esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Nanoparticles in EOR
### Cnt  
CNT is a cylinder-shaped carbon structure within a nano-size range. It was discovered by [PERSON] in 1991 [143] and classified as a fullerene type [126]. The two most common CNTs that have been used in various studies are the single-walled CNT (SWCNT) and multi-walled CNT (MWCNT) [126]. Little research has been conducted on CNTs for EOR since this type of material is relatively new [126] and not yet economical to apply on a field scale. However, CNTs possess unique structural and electronic properties [144], as well as a hydrophobic nature.  
[PERSON] and [PERSON] [144] reported that the stability of water-oil emulsion was increased in the presence of CNT/silica nanohybrid NPs. The resulting emulsion can handle coalescence and sedimentation, and can be separated easily by filtration or centrifugation processes. Similarly, [PERSON] et al. [145] flooded an SWCNT-silica nanohybrid solution into a glass bed with crushed Berea sand and found that although more energy was needed for injecting the emulsion into the sand packs, the NP could effectively improve recovery through an interfacial active catalytic reaction. [PERSON] et al. [146] flooded MWCNT into both Berea core plugs and sand packs and saw an improved dispersion stability and propagation in high temperature and salinity conditions. The NP retention was dependent on oil saturation, with more than 80% NP found in the effluent. [PERSON] et al. [92] found that using MWCNT-based nanofluid resulted in 31.8% additional recovery of heavy crude oil. [PERSON] et al. [147] conducted a comparative study on the performance of MWCNT, SWCNT, and activated carbon for the application of a Pickering emulsion in chemical EOR. The emulsions were formulated at pH of 7 with distilled water. The Pickering emulsion using MWCNT exhibited the highest stability at 0.1% and 1% salinity and moderate temperature (25 to 90 \({}^{\circ}\)C), and it successfully altered the wettability of oil-wet carbonate rock into water-wet.  
To summarize, some NP types such as silica, alumina, titanium oxide, and zinc oxide were proven to be potential EOR agents through wettability alteration and IFT reduction mechanisms. However, others such as nickel oxide, zirconium oxide, and iron oxide NPs, have shown relatively decent results in terms of the recovery improvement. There are also possibilities of combining several NPs which could potentially enhance the oil recovery. Thus, further research on nano-EOR needs to be done with varied NPs to uncover the potentials and improve the understanding on the mechanisms.
## 3 Results and Discussion
### Potential Energy Surface Scan Grid  
A potential energy surface scan was used to determine the optimized molecular structure of the (\(2E\))-HIPC molecule in its equilibrium state and to determine the most stable structure at the global minimum of the potential energy surface. The energy surface scan grid for the title molecule was performed using the AM1 method, and the N16-C17-N18-C20 and C9-C8-C7-C5 dihedral angles were rotated for various geometries. The rotation angles of the two benzene rings with respect to the imidazole ring were computed by varying the dihedral angles. A potential energy scan of the (\(2E\))-HIPC molecular geometry was performed by freezing the N16-C17-N18-C20 (SCAN1) and C9-C8-C7-C5 (SCAN2) dihedral angles and varying the dihedral angles values in 18 steps of 20\({}^{\circ}\) from \(-\)179\({}^{\circ}\) to 179\({}^{\circ}\) and from 0\({}^{\circ}\) to 180\({}^{\circ}\) for N16-C17-N18-C20 and C9-C8-C7-C5, respectively. A 3D view of the (\(2E\))-HIPC rotational conformer is shown in Figure 1. This scan revealed that the optimized molecular geometries at 170 out of 361 with N16-C17-N18-C20 and C9-C8-C7-C5 dihedral angles of \(-\)19.42\({}^{\circ}\) and 440.713\({}^{\circ}\), respectively, were the ground state conformations. The potential energy surface (PES) scan grid and the calculated optimized conformers were determined in the gas phase. The PES scan of the dihedral angle rotations resulted in 361 conformers. Two rotational global minimum energy conformers were identified for the title compound. Figure 1 illustrates the various levels of energies using colors where dark blue indicates the global minimum, light blue indicates a local minimum, red indicates a local maximum, and dark red indicates a global maximum.  
Figure 1: Potential energy surface grid of (\(2E\))-HIPC.  
Rotational conformers **I** and **II**, which are shown in Figure 2, were optimized with the AM1 and B3 LYP methods in the gas phase, DMSO, and acetonitrile. The global minimum energies and dipole moments for the conformers are presented in Table S1. Conformer **I** exhibited the lowest global minimum energies of 0.1695246 and \(-\)1084.70550 Hartree in DMSO solution using AM1 and B3 LYP, respectively, compared with the obtained values in the gas phase and in acetonitrile. This result was confirmed by the computed dipole moment values in DMSO, which were 7.1931 and 7.4136 Debye using the AM1 and B3 LYP methods, respectively. Therefore, conformer **I** was computed to have the minimum global energy, and this optimized molecular structure was used for further analysis.  
Figure 2: Rotational conformers of (\(2E\))-HIPC. (**A**) Conformer **I** by rotation of scan 1 = \(-\)71.425\({}^{\circ}\) and scan 2 = 0\({}^{\circ}\) or 360\({}^{\circ}\). Energy (B3 LYP) = 1084.7055 hartree; (**B**) Conformer **II** by rotation of scan 1 = \(-\)71.574\({}^{\circ}\) and scan 2 = 0\({}^{\circ}\) or 360\({}^{\circ}\). Energy (B3 LYP) = 1084.6857 hartree.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 2 Principle and Methods
### DBN Whole Frame  
This paper proposes an adaptive DBN particle filter, which uses a crack propagation formula as the system state equation of particle filtering and uses material parameters, observed cracks, and loads as input continuous node random variables. The steps of the DBN can be represented as follows:  
Step 1. Through the examination of accessible materials (for instance, simulations of identical structures or fatigue expansion formulas), extensive prior data on lifespan under diverse conditions of load, stress ratio, and fracture toughness among other parameters are acquired. These data act as the preliminary parameters \(\Theta_{0}\) for the crack propagation equation. Subsequently, initial particles are generated from the prior probability distribution \(P(\Theta)\) associated with these parameters by employing the Monte Carlo method, which initiates the values for each node within the DBN.  
Step 2. Once the monitoring of crack propagation in structures commences, the equivalent stress \(\Delta\sigma\) at the crack tip is determined from the observed load \(P_{obs}^{t}\). This value, together with additional prior parameters \(\Theta^{t}\), is incorporated into the crack propagation equation to derive the theoretical crack length at present, \(a_{t}\). Subsequently, this theoretical figure and the observed experimental data also serve as the basis for the state and measurement equations in particle filtering. Employing the adaptive particle filtering technique outlined in Section 2.6, particle filtering facilitates the acquisition of the posterior estimates for the current crack extension length \(\hat{a}_{t}\) and the parameters \(\hat{\Theta}_{t}\).  
Step 3. Following the acquisition of posterior parameter samples \(\hat{\Theta}_{t}\) at moment \(t\), the probability distribution of \(\hat{\Theta}_{t}\) undergoes sampling via kernel density estimation. This process inputs the derived samples into crack propagation, serving as the prior update for the crack length particles at the forthcoming moment \(t+1\). This step facilitates the retrieval of the next posterior filtering values \(a_{t+1}\).  
The structure of the overall DBN is shown in Figure 6. The uncertainties mentioned above in the DBN are represented by the nodes in the figure. Nodes are connected by arrows representing conditional probability distributions or deterministic functional relationships. The subscript \(t-1\) or \(t\) represents the time step.  
In this section, the DBN model for crack propagation is validated using both single and multiple adaptive variable datasets. Section 3.1 describes the DBN model for crack propagation with a single adaptive variable. In contrast, Section 3.2 presents the DBN model for crack propagation incorporating multiple adaptive variables.  
Figure 5: The DBN filter inference iteration.
###### Abstract  
A bi-static short-range elastic backscatter micro-lidar, named Colibri, has been developed for quantitative aerosol profiling with high range and temporal resolution within the first hundred meters. The geometric (i.e., overlap) and radiometric (i.e., lidar constant) calibrations were performed along with dark current and background noise characterizations. Results of a measurement campaign have demonstrated the capability of our system to characterize aerosol plumes with high range-resolution (<10 cm) in the short-range close to their emission sources (from 10 m). To this aim, fog-oil aerosol plumes were generated in a tunnel and characterized by using an optical particle counter. A forward inverse method without boundary conditions is presented for inverting short-range lidar profiles when no reference molecular zone is available. Lastly, we report the different retrieved lidar products, namely the distribution of aerosol layers, radiative properties (i.e., backscatter profiles), and the microphysical properties (i.e., number concentration profiles). For the validation of the proposed methodology, the lidar products were compared with measurements from the optical particle counter. Lastly, the impact of calibration errors on the lidar products is discussed through an uncertainty analysis.  
Keywords:short-range lidar; high range-resolution; high temporal-resolution; aerosols; backscattering coefficient; aerosol number concentration +
Footnote †: journal: Remote Sensing
## 4 Discussion  
Since local quantities of calcitriol are necessary for distinct tissue responses, they are dependent on the availability of 25(OH)D in the serum; consequently, the local calcitriol level should typically be higher than the serum level. Local calcitriol binds to the VDR in the cell nucleus. It is generally established that \(\beta\) cells in the pancreatic, musculoskeletal, and adipose tissues express VDR. Hence, the inability of \(\beta\) cells to convert pro-insulin into insulin can lead to vitamin D insufficiency and type 2 diabetes. One pathophysiological mechanism between obesity and vitamin D deficiency could be vitamin D sequestration and volumetric dilution. Fat-soluble vitamin D is sequestered in adipose tissues and influences a greater volumetric dilution, resulting in decreasing serum vitamin D levels especially in obese individuals. However, other possible rationales also shown in our study may be poor eating habits and differences in gene expression, such as vitamin D metabolizing enzymes, which might follow gender differences. In addition, it is thought to be mediated by adiposity that vitamin D might release from the adipocyte during a reduction in the fat tissue, eventually resulting in a reduction in MetS. One might think that increasing the vitamin D level would improve or prevent MetS. If serum vitamin D level rise and vitamin D functions properly at the cellular level, vitamin D-controlled transcript regulation such as cell differentiation, cell proliferation, and immunity may be enhanced. It includes the enhancement of the \(\beta\) cell function followed by the efficient regulation of insulin and other MetS-related metabolisms. Although our study design cannot identify the sequential relationship between MetS and vitamin D levels, several previous studies and our study have confirmed the inverse association between the serum 25(OH)D and MetS including obesity, insulin resistance, and blood pressure [19; 43; 44; 45; 46; 47]. A recent study demonstrated an unfavorable association between serum 25(OH)D and fat intake in people with metabolic genetic risk [48]. Obesity caused by a high-fat diet has been reported to modify vitamin D-related enzymes in previous studies [49; 50], which are potential mechanisms underlying the associations between MetS such as obesity and vitamin D status. However, our analysis showed that men with sufficient serum 25(OH)D levels (\(\geq\)20 ng/mL) had significantly higher protein intake, SFA intake, and lower fat, MUFA, and PUFA intake. In addition, there were no relationships between daily nutrients intake and serum 25(OH)D levels in women.
## 2 Potential Application Areas of CR-WSNs
### Health Care  
In a health care system, such as telemedicine, wearable body sensors are being used increasingly. Numerous wireless sensor nodes are placed on patients and acquire critical data for remote monitoring by health care providers. In 2011, the IEEE 802.15 Task Group 6 (BAN) [36] approved a draft of a standard for body area network (BAN) technology. Wireless BAN-assisted health care systems have already been in practice in some remote areas of developing countries, such as in Nepal and India [37; 38]. Wireless BAN for healthcare systems is suitable for areas, where the number of health specialists is relatively low.  
Medical data is critical, delay and error sensitive. Therefore, the limitation of traditional WSN, as discussed in the previous section confines the potentiality of telemedicine. The QoS may not be achieved at a satisfactory level if the operating spectrum band is crowded in convenient 'telemedicine with BAN'. The use of 'CR wearable body wireless sensors' can mitigate these problems due to bandwidth, jamming and global operability, hence improve reliability. Figure 3 presents a model forwireless BAN with CR wireless sensors. A significant amount of research has been carried out in the area of WBASN [39]. The requirements of cognitive radio implementation in wireless medical networks are discussed in reference [40].
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## Appendix A Description of the I4.0 Technological Concepts Considered in This Study
[44] do not individually address the virtualization technologies, namely VR and AR, which is something that happens in several later works [43; 45; 46]. This indicates the relevance of including a category for these technologies. The literature also suggests that both the simulation and digital twin have an impact on LM practices [8; 9]. [PERSON] and [PERSON] [51] defined the concept of a digital twin as a replica of the real world created in a digital space, bringing together different types of data on site. The replica can be used with artificial intelligence or simulations in order to help to make improvements in the context [51]. Therefore, their inclusion in this technological concept is adequate. Additive Manufacturing [3 DP]--This technological concept is unanimously adopted by other authors, such as [40; 41; 43], with similar descriptions and associated technologies. Additive manufacturing (AM) or 3D printing (3 DP) is a process by which products are produced autonomously, layer by layer [44]. It consists of versatile machines and flexible production systems that transform 3D digital models into physical products [40]. This technology is used especially for rapid prototyping and the creation of custom-made tools [44]. Manufacturing Execution Systems [MES]--MES is a useful tool for organizations that require the accurate traceability of parts, components, and assembly activities to monitor quality, cost, and lead times. The purpose of MES is to initiate, guide, respond, and report shop floor activities as they occur [96]. MES also plays a key role in the central distribution of information [44]. Regarding ERP systems, [PERSON] et al. [44] include an exclusive classification group: MES is an I4.0 technological concept, and the rest (ERP, SCADA) already existed before I4.0 began. However, other authors define more complete technological categories, integrating MES/SCADA [40] or MES/SCADA/ERP [43]. All things considered, in the present classification approach, SCADA and ERP are directly associated to MES. e-Value Chains [eVC]--Supported by the digitalization of value chain activities [44], this technological concept consists of collaborative digital platforms [43] together with suppliers, customers, and other parts of the organization. This allows for continuous connectivity, collaboration, and cooperation [44], fostering the synchronization of the production with stakeholders [43]. Slightly different classifications exist in the literature, such as smart supply chain [43]. Furthermore, several other authors mention this technological concept indirectly [4,45]. Despite not being a technological concept, as mentioned in the literature as the base concepts (IoT, Cloud, etc. ), eVC includes essential technologies for horizontal integration, and for this reason, this category must be included.
## 2 Materials and Methods  
### Expression and Purification of HoFucl  
The gene encoding _Ho_Fucl was synthesized (Bioneer, Daejeon, Korea) and cloned into pET28a(+) as an expression vector. The vector containing the synthetic _Ho_Fucl gene was transformed into competent _Escherichia coli_ BL21 (DE3) cells, which were grown at 37 \({}^{\circ}\)C on lysogeny broth (LB)-agar plates containing 50 ug/mL of kanamycin for the selection of transformants. _E. coli_ transformantsant containing the _Ho_Fucl gene were cultivated in the LB medium containing 50 ug/mL of kanamycin at 37 \({}^{\circ}\)C. When the optical density at 600 nm (OD\({}_{600}\)) reached 0.6-0.8, the expression of recombinant _Ho_Fucl was induced by adding 0.5 mM isopropyl-g-d-1-thiogalactopyranoside to the medium, and the cells were further cultivated at 18 \({}^{\circ}\)C and 180 rpm overnight. After centrifugation at 4500\(\times\)at 4 \({}^{\circ}\)C for 20 min, the collected cells were resuspended in buffer A (50 mM Tris-HCl, 200 mM NaCl, and 10 mM imidazole; pH 8.0) and disrupted using an ultrasonicator to extract the intracellular proteins. Total cell lysates were centrifuged at 25,188\(\times\)\(g\) at 4 \({}^{\circ}\)C for 30 min, and the supernatant containing the crude protein extract was loaded onto a His-Trap column pre-equilibrated with buffer A (GE Healthcare, Chicago, IL, USA). The _Ho_Fucl protein was purified using immobilized metal ion affinity chromatography. After thoroughly washing the column to remove the non-specifically bound proteins with buffer A, the bound protein was eluted with buffer B composed of 50 mM Tris-HCl, 200 mM NaCl, and 300 mM imidazole (pH 8.0). For further purification and determination of the oligomeric state of the native enzyme, the eluted protein fractions were concentrated and subjected to size-exclusion chromatography using a HiPrepTM 16/60 Sephacryl S-200 HR column (GE Healthcare), with a buffer composed of 20 mM sodium phosphate and 200 mM NaCl (pH 7.0) as the mobile phase.
# A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction
## 5 Conclusions  
Gene function prediction is an important application area of hierarchical multi-label classification. In this problem, an instance is assigned to a set of class labels, and the class labels are structured as a hierarchy. In this paper, we present a novel HMC algorithm for solving the gene function prediction problem based on the GO, the hierarchy of which is a DAG. On the basis of the characteristics of the HMC problem, two measures are designed to take into account the hierarchy constraint, and to improve the performance of the proposed algorithm. First, in order to improve the performance of base classifiers, a negative instances selecting policy associated with the SMOTE method is proposed to process the imbalanced training data sets. Second, a nodes interaction method is proposed to integrate the results of the base classifiers. Experiment results on eight yeast data sets annotated by the GO show that the proposed algorithm significantly outperforms two state-of-the-art algorithms. Due to its good performance, the proposed algorithm is expected to be a practical and attractive approach for gene function prediction based on the Gene Ontology.  
In the future work, we will do this experiment on more data sets--not only the data sets of the yeast, but also data sets of multi-cellular species, such as plants and humans. For the genes in some data sets that have many features, the appropriate feature selection method should also be considered and added to this framework. Moreover, from the aspect of the computational method, we can optimize the algorithm to improve the prediction performance and make contributions to guiding the biological validation experiments. For instance, better prediction might be achieved by changing the base classifier, or trying to use different classifiers for different nodes. Furthermore, when making decisions in the node interaction method, more nodes could take part in this process. All of the above mentioned perspectives will be considered in our future work.  
This work is supported in part by the Fundamental Research Funds for the Central Universities (Grant No. HIT NSRIF.20169), the Heilongjiang Postdoctoral Fund (Grant No. LBH-Z16081), and the Online Education Research Funds of Online Education Research Center of Ministry of Education (Quantum Education) (Grant No. 2016 YB132). All three authors made significant contributions to this research. [PERSON] and [PERSON] conceived the ideas, designed the algorithm and performed the experiments. [PERSON] and [PERSON] analyzed the results. [PERSON] drafted the initial manuscript, [PERSON] and [PERSON] revised the final manuscript.
###### Abstract  
The energy efficiency of a multi-pump system consisting of two low-power (0.75 kW) pumps operating in parallel mode and a single-pump mechanism (1.5 kW) is compared in this study. For this purpose, mathematical models, experimental data, and data retrieved from the manuals provided by the pump manufacturers are used. The single-pump system is fed by a single variable speed drive. A multi-pump system running in parallel mode consists of two pumps. One of them is driven by an induction motor connected directly to the electrical grid and equipped with a throttle. Another pump is actuated by an induction motor fed by a variable speed drive. The flowrate of the liquid in the multi-pump is controlled with the help of speed variation and throttling. In the case of the single-pump system the conventional speed control method is applied during the analysis. For both pump system topologies, the daily and annual energy consumption is obtained. As a result of conducted calculations, it was shown that the multi-pump provides 29.8% savings in comparison to the single-pump system in the case of a typical flowrate profile.  
centrifugal pump; energy efficiency; induction motor; throttling control; variable speed control +
Footnote †: journal: Applied Physics Letters B