esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Starter Microflora Population  
The number of _Lactococcus_ spp. in the kefir samples ranged from 7.8 to 9.2 log(CFU/mL) (Table 1). The type of kefir starter culture and milk protein powder used had a statistically significant effect on the _Lactococcus_ spp. population (\(p\) = 0.001 in both cases). On the other hand, this population was not influenced by the final pH of kefirs (\(p\) = 0.3761). The number of lactobacillus in the kefir samples ranged from 6.8 to 8.2 log(CFU/mL) (Table 1). As observed for lactococci, the number of lactobacillus depended on the type of starter culture and milk powder used as an additive (\(p\) = 0.001 in both cases). The effect of the final pH of kefir samples on the number of these bacteria in kefir was also not found to be statistically significant (\(p\) = 0.3669). However, it is worth noting that the lactobacillus count was generally lower than the number of lactococcal cells. The presence of yeast was not observed at a dilution of \(10^{-3}\) in any of the samples analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kefir Samples with the Addition of Milk** & **pH** & \
\begin{tabular}{c} **Lactococci Population** \\ **(log(CFU/mL))** \\ \end{tabular} &
\begin{tabular}{c} **Lactobacillus Population** \\ **(log(CFU/mL))** \\ \end{tabular} \\ \hline \multicolumn{4}{c}{**Keffir A**} \\ \hline control & 4.4 \(\pm\) 0.2 \({}^{\mathrm{f}}\) & 8.8 \(\pm\) 0.0 \({}^{\mathrm{a,b}}\) & 7.8 \(\pm\) 0.0 \({}^{\mathrm{a,b}}\) \\ MPC-80 & 5.1 \(\pm\) 0.1 \({}^{\mathrm{a}}\) & 9.2 \(\pm\) 0.0 \({}^{\mathrm{a}}\) & 8.2 \(\pm\) 0.0 \({}^{\mathrm{a}}\) \\ WPC-70 & 5.1 \(\pm\) 0.1 \({}^{\mathrm{a}}\) & 8.2 \(\pm\) 0.1 \({}^{\mathrm{d}}\) & 7.2 \(\pm\) 0.1 \({}^{\mathrm{d}}\) \\ rennet casein & 5.1 \(\pm\) 0.1 \({}^{\mathrm{a}}\) & 8.9 \(\pm\) 0.1 \({}^{\mathrm{a}}\) & 7.9 \(\pm\) 0.1 \({}^{\mathrm{a}}\) \\ sweet whey powder & 4.7 \(\pm\) 0.2 \({}^{\mathrm{d}}\) & 8.8 \(\pm\) 0.1 \({}^{\mathrm{a,b}}\) & 7.8 \(\pm\) 0.1 \({}^{\mathrm{a,b}}\) \\ whey permeate powder & 4.7 \(\pm\) 0.2 \({}^{\mathrm{d,e}}\) & 8.8 \(\pm\) 0.1 \({}^{\mathrm{a,b}}\) & 7.8 \(\pm\) 0.1 \({}^{\mathrm{a,b}}\) \\ whey powder (demineralized) & 4.7 \(\pm\) 0.2 \({}^{\mathrm{e}}\) & 8.8 \(\pm\) 0.0 \({}^{\mathrm{a,b}}\) & 7.8 \(\pm\) 0.0 \({}^{\mathrm{a,b}}\) \\ whey powder (non-demineralized) & 4.6 \(\pm\) 0.2 \({}^{\mathrm{e}}\) & 8.6 \(\pm\) 0.1 \({}^{\mathrm{b}}\) & 7.6 \(\pm\) 0.1 \({}^{\mathrm{b}c}\) \\ buttermilk powder & 4.6 \(\pm\) 0.2 \({}^{\mathrm{e}}\) & 8.9 \(\pm\) 0.2 \({}^{\mathrm{a}}\) & 7.9 \(\pm\) 0.2 \({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of pH and lactic acid bacteria count in kefir samples (mean and standard deviation calculated from six repetitions).
# The Impact of GoLo Technology on the Quality Properties of Dealcoholised Wines
## 2 Materials and Methods  
### Sample Selection  
A total of 18 wine samples from the Spanish commercial market were collected, including 6 white (B), 6 red (T), and 6 rose (R) wines from various designations of origin (DOs). The varieties of red wines included Tempranillo (T1 and T5), Cabernet Sauvignon (T2), Garnacha (T3), Merlot (T4), and Monastrell (T6). The rose wines were made from Garnacha (R1-R3) and Merlot (R4-R6). The white wines included Macabeo (W1), Verdejo (W2 and W5), Chardonnay (W3 and W6), and Airen (W4). Additional information on the designations of origin and the % of the variety of the wines studied can be found in the Supplementary Materials. The vintage year of all wines was 2023. The non-dealcohlised wine samples were first analysed and then dealcohlised using GoLo technology. This process and subsequent analysis took place over a 9-month period from January 2024 to October 2024. The samples and dealcohlisation technology were provided by a company located in central Spain. The analyses were conducted at the laboratories of the University of Murcia and the University of Castilla-La Mancha, in collaboration with a private Spanish laboratory accredited by ISO 17025 [28], which specialises in wine analysis.
###### Abstract  
The standardization of grayscale display is essentially significant for image signal communication, transmission, and terminal reading. The key step of this standardization is establishing a traceable equipment of grayscale. As a relative value, grayscale is transferred to two different absolute values to satisfy different traceability methods, including optical density for hardcopy image and luminance for softcopy. For luminance, a generation equipment is designed to build the relationship between luminance and grayscale. In this work, novel equipment is established using digital light processing (DLP) by time-frequency modulation, and the corresponding uncertainty is analyzed. The experiment result shows that this digital equipment builds the relationship between grayscale and luminance in the range of 0.16-4000 cd/m\({}^{2}\). It enables traceable measurement of grayscale to luminance on this equipment with high accuracy and can provide a standardized reference for the display of grayscale images in the fields of medicine, remote sensing, non-destructive testing, etc.  
grayscale display; image standard; digital light processing (DLP) 2020 10 2297 10.3390/app10072297
## 3 Architecture
### Operational Phases
It is also important to note that the way in which the processor acts on the incoming messages is ruled by specific custom update policies, that can be defined at will during implementation following case-specific criteria. In Figure 6 is reported the sequence diagram of the standard fault handling and recovery mechanism: this procedure can be extended to comply with specific requirements, but the base structure must always be the same. Figure 4: Data messages handling in the case of simple forwarding. Figure 5: Data messages handling with intermediate processing. Figure 6: Fault handling and recovery mechanism. For this example, we introduce the connection operator: this component, often present in practical implementations, has the main task of monitoring the network connection, eventually trying to recover the connectivity in case of faults. It is important to point out that faults can be detected by any of the modules present in the sidecar object, that must notify the event to their specific Comm Manager in any case: for example, if a Publisher throws an error generated by a message sending failure, it must notify that to its particular comm manager, that reacts accordingly. Back to our example, as soon as the connection operator detects a fault it sends a control message to the comm manager, that begins the recovery operations: publishers are then notified, and in turn they begin the recovery handling sorting all the messages currently waiting to be sent to topic-specific recovery handlers (that should be instantiated only when needed). After that the recovery initialization has been completed, the module enters the recovery mode and all the future messages coming from processors flow in the specific recovery handlers until the fault is resolved. When connection operator restores the connectivity, it immediately notifies the comm manager that in turn propagates the notifications to all the publishers: at this point the recovery handlers pass back the messages to publishers, following topic-specific recovery policies that regulates the reintroduction of the messages in terms of time, number, or even content depending on the use case implementation. After the recovery handlers have emptied, the module exits from recovery mode and message publishing operations returns to normal, eventually having in queue messages coming from the recovery handling process. It is worth noting that faults may also occur during the startup phase: in this case the mechanisms seen in this section do not apply, an specific procedures are executed depending on the use case (e.g., try to reinitialize the entire sidecar object).
## 3 Results
### Homogeneity Maps Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Cluster** & **Hemisphere** &
\begin{tabular}{l} **Coordinates** \\ **(X, Y, Z)** \\ \end{tabular} & **Z Values** & \(p\) **Values** & **Nr. Voxels** & **BA** \\ \hline Global Map: Blobs \(\geq\) 234 voxels \(Z\)\(\geq\) 2.576; peaks \(Z\)\(\geq\) 3.142. & & & & & & \\ Anterior Prefrontal cortex & L & 0, 52, 13 & 3.996 & 0.000 & 234 & 10 \\ Anterior Prefrontal cortex & R & \(-\)4, 28, \(-\)10 & 3.142 & 0.000 & 305 & 11 \\ Global Map: Blobs \(\geq\) 3 voxels \(Z\)\(\leq\)\(-\)2.577; & & & & & & \\ Peaks \(Z\)\(\leq\)\(-\)2.711 & & & & & & \\ Posterior insula/clastrum & L & \(-\)34, \(-\)7, 10 & \(-\)3.442 & 0.000 & 176 & 13 \\ Inferior frontal gyrus (Broca’s Area) & R & \(43,\) 10, 11 & \(-\)3.292 & 0.000 & 53 & 44 \\ Inferior frontal gyrus, pars triangularis & L & \(-\)51, 34, 9 & \(-\)2.732 & 0.003 & 16 & 45 \\ Dorsal anterior cingulate cortex & R & \(6,\) 0, 37 & \(-\)2.856 & 0.002 & 13 & 24 \\ Anterior prefrontal cortex & R & \(19,\) 57, \(-\)6 & \(-\)3.078 & 0.001 & 12 & 10 \\ Task Map: Blobs \(\geq\) 215 voxels \(Z\)\(\geq\) 2.577; peaks \(Z\)\(\geq\) 3.611. & & & & & \\ Anterior prefrontal cortex/Ventromedial Prefrontal cortex & L & \(-\)2, 52, 17 & 3.611 & 0.000 & 215 & 10 \\ Resting-State Map: Blobs \(\geq\) 45 voxels \(Z\)\(\leq\)\(-\)2.576; & & & & & & \\ peaks \(Z\)\(\leq\)\(-\)3.417 & & & & & & \\ Insula & R & \(47,\) 6, 0 & \(-\)5.504 & 0.000 & 1678 & 13 \\ Insula & L & \(-\)36, \(-\)7, 8 & \(-\)4.245 & 0.000 & 404 & 13 \\ Temporal pole & L & \(-\)41, 11, \(-\)12 & \(-\)3.560 & 0.000 & 109 & 38 \\ Anterior Prefrontal cortex & R & \(19,\) 57, 0 & \(-\)3.417 & 0.000 & 45 & 10 \\ PED Map: Blobs \(\geq\) 1 voxel \(Z\)\(\geq\) 2.578; peaks \(Z\)\(\geq\) 2.599. & & & & & \\ Anterior Prefrontal cortex & L & \(-\)2, 52, 13 & 3.253 & 0.000 & 122 & 10 \\ Orbitofrontal cortex & L & \(-\)6, 27, \(-\)12 & 2.909 & 0.001 & 39 & 11 \\ Anterior insula & L & \(-\)40, 12, 1 & 2.864 & 0.002 & 22 & 13 \\ Anterior Prefrontal cortex & L & \(-\)2, 55, 22 & 2.643 & 0.004 & 2 & 10 \\ Ventromedial Prefrontal cortex & L & \(-\)8, 20, \(-\)9 & 2.599 & 0.004 & 2 & 25 \\ PE Map: Blobs \(\geq\) 54 voxels \(Z\)\(\leq\) 2.579; peaks \(Z\)\(\leq\)\(-\)3.279 & & & & & & \\ Posterior cingulate cortex/retrospplenial cortex & R & \(0,\)\(-\)38, 36 & 3.279 & 0.000 & 54 & 23 \\ PE Map: Blobs \(\geq\) 256 voxels \(Z\)\(\leq\)\(-\)2.576; peaks \(Z\)\(\leq\)\(-\)3.718 & & & & & & \\ Anterior insula & R & \(45,\) 8, \(-\)6 & \(-\)5.102 & 0.000 & 849 & 13 \\ Posterior insula & L & \(-\)40, \(-\)6, 6 & \(-\)4.685 & 0.000 & 583 & 13 \\ Orbital part of inferior frontal gyrus & L & \(-\)39, 13, \(-\)9 & \(-\)4.520 & 0.000 & 453 & 47 \\ Orbital part of inferior frontal gyrus & L & \(-\)49, 33, 2 & \(-\)3.528 & 0.000 & 195 & 47 \\ \hline \hline \end{tabular} Abbreviation: BA = Brodmann Area; R = right; L = left.  
\end{table} 
Table 4: Talairach coordinates Z and P values of the clusters as calculated for each map.
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 1 Introduction
Finally, the modeling of CNT-based nanocomposites, like polymers enriched by CNTs, cannot but include the hosting polymer insulating properties. Even though there are, in the literature, very detailed and exhaustive reviews on the techniques for performing one or more steps of the multiscale modeling of carbon nanotubes, especially at the single CNT level [13; 14; 15], there is a lack of reviews specifically addressing the modeling of CNTs and CNT networks by means of equivalent circuits. This paper aims to support electrical engineers in identifying the model which is best suited to describe the properties of interest of a CNT-based conductive layer, by describing the main features of the equivalent electrical circuits available in the literature. In the following section, the different steps for the modeling of a carbon nanotube are described by increasing complexity. In Section 3, some techniques for the modeling of CNT networks are reviewed in order to propose a clear picture of the state of the art of CNT-based materials and devices simulation by means of equivalent electrical circuits.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 3 Proposed Protocol (AOMDV-ER)
### Coordinated Recoiled Node Algorithm  
In routing, usually a node floods the packet to its neighborhood and this process continues till the packet reaches its destination. However, in the proposed approach, instead of all nodes transmitting the packet, only a few eligible nodes participate in transmission. These specific nodes are called recoiled nodes and the selection of recoiled nodes is made as per the algorithm based on node's location illustrated below.  
```
begin  sdloc\(\leftarrow\)coordinates of SD line  nodeloc\(\leftarrow\)coordinates of node eligible for transmission  errpt\(\leftarrow\)error point if((nodeloc + errpt = sdloc) | | (nodeloc - errpt = sdloc))then  transmit_pkt()  else  recoil = randomTime (ms)  endif end
```  
**Algorithm 2** Coordinated Recoiled Node  
As the algorithm 2 expresses, only very few of them are eligible to further transmit the packet and the remaining retain the packet with them as transmission requires more power than sensing the network. Figure 4, shows the same strategy: node S transmits the packet to node A, B, C and node D. Further, out of these four nodes, only two eligible nodes (node B and C) can transmit the packet to their neighbors and the remaining nodes (node A and E) wait for their eligibility called recoil nodes. The eligibility of a node is based on the eligibility function of variable location from the SD line.  
The nodes which are on the SD line transmit and those which are away from the SD line will be assigned variable recoil time. Figure 5a, shows recoil time (waiting time) to node1, 2 and 3 as per the location, i.e., node 1 < node 2 < node 3. So, lower valued recoil time nodes turn out to be part of primary routes. However, if any point of time transmission is failed (due to any reason like packet loss, link error) then retransmission by the recoiled becomes inevitable. The prediction of link error is address in subsequent section.  
Figure 4: Recoil technique with recoiled nodes.
## 5 Study 2: Resilience of Roof-Mounted Solar Panels to High Wind Loads
### Aerodynamic Testing of Photovoltaic Panels on a Gable-Roofed Building
Moreover, full-scale arrays may incorporate a trim or skirt at the foremost edge for aesthetic considerations; however, it is imperative to note that the trim's configuration significantly influences the airflow dynamics around the panel. To emulate the trim's form, a contoured strip was formed from corrugated plastic sheeting and affixed to the leading edge of the panels utilizing adhesive and tape (Case 4 as shown in Figure 9d). Given the current study's objective to assess the influence of roof-mounted solar panels on wind loads exerted upon the supporting roof structure, ASCE 7-16 and 7-22 offer comprehensive guidance for wind load computations pertaining to both the unadorned roofs and those with solar installations. Utilizing the stipulations outlined in Section 30.3 of ASCE 7-16 and 7-22, one can derive the design wind pressure for the plain roof. Section 29.4.4 of ASCE 7-16 and 7-22 can be employed to evaluate the pressures acting upon the PV panels, given their parallel orientation to the roof surface and compliance with all other requisites specified in the section [73; 74]. Analogous to the components and cladding methodology, the computed pressures on the PV panels were contingent upon the zoned external pressure coefficients. In the present investigation, the layout of the solar panels consistently fell within Zone 1, as per both editions of ASCE 7.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 7 Challenges and Opportunities
### Opportunities
#### 7.2.3 Extension to Emerging Scenarios and Applications  
The third research opportunity is the extension to emerging scenarios and applications, particularly those rarely considered in existing studies. For example, the space-air-ground integrated network (SAGIN) is a promising scenario driven by large-scale LEO constellations and software-defined satellites. In SAGINs, a heterogeneous architecture with both terrestrial and non-terrestrial networks requires a higher intelligent routing protocol to find an optimal path in terms of delay and energy cost, when the UAV and satellite nodes are energy sensitive [141]. Another example is Metaverse [142], which is based on real-time, multi-media, and remote cloud services and has not been considered in the literature with GNN-based routing optimization.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Evacuation Coordination in Smart Building
#### 3.2.2 Situation Awareness
Afterwards, the situation information is ready to be consumed by semantic inference engines. We used OWL ontologies to represent information semantically in our system (user preferences, building topology, emergency knowledge, building situation). Semantic representations provide the means to easily obtain inferred knowledge. For example, if we define a class _DisabledPerson_ to represent people with at least one disability, then we can infer disabled people even though they have not been explicitly described as instances of that class. For more complex reasoning tasks, we use rules on top of our OWL ontologies, which typically add new inferred knowledge. In particular, we use rules to determine the accessibility of certain sections in the building, and to select possible evacuation routes.
###### Abstract  
Graphene and graphene oxide have shown good antibacterial activity against different bacterial species due to their unique physicochemical properties. Graphene oxide (GO) has been widely used to load metallic and metal oxide nanoparticles (NPs) to minimize their surface energy during processing and preparation, hence reducing their aggregation. In this work, GO was effectively synthesized and coated with different concentrations of zinc hydroxide Zn (OH)x using the precipitation method to prepare a GO/Zn (OH)x hybrid composite. The Zn (OH)x NPs and GO/Zn (OH)x nanocomposites were synthesized and characterized using various methods such as scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Coating GO with Zn (OH)x NPs resulted in improved aggregation of Zn (OH)x NPs as well as enhanced antibacterial activity of GO against Gram-positive and Gram-negative bacteria. Additionally, the effect of Zn (OH)x coating on the antibacterial properties of the GO/Zn (OH)x composite was systematically investigated. The synergistic effects of GO and Zn (OH)x NPs resulted in enhanced antibacterial properties of the composites compared to the pristine GO material. In addition, increasing the Zn (OH)x wt. % concentration led to an increased inhibition zone of the GO/Zn (H)x composite against Bacillus megaterium and E. coli bacteria.  
graphene oxide; zinc hydroxide; antibacterial activity; _E. coli_; nanoparticles
## 6 Natural Compounds Inhibiting Protein Expression of Metabolic Enzymes
### Inhibition of Glucose Utilization and Lipogenesis through the PI3K/AKT/mTOR Signaling Pathway  
The AKT (also known as protein kinase B, PKB) pathway is an important signaling pathway regulating glucose utilization and lipogenesis. Phosphatidylinositol 3-kinase (PI3K) catalyzes the formation of the second messenger phosphatidyl-inositol-1,4,5-trisphosphate (PIP3) on the plasma membrane, while PIP3 binds to AKT and phosphoroside dependent kinase-1 (PDK1) to promote AKT activation through its phosphorylation at T308. Activated AKT regulates cellular function by phosphorylating downstream effectors, including various enzymes, kinases and transcription factors. Therefore, AKT has been considered as a bona fide target in cancer therapies [106]. As a mammalian target of rapamycin, mTOR plays a key role in metabolic regulation through responding to upstream signals from AKT, sensing cellular material and energy reserves, and mediating the balance of sugar and lipid metabolism. Over the past decade, several studies demonstrated that resveratrol exerted its anticancer activity through attenuating AKT and mTOR regulation [52; 107; 108]. AKT and mTOR phosphorylation are important signals for increased glucose uptake and glycolysis. [PERSON] et al. reported that resveratrol could block the phosphorylation of the two proteins, leading to reduced glucose uptake and lactate production, and eventually, the autophagocytosis of ovarian cancer cells [52]. Interestingly, another study indicated that resveratrol could interrupt intracellular GLUT1 trafficking to the plasma membrane,reduce glucose uptake and eventually induce apoptosis of ovarian cancer cells without altering GLUT1 expression at the mRNA or protein levels [50]. Additionally, resveratrol could also downregulate FASN and HER2 genes to synergistically induce apoptosis of breast cancer cells [47]. The anticancer activity of resveratrol could also take place through PI3K/AKT/mTOR signaling by upregulating PTEN and attenuating AKT activation [47]. Similarly, resveratrol could also inhibit PKM2 expression through dampening mTOR activity in various cancer cells [48]. As a phytochemical compound, genistein has been reported to exhibit a series of anticancer activities, including antagonizing estrogen receptor, blocking epidermal growth factor receptor, and inhibiting AKT [36]. Consistent with its negative regulation in AKT activation, genistein was shown to repress the activities of several lipogenesis-related enzymes activated by AKT, including FASN and SCD-1 [37; 109].  
In a recent study, xanthohumol, a natural product extracted from hop plant _Humulus lupulus_ L., was demonstrated to inhibit HK2 expression by antagonizing the PI3K/AKT-GSK3\(\beta\)-FBW7 signaling axis [54]. Xanthohumol could also inhibit glucose uptake of cells, and its inhibitory activity was remarkably higher than that of resveratrol and green tea polyphenols [110]. These studies implicated great prospects of xanthohumol in both basic research and therapeutic application.