esa-sceva/satcom-chunk-collection huggingface dataset:
Illustrating and Enhancing the Biosynthesis of Astaxanthin and Docosahexaenoic Acid in _Aurantiochytrium_ sp. SK4  
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activity and therefore has been widely applied in aquaculture, nutraceuticaltical, pharmaceutical, and cosmetic industries [19]. Up to date, astaxanthin is mainly obtained by chemical synthesis and extraction from _Haematococcus phyvials_[20; 21]. The astaxanthin produced by chemical synthesis contains four different stereoisomer configurations: (3S, 3\({}^{\prime}\)S), (3S, 3\({}^{\prime}\)R), (3R, 3\({}^{\prime}\)S) and (3R, 3\({}^{\prime}\)R). The proportion of functional (3S, 3\({}^{\prime}\)S) isomer were only 25% [22]. In consideration of the advantage of astaxanthin and pathogenicity of synthetic products [23] such as synthetic \(\beta\)-carotene [24], increasing attention focused on natural astaxanthin. The strict growth conditions and quite low yields of _H. pluvialis_ have limited the application of natural astaxanthin.  
_Aurantiochytrium_ sp. SK4 belongs to Thraustochytrids that has the ability to accumulate high amounts of DHA as well as significant contents of carotenoids including astaxanthin [17]. Furthermore, by overexpressing an oxygen-involved protein, this microbe produced much higher amounts of fatty acids and astaxanthin [17], suggesting the potential of the organism to serve as a cell factory for producing such important metabolites as astaxanthin and DHA by overexpressing rate-limiting enzymes. However, the influence on the biosynthesis of DHA and astaxanthin remains largely unknown, which limits the metabolic engineering of the microbe for high-yield production of the metabolites.  
In this study, the biosynthetic pathways of astaxanthin and DHA in _Aurantiochytrium_ sp. SK4 was illustrated by characterizing the genes involved in the pathways through the analyses of the genome, transcriptome, gene functions, and metabolites. Moreover, the biosynthesis of carotenoids and fatty acids in _Aurantiochytrium_ was greatly enhanced by directing farnesyl pyrophosphate (FPP) to geranylgeranyl pyrophosphate (GGPP), the key limiting step to carotenoid production in the organism, via the expression of the _Archaeoglobus GPS_ with the ability to convert isopentenyl pyrophosphate (IPP) and dimethylallyl diphosphate (DMAPP) into GGPP.
## 3 Results and Discussion
### Starch-Iodine Absorption Spectra  
The absorption of the starch samples and iodine was attributed to the content of amylose and amylopectin. The combination of amylose and iodine produced a spiral complex which has a maximum absorption peak in the wavelength of 620-680 nm, while amylopectin formed a complex with iodine, which has a maximum absorption peak of 520-560 nm [39]. In our study, the iodine absorption spectra of the 16 BRS samples from the ABB, AAB, and AAA genome groups were obtained and are shown in Figure 6. The maximum absorption wavelengths of the BRS samples varied from 582 nm to 606 nm, which was between the maximum absorption peaks of amylose and amylopectin. The results indicated that all samples comprised both amylose and amylopectin. The value of the optical density (OD) at 620 nm (OD620) is usually used to assess the amylose content of starch [40]. It was anticipated that Meishi 1 (AAB) would exhibit a high amylose content, as indicated by the highest OD620 value. Conversely, Zhongjiao 8 (AAA) was expected to have the lowest amylose content, as indicated by the lowest OD620 value. Moreover, the ratio of amylose to amylopectin, as well as the weight of amylose molecules, impacts the aggregation of amylose molecules, resulting in different absorption peaks [41; 42]. Guangfen 1 (ABB) had the largest absorption wavelength (606 nm), followed by Saba (ABB), Maiden (AAB), Hongjiao 1 (AAA), Meishi 1 (AAB), Meishi 2 (AAB), and Poingo (AAB), among which Zhongjiao 8 (AAA) reached the lowest value (582 nm). The results indicated that the BRS samples from the ABB and AAB genome groups exhibited a greater inclination to form resistant starch than those from the AAA genome group.
## 3 New Shear Resistance Formula for the Y-Type Perfobond Rib Shear Connector
### Regression Analysis for the New Shear Resistance Formula  
A new shear resistance formula is designed to extend the application range of the shear resistance formula for the Y-type perfobond rib shear connector and to improve the accuracy of shear resistance estimation, especially for multi-rib shear connectors with more than 4 ribs and high-strength concrete. The new shear resistance formula is developed to represent the contributions of main design variables, such as number of ribs and rebars, concrete strength, rib size and material strength of steel plate, rebar size and material strength of rebar, etc., rather than the contributions of individual resistance actions, such as the end bearing, dowel effect of rib, and rebar resistance, etc. A new shear resistance formula is designed to have the basic format proposed in Equation (2), in which the first term outside of the parenthesis represents the effect of multiple number of ribs. The first term in the parenthesis is the resistance due to the transverse rebar and the second term is the contribution due to Y-type rib. The last term outside of the parenthesis is the effect of the concrete strength. Therefore, the new proposed resistance formula consists of only  
Figure 4: Comparison of experimental and calculated data (Equation (1)): (**a**) 2 ribs, (**b**) 4 ribs, (**c**) 6 ribs. (**d**) Total data.  
2 terms, that is, rebar term and rib term, and may be simpler than the current 3-term formula (Equation (1)).  
\[Q_{n}=n^{\alpha_{1}}\bigg{(}\beta_{1}d_{r}{}^{\alpha_{2}}f_{yr}{}^{\alpha_{3}}+ \beta_{2}f_{y}{}^{\alpha_{4}}\bigg{(}\frac{t}{10}\bigg{)}^{\alpha_{5}}\Big{(} \frac{w}{80}\Big{)}^{\alpha_{6}}\bigg{(}\frac{h}{100}\bigg{)}^{\alpha_{7}} \bigg{)}f_{ck}{}^{\alpha_{8}} \tag{2}\]  
where, the \(Q_{n}\) (N) represents the nominal shear resistance of multiple Y-type perfobond rib shear connectors, \(n\) is the individual number of ribs and transverse rebars, \(d_{r}\) (mm) is the diameter of a transverse rebar, \(f_{yr}\) (MPa) is the transverse rebar's yield strength, \(f_{y}\) (MPa) is the steel plate (Y-rib) yield strength, \(t\) (mm) is the steel plate (Y-rib) thickness, \(w\) (mm) is the Y-rib width, \(h\) (mm) is the Y-rib height, and \(f_{ck}\) (MPa) is the concrete strength. All the coefficients and indices, beta and alpha values are obtained through the regression fit analysis with the experimental results. The proposed formula is derived to have the application ranges for design variables adopted in Equation (2), as summarized in Table 7, which are selected based on the experimental specimens as well as numerical specimens in Table 5.
###### Abstract
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Country/Region**} & \multirow{2}{*}{**GNSS/Leveling Data Points**} & \multicolumn{3}{c}{**Global Geopotential Models (N\({}_{\text{max}}\))**} \\ \cline{3-5}  & & **EGM2008 (2190)** & **EIGEN-6C4 (2190)** & **GECO (2190)** \\ \hline Australia & 201 & 21.7 cm & 21.2 cm & 21.6 cm \\ Brazil & 1112 & 46.0 cm & 44.6 cm & 45.1 cm \\ Canada & 2691 & 12.8 cm & 12.6 cm & 13.1 cm \\ Europe & 1047 & 12.5 cm & 12.1 cm & 12.3 cm \\ Japan & 816 & 8.3 cm & 7.9 cm & 8.0 cm \\ USA & 6169 & 24.8 cm & 24.7 cm & 24.6 cm \\ All & 12,036 & 23.97 cm & 23.61 cm & 23.71 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Precision levels of the GGMs used in this study [19]. Figure 1: Location map of the 1182 GNSS/leveling UCPs in South Korea that were used in this study for the evaluation of the geoid heights in the study area.
## 1 Introduction
These parameters become even more important in designing a gripper for robotic arms with limitations in maximum load (most small industrial robots) as well as mobile robotic arms, like an autonomous robot with a robotic arm in a radiation zone [12]. Existing robotic grippers are relatively heavy, often featuring many actuators to provide dexterity, and some of these mechanisms even have counterweights or interchangeable ends. Although these features provide stability in the design and allow for different objects to grab, they result in increasing the weight of the robotic gripper, which is not ideal [12; 15]. While additional weight may not be a concern for large-scale industrial robotic arms, it is important for small-scale robots in daily applications. Lower weight and, as a result, minimal energy consumption is another advantage of using compressible material in designing the robotic end-effectors. The arm must support its weight as well as the weight of the gripper, plus the weight of whatever objects it grasps. Thus, a heavy multipurpose gripper for robotic arms can be a major drawback [16]. On the other hand, the current grippers are often designed for one application, and they must be completely changed when objects are changed [12]. This drawback makes some companies see grippers as useless in their applications. Efforts are currently underway to make grippers easily interchangeable with robotic arms, thus enabling more companies to recognize their potential. However, this technology can still be further improved [12; 17]. This paper studies a compressible robotic gripper with the ability to deform and grab objects, grasping objects with different sizes, weights, and shapes. This mechanism is made of flexible and dexterous plastic (TPU) which acts as a combination of form closure and force closure mechanism to be able to grasp objects in a range of sizes and weights [18]. The current scientific challenges in robotics gripper technology revolve around developing a grasping system capable of safely and securely gripping objects of different sizes and shapes across a wide range. Given the significance of these considerations, the primary focus of this research is to design a compressible gripper with the capability to grasp diverse objects with varying sizes, shapes, and textures.
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 3 Experiments and Results
### Fabrication of the MHOE
The angle between the incident angle \(\theta_{3}\) of projector I and the incident angle \(\theta_{5}\) of projector II is much larger than the maximum deviated angle of the MHOE, that is 38\({}^{\circ}\)\(>\) 10\({}^{\circ}\). The angle between the incident angle \(\theta_{2}\) of projector I and the incident angle \(\theta_{6}\) of projector II is much larger than the maximum deviated angle of the MHOE, that is 38\({}^{\circ}\)\(>\) 10\({}^{\circ}\). Therefore, there is no crosstalk between the left and right viewing zones. The projector (BenQ MX518F) is used to ensure the diffraction efficiencies of the MHOE. The horizontal scanning frequency is 102 KHz, and the vertical scanning frequency is 120 Hz. The resolution of the projector is 1600 \(\times\) 1200. The detailed parameters of the projector are shown in Table 1. Projection beam I is projected onto the MHOE with a pair of angles \(\theta_{2}\) = 61\({}^{\circ}\) and \(\theta_{3}\) = 81\({}^{\circ}\), which are the same as in the first step. Then, spherical wavefront array I is reconstructed in the left direction, as shown in Figure 8a. Projection beam II is projected onto the MHOE with a pair of angles \(\theta_{5}\) = 81\({}^{\circ}\) and \(\theta_{6}\) = 61\({}^{\circ}\), which are the same as in the second step. Then, spherical wavefront array II is reconstructed in the right direction, as shown in Figure 8b. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Parameters** & **Values** \\ \hline Green laser & Power & 400 mW \\ Wavelength & 532 nm \\ \hline Green sensitive photopolymer material & Thickness & 15 um \\ Resolution & 12000 line/mm \\ Sensitivity & 10 mJ/cm\({}^{2}\) \\ Averaged refractive index & 1.47 \\ \hline Micro-lens array & Pitch & 1 mm \\ Focal length & 3.3 mm \\ \hline Beam splitter & Coupling ration & 1:1 \\ \hline  & Model & MX518F \\ Projector & Resolution & 1600 \(\times\) 1200 \\  & Horizontal scanning frequency & 102 KHz \\  & Vertical scanning frequency & 102 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the optical devices in our experiments.
## 4 Implementation and Performance Analysis of CAES-Based Image Encryption  
After designing the CAES algorithm shown in Figure 7, this proposed CAES was applied to image encryption, and its encryption performance and security were analyzed. Here, Python was used as the developing environment. The program modules used included Pycrypto, Numpy, Numpydoc, OpenCV, and Matplotlib. In this image encryption, PNG images were used as the plain text data. The encrypted files could also be displayed normally by pixel processing. At the same time, the encrypted pixel data were stored in bytes as the test data for the National Institute of Standards and Technology (NIST) test. In this application, the master-slave hyperchaotic Henon Maps in Equations (1) and (2) were used with a synchronization controller from Equation (6) to synchronize dynamic random key generators with SHA256, as shown in Figure 7. The encryption and decryption of the CAES flow chart are shown in Figures 8 and 9, respectively.
# Towards Design and Development of Security Assessment Framework for Internet of Medical Things
## 3 Security Assessment of IoMT
### Attack Vectors  
1. Storage of data in the cloud--Improper encryption, authentication, and access control of the cloud.
2. Gateway connection to the cloud--Insufficient security mechanisms for device connectivity with the cloud.
3. Storage of data in the gateway--Inadequate policy framework as well as a lack of strong encryption, verification, and authentication protocols at the entry point.
4. Software/hardware/firmware vulnerabilities--Lack of proper guidelines for secure software development, insufficient update check firmware, and the application of malicious embedded hardware.
5. Communication protocol--Appropriate security services are ineffective, as is the use of an old proprietary procedure and improper protocol setup.
6. App connectivity--Lack of security control in app connectivity to the gateway by Wi-Fi.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 3 Preliminaries
### Place Recommender System  
Due to the growth of Location-Based networks (LBN), the need for providing recommendations to individuals according to their interests is very much appreciable. In this case, the location-based services contributing more effectively to this purpose [2]. Recommendation system (RS) is one of the most popular features provided by the LBSNs. RS has different types of applications based on user choices. It can find the user's interest depending on the user's previous activities and can suggest the user on unvisited places. It can also provide a suitable place for a group of users to meet together.
# A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model
## 1 Introduction
Among various studies on the safety issues in HRI involving rehabilitation robots, some have optimized the robots to specifically reduce safety hazards. For example, a filter algorithm was designed and applied to cope with the accidental triggering of dangerous robot motions [16]. Alternatively, the possibility of secondary injury caused to patients can be reduced by optimizing the control strategy [29]. In another study, a safe strain diagram of muscles was built based on the biochemical model [30], and this was incorporated into the robot trajectory planning strategy to improve system safety. However, none of the described approaches can entirely circumvent the aforementioned safety hazards. According to the literature review, a common solution to these hazards is to add a complex mechanism and sensors to the original equipment to limit the robot workspace. However, this will increase the complexity of the equipment and increase the costs, and this is not conducive to the use and popularization of rehabilitation robots [31]. In this paper, we propose a safe admittance boundary algorithm based on the space classification model for the force-sensing collaborative robots that have already been put into large-scale production. Virtual workspace constraints are imposed in the control layer (Figure 1) to elevate HRI safety in robot-assisted active training. First, the method for solving the workspace of the FK model is improved. The Monte Carlo method is combined with voxel center filtering to perform point cloud isodensification of the human and robot workspaces. Next, the nearest neighbor density is calculated in the point cloud. On this basis, a space classification model is built and integrated with admittance control. A variable stiffness admittance strategy is designed accordingly to construct the safe admittance boundary. Finally, the HRI safety is verified experimentally. In the simulation environment, the proposed algorithm is subjected to space verification experiments and out-of-bounds experiments using the dynamic arm simulator (DAS). The ability of the proposed algorithm to improve system safety is thus verified. This paper presents three main contributions:  
1. An algorithm model is proposed for identifying the safe space range based on the density of neighboring points in the point cloud. 2. The working space of the robot's end degree of freedom is morphologically and reasonably restricted in the robot control layer. 3. This algorithm can improve the safety of HRI for collaborative robots in the field of upper limb rehabilitation. The safe admittance boundary algorithm is described in four sections. Section 1 provides an overview of the algorithm. Section 2 introduces the point cloud isodensification of the workspace in the FK model. Section 3 describes the construction of the space classification model based on the nearest neighbor density in the point cloud. Section 4 presents the variable stiffness admittance strategy.
## 3 Advantages of the Proposed Configuration
### The Attenuation Chains as Seismic Sensors  
Sensing the changing distance of the seismic attenuation filters with respect to the surrounding rock at various heights along the attenuation chains provides a measurement of the linear and tilt movements for active damping of the pendulum modes. It is also important to reduce the movements induced on the test masses by the micro-seismic peak [27; 28]. In this regard, it should be noted that after acquisition of Fabry-Perot lock, each pair of test masses effectively form a single, rigid inertial mass hanging from two widely separated points subjected to the seismic noise of two vertices of the triangle. After the Michelson lock acquisition, the four test masses of each interferometer form an even larger composite inertial mass with suspension points distributed in all three facility vertices. Optical levers, augmented with fringe counting, provide length sensing between each test mass and the surrounding rock. In the Einstein Telescope triangular topology, the six locked detectors provide multiple independent measurements of the low-frequency seismic motion with two different suspension frequency tunings and three orientations. These redundant signals can be combined to form synthetic seismometer measurements of the low-frequency seism at the three vertices with a noise floor substantially below what can be achieved with conventional sensors. These signals can be used to suppress the amplitude of low-frequency motion caused by micro-seismic noise. All of this contributes to reduce the residual motion of the test masses and ultimately control noise.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 3 Numerical Simulation
### Numerical Results  
The results from the numerical simulation are contrasted against velocities recorded by the YNB and MDJ seismic stations. Figure 5 presents this comparison, and the velocity evolution based on the numerical simulation is portrayed in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\rho\) & \(D\) & \(P_{IC}\) & \(A_{juel}\) & \(B_{juel}\) & \(R_{1 juel}\) & \(R_{2 juel}\) & \(\omega_{j\mid t\mid}\) & \(E_{0}\) \\ (**kg**:\(\mathbf{m}\)**-**3**) & (\(\mathbf{m}\)**:\(\mathbf{s}\)**-**1**) & (**GPa**) & (**GPa**) & (**GPa**) & (**GPa**) & \(R_{1 juel}\) & \(R_{2 juel}\) & \(\omega_{j\mid t\mid}\) & (**j**:\(\mathbf{m}\)**-**3**) \\ \hline
1630 & \(\epsilon\)930 & 27 & 371 & 0.743 & 4.15 & 0.95 & 0.3 & \(1\times 10^{9}\) \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters of TNT [52].  
Figure 5: Comparison of measured and simulated amplitude in velocities at (**a**) YNB and (**b**) MDJ seismic stations.  
As illustrated in Figure 5, the results demonstrate a high degree of alignment between the simulated and observed data, both in terms of wave arrival times and amplitude in velocities. For instance, the discrepancy between the observed and simulated peak amplitude in velocities at the YNB and MDJ seismic stations within specific timeframes remains below 9%. The results indicate that, for the specific earthquake source and area under consideration, a TNT explosion can effectively simulate seismic energy propagation. However, it is important to confirm this applicability whenever the source characteristics or site conditions vary, as both source parameters, such as fracture dynamics, and site characteristics significantly influence peak ground velocity.  
Figure 7 showcases the stress distribution at distances extending 45\({}^{\circ}\) horizontally from the epicenter, positioned 578 km below the surface. The relationship between distance and stress, derived from numerical simulation results, is shown in Equation (18), highlighting an exponential power decrease in stress amplitude with increasing distance:  
\[\sigma=60.1\times d^{-1.32}, \tag{18}\]  
where \(\sigma\) is the effective stress and \(d\) is the distance from the hypocenter.  
Figure 6: Seismic-wave amplitudes in the velocity distribution from the numerical simulation.  
Figure 7: Stress distribution at various distances extending 45\({}^{\circ}\) horizontally from the epicenter located 578 km below the surface.
## 2 Materials and Methods
### Model Performance  
The mean absolute error represents the average of the absolute difference between the actual and predicted values in the dataset. It measures the average of the residuals in the dataset.  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}}\big{|}Y_{i}-\hat{Y}\big{|} \tag{5}\]  
(best value = 0; worst value = +\infty) where \(\hat{Y}\) is the predicted value of \(Y\).  
The mean squared error represents the average difference between the actual and predicted values in the dataset. It measures the variance of the residuals.  
\[MSE=\frac{1}{N}{\sum\limits_{i}^{N}}\big{(}Y-\hat{Y}\big{)}^{2} \tag{6}\]  
(best value = 0; worst value = +\infty).  
The root mean squared error is the square root of the mean squared error. It measures the standard deviation of the residuals.  
\[RMSE=\sqrt{MSE}=\sqrt{\frac{1}{N}}{\sum\limits_{i=1}^{N}}\big{(}Y_{i}-\hat{Y} \big{)}^{2} \tag{7}\]  
(best value = 0; worst value = +\infty).  
The coefficient of determination or R-squared represents the proportion of the variance in the dependent variable that the linear regression model explains. It is a scale-free score; irrespective of the values being small or large, the value of R square will be less than one.  
\[R^{2}=1-\frac{\sum\big{(}Y_{i}-\hat{Y}\big{)}^{2}}{\sum\bigg{(}Y_{i}-\hat{Y} \bigg{)}} \tag{8}\]  
(worst value = -\infty; best value = +1) where \(\hat{Y}\) is the mean value of \(Y\).  
RMSE and R-squared quantify how well a linear regression model fits a dataset. The RMSE indicates how well a regression model can predict the value of a response variable in absolute terms. In contrast, R-squared indicates how well the predictor variables can explain the variation in the response variable [45].