esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Neointimal Hyperplasia  
Neointimal hyperplasia (NIH) and thrombosis are the biological processes that drive the majority of vascular implant failures. NIH is a wound response that promotes blood clot formation in vessels in response to significant luminal injury. Vascular treatments, including percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), arteriovenous grafting (AVG), and arteriovenous fistula (AVF) establishment, are major contributors in the development of NIH and thrombosis, despite their substantial benefits. NIH is classified into two types: (1) arterial NIH caused by mechanical trauma during PCI, and (2) venous graft NIH resulting from CABG, AVG and/or AVF formation in haemodialysis patients [1; 2].  
The development of NIH, as depicted in Figure 1, can initially be due to injury of the delicate inner endothelium vessel layer during the implantation of prosthetic devices. For example, the overexpression of deployed stent [3]. Endothelial damage induces a complex inflammatory response, which triggers the migration and proliferation of vascular smooth muscle cells (VSMCs) at the tunica intima. Cell accumulation in the tunica intima layer thickens the arterial wall and leads to a loss of patency [4]. Later, affected arterial sites are highly susceptible to the build-up of lipoprotein, which accelerates the progression of atherosclerosis [5].  
Many research studies have proven that the stent type and shape can induce an inflammatory response [6]. Therefore, stents have been through several generations to enhance clinical outcomes and mitigate side effects after deployment. Bare metal stents (BMS) are associated with a high risk of restenosis and the need for repeat revascularization, whereas drug eluting stents (DESs) have shown more favourable outcomes in patients by reducing the risk of in-stent restenosis (ISR) [7; 8]. On the other hand, vascular access, used for haemodialysis's treatment in chronic kidney disease (CKD) patients, are at a high risk of failure too due to NIH [9; 10]. The failure of these accesses cannot be corrected and rather, patients will have to undergo another procedure to establish another access.
## 3 Digital Beam-Forming  
Digital beam-forming (DBF) is a marriage between antenna technology and the evolution of digital devices. It is an advanced approach to steer phased array antennas. The synthetic narrow beam enhances the directional resolution of the radio system. The number of array elements and space between them determine the beamwidth and level of sidelobes [20]. The directional pattern of a uniform line array can be expressed as:  
\[|F(\theta)|=N\frac{\sin(\frac{N\pi}{\lambda}d(\sin\theta-\sin\theta_{B}))}{ \frac{N\pi}{\lambda}d(\sin\theta-\sin\theta_{B})} \tag{1}\]  
where \(F(\theta)\) is the array pattern, \(\theta\) is the azimuthal angle, \(d\) stands for the distance between adjacent antennas, \(\lambda\) is the wavelength of the transmitting signal. \(N\) is the number of array elements, \(\theta_{B}\) is the maximum value of the array beam pointing in azimuthal direction. The array pattern is represented as the sinc function, and the maximum value is \(N\)[21].  
Phased array antennas are used to steer a narrow beam over an arc from a fixed antenna array. When \(\left.\right|F(\theta)\left.\right|=1\), the maximum value of the array beam pointing is at \(\theta_{B}\) (\(\theta=\theta_{B}\)):  
\[\theta_{B}=\arcsin(\frac{\lambda}{2\pi d}\Delta\Phi_{B}) \tag{2}\]  
where \(\Delta\Phi_{B}\) is the phase difference between adjacent antennas and can be changed to steer the array main beam direction. At each transmitter the direction is adjusted with the application of a systematic phase delay to the signal sent to the antenna.
## 2 Materials and Methods
### Numerical Investigation  
#### 2.4.1 Fundamental Theory of XFEM  
XFEM adds additional degrees of freedom to the nodes near the crack surface and the crack tip, and an enhancement function (a shape function satisfying appropriate properties)  
Figure 6: Schematic diagram of strain gauge position: (**a**) Support plates; (**b**) Girder outer web; (**c**) Girder inner web.  
Figure 7: Acoustic emission instrument and sensors.  
is used to construct nonlinearity and capture a singular stress field at the crack tip. The asymptotic displacement function is as follows [20]:  
\[u=\sum_{i=1}^{N}N_{i}(x)[u_{i}+H(x)a_{i}+\sum_{\alpha=1}^{4}F_{\alpha}(x)b_{i}^{ \alpha} \tag{1}\]  
where \(N_{i}(x)\) is the standard shape function of nodal displacements, usually a continuous interpolating polynomial; \(u_{i}\) stands for the displacement vector of the standard finite element method nodes; \(a_{i}\) and \(H(x)\) are the enriched nodal degrees of freedom vector and the discontinuous step function for element nodes across the crack surface and \(b_{i}^{\alpha}\) and \(F_{\alpha}(x)\) are the enriched crack tip nodal degrees of freedom vector and the elastic asymptotic crack tip function, respectively.  
As illustrated in Figure 8a, the first part in square brackets of Equation (1) applies to all standard nodes for the continuous part of the finite element model, the second part applies to nodes in elements divided by the crack, and the third part applies to nodes in elements intersected by the crack tip. When there is no crack, the equation is reduced to the displacement function of the standard finite element method.  
In Equation (1), the Heaviside enhancement function is \(1\) on one side of the crack surface and \(-1\) on the other side, and \(H(x)\) is expressed as [24]:  
\[H(x)=\left\{\begin{array}{ll}1&(x-x^{*})\cdot n\geq 0\\ -1&\text{otherwise}\end{array}\right. \tag{2}\]  
where \(x\) is a Gaussian integration point; \(x^{*}\) stands for the point on the crack surface nearest to \(x\) and \(n\) is the outward unit normal vector perpendicular to the crack at point \(x^{*}\). The normal and tangential coordinates of the crack are illustrated in Figure 8b.  
In Equation (1), the crack tip enhancement function is a set of linearly independent bases extracted from the analytical solution of the crack tip displacement field based on LEFM. For isotropic materials, the expression of \(F_{\alpha}(x)\) is as follows [20]:  
\[F_{\alpha}(x)=[\sqrt{r}\sin\frac{\theta}{2},\sqrt{r}\cos\frac{\theta}{2},\sqrt {r}\sin\theta\sin\frac{\theta}{2},\sqrt{r}\sin\theta\cos\frac{\theta}{2}] \tag{3}\]  
where, as shown in Figure 8b, (\(r\), \(\theta\)) are the coordinates in a polar coordinate system with the origin located at the crack tip, and \(\theta=0\) indicates the direction tangential to the crack.
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 4 PDDQN-HHVBF Protocol
### Setting of Candidate Relay Nodes  
In the above, we have introduced in detail how to establish a virtual pipeline when a node forwards data. In this part, we will introduce how to determine a candidate relay node when a node within a certain pipeline forwards data. Before the sending node data, it first sends a request information packet, which requests all node location information, energy information and candidate relay node information around the sending node. Nodes in M-UWSNs are defined as Formula (10):  
\[N=\left\{n_{1},n_{2},n_{3},\ldots,n_{m}\right\} \tag{10}\]  
\(n\) and \(m\), respectively, represent the sensor node and sensor node number, as shown in Figure 5:  
Figure 4: The schematic diagram of virtual pipelines in M-UWSNs.  
Figure 5: The schematic diagram of candidate relay nodes.  
\(N_{i}\) is the node that pre-forwards data, first find the neighbor node of \(N_{i}\), when \(n_{j}\) satisfies the Formula (11), it will represent the neighbor node belonging to \(n_{i}\):  
\[\left|\overrightarrow{N_{i}n_{j}}\right|\leq R,\quad n_{j}\in N \tag{11}\]  
after finding the neighbor nodes of \(N_{i}\), all neighbor nodes need to judge whether they belong to the nodes in the virtual pipeline established by \(N_{i}\) and Sink node. As shown in Figure 3, the angle between \(\left|\overrightarrow{N_{i}n_{j}}\right|\) and \(\left|\overrightarrow{N_{i}Sink}\right|\) is \(\theta_{j}\). When the \(\theta_{j}\) satisfies Formula (12), it means that \(n_{j}\) belongs to the node in the virtual pipeline established by \(N_{i}\) and the Sink node, that is, \(n_{j}\) is the candidate relay node when \(N_{i}\) node pre-forwards data:  
\[0\leq\theta_{j}\leq\frac{\pi}{2}\to n_{j}\in C_{candidate} \tag{12}\]  
\(C_{candidate}\) represents the set of candidate relay nodes of the sending node \(N_{i}\). All candidate relay nodes of \(N_{i}\) can be selected according to the above formula. At the same time, in order to prevent the candidate relay node from drifting out of \(C_{candidate}\) due to the movement of ocean currents, which leads to the problem of routing holes. All candidate relay nodes will calculate their own candidate relay node number \(Num_{can}\) and pass it to the pre-forwarding data node. Larger \(Num_{can}\) means that the more relay nodes can be selected, the less possibility of routing holes.
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 4 Experimental Results and Discussion
### The Parameters Selection Discussion  
For proposed method, these parameters \(K,\mu,\beta,\tau,\lambda,\theta\) need to be given to run the algorithm. For \(K\), we find that when it is smaller, the cartoon image will become smoother. Once \(K\) is determined, we fix \(\tau=2\) and change the other parameters, we find that when \(\beta\leq 0.05\) and it is smaller and smaller, the cartoon image will become smoother and smoother. To see these experimental phenomena more clearly, we take the Finger image as an example and fix \(\beta=0.02,\ \mu=0.1,\ \lambda=0.2,\ \theta=0.5,\ \tau=2\) to change \(K\) and obtain the flowing results in Figure 1.  
For \(\lambda\), if it is larger and larger, the pseudo edge \(|\omega|\) image will get thicker and thicker, the cartoon becomes more and more blurred. In short, we choose \(K=0.001\) in the following experiments, other parameters are adjusted according to different test images.  
The test images are in Figure 2 and the stopping condition is \(\varepsilon=10^{-2}\) or the maximum 400 iterations. The decomposition results are compared with them using [4; 5; 8; 11; 12; 15; 18; 26; 39; 40], respectively.
## 2 Materials and Methods  
This article is based on the analysis of GPS products, LEO precise orbit products, and LEO GPS observation datasets obtained from different sources. The second-order, time-difference carrier phase liner combination is presented. Then, based on the relationship between the STP value and the satellite's acceleration, the STG cycle slip detection and repair approach is described in detail.
## 3 Parameter and Performance Comparison
### Execution Speed
#### 3.2.2 Execution Speed of Digital Signature Algorithms on x86/64 Platforms and Energy Consumption  
Data from [38] show the execution speed on the x86/x64 platform with an i7-6700 processor, and [27] provides execution time data for the i7-1165G7 processor for Dilithium and Falcon, as shown in Figure 7. Both [38] and [39] provide data on energy consumption, concluding that energy consumption is roughly proportional to the speed, i.e., the execution time, as power consumption has much smaller deviations (maximum 20% in [38], maximum 50% in [39]) compared to execution speed (which varies by several orders of magnitude).  
Figure 6: Execution speeds (in thousands of clock cycles) of digital signature algorithms on the Arm Cortex M4 platform according to [37] (“Dilithium 1”, “Falcon 1” and “Sphincs+” bars), [9] (“Falcon 2” bars) and [21] (“Dilithium 2” bars).
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.4 Internal Heat Dissipation from the Spacecraft  
While the S/C is operational, internal heat is generated by its electrical components. In this analysis, the heat dissipation is directly correlated with the S/C's CONOPS (Concept of Operations) and its power consumption, with thermal dissipation considered as a portion of the total power used. In this paper, the two cases considered are that 50% or 100% of the power consumption is converted to heat emissions.
## 1 Introduction
Specifically, queuing models are frequently employed to analyze delay performance [33; 34; 35]. The standard queuing system is typically represented by A/B/C/K/N/D, where A denotes the arrival time distribution, B represents the service time distribution, C indicates the number of servers, K signifies queue capacity (sometimes considered to be infinite), N represents the population of customers (often assumed to be infinite), and D signifies the service discipline, typically First Come First Served (FCFS) [9; 14; 36]. One of the most commonly used queuing models is the M/M/s/N model, which uses a finite queuing length constraint with an equal amount of charging piles in each station, where both the arrival rate and the service rate are assumed to have a Poisson distribution [31]. Furthermore, [PERSON] et al. [10] determined the charging demand and charging station candidates for queuing simulation by using regression and logarithmic transformation. An alternative path to enhance realism and optimize research efficiency involves gridding the map [11; 37]. [PERSON] et al. [30] aimed to reduce energy loss for EVs and the grid by determining the optimal location and size of the charging stations. Meanwhile, [PERSON] et al. [38] conducted an empirical study that combines charging convenience, charging cost, and charging time into a simplified single-objective optimization framework with limited deployment options. In summary, a notable limitation of the current methodologies lies in their reliance on the assumption of an infinite supply of charging piles, neglecting the potential impact of waiting delays. Thus, the optimization of charging station deployment for electric taxis, with a focus on service quality, has been insufficiently addressed [17]. Most research endeavors have centered on maximizing either total costs or demand coverage, with service-related factors, such as customer loss, often being marginalized and monetized for optimization purposes. To meet future electric taxi charging demands, this empirical study aims to optimize the deployment of charging stations to enhance service quality by reducing waiting times and the potential number of unserved taxis. A heuristic algorithm known as Improved K-means iterated with Queuing Theory (IKQT) is proposed for the charging deployment planning process. Trajectory data from Shanghai Qiangsheng Taxi's GPS tracking from April 2018 are utilized for model validation.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 2 Theory Introduction  
### SDP Technology  
As a new image processing method that presents the original signal of the time series in an intuitive image, the SDP method is able to fully characterize the gear vibration signal. Assuming that the time-domain vibration signal is \(X=\{x_{1},x_{2},\ldots,x_{i},\ldots,x_{n}\}\), the time-domain vibration signal is converted to a polar plot by the SDP technique, and this point is \(S=[r(i),\theta(i),\phi(i)]\). The conversion diagram of the SDP technique is shown in Figure 1.  
In Figure 1a, \(x_{\min}\) and \(x_{\max}\) are the minimum and maximum amplitudes of the vibration signal, respectively, and \(x(i)\) and \(x(i+l)\) are the corresponding amplitudes of the vibration signal at time \(i\) and time \(i+l\), respectively. The converted coordinates can be obtained by putting the vibration signal through the SDP technique, as shown in Figure 1b. The relationship of the images is built before and after processing with the SDP technique, as follows [40]:  
\[r(i)=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}} \tag{1}\]  
\[\theta(i)=\theta+\frac{x_{i+l}-x_{\min}}{x_{\max}-x_{\min}}\zeta \tag{2}\]  
\[\phi(i)=\theta-\frac{x_{i+l}-x_{\min}}{x_{\max}-x_{\min}}\zeta \tag{3}\]wherein, \(r(i)\), \(\theta(i)\), and \(\phi(i)\) denote the radius of the polar coordinates and the deflection angles along the mirror symmetry plane, counterclockwise and clockwise, respectively. In addition, \(\theta\) and \(\xi\) are the mirror plane of the symmetry angle and the angular gain factor, respectively, where \(\xi\leq\theta\).
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 2 Cell Membrane and Lipids of _Sulfolobus_ Spp.
### Membrane Regulators  
Three primary functions have been attributed to biological membranes. First, they prevent the entrance of toxic substances into the cell. Secondly, they provide a framework for the channels and receptors responsible for the inward and outward transport of nutrients, ions and waste products. Thirdly, they provide separate environments between organelles. Membrane regulators, such as sterols, have the ability to regulate the membrane dynamics in Bacteria and Eukarya. They are known to protect the membrane from amphipathic toxins and stabilize its structure [75].  
In a current review, [PERSON] et al. suggested possible members of archaeal membrane regulators. Polytepenes, like carotenoids, polyprenols, quinones and apolar polyisoprenoids are likely to function as those, and help the cell membrane to adapt to physiological and physicochemical stressors. Sulfolobales feature all four of the proposed membrane regulators. As for carotenoids, \(\beta\)-carotene and zeaxanthin have been shown to be present in Sulfolobales. In Bacteria and Eukaryotes this said polyterpene has an antioxidant effect and intervenes with the membrane's physicochemical properties. However, the function in Archaea is still unknown, together with the insertion process into the monolayer membrane of _Sulfolous_ spp. Polyprenols are involved in membrane protein glycosylation and protect cells against free radicals. Its length of side chains and number of unsaturations in polyphenyl-alcohol were associated with environmental conditions such as temperature. Althoughquinones have not been found in all Archaea, sulfolobusquinones are present in Sulfolobales. Besides its involvement in the transfer of electrons and protons in the respiratory chain, this potential archaeal membrane regulator is engaged in membrane modulation and stabilization. In _Saccharolobus solfataricus_ (previously _Sulfolobus solfataricus_), the quinone side chains are affected by oxygen availability and carbon source. Apolar polyisoprenoids' function is still being investigated. Longer apolar isoprenoids are present in hyperthermophilic _Thermococales_, as well as extreme halophiles such as _Haloferacales_ and _Halobacteriales_, while thermoacidphilic Sulfolobales synthesize mainly short ones. This can be due to the fact that long-chain polyisoprenoids are correlated to bilayer and not monolayer, which _Sulfolobus_ membrane is mainly comprised of due to the high tetraether lipid content. In the bilayer, this polyterpene acts as a membrane stabilizer towards stress factors [76].
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 5 Necessity and Methods to Increase the CFRP Conductivity  
In recent years, CFRP has been widely applied in many fields, particularly in aircrafts. Compared to metal materials, CFRPs can significantly decrease the weight of structural parts and have been increasingly widely applied. Epoxy resins with good properties have wide applications, but they show the undesired electrical insulation and limited the global electrical conductivity of CFRPs [166]. High electrical conductivity is required in aircrafts for lightning protection and EMI shielding [141; 167; 168]. [PERSON] and co-workers showed how the alignment of carbon materials can be used to improve the conductivity [169; 170]. The results showed that when the current was parallel to the alignment direction, the specimen reached the highest conductivity (approximately 23 S/cm). As the specimen rotated away from parallel alignment, the conductivity decreased and finally reached the minimum value (15 S/cm) when the current flowed perpendicularly to the alignment direction, as shown in Figure 6. Similar results were also obtained by [PERSON] and co-workers [171].  
Carbon black (CB), carbon nanotubes (CNTs), and other nano-particles, as well as metal fibers, have also been used to improve the mechanical performance of composites. Key performances of the systems constructed with CFRPs have been significantly improved by the above products of nanotechnology. New plastic materials, such as polyaniline (PANI), are introduced as an effective method to improve the conductivity of CFRPs [141; 172].  
Figure 6: Conductivity of aligned mesoporous carbon films as a function of the orientation of the electrodes to the alignment direction of the cylindrical mesoporous [169].
## 4 Simulation and Results
Figure 7: BER performance of the new SRC-Log techniques in Comparison with the previous precoding-companding techniques in the linear amplifier cases. In addition to the case of OFDM w/o PAPR reduction (solid line). Figure 8: BER performance of the new SRC-Log compared with the previous precoding-companding techniques and OFDM without PAPR reduction in AWGN channel. In addition to the case of an ideal amplifier (solid line). Figure 9: BER performance of the new SRC-Log compared with the previous precoding-companding techniques and OFDM without PAPR reduction in Rayleigh channel. In addition to the case of an ideal amplifier (solid line). Figure 11: ACPR comparison of the proposed SRC-Log and the previous precoding- companding techniques, in addition to the cases of OFDM w/o PAPR reduction in the presence of nonlinearity. Figure 10: EVM comparison of the proposed SRC-Log and the previous precoding- companding techniques, in addition to the cases of OFDM w/o PAPR reduction in the presence of nonlinearity. From the above discussion, it can be concluded that the proposed SRC-Log is the best practical precoding-companding technique from different points of view. Although the proposed technique (SRC-Log) introduces better performance with small computational complexity compared with other techniques; its performance is limited in very small IBO values (e.g., IBO \(<\) 2 dB) as all PAPR reduction techniques. It means that HPA does not work with maximum efficiency, and power loss occurs at low IBO values. Recently, some works tried to reduce the PAPR by controlling the power allocated per subcarrier. Power allocation [43; 44] and PAPR reduction can be jointly used to optimize the PAPR in OFDM systems. Figure 12: Comparison between SRC-Log and PTS with 215 alternatives. In the case of 4, 16, and 64-QAM. Figure 13: Comparison of computational complexity of SRC, Log, and Hybrid SRC-Log, based on (17), (19), along with PTS [15] with V = 16 and W = 2.