esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Sample Homogenization--Dry, Wet and Cryogenic Grinding
#### 2.1.2 Wet Grinding
A subsample (25 g) of corn or wheat flour was taken and mixed with water (sample to water mass ratio, 1/1) and 2 g of the resulting slurry was taken for extraction and LC-MS/MS analysis. Using this procedure, the impact of potential homogeneity concerns is minimized. Furthermore, this approach builds on the selection of a subsample size (e.g., 25-50 g) previously shown to be representative of the initial sample, an approach that would allow scaling to small analytical test portions. An additional benefit of small test portions is the significant reduction in solvents used and waste generated, making sample analysis more cost-effective.
###### Abstract  
As a traditional Chinese drink, tea is favored for its rich flavor and its medicinal functionality. In this study, the in vitro bioactivities of Wushanshencha (WST; a local tea from Chongqing, China), which is processed mainly from the leaves of the wild _Malus hupehensis_ (Pamp.) [PERSON]). We assessed the scavenging capacity of tea extracts on 1, 1-diphenyl-2-picrylhydrazyl (DPPH); 2, 2\({}^{\prime}\)-azino-bis (3-ethylbenzthiazol-6- sulphonic acid) diammonium salt (ABTS); and hydroxyl (OH) free radicals, and demonstrate the high antioxidant activity and dose-dependent relationship of these extracts. We also detail the anti-mutagenic effect of these tea extracts against the _Salmonella typhimurium_ TA98 strain induced by the 2, 7-diaminofluorene (2, 7-AF) mutagen and the TA100 strain induced by the N-methyl-N\({}^{\prime}\)-nitro- N- nitrosoguanidine (MNNG) mutagen at concentrations of 1.25 and 2.50 mg/plate, respectively, with the high-dose groups showing better results. We investigated the anticancer mechanisms of WST extracts (40, 100, and 160 mg/mL) in HepG2 human hepatoma cells via 3-(4, 5-dimethyl-2-thiazol)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) assay and quantitative real-time reverse transcription-polymerase chain reaction (RT-qPCR). The results showed that the proliferation of HepG2 cells was significantly inhibited in a dose-dependent manner by the tea extracts. Moreover, apoptosis in HepG2 cells was induced via upregulation of Caspase-3, Caspase-7, Caspase-8, Caspase-9, p21, p53, and Bax as well as downregulation of Bcl-2 apoptosis-associated factors, as assessed via mRNA expression levels after treating with WST extracts. The expression of inflammation-related factors, e.g., NF-kB, and Cox-2, was significantly downregulated by the WST extracts, demonstrating its inflammatory properties. Together, these observations indicated that WST extracts have anti-inflammatory and anti-cancer properties. In addition, high-performance liquid chromatography (HPLC) analysis showed that WST extracts contained chlorogenic acid, 4-hydroxycinnamic acid, isoquercitrin, taxifolin, quercetin, rosmarinic acid, myricetin, baicalin, neosperidin dihydrohalcone, and quercetin. As such, WST appears to be an effectively functional drink, due to its rich functional components and anti-cancer activity.  
Keywords:Wushanshencha; anti-oxidant; anti-mutagenic; anti-cancer; high-performance liquid chromatography
# Pitting Corrosion in AISI 304 Rolled Stainless Steel Welding at Different Deformation Levels
## 1 Introduction
Alike, when materials obtained by cold rolling are welded, the stress state changes due to the effect of heat and the apparition of new stresses during the cooling of the weld, especially if deformation is impeded. These welding stresses depend on the energy applied and the heat transmission conditions during the welding process [17]. Cold rolling and welding are part of various manufacturing processes. So, the combined effect of both in the stresses increment, and its influence in corrosion, can be present in many manufactured products. The effect of the residual stresses generated during products manufacturing of stainless steel on the stress corrosion cracking susceptibility is presented by [18]. Other works listed below focused on the pitting corrosion, the factors that cause it and their effects: In [19] a summary of the effects of possible factors in the pitting corrosion of metals is given; in [20] cold working was discussed as one factor on the mechanism and rate of pitting corrosion; in [21] the pitting generation caused by applied stress has been studied; in [22] an overview of the critical factors influencing the pitting corrosion of metals is provided; in [23] a method of evaluating strength reduction due to the pitting corrosion is established; in [24] a quantitative analysis of the effect of Type I residual stresses on the occurrence of pitting and stress corrosion is investigated; in [25] the effects of the micro-plasma arc welding technique on the pitting corrosion of different zones of an AISI 316L stainless steel were studied; in [12] the effect of low intensity X-rays ionizing radiation on TIG-welds of AISI 304 stainless steel using AISI 316L as filler rods is analysed. In this work, a batch of welded samples of AISI 304 stainless steel were subjected to different levels of cold work to change the shape of grains, create residual stresses and damage the surface. The influence of those changes in the pitting corrosion resistance of the welds was evaluated by the immersion of the samples in a FeCl\({}_{3}\) solution according to a standardized test and quantified by the pits number [8; 26; 27; 28; 29]; while deformation levels were measured as the thickness reduction of the metal samples (cold work level). A mathematical relation between the cold work level and the number of pits is proposed for the tested samples.
## 3 Proposed Framework
### Localization with Combined Dictionary  
In our earlier work, we introduced an MJPF approach for localizing an AV using static LiDAR-based landmarks. Specifically, the results presented an in-depth analysis where individual track dictionaries (e.g., buildings, poles, trees) were used to estimate ego-vehicle trajectories. It was demonstrated that each track, treated separately, could yield highly accurate localization results if treated as a stable landmark.  
However, real-world driving scenarios are far more complex. Multiple static tracks can appear simultaneously, and certain time intervals might exist with no observations. This motivates the need to combine the information contained in the different dictionaries  
Figure 6: DBN model during the testing phase with separate track predictions.  
(one dictionary per track) into a unified framework. By doing so, we can better handle the following situations:  
1. Simultaneous Track Interactions: When multiple static tracks are observed at the same instant, combining dictionaries can refine ego-vehicle position estimates by leveraging cross-track constraints.
2. Extended No Observation Periods: In some time intervals, no new LiDAR observations of static tracks may be available (e.g., occlusion or sensor dropout). Incorporating combined dictionaries can help maintain stable localization by relying on prior learned interactions across multiple tracks.
3. Isolated Track Interactions: Even when only one track is observed, the combined dictionary framework can cross-validate with other track dictionaries (now unused) to check for consistency, thereby reducing ambiguity.
## 2 Research Background
### Power Production from Biomass  
Regarding power production from biomass, most of the experimental facilities integrate an internal combustion engine (ICE) into the gasifier, coupled to a power generator. Accordingly, specific arrangements are necessary to make ICEs work with syngas, which has been a critical point as they usually operate with gasoline, diesel, and natural gas as fuels. Most of the engines available in the market are designed to operate with gasoline. Consequently, it is difficult to find a high efficiency engine that properly works with syngas. However, if the proper changes are made, an ICE can properly work with syngas; although, the obtained power is slightly lower [31]. There are examples in the technical bibliography of experimental adaptations of ICEs to be fed with syngas [32], such as the prototype designed by General Electric under the name \"Jenbacher Gas Engine\" [33]. Lastly, advanced numerical techniques have shown interesting results in dual-fuel engines powered with syngas; although, there is still an open issue related to the storage and transportation of the fuel that needs to be overcome [34].
## 3 Results  
Preliminary results suggest that blockchain integration can be achieved without sacrificing performance, while markedly improving transparency. WEave's extended framework was tested via simulation across multiple product domains, showing acceptable performance overhead (in transaction latency and resource usage). In other words, adding circular lifecycle tracking did not bottleneck the system, and audit queries (e.g., retrieving a part's full history) remained efficient. Similarly, [PERSON] et al. [38] report that their blockchain-based Triple Retry circularity framework introduced only minimal delays in exchange for significantly stronger control of reverse supply chain activities. Some studies, including ours, have started defining new metrics to quantitatively evaluate such systems. For instance, WEave's extension proposes metrics for \"lifecycle traceability\" and \"reuse auditability\"--measuring how completely and easily a product's journey (including second-life stages) can be reconstructed on-chain. These metrics go beyond traditional supply chain KPIs, reflecting circular economy priorities like the percentage of recycled content traced or the turnaround time for verifying a returned item's provenance. While not all publications report numerical benchmarks, the qualitative improvements are consistently highlighted. [PERSON] and [PERSON] [39], in a broad review, conclude that a Circular Economy Blockchain (CEB) architecture can enable new circular business models by boosting data transparency and stakeholder trust, which ultimately translates to better performance on sustainability metrics. They note that blockchain's unique features (immutability, decentralization, tokenization) allow companies to monitor circular KPIs in real-time and automate compliance with sustainability standards. Likewise, [PERSON] et al. [40] demonstrate through use cases how blockchain can serve as an immutable tracking service, a smart contract enforcement tool, a marketplace platform, and an incentive mechanism (via tokens) within reverse logistics networks. By evaluating transportation and collection processes under different blockchain configurations, they show improvements in data reliability (no loss or alteration of records), security in multi-party coordination, and even user engagement (thanks to reward tokens). In summary, the literature to date indicates that blockchain-enabled circular systems can be scalable and effective, providing measurable gains in supply chain visibility and accountability that were previously unattainable with linear supply chain IT systems.  
Next, we summarize the qualitative capabilities achieved by the extended WEave framework and then detail the quantitative results from our simulations. Sections 3.1 and 3.2 correspond to the two major experiment tracks: (1) the extended apparel supply chain with circular flows, and (2) the generalized cross-industry (furniture) scenario.
applied sciences  
Chapter 1 Cluster Thinning Improves Aroma Complexity of White Marastina (_Vitis vinifera_ L.) Wines Compared to Defoliation under Mediterranean Climate  
[1]  
[2]  
[3]  
[4]  
[5]  
[6]  
[7]  
[8]  
[9]  
[10]  
[11]  
[12]  
[13]  
[14]  
[15]  
[16]  
[17]  
[18]  
[19]  
[20]  
[21  
was reported. A low crop level affected geranyl acetate, but not C\({}_{13}\) norisoprenoids, C\({}_{6}\) alcohols, and higher alcohols. On the contrary, the concentration of some esters increased with a higher crop level (20 buds per vine) [9].  
Marasitina is an old white grape variety (_Vitis vinifera_ L.) mostly cultivated in the coastal regions of Croatia (Dalmatia). It produces high-quality wines with fruity flavors and moderate acidity. Marastina is used for dessert-type premium wines called Prosek in Croatia. Bunch rot and powdery mildew can be a problem due to relatively compact clusters and thin berry skin. Marastina is known in Greece and Italy as Pavlos and Malvasia Bianca Lunga (Malvasia del Chianti), respectively [10]. While many studies indicate that canopy-management techniques can result in improved fruit and wine quality, too little fruit or too many leaves can turn the vine toward overcropping and eventually lead to unbalanced wines. There is no information of the impact of canopy techniques on the aroma complexity of Marastina wines. Even more, results on other white varieties grown on dry and shallow karstic soils are scarce, and only a few papers [3,8,9] reported variations in individual wine aroma classes after application of defoliation or cluster-thinning techniques. The effects of three defoliation times (preflowering, after berry set, and at veraison) and cluster thinning at veraison on the aroma profile of white wines have not been compared under the same vineyard conditions previously. The controversial results of the effect of viticulture techniques among other varieties under different climate conditions are not applicable to the improvement of the aroma potential of neutral variety Marastina under Mediterranean conditions.  
Here, we fill this information gap and compared the effects of five different canopy techniques: defoliation at preflowering (PF), after berry set (BS), or at veraison (V); cluster thinning at veraison (CT); and an untreated control (C) on the agronomic qualities, cluster architecture, and the physiochemical and aroma composition of cv. Marastina wines. This will provide practical information to optimize cultivation and targeted manipulation of aroma compounds to improve sensory attributes of Marastina wines.
## 4 Results
### Experimental Setup  
The region of interests cover a space of 1000 m \(\times\) 1000 m \(\times\) 1000 m. We assume that the anchors are randomly deployed at the bottom and the length of tethering wires are also randomly generated, while the sink nodes are stationary at (333, 333, 1000) m and (666, 666, 1000) m. We consider UWSNs with different sizes of 10 and 100 sensor nodes, respectively. The sensors use Orthogonal Frequency-division Multiplexing (OFDM) modulation which allows simultaneous transmission from several users.  
The simulation parameters are shown in Table 1. Each sensor node has a communication range of 300 m with initial energy of 100 J. The packets are set to the length of 1000 bit with the \(TTL\) of 10 time slots. Sensor nodes move passively at a maximum speed of 100 m per time slot. The coefficient of energy consumption \(e_{s}\) and \(e_{r}\) are set to \(5\times 10^{-8}\) J/bit and \(10^{-8}\) J/(bit \(\cdot\) m\({}^{2}\)), respectively. The decaying factor of Vol, i.e., \(\alpha\), is set as 0.5, while the learning discount factor \(\gamma\) is set as 1, which speeds up the learning rate. All simulation results are acquired with runs of 100 times.
# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Synthesis of the Results  
This review consists of the detail investigation of 36 studies, represented in two clusters (one cluster contains both direct and indirect conventional monitoring-based studies) which were effective in detecting scour damage either by processing vehicle signals, signals directly gathered from the bridge, or estimating scour depth using machine learning algorithms. The synthesis of their results was provided below.
## 2 Theory  
Cluster algorithm is the process of grouping items with similar features together. The traditional k-means clustering algorithm generally uses Euclidean distance as a metric to measure the similarity between objects. Similarity is inversely proportional to the Euclidean distance between data. The greater the similarity between data, the smaller the distance. This algorithm requires specifying the initial number of clusters and initial cluster centers in advance, continuously updating the positions of cluster centers based on the similarity between data objects and cluster centers. Despite its simplicity and low time complexity, the k-means clustering algorithm randomly selects initial cluster centers, making the hierarchical results relatively sensitive to the choice of initial cluster centers and lacking determinism. This issue is addressed by the improved k-means methods based on density and maximum distance classification [21].  
The improved k-means method based on maximum distance and maximum density first calculates the distance and average distance between any two data points in the dataset, as well as the density and average density between any two data points. It sets an empty dataset T and F, adds all data objects with density greater than the average density to dataset T, selects the data object with the maximum density in T as the first cluster center, and adds these data to the empty dataset F. Subsequently, it calculates the data in dataset T that is farthest from dataset F and adds it to dataset F. This process is repeated until dataset F contains K data points, which are considered the final cluster centers. Finally, using the obtained K cluster centers, the Euclidean distance is calculated for each data point, assigning the data point to the nearest cluster center, forming K clusters. For each cluster, the average value of all data points is calculated to obtain new cluster centers, replacing  
Figure 1: Ultra-short baseline positioning system.  
of maximum distance and maximum density, achieved through the computation of metrics such as distance, density, and average distance among data objects, providing a stable approach for selecting initial cluster centers. Specifically, we initiate the process by calculating the distance and average distance between any two data points, as well as the density and average density. Subsequently, by setting a threshold, data objects with density exceeding the average are added to a temporary dataset (T). Within T, the data object with the maximum density is chosen as the first cluster center. Following this, we iteratively select additional cluster centers based on the proximity of distances until achieving K final cluster centers. Throughout the assignment of data points and the update of cluster centers, we employ methods such as Euclidean distance to form the final K clusters. The key advantage of this improved method lies in its ability to consistently select initial cluster centers, thereby enhancing the accuracy of clustering.
## 3 Materials and Methods
### XO Fluorescence Quenching  
The fluorescence spectra of XO and the effects of samples on XO were performed using a Hitachi F-7000 fluorometer (Tokyo, Japan). The fluorescence quenching effect was evaluated to determine the binding constants between compounds and XO based on the procedures reported by previous literatures with some modifications [34]. 2.0 mL of XO solution (1.0 umol/L in phosphate buffered saline, pH 7.4) was poured into a fluorescence quartz cuvette and the fluorescence emission spectra was recorded as blank from 300 nm to 450 nm under an excitation wavelength at 280 nm. Then, 10 mL of sample (0.1 mmol/L in dimethyl sulfoxide (DMSO) for gardenin B and 1.0 mmol/L in DMSO for eupatorin) was added dropwise into the cuvette containing XO solution. The fluorescence emission spectra under the same condition and the fluorescence intensities at 337 nm were recorded. Each test was performed in triplicate. The fluorescence quenching of XO with gardenin B and eupatorin was described using the following Stern-Volmer equation [35]:  
\[\mathrm{F_{0}}/\mathrm{F}=1+K_{\mathrm{q}}\tau_{0}[\mathrm{Q}]=1+K_{\mathrm{sv }}[\mathrm{Q}] \tag{2}\]  
where F\({}_{0}\) is the fluorescence intensity of XO at 337 nm, F is the fluorescence intensity at 337 nm in the presence of the sample, [Q] is the corresponding sample concentration, \(K_{\mathrm{q}}\) and \(K_{\mathrm{sv}}\) are the quenching constants, and \(\tau_{0}\) is the average lifetime (6.2 ns).  
The relationship between fluorescence quenching intensity at 337 nm and sample concentration was described by the binding constant formula:  
\[\mathrm{Log_{10}(F_{0}-F)/log_{10}}K_{\mathrm{a}}+n\mathrm{log_{10}[Q]} \tag{3}\]  
where F\({}_{0}\) is the fluorescence intensity of XO at 337 nm, F is the fluorescence intensity at 337 nm in the presence of the sample, [Q] is the corresponding sample concentration, \(K_{\mathrm{a}}\) is the binding constant, and \(n\) is the number of binding sites per XO molecule. All the samples showed no emission spectra in the scanned range under excitation.
## 2 Materials and Methods
To evaluate the limit of detection (LoD; i.e., the lowest copy number reliably detected of the _Xf_ genome) of the cLAMP assay for the amplification of the _Xf_ target DNA in almond crude extracts, 6-days-grown bacterial cells of _Xfm_ strain ESVL ST6 on PD3 agar plates [27; 28] were employed. The cell suspension was 10-fold serially diluted up to 10 CFU/mL (starting from an OD\({}_{600}\) value = 0.5, equivalent to 4 \(\times\) 10\({}^{8}\) CFU/mL; [29]) in healthy crude almond sap and then subjected to alkaline and neutralization treatments before the test, as specified above. NIC and NTC were also included. The formation of amplicon DNA was assessed by observing the color shift from pink (negative) to yellow (positive) after 30 min of incubation at 65 \({}^{\circ}\)C, due to the presence of phenol red, a pH indicator present in the master mix. The cLAMP assay was performed with a final volume of 25 \(\upmu\)L, including, as follows: 12.5 \(\upmu\)L Bst 2.0 WarmStart(r) DNA Polymerase 2x Master Mix (New England Biolabs, Ipswich, MA, USA); 2.5 \(\upmu\)L LAMP primer mix (0.2, 1.6 and 0.4 \(\upmu\)M of F3/B3, FIP/BIP and LF/LB primers, respectively); 1 \(\upmu\)L of neutralized and diluted crude DNA extract and double-distilled water to a final volume; and 25 \(\upmu\)L of mineral oil, added to each tube to cover the LAMP mix to avoid possible evaporation and contaminations. The consistency of the reaction for some tubes was also evaluated using 1.2% agarose gel electrophoresis run in Tris-acetate-EDTA (TAE 1x) buffer and stained with GelRed. The performance criteria, such as the diagnostic sensitivity (proportion of infected samples testing positive compared with the number of positive results from an alternative test, considered to be the gold standard) and specificity (calculated as the ratio of samples testing negative compared with the results from the alternative test) of the assay, were calculated according to Ref. [30]. A statistical comparison of correspondence between the results of positive and negative samples in both tests was performed by [PERSON]'s exact test on a contingency table. For all the tested samples (positive and negative) in cLAMP and qPCR, respectively, receiver operating characteristic curve (ROC) analyses were calculated by plotting the true positive rate (sensitivity) and the false positive rate (1-specificity) with a 95% confidence interval (CI). The area-under-the-curve (AUC) values were also calculated to evaluate the performance of both assays.