esa-sceva/satcom-chunk-collection huggingface dataset:
# Metabolic Engineering _Escherichia coli_ for the Production of Lycopene
## 2 Metabolic Engineering of Two Major Pathways in _E. coli_ for Lycopene Production  
### The Primary Biosynthetic Pathways for Lycopene Production  
The MEP and MVA pathways are the major pathways producing the precursors of lycopene, IPP, and DMAPP. As shown in Figure 1, glyceraldehyde 3-phosphate (G3P) and pyruvate are condensed to form 1-deoxy-d-xylulose-5-phosphate (DXP) by DXP synthase (DXS), followed by the conversion into MEP under the catalyzation of DXP reductoisomerase (DXR or IspC) in the MEP pathway. Further, a series of enzymes, including 4-diphosphocytidyl-2C-methyl-d-erythitol (CDP-ME) cytidyltransferase (IspD), CDP-ME kinase (IspE), 2C-methyl-d-erythitol-2,4-cyclo-diphosphate (MEC) synthase (IspF), 4-hydroxy-3-methyl-2-(_E_)-butenyl-4-diphosphate (HMBPP) synthase (IspG), and HMBPP reductase (IspH), successively catalyzes the conversion of MEP into IPP, along with the respective intermediates of CDP-ME, 4-diphosphocytidyl-2C-methyl-d-erythitol-2-phosphate (CDP-MEP), MEC, and HMBPP. Subsequently, isopentenyl-diphosphate isomerase (IDI) catalyzes the isomerization of IPP to DMAPP [24; 25]. The MVA pathway initiates with acetyl-CoA, which is converted into MVA through three reactions, catalyzed by acetoacetyl-CoA thiolase (ACCT), 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthase (HMGS), and HMG-CoA reductase (HMGR), respectively. Subsequently, MVA is converted to mevalonate-5-phosphate (MVAP) catalyzed by mevalonate kinase (MK). The transformation of MVAP into IPP is reported through different pathways. The one in eukaryotes involves two reactions successively catalyzed by MVAP kinase (PMK) and MVAPP decarboxylase (MDD), while the other pathway in archaea harbors two reactions catalyzed by MVAP decarboxylase (MPD) and isopentenyl phosphate kinase (IPK), respectively [26]. After the production of IPP and DMAPP, the condensation reaction between the two intermediates occurs with the formation of geranyl diphosphate (GPP). Further, farnesyl pyrophosphate (FPP) is formed from GPP catalyzed by FPP synthase (IspA). FPP, in turn, is catalyzed by geranylgeranyl diphosphate (GGPP) synthase (CrtE), phytoene synthase (CrtB), and phytoene desaturase (CrtI) to successively form GGPP, phytoene, and finally lycopene [15; 27]. The exogenous genes _crtEBI_ from various sources can exhibit differential activities when introduced into the host strains. For instance, when different carotenoid genes _crtEBI_ were introduced into _E. coli_, higher lycopene yield and cell growth were reached using the genes derived from _Pantoea agglomerans_ compared with those of _Pantoea ananatis_. Furthermore, it was identified that _crtE_ was responsible for the difference between the engineered _E. coli_ strains harboring the _crtEBI_ genes of _P. agglomerants_ and _P. ananatis_, respectively [15]. To summarize, the sole utilization of the endogenous MEP pathway or co-expression of the MEP and heterogeneous MVA pathways, and the subsequent expression of three key enzymes, CrtE, CrtB,  
Figure 1: The metabolic pathways for lycopene production.
## 3 Results and Discussion  
### Variation of Optical Performances of Water-Based Topcoat Paint Film  
The color difference change in water-based topcoat paint film is shown in Table 1. The color difference of the water-based topcoat paint film increased with the content of microcapsules increased, from 0.6 to around 5.0. This is because the microcapsule is a kind of white powder and may affect the original color of wood. Because of the uneven distribution of white powder, the color of the water-based topcoat paint film surface is also uneven, resulting in a larger color difference [21]. When the content of microcapsules with different core-wall ratios was less than 10.0%, the color difference was smaller.  
The gloss measured is shown in Table 2. The gloss of water-based topcoat paint film declined gradually with the rise in the content of microcapsules. When the content of microcapsules was 0-5%, the gloss of water-based topcoat paint film declined more obviously. When the content of microcapsules increased, the gloss of water-based topcoat paint film decreased slowly. This is because the surface of the water-based topcoat paint film without microcapsules is smooth, and the microcapsules particles added lead to uneven surface, which enhanced diffuse reflection and reduced gloss [22]. The gloss of the water-based topcoat paint film with the microcapsules core-wall ratio of 0.67:1 and the content of 0-10% is higher than others.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Content of** **Microapsules (\%)**} & \multicolumn{6}{c}{**Color Difference**} \\ \cline{2-7}  & **0.42:1** & **0.50:1** & **0.58:1** & **0.67:1** & **0.75:1** & **0.83:1** & **0.92:1** \\ \hline
0 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 \\
5.0 & 1.8 & 1.5 & 1.5 & 1.6 & 1.3 & 1.2 & 1.6 \\
10.0 & 2.8 & 2.2 & 2.3 & 2.0 & 1.5 & 2.1 & 2.9 \\
15.0 & 3.6 & 3.2 & 2.8 & 2.4 & 2.4 & 2.7 & 4.1 \(\pm\) 0.1 \\
20.0 & 4.4 & 3.3 & 3.2 & 3.5 & 4.4 & 3.4 & 4.4 \(\pm\) 0.1 \\
25.0 & 5.1 & 5.5 & 5.0 \(\pm\) 0.2 & 4.8 \(\pm\) 0.1 & 4.7 \(\pm\) 0.1 & 3.9 \(\pm\) 0.1 & 5.3 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variation of color difference of water-based topcoat paint film.
## 2 Related Works
### Agricultural Product Quality Grading  
Quality grading algorithms have been applied to edible agricultural products, based on different features. The Back Propagation (BP) Neural Network and Support Vector Machine (SVM) were utilized to grade pear appearance quality based on shape, surface color, and defects according to industry standards, achieving an accuracy of 80.5% [14]. Machine learning ensemble models (XGBoost, LightGBM, and Linear models) were employed to estimate apple ripeness by training spectral sensor data, facilitating apple quality grading based on a custom relationship between the ripeness and quality levels, achieving an accuracy of 80.5% [15]. The relationship between the RGB and HSV color maps of mushrooms' freshness was analyzed, and the grading of two quality classes of mushrooms (fresh and deteriorated) were carried out using an Artificial Neural Network (ANN) and SVM, with accuracies of 94.7% and 93.4%, respectively [16]. However, the above adopted grading methods lacked textual data, and were based on existing grading standards, which are insufficient for classifying orchids that lack defined indicators and grading standards. Especially, the outputs and features of these models do not clearly represent the key features affecting quality, nor their importance, providing limited guidance for the grading results of agricultural production processes. In contrast, this study fully utilizes both image and textual features of orchids, effectively uncovering the impact mechanism of key features on the orchid's quality and value. This provides a scientific basis for _Rchb. f._ cultivation and online transactions.
# A Novel Dual-Rotor Ultrasonic Motor for Underwater Propulsion
###### Abstract  
Micro underwater vehicles (MUVs) have been highlighted recently for underwater explorations because of their high maneuverability, low price, great flexibility, etc. The thrusters of most conventional MUVs are driven by electromagnetic motors, which need big mechanical transmission parts and are prone to being interrupted by the variance of ambient electromagnetic fields. In this paper, a novel dual-rotor ultrasonic motor with double output shafts, compact size, and no electromagnetic interference is presented, characterized, and applied for actuating underwater robots. This motor was composed of a spindle-shaped stator, pre-pressure modulation unit, and dual rotors, which can output two simultaneous rotations to increase the propulsion force of the MUV. The pre-pressure modulation unit utilized a torsion spring to adjust the preload at the contact faces between the stator and rotor. The working principle of the ultrasonic motor was developed and the vibration mode of the stator was analyzed by the finite element method. Experimental results show that the no-load rotary speed and stalling torque of the prototype ultrasonic motor were 110 r/min and 3 mN-m, respectively, with 150 V peak-to-peak driving voltage at resonance. One underwater robot model equipped with the proposed ultrasonic motor-powered thruster could move at 33 mm/s immersed in water. The dual-rotor ultrasonic motor proposed here provides another alternative for driving MUVs and is appropriate for developing specific MUVs when the electromagnetic interference issue needs to be considered.  
p ezoelectric actuator; underwater robot; propeller; torsional spring; dual-rotor +
Footnote †: journal: Applied Physics Letters
## 3 Experimental Results and Discussion
### Sensitivity Analysis of Coding Schemes: OvA and OvO  
The multi-classification task can be performed using either of the coding schemes: One-versus-All (OvA) or One-versus-One (OvO). We experimented with both schemes without augmentation, and the results of both are given in Table 8. We found that the OvO scheme performs better than OvA with deep-layered architecture that starts from scratch. The reverse has been found true otherwise, i.e., the OvA performs better for the multi-class cases when used with pre-trained CNN architecture (transfer learning). When the proposed system starts from scratch with the OvA scheme, the results have been found slightly lacking than OvO, which is in agreement with [27].
## 2 Materials and Methods
### Study and Control Group Description  
A total of 29 patients were included in the study, 19 women and 10 men. No adverse effects of the procedure were recorded in early and late follow-up. The PRP and the control group were comparable with respect to age (63 vs. 59) and BMI (30.5 vs. 31). Of the 29 participants, only four declared history of trauma at the onset of symptoms, 3 of whom were in the PRP group. There was a substantial difference in the involvement of opposite extremity between both groups. In the PRP group, 37% of participants had pain in an opposite extremity. On the contrary, over 75% of patients in the control group reported complaints about the function of an opposite lower extremity. All individuals enrolled in the study were provided with some form of conservative treatment prior to the surgery, including physiotherapy, viscosupplementation, or steroid injection. The most common form of conservative treatment was physiotherapy, attended by 75% of patients from the PRP group and 84.6% of patients from the control group. In 25 individuals, grade IV cartilage lesions were diagnosed. The remaining four presented grade III cartilage lesions. The entire study and the control group presented grade III or IV cartilage lesions in the knee. The most common site of cartilage defect in the PRP group was medial femoral condyle, whereas in the control group, it was medial tibial condyle. The medial compartment of the knee and patella-femoral joint presented the majority of chondral lesions locations. Cartilage lesions on lateral femoral condyle were found in 4 cases and 2 in lateral tibial condyle. Concomitant knee lesions included meniscal tears (n = 20), which were excised during the surgery. The medial meniscus was mostly involved in the degenerative process. 62.5% of individuals in the PRP group had medial meniscus lesions. A comparable number of participants presented lesions of the medial meniscus in the control group (61.5%). The results are shown in Table 1.
## Materials and Methods
### Remote Sensing Data and Preprocessing
#### 2.2.1 Remote Sensing Data Acquisition  
The ZY-3 MUX data were acquired from the Satellite Surveying and Mapping Application Center (SASMAC) of the National Administration of Surveying, Mapping and Geo-information of China (NASG) [53]. The GF-1 WFV data were acquired from the Gaofen satellite data and information service system (GFDIS) [54], and the HJ-1 CCD data were acquired from the China Centre for Resources Satellite Data and Application (CRESDA) [55]. The surface reflectance data of Landsat-8 OLI for the study area was acquired from the United States Geological Survey (USGS) [56]. The Landsat-8 OLI data was used to evaluate the data stability of different sensors among ZY-3 MUX, GF-1 WFV and HJ-1 CCD. The acquisition information for these satellite data sources is provided in Table 2. All of the images over the study area were cloudless.
### Paper Contributions and Organizations  
Given the previously highlighted challenges arising from the imminent widespread adoption of EV charging, it is imperative to devise novel strategies that reduce the PAR value and energy cost of the electric grid and effectively address the impact of the fleet of EVs in residential buildings. Although EV charging strategies have been explored, a critical component--load modeling--often relies more recently on traditional statistical methods, such as probabilistic models [13] and Monte Carlo simulations [24; 25; 26]. These approaches face challenges when dealing with non-linear patterns or non-stationary data, potentially resulting in significant deviations in predicting EV charging demand. Moreover, they underutilize the fleet-level characteristics of EVs, which include diverse real-life charging loads, varying vehicle models, and battery capacities (e.g., long-range, mid-range, and short-range), as well as the influence of weather events. Additionally, limited studies have addressed charging scenarios, particularly in the context of cost optimization and Peak-to-Average ratio (PAR) management to achieve peak shaving and valley filling of the grid load profile [13]. Therefore, improvements are needed to develop both overnight and workplace charging scenarios in residential buildings using reliable load predictions.  
This paper proposes three charging strategies based on the STLF approach, namely \"the CEEMDAN, the SD methods, and deep neural networks, [12]\" to address the problem of peak charging load for EV fleets in residential buildings. For this purpose, the simulation results of aggregated charge power during the day three-day-ahead prediction period in two seasons, spring and winter, are further analyzed under fifteen charging strategy combinations. PAR values and energy cost of all charging strategies were analyzed to provide the impact of EV charging on the aggregated peak in residential buildings.  
The contribution of the presented paper is the application of prediction hybrid machine learning method on charging strategies to achieve peak shaving and valley filling of the grid load profile, through the following means:  
* Studying the effect of various charging load shifted (%) from evening to overnight or from home to workplace/other charging sites or mix overnight and workplace/other charging sites for residential buildings in Canada
* Comparing the predicted charging model with various combinations of overnight and workplace charging shifts (%)Evaluating the effectiveness of EV charging strategies on PAR minimization and energy cost reduction.  
The following section provides the theoretical background of our method. Section 3 describes the experimental results and validation. Section 4 presents future work. Section 5 concludes.
## 3 Materials and Methods
### Comparison of ANAC074 Binding with the Four cis-Acting Elements  
To compare the binding activity of ANAC074 to NACRS, MybSt1, NRS1, and NRS2, the effector construct pROKII-ANAC074 was co-transformed with each reporter constructs pCAM-NRS1, pCAM-NRS2, pCAM-MybSt1 or pCAM-NACRS into tobacco leaves using particle bombardment method (Bio-Rad, Hercules, CA, USA). The empty pCAMBIA1301 was transformed and used as positive control. Three independent experiments were performed.  
Statistical analyses were carried out using SPSS 16.0 (SPSS Inc., Chicago, IL, USA) software. Data were compared using Student's t test. Asterisks indicate a significant difference compared with NACRS (* \(p\) < 0.05).  
Analysis of the Interaction of the Other Ten Arabidopsis NAC Family Members with ANAC074-Binding Elements  
For effector vectors construction, the ORFs of AT3G44350, AT2G33480, AT3G56520, AT3G04420, AT4G01540, AT2G43000, AT5G50820, AT1G69490, AT1G77450 and AT4G28500 were respectively cloned into pGADT7-Rec2 (designated as: pGADT7-AT3G44350, -AT2G33480, -AT3G56520, -AT3G04420, -AT4G01540, -AT2G43000, -AT5G50820, -AT1G69490, -AT1G77450 and -AT4G28500) at the site between SMART III Sequence and CDS III Sequence using the infusion method. Each of the 10 effector vectors containing NAC gene was co-transformed with reporter vectors pHIS2-NRS1-D4, -D7, -D6, -D8, pHIS2-NRS2-D4, -D5, -D6, -D8, or pHIS2-MybSt1 into yeast cells. The bindings of the 10 NACs to the elements harboring in pHIS2 were analyzed using the Y1H system [23; 24].
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 1 Introduction
This study utilizes a dataset based on coffee futures contracts to validate the proposed approach--a financial instrument characterized by high data variability and volatility. The dataset integrates meteorological information, economic indicators (including coffee prices, exchange rates, and trade data), and additional contextual variables relevant to smart contract execution [12]. The IMWV model is developed following the CRISP-DM methodology and structured across multiple phases: business understanding, data exploration, preprocessing, modeling, evaluation, and implementation [9]. This ensures a rigorous data integrity assessment by leveraging multi-label classification techniques and ensemble learning methods [13]. Ultimately, this research advances blockchain-based data validation by introducing a structured framework that enhances the reliability of oracle-fed data in smart contracts [14]. The proposed IMWV model is expected to mitigate risks associated with erroneous or manipulated information, thereby strengthening the security and trustworthiness of blockchain applications [15]. This document is structured to enhance reader comprehension. Section 2 offers a concise literature review and outlines key concepts to frame the research topic. Section 3 details the methodologies employed to conduct the study, illustrating and analyzing the supply chain used to validate data integrity in coffee futures contracts. Additionally, it presents the design and evaluation of the IMWV proposal and the smart contract, discusses the research findings, and compares them with related studies. Finally, Section 4 summarizes the conclusions, highlighting the main findings and proposing directions for future work, ensuring a logical and coherent flow of content throughout the document.
## 9 Experimental Assessment and Analysis
### Experimental Method  
It is worth recalling that the purpose of our algorithm is to generate facial movements during phonation for natural man-machine communication. To this end, we first estimate articulatory movements from spoken speech using our \(Fuzzy-Genetic\) optimization algorithm and then we transform the articulatory parameters into facial movements parameters. Many authors, for example [PERSON] et al. [57] have shown in fact that there exists a strong relation between articulatory and facial movements. Facial parameters are finally used to move the points corresponding to the involved facial muscles in a Geoface-articulated bone model. To measure the goodness of the generated facial movements we firstly test the quality of the synthesized speech in order to be sure that it is sufficient for supporting the subsequent optimization algorithm and then the quality of facial movements. As a matter of fact fact, the better the voice generated, the better the facial movements. The test of the generated speech is carried out with objective and subjective tests. We first test the objective quality of the generated formant transitions, which must be quite close to those extracted from the spoken voice. Then, the naturalness, quality and synchronization between facial movements and artificial vocalization are assessed with subjective experiments.  
Clearly, the synthetic speech is not confused with naturally spoken speech, whose intelligibility mainly depends on its SNR. Many authors pointed out that since the perception of spoken speech is normally accompanied by face movement, the visual part of speech should be an important perception element [58]. For this reason, graphical talking heads synchronized with synthetic speech has been developed [59]. Subsequently, the perceptual benefits of the visual speech have been experimentally highlighted. We developed a talking head too, and in this section its performance over existing systems is reported.  
The decrease of intelligibility or naturalness of synthetic speech can be modeled as a noise component added to the original speech, introducing the concept of \(EquivalentNoiseLevel\). We use this concept to predict, when not available, results on the benefits of the audio-visual speech over only audio. In this paper, we assume that visual speech is obtained with the viseme-driven approach described in Reference [60] for all the considered speech synthesizers. In viseme-driven speech animation, each key pose is associated with a viseme, that is the position of the lips, jaw and tongue when producing a particular sound [61]. In Reference [60], the results of a subjective test of naturalness of the viseme-driven system are reported. In the test, 36 participants with normal hearing and vision tested individually the visual speech synthesis of a number of words with meaning. The average naturalness was about 0.84. This coefficient was used to weight the audio-visual subjective results of our tests.
## 5 Influence of the Inclination Angle
### Displacement  
Figure 11 illustrates the displacement field of the rough slope considering joint inclination angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). It should be noted that the length of the vector arrow reflects the value of its corresponding displacement. The self-weight slope is in a vertical downward compression state after loading when the joint inclination angle is 0\({}^{\circ}\)  
Figure 10: The development of tension fractures at the top of the stratified rock slope.  
as shown in Figure 11a. Moreover, the slope displacement vector is tangential to a section of the slip surface, resulting in the sliding of the entire slope. Figure 12 shows the displacement vectors of the slope, which show the distribution of loose bodies, which means the potential slide parts of the slope. Regarding the slope with a joint inclination angle of 30\({}^{\circ}\) (Figure 11b), the upper part experiences dense compression displacement, resulting in rapid destruction of the top of the slope through a significant shear slip, leading to the complete destruction of the slope. For a joint inclination angle of 45\({}^{\circ}\), the displacement vector on the slope surface is sparse and parallels the slope surface tangent, as shown in Figure 11c. Nonetheless, the displacement is primarily concentrated in the center of the slope surface, suggesting the slide of the entire slope as a single entity, with the rock mass falling locally in the center of the slope surface. Upon applying a 60\({}^{\circ}\) joint inclination angle to the slope model (Figure 11d), the displacement vector along the slope surface is tangential to it, resulting in a dense displacement distribution within the slip surface rupture range. At a joint inclination angle of 90\({}^{\circ}\), shown in Figure 11e, the compression displacement vector remains along the surface tangent, causing the rock on the slope surface to slide down along the slope. The dense displacement vector observed at the base of the slope implies that the sliding rock continually accumulates, resulting in severe displacement.  
Figure 11: Sliding displacement vector of rough slope under different joint inclination angles. 1—Tension fractures in green; 2—Shear plane in red.