esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of SAFE, SDE and SPME Extracts by GC-O
Another meaningful difference was the \"popcorn\" aroma in the SDE extract, apparently caused by high temperature application, which suggests the possibility of artifact formation during this procedure. All other differences were mostly caused by the fact, that SAFE and SDE techniques are exhaustive extraction types, while SPME allows for the extraction of the compounds from the volatile phase depending on their partition coefficients. Additionally, it is a selective technique, the selectivity of which is fiber-dependent, so the volatile profile could differ from the two remaining isolation techniques. Summarizing, the best option is the application of both SAFE aided with SPME extraction to achieve a reliable odor active compounds profile. Although SDE was popular and often used in early studies with _Brassica_ vegetables, the high temperature of extraction and long extraction time makes it unsuitable in the analysis of fresh vegetable volatiles. However, in certain cases, SDE can provide bigger peaks of thermally stable compounds than the other two techniques and can aid in their identification. Some of the identified aroma components in broccoli have already been described in literature. Methanethiol is a meaningful aroma component in cheese, as well as different alcoholic beverages [15] and also appeared to be the primary contributor to the objectionable odor that broccoli develops when held in anaerobic conditions [17]. The odour of this compound was described as \"intensely putrid, fecal-like aroma\" and might be the reason for the rejection of broccoli by many consumers. Dimethyl disulfide has been detected as a major volatile sulfur compound in the headspace of broccoli [16] as well as cabbage [18], asparagus, corn, and tomato paste [19] and is considered to be a secondary product of primary C-S lyases action on S-methyl-L-cysteine sulfoxide [20]. Dimethyl trisulfide was also detected in _Brassica_ vegetables before, such as cabbage or broccoli [16]. Methyl thiocyanate, was also previously identified in broccoli stored under a low oxygen atmosphere [16] and in cabbage [4]. Hexyl isothiamate was the only identified isothiances in the extracts and was the source of nice aroma \"broccoli-like\"; this component was previously detected in Japanese and Kenyanadish [21].
## Materials and Methods
### Molecular Docking  
The docking calculations were performed on (-)-epicatechin ((2R,3R)-2-(3,4-dihydroxypropyl)-3,4-dihydro-2H-chromene-3,5,7-triol), chlorogenic acid ((1S, 3R, 4S, 5S)-3-[(E)-3-[3,4-bis(oxidanyl)]phen yl]prop-2-enoyl]oxy-1,4,5-tris(oxidanyl)cyclohexane-1-carboxylic acid), rutin (2-(3,4-dihydroxyyphenyl) -5,7-dihydroxy-3-[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-[[(2R,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyl-ox an-2-yl]oxymethyl]oxan-2-yl]oxy-chromen-4-one), salicin ((2R,3S,4S,5R,6S)-2-(hydroxymethyl)-6-[2-(hydroxymethyl)]phenoxy]oxane-3,4,5-triol) and donepezil ((2R)-5,6-dimethoxy-2-[[1-(phenylmethyl)] piperidin-4-yl]methyl]-2,3-dihydroend-1-one) (Figure 1).  
Chemical structures of ligand molecules were taken from PubChem database ([[http://pubchem.ncbi.nlm.nih.gov/](http://pubchem.ncbi.nlm.nih.gov/)]([http://pubchem.ncbi.nlm.nih.gov/](http://pubchem.ncbi.nlm.nih.gov/))). The structures of molecules were geometry optimized using the software Avogadro 2.0 following the MMFP94 method [23]. Three-dimensional crystallographic structure of human AChE enzyme (pdb code: 4 ey7) complexed with donepezil [24] was retrieved from Protein Data Bank (PDB) ([[http://www.rcsb.org/](http://www.rcsb.org/)]([http://www.rcsb.org/](http://www.rcsb.org/))). Before docking the ligands into the enzyme, it was prepared by removing all water molecules, heteroatoms, any co-crystallized solvent and the ligand. AutoDockTools (ADT; version 1.5.6) was used to add hydrogens and partial charges for protein and ligands using Gasteiger charges [25]. The grid box was designed to include the active and peripheral site of human AchE. The dimension of the grid box was \(60\times 60\times 60\) with distance of 0.375 A between points, centered at coordinates x = \(-\)14.108, y = \(-\)43.833, z = 27.67. Molecular docking was conducted using AutoDock 4.2.3. program package (Molecular Graphics Laboratory, La Jolla, CA, USA). Docking simulations were performed using Lamarckian Genetic Algorithm [25] with standard docking procedure for rigid receptor and flexible ligand. A total of 25 runs along with \(25\times 10^{5}\) energy evaluations and 27,000 iterations were carried out. Other parameters were set to default. Conformations of docked structures with the lowest binding energy were considered as the most favorable docking pose. Discovery Studio Visualizer 4.5. (DSV; Dassault Systemes BIOVIA, San Diego, CA, USA) was used to visualize binding interactions and produce the figures. In order to validate the docking procedure, docking was performed with the co-crystallized AChE: Donepezil, which was re-prepared in DSV. The RMSD value between re-docked conformation and the original structure, which was \(\leq\)1.5A, indicated the reliability of the binding ability prediction of new ligands.  
Figure 1: Structures of ligand molecules used for molecular docking: **a**, donepezil; **b**, (-)-epicatechin; **c**, chlorogenic acid; **d**, salicin; **e**, rutin.
# Deadlocks Detection in Multithreaded Applications Based on Source Code Analysis
## 5 Deadlock
### DL3 Application Model  
The last scenario considered in [16] refers to the code in which the recursively called function attempts to perform the locking operation every time, which causes a deadlock phenomenon. Figure 6 presents a graph of the DL3 application operation. The sample DL3 application implements the S4 scenario, according to the model in Section 3, the source code of DL3 application is as follows:  
Figure 5: DL2 application operation graph.  
\(T_{DL3}=(t_{0},t_{1})\), \(U_{DL3}=(\{t_{0}\},\{t_{1}\},\{t_{0}\})\), \(R_{DL3}=\{\{counter\}\}\), \(O_{DL3}=\{o_{0,1},o_{0,2},o_{0,3},o_{0,4},o_{0,5},o_{1,1},\) \(o_{1,2},o_{1,3},o_{1,4},o_{1,5},o_{1,6},o_{1,7},o_{1,8}\}\), \(Q_{DL3}=\{(m,PMD)\}\), \(F_{DL3}=\{(o_{0,1},o_{0,2}),(r_{1},o_{0,2}),(o_{0,2},o_{0,3}),\) \((o_{0,3},o_{0,4}),(r_{1},o_{0,4}),(o_{0,4},o_{0,5}),\) \((o_{1,1},o_{1,2}),(q_{1},o_{1,2}),(o_{1,2},o_{1,3}),\) \((o_{1,3},r_{2}),(o_{1,3},o_{1,4}),(o_{1,4},o_{1,5}),\) \((o_{1,5},r_{1}),(o_{1,5},o_{1,6}),(o_{1,6},o_{1,1}),\) \((o_{1,6},o_{1,7}),(o_{1,3},o_{1,7}),(o_{1,7},q_{1}),\) \((o_{1,7},o_{18}),(o_{1,8},o_{1,6})\}\).  
Like in the DL2 application, also in the DL3 application, there is a deadlock phenomenon, which is the result of the circular nature of the operation. In the DL2 application, the deadlock was due to incorrect placement of locking and unlocking operations, while in the case of the DL3 application, the operations are correctly placed. The difference is that in the case of recursive calls, one should use the pthread_mutex_lock function with the PMR type lock. This type guarantees that if the recursive function tries to re-lock, the thread ID will be checked, and the internal lock counter will be incremented, which will allow further operation.  
Recursion in the operation graph is manifested by the presence of a path that leads from the last operation to the first. This path may consist of one edge, but it is not a rule, as it is the case in the DL3 application. In the case of recursive functions, it should be borne in mind that there is always a run that ends at the last operation or an operation from which no other edges are derived. Therefore, in this case, to detect a resource conflict causing a deadlock, one must determine whether the thread is calling the function recursively and whether it is using a PMR type lock. If the lock type is different, a deadlock will occur. So the third lemma is as follows.  
Lemma 3: _If \(t_{i}\) thread of \(P\) application calls a recursive function that does not use PMR type locks, then the deadlock phenomenon will occur._  
Proof: Lemma is a direct consequence of the assumptions of the invocation of recursive functions.
## 3 Rate Analysis of MU-PhC-MIMO VLC Systems
### Approximate Expression Based on ZF Scheme  
In this subsection, we discuss the approximate expression for system rates under the ZF scheme, where interference between users can be eliminated as much as possible, meaning \(\sum_{j=1,j\
eq k}^{K}\zeta n_{\text{s}}g_{k}^{T}w_{k}S_{j}=0\). (8) and (9) can be re-expressed as  
\[\begin{split}&\xi_{k,1}=\frac{\big{(}-\zeta n_{\text{s}}g_{k}^{T}w_{k}+ \zeta g_{k}^{T}d+n_{\text{b}}\big{)}^{y_{k}}}{y_{k}!}\exp\Bigl{(}-\zeta n_{ \text{s}}g_{k}^{T}w_{k}+\zeta g_{k}^{T}d+n_{\text{b}}\Bigr{)};\\ &\xi_{k,2}=\frac{\big{(}\zeta n_{\text{s}}g_{k}^{T}w_{k}+\zeta g _{k}^{T}d+n_{\text{b}}\big{)}^{y_{k}}}{y_{k}!}\exp\Bigl{(}\zeta n_{\text{s}}g_ {k}^{T}w_{k}+\zeta g_{k}^{T}d+n_{\text{b}}\Bigr{)}.\end{split} \tag{12}\]  
Even after substituting (12) into (11), the form remains complex and could hinder subsequent optimization operations. Consequently, we derive a concise approximate expression for (11), and the detailed process can be found in the proposition below.  
**Proposition 1**.: _When \(n_{\text{b}}\geq 40\), the asymptotic approximation expression of (7) is_  
\[\begin{split}\widetilde{R}_{k}=1-\frac{1}{2\ln 2}-\frac{ \mathcal{F}(\frac{n_{\text{s}}g_{k}^{T}w_{k}}{\sqrt{\lambda_{k,1}}})+\mathcal{ F}(\frac{n_{\text{s}}g_{k}^{T}w_{k}}{\sqrt{\lambda_{k,2}}})}{4\ln 2},\end{split} \tag{13}\]  
_where \(\lambda_{k,1}=-\zeta n_{\text{s}}g_{k}^{T}w_{k}+\zeta g_{k}^{T}d+n_{\text{b}}\), \(\lambda_{k,2}=\zeta n_{\text{s}}g_{k}^{T}w_{k}+\zeta g_{k}^{T}d+n_{\text{b}}\) and_  
\[\begin{split}&\mathcal{F}(\frac{n_{\text{s}}g_{k}^{T}w_{k}}{ \sqrt{\lambda_{k}}})=\int_{-\infty}^{+\infty}\frac{\exp\left(-\frac{(\varrho +\frac{n_{\text{s}}g_{k}^{T}w_{k}}{2\sqrt{\lambda_{k}}})^{2}}{2}\right)+\exp \left(-\frac{(\varrho-\frac{n_{\text{s}}g_{k}^{T}w_{k}}{2\sqrt{\lambda_{k}}}) ^{2}}{2}\right)}{\sqrt{2\pi}}\times\\ &\ln\left(\exp\left(-\frac{(\varrho+\frac{n_{\text{s}}g_{k}^{T}w_{ k}}{2\sqrt{\lambda_{k}}})^{2}}{2}\right)+\exp\left(-\frac{(\varrho-\frac{n_{ \text{s}}g_{k}^{T}w_{k}}{2\sqrt{\lambda_{k}}})^{2}}{2}\right)\right)\text{d} \varrho.\end{split} \tag{14}\]  
**Proof of Proposition 1.** See Appendix A.  
According to (13), we obtain the asymptotic approximation expression for the weighted sum rate of the MU-PhC-MIMO VLC system as follows:  
\[\sum_{k=1}^{K}\mu_{k}\widetilde{R}_{k}=\sum_{k=1}^{K}\mu_{k}\Big{[}1-\frac{1} {2\ln 2}-\frac{\mathcal{F}(\frac{n_{\text{s}}g_{k}^{T}w_{k}}{\sqrt{\lambda_{k,1}}}) +\mathcal{F}(\frac{n_{\text{s}}g_{k}^{T}w_{k}}{\sqrt{\lambda_{k,2}}})}{4\ln 2} \Big{]}. \tag{15}\]
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Parallel Model Training and HPC Integration
#### 2.4.4 Integration of Spark, MPI, and GPU  
Figure 4 illustrates a key aspect of our methodology, which is the seamless integration of these frameworks. Spark handled the data loading and preprocessing, ensuring that when the training phase began, data was already partitioned and readily accessible in memory on the machine. Then, MPI took over to coordinate the parallel training tasks, and each MPI process invoked TensorFlow for the portions of training that could benefit from GPU acceleration. We carefully managed the interface between Spark and MPI: intermediate datasets (like the final feature matrix for training) were saved as distributed files (Parquet format) and then read by each MPI process locally. This approach avoided heavy data movement through the MPI communicator--each rank could directly read its portion of data from the local disk (thanks to HDFS's replication on the single node, data locality was ensured [18]). Once training was completed, results (model parameters, metrics, logs) were gathered. Spark was then used again to aggregate these results, for example, merging the logs from each rank into a unified view and storing final models. By combining Spark's ease of data handling with MPI's control over parallel execution and GPUs' raw computational power, the pipeline maximized resource utilization. This cohesive use of multiple HPC tools in one workflow is a distinguishing feature of our approach compared to previous efforts that might have used these tools in isolation.  
In developing the pipeline, we also implemented strategies for efficient resource use and fault tolerance. Data were stored and exchanged in columnar format (Parquet) to speed up 1/O and allow selective loading of features, which reduced memory overhead [19]. We monitored memory usage throughout and used Python's garbage collection triggers to preemptively clear buffers between pipeline stages, preventing memory bloat when handling large, in-memory datasets. For fault tolerance, we relied on Spark's lineage-based recovery--if a data partition read failed or a node process died, Spark could recompute lost partitions automatically [21]. Similarly, for the MPI processes running long training jobs, we integrated periodic checkpointing: model weights and optimizer states were saved at intervals so that if a process or the program crashed, it could be resumed from the last checkpoint, following established MPI checkpointing practices [21]. These measures, while not the primary focus of this study, ensured that the pipeline was robust as well as fast. In summary, the methods above detail an HPC-driven approach that spans from data ingestion to model output, specifically tailored to improve both the scalability (ability tohandle more data and more complex models) and efficiency (faster execution) of urban water demand forecasting tasks.
## 2 Various PPF Strategies
### PPF of Hypercrosslinked Monoliths
The hypercrosslinked monolithic capillary columns were tested in the separation of alkylbenzenes by RPC and the column efficiencies were found to be higher than 70,000 plates/m. The high stability of post-polymerization hypercrosslinking modification of monolithic capillary columns in HILIC was investigated [17]. A poly(styrene-_co_-vinylbenzyl chloride-_co_-divinylbenzene) monolith was prepared by an in situ thermal polymerization. The monolithic surface was then subjected to PPF with 4,4'-azobis(4-cyanovaleric acid) followed by a surface grafting polymerization of dimethyl(3-sulfonylpropyl)ammonium hydroxide to obtain hypercrosslinked monolith, see Scheme 8. The resulting monolith was used for the separation of small polar compounds in the HILIC mode. The results showed that the grafting time is the major variable for controlling the retention of analytes. The columns showed a dual retention mechanism, including RP and HILIC. The stability and the long lifetime of the columns were obvious. The hypercrosslinked monolith was also used for the separation of polar phenolic acids in the HILIC, 1- and 2D LC. The preparation of a hypercrosslinked monolith under nucleophilic substitution was developed for RPC and HILIC using capillary LC [18]. The poly(styrene-_co_-vinylbenzyl chloride-_co_-divinylbenzene) monolith was prepared by in situ radical polymerization. The diaminoalkanes (e.g., 1,2-diaminoethane, 1,4-diaminobutane, 1,6-diaminohexane and 1,8-diaminooctane) were used in the hypercrosslinking process of the monolith through the nucleophilic substitution reaction. The column efficiency was improved when using the longest diaminoalkane with 1,8-diaminooctane. In order to improve the monolith for use in HILIC mode, the hypercrosslinked monolith was further modified with 2-aminoethanesulfonic acid. This proposed monolith provided dual-retention mechanism involving RPC and HILIC. The separation of low-molecular phenolic acids in both RPC and HILIC modes was demonstrated and the hypercrosslinking led to a significant separation performance of alkylbenzenes in RPC. Figure 1: The structure of 4,4’-bis(chloromethyl)-1,1’-bibhenyl, \(\alpha\)-\(\alpha\)’-dichloro-p-xylene and formaldehyde dimethyl acetal [16].
## 4 Results and Discussion
### Statistical Features
), which is useful to evaluate the degree of relationship between the isotopic specific activities and the samples' geolocation. Results of the PCA allow to group the sampling sites in four clusters that seem to be comparable to each other (Figure 3) in terms of activity concentration of \({}^{226}\)Ra, \({}^{232}\)Th and \({}^{40}\)K. Specifically, sites ID1 and ID2 show positive correlations with \({}^{40}\)K-specific activity but negative linear relationships with \({}^{226}\)Ra and \({}^{232}\)Th ones, whereas sites IDs 3, 4 and 5 are negatively correlated to the activity concentrations of \({}^{226}\)Ra, \({}^{232}\)Th and \({}^{40}\)K. The sites ID6 and ID7 show instead a positive correlation with values of \({}^{226}\)Ra- and \({}^{232}\)Th-specific activities, and exhibit a negative correlation with \({}^{40}\)K. Finally, sites ID8 and ID9 show positive correlations of the three investigated radionuclides' activity concentrations. We believe that the differences of \({}^{226}\)Ra-, \({}^{232}\)Th- and \({}^{40}\)K-specific activities among the identified clusters mainly depend on the compositional/mineralogical features of rocks from which the analyzed river sediments derive. The sediments collected along the rivers located in the Tyrrhenian side (ID1 and ID2) record the chemical fingerprint of the granitoid rocks from the Serre Massif. On the contrary, the Ionian river sediments (ID6, ID7, ID8 and ID9) reflect the chemical and mineralogical composition of the outcropping Late Cretaceous and Cenozoic terrigeous sediments. Moreover, the ID6 and ID7 rivers are characterized by a component provided by the erosion of the Aspromonte Unit, whereas clasts from the upper crustal complexes (Stilo-Pazzano and Mammola) and from granitoids supply the ID8 and ID9 rivers. Regarding the rivers on the western and southern margin of the Aspromonte Massif (ID3, ID4 and ID5), the main lithologies occurring within the drainage basins come from the Aspromonte and the Stilo Units.
## 3 Datasets and Evaluation Metrics
### PIQUE  
The Perception-based Image Quality Evaluator is another no-reference metric. Its evaluation is based on detecting perceptible distortions (such as noise or blocking) in image blocks using heuristics and thresholds, followed by aggregating the distortion scores from the identified blocks. The calculation is algorithmic and rule-based, lacking a single equation that summarizes it [99]. Lower PIQUE values indicate better visual quality.
###### Abstract  
In this paper, we propose an active robotic 3D reconstruction methodology for achieving full object 3D reconstruction. Existing robotic 3D reconstruction approaches often struggle to cover the entire view space of the object or reconstruct occluded regions, such as the bottom or back side. To address these limitations, we introduce a two-stage robotic active 3D reconstruction pipeline, named See-Then-Grasp (STG), that employs a robot manipulator for direct interaction with the object. The manipulator moves toward the points with the highest uncertainty, ensuring efficient data acquisition and rapid reconstruction. Our method expands the view space of the object to include the entire perspective, including occluded areas, making the previous fixed view candidate approach time-consuming for identifying uncertain regions. To overcome this, we propose a gradient-based next best view pose optimization method that efficiently identifies uncertain regions, enabling faster and more effective reconstruction. Our method optimizes the camera pose based on an uncertainty function, allowing it to identify the most uncertain regions in a short time. Through experiments with synthetic objects, we demonstrate that our approach effectively addresses the next best view selection problem, achieving significant improvements in computational efficiency while maintaining high-quality 3D reconstruction. Furthermore, we validate our method on a real robot, showing that it enables full 3D reconstruction of real-world objects.  
active 3D reconstruction; object full 3D object reconstruction; robot vision Article
## 3 Results
### Experimental Validation  
The applicability of the method presented herein, summarized in Figure 1, has been demonstrated before [21]. The results are briefly outlined here using the drugs fluoxetine and AICAR. AICAR and fluoxetine were previously reported to reduce Nile red fluorescence in _C. elegans_[17], and were therefore selected as positive controls for the validation of our image processing method. The two agents were tested in three independent experiments and the images were evaluated using the presented workflow. The mean fluorescence of three experiments is shown in Figure 7B. Fluoxetine significantly reduced fluorescence to 58.0% (\(\pm\)5.9) at 100 \(\upmu\)M and 75.6% (\(\pm\)4.1) at 10 \(\upmu\)M, while AICAR significantly reduced fluorescence to 42.9% (\(\pm\)12.4) at 250 \(\upmu\)M and 50.6% (\(\pm\)11.9) at 100 \(\upmu\)M, compared to vehicle-treated worms. Biochemical TAG quantification showed a similar reduction of TAG with a TAG/protein ratio of 47.8% after treatment with 100 \(\upmu\)M fluoxetine and 90.5% with 100 \(\upmu\)M AICAR (Figure 7C).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ETS** & **Sensitivity** & **Specificity** & **ACC** & **MCC** & **Precision** \\ \hline
**ETS1** & 1.0000 & 0.9980 & 0.9980 & 0.8592 & 0.7627 \\ \hline
**ETS2** & 0.9999 & 0.9977 & 0.9977 & 0.7952 & 0.6817 \\ \hline
**ETS3** & 1.0000 & 0.9993 & 0.9993 & 0.8453 & 0.7602 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean performance of machine learning classification combined with binarization obtained for external test sets ETS1-3.  
Figure 6: Mean performance metrics of segmentation on three different external test sets. Bars represent the mean value of TPR, TNR, ACC, MCC, PPV, and F1 of ETS1-3 \(\pm\) SD.