esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Adherence to the Host
#### 2.1.2 RyfA  
In addition to PapR, P-fimbriae are regulated by another sRNA called RyfA. It is a 305-nt sRNA implicated in oxidative and osmotic stress responses, biofilm formation, and swarming motility in _E. coli_ and _S. dysenteriae_([PERSON] _et al._, 2015; [PERSON] _et al._, 2016). In UPEC strain CFT073, RyfA regulates genes involved in metabolism, respiration, and motility ([PERSON] _et al._, 2015). Interestingly, _Stigella dysenteriae_ encodes two copies of RyfA namely RyfA1 and RyfA2, which are present in tandem in its genome and share about 95% in sequence identity ([PERSON] _et al._, 2015). RyfA1 but not RyfA2 was shown to be regulated by a second sRNA called RyfB1 and the overproduction of RyfA1 disrupts the virulence-associated process of cell-to-cell spread and reduces the expression of _ompC_, a key outer membrane protein essential for _Stigella_ pathogenesis.  
RyfA is crucial for _UPEC_ pathogenicity by regulating fimbrial expression, modulating _fm_ genes for type 1 fimbrie and _pap_-operons for P-fimbriae production. Additionally, _rpfA_ mutants showed a 3.5-fold and 6.3-fold reduction in _papA_ and _papA_2_ expression and 2.02- and 3.16-fold reduction in _papC_ and _papC_2, respectively, confirming its role in _pap_-operon regulation. Functional studies show that _rpfA_ mutants exhibit significantly reduced fitness in a murine UTI model, with a 124-fold decrease in bladder colonization and a 13-fold reduction in kidney colonization compared to the wild-type strain ([PERSON] _et al._, 2015). These findings suggest RyfA's role in adhesion and UPEC virulence.
## 4 Types of Catalysts Used in Transesterification
### Biocatalysts
#### 4.6.1 Immobilization of Lipases  
Entrapment  
Entrapment is employed in order to immobilize the enzymes physically in which lipase is trapped in polymeric network, for example microcapsules that allows substrate to pass through still holding the lipase. Generally, entrapment of lipase is performed by placing it in monomeric phase solution in situ and polymerization results in entrapment. As compared to physical adsorption, the entrapment method of immobilization give more stability and protection from surroundings such as gas bubbles, hydrophobic solvent and mechanical stress [145].  
_Pseudomonas ecpaciu_ lipase was immobilized in phyllosilicate sol-gel matrix and enzyme catalyzed production of alkyl esterified from grease and tallow was investigated, >95% ethyl esters were produced and possessed 5-time reusability without considerable loss of activity was noticed. Nine lipases were tested in enzymatic transesterification of soybean oil with both methanol and ethanol. _Pseudomonas ecpaciu_ was immobilized on sol-gel support resulted in highest yield of alkyl esters. After an hour, the yield of fatty acid methyl esters (FAME) was 67% mol, while fatty acid ethyl esters (FAEE) yield was 65% mol. Biodiesel from palm oil was synthesized via enzymatic catalysis by using methanol as well as ethanol. _Candidia rugosa_ lipase entrapped in activated carbon support and four-time reusability test results highest yield of fatty acid alkyl esters. An hour after the reaction, FAME and FAEE yield was 70% and 85% mol, correspondingly. _Pseudomonas ecpaciu_ lipase was immobilized by entrapping it in polyallyamine-mediated biometric silica and used to synthesize biodiesel from soybean and waste cooking oil as feedstock. The soybean oil was trans-esterified under optimum conditions were 75% n-hexane, 43.6 \({}^{\circ}\)C, substrate molar ratio 4.3% and 79% predicted value and 76% experimental yield of biodiesel was achieved. Optimum conditions for transesterification of waste cooking oil were 38% n-hexane, 43.3 \({}^{\circ}\)C, substrate molar ratio 5% and 68% predicted value and 67% experimental yield of biodiesel was attained. The entrapped lipase stored for a month at 4 \({}^{\circ}\)C and efficiency of the catalyst remained the same. In contrast to covalent bonding, a comparatively simple technique is employed while maintaining the stability and catalytic activity of enzyme [136].
# Influence of Posture Change on Train Running Safety under Crosswind
## 2 Numerical Methodology
### Numerical Method  
When simulating flow fields with large Reynolds numbers, various methods such as Large Eddy Simulation (LES) and Detached Eddy Simulation (DES) demonstrate a significant advantage in accurately describing the exact change in flow structures. However, as the mesh resolution and time-step become more rigorous, numerical performance suffers, making it challenging to complete the work in this analysis using these techniques. Additionally, in simulating flow systems analogous to those in this study, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations turbulence model is widely used. In the current study, the 3D unsteady RNG k-\(\varepsilon\) turbulence model was used to simulate the flow field as the train passes through a crosswind region.  
The calculations were carried out using the commercial code STAR CCM + 13.06. The pressure data was obtained with the use of a pressure-based solver based on the finite volume method (FVM). A second-order upwind scheme was used to discretize the convection and diffusion concepts. An implied scheme with second-order accuracy was applied to solve the time term, and the time-step was set as 5 \(\times\) 10\({}^{-4}\) s, giving a courant number less than 1. A total of 20 iterations were implemented in each time-step, and the residual of each turbulent equation was at least 10 \(\times\) 10\({}^{-5}\) at each step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Degree of Freedom** & **Leading Car** & **Middle Car** & **Trailing Car** \\ \hline Rolling rotation (\({}^{\circ}\)) & 6 & \(-6\) & 6 \\ Pitching rotation (\({}^{\circ}\)) & 1 & \(-1\) & 1 \\ Yawing rotation (\({}^{\circ}\)) & 1 & \(-1\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Posture assigned to each car.  
Figure 1: Geometric models employed in the numerical simulation: **(a)** the realistic CRH3 train; **(b)** the CAD model train; **(c)** front view of the train; **(d)** length components of the train; **(e)** train model with specific posture, according to Table 1.
## 3 Metrics for Geofence Digital Signature (GDS) Generation
### Spatial Features for Magnetic Field  
The smartphone's magnetometer can collect the three-axis parameters of the magnetic field \(S_{x}\), \(S_{y}\) and \(S_{z}\). In addition, the pitch \(P\), roll \(R\), and azimuth \(\psi\) parameters can be sensed by the smartphone's accelerometer. These last three parameters are the output from the mobile orientation sensors or the position sensors which are used in determining the smartphone's orientation [24]. The magnetometer field strength \(S\) can be given by  
\[S=\sqrt{S_{x}^{2}+S_{y}^{2}+S_{z}^{2}}. \tag{10}\]  
According to [25], the horizontal and vertical parameters of the magnetic field \(S_{H}\) and \(S_{V}\) can be calculated as follows  
\[S_{V}=-S_{x}\sin(P)+S_{y}\sin(R)+S_{z}\cos(P)\cos(R), \tag{11}\]  
\[S_{H}=\sqrt{S^{2}-S_{V}^{2}}. \tag{12}\]  
Using the parameters \(S_{H}\) and \(S_{V}\) in the GDS generation can help in reducing the magnetometer orientation effect. Therefore, the mind features \(S_{x}\), \(S_{y}\), \(S_{z}\), \(P\), \(R\), \(\psi\), \(S_{V}\),are used to represent the spatial features for the magnetic field. Each feature is represented by the mean and the interquartile range (\(IQR\)) statistics, which can be given as follows,  
\[\mu=\frac{1}{c}\sum_{i=1}^{c}x_{i}, \tag{13}\]  
\[IQR=Q_{3}-Q_{1}, \tag{14}\]  
where \(\mu\) is a central value of the data. Meanwhile, \(IQR\) is the difference between the upper quartile \(Q_{3}\) corresponds with the 75 th percentile of the data and the lower quartile \(Q_{1}\) corresponds with the 25 th percentile of the data.
## 2 Model Formulation
We apply a similar approach as above so that the population dynamics of non-reproductive workers is given by  
\[\frac{dN_{w}}{dt} = \underbrace{\frac{eN_{g}\beta_{n}N^{\delta_{n}}}{\beta_{g}N_{g}^ {\delta_{g}}+\beta_{n}N^{\delta_{n}}}}_{\text{newborn}}+\underbrace{\max\{C_{ g}-C_{w},0\}N_{g}}_{\text{switch from gamergates to worker}} \tag{5}\] \[-\underbrace{\max\{C_{w}-C_{g},0\}N_{w}}_{\text{switch from worker to gamergates}}-\underbrace{d_{w}NN_{w}}_{\text{Mortality}}\]  
We provide the flow diagram of our mathematical model in Figure 1 and the related parameters in \[E_{w}=\beta_{n}N^{\delta_{n}},\]0 Based on the discussions above, we have the following nonlinear equations to model nonlinear hierarchy in social insect colonies:  
\[E_{w}=\beta_{n}N^{\delta_{n}},\]1 Schematic flow diagram for our model. \[E_{w}=\beta_{n}N^{\delta_{n}},\]2 \[E_{w}=\beta_{n}N^{\delta_{n}},\]3 (7) \[E_{w}=\beta_{n}N^{\delta_{n}},\]4  
**Remark 1**. : _The addition of relative costs is the unity, i.e., \(C_{w}+C_{g}=1\), with all costs being non-negative [36; 37]. The values of \(C_{w},C_{g}\) could determine the survival rate of eggs (e.g., brood care, foraging), and the interchangeable rate between gamergates and workers, [32]. Because non-reproductive workers perform risky tasks such as foraging, while gamergates remain in the nest and produce brood [38; 39], it would be reasonable to assume that \(d_{w}>d_{g}\), i.e., the mortality rate of workers is larger than that of gamergates._  
Notice that \(N_{w}=N-N_{g}\), so that we can reduce our proposed system (6)-(8) to the following two equations:  
\[E_{w}=\beta_{n}N^{\delta_{n}},\]5 \[E_{w}=\beta_{n}N^{\delta_{n}},\]6  
which allows us to explore the following research questions theoretically:  
1. What are the key life-history parameters that determine the dynamics of the different classes in the colony \(N\), \(N_{g}\), \(N_{w}\)? 2. How do the metabolic parameters, such as \(\beta_{g}\), \(\beta_{n}\), and the exponent parameters \(\delta_{g}\), \(\delta_{n}\) affect the colony size, \(N\), and the size of the reproductive class \(N_{g}\)? 3. How do the colony size, \(N\), and the number of established gamergates, \(N_{g}\), regulate each other? \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Description** & \begin{tabular}{c} **Best** \\ **Estimation** \\ \end{tabular}  & **Range** & **Units** & **Reference** \\ \hline \(e\) & Egg-laying rate & 2.42 & \([0.5,2.42]\) & eggs/day & Estimated \\ \(d_{g}\) & Natural death rate & 0.00028 & \([0.0001,0.0025]\) & 1/day & [27] \\ \(d_{w}\) & Natural death rate & 0.0025 & \([0.0025,0.005]\) & 1/day & [27] \\  & \(\mathrm{Normalized}\) & & & & \\ \(\beta_{g}\) & metabolic rates for & 33.00 & \([25,50]\) & \(\mu\mathrm{W}/\mathrm{gr}\) & Assumed \\  & \(\mathrm{Normalized}\) & & & & \\ \(\beta_{n}\) & metabolic rates for & 74.60 & \([45,150]\) & \(\mu\mathrm{W}/\mathrm{gr}\) & Assumed \\  & \(\mathrm{\,\
## 2 Kinematic Constraint Equations  
A robot-trailer system consists of a four- or two-wheeled robot with Mecanum wheels and a two-wheeled trailer with conventional wheels located on one axis. All wheels of the robot-trailer system have permanent contact with the surface.  
The center of mass \(C\) is on the longitudinal axis of the robot's body so that the track width is \(2I_{C}\). The quantity \(\rho\) denotes the distance from the center of mass \(C\) to the rear and front axes so that the wheelbase is \(2\rho\). The coordinates of the center of mass \(C\) are described in the geodetic reference coordinate system \(XOY\) by means of \(x_{C},y_{C}\). The robot's azimuth angle \(\psi\) is defined by the inclination of the robot's longitudinal axis with the axis \(OX\).  
The center of mass of the trailer's body \(S\) is again symmetric to the track width \(2I_{S}\). The coordinates \(x_{S},y_{S}\) of the trailer's center of mass are given in the reference system \(XOY\). The angle between the trailer's longitudinal axis and the fixed axis \(OX\) is \(\chi\).  
Each wheel of the robot and the trailer has a radius of \(R_{C}\) and \(R_{S}\), respectively. The angles of rotation of the wheels are \(\varphi_{i}\) (\(i=1,\ldots,4\) or \(i=1,2\)) for the Mecanum robot and \(\theta_{i}\) (\(i=1,2\)) for the trailer, Figures 1 and 2.  
The robot and trailer are connected by two rods. The rods are orthogonal to the wheel axles and are fixed in the middle of the robot and trailer bodies. The rods are interconnected at point \(Q\) by a joint so that they can rotate freely relative to one another. The distances between the connection point of the rods \(Q\) and the centers of mass of the robot, as well as the trailer, are \(b_{C}\) and \(b_{S}\), respectively.  
The body-fixed coordinate system \(\xi\eta\zeta\) is introduced, which has its origin at the center of mass \(C\) of the Mecanum robot. The axis \(C\xi\) is the longitudinal axis pointing forward, \(C\eta\)  
Figure 1: Four-wheeled robot and two-wheeled trailer system (top view).  
is the lateral axis, and \(C\zeta\) denotes the vertical axis pointing upward. Consequently, \(V_{C\xi}\) and \(V_{C\eta}\) denote the body-fixed longitudinal and lateral velocities, respectively.  
The body-fixed velocity components \(V_{C\xi}\) and \(V_{C\eta}\) can be described by the geodetic velocity components \(\dot{x}_{C}\) and \(\dot{y}_{C}\) by  
\[\begin{split} V_{C\xi}=&\dot{x}_{C}\cos\psi+\dot{y}_ {c}\sin\psi\,,\\ V_{C\eta}=&-\dot{x}_{C}\sin\psi+\dot{y}_{c}\cos\psi\,, \end{split} \tag{1}\]  
and vice versa by  
\[\begin{split}\dot{x}_{C}&=V_{C\xi}\cos\psi-V_{C\eta} \sin\psi\,,\\ \dot{y}_{C}&=V_{C\xi}\sin\psi+V_{C\eta}\cos\psi\,. \end{split} \tag{2}\]
## 2 Materials and Methods
### Assessments
#### 2.3.2 Laboratory Data  
Blood, urine and fecal samples were collected in the screening process, at the baseline, in the follow-up, and at the end of the study (Figure 2). The biochemical results--including those of the total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), triglycerides (TGs) and fasting blood sugar (FBS) levels--were determined from the blood using the automated machine at the AMS Clinical Service Center, Chiang Mai University, Chiang Mai, Thailand. Other biomarkers in the blood, such as immunoglobulin A (IgA), lipopolysaccharide-LPS, and inflammatory chemokines/cytokines, were determined using an ELISA commercial kit (MyBioSource(r), San Diego, CA, USA for LPS, Elabscience(r), Houston, TX, USA for IgA, MyBioSource(r), San Diego, CA, USA for IL-10, MyBioSource(r), San Diego, CA, USA for TNF-\(\alpha\)). The plasma TAC was determined with a 2,2'-azinobis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS) radical scavenging capacity assay [14; 15]. The determination of MDA was achieved by the TBARS method, using 50 uL of the sample reacted with TBA and TCA heated  
Figure 1: The enrollment and study flowchart.  
at 100 \({}^{\circ}\)C for 30 min, and with the tube cooled in cold water. The colored complex of MDA-(TBA)2 that occurred was measured using a spectrophotometer at 532 nm [16; 17], and the reduced GSH in the plasma was determined using a recycling assay of DTNB [18].  
Urine samples were collected from the subjects in order to determine their intestinal permeability. The subjects were given mannitol and lactulose at a ratio of 1:2 dissolved in water. The subjects were required to collect urine within six hours after taking the mannitol and lactulose [19]. The volume of total urine from each subject was measured, and the intestinal permeability was analyzed using an ELISA commercial kit (EnzyChromTM, BioAssay, Hayward, CA, USA).  
Fecal samples were collected in order to determine the short-chain fatty acids using high-performance liquid chromatography (HPLC) with the following conditions: Shodex SH1011 as a column, 5 mM sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) as a mobile phase, a flow rate of 0.6 mL/min at 210 nm and 75 \({}^{\circ}\)C [20; 21].
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 3 Problem Definition  
In this section, we first introduce the system model and, then, the resource allocation problem for V2V communication inside vehicular clusters is formulated.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 2 Review of [PERSON] et al.'s Scheme
### Inefficiency of Password Updating  
By analyzing some of the related memory-based authentication schemes [15; 16; 17; 18], we found that a trend in password updating operations was to carry out this operation without help from a server. However, in [PERSON] et al.'s scheme, when a client wants to update his password, he must log in and establish a session key with the server even when the client does not want to access any of the server's services. Although this is not necessarily wrong, it is absolutely not efficient.
## 2 Materials and Methods
### Dosimetric Characterization
#### 2.2.1 Heating Rate Optimization  
A group of samples was identified, encapsulated and labeled. The samples were irradiated to a test dose of 10 Gy under standard irradiation conditions. Thermoluminescence measurements were obtained after 24 h to eliminate the low-temperature peaks associated with shallow traps, using different heating rates ranging from 1 \({}^{\circ}\)C s\({}^{-1}\) to 15 \({}^{\circ}\)C s\({}^{-1}\) to identify practically and economically effective heating rates.
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 3 Simulation Conditions and Model Validation  
### Powder Characterization  
Particle size distribution (PSD) is a critical property influencing the spreading behavior of Ti65 powder. The PSD of Ti65 powder (provided by Xi'an Bright Laser Technologies Co., Ltd., Xi'an, China) used in experiment is statistically analyzed and presented in Figure 1a. Measurements obtained using a laser particle size analyzer (Mastersizer 3000, Malvern Panalytical(r), Malvern, UK) indicate that the distribution approximates a normal distribution, with \(D_{50}\) and \(D_{90}\) values of approximately 38.7 \(\upmu\)m and 61.5 \(\upmu\)m, respectively. The inset image, obtained via Zeiss field emission scanning electron microscopy (SEM) (Zeiss, Oberkochen, Germany), reveals that the Ti65 powder particles are nearly spherical in morphology. Accordingly, the particles are assumed to be spheres in the simulations. Detailed information regarding the chemical composition of the Ti65 powder is provided in Figure 1b.
## 4 Experimental Results
### Case Study  
In this subsection, we describe conducted case studies on the OpenAI and Panda robot arm scenarios to demonstrate the advantages of the proposed DDPG-DMP over other baselines. We focus particularly on comparing it to the original DDPG, SAC, and TD3 that employ neural network-based policies, in terms of their impacts on robotic arm control behaviors. Figure 5 shows the screenshots of a robot arm controlled by the trained DDPG and DDPG-DMP in one complete rollout (100 steps) in the Pusher-v0 task, depicting the robot's state at steps \(0,25,50,75\), and 100 from left to right. It was clearly observed that DDPG's torque control lacked proper coordination between multiple joints, causing excessive robotic arm movement. Between steps 25 and 50, the robot end-effector pushed the object far away from the target position before reversing direction to complete the task. In contrast, the proposed DDPG-DMP generated gentler control actions, smoothly guiding the robot arm's end-effector towards the object from steps 0 to 75, then steadily pushing it to the target within the final 25 steps. Figure 4 illustrates the trajectories of joint angle positions and angular velocities for three crucial joints (wrist3, wrist4, wrist5) using DDPG and DDPG-DMP. Owing to the characteristic of the DMP, our method controlled each joint smoothly in terms of both angle and angular velocity. In contrast, the original DDPG without employing the DMP-based policy not only exhibited larger joint angle fluctuations but also produced severe jitter in joint angular velocities, further leading to excessively large robot arm control actions and violent swings in the end-effector position.  
Furthermore, we analyzed the superiority of DDPG-DMP in directly generating joint angle control trajectories in the PandaReachJoints-v2 task compared to TD7 and SAC(DDPG and NDP were not included in this comparison due to their poor control performance). Figure 7 compares the trajectories of all seven joint angle signals in one complete rollout for one target position after 175,000 steps' training using the aforementioned methods (joints are represented by indices 0 to 6, from the base to the joint closest to the end-effector). It was observed that in controlling the base joints (index 0, index 1) and shoulder joint (index 2), all three methods generated relatively stable control trajectories while DDPG-DMP achieved relatively smoother and smaller-amplitude joint angle trajectories. For the joints responsible for controlling the first segment of the robotic arm (index 3 and 4), TD3 and SAC exhibited noticeable jitter, negatively impacting the coordination of all seven joints. In contrast, DDPG-DMP consistently generated smooth trajectories, effectively avoiding joint jitter. For the last two joints (indices 5 and 6), our method's control trajectories demonstrated clear superiority in both appropriate joint movement range and smooth angle changes. This led to superior control performance and a high success rate.
## 6 Implementation and Simulation  
This section presents and compares simulations of the original and modified protocols using the KNN algorithm. Table 1 shows that both simulations used the same parameters.  
To reduce the network's energy consumption, all dead nodes in the network will be removed. Some CHs in the network will act as normal nodes after the energy capacity of every CH in the network is checked by calculating the distance between every cluster head to the base station, and the CH energy consumption will be checked to see if it is over the threshold. Figure 8 shows the implementation of the KNN algorithm to improve the clustering of CHs. The clustering is done in the following phases for each cluster head:  
- **Phase A:** Implement the network with Table 1 parameters and calculate the initial cluster areas and cluster heads using the original protocol and assigned coordination of the cluster heads.  
- **Phase B:** Nodes select nearby CHs using the K-Nearest Neighbours algorithm.  
- **Phase C:** Prepare the network for the next round and repeat Phase B (without nodes that selected CH from Phase B).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Definition** \\ \hline \(x\times y\) & 100 m \(\times\) 100 m & Area of network, dimensions \\ \(n\) & 50 & Number of nodes in the network \\ \(R_{max}\) & 5000 & Maximum number of rounds \\ \(P_{opt}\) & 0.1 & The probability of a node to become a CH \\ \(E_{elec}\) & 50 nJ/bit & Energy dissipation per bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) & Energy dissipation for free space \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) & Energy dissipation for multipath delay \\ \(E_{Rx}\) & 50 nJ/bit & Receiving energy of sensor \\ \(E_{D}\) & 5 & Data aggregation energy \\  & nJ/bit/message & \\ \(P_{x}\) & 0.1 & Probability of a node to become a cluster head \\ \(L\) & 4000 bits & Packet size \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used to implement the proposed protocols in MATLAB.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods  
With the development of computer level, intelligent optimization methods have been proposed one after another, being widely used in various disciplines due to their unique advantages of strong universality and efficient solution of complex problems. At present, intelligent optimization methods can be divided into two categories: traditional intelligent methods and artificial intelligence methods.