esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
###### Abstract  
D-allulose is a rare sugar with promising applications in food and health industries, owing to its low caloric value and multiple health benefits. In this study, we systematically investigated a thermostable D-allulose 3-epimerase (TcDAEase) from _Thermogemmatispora carboxidivorans_ for food-compatible continuous production. The enzyme exhibited remarkable thermostability, with over 70% activity retained at 80 \({}^{\circ}\)C, and showed broad pH tolerance across the range of 8.0 to 13.0. Notably, TcDAEase exhibited high catalytic activity toward D-allulose and D-fructose even without the addition of metal ions. Moreover, food-grade Mg\({}^{2+}\) was identified as enhancing enzyme activity by 14.3%, thus ensuring compliance with Generally Recognized as Safe (GRAS) standards for food applications. To improve industrial applicability, the enzyme was immobilized using a chitosan-diatomataceous earth (DE) matrix via three-step adsorption-crosslinking-embedding strategy. The immobilized TcDAEase achieved 48.7% \(\pm\) 2.4% activity recovery and retained 90.3% \(\pm\) 1.5% activity over seven reaction cycles. Furthermore, continuous production of D-allulose was realized in a packed-bed reactor, operating stably at 60 \({}^{\circ}\)C, pH 8.0 and 5 mM Mg\({}^{2+}\) for 150 days, producing 756 kg of D-allulose with a conversion yield exceeding 89.7% of the theoretical maximum. Overall, this study provides a feasible strategy for the continuous and efficient production of high-value-added D-allulose in the food industry.  
D-allulose; D-allulose 3-epimerase; _Thermogemmatispora carboxidivorans_; immobilization; continuous production
# Long-Term Analysis of Wave Climate and Shoreline Change along the Gulf of California
###### Abstract  
The last ten years have shown that Climate Change (CC) is a major global issue to attend to. The integration of its effects into coastal impact assessments and adaptation plans has gained great attention and interest, focused on avoiding or minimizing human lives and asset losses. Future scenarios of mean sea level rises and wave energy increase rates have then been computed, but downscaling still remains necessary to assess the possible local effects in small areas. In this context, the effects of CC on the wave climate in the Gulf of California (GC), Mexico, have received little attention, and no previous studies have tackled the long-term trend of wave climate at a regional scale. In this paper, the long-term trends of the wave height, wave period and wave energy in the GC were thus investigated, using the fifth-generation climate reanalysis dataset (ERA5). The long-term shoreline evolution was also examined from historical Landsat images, so as to identify erosional hotspots where intervention can be prioritized. The results indicate that both the mean and extreme wave regimes in the GC are getting more energetic and that two-thirds of the coast is suffering chronic erosion. A discrepancy between the trends of the wave period and wave height in some regions of the Gulf was also found. Finally, the importance of natural processes, human activity and CC in the shoreline change is highlighted, while addressing the need for future permanent field observations and studies in the GC.  
shoreline change; wave climate; climate change; Gulf of California +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Airborne Imaging Spectroscopy Data  
APEX is an airborne imaging spectrometer measuring radiation in the spectral range between 350 nm and 2500 nm in 284 contiguous spectral bands [60]. It was operated at an altitude of 6500 m a.s.l. over the study area on 12 July 2013, around 11:30 a.m. local time. The solar zenith angle and the solar azimuth angle were 28.1\({}^{\circ}\) and 139.1\({}^{\circ}\), respectively. Surface height differences and slope changes cause a varying ground sampling distance across the flight line (i.e., between 1.5-3.0 m), and data were resampled to a 2 m pixel grid.  
APEX data were geometrically corrected using a parametric geocoding approach (PARGE) [61]. Based on 15 ground control points that represent topographic diversity, a root mean square error (RMSE) of 3.2 m \(\pm\) 1.4 m was calculated. Considering the final aggregated pixel size of 30 m \(\times\) 30 m in our analysis, the achieved geometrical accuracy is acceptable. The atmospheric correction software (ATCOR-4) was used to compensate for atmospheric effects, to minimize illumination changes due to topography, and to eventually retrieve top-of-canopy hemispherical conical reflectance factor (HCRF) data [62] (for terminology see [63]). ATCOR-4 uses look-up-tables (LUT) of atmospheric functions (i.e., up- and down-welling transmittances, path scattered radiance, spherical albedo), pre-calculated with the atmospheric radiative transfer code MODTRAN-5 [64]. We assumed a mid-latitude summer atmosphere type and a rural aerosol model and estimated water vapor as well as aerosol load pixel-wise to select LUT entries for subsequent compensation of atmospheric absorption and scattering effects. We further applied the ATCOR-4 supported correction of topography induced illumination changes using a coarse resolution (i.e., 25 m spatial resolution) digital elevation model.
# Nozzle Design of Plug-and-Play Passive Pre-Chamber Ignition Systems for Natural Gas Engines
###### Abstract  
To evaluate the significance of the geometrical parameters of a passive pre-chamber on engine performance, this study investigated the design of a plug-and-play passive pre-chamber in a 15 L heavy-duty natural gas engine. Multi-dimensional numerical investigations were conducted for parametric studies involving lateral angle, orifice diameter, and vertical angle. A compressive flow solver was employed for Navier-Stoke equations, coupled with detailed sub-models and a chemical kinetic scheme. The combustion model was calibrated and could well predict the engine combustion and operating performance. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for experimental tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that, considering the trade-off between the ignition energy and the scavenging efficiency, the ratio of the pre-chamber to clearance volume is recommended to be 0.2-0.7%, and the corresponding area-volume ratio is 0.003-0.006 mm\({}^{-1}\). Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% of fuel consumption, which presents a significant improvement in fuel economy.  
natural gas engine; turbulent jet ignition; pre-chamber; scavenging; fuel consumption Article
###### Abstract  
A generalized scientific review with elements of additions and clarifications has been carried out on the methods of theoretical research on the astrophysical properties of crystals with ionic-molecular chemical bonds (CIMBs). The main theoretical tools adopted are the methods of quasi-classical kinetic theory as applied to ionic subsystems relaxing in layered dielectrics (natural crystals, crystal hydrates, various types of ceramics, and perovskites) in an electric field. A universal (applicable for any CIMBs class crystals) nonlinear quasi-classical kinetic equation of theoretical and practical importance has been constructed. This equation describes, in complex with the Poisson equation, the mechanism of ion-relaxation polarization and conductivity in a wide range of polarizing field parameters (0.1-1000 MV/m) and temperatures (1-1550 K). The physical model is based on a system of non-interacting ions (due to the low concentration in the crystal) moving in a one-dimensional, spatially periodic crystalline potential field, perturbed by an external electric field. The energy spectrum of ions is assumed to be continuous. Elements of quantum mechanical theory in a quasi-classical model are used to mathematically describe the influence of tunnel transitions of hydrogen ions (protons) during the interaction of proton and anion subsystems in hydrogen-bonded crystals (HBC) on the polarization of the dielectric in the region of nitrogen (50-100 K) and helium (1-10 K) temperatures. The mathematical model is based on the solution of a system of nonlinear Fokker-Planck and Poisson equations, solved by perturbation theory methods (via expanding solutions into infinite power series in a small dimensionless parameter). Theoretical frequency and temperature spectra of the dielectric loss tangent were constructed and analyzed, the molecular parameters of relaxers were calculated, and the physical nature of the maxima of the experimental temperature spectra of dielectric losses for a number of HBC crystals was discovered. The low-temperature maximum, which is caused by the quantum tunneling of protons and is absent in the experimental spectra, was theoretically calculated and investigated. The most effective areas of scientific and technical application of the theoretical results obtained were identified. The application of the equations and recurrent formulas of the constructed model to the study of nonlinear optical effects in elements of laser technologies and nonlinear radio wave effects in elements of microwave signal control systems is of the greatest interest.  
**Keywords: crystals with ionic-molecular chemical bonds (CIMBs); hydrogen-bonded crystals (HBCs); generalized nonlinear quasi-classical kinetic equation of ion-relaxation polarization and conductivity; Fokker-Planck nonlinear kinetic equation; quantum tunneling diffusional relaxation polarization (for the proton subsystem in HBCs); dielectric loss tangent**
## 3 Challenges and Potentials of Soft Robots
### Actuation
Applying voltage to the electrodes causes the polymer to deform due to the ionization process and the motion of ions between the two electrodes [68; 69; 70]. Shape memory alloys (SMAs) are smart materials that react to heat stimuli. When applying high temperature to the SMA, it deforms into a certain shape and is restored to its original shape after heat is removed. The heat is usually provided through electrical heating using high voltage. SMAs are used as actuators in soft robotics, as they can be embedded to drive a soft material such as polydimethylsiloxane (PDMS) [71; 72]. The use of smart actuators is prominent in underwater robotics [73] due to the favorable operating conditions for smart materials in water. In addition, smart materials can be directly embedded within the elastically deformable body of the robots, making them a good option for biomimetic applications. For example, biomimicry of jellyfish was implemented using DEAs [67] and using SMAs in the case of Robojelly [74]. Manta ray biomimetic robots were actuated using IPMCs [68] as well as SMAs [75]. A biomimetic crawling starfish used actuated legs made from embedded SMA wires cast in PDMS [71]. Another group developed a soft robotic arm inspired by octopus tentacles using cables and SMA springs [72]. The SMA springs help mimic the muscular hydrostat of the octopus's arm by providing transversal contraction. Smart actuators provide an advantage in terms of their compact size and weight, and high actuation biomimicry resembling real fish swimming modes. However, they require high-voltage sources and are hard to control. The various soft robotic platforms are shown in Table 1, classifying their biomimetic inspiration, actuation types, swimming modes, and level of compliance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Robot** & **Biomimicry** & **Acuation** & **Swimming** & **Compliance** \\ \hline
[37] & Multi-Joint Fish & Carangiform Fish & Electric Actuators (Servomotors) & BCF Undulation & Medium \\ \hline
[69; 70] & Biomimetic Fish & Fish & IPMC & BCF/MPF Oscillation & Medium \\ \hline
[40; 59; 63] & SoFi & Fish &
\begin{tabular}{c} FEA (Pneumatic/Hydroallic) \\ \end{tabular} & BCF Undulation & High \\ \hline
[41] & Stingray Robot & Stingray &
\begin{tabular}{c} Electric Actuators \\ (Servomotors) \\ \end{tabular} & MPF Undulation & Medium \\ \hline
[51] & Octopus Arm & Octopus &
\begin{tabular}{c} Motor-driven \\ Cables \\ \end{tabular} & Crawling & High \\ \hline
[72] & Octopus Arm & Octopus &
\begin{tabular}{c} Motor-Driven \\ Cables/SMA \\ Springs \\ \end{tabular} & - & High \\ \hline
[76] & Octopus Robot & Octopus &
\begin{tabular}{c} Motor-Driven \\ Cables/SMA \\ \end{tabular} & Crawling & Medium \\ \hline
[66] & Cuttlefish Robot & Cuttlefish & DEA & Jet Propulsion & Medium \\ \hline
[74] & Robojelly & Jellyfish & SMA & Propulsion & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of various underwater soft robotic systems.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 4 Experiments
### Evaluation Metrics  
In our experiments, we conducted two types of cross-modal retrieval tasks: retrieving texts using image queries (denoted as \"I2T\") and retrieving images using text queries (denoted as \"T2I\"). Next, we utilized two standard hashing performance protocols, Hamming ranking and hash lookup [57], to assess the effectiveness of our method and its competitors.For the Hamming ranking protocol, we utilized mean average precision (mAP) to measure accuracy, while precision-recall curves (P-R curves) were employed for the Hash lookup protocol. For mAP and P-R curves, we considered images and texts to be similar if they shared at least one label; otherwise, they were considered dissimilar. Specifically, given a query \(q_{i}\), the average precision (AP) of the top-\(N\) results is defined as:  
\[AP(q_{i})=\frac{1}{N}\sum_{r=1}^{R}p(r)d(r), \tag{22}\]  
where \(N\) is the number of relevant instances in the result set, \(R\) represents the total amount of data. \(p(r)\) denotes the precision of the top- \(r\) results. If the \(r\)-th retrieved result is relevant to the query instances, \(d(r)=1\); otherwise, \(d(r)=0\). The mAP value is defined as the average AP across all queries \(q_{i}\):  
\[mAP=\frac{1}{M}\sum_{i=1}^{M}AP(q_{i}), \tag{23}\]  
where \(M\) represents the number of queries.  
Figure 4: Some examples from the MIRFLICKR-25K and NUS-WIDE datasets. (**a**) MIRFLICKR-25K. (**b**) NUS-WIDE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Query** & **Retrieval** & **Text Feature** \\ \hline MIRFLICKR-25K & 5000 & 2000 & 18,015 & 1386 d \\ NUS-WIDE & 5000 & 2000 & 184,577 & 1000 d \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details about the datasets used in our experiments.
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## 3 Results
### Training Results
#### 3.1.2 Training Effects  
Using the same test set (50 images), the five models obtained from the training are evaluated. The bolt and bolt corner point detection was run on the test set with a confidence level requirement of 0.5. The average detection time per image was 0.020 s. The correct detection of bolts and the relatively accurate detection of key points were counted separately, and the detection results are shown in Table 5. The results in the table are the true number and the number of correct detections for each dataset. From the table, we can come to the following conclusions:  
1. The YOLOv7-pose model has a very excellent detection performance; even if only 100 images are used for training, it can still obtain a high detection correct rate;
2. Without adding real images to the dataset, the model trained from a pure synthetic dataset can identify bolts and key points in real images with relative accuracy when the images are well-lit and clear;
3. Compared with Keypoint-RCNN, the YOLOv7-pose shows better performance in the key-point detection correct rate.
4. In the detection results of Keypoint-RCNN, there are individual cases where the same corner point is not detected as different and some corner points are not detected correctly, as shown in Figure 19. Such errors would have a significant impact on further angle calculations.
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_Article_  
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the actual specimen state, which includes not only the internal state but also the surface state [8; 9]. Thermal conductivity (\(\kappa\)) is related to thermal diffusivity (\(\alpha\)) in the following equation:  
\[\kappa=\alpha\cdot\varrho\cdot\text{Cp} \tag{2}\]  
where \(\varrho\) is the material density and Cp is the heat capacity. The heat capacity and material density are defined according to the composition of the material, because they are physical properties. Thermal diffusivity refers to the rate at which heat travels from a high temperature to a low temperature in a material with a temperature difference. Therefore, since physical and chemical changes to the heat transfer path affect thermal diffusivity [10], the thermal conductivity for a material can be determined from its thermal diffusivity.  
Laser flash analysis (LFA) is a thermal diffusivity measurement method in which a shot laser pulse irradiates a single sample face, and then an infrared (IR) detector monitors the temperature increase of the opposite side of the sample; the thermal diffusivity is calculated from the temperature increase versus the time profile [7; 10; 11]. The advantage of the LFA method is that it can be used to investigate small or thin samples from below zero to high temperatures (approximately 200 K). In addition, thermal diffusivity reflects the material state in terms of microstructure, precipitation, defects, etc., when measured with LFA.  
Al-Mg-Si alloys are widely used as heat sink materials. The 6 XXX alloys are particularly advantageous due to their medium strength, excellent formability, high thermal conductivity, and low cost [12; 13; 14; 15]. Mg and Si are the major elements in the 6 XXX series, and they exist in the form of the Mg\({}_{2}\)Si phase in Al. The type of precipitation phase can be controlled according to the heat treatment, and the type of Mg\({}_{2}\)Si phase can be used to determine the alloy properties. The precipitations of the Mg\({}_{2}\)Si unstable phases improve the strengths of the alloys [13; 16; 17]; in particular, among the various Mg\({}_{2}\)Si precipitation phases, the \(\beta^{\prime}\) phase contributes most to improving the strength.  
Thermal diffusivity can also be improved by the precipitation of solute in a matrix [10; 18; 19]; however, few studies have been carried out on the types of Mg\({}_{2}\)Si precipitates that contribute to thermal diffusivity. The aim of this paper is to study the effects of Mg\({}_{2}\)Si content and phase precipitation on the thermal diffusivity of Al-Mg\({}_{2}\)Si alloys. In addition, the measurements of as-quenched and aging-treated specimens are compared in terms of factors such as thermal diffusivity and heat flow. This study has been carried out for the purpose of determining the influence of Mg\({}_{2}\)Si content and aging treatment on thermal diffusivity, and to apply the design of a high thermal conductivity alloy and its heat treatment condition.
# Using an Uptake Enhancer to Mitigate Nitrogen Leaching While Enhancing Uptake Efficiency
## 2 Materials and Methods  
### Experimental Site and Materials  
The study was conducted in the laboratories and glasshouses of the Department of Soil, Crop, and Climate Sciences at the University of the Free State in Bloemfontein (29\({}^{\circ}\)07\({}^{\prime}\)S, 26\({}^{\circ}\)11\({}^{\prime}\) E), South Africa. The greenhouse temperature was maintained at 23/15 \(\pm\) 2 \({}^{\circ}\)C during the day and nighttime, respectively. The study was carried out in August 2022 over a period of 16 weeks using a sandy loam soil type as a growing medium to investigate the effect of soil on the movement of N in combination with the uptake enhancer. PVC columns of 60 cm in height (made up of six 10 cm rings taped on top of each other) were used to conduct the experiment. The PVC columns were then closed at the bottom using 2 \(\upmu\)m netting to prevent the soil from passing through the bottom during the duration of the experiment. Four replicated PVC columns of each treatment were placed together in a container to collect leachate during the experiment. Each PVC column was packed with 5 kg of sandy soil to obtain a bulk density of 1000 kg m\({}^{-3}\). For irrigation purposes, field water capacity was maintained by applying gravimetric methods until harvest. To do this, the columns were irrigated with distilled water until saturation to ensure maximum water-holding capacity and then weighed. Following a 48 h drying period, the columns were reweighed to determine water loss due to evaporation and transpiration. This weighing process was repeated every two days to ensure that the soil remained at field water capacity. The calculated differences in weight from one period to the next allowed us to precisely monitor water loss and maintain optimal soil moisture levels by refilling the lost water. Three oats seeds (cultivar SSH4817--with a medium to long growth season, suited for grazing and hay production, high biomass, and good regrowth) were planted per column, spaced about 3 cm apart, whereafter thinning was carried out at the two-leaf growth stage and only one plant was left to grow in each PVC column. Early thinning was carried out to avoid the possible future complication during root mass measurement. The experiment was subjected to a complete randomized block design, with six different treatments and four replicates per treatment. One part of the experiment was constructive to enable measurements of leaching at five depth intervals. Another part of the experiment was employed for destructive purposes, entailing the monitoring of crop N uptake at four growth stages. For this purpose, additional PVC columns were added to include sampling at four growth stages. PVC columns were moved around randomly within the blocks every 4 to 5 days to expose the crops to the same conditions that could apply to different areas in the glasshouse and to prevent possible issues like shading or ventilation.