esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Drought Detection  
The root-zone soil moisture percentile is utilized to detect and characterize drought [28; 29; 72]. The root zone soil moisture percentiles are calculated for each grid in each time step with reference to the period of 1 January 1984 to 31 December 2014. Drought intensity classifications are adopted from the National Drought Mitigation Center (NDMC) of the United States Drought Monitor (USDM), which defines five drought categories as in Table 2.
## 5 Simulation
### Design of Event Conditions and Controllers
As shown in Table 1, on the one hand, the sampling numbers for centralized event-triggered control in [21] are less than those for the decentralized relative event condition in [25]. This accords with the analysis in [24], that is, the decentralization would weaken the sampling performance. On the other hand, for each channel, the total samplings numbers for the proposed scheme (i.e., 900 samplings for the controller-to-actuator channel and 1162 samplings for the sensor-to-controller channel) are less than those for the scheme in [21] (i.e., 1654 samplings for the controller-to-actuator channel and 1392 samplings for the sensor-to-controller channel). This illustrates that, in this simulation, the integral-based event condition can save more communication resources than the relative event condition even though the latter operates in a centralized way. Additionally, for the proposed scheme, the sampling performance of \(y_{1}\) is better than that of \(u_{1}\) and \(y_{2}\). To balance the sampling performance of these nodes, one can properly decrease \(\sigma_{y}^{1}\) and increase \(\sigma_{u}^{1}\), \(\sigma_{y}^{2}\) within the range decided by Theorem 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **The Proposed Scheme** & **The Scheme in [21]** & **The Scheme in [25]** \\ \hline \(u_{1}\) & 900 & 1654 & 3180 \\ \(y_{1}\) & 331 & 696 & 748 \\ \(y_{2}\) & 831 & 696 & 1803 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sampling numbers for different event-triggered control schemes. Figure 4: **(top row)**: evolutions of inter-event times in nodes \(y_{1}\); (**bottom row**): evolutions of inter-event times in nodes \(y_{2}\). Figure 3: **(top row)**: input signal; (**bottom row**): evolutions of inter-event times in node \(u_{1}\). Figure 2: Trajectories of the plant state and the observer error. (**a**) state trajectories; (**b**) observer error trajectories.
## 5 Numerical Results
Once again, buckling temperatures based on TD material properties are always more conservative that predictions based on TI material properties, independently of the \(A_{R}\). Figure 4: Effect of different CNT profiles on the critical thermal buckling load considering \(L/R=2\), \(V_{CNT}^{*}=0.17\), \(A_{R}=1000\), \(w=0.425\), \(N_{r}=N_{s}=0\). Figure 3: Effect of different CNT profiles on the critical thermal buckling load considering \(L/R=2\), \(V_{CNT}^{*}=0.17\), \(A_{R}=1000\), \(w=0\), \(N_{r}=N_{s}=0\). TI: temperature-independent; TD: temperature-dependent. A further systematic investigation is provided to evaluate the influence of wavy CNTs volume fractions \(V_{CNT}^{*}\) on the thermal buckling load of the cylindrical shell. Three increasing values are here assigned to the \(V_{CNT}^{*}\), namely, 0.12, 0.17, 0.28, while considering the double possibility of TI and TD material properties. Figure 5(a)-e depict all the curves in terms of \(\Delta T_{cr}\) vs. \(R/h\), for each distribution and \(V_{CNT}^{*}\) previously mentioned, and for fixed values of \(L/R=2\), \(A_{R}=500\), and \(w=0.425\). Interestingly, an increased value of \(V_{CNT}^{*}\) causes a considerable increase of the critical thermal buckling load, whose entity varies with the selected pattern of CNTs within the structures, as well as with the selected TI or TD material properties. For the sigmoidal pattern, we also study how the exponential parameter \(s\) can affect the thermal buckling load of the stiffened cylindrical shell reinforced by wavy graded CNTs. This parametric analysis is repeated for different combinations of \(A_{R}\) and waviness indexes \(w\). According to the plots in Figure 7, \(\Delta T_{cr}\) decreases for increasing \(s\), whose variation is more pronounced for wavy CNTs (more specifically, for \(w=0.425\)), and reverts to a constant value for high values of \(s\). This agrees with the sigmoidal trend of Figure 2, where increasing values of \(s\) yield to the mechanical behavior of a laminate made by two different laminae and properties. The last parametric investigation finally considers the possible presence of ring and/or string stiffeners with a fixed geometry, i.e., \(h_{s}=h_{r}=b_{s}=b_{r}=0.004\) m. A varying number of ring and/or string stiffeners is assumed at the inner or outer surfaces of the shell, as combined in Table 5. Based on results listed in Table 5, the critical thermal buckling load for stiffened shells increases significantly compared to the un-stiffened cases, especially when the same number of rings and strings is located at the external surfaces of the cylindrical shell. As also expected, the thermal buckling behavior improves as the number of rings and strings increases. A similar investigation could be repeated for other combinations in terms of stiffener numbers and locations on the structure, but it is here not reported for the sake of brevity. Figure 7: \(\Delta T_{\sigma}\) vs. \(s\) for different combinations of aspect ratios \(A_{R}\) and waviness indexes \(w\) considering \(L/R=2\), \(R/h=200\), \(V_{CNT}^{*}=0.28\), \(N_{r}=N_{s}=0\).
# Demand-Response Control in Smart Grids
## 2 State of the Art
First, a double-sided auction occurs when a person can act as both auctioneer and bidder at the same moment. In a one-sided auction, on the other hand, a participant can only function as the auctioneer or bidder. Second, whether an auction is a single-item or a multi-item (combinatorial) auction is determined by the number of items or services being sold. The nature of the auction differs depending on whether the objects being auctioned are divisible (meaning that they may be shared between two or more people) or indivisible (meaning that they can not be split). Third, whether an auction is single-dimension (also known as uni-attribute) or multi-attribute is determined by the number of attributes in the bid. Bidders supply information about other qualities in addition to the bid amount in single-dimension auctions, but in multi-attribute auctions, bidders provide information about other attributes in addition to the bid amount (e.g., time or quality). Fourth, an auction is a first-price auction if the winner pays the exact amount it provided in its bid; otherwise, it is a second-price auction if the price is conditioned by the non-winning bids. Finally, an auction can be classed as forward or reverse depending on the function of the competitors. As previously stated, in forward auctions, the auctioneer sells products or services to bids, but in reverse auctions, the auctioneer is the buyer, and bidders compete to sell their services to the auctioneer. Furthermore, auctions can be classified as open-cry or sealed-bid. Participants in open-cry auctions compete openly against one another, making their bids public and expressing their preferences to the other auctioneers. Agent privacy is traded for the sake of transparency in these procedures. Iterative auctions, in which participants can adjust their bids openly in response to the other participants' bids, are a good illustration of this type of auction. If bidders increase their offers during the sale, it is an English auction; otherwise, bidders in Dutch auctions decrease their offers downward. In contrast, in sealed-bid auctions, the agents' privacy is protected because all bidders make sealed bids to the auctioneer at the same time. As a result, no one knows how much the other bidders have bid (as shown in Figure 1).
## 2 Materials and Methods
### Method Validation Parameters  
Method validation was conducted on method 3 following the ICH guidelines for the parameters of linearity, accuracy, precision and sensitivity [22]. To satisfy the validation requirements, 3 calibration curves with 7 points were built on 3 different days and from 2 different operators. For the parameter of linearity, the calibration interval was extrapolated from the range of analytical response with a R\({}^{2}\) linearity coefficient > 0.998. The repeatability of the calibration curves was assessed by comparing the relative standard deviation RSD % of their slopes. The absence of a CRM matrix of _Boswellia_ without boswellic acids led us to use the solvent as matrix for the studies of accuracy and precision. The accuracy was expressed in terms of accuracy BIAS % (Acc. BIAS %) using a low (5 mg/L \(n\) = 3), a medium (20 mg/L \(n\) = 3) and a high concentration level (100 mg/L \(n\) = 3). The results were expressed as % of difference between the real and the measured value. Intermediate precision was calculated based on the levels used for the accuracy step as relative standard deviation (RSD %) among the measurements (_n_ = 3) for each level. Sensitivity was measured in terms of LOD and LOQ (limit of detection and limit of quantitation, respectively) based on the standard error \(\sigma\) of the calibration curve. The \(\sigma\) value is divided by the value of the average slope and multiplied 3-fold for the LOD and 10-fold for the LOQ.
## 6 Nano-Particles and Nano-Structures
### Gold Nano-Particles
The thiol layer was responsible for the primary loading of 20 nm AuNPs whilst the secondary PEI layer supported the immobilization of 10 nm AuNPs. Ultimately, the column demonstrated both an increase in available surface area and an increase in separation efficiency. As the total content of gold was lower than in previously reported columns [131,134], this increase in efficiency may have been due to the hypercross-linking step alone. Once the AuNPs have been immobilized, the surface can be used for additional functionalization. For example, [PERSON] _et al_. have used the AuNP surface to produce switchable surface chemistries [131]. By exploiting the interaction between thiols and gold, a number of molecules were used to produce RPLC functionality, ion exchange functionality, and charged groups for CEC separations. Each stationary phase demonstrated the separation ability and the stability of the AuNP coating, using model analytes of three proteins: ribonuclease A, cytochrome C, and myoglobin. This method of coating is also temperature sensitive, as described by [PERSON] _et al_. [133], wherein the AuNP surface can be regenerated using temperatures of above 80 \({}^{\circ}\)C and water to remove thiols. An example of immobilized AuNPs upon a polymer monolith is shown in Figure 6.  
Figure 6: FE-SEM micrograph of a poly(LMA-_co_-EDMA) monolith with aminated poly(GMA) grafted chains to support AuNP immobilization. Reprinted from [9]. Copyright (2013), with permission from Elsevier. Immobilized AuNPs can also be utilized in the preparation of affinity phases. [PERSON] _et al_. produced poly(EDMA) monoliths within the confines of polypropylene pipette tips, which were subsequently modified with AuNPs [129]. The monolith ultimately expressed a lectin, which could be selectively used to extract glycoproteins from complex mixtures. The pipette tip demonstrated an extraction efficiency of up to 37% and a recovery efficiency of 86% to 100%. A segmented AuNP monolith was produced by [PERSON] _et al_. [9], wherein, similar to the photo-grafting work performed by [PERSON] _et al_. [8], a column expressing two discreet zones was prepared. A section of column dedicated to AuNP immobilization was followed by a RP zone, and using an elution technique [133], cysteine containing peptides could be extracted and separated, within a single column. In addition, [PERSON] _et al_. produced an AuNP modified monolithic enzymatic microreactor within a capillary for CE [130]. The AuNPs were modified with \(\alpha\)-glucosidase and used in the screening of \(\alpha\)-glucosidase inhibitors in a range of natural products.
# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach  
[PERSON] and [PERSON]  
1 Politecnico di Bari, Dipartimento di Ingegneria Elettrica e dell'Informazione (DEI), 70125 Bari, Italy; [EMAIL_ADDRESS]  
Footnote 1: email: {M  
New approaches have been developed for an early and accurate diagnosis of melanoma, such as biophysics analysis [11], imaging techniques (i.e., dermoscopy), diagnostic algorithms, and image analysis-based criteria [12; 13]. Nevertheless, clinical diagnosis is still challenging since it depends on human vision and physician experience and expertise. For these reasons, computer-aided detection and diagnosis (CADe and CADx) systems are becoming effective means to help specialists in detecting and preventing neoplastic pathologies [14]. Image segmentation is a fundamental phase of CADe systems and it is a difficult task in the specific case of dermoscopic image analysis for skin lesion classification. In fact, low contrast between lesion and skin area around lesion, irregular lesion border, artifacts (such as bubbles or body hairs), blood vessels, lack of color uniformity inside nevi, and depigmentation make the segmentation a challenging task. An accurate segmentation is crucial for a correct diagnosis because it makes it possible to extract highlight information on border structure such as asymmetry and lesion area irregularity [15; 16].  
In this paper the lesion segmentation problem is addressed: An automatic and real-time method for dermoscopic images able to segment skin lesions and identify/highlight border also in the presence of imperfections (such as hairs, bubbles) is presented. In the proposed method, a preprocessing phase aimed at reducing the image noise and at preserving all the spatially correlated information was first performed using the singular value decomposition (SVD) technique. The preprocessed Red Green and Blue (RGB) image under test was afterwards decomposed into a frame of bit-plane layers. In order to extract all the coherent information, thus preserving the contour transitions, a bit-plane binary orthogonalization-like was then implemented and the image segmentation was achieved by appropriately summing up the mostly correlated singular vectors. Morphologic techniques were conveniently used to reduce spurious pixels in segmented regions.  
Adopting the publicly available PH\({}^{2}\) database [17] as test bench, experimental results showed good performance and easy hardware development.  
In Section 2, a brief review of the most recent and popular segmentation methods indicated in literature is given. Section 3 deals with the techniques used for the procedure development, while in Section 4 the conceived method is described in details, and in Section 5 the database used to test the algorithm is presented. Experimental results and discussions are highlighted in the last section of the paper.
## 3 Results
### Effects of Synthetic Data on the Training  
In Section 2.3, we determined the baseline mAP of 86.5 % on real world data at our desired input image size. This allows us to examine the effect of adding supplementary synthetic data during training on the detector's performance. For this, we successively add more and more subsets of our synthetic dataset to the set of training samples. These subsets exhibit different characteristics as described in the following. In the first trial, we add the synthetic scenes 1, 2, and 3 to the existing training set. In total, these scenes show 2130 pedestrian worker instances in 750 images. The amount of provided information is not significantly enhanced by these scenes. It only increases the sheer number of samples as the scenes imitate the real world samples, which have already made up the training dataset beforehand. This results in an increase of the mAP by 1.7 percentage points to a value of 88.2 %. Next, we further extend the training dataset by adding the synthetic scenes 4, 5, and 6, which consist of 1510 images showing 5170 worker instances in total. The compositions of these scenes differ from those of the scenes before, but the environmental conditions remain similar. Trained on the resulting dataset, our detector yields a mAP of 88.7 %. This is an increase of only 0.5 percentage points in comparison to the previously trained detector. By subsequently adding scene 7 containing 2490 worker instances in 620 images, we incorporate different lighting conditions into our training dataset. The scene is brightly illuminated, which leads to large cast shadows. Workers are consistently located in over- and underexposed areas. By this, the mAP drastically increases by 5.2 percentage points to 93.9 %. Further training samples of scene 8, which exhibit environmental conditions similar to scene 7, have only little effect on the detection quality. Although this scene provides another 2490 worker instances in 620 images, the mAP only increases to 94.0 %. The bar graph in Figure 4 summarizes the increase of the mAP over the successively added subsets of our synthetically generated dataset.
###### Abstract
It was previously proven that the inhibition pattern (the effectiveness of the action) is strongly tailored by the substrate used. [PERSON], [PERSON], and [PERSON] [11] have reported that the type of substrate used to carry out the reaction determines the effectiveness of the tested fennel (_Foeniculum vulgare_) PPO inhibitors and also has a significant impact on the inhibition type. The 0.1 mM ascorbic acid was the most effective using pyrogallol (25% inhibition of PPO activity), and the weakest using 4-terbutylcatechol (10% inhibition of enzyme activity) as a substrate. Moreover, the 0.1 mM L-cysteine solution inhibited approximately 36% PPO activity with catechol and almost twice more with pyrogallol. [PERSON], [PERSON], and [PERSON] [11] also showed that non-competitive inhibition was observed for sodium metabisulfite and glutathione using catechol, and 4-terbutylcatechol as a substrate, while uncompetitive and competitive inhibitions were detected using pyrogallol, respectively. Similarly, for truffle PPO, it has been reported that sodium metabisulfite acted as a non-competitive inhibitor in the presence of catechol, while it was uncompetitive when tyrosine was used [12]. Furthermore, uncompetitive inhibition was found for benzoic acid and thiourea using catechol and 4-terbutylcatechol, but non-competitive inhibition was found when pyrogallol was used. The research results cited above allow for the hypothesis that, for studying the activity and kinetics of enzymatic reactions, as well as the enzyme activity model, it would be more appropriate to use an endogenous substrate (i.e., polyphenic compounds isolated from the studied plant material). This would enable a better representation of the natural environment in which these enzymes exhibit activity, allowing for a more accurate characterization of the enzymes and a deeper understanding of various processes. It is also obvious that both shredding and storage cause stress, which may promote the expression of new isoenzymes inactive in fresh tissues [13; 14]. Importantly, these isoenzymes usually differ in the case of the inhibitory pattern. In sum, the aware development of new tools reducing enzymatic browning in cut vegetables and fruit needs extended knowledge about enzymes charged in that process. Therefore, our research is focused on the partial purification and the biochemical characterization of PPO isolated from stored shredded iceberg lettuce (enzymatic pattern responsible for enzymatic browning). It is important that different classes of potential inhibitors (commonly used PPO inhibitors, and natural plant extracts) were tested using lettuce phenolics as endogenous substrates.
## 3 Results and Discussions
### Coupled Flow and Noise Simulations for a Wind Turbine Array
#### Modeling of the Wind Turbine ANSource  
As is shown in the above AD simulation, every wind turbine has different inflow conditions because some wind turbines are in the wake of another, which leads to different ANSources.  
Figure 10: Comparisons of the normalized wake velocity from LES and RANS simulations, for down-stream locations of 5D and 7D after a single wind turbine.  
Figure 9: (a) Mesh topology; (b) stream-wise flow with multi-wakes.  
The simulation were conducted for a free-stream wind velocity of 8 m/s. For a receiver at 120 m far away, the detailed noise spectra solely from the first upstream wind turbine are shown in Figure 11. The noise spectra for other downstream wind turbines will be shown in the later sections. As shown in the figure, the total SPL spectrum is name as SPL-TOTAL which is logarithmically summed from several noise mechanisms. These noise mechanisms include: noise from blade tip (SPL-TIP); from trailing edge bluntness (SPL-TEBLUNT); from the shedding of laminar boundary layer vortex (SPL-LBLVS); from flow separation (SPL-SEPARATION); from turbulent boundary layer trailing edge (SPL-TBLTE); from turbulent inflow (SPL-INFLOW) [14; 15; 16]. Later, the total noise spectrum will be applied into the noise propagation computations.  
Although the flow and the noise source are modelled without wind and temperature gradients, in reality the effective sound speed profile will be affected by wind and temperature profiles. It was shown that the combinations of wind speed and temperature gradient forms different types of sound speed profiles [35]. Three idealized initial sound profiles are considered in the following sound propagation simulations. In Figure 12a, a uniform sound speed at temperature of 15 \({}^{\circ}\)C is assumed at the left boundary. The wind turbines are separated with a normalized distance of seven rotor diameters. For example, the second wind turbine is located at \(x=560\) m and the third wind turbine is located at \(x=1120\) m. Inside the computational domain, the sound speed is superimposed with the wind turbine wake field. Similarly, Figure 12b,c represent downward and upward sound speed contours. These sound profiles merge with the wind turbine multi-wakes and show distinct characteristics. The sound profiles have significant effects on wind turbine noise propagation as shown in the next subsection.  
Figure 11: Aerodynamic noise source spectra of a single wind turbine.