esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Ex vivo computer controlled circulatory reactors are advantageous for the investigation of circulatory systems. So far, most of the models have dealt with laminar or pulsatile flow. This study aimed to monitor blood vessel and vessel graft compliance continuously under physiological flow in real time. Methods: Human common iliac arteries and silicon tubes served as interposition grafts. Changes in wall diameter and displacement were analyzed. The artificial circulatory system (ACM) presented an \"artificial heart\" able to simulate various ejection pressures, ejection volumes (EV), and frequencies of pulsation (FP). ACM was validated by comparing medical data reconstructed with the 2D-speckle-tracking-technique (2 DSTT). Results: Silicon tubes were more rigid compared to iliac arteries, as changes in diameter were approximately 48% lower (0.56 \(\pm\) 0.007 mm vs. 0.83 \(\pm\) 0.016 mm, \(p\) < 0.0001, for EV = 70 mL and FP = 60 min\({}^{-1}\)). Wall displacement was 2.3-fold less pronounced in silicon tubes (1.45 \(\pm\) 0.032 mm vs. 5.79 \(\pm\) 0.043 mm for iliac arteries (\(p\) < 0.0001)). FP and EV did not further increase differences in wall displacement between both types of grafts. There were no significant changes between results gathered from ACM and 2 DSTT. Conclusions: The ACM was successfully validated by 2 DSTT with the use of selected grafts. It may become a useful tool to investigate different types of vascular grafts.  
artificial vessels; wall displacement; vessel diameter; vessel dilatation; pulsatile flow; circulation +
Footnote †: journal: Applied Mechanics and Physics
# Performance Prediction and Design of Stratospheric Propeller
## 5 Finite Element Analysis
### Propeller Model  
A three-dimensional mesh was generated in ICEM. Figure 4 shows the computational domain, which consisted of two components, an internal rotation domain, and an external static domain. The upstream distance of the static domain was 10 times the diameter of the propeller, the downstream one was 20 times, and the diameter of the static domain was 10 times the diameter of the propeller. The rotation domain's speed is \(n_{s}\), whereas the static domain is stationary. The interface boundary condition was used as the exchange data between the static and the rotation domains, the velocity inlet and pressure outlet were adopted as the boundary conditions of the static domain, and the surface of the propeller was set as the wall.  
Figure 3: Mesh of the airfoil.  
The rotational domain was composed of 5.55 million meshes, the blade surface was a triangular mesh, and the boundary layer around the surface was an hexagonal mesh, which is shown in Figure 5. A tetrahedral mesh was used to fill the space between the propeller surface and the interface.  
The static domain is shown in Figure 6, which composed of 1.97 million hexagonal meshes, which were generated based on a three-dimensional O-Block.  
FLUENT with a pressure-based solver was used to perform the CFD analyses. The control equation was discretized using the finite volume method, and the RANS equation and the \(k\)-\(\omega\)\(SST\) Low-Re corrections turbulence model were adopted. The multiple reference frame (MRF) model was used to calculate the rotational motion of the propeller. The pressure-velocity coupling algorithm and the second-order upwind scheme were applied. The parameter \(y^{+}\) is the dimensionless wall distance for a wall-bounded flow, which was required to be less than 1 according to the boundary layer mesh on the propeller. The CFD results showed that \(y^{+}\) was approximately 0.7 along the blade length in this model.  
Figure 4: Domain and boundary of the propeller.  
Figure 5: Mesh near the propeller.  
Figure 6: Mesh of the propeller.  
According to the propeller similarity theory, the performance at different altitudes should be consistent when the Reynolds number and the advance ratio are equal.  
\[\left\{\begin{array}{c}Re=\frac{\rho_{0}n_{0}D_{0}^{2}}{\mu_{0}}=\frac{\rho_{1}n _{1}D_{1}^{2}}{\mu_{1}}\\ \lambda=\frac{V_{0}}{n_{0}D_{0}}=\frac{V_{1}}{n_{1}D_{1}}\end{array}\right. \tag{25}\]  
For the same propeller model, \(D_{0}=D_{1}\) the scaling ratio can be determined by:  
\[\left\{\begin{array}{c}\eta_{\rho}=\frac{\rho_{1}}{\rho_{0}}\\ \eta_{n}=\frac{n_{1}}{n_{0}}-\frac{\rho_{0}\mu_{1}}{\rho_{1}\mu_{0}}\\ \eta_{V}=\frac{V_{1}}{V_{0}}=\frac{n_{0}}{n_{1}}\end{array}\right. \tag{26}\]  
The atmospheric conditions at different altitudes are shown in Table 2. \(H=20\) km was selected as the baseline. The scaling ratio is listed in Table 3.
## 2 Background and Related Work
### Consensus Protocols Based on Verifiable Random Function
In each group of the entire Dfinity network, there is a pair of a public key \(PK\) and a private key \(SK\), but every member of the group can only be acquainted with a part of the private key, denoted as \(SK_{i}\), instead of the complete \(SK\). Each member can sign one message \(X\) with its piece of signature and produce the corresponding short signature \(SIG_{i}(X)\). With at least \(k\) short signatures, the complete signature \(SIG(X)\) for that message \(X\) can be computed, and the \(SIG(X)\) here is exactly the same as the signature signed directly with the private key \(SK\).
# A Coupled Multiscale Approach to Modeling Aortic Valve Mechanics in Health and Disease
## 2 Materials and Methods
### Implementation of FE\({}^{2}\) Approach to Aortic Valve Leaflet Tissue
#### 2.2.1. Macroscale Model  
Our geometric representation of the AV leaflet is shown in Figure 5 via the mesh. Dimensions were obtained from [PERSON] et al. [35], [PERSON] [6], [PERSON] [4], and [PERSON] [36]. Eight node mixed-formulation elements were used in a hexahedral mesh generated with FEAP's built-in tools and a custom algorithm. We exploited the symmetry and model of only half of the leaflet with appropriate boundary conditions at the symmetry plane. The remaining edges around the leaflet were fixed as in the experiments.  
As is typical in AV leaflet literature, we define the circumferential, radial, and transmural (CRT) curvilinear basis. Referencing Figure 5, the circumferential direction is tangential to the curved surface and orthogonal to the symmetry plane, the radial direction is tangential to the curved surface and orthogonal to the circumferential direction, and the transmural direction is orthogonal to the curved surface through the thickness of the leaflet.  
An important characteristic of the AV leaflet is its natural curvature (hence the name cusp). Exact data on the curvature of the cusp used in the experiments of [PERSON] are not available, so we approximated the surface curvature with the following out-of-plane (OOP) surface deformation:  
\[\omega(x,y)=\frac{r\rho^{2}}{\pi^{2}}\bigg{(}\cos\bigg{(}\frac{\pi}{\rho}(x-x _{0})\bigg{)}+1\bigg{)}\bigg{(}\cos\bigg{(}\frac{\pi}{\rho}(y-y_{0})\bigg{)}+1 \bigg{)}, \tag{19}\]  
where \((r,\rho)\) are parameters that control the shape and are dictated by the experimental geometry. \((x_{0},y_{0})\) is the planar center of curvature in a Cartesian system. We define \(x_{0}=0\) for symmetry, leaving \(y_{0}\) as a free parameter.  
The trilayer structure of the AV leaflet tissue is explicitly modeled with three discrete layers (ventricularis, spongiosa, and fibrosa). Interconnecting fibers through the thickness are modeled with perfectly bonded layers [37; 38]. The in-plane fibers are embedded in the RVE via (20), but the OOP orientations are computed from (19) in the macroscale mesh and  
Figure 5: Macroscale AV leaflet (symmetric) mesh generated with a custom algorithm and FEAP built-in tools. The \(r\) and \(c\) directions represent a curvilinear set of coordinates in the surface of the leaflet and \(t\) is the coordinate orthogonal to the surface of the leaflet. Paraview was used for visualization [13].  
passed down to each RVE. Note that due to the lack of available data, we did not pre-stress the layers [39].
## 4 Design of Multi-Constraint Optimized Genetic Algorithm
### Task Planning Oriented Genetic Algorithm  
#### 4.3.1 Multiple Population Based on Task Cluster  
As analyzed above, all related tasks are aggregated together according to the service relevance, forming a group of independent task clusters. The so-called service relevance here has two meanings: the direct relevance and indirect relevance. Assuming two tasks \(\tau_{i}\) and \(\tau_{j}\) require the same type of service, then, \(\tau_{i}\) and \(\tau_{j}\) are considered to be directly related. For two direct related tasks \(\tau_{i}\) and \(\tau_{j}\), if task \(\tau_{m}\) is directly related with \(\tau_{i}\), but not related with \(\tau_{j}\), \(\tau_{m}\) and \(\tau_{j}\) are seen as indirect related. All tasks that are service related will be put into the same cluster. In addition, the chromosome segments of tasks in same cluster will be assembled together, as a fundamental chromosome for an evolutionary population. Thus, an original large solution space is decomposed into multiple subspaces, which will greatly reduce the complexity and cost of the full solution procedure.  
Of course, after employing the conception of task cluster, there will also introduce two-level solutions: the local solution for each chromosome and global solution for one planning problem. It is easy to prove that for one globally solvable problem, it must be locally solvable, i.e., as long as there is a global solution for a problem, it must exist a set of local solutions. Therefore, the solving procedure can be separated into the following four steps.  
* Step 1: For every population \(P_{j}\) (\(j=1,2,...,n\)\(n\)_is the total number of populations_), search all possible solutions under **Constraint 2-Constraint 6**, and form its corresponding solution set \(\zeta_{P_{j}}^{\pi}\). If there does not exist a set \(\zeta_{P_{j}}^{\pi}\) that is \(null\), all populations are solvable, go to next, otherwise return \(False\).
* Step 2: Iterate through the combinations of local solutions among all \(P_{j}\), and add the globally solvable solutions into the set \(\zeta_{global}^{\pi}\); If \(\zeta_{global}^{\pi}\) is not \(null\), then, go to next, otherwise, there is no feasible solution, return \(False\).
* Step 3: Evaluate the efficiency of all solutions in \(\zeta_{global}^{\pi}\) using an instantiated function \(F(\pi)\) as shown in Formula (4).
* Step 4: Select one optimal solution from \(\zeta_{global}^{\pi}\) and return.  
It can be said that service relevance based on task clustering is the first step for optimizing this problem, and the other obvious advantage of this method is the potential for improving the computing efficiency via parallel hardware.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.4 GSST Model  
The GSST model [66] is based on the SST technique and produces all the necessary rain attenuation statistics at any frequency, elevation angle, or polarization. The derivation idea of this standalone attenuation formula is given in [105]. The algorithmic rule of this model is shown in Algorithm 4, where the significance of related parameters can be checked in Figure 2.  
Advantages  
The model can faithfully convert the rainfall rate into rain attenuation only from the probability distribution of rain. It can also produce rain attenuation time series at an arbitrary polarization and frequency for every slant path over an approximately \(10^{\circ}\) elevation angle.  
Limitations  
It considers \"isotropy\" of rainfall, which does not consider the \"denser inner side\" and comparatively \"lighter outer side\" of the rain structure. This model's main drawback is that the SST technique needs to consider the velocity of wind, which is not known accurately, and the model has not been directly verified outside the temperate region.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 3 Data Description and Preprocessing  
### Dataset Overview  
The dataset used in this study contains most of the parameters that impact the fatigue life of coiled tubing used in lab- and full-scale tests. The full-scale fatigue test aims to simulate the actual state in full scale from the coiled tubing reel through the arch. Then, the straightener is inserted into the well, as shown in Figure 3. Due to its complexity, the required equipment and cost, only a small number of such tests have been conducted. Generally, only service companies have performed this test to enhance their tracking processes and ensure successful service delivery [33; 34; 35]. Unlike the full-scale fatigue test, the lab-scale test contains a simplified design that uses a segment of CT (+/ - 6-10 feet)  
Figure 1: Machine learning in material fatigue.  
Figure 2: Conventional vs. proposed ML methodology for CT fatigue life estimation.  
fixed on one side in the testing machine, as shown in Figure 4, while bending cycles are applied from the other side until failure [30; 37; 38].  
The dataset is as follows:  
* Grade: Integer representing the CT material grade.
* CT Diameter: A continuous variable representing the outside diameter of the CT.
* Wall thickness: A continuous variable representing the thickness of coiled tube.
* Welding type: A categorical variable with different welding types used between CT segments (bias, manual, orbital, etc.).
* Radius: A continuous variable representing the bending radius of the testing machine.
* Pressure: Integer indicating the applied pressure while testing.
* N cycle: The target variable representing the number of cycles before failure.
## 3 Results
### Elbow Joint Torque Model  
The torque of the elbow joint is the main idea for modeling the nerve signal for the bicep muscle; it will be used as input data in ANN. The data is collected for elbow joint torque when the subject started lifting the right hand from angle \(\theta_{\text{ej}}=0^{\circ}\) to \(\theta_{\text{ej}}=90^{\circ}\), which refers to elbow joint without movement of the shoulder joint with an increment of 0.03\({}^{\circ}\) in flexion elbow joint and decrement of 0.0225\({}^{\circ}\) in the extension elbow joint. Each increment and decrement in the angle are aligned with the sample number of the SEMG signal. The torque for the elbow joint is determined by using the standard torque Equation (11) [2].  
\[\tau=(r_{load}F_{load})+(r_{arm}F_{arm})\cos\theta \tag{11}\]  
Figure 14: Fit model for the relationship between amplitude SEMG signal and loads.  
where \(r_{load}\) is the distance from the elbow joint to the load, \(F_{load}\) is known as in Equation (10), \(F_{arm}\) is the force due to the mass of the lower arm and \(r_{arm}\) is the distance from the elbow joint to the center of mass of the lower arm which is half of \(r_{load}\).  
The segment mass can calculate by Equation (12) [18].  
\[m_{seg}=p_{i}m_{total} \tag{12}\]  
where \(m_{seg}\) is segment mass, \(p_{i}\) is a percentage segment mass from the table and \(m_{total}\) is the total body mass.  
The segment length can be calculated by Equation (13) [18].  
\[l_{seg}=p_{i}/l_{total} \tag{13}\]  
where \(l_{seg}\) is segment length, \(p_{i}\) is a percentage segment length from the table, and \(l_{total}\) is the body length.  
The mass of the lower arm (forearm and hand) can be calculated by Equation (14).  
\[M_{arm}=P_{i}M_{total} \tag{14}\]  
where \(M_{arm}\) is the mass lower arm, \(M_{total}\) is the total body mass, \(P_{i}\) is segment mass from anthropometric Table 4. Which can be used to estimate the segment mass and segment length for different body parts.  
The mass of subject C is 67 kg, \(P_{i}\) equal 0.022 for forearm and hand then the \(M_{arm}\) for Subject C is equal 1.474 kg. \(F_{arm}\) is equal to \(M_{arm}\) multiplied with gravity, this means the \(F_{arm}\) is 14.4452 Newton. \(r_{load}\) is 0.32m from elbow joint to load, \(r_{arm}\) is 0.160 m half of \(r_{load}\). This research will rewrite the torque equation in another form as shown in Equation (15).  
\[\tau=(r_{load}\ ((0.016\ Amp\ +0.388)\ g)\ )+((r_{load}\ /2)((P_{i}\ M_{ total})g))\cos\theta \tag{15}\]  
As can be observed in Figure 15a, the resistance forces are \(F_{load}\) and \(F_{arm}\), and distance from elbow joint \(r_{load}\) and \(r_{arm}\). Figure 15b shows the result of the torque for Subject C with three loads.
###### Abstract  
In this paper, we propose three mechanisms to reduce the broadcast storm problem in wireless mesh networks based on the Named-Data Network (NDN) architecture. The goal of our mechanisms is to reduce the number of content requests forwarded by nodes and consequently, increase the network efficiency. The first proposed mechanism, called Probabilistic Interest Forwarding (PIF), randomly forwards content requests. The second mechanism, called Retransmission-Counter-based Forwarding (ReCIF), decides to forward content requests based on the number of retransmissions by adding a counter to the header of requests. The third mechanism, called ReCIF+PIF, combines the features of PIF and ReCIF to suppress content requests. We compare the performance of our mechanisms with both the NDN default forwarding mechanism and the Listen First Broadcast Later (LFBL) mechanism. Our proposals outperform the default NDN forwarding mechanism by up to 21% regarding the data delivery rate in dense networks and provide a 25% lower delivery delay than the default NDN. Our mechanisms accomplish this performance by only reducing the number of content requests forwarded by nodes. One of our mechanisms, PIF, outperforms LFBL regarding the data delivery rate and delivery delay by up to 263% and 55%, respectively, for high network contention levels.  
named-data networking; information-centric networking; NDN; wireless networks; broadcast storm +
Footnote †: journal: Information Information Systems
## 6 Results
### Performance of Converter in Stand-Alone
#### 6.1.2 DC-DC Full Bridge Closed Loop Performance  
In the DCMG proposed there are several DC-DC full bridge converters versions, in this subsection some of this versions results are presented, PECs selected to test are 400 W at 48 V, 1.2 kW at 190 V and 3.3 kW at 190 V. First, 400 W at 48 V topology supplies six 60 W tandems Cuk driver-LED luminary and the simulation is relative to this dynamics. Simulation results are shown in Figure 17.  
Simulation test starts with 100% load condition, then tandems are disconnected one by one until converter remains at 10% load operation, only with bleeding resistor connected. Further, tandems are connected one by one to meet 100% load condition. In this case, inductor current and output current is the same. Inductor current ripple keeps its value according the criteria and converter keeps on CCM during the test, Figure 17a. There are not significant overshot in DC bus at luminaries connection/disconnection sequence, Figure 17b. Converter DC-DC full bridge 1.2 kW at 190 V output version behavior is tested, see Figure 18.  
Again, simulation starts at 100% load condition, then 22.5% load steps are removed until 10% nominal load. At 1 s, 22.5%, load steps are connected back until meet 100% load condition again. Smooth transients presence is noticed in inductor current ripple and CCM condition remains during the test, see Figure 18a. No significant overshot appears in DC  
Figure 17: DC-DC full bridge converter 400 W at 48 V with state feedback controller. (a) inductor current, (b) output voltage.  
Figure 18: DC-DC full bridge converter 1.2 kW at 190 V with state feedback controller. (a) inductor current, (b) output voltage.  
bus at load connection/disconnection sequence, Figure 18b. Finally, converter DC-DC full bridge 3.3 kW at 190 V version behavior is tested in the same way than before mentioned versions, see Figure 19.  
Test simulation starts at 100% load condition, then 22.5% load steps are disconnected, until final load at 10% nominal power. At 1 s, 22.5% load steps are connected back until meet 100% load condition again. Current ripple increases at 55% load condition but ripple current criteria is met and CCM reamins during the test, see Figure 19a. Smooth transients apear in DC bus, with no overshot, at load connection/disconnection sequence, see Figure 19b. All the DC-DC full bridge converters versions remaining are tested in the same way, similar results were obtained in all cases.
## 3 Results and Discussion
### Effect of HHP on Vitamin C Content in Kiwifruit Pulp Beverage
HT (85 \({}^{\circ}\)C for 10 min), HHP (high hydrostatic pressure, 400–600 MPa for 5–15 min). Values with different letters are significantly different (\(p<0.05\)). All data were expressed as means \(\pm\) SD, \(n=3\). vitamin C gradually changed from aerobic degradation to anaerobic degradation, while the rate of aerobic degradation is greater [35]. During the whole storage period, HHP treatment at 400 MPa for 15 min maintained significantly (\(p<0.05\)) higher VC content and showed better effect on maintenance of vitamin C. This suggested that HHP treatment at 400 MPa for 15 min protected vitamin C of kiwifruit pulp beverage against long-term degrading processes. Nevertheless, with higher HHP of 600 MPa for 15 min, the VC content of samples decreased sharply and was even lower than that in heat-treated samples, which revealed that extremely high pressure could cause more loss in vitamin C. Similar results were obtained by [PERSON] et al. [36] who treated acidified Granny Smith apple puree at 600 MPa, found the total vitamin C content fell significantly.
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 4 Results and System Evaluation
### Performance Analysis  
This article [47] explores a method for encoding, packaging, and compressing data in heterogeneous Internet of Things (IoT) networks using satellite communication channels. A crucial aspect of this research is the reduction in the cost of transmitting messages via satellite communication channels, which is significant for data collection in areas with underdeveloped infrastructure. The authors developed a method that combines multiple data encoding and packaging methods to enhance data transmission efficiency.  
* The method reduces the data volume with a common linguistic feature ranging from 30 to 2048 bytes by 10-17 times (4.6 times due to serialization and 2.13-3.6 times due to the authors' proposed archiving method);
* The cost reduction in data transmission leads to an increased economic efficiency of satellite communications for organizing data transmission networks in remote areas lacking telecommunication infrastructure;
* The method can be further refined and generalized for satellite IoT scenarios using various combinations of data transmission technologies, encoding formats, and container sizes.  
IRIDIUM 9602 SBD Satellite Transceiver:
* The maximum size of the sent message is 340 bytes;
* The maximum size of the received message is 270 bytes.  
IRIDIUM CORE 9523 Satellite Transceiver:
* The maximum packet size for transmission is 1960 bytes (SBD);
* The maximum packet size for reception is 1890 bytes (SBD).  
Depending on the amount of data being compressed, the compression ratio varies.  
This approach is assumed to be used, as it has proven its effectiveness in data compression [48]. Satellite communication, in turn, allows the vehicle to be tracked more accurately at any point.  
The use of compression technology is extremely important for a large number of vehicles connected to the system. With an increase in their number, the amount of transmitted data increases, as shown in Figure 4.  
Figure 4: Observed data processing load.