esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
AHP analytic hierarchy process  
MSE mean squared error  
SFS sequential forward selection  
SBS sequential backward selection  
Symbol  
MSE  
\(N_{data}\) Number of data  
## Abbreviations  
\begin{tabular}{l l} \(y_{i}\) & Estimated weighting factor \\ \(\dot{y}_{i}\) & True weighting factor \\ Feature extraction \\ \(N\) & Number of jobs \((i=1,2,\ldots,N)\) \\ \(M\) & Number of machines \((k=1,2,\ldots,M)\) \\ \(w_{f}\) & Weight of objective function\(f\) \\ \(\rho_{i}\) & Weight of job \(i\) \\ \(p_{i,k}\) & Processing time at job \(i\) at machine \(k\) \\ \(S_{ij}\) & Setup cost when switching from job \(i\) to job \(j\) \\ \(C_{k}\) & Completion time of job \(i\) at machine \(k\) \\ \(L_{ik}\) & Delay in delivery of job \(i\) at machine \(k\) \\ \(\delta_{ijk}\) & 1 when job \(j\) is processed immediately after job \(i\) at machine \(k\), 0 \\  & otherwise \\ \(y_{ik}\) & 1 when job \(i\) is processed at machine \(k\), 0 otherwise \\ \(C_{k}\) & Maximum completion time of machine \(k\) \\ \(\overline{C}\) & Mean of completion time of each machine \\ \(D_{k}\) & Total delivery date of job processed of machine \(k\) \\ \(\overline{D}\) & Mean of the delivery date of job processed by each machine \\ \(\tau_{s,i}\) & Starting time order of job \(i\) in ascending order \\ \(\tau_{p,i}\) & Weight order of job \(i\) in ascending order \\ \(\tau_{p,i}\) & Processing time order of job \(i\) in ascending order \\ \(\tau_{d,i}\) & Delivery time order of job \(i\) in ascending order \\ \(\tau_{p/p,i}\) & \(\rho/p\) order of job \(i\) in ascending order \\ \(\tau_{p/d,i}\) & \(\rho/d\) order of job \(i\) in ascending order \\ \(\tau_{1}/pd,i\) & \(1/pd\) order of job \(i\) in ascending order \\ \(\tau_{p/pd,i}\) & \(\rho/pd\) order of job \(i\) in ascending order \\ \(\sigma_{c}^{2}\) & Variation of the completion time \\ \(r_{s}\) & [PERSON]'s rank correlation coefficients \\ \(\sigma_{d}^{2}\) & Variance of delivery time setting for each machine \\ \(U\) & Sum of completion time of each machine \\ \(\mu\) & Shortest processing time selectivity \\ Feature selection \\ \(z\) & Feature \\ \(S\) & Subset of the selected features \\ \(z^{+}\) & Feature that improves accuracy the most when adding features to subset \\ \(S^{+}\) & Subset of the features when feature \(z^{+}\) is added \\ E & Constant expressing the degree of tolerance \\ \(n_{s}\) & Number of selected features \\ Inverse optimization \\ \(w_{0}\) & Initial weighting factor \\ \(I\) & Maximum number of iterations \\ \(x_{\beta}^{true}\) & Solutions with known weighting factor \\ \(\alpha\) & Learning rate \\ \(B\) & Number of problem instances \\ \end{tabular}
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
###### Abstract
Hence, adequate HPP exposure reduces virulence-related functional genes, including _lhy_, _actA_, and invading surface proteins internalins A, B, G and H. However, HPP demonstrated increased expression of numerous DNA repair genes, transcription-associated genes, including RNA polymerase \(\alpha\) subunit, delta factor, and those encoding proteins involved in transcription termination/antitermination activities (e.g., _nusA_, _nusG_ and _rho_) as well as translation-apparatus-related genes, consisting of genes involved in the assembly, maturation and stabilization of ribosomal protein, tRNA and rRNA. Moreover, HPP assisted _L. monocytogenes_ in translation, with 46-58 ribosome and translation-associated genes significantly upregulated and merely 3-7 genes downregulated. The expression of two cold-shock protein genes, _cspB_ and _cspl_, was greatly increased, but certain heat-shock protein genes (_groEL_, _htrA_, _dnaK_, _clpC_ and _clpE_) were repressed and the expression of others (_clpQ_, _clpP_ and _clpX_) was only slightly increased under HPP stress. HPP also stimulated an increase in protein channel complexes, motility and chemotaxis, and lipid and cell wall biogenesis [11]. More recently, the gene expression profiling of _L. monocytogenes_ strains (barotolerant RO15 and barosensitive ScottA) by HPP (200 and 400 MPa/ 8 min) damage recovery at cooling temperature (8 \({}^{\circ}\)C) was investigated [12]. HPP induced protein folding, the phosphotransferase system, and cobalamin biosynthesis, resulting from SigB-initiated general stress response. Cell division-associated genes were downregulated, but peptidoglycan synthesis genes were upregulated, inferring that cell wall repair played a role in HPP damage recovery. Furthermore, the ncRNAs Rli47 and Rli53 might also play a role in HPP damage recovery [12]. Label-free quantitative proteomics to explore _L. monocytogenes_ inactivation mechanisms under HPP remains lacking. Limited quantitative proteomics studies have successfully applied to discover the effects of combining HPP and electrolyzed water to inactivate _Bacillus cereus_ spores [13], _L. monocytogenes_ from our previous work [14], _Campylobacter jejuni_ 81-176 [15], and _Lactobacillus sanfrancisensis_[16]. The objective of the present study was to evaluate the efficacy and mechanism of HPP at 200 and 400 MPa in inactivating _L. monocytogenes_ (from sub-lethal to lethal injuries) by label-free quantitative proteomics.
## 1 Introduction  
In 1998, [PERSON] presented the vision of a \"Digital Earth\" embedding geo-referenced data in a multi-resolution, three-dimensional representation of the Earth, facilitating collaboration to understand the interaction between human impacts and the environment [3]. With the rapid development of both commercial and scientific 3D geobrowsers representing the earth, technological development has approached, although not reached, [PERSON]'s vision. Virtual globes can be defined as geobrowsers, based on a globe metaphor, that integrate multi-scale and multi-temporal data from various sources. They have become a popular tool, and expectations regarding their utility as planning and community engagement tools are high, but are tempered by both technical limitations and ethical issues [1,2,4].  
In a position paper for the Vespucci Initiative for the Advancement of Geographic Information Science, [PERSON] _et al_. [3] set up a research agenda on the use of virtual globes. In addition to research in areas such as information integration and governance models, they called for case studies incorporating up-to-date modeling and indicators that would visualize abstract concepts in space such as \"quality of life\" or \"vulnerability\". Personal anecdotal evidence from discussions with representatives of community groups and local government also points to the need for collaborative frameworks employing easily accessible tools to visualize, explore and assess development alternatives, as well as local climate change vulnerability, impacts, and response options, within their spatial context [5].  
This paper responds to [PERSON] _et al_.'s [3] call for case studies, and builds on [PERSON] and [PERSON]'s [1] previous discussion of virtual globes, particularly related to the potential benefits of virtual globes in providing access to visual information, stimulating citizen interest, and providing representative views. Using two bottom up participant-driven introductory examples, and the Kimberley Climate Adaptation Project (KCAP) as a case study, the paper examines how far potential benefits can be fulfilled in practice. The paper demonstrates how, when used in tandem with other media, virtual globes can: (i) provide access and a shared platform for diverse spatial inputs from multiple stakeholders; (ii) raise interest and awareness of complex spatial environmental data and modeling outputs; and (iii) help to present locally relevant issues, vulnerabilities and possible response actions. However, several issues have been observed with their use that limit virtual globes' effectiveness in planning processes, including: (i) issues of visual representativeness at global, regional and object scales; (ii) affective responses; and (iii) the risk of misinterpretation. These are examined in greater detail in the Kimberley case study in Section 4. The paper concludes with observations about how to address the identified limitations and risks, and how and when such tools might best be employed in collaborative planning settings.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 2 NTF-Based Digital Twin Ecosystem in Aviation Industry
### Architecture of NFT-Based System for Aviation Applications  
The architecture in Figure 3 illustrates a comprehensive NFT-based system for aviation applications, seamlessly integrating blockchain technology with existing aviation infrastructure.  
At the top level, a user interface provides the entry point for users to interact with the system. This is supported by an identity and access management component, ensuring secure authentication and authorization for all users and system elements. An application programming interface (API) gateway manages and routes external requests to appropriate services within the system.  
Figure 3: Architecture of NFT-based system for aviation applications.  
The central application layer acts as the core, orchestrating data flow and operations between various components. It connects to several key elements: cloud infrastructure, which includes off-chain storage for handling large datasets unsuitable for blockchain; a blockchain layer, housing smart contracts for automating rules and agreements, and NFT tokens representing unique digital aviation assets such as aircraft parts, maintenance records, or flight logs; and an integration layer that facilitates communication with external systems.  
The blockchain layer is particularly significant, using the immutability and transparency of blockchain technology to enhance trust and efficiency in aviation processes. Smart contracts can automate complex procedures such as maintenance scheduling or parts tracking, while NFTs provide a secure, verifiable method for digitally representing and transferring ownership of aviation assets.  
The integration layer plays a crucial role in connecting the blockchain system with legacy systems, specifically existing aviation systems. This ensures seamless data flow and interoperability between the new blockchain-based platform and current aviation infrastructure, allowing for gradual adoption and minimizing disruption to existing operations.  
This architecture is designed to address specific challenges in the aviation industry, such as supply chain management, maintenance tracking, and regulatory compliance. By combining blockchain's security and transparency with the NFT's unique asset representation capabilities, the system offers a robust platform for modernizing aviation operations, enhancing safety, and improving efficiency across the industry.
## 3 Simulation Methodology  
To obtain optimal system configuration based on the economic and emission impact, various hybrid systems are considered and an optimum scenario was determined by using an optimization software where each system configuration is represented as a mathematical model compelled by various constraints. This paper seeks to minimize the daily operation costs of producing electrical energy for healthcare facilities in remote areas as well as the emission emanating from fossil fuel generators. The hybrid systems are designed in such a way that they meet the peak load demand of the healthcare facility and supply its load based on the availability of PV and wind resources with a backup diesel generator. Therefore, the objective function and the model constraints can be mathematically expressed as follows:  
\[Min\left[\alpha_{1}\sum_{DG,t}C_{DG}(P_{DG,t})+\alpha_{2}\sum_{DG,t}E_{DG}(P_{ DG,t})\right] \tag{7}\]  
Subject to the following constraints:  
\[\sum_{i=1}\alpha_{i}=1 \tag{8}\]  
\[\sum_{DG,t}P_{DG,t}+P_{WT,t}+P_{PV\_B,t}=D_{t} \tag{9}\]  
\[P_{PV,t}\geq P_{B,t}+P_{PV\_B,t} \tag{10}\]  
\[P_{DG}^{\min}\leq P_{DG,t}\leq P_{DG}^{\max} \tag{11}\]\[0\leq P_{WT,t}\leq P_{WT}^{\max} \tag{12}\] \[0\leq P_{PV,t}\leq P_{PV}^{\max}\] (13) \[SOC(t)=SOC(0)+\beta_{c}\underset{i}{\sum}P_{c}^{B}(i,t)-\beta_{d} \underset{j}{\sum}P_{d}^{B}(j,t) \tag{14}\]  
where \(C_{DG}\) and \(E_{DG}\) are the operation costs of diesel generators and emissions contribution from diesel generators, respectively;\(P_{DG,t}\), \(P_{WT,t}\) and \(P_{PV,t}\) represent the optimal power produced by diesel generators DG, wind turbines and solar PV at time t, respectively; \(P_{B,t}\) represents the charging and discharging capacity of the battery storage; \(P_{PV,B,t}\) represents the total power flowing in the PV and battery storage system at time t; \(D_{t}\) is the hourly electrical load of the rural clinic; \(P_{DG}^{\min}\) and \(P_{DG}^{\max}\) are the minimum and maximum power rating of the diesel generators DG, respectively; and \(P_{WT}^{\max}\) and \(P_{PV}^{\max}\) represent the maximum power rating of wind turbines and the solar PV system, respectively.  
The objective function in Equation (7) aimed at minimizing the costs of operation and emissions of the diesel generators. These are two incomparable objectives, hence, a weighting factor constraint such as in Equation (8) states that the sum of all weighting factors equals unity [29]. The power balance equation is given in Equation (9) and it indicates that the sum of the power produced from all the power sources must equal the energy demand by the load at any time t. Constraint (10) states that the total forecasted power by solar PV must be equal or greater than the total power obtainable from the charging and discharging of the battery storage system and the load power. Equations (11)-(13) are the boundary limits for the diesel generators, wind turbines and the solar PV system. Equation (14) is the state of charge constraint and it limits the available capacity of the battery.
## 3 Results and Discussion
### OAVs of Odor-Active Compounds in Six CSSs  
To evaluate further the contributions of the 28 odor-active compounds to the aromas of the six CSSs and to screen for the key odorants, their OAVs were calculated based on their obtained concentrations and odor detection thresholds in water, and the results are shown in Table 5.  
Of the 28 odor-active compounds, 27 odorants in some CSSs yielded OAVs \(\geq 1\), and their OAVs were vastly different. Only octanal had an OAV \(<\) 1 in all six CSSs; it did not contribute to the odors. The number of odorants with OAVs \(\geq\)1 in C1, C2, C3, J1, J2 and J3 was 26, 21, 27, 22, 23 and 25, respectively. In most samples, methanol, 3-methylbutanal, 2-methylbutanal, HEMF, guaiacol and benzeneacetaldehyde had higher OAVs than the other odor-active compounds; they contributed the most to the overall odor profile and imparted cooked potato, malty, caramel-like, smoky and honey-like odors to the six CSSs, and these odors also comprise the characteristic notes of GSS. In the six samples, there were much bigger differences among the OAVs of methionol (OAVs = 20-583), 2-ethyl-3,5-dimethylpyrazine (OAVs = 7-263), 4-ethylguaiacol (OAVs = 10-136), 3-methylbutanol (OAVs = 2-74), DMHF (OAVs = 6-342), dimethyl trisulfide (OAVs = 31-317), 4-ethylphenol (OAVs = 1-30) and ethyl 3-methylbutanoate (OAVs = 2-34); these odorants caused the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \cline{3-8} \multicolumn{1}{c}{**No.**} & **Compound** & \multicolumn{3}{c}{**Chinese CSS Samples**} & \multicolumn{3}{c}{**Japanese CSS Samples**} \\ \cline{3-8} \multicolumn{1}{c}{} & & **C1** & **C2** & **C3** & **J1** & **J2** & **J3** \\ \hline \multirow{4}{*}{23} & carboxylic acids & & & & & & \\
23 & acetic acid & 207266 \(\pm\) 16707 & 60588 \(\pm\) 773 & 215125 \(\pm\) 17321 & 57948 \(\pm\) 4717 & 143403 \(\pm\) 5401 & 406726 \(\pm\) 4668 \\
35 & butanoic acid & 2844 \(\pm\) 23 & 2917 \(\pm\) 4  
six CSSs to have some different notes. The OAVs of the other odorants were close; these odorants had similar contributions to the odors of six CSSs.  
According to the OAV results, 27 odorants identified in different CSSs were further screened as key odorants contributing to the characteristic aroma of CSS. Except for EHMP, the other odor-active compounds had been identified as key odorants of GSS. Therefore, according to the results obtained, it was concluded that the key odorants of CSS should be same as those of GSS. The question of whether CSSs contain more nutritional components requires further study.
## 2 Materials and Methods
### Cell Viability: MTT Assay  
The cell viability was determined by an MTT colorimetric assay. MSCs were seeded in 96-well plates at a density of 5 \(\times\) 10\({}^{3}\) cells/well. The culture medium was replaced every 48 h. Cell viability was measured every 24 h up to day 7, the culture medium was removed, and 100 \(\mu\)L of 0.5 mg/mL 3-[4,5-dimethylthiazol-2-yl]-2,5- diphenyl-tetrazolium bromide (MTT) (Sigma-Aldrich, Saint Louis, MO, USA, Sigma) in complete culture medium was added. Cells were incubated with MTT at 37 \({}^{\circ}\)C for 4 h. During the incubation period, MTT was reduced to purple insoluble formazan precipitates by the activity of mitochondrial dehydrogenases in viable cells. Then, the medium was removed, and formazan crystals in the wells were dissolved in 100 \(\mu\)L of dimethyl sulfoxide (DMSO, Sigma Aldrich, Saint Louis, MO, USA). The optical density was measured at 544 nm with a 96-well microplate reader (FLUOstar Optima, BMG Labtech, Offenburg, Germany) using DMSO as a blank control.  
The population doubling time (PDT) was calculated using the following equation:  
PDT = duration (hours) \(\times\) log(2)/log(FinalConcentration) - log(InitialConcentration).
# Tropical Dry Forest Diversity, Climatic Response, and Resilience in a Changing Climate
## 2 Components of Tropical Dry Forests
### Phenological Cycles  
TDFs of the Neotropics have a mix of deciduous and evergreen species, with at least 50% of the species exhibiting a deciduous nature [16]. The phenology of the TDF, seasonal cycles of the expression in TDFs, include periods of tree growth and dormancy, green-up and senescence, flowering, and fruiting [57]. In TDFs, these patterns are driven by seasonal patterns variations in precipitation with the wet season bringing green-up and growth and the dry season resulting in senescence and dormancy [57]. This phenological cycle and its variation across latitudinal gradients have been identified as essential for proper classification of the dry forest and as a research priority; however, there is still a significant gap in the direct quantification of its phenological signature [14; 15; 16; 21]. Additionally, remote sensing of phenological components, such as green-up, is not accurate, with a 1-3 week discrepancy between satellite and in situ data for the start of green-up. Satellite data, instead, corresponds to the peak rate of change in the green-up [58].  
In the few studies that have analyzed this pattern, the level of deciduous species is thought to be related to the duration and severity of the dry season, variability in soil moisture, and micro-scale environmental conditions including rooting depth, temperature, and plant available water content [14; 41; 59; 60]. Additionally, there are differences in phenological cycles across countries and successional stages. Leafing intensity for early successional stages during the dry season and at the beginning of the rainy season is highest away from the equator (Mexico and Brazil), while it is reduced in equatorial regions (Costa Rica) [61]. In Costa Rica, the total phenological cycle varies between 150-358 days, depending on the presence of late-season rains, which extends the forests' maturity period [62]. Leafing intensity between successional stages is not significantly different; however, there are significant differences in the number of species that have 50%-100% of their leaves in the Brazilian and Costa Rican rainy seasons between stages [61]. Under drier conditions, all stages experience earlier senescence; however, increased senescent individuals is gradual in the early stages but abruptly changes in intermediate and late stage forests [63].
## 4 Results
### Identification of Critical Scenario Components  
As for NMI data points in which the genotype was K1, objects that caused a temporary obstruction of view were investigated as critical scenario components. Table 3 shows the investigation results for the occlusion-causing objects. Oncoming vehicles coming from the opposite direction and preceding vehicles running in front of the ego vehicle caused most of the temporary occlusion. Among the objects, oncoming right-turning vehicles accounted for approximately 67% of the objects that obstructed the driver's view and dynamically created an occlusion.  
In the case of NMI data points with the genotype F2, the expectation of drivers was first investigated. Table 4 shows the investigation results for the driver's expectation in the right-turn scenario. Most of the expectations considered the driving behavior of the oncoming vehicle. Drivers often expected that oncoming vehicles would not enter the intersection under yellow or red lights, leading to errors. Thus, when the vehicle turns right at the intersection, the change in traffic lights is considered as an environmental factor leading to the expectation of certain behaviors.  
Analysis results identified two critical scenario components related to genotypes K1 and F2, respectively. The first environmental component was dynamic occlusions caused by oncoming right-turning vehicles. The second component was changing traffic lights. These scenario components were identified explicitly through analysis of driving data, as shown above.