure 4: Simple linear regression model between NDVI derived from the ArborCam image (bands, four and seven) with aboveground carbon content for all 47 quadrats, and quadrats partitioned into two groups: herbs, grasses, and sedges (41 quadrats), and _J. kraussii_ (6 quadrats).  
Figure 5: Spatial distribution of aboveground carbon content in Saltmarsh derived from the ArborCam NDVI image calibrated using the herbs, grasses, and sedges simple linear regression model.
# Resveratrol: A Review on the Biological Activity and Applications
## 16 Pharmacokinetics of Resveratrol: Challenges in Absorption, Metabolism, and Therapeutic Efficacy  
Despite being well-absorbed orally, RSV presents significant PK challenges that hinder its clinical utility in chemotherapeutics. Its oral bioavailability is only about 70%, yet its systemic availability is reduced to a mere 5% due to extensive hepatic and intestinal metabolism via glucuronidation and sulfation, leading to less active metabolites. Additionally, oxidative enzymes, air oxidation, light exposure, and high temperatures can induce _trans_-to-_cis_ conversion, further diminishing its efficacy. Compounding these challenges is its poor water solubility of approximately 0.03 mg/mL, which restricts its absorption and systemic circulation [256].  
Recent research has focused on new delivery systems to address these PK barriers. Nanoformulations show promise in enhancing solubility, preventing degradation, and controlling drug release kinetics. Other strategies, including metal-based carriers, derivative compounds, and polyhedral formulations, are also being explored [257].  
Despite these efforts, evidence shows that RSV is quickly metabolized and excreted as sulfate and glucuronide conjugates. Studies involving human urine and blood samples consistently reveal low levels of unmetabolized RSV and high levels of its conjugated metabolites, indicating rapid metabolism and extensive enterohepatic recirculation. For instance, a study on oral RSV administration found that only 2% of the dose was excreted unchanged, confirming an oral bioavailability as low as 5% [258].  
Moreover, its bioavailability varies among individuals, influenced by factors like diet composition and metabolism. While complex meals may improve absorption, obtaining the necessary therapeutic doses remains challenging. The variability of RSV's bioavailability, particularly in cancer patients who may experience altered metabolism, suggests a significant gap in understanding its therapeutic efficacy and warrants further investigation [257].
## 4 Results and Discussions
To obtain the predictive path loss modelling results, first we divided the path loss data into portions and employed the grid search-based hyperparameter tuning method to generate configurations for the parted data chunks for training and testing. The performance results of the proposed method were evaluated and reported for each machine learning algorithm described in Table 1, using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Coefficient of Efficiency (COE), Correlation Coefficient (R) and Standard Deviation Error (STD) [65]. Secondly, the predictive path loss modelling was conducted for three study locations using the standard Bayesian optimization for hyperparameter tuning. The results were compared to our first results using the grid search-based hyperparameter tuning method.
## 2 Related Work  
### Robot Control Policy Learning  
Data-driven learning algorithms are widely employed to train control policies, which can be classified into supervised learning methods, reinforcement learning methods, and recently developed deep reinforcement learning methods. The supervised learning methods take the state-action pairs of demonstration data as the training samples to learn mapping relationships between the states and actions [11], which have been successfully deployed in manipulation tasks [12; 13], driving [14], and navigation [15]. However, generally, the policies learned with supervised learning methods are deeply influenced by the quality of demonstrations. Typically collecting high-quality demonstration data, especially in the field of robots, is not a trivial task [16; 17].  
Reinforcement learning methods train the robot agents to obtain optimal policies through trial and error [18; 19; 20; 21; 22]. Reinforcement learning has led to many successes in the domain of robot control when low-dimensional state space or action space is available [23; 24]. However, reinforcement learning shows limited success in continuous cases.  
Deep reinforcement learning methods combining reinforcement learning with deep neural networks have shown great potential in addressing high-dimensional and continuous action-state space for robot control policy learning. Deep Q-network (DQN) [6] has been implemented to train reaching skill in 2D space for three-link robots [25; 26]. In addition, deep deterministic policy gradient (DDPG) [27], trust region policy optimization (TRPO) [28], asynchronous advantage actor-critic (A3C) [29], generalized advantage estimation (GAE) [30], and proximal policy optimization(PPO) [31] have also been implemented in certain robot simulated control tasks, such as stacking blocks, hopping, or walking. Guided policy search (GPS) [32] is a rare method that can directly train control policies on real-world robots. Although deep reinforcement learning methods show high potential for control policy learning, due to large amounts of training data notoriously to collect, acquiring manipulation skill policies for real-world robots through DRL is time-consuming and cumbersome.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 5 Techno-Functional Properties and Application of Pectin
### Pectin Gel
A large number of active Ca\({}^{2+}\)-binding sites contributes to the formation of percolating network structures and improved rheological properties of the gels (faster formation kinetics, enhanced viscosities, and higher elastic modulus) [123]. Indeed, viscoelastic materials combine two different properties. The term \"viscous\" means that they deform when exposed to an external force, whereas the term \"elastic\" implies that once a deforming force has been removed, the material will return to its original configuration. The viscoelastic response of material is measured by complex moduli of elasticity (G', the storage modulus and G\", the elastic modulus). Watermelon pectin at 5% (\(w/v\)) showed a weak gel-like behavior with a typical shear thinning behavior. Petkowicz et al. (2017) showed that the elastic modulus was higher than the viscous modulus for watermelon pectin at 5% (\(w/v\)) over the analyzed frequency range and the moduli increased with increasing frequency. Therefore, this solution behaved as a weak gel. Negatively charged GalA and electrostatic interactions between positively charged side chains of the protein are responsible for this behavior of watermelon rich pectins. Furthermore, watermelon pectin had greater values of elastic modulus than those described for pectin obtained from apple pomace by enzymatic and chemical methods and from cacao pod husks using nitric acid [6].
## 3 Control Layer Architecture
### Context Measurements Dissemination  
The node that receives a context measurement stores it in an indexed list. Considering \(\{n_{1},\ldots,n_{z}\}\) as the set of nodes in the network at time \(t\), the indexed list of received context measurements for the node \(n_{i}\) for \(1<i\leq z\) is represented as \(M_{i}=[m_{j}\mid 1\leq j\leq z]\) where \(M_{i}(j)=m_{j}\).  
Figure 2 shows how a node (\(n_{1}\)) disseminates its context measurement when comes into range with another node (\(n_{2}\)). The control metadata are shared bidirectionally, hence, \(n_{2}\) will follow the same flow when it is its turn to do the dissemination.  
In Figure 2, _step_\(s_{1}\), when node \(n_{1}\) contacts node \(n_{2}\), \(n_{1}\) takes a local context indicator reading, \(m_{l}\). In _step_\(s_{2}\), \(n_{1}\) aggregates this context reading along with all the context measurements received by contacted nodes stored in the list \(M_{1}\). The aggregation process is described in Section 3.3. In _step_\(s_{3}\), the aggregated measurement, \(m_{1}\), is shared with the contacted node \(n_{2}\) which stores it in its context measurements list \(M_{2}\) (_steps_\(s_{4}\) and \(s_{5}\)). At this point, \(n_{2}\) follows the same flow to calculate \(m_{2}\) with the slight difference that it will not use the entry \(M_{2}[n_{1}]\). This entry contains the received measurement \(m_{1}\) built out of the information provided by \(n_{1}\). Using \(m_{1}\) would interfere with the network perception \(n_{2}\) is about to share with \(n_{1}\).
## 3 Results  
Table 2 illustrates the influence of Polish learners' tonal performance in Mandarin, revealing a significant positive coefficient of 0.294 for Polish learners. This suggests that compared to native Taiwanese Mandarin speakers, Polish learners tend to have a higher pitch in their tone production. The reference tone used in this analysis is Tone 4, both in the first and second syllables, against which the other tones (T1, T2, T3) are compared. Significant differences were observed between Tone 4 and the other tones. Specifically, in the first syllable, T1 showed a significant positive effect on the dependent variable with a coefficient of 0.231. Conversely, T2 and T3 demonstrated significant negative effects with coefficients of \(-\)0.295 and \(-\)0.293, respectively. These results indicate a clear deviation in tonal production between T1 and the lower pitch tones T2 and T3 when compared to T4. In the second syllable, a similar pattern emerged. T1 had a positive impact with an estimated coefficient of 0.279, suggesting a higher pitch effect similar to the first syllable. However, T2 and T3 exhibited negative impacts with coefficients of \(-\)0.137 and \(-\)0.263, respectively, indicating a lower pitch effect compared to T4. These findings underscore that the overall tonal register of L1-dominant Polish speakers learning Mandarin (L2) is relatively elevated compared to that of native Taiwanese Mandarin speakers. The data from Table 2 provide a statistical validation of these tonal differences, highlighting the distinct tonal acquisition patterns observed in Polish learners of Mandarin. Given the significant differences between the two groups, we further analyzed the T-values of the tones in different positions using pairwise comparisons, after adjusting for other variables. For example, considering the tones in the first syllable: when T1 occurs in the first syllable, our data indicate significant differences between T1 and T2, T3, and T4, as shown in the SIG column. The only exception is that T2 cannot be produced distinctively from T3, with a SIG value of 0.859. Combining the data shown in Tables 3 and 4, we found that T2 and T3 produced in the first syllable of a disyllabic word do not distinguish from one another effectively. This demonstrates that the production of T2 and T3 in the first syllable can easily be confused. The total trajectories of all tonal combinations are shown in Figure 1. Generally, L1-dominant Polish speakers learning Mandarin (L2) exhibited a higher tonal register compared to TM natives. As detailed in Figure 1, the production of the first syllable (points 1 to 11) for the tonal combinations T1T1, T1T2, T1T4, and T2T1, when compared to TM natives, reveals significant differences. Similarly, the production of the second syllable (points 12 to 22) for the tonal combinations T1T1, T1T2, T3T2, and T4T2, indicates that the first syllable with T1 and the second syllable with T2 present more difficulties for L1-dominant Polish speakers learning Mandarin (L2). Notably, our results show that T3 and T4 seldom pose significant challenges for L1-dominant Polish speakers learning Mandarin (L2).
## 7 Routing Protocols proposed by Spanish Universities
### SHRP (Simple Hierarchical Routing Protocol)
Both of them are two hops from the sink node; however the processing time is greater in topology 2 because NC3 node in topology 3 has more neighbors, so NC3 node has to process more NMI messages and calculate more in order to have the best route until sink node. With these experiments the conclusion is that SHRP protocol has a media convergence time of 8 ms for each direct link that has to exchange NMI messages, and this time increases linearly with the increase of neighbor nodes. The second experiments that we have done are the comparison of SHRP protocol and HTS (_Hop to Sink Protocol_) protocol. The HTS protocol is a simple routing protocol that we have proposed and implemented. The only metrics that it uses are the number of hops and the sink node has to receive all routing table from all nodes to decide the best route and send the decision to each node. HTS protocol looks like a link state protocol but just sink node knows all routes from all NC nodes. In this study there are the same topologies shown in Figure 4 with the same parameters previously described. As we can see in Figure 9, the convergence time for the first topology reached 20 ms. With SHRP we had a maximum convergence time of 9 ms. Now in topology 2 (Figure 10), the maximum Convergence Time was 35 ms, meanwhile with SHRP protocol it was 24.7 ms. In Figure 11 is shown the Convergence Time of HTS in topology 3. As can be seen the maximum Convergence Time was 31 ms and with SHRP protocol it was 18.5 ms. These experiments shows that SHRP protocol has a better convergence time with respect to a simple protocol such as HTS, that just chooses the neighbors based on the number of hops to the sink node. Link state protocols like HTS, where each node has to know all the routes to the sink node, can have a large convergence time, being a problem when there is a topology that changes constantly as in wireless sensor network, as we have low battery time, interference and obstacles problems. So we can conclude that protocols that just need to have neighbor information to decide the best route shows to have a lower convergence time, being more interesting to WSN. Figure 2:1 Comparison of Convergence Times.
## 2 Methods
### Image tilt Correction  
A misalignment between the OCT scan beam and the axis of the optic nerve during B-scan acquisition can result in significant image tilting [11]. Thus, the angle \(\gamma\) change could be significantly different between the left and right zone of the same B-scan, or even opposite after LP. For example, in Figure 3, the LP induced \(\gamma\) increased from \(-1.31^{\circ}\) to \(2.34^{\circ}\) in the left zone, while it decreased from \(2.44^{\circ}\) to \(0.77^{\circ}\) in the right zone.  
This tilting artifact can be eliminated by averaging the ppRPE/BM layer angles of the left and right zones from the same cross-sectional B-scan. This averaged ppRPE/BM layer angle was referred to as the cross-sectional ppRPE/BM layer angle and denoted as \(\Gamma\). A positive \(\Gamma\) indicates a V-shaped ppRPE/BM layer. The angle \(\Gamma\) in Figure 3 was \(0.57^{\circ}\) before LP, and increased to \(1.56^{\circ}\) after LP. In addition, this averaging method could also eliminate the artifact induced by the tilting of B-scan relative to the scanning window. Figure 4 illustrates one example in which the OCT scan had a tilt angle of \(1.88^{\circ}\) against the scanning window. The \(\gamma\) was measured as \(4.78^{\circ}\) in the left zone and \(-0.38^{\circ}\) in the right zone. The angle \(\Gamma\) was calculated as \(2.20^{\circ}\). After we readjusted the scan to its correct position by rotating an angle of \(1.88^{\circ}\) clockwise, the \(\gamma\) became \(6.19^{\circ}\) in the left zone and \(-1.98^{\circ}\) in the right zone. The corresponding \(\Gamma\) was calculated as \(2.11^{\circ}\), which was only \(0.09^{\circ}\) less than the one without tilt correction.  
Figure 3: B-scans before and after LP in the temporal-nasal direction from the same individual. The blue dashed line marks the ppRPE/BM layer before LP, and the blue solid line marked the ppRPE/BM layer after LP. To visualize the angle \(\gamma\) change difference, the blue dashed line in the B-scan before LP was shifted vertically to the B-scan after LP.
## 3 Antibiofilm Peptidomimetics
Olygo-acyl lysyl antimicrobial peptides (OAK), oligomersof acetylated lysines with alternating pattern of hydrophobic residues (amino acyl chains) and cationic amino acids similar to previously mentioned peptidomimetics, have also shown not only antimicrobial but also antibioofilm activity. The most active representative was lapidated OAK that displayed potency in killing preformed biofilm of _Streptococcus mutans_, however only at significantly higher concentrations (e.g., \(\geq\)0.5 mM) within one hour of exposure [80] (Table 3). As a different approach, several peptidomimetics were designed with an alternative backbone. For example, a short amphiphilic cationic peptidomimetic 23b contained-amino-[1,10-biphenyl]-3-carboxylic acid backbone with biphenyl attached with hydrophobic Trp and cationic aminoethyl guanidine [81]. Such peptidomimetic was able to disrupt biofilms of _S. aureus_ and _E. coli_ (41% and 39% respectively) at 250 \(\upmu\)M (Table 3). These findings may indicate that the increased cationic charge and hydrophobicity potentiates the activity against planktonic cells, however does not facilitate disruption of the large aggregates  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & **Structure** & **Bacteria** & **Activity** & **Ref. ** \\ \hline
1 & & _P. aeruginosa_ & Inhibition/Prevention & [75] \\ \hline
1-C13\({}_{\text{tmer}}\) & & _P. aeruginosa_ & Inhibition/Prevention & [75] \\ \hline Y-36 & & _P. aeruginosa_ & & [76] \\  & & & Inhibition/Prevention & \\  & & _MRSE_ & & \\ \hline  & & _E. coli_ & & \\  & & _P. aeruginosa  
of bacteria surrounded by an extracellular matrix. Other ultra-short dipeptidomimetics designed using spermine backbone, inhibited MRSA biofilm formation at sub-MIC concentrations. Though both showed better killing efficacy than vancomycin, 1c and 1d were able to kill viable cells of preformed biofilms only at concentrations higher that MIC, indicating no antibiofilm specificity [82]. Further modifications led to the discovery of dialkyl cationic amphiphile peptidomimetics bearing two identical length lipophilic alkyl chains and one non-peptide amide bond, still keeping the previously established hydrophobicity-cationicity ratio. The most potent dialkyl cationic amphiphile 4g was not able to show high potency while inhibited 80% of the _S. aureus_ and _E. coli_ biofilms at 18-50 \(\times\) MIC, while eradicated preformed biofilms at values corresponding to 64-fold of MIC values [83]. Though mentioned peptidomimetics have displayed some antibiofilm activity, it remains far from desirable. Overall, more investigations are needed in this field in order to understand how to enhance antibiofilm profile of peptidomimetics.