esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Deficit Round Robin with Fragmentation (DRRF) (for Best Effort Traffic)  
In the previous section, we described the basic idea of two common scheduling algorithms: GPS and DRR. Although GPS can achieve perfect fairness with frame utilization, the resource allocation results in a large overhead. On the other hand, DRR can mitigate the effect of packet fragmentation. However, again full frame utilization cannot be achieved. In other words, some spaces may be left unused in a Mobile WiMAX frame in case the next full packet does not fit in that space. We will also show the simulation results to illustrate these claims (See Section 7). As a result, we introduce a modification of DRR, the so-called Deficit Round Robin with Fragmentation (DRRF) [10].  
DRRF is basically a combination of GPS and DRR. In fact, DRRF is similar to DRR, but allows packet fragmentation for the purpose of achieving full frame utilization, that is, in case there are some left-over spaces within a frame, DRRF allocates those left-over spaces to some mobile stations.  
Table 2 shows an example of DRRF. Again, we use the same example as in DRR (See Section 2.2). Four frames are illustrated as an example (in 5 rounds). In each round, the deficit counter is increased by the quantum size, 75. Similar to the DRR, the packets with the deficit counter greater than the packet size are scheduled. However, to achieve full frame utilization, a fragmented packet is allowed as well. In other words, a fragmented packet with the deficit counter greater than the fragment size is also scheduled. In this table, there are 2 rounds in the first two frames. In frame 3, there is no round and only one round for the fourth frame. In each frame, some fragmented packets may be also transmitted (we indicate these by bold numbers.)  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline Frame No. & \multicolumn{3}{c|}{1 st} & \multicolumn{3}{c|}{2 nd} & \multicolumn{3}{c|}{3 rd} & \multicolumn{3}{c|}{4 th} \\ \hline Round & 1 & 2 & & & 3 & 4 & & & \\ \hline MS\#1 & 0/75 & 0/150 & 125/25 & 0/25 & 0/100 & 0/175 & **100**/75 & **25**/50 & 0/50 & 0/12
## 5 Performance Evaluation  
### Simulation Parameters  
In the simulation experiments, network lifetime has two definitions: First Node Dies (FND), the time when the first node dies in network and Last Node Dies (LND), the time when the last node dies. The parameters of simulations are listed in Table 3. Unless otherwise specified, every simulation result shown below is the average of 200 independent experiments where each experiment uses a different randomly-generated uniform topology of sensor nodes. For simplicity, we assume the probability of signal collision and interference in the wireless channel is ignorable and the radio transmitter, radio amplifier and data fusion unit are the main energy consumers of a sensor node, so we only calculate the energy consumption of these three components in the simulation. In simulation, we use the same radio model shown in [5] for the radio hardware energy dissipation. This radio model has been widely adopted in several studies [3; 6; 10-11].  
Figure 6: Routing tree construction.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods
### Association Rule-Driven Probabilistic Map Update Rules  
Here, based on the Bayesian probability update rule [16] and considering the association characteristics of the target, the update of an association rule-driven TPM is given as follows:  
1. Grid \((m,n)\) without access: \[p_{mn}(k+1)=\tau_{1}p_{mn}(k)\] (7)  
Figure 2: Two kinds of association rule TPM updates. (**a**) initial stage, (**b**) find target \(A\) and the TPM is updated according to rule \(S_{i}\), (**c**) find the second target \(B\), (**d**) the TPM is updated according to rule \(S_{i+1}\).  
where \(P_{mn}(k)\) and \(p_{mn}(k+1)\) represent the probability of grid \((m,n)\) at time \(k\) and \(k+1\), respectively; and \(\tau_{1}\in[0,1]\) is the dynamic information factor.
2. Grid \((m,n)\) has access, and \(f_{g}=1\): \[p_{mn}(k+1)=\frac{p_{d}p_{mn}(k)}{p_{f}(1-p_{mn}(k))+p_{d}p_{mn}(k)}\] (8) where \(p_{d}\in[0,1]\) and \(p_{f}\in[0,1]\) represent the detection probability and false-alarm probability of the magnetic detector, respectively; if the target in the grid \((m,n)\) is detected, the TPM of other grids is updated based on \(S_{i}\) as follows (here, take grid \((o,p)((o,p)\
eq(m,n))\) as an example): \(\bullet\) High-probability area, \(hp\): \[p_{op}(k+1)=\frac{1}{s_{hp}}\sum_{i=1}^{s_{hp}}\tau_{hp}p_{mn}(k)\] (9) where \(p_{op}(k+1)\) represents the probability of the grid \((o,p)\) at time \(k+1\); \(\tau_{hp}\) is the probability enhancement factor; and \(s_{hp}\) is the number of grids that conform to the high- probability area, \(hp\). \(\bullet\) Low-probability area, \(lp\): \[p_{op}(k+1)=\frac{1}{s_{lp}}\sum_{i=1}^{s_{lp}}\tau_{lp}p_{mn}(k)\] (10) where \(\tau_{lp}\) is the probability reduction factor; and \(s_{lp}\) is the number of grids that meet the low-probability area, \(lp\). \(\bullet\) Uncertain probability area, \(mp\): \[p_{op}(k+1)=\tau_{2}p_{mn}(k)\] (11) where \(\tau_{2}\in[0,1]\) is the dynamic information factor.
3. Grid \((m,n)\) has access, and \(f_{g}=0\): \[p_{mn}(k+1)=\frac{(1-p_{d})p_{mn}(k)}{(1-p_{f})(1-p_{mn}(k))+(1-p_{d})p_{mn} (k)}\] (12)  
If the target in grid \((m,n)\) is detected and based on association rule \(S_{i}\), the probability of other grids is updated as (8)-(10).
## 2 Main Results
### Controller Design and Stability Analysis
According to the definitions of \(C\) and \(k\) in (42),we conclude that \(z_{1}\) and \(z_{2}\) eventually converge to arbitrarily small compact sets by choosing appropriate design parameters. The proof is completed. **Remark 5**. : _In this paper, the barrier Lyapunov theory integrated with the design procedure of backstepping is applied to deal with state constraints. The change range of backstepping error variables \(z_{1}\) and \(z_{2}\) is restricted by the design parameters \(k_{a1}\) and \(k_{a2}\). If \(|z_{1}|\to k_{a1}\) and \(|z_{2}|\to k_{a2}\), the barrier Lyapunov function will approximate infinity. Hence, the variation of \(z_{1}\) and \(z_{2}\) is always limited to \(|z_{1}|<k_{a1}\) and \(|z_{2}|<k_{a2}\). This ensures that the bounds of the system state constraints are not violated in the process of helicopter's yaw channel tracking, satisfying the limits of helicopter yaw motion space and improving its operational security._  
**Remark 6**. : _The investigated control strategy is an adaptive neural fault-tolerant control, and it is employed in a UAV helicopter SISO non-affine nonlinear yaw control system to realize tracking errors in arbitrarily small compact sets; As far as we know, there are few conclusions about adaptive neural network fault-tolerant control for non-affine nonlinear yaw control system faced with the uncertainties in system conversion, unknown disturbances, actuator faults, input saturations, and full-state constraints. The MIMO systems can also make use of this control scheme, but the universality of the proposed controller needs further study._
## 3 Simulation Method
### Overset Mesh Technology  
The overset mesh technique, also called overlapping or chimera grids, was applied. A new internal interface node was created within the overset mesh region. This volume-type interface enables the coupling of solutions on the domains using automatically generated sets of acceptor cells in one mesh and donor cells in another mesh. Varying values of the donor cells affect the values of the acceptor cells based on interpolation. This method can handle complex geometries and body motions in dynamic simulations.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 3 Background
### Speed Transition Matrix  
The STM is a novel traffic data representation and modeling technique that captures the vehicle's speed at the movement between two consecutive road segments called transition [34]. It is used to represent the speed probability change, and therefore it represents the speed probability distribution at one transition in one time interval. The transition is defined as a spatial change in vehicle trajectory when traveling from edge \(e_{i}\) to edge \(e_{i+1}\) in time interval \(\Delta t\). As a traffic parameter under observation, the relative harmonic speed is used. The speed is relative to the speed limit on the observed edge. Two examples of the transition are visually represented in Figure 2 with red and green colors. Transitions describe the vehicles that are traveling between edges \(h-f\), and \(l-g\) with corresponding STMs. The STMs represent a very different traffic pattern: (i) On the left-hand side, the traffic congestion with very low origin and destination speeds, and (ii) on the right-hand side, stable traffic flow with origin and destination speeds around 60% of the speed limit. The red circles show the CoM for represented traffic patterns. It can be observed that the position of the CoM is one of the most important parameters when estimating the traffic state, and the next chapters will debate how to use the position of the CoM for anomaly detection.  
In this paper, 5% is chosen as the discretization period, and 100% is the maximal possible speed, which resulted in matrix dimensions of 20 \(\times\) 20. The STM can be represented as:  
Figure 1: Example of two possible anomaly types: (**a**) Sudden breaks and (**b**) intense accelerations.  
Figure 2: Transitions (**center**), congested STM (**left**), and normal traffic STM (**right**) examples on a simple road network.  
\[\mathbf{X}(\Delta t)=\quad\begin{pmatrix}p_{11}&p_{12}&\ldots&p_{1n}\\ p_{21}&\ddots&&\vdots\\ \vdots&&\ddots&\vdots\\ p_{m1}&\ldots&\ldots&p_{mm}\end{pmatrix} \tag{1}\]  
where \(p_{ij}\) represents the probability of transitioning from speed value \(i\) to \(j\).
## 3 Background and Previous Related Work
### Dlt
#### 3.3.4 Public Key Cryptography (PKC)  
Known as asymmetric cryptography, PKC uses a pair of keys, namely a public key and a private key. In DLT, public keys serve as user addresses, while private keys authorize transactions and asset transfers. Examples of PKC used in DLT are the RSA algorithm and Elliptic Curve Cryptography (ECC).
# A Data Processing Framework for Polar Performance Diagrams
## 3 The hrosailing Framework
### From Real World Measurements to Polar Diagrams
In other words, for resolutions \([\text{TWS}_{1},\dots,\text{TWS}_{n}]\), \([\text{TWA}_{1},\dots,\text{TWA}_{m}]\) the corresponding boat speeds are calculated with an interpolation method \(I\) and a neighbourhood \(N:\mathbb{R}^{2}\to 2^{\mathbb{R}^{2}}\) as  
\[\text{BSP}_{i,j}=\text{I}(\text{TWS}_{i},\text{TWA}_{j},S_{i,j})\]  
where  
\[S_{i,j}=\{(\text{TWS},\text{TWA},\text{weight})\in\text{WPTS}\ :\ (\text{TWS}, \text{TWA})\in\text{N}(\text{TWS}_{i},\text{TWA}_{j})\}.\]  
The PointCloudExtension basically works in the same way, except that the (TWS, TWA) pairs, which have to be interpolated, are generated first by a sampling method. You can also apply the CurveExtension which generates a PolarDiagramCurve from weighted points via regression. While the framework can always be extended by customised methods, the following basic tools are included:  
1. DataHandler for csv-files, arrays and files containing NMEA sentences;
2. CylindricMeanWeigher which produces higher weights for points whose BSP is roughly the mean of the BSP points in a given cylindric neighbourhood and \(\text{CylindricMemberWeigher which produces higher weights for points with more neighbours in a cylindric neighbourhood;
3. FluctuationWeigher which assigns low weights for data which strongly fluctuates in time (locally) and high weights for data which is nearly constant in time (also locally);
4. BoundFilter which only keeps points whoose weights surpass a given threshold and \(\text{QuantileFilter which keeps a given percentage of the weighed points;}\)
5. classical interpolators, such as the inverse distance interpolator for nongridded data and the improved inverse distance interpolator [20];
6. a more general interpolator, namely the ArithmeticMeanInterpolator, i.e., I calculates the weighted mean with weights \(e^{-\kappa w|(\text{TWS},\text{TWA})-(s,\rho)|}\) for each point \((s,a,w)\in\text{WPTS}\);
7. least square regression methods (Section 2.2.1 of [22]) and orthogonal distance methods [23];
8. sampling methods such as Fibonacci samples (inspired by [24]) and Archimedean samples (inspired by [25]), these produce sample points in the convex hull of available (TWS, TWA) data points with equidistantly distributed angles on the spiral \(\sqrt{\beta}(\cos(\beta),\sin(\beta))\) and equidistantly distributed points on the spiral \(\beta(\cos(\beta),\sin(\beta))\) respectively;
9. neighbourhoods such as Ball, Ellipsoid, Cuboid or Polytope;
10. multiple model functions representing common types of polar diagrams. To conclude, the introduced pipeline model contains a range of flexible tools for handling different types of issues that might occur when processing the sailing data. To the best of our knowledge, established tools only provide ways to generate polar diagrams by a \"black box\" function.
## 4 Performance Evaluation
### Location Response Time  
In order to evaluate the average location response time of multiple sinks, we fixed the \(\alpha=200\) m and varied the number of sinks from 1 to 10 in steps of 1. In ALS and SLS, from Figure 7(a), it can be seen that the location response time slightly increases with the number of sinks. This is because when multiple sinks are deployed in the network, location query messages always have to travel until they find all sink's location information. ALS shortens the location response time than SLS, because ALS utilizes the first response among the other responses. However, because SDLS constructs anchor system by source node, the location response time remains fairly constant regardless of the number of sinks in the network. Although TTDD gets also fixed source nodes, it takes much time because TTDD constructs grid structures for each source node.  
In order to evaluate the average location response time of multiple sources, we fixed the \(\alpha=200\) m and varied the number of sources from 1 to 10 in steps of 1. In SDLS, from Figure 7(b), it can be seen that the location time slightly increases with the number of source. This is because when multiple sources are deployed in the network, location query messages always have to travel until they find all source's location information. However, until source nodes are 10, SDLS is less time than ALS to get location information. TTDD is also same that the location time increases with the number of source nodes.
###### Abstract  
Currently, balanced armature (BA) receivers are frequently used in earphones, owing to their small size and superior sound quality. However, the reliability of BA receiver earphones has become a considerable challenge, as they easily fail when subjected to external forces, especially during drop impacts. In addition, the original gasket cannot protect the BA receiver well. Therefore, this article focuses on improving the reliability of BA receiver earphones by designing a novel concept for the gasket. Based on a simplified model and analysis methods, the maximum von Mises stress on the armature with different drop directions and the maximum von Mises stress point must first be determined. The gasket was divided into two parts, one for linking and the other for shock absorption. This article focused on the design of the shock absorption structure. A novel concept gasket was proposed, and the analysis results showed that the gasket improved the shock absorption performance. For demonstrating the validity of the shock absorption performance of the novel concept gasket, three confirmatory experiments were performed: the drop impact test, X-ray photography, and sound performance, which included the sound pressure level and total harmonic distortion. The analysis results were experimentally verified.  
balanced armature receiver; finite element method; drop impact test +
Footnote †: journal: Applied Sciences
## 1 Introduction
[16] performed finite element simulations for L- and inversed L-channel anchors (where \"inversed\" indicates that the angle is in a sitting position or has no top flange) embedded in a concrete slab, and suggested a design equation based on the parametric study results. JSCE [17] provides design equations for stud anchors and L-channel anchors, and [PERSON] et al. [18] modified the JSCE's design equation for the design of L- and T-channel anchors based on their push-out test results with unreinforced concrete slabs (to simulate composite sandwich structures). It is noted that the current version of Eurocode 4-04 [19] provides a design equation only for stud anchors. Each of these previous studies provides a valuable insight into the design of steel anchors but the majority of the previous studies, including the current design codes, are for channel anchors with full-length welding. Considering the fact that steel anchors with partial-length welding are also widely used in practice with the increasing application of U-shaped or built-up sections, more experimental studies are required for partially welded channel anchors, and the applicability of design equations to the partially welded channel anchors needs to be examined. Figure 1: Application of steel anchors to UH-beams: (**a**) UH-beam with Z-channel anchors; (**b**) UH-beam with conventional stud anchors. In the present study, to investigate the behavior of various steel anchors, push-out tests were performed for 13 test specimens. Test parameters included the geometry of steel beams (H-beams and UH-beams) and the type of steel anchors (stud anchors, and Z- and C-channel anchors with full- or partial-length welding). Test results were compared with predictions by existing design equations for steel anchors, and the applicability of the equations to the partially welded channel anchors was discussed.
# Effects of Supplementation of Different Proteins on the Rheological Properties of Liquid Whole Eggs
###### Abstract  
This study aimed to evaluate the impact of increasing total protein content on the rheological properties of liquid whole eggs. The study fills the gap between fundamental science and industrial application by exploring the potential for developing high-protein, functional egg products for health-conscious consumers and the food industry. Liquid whole egg samples were enriched with different percentages of egg white and whey proteins. Proteins were added either before or after heat treatment, followed by homogenization, to analyze the effects on rheological behavior. Results indicated that whey protein samples exhibit near-Newtonian behavior due to their high solubility and minimal protein interactions, while egg white protein samples, especially at higher concentrations, induce shear thinning behavior and increased viscosity due to their water-binding capacity and partial heat-induced denaturation. Flow behavior index (n) decreased by 62.7 \(\pm\) 5.7% and 25.2 \(\pm\) 1.8% for heat treated with 10% added egg white and whey protein samples, respectively; meanwhile, it decreased by 2.64 \(\pm\) 0.06% for 10% of added egg white protein and increased by 12.0 \(\pm\) 1.0% for 10% of whey protein when heat treatment was induced prior to protein additions. The findings from this study offer valuable insights for developing functional food enriched with whey and egg white proteins.  
liquid whole eggs; egg white proteins; whey proteins; rheological behavior Article
## 3 Preliminaries
### Coverage Perception Model  
It is assumed that in the monitoring area \(A\), the coverage model of each underwater sensor node is a sphere with the sphere's center as the node's coordinates and \(r_{j}^{s}\) as its radius of perception. The communication range is also a sphere, with radius \(r_{j}^{c}\). To ensure the connectivity of the network, the radius of communication is set to be greater than or equal to twice the radius of perception; that is, \(r_{j}^{c}\geq 2r_{j}^{s}\)[21]. Assume the Euclidean distance \(d\left(e_{i},s_{j}\right)\) between event \(e_{i}\) and sensor node \(s_{j}\) is  
\[d\left(e_{i},s_{j}\right)=\sqrt{\left(x_{j}-x^{\prime}_{i}\right)^{2}+\left(y _{j}-y^{\prime}_{i}\right)^{2}+\left(z_{j}-z^{\prime}_{i}\right)^{2}}, \tag{1}\]  
where coordinate \(\left(x_{j},y_{j},z_{j}\right)\) is the coordinate of node \(s_{j}\) and coordinate \(\left(x^{\prime}_{j},y^{\prime}_{j},z^{\prime}_{j}\right)\) is the coordinate of event \(e_{i}\). The probability that the defined event \(e_{i}\) is covered by the sensor node \(s_{j}\) is \(p\left(e_{i},s_{j}\right)\). A Boolean sensor coverage model is used to simplify the computation, and the probability is a binary function [22]:  
\[p\left(e_{i},s_{j}\right)=\left\{\begin{array}{ll}1,&d\left(e_{i},s_{j} \right)\leq r^{s},\\ 0,&otherwise.\end{array}\right. \tag{2}\]  
If \(d\left(e_{i},s_{j}\right)\leq r^{s}\), node \(s_{j}\) covers event \(e_{i}\). In this case \(P\left(e_{i},s_{j}\right)\) equals 1; otherwise, it is equal to 0. Similar to the calculation process for a two-dimensional sensor coverage area, the probability that the underwater three-dimensional space event \(e_{i}\) is covered by node set \(S\) is \(P\left(e_{i},S\right)\), where  
\[p\left(e_{i},S\right)=p\left(e_{i},s_{1}\right)\lor p\left(e_{i},s_{2}\right) \vee\cdots\lor p\left(e_{i},s_{N}\right)=1-\prod_{i=1}^{N}(1-p\left(e_{i},s_{ i}\right)). \tag{3}\]  
**Definition 1**.: _According to the preceding analysis, the relative effective coverage degree of event \(e_{i}\) can be described as [19]:_  
\[D_{A}\left(e_{i}\right)=\sum\limits_{s_{j}\in S}\frac{p\left(e_{i}s_{j}\right)}{ 1+\sum\limits_{e_{i}\in E}I\left(d\left(e_{i},s_{j}\right)\leq r^{s}\right)}, \tag{4}\]  
_where \(I(\cdot)\) is an indicator function, that is, when the condition \(d\left(e_{i},s_{j}\right)\leq r^{s}\) is satisfied, \(I\left(d\left(e_{i},s_{j}\right)\leq r^{s}\right)\) is equal to \(I\); otherwise, it is 0. \(\sum\limits_{e_{i}\in E}I(d(e_{i},s_{j})\leq r^{s})\) indicates the number of adjacent events \(e_{i}\) for node \(s_{j}\)._
## 5 Comparative Evaluations
### Evaluation Results for Robustness  
Figure 12 shows the evaluation results for the basic robustness tests of the LSB, DSS, MC, and proposed methods, where the horizontal axis shows the bit rate and the vertical axis shows the BER of each attack process. As we can see, the LSB method is not robust (i.e., does not have a BER greater than 10%) against any attacks except Requant-24. The DSS method is robust (BER less than 10%) except for G723.1. The MC method and the proposed method are robust (BER less than 10%) except for the Requant-8 and G723.1 encodings. Unfortunately, speech watermarking using LP residuals is fundamentally vulnerable to G726.