esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Various Strategies for Enhancing the Production of Biofuel
### Engineering Microbial Pathways to Enhance Tolerance Towards Bifuel  
Microorganisms possess pathways that produce compounds that can be used for producing biofuels. As these compounds are anti-microbial in nature, microbes generally produce them in much lower amounts. So, there is a need to enhance the production of these compounds along with developing microbial tolerance towards them. This tolerance is often complex and mostly associated with stress response methods [46]. To induce biofuel tolerance in microbes, many strategies were employed. Some of them were modifying membrane proteins, expressing heat shock proteins (HSPs) or efflux pumps in host microbial cells, and activating the genes related to stress response [46; 47]. When efflux pump-related operon is cloned into host cells, they enhance the tolerance by exporting the toxins out of the cells [46]. Different microbes have different efflux pumps, for instance _E. coli_ has pumps which specifically pump out toluene, and others like acrAB-tolC pumps facilitate the pumping of toxins like octaanes, heptanes, and hexane [47]. Generally, HSPs are produced to assist the proper folding of proteins along with their synthesis and transport. As a response to stress, their HSP production increases, as they help in the prevention of protein aggregate formation. In a study by [PERSON] et al., it was reported that the HSP-related genes like htpG, rpoH, ibpAB, and dnaJ were observed to be affected when _E. coli_ was exposed to butanol [48]. It was also reported that the plasma membrane composition alters when microbes are allowed to interact with solvents; this change is to prevent the entry of those solvents into the cell [46]. The membrane composition is altered by converting cis fatty acids in the membrane to trans with the help of the cis-trans isomerase enzyme. By converting cis-fatty acids to trans, the membrane fluidity changes, affecting its permeability, and in turn leading to the solvent tolerance [49].
## 1 Introduction
In order to solve this problem, the aim of this work is to design and test a hybrid data transmission network and its concept with extended capabilities, making it possible to simultaneously transmit data from solar power plant sensors and various monitoring subsystems using RF modules, Wi-Fi technology, GSM networks, etc., and monitoring and evaluating the operating parameters of the solar power plant using various software subsystems (e.g., mobile applications).
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 3 Design of Wear Characterization Experiments  
### Static Experiments  
Wear characterization experiments are conducted as a series of clutch closing cycles characterized by the target values of the following four closed-loop controlled operating  
Figure 2: Illustration of time responses of clutch slip speed \(\omega_{m}\), torque \(M_{2m}\), closing time \(t_{2}\) and temperature \(T_{dm}\) controlled in closed-loop manner with target values: \(\omega_{0R}\) = 2000 rpm, \(M_{2R}\) = 40 Nm, \(t_{2R}\) = 5 s, \(T_{dR}\) = 80 \({}^{\circ}\)C (a) including zoom-in detail of single closing cycle (b) [25].  
parameters (see Section 2): clutch temperature (\(T_{dR}\)), initial slip speed (\(\omega_{0R}\)), closing time (\(t_{2R}\)) and clutch torque (\(M_{zR}\)). The wear rate \(w\) is defined as the ratio of clutch worn volume \(V_{w}\) (expressed in mm\({}^{3}\)) and corresponding cumulative dissipated energy (expressed in M)):  
\[w=V_{w}/\sum_{i=1}^{n}E_{dis,i}\left[\mathrm{mm}^{3}/\mathrm{MJ}\right], \tag{1}\]  
where \(n\) represents the number of considered closing cycles with the same operating parameters, and \(E_{dis,i}\) is the dissipated energy during the \(i\)th cycle.  
Three distinctive levels of each operating parameter are considered: \(T_{dR}\in\{120,\,170,\,240\}\)\({}^{\circ}\)C, \(\omega_{0R}\in\{1200,\,1700,\,2800\}\) rpm, \(t_{2R}\in\{0.9,\,2.15,\,3.4\}\) s, \(M_{zR}\in\{25,\,50,\,75\}\) Nm. The basic/initial design of experiments (DOE) is derived from all combinations of individual parameters' levels (i.e., full factorial design), resulting in \(3^{4}=81\) experimental operating points in total (characterization points shown by blue circles in Figure 3). Operating parameters of several characterization points are adjusted (see, e.g., empty blue circles), since they could not be executed due to the test rig limitations. Additional 20 interpolation points are derived from the target operating space by maximizing their distance from the basic 81 points (green circles in Figure 3). Finally, 20 extrapolation points (red circles in Figure 3) are placed outside of the target operating space and close to the edge of individual parameters' feasible operating region. The characterization and extrapolation points are used for wear rate model parameterization (modeling points), while the interpolation points serve as a basis for model validation.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 3 Results and Discussions
### Comparisons of Image Enhancement Methods
The average computational time for each algorithm across all datasets is summarized in Table 3 below:  
Due to its logarithmic complexity, our method has a similar computational expense to the AMSR, though it is slower than traditional methods like CLAHE and SSR.
## 4 Numerical Simulation and Discussion
### Governing Equations and Constitutive Models
#### 4.1.2 Heat Transfer  
The heat transfer process in a saturated porous medium consists of convective and diffusive fluxes. The energy balance equation for heat transfer is  
\[(\rho C)_{eff}\frac{\partial T}{\partial t}+\rho_{w}C_{w}u\cdot\
abla T+\
abla \left(-\kappa_{eff}\
abla T\right)=0, \tag{4}\]  
where \(\phi\) is the porosity. \((\rho C)_{eff}=\phi\rho_{w}C_{w}+(1-\phi)\rho_{s}C_{s}\) is the equivalent volume heat capacity, \(C_{w}\) is the specific heat capacity of water, \(C_{s}\) is the specific heat capacity of solid, and \(\rho_{s}\) is the solid density [43]. The fluid velocity \(u\) is included in the convection term. \(\kappa_{eff}=\phi\kappa_{w}+(1-\phi)\kappa_{s}\) denotes the equivalent heat conductivity of a porous medium, \(\kappa_{w}\) is heat conductivity of water, and \(\kappa_{s}\) is the heat conductivity of solid.
## 4 Discussion
### Limitations and Prospect  
Previous studies mainly discussed vegetation dynamics using a specific index, and explored potential reasons in a single region. This study revealed the inconsistency between greenness and productivity in three major agglomerations from 2001 to 2020, and emphasized the crucial role of climatic factors and land use change in shaping these ecological changes. This study provided insights into the vegetation dynamics within the agglomerations and highlighted the regional differences in their responses to external factors, which is crucial for developing targeted policies and management strategies aimed at enhancing urban sustainability.  
There are also many limitations in this study. First, this study solely assessed the inconsistency between greenness and productivity, without delving into their correlation. Comprehending the linkages between LAI and GPP is vital to mechanistically explaining how LAI translates into GPP [58]. Future research should explore this relationship in depth to gain insights into the ecological processes driving vegetation growth. Second, this study simply analyzed the impacts of climate and land use change on greenness and productivity. To fully reveal the underlying mechanisms of regional vegetation change, future studies should delve into the contribution rates and impact paths of these two factors. This would provide a deeper understanding of how these external forces shape vegetation patterns and dynamics.
## 2 Structure and Working Principle
### Working Principle  
The axial flux motor (AFM) features a unique structure that operates based on the principle of electromagnetic induction. As shown in Figure 2, the motor's magnetic flux predominantly flows along the axial direction. The magnetic field produced by the stator interacts with the magnetic field of the rotor's permanent magnets, forming a closed magnetic flux circuit. When the stator windings are energized, they produce a magnetic field that interacts with the field generated by the rotor's permanent magnets, creating torque to drive the motor. The permanent magnets generate a stable magnetic field, which interacts with the stator's magnetic field to produce an electromagnetic force, ultimately driving the rotor. This interaction between the magnetic fields of the stator and the rotor allows for the rotor to convert electromagnetic energy into mechanical energy, which is output by the motor.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 2 Shearer Cutting Unit Detection in Infrared Thermal Images
### Feature Capture of a Shearer Cutting Unit in Infrared Thermal Images
#### 2.2.1 Binarization Processing  
By setting the gray value of pixels in an image to 0 or 255, a binary image can be obtained. The amount of information in the image is greatly reduced, making the object features more obvious. Since the image is segmented, many pixels with similar gray values collectively form a specific region. For example, there is a distinct difference between the gray values of different objects, so we can reset the gray values of image pixels by a given threshold value. Because the infrared thermal image in this paper used the iron-red color palette, the rgb2 gray function of MATLAB was first applied to gray-scale processing, and then the im2 bw function was used to achieve binarization. The binarization result of an infrared thermal image is shown in Figure 3.
## 3 Results and Discussions
### Radar Feature Analysis
It can be inferred from Figure 8 that SAR image bands can be used to improve the classification of land covers, in comparison with using the spectral bands of an image alone. It is interpreted from Figure 8 that mainly three types of land cover can be classified. Region A, which was shown as green patches in Figure 8, exhibited the highest backscattered power in June. This is because the shrubs in the Gobi Desert grow stronger in the summer than in the winter (channel B) or fall (channel R). Accordingly, region B, which is shown as bright yellow patches in Figure 8, is revealed as vegetation because of high backscattering in both channel R (SAR image acquired in Feb) and channel G (SAR images acquired in June). This is because the biomass of vegetation stays relatively high from spring through summer in the study area. Region C is interpreted as the center of Dunkuotan Village because the dense white spots are shown. This is because the residential buildings are proved to backscatter high in the SAR image throughout the year; i.e., there is no seasonal variance found between winter and summer for buildings in the SAR images. SAR images are verified to improve the detectability of buildings and many types of land cover, in comparison with when optical images are deployed only.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 3 Experimental Results and Analysis  
We implemented our algorithm in the C++ programming language and conducted experiments on a notebook computer equipped with a Ryzen 7 3750H CPU, 16 GB memory, and the operating system is Windows 10. To evaluate our algorithm, we used five HDR RGBE images (HDR 1 to HDR 5) which had been used as test images in HDR data embedding literature [11; 12; 13; 14; 15]. In addition, we adopted five standard LDR color images (LDR 1 to LDR 5) collected from the USC-SIPI image database which have been utilized in several data hiding or steganographic schemes [16; 17; 36]. Our original test images are presented in Figure 3. Additionally, all HDR images presented in this paper were tone mappedusing [37] prior to display for visualization purpose. In addition, we use tone-mapped images for image quality assessments presented later in this section.
## 4 Discussion and Conclusions
### Discussion
#### 4.1.2 Characteristics of Flat Digging Control  
Due to the normal excavation operation of the grab, grooves will be formed at the bottom, as shown in Figure 22.  
In contrast with the traditional excavation method, the grab level excavation described in this paper can achieve high flat digging precision. The steps by which such flat digging control is achieved were as follows:  
(1) In Formula (12), the size parameters \(a\), \(b\), \(c\), \(d\), and \(e\) of grab were determined, the kinematic equation established according to the kinematic relationship, and the variable parameters determined; (2) according to the set depth of flat digging, the motion trajectory of the natural excavation stage was calculated; (3) through the control movement of the closing wires and hoist wires, the upper block compensation method was used to carry out flat digging, and the movement trajectory calculated; (4) in this study, the flat digging precision was calculated and the parameters obtained as output. We found that reasonable surface accuracy could be obtained through flat digging grab control.  
However, there are several limitations in this research. First, the property parameters of materials in DEM simulation were derived from their engineering characteristic parameters, and their complete accuracy cannot be guaranteed. Material parameters need to be verified  
Figure 22: Normal digging grooves.  
by experiments to ensure the accuracy of experimental parameters; second, the material studied in this paper was exclusively sand, and there is a lack of research into other materials. It is necessary to further study the mechanical test of the grab digging material; third, this test was carried out on a land test site. Sea testing of the grab dredger may produce different results. Economic effects, weather factors and seawater flow should all be considered in future studies. The sea test will be completed in our follow-up work, which will further fulfil the research goal set out in this paper.
# Controlling Traffic Congestion in a Residential Area via GLOSA Development
## 2 Materials and Methods
### Data Collection  
Through the available data and information as shown in Figure 1, and data from the general traffic data (GTD) [10], as per [PERSON] et al., calculating congested travel by vehicle miles involves multiplying the congested segment length by the vehicle volume summed across all congested segments. Show the Figure 2 average traffic volume.  
The percent of congested travel is an expansion of the congested travel measure, providing an assessment of the extent of congestion [25; 26]:  
* In Figure 1A, a section is shown in red, which runs between point A (33.30229621555221, 44.3564187464969) and point B (33.281860, 44.347720).
* Rush time is from 7 am to 10 am (3 h).  
Figure 2: Average traffic volume for the actual state.  
* The cross-sectional area of the road is 10.5 m.
* The vehicle length and safety distance 6 m.
* Each road has three lanes, and the width of the lane is approximately 3 m.
* Vehicle travel time for the distance between the two points is 4 min during the rush time.  
Using the previous equations, we can calculate the maximum traffic volume during rush hour. Since the rush congestion time is 3 h, the total number of minutes is 180 min. If the vehicle travel time for the distance between the two points (2.6 km) is 4 min, then the total number of vehicles that can pass through the road during rush hour is as follows:  
* Capacity of one lane = (2.6 \(\times\) 1000/6) = 433 vehicles.
* Total capacity of road = 433 \(\times\) 3 = 1299 vehicles.
* Therefore, the total capacity of each lane is approximately (240/433) = 1 vehicle per 2 s.  
This is much higher than the maximum traffic volume, which means that the road is experiencing congestion during rush hours. To this congestion, several measures can be taken, such as increasing the traffic volume, implementing traffic management strategies, and encouraging the use of public transport.  
Figure 3 illustrates the basic parameters involved in the simulation procedure, which includes the following:  
* Number of simulations run to collect data; the time interval is the time between each simulation run.
* Vehicle travel time evaluation is the method used to evaluate the travel time (average).
* The number of vehicles recorded is the total number of vehicles recorded during the simulation.
* Average travel time(s) of vehicles in the network is the average travel time for all vehicles in the network.
* Distance traveled is the total distance traveled by all vehicles in the network.  
Figure 3: Basic parameters (A: vehicle input start; B: vehicle input end; C: signal heads; D: vehicle travel time results; E: signal times table windows; F: vehicle).
## 2 Materials and Methods
### Implant Stability Measurement  
Primary stability was checked using the Oostell device (Oostell Inc. W&H Dentalwerk, Salzburg, Austria) (Figure 4) by connecting a smart peg to the implant and measuring the value of the implant stability quotient (ISQ). The Oostell device allows for the evaluation of an implant's stability by resonance frequency analysis (RFA). Implant stability is measured as the implant stability quotient (ISQ), with values ranging from 1 to 100. An ISQ greater than 70 is regarded as the most favorable for implant stability, whereas ISQ values below 60 indicate low primary stability. The ISQ is related to the lateral stability of the implant, which depends on the rigidity of the bond between the bone and the implant surface [20].
## 2 Related Work
### License Plate Recognition in Real Road Environments  
In real road environments, perspective effects and geometric distortions caused by the relative position and angle between the camera and the vehicle pose major challenges for license plate recognition. A YOLOv8-based vehicle tracking system was therefore developed in a study [23]. To address geometric transformations of license plates captured from various viewpoints, this system utilized 3D geometric modeling to estimate the intrinsic and extrinsic parameters of the camera and develop an algorithm that can transform distorted license plate images to a frontal viewpoint based on these parameters. It also enabled the restoration of partially occluded license plates to their complete form by fusing multi-view information, achieving an 18% improvement in detection performance compared to existing methods.  
A license plate identification system robust to geometric distortions through a customized YOLO architecture based on YOLOv8 was built in another study [24]. An affine transformation-based normalization module was designed to effectively handle trapezoidal deformations of rectangular license plates caused by perspective effects. The proposed method achieved a high recognition accuracy of 92.3%, even in situations where the camera angle was tilted up to 45 degrees, demonstrating stable performance particularly against rapid viewpoint changes occurring on highways or at intersections.  
A preprocessing module that utilizes homography transformation to convert license plates captured from various camera viewpoints to a standard viewpoint was developed in a study [25], addressing geometric distortion issues in an integrated system for real-time vehicle detection and license plate recognition. A dynamic geometric correction algorithm was introduced to respond to complex vehicle trajectories and various entry angles occurring in intersection environments. The proposed system was designed to track vehicle movements in real-time while simultaneously predicting and correcting geometric deformations of license plates, achieving a high license plate recognition success rate of 97.1%.
## 3 Results and Discussion
### Determining Confidence Interval  
The confidence interval (CI) is calculated by:  
\[CI=\sqrt{F_{\alpha}(1,f_{e})\cdot V_{e}\cdot\left(\frac{1}{N_{e}}+\frac{1}{R} \right)} \tag{3}\]  
where, \(f_{e}\) is the freedom degrees of error (\(f_{e}=2\)), \(V_{e}\) is the average error (\(V_{e}=0.1206\)), \(F_{\alpha}(1,2)\) is the Fisher coefficient corresponding to the confidence level (\(\alpha\)) of 90% (\(F_{\alpha}(1,2)=8.5263\)), \(R\) is the number of iterations of an experiment, and \(N_{e}\) is the number of effective iterations which can be computed as follows:  
\[N_{e}=S_{sum}/\left(1+A_{f}\right)=27/(1+2+2+2)=3.857 \tag{4}\]  
where, \(S_{sum}\) is the total number of experiments and \(A_{f}\) is the total freedom of all averaged parameters. Substituting \(F_{\alpha}\), \(V_{e}\), \(R\) and \(N_{e}\) into (3) we have:  
\[CI=\sqrt{8.5263\cdot 0.1206\cdot(\frac{1}{3.857}+\frac{1}{3})}=0.78\]  
Accordingly, at a confidence level (\(\alpha\)) of 90%, the grinding time is predicted with the optimum level of the input parameters \(a_{ed3}/R_{PM3}/F_{c3}\) as follows:  
\[(10.06-0.78)\leq\overline{t_{c,op}}\leq(10.06+0.78)\]  
Or:  
\[9.28\text{ min}\leq\overline{t_{c,op}}\leq 10.84\text{ min}\]  
In addition, a confirmation experiment was conducted to evaluate the appropriateness of the optimum parameters. The experimental results and predicted values are given in Table 8. Since the error between experimental and predicted value was appropriately 6.6% of the range, the experimental work is said to be satisfactory.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 4 The RCS Features of the CR Array in Multi-Resource SAR
### The RCS Reduction Trend and Systematic Bias Analysis
Additionally, the measured RCS of DR4 in both ascending and descending TSX data shows substantial disparities with theoretical values, exceeding 5 dBsm in this case. These discrepancies may arise from environmental influences or alignment deviations during CR installation [8,22].