esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The SPME-Arrow method is a novel extraction technology which has been employed in the analysis of VOCs from grape skins. This method has proven to be fast and efficient for the analysis of both free and bound VOCs from grape skins. In this study the evaluation of five commercially available fibre coating materials was done and determined that DVB/CWR/PDMS coating is superior in the extraction efficiency compared to other fibres. A total of 53 free and 84 bound VOCs were analyzed and identified by SPME-Arrow method. Since the SPME-Arrow is very sensitive to experimental conditions, the process was optimized by employing Box-Behnken experimental design. For free VOCs the optimum extraction conditions are: extraction temperature 60 \({}^{\circ}\)C, incubation time 20 min, exposure time 49 min, and desorption time 7 min at 250 \({}^{\circ}\)C. Optimum extraction process for bound VOCs includes removal of free volatiles by heating of the sample, followed by acid hydrolysis. The optimum SPME-Arrow conditions are: extraction temperature 60 \({}^{\circ}\)C, incubation time 20 min, exposure time 60 min, desorption time 7 min at 250 \({}^{\circ}\)C. Application of the optimized method provides a powerful tool for establishment of the global volatile profile of grape skins. Furthermore, the method is automatized and can be applied to a large number of samples for expeditious analysis of major classes of volatile organic compounds in grape skins.  
The following are available online. Table S1: Parameters of the identification and physio-chemical properties for free volatile compounds. Table S2: Parameters of the identification and physio-chemical properties for bound volatile compounds. Table S3: Validation parameters of the optimized SPME-Arrow GC-MS method.  
Conceptualization, I.S. and I.T.; methodology, I.T.; formal analysis, D.P., P.S.; investigation, I.S. and P.S.; resources, I.S. and E.M.; writing--original draft preparation, I.S.; writing--review and editing, I.T. and J.K.K.; supervision, D.P.; funding acquisition, E.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & \multicolumn{3}{c}{**Factor Levels**} \\ \hline Coded levels & \(-1\) & 0 & 1 \\ A: Extraction temperature (\({}^{\circ}\)C) & 40 & 50 & 60 \\ B: Incubation time (min) & 10 & 20 & 30 \\ C: Exposure time (min) & 30 & 45 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Independent factors and their levels used in the BBD for optimization of SPME Arrow extraction for determination of bound volatile compounds.  
**Funding:** This research was funded by the Centre of Excellence for Biodiversity and Molecular Plant Breeding, grant number CoE CroP-BioDiv KK.01.1.1.01.005.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** Not available.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Graph Separation
#### 4.5.2 Graph Clustering  
Graph clustering involves grouping vertices into clusters based on their connectivity patterns. The goal is to identify dense subgraphs where vertices within a cluster are more densely connected to each other than to vertices outside the cluster. Graph clustering has numerous applications, including social network analysis, recommendation systems, and biological network analysis.  
**Definition 16** (Clustering).: _Given a graph \(G=(V,E)\), a cluster \(C\in V\) is a non-empty subset of \(V\), representing a group of \"similar\" vertices. These vertices are densely connected within the group but sparsely connected to those outside it._  
SCAN, a fundamental structural graph clustering algorithm, serves as a cornerstone for graph data management and analysis. Despite its importance, computational intensity of the method creates significant runtime challenges, restricting its real-world adoption. To overcome this limitation, [PERSON]. [120] developes anySCAN, an innovative interactive solution optimized for multicore processors. The study in [118] enhances the DynStrClu approximate clustering algorithm based on Jaccard similarity, addressing a key limitation of traditional SCAN clustering which requires careful parameter tuning for optimal results. The work presented in [119] introduces DPISCAN, a thread-level parallel processing approach implemented on Apache Spark's distributed framework.  
**Algorithm analysis.** We analyze different type of graph separation algorithms over dynamic graphs. As shown in Table 8, most algorithms support incremental dynamic graphs. For the time complexity, Ref. [113] has to check the set of candidate vertices for movement in partition \(P_{i}\). Additionally, for the time complexity of clustering, Ref. [118] returns clustering result satisfying the approximation probability \(1-p_{f}\) after up to \(M\) edge updates.  
In [119], \(\delta\) is the communication overhead in the Apache Spark cluster, and \(T\) is the number of executor nodes in the Spark cluster.  
**Discussion of large-update scenarios.** In our survey, we categorize dynamic graph algorithms into three paradigms based on how they handle updates: single update algorithms (process updates one-by-one), batch update algorithms (process grouped updates simultaneously), and streaming algorithms (process updates continuously as they arrive). Our analysis reveals that as the number of updates grows, computational costs increase significantly for both single update and batch update approaches. This scalability limitation motivates the need for streaming algorithms, which are specifically designed for dynamic graphs with continuous updates (referred to as \"streaming graphs\"). While our survey covers the existing single and batch algorithms, we identify a notable research gap in streaming dynamic graph algorithms. Current approaches often struggle to maintain efficiency under sustained high update rates.
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
###### Abstract  
Amine neurotransmitters (NTs) are crucial in the central nervous system, and dysregulation in their levels is implicated in a spectrum of neurological disorders. Thus, a precise and timely assessment of their concentrations is critical for early diagnosis and treatment efficacy monitoring. Graphene-based field effect transistors (GFETs) have become a ground-breaking instrument in the detection of these NTs because of their exceptional electrical characteristics and adaptability. This paper summarises the significant advancements in GFET biosensors in amine NT detection and highlights developments in the selectivity, sensitivity, and limit of detection (LOD) attained by selecting various graphene materials and functionalisation approaches.  
graphene; GFET; biosensor; neurotransmitters
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 2 HSI Inpainting Model and Mechanism  
### HSI Inpainting Model  
The HSI inpainting problem can be modeled as a reconstruction problem of signals acquired under incomplete observation. Assuming that the noise is additive, the inpainting model can be expressed as follows:  
\[\mathbf{y}=\mathbf{M}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{x}\in\mathbf{R}^{n_{v}}\) is the vectorized form of column stacking of clean and defect-free HSI \(\mathbf{X}\in\mathbf{R}^{L\times n_{s}}\), or \(\mathbf{x}=\text{vec}(\mathbf{X})\). Therefore, \(n_{v}=L\times n_{s}\), \(L\) is the number of spectral bands in the HSI and \(n_{s}\) is the number of pixel spectral vectors. Vector \(\mathbf{M}\in\mathbf{R}^{n_{m}\times n_{v}}\) is a mask chosen for the observable portion of \(\mathbf{X}\) that consists of two elements, 0 and 1. There is only one value of 1 in each row, and \(n_{m}\leq n_{v}\). Finally, \(\mathbf{y}\in\mathbf{R}^{n_{m}}\) is the vector form of the observed incomplete HSI, and the additive noise is \(\mathbf{n}\in\mathbf{R}^{n_{m}}\).  
In this paper, we assume that the mask matrix \(\mathbf{M}\) is user-provided. In addition, \(\mathbf{y}\) is also known. Thus, the image inpainting problem is converted into a reconstruction problem for \(\mathbf{x}\).
## 2 System Model  
As shown in Figure 1, a multi-user two-tier heterogeneous network scenario is considered in the paper, which includes a single massive MIMO enabled macro-base station (MBS), multiple single-antenna small base stations (SBSs) and single-antenna users. The MBS and SBSs differ in transmit power, size, density and the number of antennas. Let\(\mathcal{J}=\{0,1,2,...,J\}\) be the set of BSs, where index 0 denotes the MBS and the others are SBSs. \(\mathcal{K}=\{1,2,...,K\}\) is the set of users. It is assumed that all users distribute uniformly within the coverage area of the MBS and each user is associated with only one BS at a time.
## 6 Result and Discussion
### Standard Shapes Comparison
#### 6.1.2 Heavy Inhomogeneity Cases (\(A=0.51\))  
Simulations parameters are shown in Table 2 for the pair Air/Dichlorodifluoromethane (R12) (\(A=0.51\)) as in [1; 6]. The timestep is \(Dt=10^{-9}\) s as before.  
Figure 5: Shape comparison between results [6] (left column) and smoothed particle hydrodynamics (SPH) results (right column) for \(A=-0.79\). See Figure 3 for the equivalent experimental data [1].  
In addition, in this case, the SPH results (Figure 6) show good agreement with the available experimental data (Figure 4). With respect to the CFD simulations, the SPH results look more reliable especially at longer times (e.g., the jellyfish head shape in the last figure of Figure 6). Again, the small timescale differences are discussed in the next section.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \(\mathbf{N_{p,1}}\) & \(\mathbf{N_{p,2}}\) & \(\mathbf{N_{p,3}}\) & \(\mathbf{N_{p,4}}\) & \(\mathbf{\rho_{1}}\) & \(\mathbf{\rho_{2}}\) & \(\mathbf{\rho_{3}}\) & \(\mathbf{P_{r}}\) & \(\mathbf{h_{1}}\) & \(\mathbf{h_{2}}\) & \(\boldsymbol{\tau_{s}}\) & \(\boldsymbol{\alpha}\) & \(\mathbf{Ma}\) \\ \hline
125,050 & 244,464 & 5536 & 42,121 & 5 & 1.16 & 3.65 & 20 & 1.15 dL & 0.5 dL & \(1.39\cdot 10^{-11}\) & 0.1 & 1.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational set-up for the Air-R12 system: \(N_{p,1}\), \(N_{p,2}\), \(N_{p,3}\), \(N_{p,4}\) are the number of particles of type 1, 2, 3 and 4. \(\rho_{1}\), \(\rho_{2}\) and \(\rho_{3}\) are the density of particles of type 1, 2, and 3 expressed as \(\mathrm{Kg\,m^{-3}}\). \(P_{r}\) is the pressure ratio. \(h_{1}\) is the smoothing length of particles type 1. \(h_{2}\) is the smoothing length of particles type 2 and 3. \(\tau_{s}\) is the dimensionless time step of the simulation. \(\alpha\) is the dimensionless factor controlling the dissipation strength. \(Ma\) is the shock wave Mach number in the simulation.  
Figure 6: Shape comparison between CFD results [6] (**left** column) and SPH results (**right** column) for \(A=0.51\). See Figure 4 for the equivalent experimental data [1].
# Specific Modeling Issues on an Adaptive Winglet Skeleton
## 1 Introduction
### General Overview of the Winglet System  
Among the just mentioned four AIRGREEN 2 morphing devices which may be considered for similar purposes, this study focuses on specific modeling issues addressed during the advanced design of a finger-like mechanism-based morphing winglet developed by CIRA. Such an integrated morphing system, capable to reduce wing-bending moments and increase aircraft flight stability in response to changing flight conditions, has proved to provide significant aerodynamic benefits, estimated on the order of 2.5% LoD at high CL with respect to the optimal passive winglet counterpart [23].  
The morphing winglet system consists of two \"finger-like\" mechanisms realizing two independent morphing tabs moving both upward and downward. Such deflections are driven by dedicated electromechanical actuators [24]. One of the major advantages of this architecture is the capability to move the individual surfaces either synchronously or independently to different angles. This ensures smoother morphing winglet aeroshapes and a more efficient distribution of the span-wise aerodynamics, as estimated by computational fluid dynamics (CFD) simulations (Figure 1).  
The structural layout of the morphing winglet consists of a passive and an active part [25]. The former is made of laminate skin panels and a torsion box consisting of spars and ribs. The latter incorporate two hinged mechanisms covered by a segmented skin. By means of linking rods, hinged on non-adjacent blocks, the morphing capability is enabled by the relative rotations of three adjacent blocks, which are free to rotate around the hinges on the camber line, thus physically turning the camber line into an articulated chain of consecutive segments. Each tab is, therefore, a single degree of freedom (SDOF) system; if rotation of any of the blocks is prevented, no change in shape can be obtained. On the contrary, if an actuator moves any of the blocks, all the other blocks follow the movement accordingly.
###### Abstract  
Flow velocity field measurement is important for analyzing flow characteristics of oil-water two-phase immiscible flow in vertical well. Digital particle image velocimetry (DPIV) is an effective velocity field measurement method that has overcome single point measurement limitation of traditional instruments. However, multiphase flow velocity fields generated by DPIV are often accompanied by local false vectors caused by image mismatching, which leads to measurement results with low accuracy. In this paper, the reasons for oil-water two-phase immiscible flow image mismatching in inner diameter 125 mm vertical pipe is identified by studying the DPIV calculation process. This is mainly caused by image noise and poor window following performance that results from poor deformation performance of the interrogation window. To improve deformation performance of the interrogation window, and thus improve the accuracy of the algorithm, iterative closest point (ICP) and moving least squares (MLS) are introduced into the window deformation iterative multigrid algorithm in DPIV postprocessing algorithm. The simulation showed that the improved DPIV algorithm had good matching performance, and thus the false vector was reduced. The experimental results showed that, in light of the present investigation, on average, the improved DPIV algorithm is found to yield an accuracy improvement of \(\sim\)6%; the measurement uncertainty and reproducibility of the improved DPIV algorithm were \(0.149\times 10^{-3}\) m/s and \(1.98\%\), respectively.  
oil-water two-phase immiscible flow; vertical pipe; digital particle image velocimetry; window deformation iterative multigrid; iterative closest point; moving least squares +
Footnote †: journal: Applied Physics Letters
## 4 Computational Results
### Randomly Generated Instances Studies  
In these tests, we utilize two sets of randomly generated instances to evaluate the performance of the proposed algorithm. For the first set of small-size instances, the range of jobs is [20; 100]. For the second set of large-size instances, the number of jobs is set as 500, 1000, 2000, 3000, 4000, and 5000. The processing time \(t_{i}\) is randomly generated from a uniform distribution (30, 210) min and the power consumption per hour \(p_{i}\) is randomly  
Rule 1: If \(t_{i}\leq\max_{k\in A}\{I_{k}\}\) and \(kk=\operatorname*{argmin}_{k\in A}\{t_{i}\leq I_{k}\}\), where \(\operatorname*{argmin}_{k\in A}\{t_{i}\leq I_{k}\}\) denotes the minimal index \(k\) for \(t_{i}\leq I_{k}\), then job \(i\) can be directly inserted into the off-peak period \(kk\) and the job no longer traverses all the non-full periods. Rule 2: If \(t_{i}\leq\min_{k_{\ell}\in\Gamma}\Big{\{}I_{k_{\ell}}\Big{\}}\), \(\max_{k\in A}\{I_{k}\}>0\) or \(\max_{k^{\prime}\in B}\{I_{k^{\prime}}\}>0\), then job \(i\) no longer traverses on-peak periods. As mentioned above, when there are no jobs that need to traverse non-full on-peak periods, the time complexity of GIH-F is O(\(n^{2}m\)) and GIH is O(\(n^{2}m^{2}\)). This implies that GIH-F is \(m\) times faster than GIH, theoretically, and the experimental data of the small-size instances in Table 5 can verify this conclusion. From Table 5, we can see that \(R\) and \(m\) are almost the same order of magnitude. In addition, all the small-size instances can be solved within 0.02 s using GIH-F. By and large, the computation time increases slightly with \(n\), and parameter \(e\) has no significant effect on the computation time, which indicates that the algorithm is quite stable. In addition, it can be seen from Table 5 that the smaller the parameter \(e\) (i.e., the shorter the makespan), the higher the total electricity cost. Therefore, in a specific instance, the decision-makers can obtain a set of Pareto solutions by adjusting the makespan, and they can choose a solution according to actual needs. What is more, it is amazing to see that our algorithm not only greatly improves computation speed but also further improves the accuracy. Table 6 shows that the number of periods increases quickly with the number of jobs. Since the time complexity of GIH is O(\(n^{2}m^{2}\)), it's runtime will rise sharply. To ensure the feasibility of the contrast tests, we add two rules (i.e., Rule 1 and Rule 2) to improve the computational speed of GIH without changing the computational accuracy. Intuitively, the \(CT_{F}\) is significantly less than \(CT_{H2}\), which means that the designed filtering mechanism is efficient in dealing with large-scale instances. Specially, as \(n=5000\) and \(e=2.0\), our algorithm can solve a randomly generated instance within 1 min and maintain the same accuracy as GIH2, while GIH2 takes nearly 39 h, let alone GIH. Note that when \(e\) is set as 3.0, the given makespan is very abundant and there is no job processed within an on-peak period in our experimental
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 2 Materials and Methods  
### Reagents and Materials  
CBZ was purchased from Sigma-Aldrich (St. Louis, MI, USA). Acetonitrile HPLC-grade ([PERSON], Barcelona, Spain) was used as the organic eluent in the high-performance liquid chromatographic (HPLC) method. Sodium chloride was obtained from Fisher Scientific (Hampton, NH, USA), formic acid 98-100% was acquired from VWR (Alfragide, Portugal), hydrochloric acid 37% p.a. was obtained from Scharlau (Barcelona, Spain), and sodium hydroxide was purchased from Merck (Darmstadt, Germany). A stock solution of CBZ (1000 mg/L) was prepared in acetonitrile and stored at \(-10\)\({}^{\circ}\)C. The working solutions were prepared daily by diluting the stock solution with ultrapure water before use. Ultrapure water (18.2 M\(\Omega\).cm resistivity) was obtained using a purification system from Millipore (Molsheim, France). The CBZ working solution had an initial pH of 5.  
A boron-doped diamond (BDD) electrode (DIACHEM(r) electrode type, 100 \(\times\) 20 \(\times\) 2 mm, niobium substrate, both sides coated, multilayer--from CONDIAS, Itzehoe, Germany) and a mixed metal oxide (MMO) electrode (titanium-coated with RuO\({}_{2}\)-IrO\({}_{2}\)-TiO\({}_{2}\), 10.50 \(\upmu\)m thickness, 100 \(\times\) 20 \(\times\) 2 mm, UTronTechnology, Youchuang, China) were used as the anode, and stainless steel (STS) (AISI-304, austenitic grade, 100 \(\times\) 20 \(\times\) 2 mm) was used as the cathode in the 2D and 3D electrochemical experiments. An HQ Power, adjustable DC power supply, model PS3020 (Velleman(r), Gavere, Belgium) with an adjustable output voltage of 0-30 V and an adjustable output current of 0-20 A was used to carry out the electrochemical processes.  
Vineyard pruning residues from Touriga Nacional (TN), gently provided by Sogrape Vinhos, S.A. (Porto, Portugal), sampled at Quinta dos Carvalho (Dao wine region) in 2015, were used to produce the biochar. The vineyard pruning residues were pyrolyzed in an industrial oven by Ibero Massa Fluestal (Aveiro, Portugal), as previously described by [PERSON] et al. [38]. Briefly, the biomass was pyrolysed (without any nitrogen or carbon dioxide supply): 8 h of heating time, 14 h of holding at 500 \({}^{\circ}\)C, and cooling for 18 h until room temperature. The obtained biochar was milled (ZM200, Retsch, Haan, Germany) and sieved (AS 200 Basic Retsch, Haan, Germany) to obtain two fractions with particle sizes of 1-2 mm and \(<\)75 \(\upmu\)m that were used in the experiments. The moisture and ash contents of the biochars were determined following the ASTM D1762 standard.  
One polymer-based spherical activated carbon (PBSC) was supplied by SARATECH(r) reference 102282 (Blucher, Erkrath, Germany).
## 3 Results
### Feasibility and User Experience During Follow-Up at Home  
#### 3.2.1 Compliance  
Of the 30 participants who completed IRF phase of the study, 23 (76.7%) completed home follow-up. The remaining seven did not participate due to the inability of research staff to contact the participant after multiple attempts (four), a change in medical status (two), and moving out of state (one). Follow-up visits were completed an average of \(124.2\pm 33.1\) days following discharge from IRF. Participants were given the option to receive study equipment at their home (\(n\) = 6) or at Froedtert Hospital before or after a clinical visit (\(n\) = 17). In the 44 participant days of at-home silent monitoring, 81.9% of potential silent monitoring data were collected. The reasons for missing data were less clear in the home setting compared to the IRF. Only one participant called to report technical issues with the system, which resulted in less than 1 h of monitoring time for both days for that participant. No other technical failures were reported by users, although we suspect such issues were probable based on IRF findings. The average compliance time across participants was 96.3 \(\pm\) 9.9% and 94.6 \(\pm\) 12.9% for the MI and LI sides, respectively. All devices were successfully returned via pre-paid packaging by the participants.