esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The integration of spark discharge with diamond wire sawing is proved to be effective in improving MRR of Al\({}_{2}\)O\({}_{3}\) ceramic. Surface quality of Al\({}_{2}\)O\({}_{3}\) ceramic and wear of diamond wire in diamond wire sawing combined with oil film-assisted ECDM were analyzed and the conclusions were drawn as follows:  
1. The integration of spark discharge with diamond wire sawing facilitates the spalling of Al\({}_{2}\)O\({}_{3}\) ceramic, which results in the increase of surface roughness. The roughness in parallel and perpendicular directions increases by 7.30% and 16.7%, respectively when 46 V is applied. The surface roughness increases with the applied DC voltage and when the applied voltage is 58 V, the roughness increases by 116% and 38.2%, respectively.
2. There are no recast layer and micro cracks on the machined surface by diamond wire sawing combined with oil film-assisted ECDM. The material removal is due to mechanical failure instead of melting.
3. The Na and Cl elements are left on the machined surface, but the material of the diamond wire does not transfer to the machined surface. Through analysis of the element content on the surface of the workpiece, it can be concluded that the oil film will not decompose to produce a carbon layer attached to the surface of the workpiece, which will have a negative impact on the material removal rate of the workpiece.
4. Micro craters are formed on the matrix of diamond wire, and the removal of Ni material increases the chance of diamond grits pull-out.  
Conceptualization, [PERSON]; writing, [PERSON]; methodology, Z.J.; writing--original draft preparation, K.Z.; data curation, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]; investigation, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52275400); Fundamental Research Funds for the Central Universities (FRF-TP-19-003A3).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: SEM of diamond wires after (**a**) diamond wire sawing combined with oil film-assisted ECDM (58 V) and (**b**) diamond wire sawing.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Nozzle Direction on Mixing Uniformity  
The influence of four structural parameters--nozzle direction, nozzle height, nozzle radius, and nozzle radius--on the gas volume fraction of the jet-stirred tank is analyzed in this study. The geometrical model of these structural parameters is shown in Figure 13. Three main nozzle orientations are used in the jet-stirred tank: upward injection, downward injection, and circumferential injection. The nozzle radius for these three models is 5 mm, and the nozzles for the upward and downward jets are angled at 150\({}^{\circ}\) relative to the horizontal plane.  
Figure 14 illustrates the instantaneous distribution of the gas volume fraction in the jet-stirred tank. The figure reveals that gas does not achieve uniform mixing inside the tank upon entering from the nozzle, whether injected upward or downward. Instead, it rapidly ascends along the walls and exits through the outlet. This flow pattern leads to a non-uniform distribution of gas within the tank, predominantly concentrated near the walls, resulting in significant variations in the gas volume fraction across different tank regions. Conversely, circumferential injection achieves a more uniform distribution of the gas volume fraction.  
Figure 12: Gas volume fraction curve with height for different aeration volumes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **AF = 0.10** & **AF = 0.15** & **AF = 0.20** & **AF = 0.25** & **AF = 0.30** \\ \hline Average & \(7.19\times 10^{-3}\) & \(1.11\times 10^{-2}\) & \(1.45\times 10^{-2}\) & \(1.82\times 10^{-2}\) & \(2.26\times 10^{-2}\) \\ Variance & \(5.57\times 10^{-6}\) & \(1.48\times 10^{-5}\) & \(2.36\times 10^{-5}\) & \(4.08\times 10^{-5}\) & \(6.82\times 10^{-5}\) \\ DV & \(2.08\times 10^{-17}\) & \(5.58\times 10^{-17}\) & \(8.90\times 10^{-17}\) & \(1.54\times 10^{-16}\) & \(2.58\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average and variance of different aeration rates.  
Figure 15 depicts gas volume fraction versus height curves for three different nozzle orientations. Upon observation, it is evident that gas volume fraction levels are generally higher across all points when using circumferential jetting compared to the other two orientations. Given the significant impact of the gas volume fraction on reaction efficiency and mixing uniformity in practical applications, the circumferential injection was selected as the preferred nozzle direction for subsequent experiments in the jet-stirred tank.
## 4 Results
### Hyperspectral Mapping Alteration Minerals  
Generally, the rock chips are larger than the thin sections, which means that the results of hyperspectral mineral mapping can indicate the mineral types and their distributions at a larger scale. Figure 7 illustrates that the results of hyperspectral mineral identification are in excellent agreement with the overall mineral identification of the sample and microscopic identification. G 05, G 06 and G 07 are altered surrounding rocks, and the following is a detailed description of the form of alteration zoning.  
G 05, adjacent to the high-grade iron ore, is the altered wall rock and mainly contains garnet, chlorite and magnetite, in which garnet accounts for about 75% and the chlorite accounts for about 20%. Chlorite grows around garnet and contains a few fine grains of quartz. The garnet grains are relatively complete and are infilled slightly by chlorite. As shown in Figure 7, some small magnetite grains are contained in garnet and show straight boundaries with garnet and chlorite.  
G 06 mainly consisted of garnet and chlorite, in which garnets accounted for about 90% of the whole sample; chlorite accounted for 10%. The distribution was concentrated for chlorite, while dispersed for  
Figure 7: Identified minerals derived from the HySpex image.  
garnet. The garnet always displayed cataclastic texture, and was infilled and replaced by chlorite as veins. Compared to G 05, The content of chlorite increased obviously.  
G 07 is mainly composed of chlorite, garnet and quartz, in which garnet accounts for about 40%, chlorite accounts for about 30% and quartz accounts for about 30%. Disseminated chlorite grows around garnet and infills cracks in garnet, and quartz veins filled the boundary between garnet and chlorite. Garnet, including a few fine grains of magnetite, is infilled and replaced by chlorite and quartz. It is worth noting that there is a quartz veinlet on the upper part, which is supposed to be the late stage of alteration. The concentrating degree of garnets is decreasing from the proximal to distal part of high-grade iron ore.  
The spectrum results are consistent with the observation of hand specimens and results under a microscope. The relative contents of garnet in the three samples are higher than the identification results under microscope, possibly resulting from the loss of chlorite, a soft phyllosilicate, during rock chipping.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 1 Introduction
Based on digital image processing and histogram specification theory, data from the CZI instrument on board the HY-1C/D satellites were used to study an image mosaicking color consistency processing method in the Antarctic region. Single-image illumination homogenization processing and multi-image color consistency processing were studied. In the experiment, a color segmentation matching method based on a threshold value is proposed to achieve color consistency between adjacent images and achieve a good effect. In the second chapter, satellite data information, data preprocessing, and image color-matching methods are introduced. In Section 3, the results of nonuniformity correction and image color matching are introduced. In Section 4, the quality of mosaic images is analyzed qualitatively and quantitatively, and the problem of overstretching in color matching is discussed. The full text is summarized in Section 5.
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 4 Experimental Results and Analysis
### Effects of the Data Augmentation  
In this section, we evaluate the data augmentation of blurred images. Let \(M_{na}\) denote the model trained on the UAVT-3 training set, \(M_{a}\) denote the model fine-tuned using blurred images with distortion \(D_{f}^{i}\) from \(M_{na}\), where \(i\) denotes the blur type (fog blur and motion blur), \(j\) denotes the distortion degree, and three distortion degrees were selected in this work. There are 12 \(M_{a}\) models evaluated on the corresponding test set, i.e., \(i\) and \(j\) were kept the same in training and testing. The experiment's results are shown in Table 3. \(\sigma\) and \(K_{m}\) denote the blur factors of fog blur and motion blur; larger \(\sigma\) and \(K_{m}\) means greater distortion. For the fog blur, it can be seen that when \(\sigma\) was set as 0.004, 0.006, and 0.008, the (P, R, mAP) of model \(M_{na}\) are (86.6%, 85.0%, 90.7%), (84.1%, 70.0%, 78.2%), (83.7%, 58%, 66.7%), while those of model \(M_{a}\) is (92.8%, 98.6%, 99.0%), (93.8%, 98.6%, 99.1%), (98.2%, 99.2%, 98.8%). The (P, R, mAP) increased (6.2%, 13.6%, 8.3%), (9.7%, 28.6%, 20.9%), (14.5%, 41.2%, 31.2%), and the average increase is (13.4%, 27.8%, 20.1%). It can be concluded that the P, R, and mAP of model \(M_{na}\) reduced greatly with the increase in \(\sigma\). However, the accuracy of model \(M_{a}\) improved greatly after fine-tuning using the data augmentation of blur images. The same conclusion can also be obtained from the motion blur part. The P, R, and mAP values of \(M_{na}\) decrease significantly with the increase in \(K_{m}\), and the accuracy of model \(M_{a}\) improved greatly after fine-tuning the motion-blurred images. The average improvement of (P, R, mAP) is (11.8%, 32.1%, 26.3%) when \(K_{m}\) was set as 55, 75, and 100. Therefore, it can be concluded that data augmentation of blur images can greatly improve the accuracy of the blurred images, i.e., improve the generalization ability in practice.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities
### The International Space Station
#### 5.2.4 Controls  
To be able to study reduced gravity as an independent experimental variable, space-flight experiments should be accompanied by a series of control experiments performed on the same set of samples. These should include 1-g ground controls, simulated-microgravity ground controls, and in-flight 1-g controls. Ground controls are normally performed after the mission completion using the same environmental parameters registered during the whole mission, like relative humidity, temperature, and pressure. The use of an on-orbit centrifuges for in-flight 1-g controls can provide assurance that samples are exposed to the same Space environmental factors, except for microgravity. On the ISS, centrifuges suitable for small samples are available for performing 1-g controls and, more recently, a system for long-term rodent investigations has also been implemented [80; 81]. Lack of in-flight 1-g controls leads to difficulties in discerning between the effects of microgravity from other aspects of spaceflight, such as vibration, radiation exposure, etc [82]. Moreover, controls are important to identify the effects due to hardware design or sample conditions, like alterations in gas exchange (due to the lack of convection in Space) and temperature control, among others. For example, early studies in plant response to microgravity were compromised by the build-up of the gaseous hormone ethylene and the lack of appropriate controls [82; 83].
## 2 Literature Reviews  
### Fault Detection and Diagnosis Models  
Generally, fault detection and diagnosis models can be divided into model-based, knowledge-based, and data-driven approaches [6]. The model-based and knowledge-based methods [16; 17; 18; 19; 20] are usually based on domain knowledge of the physical process and an understanding of the mathematical functional relationships between inputs and outputs for a specific process. Modelling-based methods, however, require extensive parameter setups [21], so they are not easily implemented as comprehensively as they could be for fault detection in real manufacturing systems. For knowledge-based methods to be successful, experts and domain expertise are critical. For the purpose of fault detection and diagnosis, data-driven methods [6; 15] are commonly used to choose features from historical data and utilized to facilitate the fault detection process. Data-driven methods utilize statistical analysis, data mining, and machine learning techniques, which allow for the learning of the process and equipment condition from historical data.  
Data-driven methods are more appropriate for fault detection and diagnosis in semiconductor manufacturing, due to the complex, interrelated structure of process steps and the complexity of the fault model, when compared to model-based and knowledge-based methods, which both rely on rigorous modeling techniques and domain expertise [22; 23; 24; 25]. For example, when using model-based methods to analyze different process variation and equipment condition due to different recipes for setting the process parameters, different models are required to be used. It is difficult to identify all the variations in a process by using a single mathematical or physical model [26].
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 3 Comparative Model Test: The First Generation T-Shaped Joint and the Traditional I-shaped Joint
### Discussion  
Compared to the conventional I-shaped joint, the proposed T-shaped joint exhibited a slight improvement in terms of crack resistance performance (i.e., the mean value of the two schemes were 4.96 MPa and 3.39 MPa, respectively). Moreover, the weak interface of the T-shaped joint was set to be far from the peak region of the negative bending moment, which dramatically reduced the cracking risk.  
Note that in general, the crack is developed at the joint interface, and there was no visible crack generated at the UHPC matrix and the joint center (corresponding to the pier top position). This phenomenon demonstrates that the joint interface is the key issue of the design.  
The shrinkage test indicated that the maximum shrinkage strain of the steam curing part and the natural curing part was 173 \(\mu\epsilon\) and 164 \(\mu\epsilon\), respectively. In reference to the UHPC axial tensile test in [31], the cracking strains of plain UHPC with different fiber content (2-3.5%) were in the range of 192-198 \(\mu\epsilon\). Therefore, for plain UHPC without reinforcement, shrinkage-induced cracking was easily developed at the successively poured UHPC interface. In the present test, before loading, no visible crack was found in the UHPC panel due to the introduction of steel bars. Note that in the first generation T-shaped joint design, there was no reinforcement between the UHPC transverse beam and the post-cast T-shaped joint (Figure 8a), which may lead to shrinkage cracks in the lower part. Besides, the rough surface quality of the UHPC transverse beam is uncontrollable, and it has a great influence on the joint strength. Consequently, we replaced the UHPC transverse beam with a steel diaphragm, and two steel stiffeners were added to each girder (Figure 8b). The optimized scheme was named as the second generation T-shaped joint.  
The second generation T-shaped joint can further reduce the weight of the prefabricated composite component. The steel diaphragm can also be employed as the formwork for casting UHPC, which speeds up the on-site construction time. The following section validates the effectiveness of the optimized scheme through a scaled model test, and the design and calculation method are also presented.
## 2 Nonlinear Finite-Element Modeling
### Material Model
#### 2.5.2 Compressive Stress-Strain Relationship  
The stress-strain models proposed by the authors [22] for plain LWAC and SFLC were introduced in the FE model, which was expressed as follows.  
Ascending:  
\[y=ax+(3-2a)x^{2}+(a-2)x^{3} \tag{17}\]  
Descending:  
\[y=\frac{k_{1}\lambda x}{k_{1}\lambda-1+x^{k_{2}\lambda}} \tag{18}\]  
where \(y=f\!/\!f_{\mathrm{c}}\)'; \(x=\varepsilon/\varepsilon_{\mathrm{c}};f_{1}\) is the general stress in the compression; \(\varepsilon_{1}\) is the general strain in the compression; \(f_{\mathrm{c}}\)' is the peak stress; \(\varepsilon_{\mathrm{c}}\) is the strain corresponding to the peak stress; \(E_{\mathrm{c}}\) is the elastic modulus of the concrete; and \(a\), \(\lambda\), \(k_{1}\), and \(k_{2}\) are the modified factors, as defined in Table 1.  
The conversions established by [PERSON] and [PERSON] [23] were employed to generate the modified compressive stress-strain model. The expressions for the initial modulus of elasticity and strain corresponding to peak stress can be written as:  
\[E_{\mathrm{c}}=0.0736(f_{\mathrm{c}}^{\prime})^{0.3}\rho_{d}{}^{1.51} \tag{19}\]  
\[\varepsilon_{\mathrm{c}}=(0.71f_{\mathrm{c}}^{\prime}+168)\times 10^{-5} \tag{20}\]  
where \(\rho_{d}\) is the oven-dry density of concrete.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **LWAC** & **SFLC 1** \\ \hline \(a\) & \(1.797E_{\mathrm{c}}\varepsilon_{\mathrm{c}}/f_{\mathrm{c}}^{\prime}-1.264\) \\ \(\lambda\) & \(1/[1-(f_{\mathrm{c}}^{\prime}/\varepsilon_{\mathrm{c}}E_{\mathrm{c}})]\) \\ \(k_{1}\) & \(k_{1}=(37.15/f_{\mathrm{c}}^{\prime})^{3.062}\) & \(k_{1}=-0.220r-0.017f_{\mathrm{c}}^{\prime}+1.758\) \\ \(k_{2}\) & \(k_{2}=(41.25/f_{\mathrm{c}}^{\prime})^{1.544}\) & \(k_{2}=0.299r+0.014f_{\mathrm{c}}^{\prime}-0.505\) \\ \hline \hline \end{tabular}  
* \(r\) = volume fraction of steel fibre in %.  
\end{table}
Table 1: Parameters for compressive stress–strain model. LWAC: lightweight aggregate concrete, SFLC: steel fiber-reinforced lightweight aggregate concrete.  
Figure 3: Illustration of the yield surfaces and deviatoric plane of the concrete damaged plastic (CDP) model [19]: (**a**) Yield surfaces of the CDP model; (**b**) Deviatoric plane of the CDP model (\(\sigma_{i0}\) is the tensile stress corresponding to the end of the linear portion in the ascending branch).
## 4 Isoprenoid Production in Methanotrophs
### C5: Isoprene  
Isoprene, a volatile 5-carbon hydrocarbon, is an important platform chemical for the production of polyisoprene (for tires and the rubber industry), elastomers (for sporting goods), and isoprenoids (for adhesives and pharmaceuticals) [40; 41]. Isoprene is produced by direct separation from the oil cracking fraction and dehydration of C5 isoalkanes and isoalkenes. However, seven gallons of crude oil are needed to make one gallon of isoprene. In other words, demand is higher than supply, and studies are being conducted to biologically synthesize isoprene to solve this problem. Biologically, isoprene is produced by various organisms such as microorganisms, plants, and animals through the MVA and MEP pathways [42]. Currently, algae and cyanobacteria are used as biomass for biological isoprene production. These have the advantage of being renewable resources, but more than half of the mass is composed of oxygen, which results in low conversion efficiency. This is because the oxygen content of the isoprene is lower than the oxygen content of the cellulosic biomass, so oxygen must be removed as waste [43]. Therefore, it is still not suitable for commercial production.  
To solve this problem, studies are being conducted to synthesize isoprene from methane gas through methanotrophs. Methanotrophs have genes encoding the enzymatic components of the MEP pathway. Therefore, if methanotrophs have an isoprene synthase, it can synthesize isoprene from both methane and methanol (Figure 1). Recently, recombinant technology of methanotrophs has been developed, and the isoprene synthase (_ispS_) derived from sweet potato was expressed in _M. alcaliphium_ 20Z to synthesize 50 mg/mL of isoprene from methanol (Table 2) [44].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product** & **Strain** & **C1 Feedback** & **Process** & **Productivity** & **Reference** \\ \hline \multirow{2}{*}{Isoprene} & _Methylomicribuium_ & \multirow{2}{*}{Method} & \multirow{2}{*}{Expression of the isoprene synthase (_isy5_)} & \multirow{2}{*}{50 \(\mathrm{\SIUnitSymbolMicro mL}\)} & \multirow{2}{*}{[44]} \\  & _calciphium 202_ & & & & \\ \hline Limonene & _Methylomonas_ sp. 16a & Methane & Expression of the limonene synthase (_int5_) & 0.5 ppm & [45] \\ \hline \multirow{2}{*}{Astaxanthin} & _Methylomonas_ sp. 16a & \multirow{2}{*}{Methene} & Expression of the geranylgeranyl diphosphate synthase (_crf1_), phytonee & \multirow{2}{*}{2.4 mg/gDCW} & \multirow{2}{*}{[46]} \\  & & synthase (_crf8_), phytonee desaturase (_crf1_), lycopene cyclase (_crf7_), & & & \\  & & beta-carotene ketolase (_crf9_), and beta-carotene hydroxylase (_crf2_) genes & & \\ \hline \multirow{2}{*}{Astaxanthin} & _Methylomonas_ sp. 16a & Methane & Use of transposon promoter-probe vectors for enhancing C40 carotenoid & 2 mg/gDCW & [47] \\ \hline \multirow{3}{*}{Vitamin B2} & _Methylocystis_ sp. & \multirow{2}{*}{Methene} & Exponential phase & 0.05 \(\mathrm{\SIUnitSymbolMicro mL}\) & \multirow{2}{*}{[48]} \\  & Strain M & & Late exponential phase & 0.05 \(\mathrm{\SIUnitSymbolMicro mL}\) & \\ \cline{
# Biological Decontamination by Microplasma
## 4 Discussions
In addition to the energy efficiency, the microplasma electrodes' system and power supplies are relatively cheaper to produce and integrate as portable devices due the low discharge voltage that requires reduced-size power supplies and less insulation. The microplasma shown in this research is suitable mostly for carrying out decontamination for relatively flat surfaces, and the results obtained with already-grown bacteria highlight the use of microplasma technology as a prophylactic measure. The colony-forming units of _P. aeruginosa_ were difficult to count; thus, the calculations of D values were not carried out. Considering the higher initial concentration of CFU/mL and an inhibition of 100% on an area with 45 mm diameter, after the microplasma treatment of _P. aeruginosa_ at \(-\)1.7 kV, the D values in Joule would be lower than what was calculated for _E. coli_ and _S. aureus_. Classic decontamination technologies use heat or chemicals, but these methods are not energy efficient in the case of heat treatment or are potentially harmful to humans, as in the case of chemical decontamination. The heat sterilizers commonly use hot air at 170 \({}^{\circ}\)C for 60 min to achieve sterilization. In order to decontaminate surfaces with hot air, the energy consumption is high compared with microplasma treatment, and some materials would also not be suitable for the heat-decontamination process at high temperatures. Liquid chemicals can be used for surface decontamination, but due to the toxicity of the liquid, the process will require additional rinsing after using water that may not be sterile. The only limitation of microplasma for the biological decontamination of surfaces compared with heat and chemicals is the requirement of relatively flat surfaces to be decontaminated.