esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Particle Swarm Optimization (PSO)  
This population-based optimization technique was inspired by the movement of flocks of birds and schools of fish. It uses social interactions to find the best solutions. The swarm is randomly initialized with a population of solutions that are called particles (or agents). The search for the optimal solution is repeated in iterations, during which these particles move around the search space according to a mathematical formula that governs the position and velocity of the particles. The motion of each particle is affected by the best solution that has been achieved so far by that particular particle and is guided to the known best positions within the search space, which are adjusted when better positions are discovered by other particles in the swarm. Hence, the swarm moves toward the optimal solution [15].  
In this study, the velocity was modeled mathematically, as stated in Equation (1), where \(v_{id}(t)\) is the velocity of the particle \(i\) in dimension \(d=1,\ldots,n_{p}\) at time step \(t\), \(w\) is the inertia weight, \(r_{1}\) and \(r_{2}\) are random values \(\in[0,1]\) from a uniform distribution, \(c_{1}\) and \(c_{2}\) are positive acceleration constants, \(p_{id}(t)\) is the best position that the particle \(i\) has visited since the first time step with \(d\) dimensions at time \(t\), and \(g_{d}(t)\) is the best global particle position. The position was also modeled mathematically, as stated in Equation (2), where \(x_{id}(t)\) is the position of the particle and \(v_{id}(t+1)\) is the velocity of the particle \(i\) in dimension \(d\) at time step \(t+1\)[32].  
\[v_{id}(t+1) =w\,.\,v_{id}\] \[+r_{1}c_{1}\,.\,[p_{id}(t)-x_{id}(t)] \tag{1}\] \[+r_{2}c_{2}\,.\,[g_{d}(t)-x_{id}(t)]\]  
\[x_{id}(t+1)=x_{id}(t)+v_{id}(t+1) \tag{2}\]
## 1 Introduction
### Review of Current Research
The nonlinear dynamic analysis model of the track was established to study the influence of additional track geometric irregularity and track stiffness irregularity caused by slab track deformation on the dynamic response of the vehicle-track-bridge system. [PERSON] et al. [22; 23] calculated the temperature field distribution of CRTS II slab track using the Fourier heat conduction equation. The CRTS II slab ballastless track model on a subgrade section was established, and the vertical deformation of slab track under the action of overall temperature and temperature gradient was analyzed. The results showed that the temperature gradient has a great influence on the deformation of the slab track. [PERSON] et al. [24] established the simulation model of CRTS I ballastless track, and analyzed the two working conditions of the internal temperature of the slab track, namely 'upper hot and lower cold' and 'upper cold and lower hot'. When the internal temperature of the slab track is 'hot up and cold down', the slab track is in the state of the middle arch, and the maximum value is 0.65 mm. When the internal temperature of the slab track is 'upper cold and lower hot', the slab track is in the state of central subsidence, and the maximum value is 1.37 mm. [PERSON] et al. [25] tested the temperature and deformation of CRTS I ballastless track structure on a subgrade section and bridge section of the Harbin-Dalian Railway and obtained the temperature field distribution and deformation of the slab track. The test results showed that the displacement at the corner of the slab track reached 1.26 mm within a day. The difference in the slab surface temperature reached 23 \({}^{\circ}\)C. The CRTS I ballastless track model was established to study the influence of slab thickness and temperature difference on the warping deformation of slab tracks. The research on slab ballastless tracks is mostly focused on CRTS I and CRTS II type slab tracks. There are few related studies on CRTS III type slab tracks, and most of them lack field test research. Therefore, the simulation and experimental study of CRTS III slab track discussed in this paper can provide the theoretical and experimental basis for subsequent maintenance and repair, reduce the economic and personnel costs of maintenance and repair, and achieve the purpose of green maintenance.
# Estimation of the Frequency Response Function of the Rotational Degree of Freedom
## 2 Theoretical Background  
### Receptance of Euler-Bernoulli Beam  
The coordinates and directions of the displacement, rotation, force, and moment of the substructure are illustrated in Figure 1. The \(i\) and \(j\) at the ends indicate the measurement and excitation positions, respectively. In this study, as illustrated in Figure 1, the out-of-plane mode is considered solely, and the axial direction is not considered. The full receptance matrix according to the RCSA formula can be expressed as:  
\[R_{ij}=\left[\begin{array}{cc}H_{ij}&L_{ij}\\ N_{ij}&P_{ij}\end{array}\right]=\left[\begin{array}{cc}x_{i}/F_{j}&x_{i}/M_{ j}\\ \theta_{i}/F_{j}&\theta_{i}/M_{j}\end{array}\right] \tag{1}\]  
where \(H_{ij}\) is the displacement/force FRF, \(L_{ij}\) is the displacement/moment FRF, \(N_{ij}\) is the rotation/force FRF, and \(P_{ij}\) is the rotation/moment FRF.  
Figure 1: Coordinate system of the beam.  
In the free-free boundary condition of the beam, the cylindrical direct and cross receptance can be expressed using the Euler-Bernoulli beam theory, as expressed in Equations (2) and (3), respectively [14; 15; 16; 17]:  
\[R_{ij}=\left[\begin{array}{cc}H_{ii}&L_{ii}\\ N_{ii}&P_{ii}\end{array}\right]=\left[\begin{array}{cc}\frac{-C_{1}}{EI^{ \lambda 3}C_{7}}&\frac{C_{2}}{EI^{\lambda 2}C_{7}}\\ \frac{C_{2}}{EI^{\lambda 2}C_{7}}&\frac{C_{3}}{EI^{\lambda 2}C_{7}}\end{array}\right] \tag{2}\]  
\[R_{ij}=\left[\begin{array}{cc}H_{ij}&L_{ij}\\ N_{ij}&P_{ij}\end{array}\right]=\left[\begin{array}{cc}\frac{C_{4}}{EI^{ \lambda 3}C_{7}}&\frac{-C_{5}}{EI^{\lambda 2}C_{7}}\\ \frac{C_{5}}{EI^{\lambda 2}C_{7}}&\frac{C_{6}}{EI^{\lambda 2}C_{7}}\end{array}\right] \tag{3}\]  
where \(E\) is the elastic modulus, \(I\) is the area moment of inertia, \(\eta\) is the damping ratio, \(L\) is the length of the cylinder, and \(\lambda\) can be expressed as Equation (4).  
\[\lambda^{4}=\frac{\omega m}{EI(1+i\eta)L} \tag{4}\]  
\[\text{where,}\left\{\begin{array}{c}C_{1}=cos\lambda Lsinh\lambda L-sin \lambda Lcosh\lambda L\\ C_{2}=sin\lambda Lsinh\lambda L\\ C_{3}=cos\lambda Lsinh\lambda L+sin\lambda Lcosh\lambda L\\ C_{4}=sin\lambda L-sinh\lambda L\\ C_{5}=cos\lambda L-cosh\lambda L\\ C_{6}=sin\lambda L+sinh\lambda L\\ C_{7}=cos\lambda Lcosh\lambda L-1\end{array}\right..\]
## 4 Experiments and Analysis
### Model Performance Evaluation  
To respond to RQ1, all models were trained using the designated training set and evaluated across various tasks. Two types of tasks were set up: one-to-one function similarity detection and one-to-many homologous function search. Furthermore, the detection efficiency of GMN+ and baseline methods, in terms of time cost, were assessed.
## 3 Results
### Driving Scenarios and Environment Perception
As shown in Figure 13, the vehicle follows accurately the speed profile and the longitudinal acceleration reaches up to 8 m/s\({}^{2}\) while the longitudinal velocity increases from 0 up to 13.8 m/s, as illustrated in Figure 11b,c, respectively. The front wheels steering angle \(\delta\) decreases up to \(-\)0.04 rad and then remains almost constant at \(-\)0.02 rad (Figure 13a). The cross-track error \(e_{1}\) and the relative yaw angle \(e_{2}\) are always within \(\pm\) 0.1 m (Figure 13e). In all the considered maneuvers, the vehicle follows the planned trajectory and respects the feasibility constraints imposed by the actuators, in terms of maximum front wheel steering angle, and vehicle dynamics, while maximizing its longitudinal speed. Figure 13: Results obtained during the right turn maneuver in MIL configuration (see Figure 10). Measured vehicle states: (**a**) Front wheels steering angle \(\delta\); (**b**) Longitudinal acceleration \(a_{z}\); (**c**) Longitudinal speed reference \(V_{ref}\) (red, dashed) vs. Actual longitudinal speed \(V_{x}\) (black, solid); (**d**) Lateral speed \(V_{y^{\prime}}\); (**e**) Cross track \(e_{1}\) (black, solid) and Relative yaw angle \(e_{2}\) (blue, solid).
## 5 Elastic Scaling
### Results of Elastic Scaling  
Elastic scaling in this context concerns data center resource reconfiguration by minimizing rapid hardware changes and matching demands by populating an accurate machines list. By applying these metrics to Google and Alibaba scaling using the real trace, and normalizing the values to the max value, the training input of the trace is represented as a percentage. It is clear that in Figures 16 and 17, the Alibaba scaling factor is very high (6 to 7 times the demand), which means resources are being wasted. The Upper Bound \(UB\) value is also normalized to be a percentage. For Google trace, \(SF\) values are 0.85 and 1.611, which means \(LB\) violation is higher (0.78) in workload type 1 than (0.565) in workload type 2. Machine count \(MCount\) represents machine participation in provisioning for that sample. Normalized to a maximum number of available machines, \(TO\) represents the number of machine transitions normalized to the maximum movements in a Bayesian network. For both Alibaba and Google traces, \(TO\) is higher than \(MCount\) because one machine can jump between states more than one time. For Alibaba, most of the machines are participating in the provisioned resources from the service starting point, which increases the \(ST\) and \(UB\) factors. However, for Google, resources are added gradually, which increases the \(LB\) ratios and reduces \(ST\) costs. Further, the cloud manager needs more time to check the higher number of machines.  
Table 5 summarizes the mathematical notation symbols and their definitions. In the next section, LSTM machine learning will be used to enhance these scaling metrics by optimizing elastic scaling.
# Rentable CDN Using Blockchain and Proof of Provenance
## 4 Analysis
### Scalable Collusion Attack Simulations  
As explained in the introduction, the proposed rentable CDN is not bound to a specific Blockchain or p2p software. The reference software is built using Ethereum as a mainchain, and another Blockchain as a sidechain which supports delegated proof of stake. Ethereum is used to secure the interactions between Ethereum and the side chain, while the side chain is used to support all necessary transactions to run the rentable CDN. For p2p communication, Bittorent protocol is simulated.  
To measure how the proposed method improves upon the exiting work, collusion simulations are conducted. In the simulations, we measure the difference in download speeds and Downloader's loss due to collusion. Since the performance can vary depending on hardware, peers, location, and file availability, we try to simulate an environment with an average network connection.  
Figure 8 shows the simulation result when 700 MB file is downloaded with 50 honest Uploaders and 10 malicious Uploaders. Theta network and the proposed method are compared in terms of how long it takes to download the file. \"Loss due to collusion\" represents the loss in payment (relative to file size) for Downloader due to collusion in Theta network; the proposed method does not have any loss due to collusion because of the proof of provenance system. The graph clearly shows that the proposed method has a slightly faster download speed then Theta network. This not a surprising result because the proposed method's download speed is not affected by malicious Uploaders. The close view of the figure also shows that \"loss due to collusion\" only goes up to 0.37%. This is a negligible loss as Theta network has claimed, however, we also observe that the maximum loss due to collusion occurs within the first 5 seconds of the connection. This means that the malicious Uploader only needs to connect for 5 seconds and then attack another Downloader. This inherent structure allows a single malicious Uploader to scale their attack to the whole network.  
Figure 9 shows extended simulation results when collusion attack is scaled, i.e., overwhelming number of fake malicious Uploaders disrupt the network. In the simulation, there are 6 and 12 times more malicious Uploaders than the honest Uploaders. The figure clearly shows that contents take longer time to download in Theta network than the proposed method, while the loss due to the collusion increases to a significant amount. The content takes longer time to download for Theta network because malicious Uploaders take up the download connection spots that honest Uploaders could have connected to.  
Figure 8: Download speed comparison between Theta network and proposed method. (**a**) 50 honest Uploaders and 10 malicious Uploaders. (**b**) close-up view of the left figure.
# A Programmable SRv6 Processor for SFC
## 4 Application and Validation  
### Prototype System  
This paper's prototype system is implemented using the Corundum prototype platform based on a VCU118 FPGA development board [31]. Corundum is an open source, FPGA-based NIC prototype platform for 100 Gbps and higher rate network interface development. Corundum is composed of three nested modules, namely the FPGA top module, interface module, and port module. The top module mainly includes PCIe IP core, DMA interface, PTP hardware clock and Ethernet interface, as well as one or more interface module instances. Each interface module includes an Ethernet interface at the operating system level, queue management, packet descriptors, and completion processing logic, and one or more port module instances. The port module provides the AXI-Stream interface to the MAC, as well as the send scheduler, send and receive engine, and other content. Corundum includes core functions for real-time and high-speed operations, including high performance data paths, 10G/100G Ethernet interfaces, third-generation PCIe, and scalable queue management and transmission scheduling capabilities. With a combination of multiple network interfaces, multiple ports in each interface, and transmission scheduling functions in each port, Corundum can be used for the development and testing of advanced network applications. Corundum supports a variety of FPGA, including the VCU118 development board.  
The prototype system test environment is shown in Figure 5. In our system, Corundum is connected to a machine equipped with an Intel Xeon E5645 CPU clocked at 2.40 GHz via a peripheral component interface express (PCIe), and to the Spirent FX3-100 GO-T2 tester [32] via quad small form-factor pluggables (QSFP28). The SRv6 processor is constructed by combining two RMT pipelines with a deparser plus module.
## 3 PCC\({}^{2}\)LS for the Minimum Connected Dominating Set Problem
### Initial Solution Construction Procedure
#### 3.1.2 InitConstructCDS Procedure  
In this process, there are three main components: initialization, simplification, and construction. During the initialization phase, in line 1 of Algorithm 1, the _Dscore(\(v\))_ value of each vertex \(v\in V\) is initialized to its degree plus one.  
In the simplification phase, we apply three simplification rules to reduce the search space for the algorithm. Specifically, we follow a vertex sequence to assess whether a vertex should be fixed in the solution or removed from the graph. Vertices intended to be fixed in the solution are added to set \(S_{1}\), whereas those to be removed are appended to set \(S_{2}\). Thereafter, vertices from \(S_{1}\) are integrated into the candidate solution \(D\), while those from \(S_{2}\) are incorporated into the set _REset_. At line 6, vertices from the _REset_ are eliminated from the set \(V\).  
In the construction phase, we adopt a greedy strategy based on the _Dscore_ values to generate an initial solution. More specifically, if the current candidate solution \(D\) is an empty set, at line 8, to break the randomness, we select an initial vertex with the biggest _Dscore_ value from the \(V\) set. Subsequently, at line 9, the chosen vertex \(v\) is added to the set \(D\). If the \(D\) is not empty, the algorithm iteratively selects vertices with the biggest _Dscore_ values from the set \(N(D)\) and adds them to the \(D\) until a feasible solution is formed (lines 10-12). Finally, at line 13, once \(D\) forms a feasible solution, Algorithm 1 ends and returns the initial solution \(D\).  
```
0: an undirected, connected graph \(G(V,E)\)
0: an initial connected dominating set \(D\)
1. Initialize _Dscore_\(\leftarrow\)\(1N[v]\) for all \(v\) in \(V\);
2. \(D\gets REset\leftarrow\emptyset\);
3. while\(v\in V\backslash(D\bigcup REset)\) in chronological order satisfying any simplification rules then
4. obtain a fixed vertex set \(S_{1}\) and a deleted vertex set \(S_{2}\) based on simplification rule;
5. \(REset\gets REset\bigcup S_{2}\) and \(D\gets S_{1}\);
6. \(V\gets V\backslash REset\);
7. if\(D=\emptyset\)then
8. \(v\leftarrow\) select one vertex with the biggest _Dscore_ value from set \(V\);
9. \(D\gets D\cup\{v\}\);
10. while\(D\) is not a _CDS_then
11. \(v\leftarrow\) select one vertex with the biggest _Dscore_ value from \(N\)
12. \(D\gets D\cup\{v\}\);
13. return\(D\);
```  
**Algorithm 1**InitConstructCDS( )  
Figure 1: An example of applying simplification rules.
## 4 Analysis of Test Results
### Pressure Rise Ratio versus Mass Flow Rate  
Figure 11 shows the variation of the pressure rise ratio versus mass flow rate at different Mach numbers. Overall, when the inlet is at a small opening (\(K\leq 25\%\)), the larger the mass flow rate, the smaller the pressure rise ratio; when the inlet is at a large opening (\(K>25\%\)), the pressure rise ratio shows a trend of first increasing and then decreasing with the increase in the mass flow rate. When the mass flow rate is constant and the opening is less than 50%, the larger the opening, the larger the pressure rise ratio. However, with the continuous increase in the opening, the pressure rise ratio begins to decline at this time.
## 3 Aims  
The main aim of the work is to map the crop areas over a large territory through the combination of two mapping techniques, each individually incomplete for the purpose. The first technique is based on spaceborne remotely sensed data that is able to cluster the territory into homogeneous areas by analyzing the satellite images over a relatively long time interval, but it is not able to identify the crop kind. The second technique is able to identify the crop kinds that potentially could grow in the territory given the agro-climatic conditions but is not able to determine which are actually grown. The combination of the space-based crop mapping and the agro-climatic mapping aims at a crop classification sufficiently accurate to provide the exposure information for weather related risk assessment. Our fusion concept is illustrated in Figure 1, where the information contribution from each branch of the confluence is visually highlighted. The second aim of the research is to make the methodology suitable to be easily applied everywhere. This implies the use of only data available at a global scale and the development of unsupervised techniques.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 4 Particle Swarm Optimization  
PSO is an artificial intelligence technique inspired from the social behavior of birds, and it is used to approximate the solutions for optimization problems involving a large search space [50]. An individual amongst the bird population is known as a particle, and it represents the possible solution (a bird in the flock) for a given problem. The fitness of each particle indicates how close it is from the ideal solution. The solution has definite boundaries, and the particles move within the \(D\)-dimensional boundaries (flying space for birds) [51]. Let \(P\) represent the total number of particles (birds or solutions) and \(S_{j}^{\tau}=[S_{j1}^{\tau},S_{j2}^{\tau},\ldots,S_{jD}^{\tau}]\) represent the position of the \(j\)th particle \(1\leq P\leq D\) at iteration \(\tau\), where \(S_{jD}^{\tau}\) indicates the position of particle in the \(d\)th \(1\leq d\leq D\) dimensional space. At iteration \(\tau\), the velocity of the \(j\)th particle can be denoted as \(V_{j}^{\tau}=[V_{j1}^{\tau},V_{j2}^{\tau},\ldots,v_{jD}^{\tau}]\), which varies in the range \([-V_{max},V_{max}]\). During each iteration, the fitness of the particles is evaluated to indicate their merit. The position of particles depends on two key parameters: (1) personnel best (\(Pb^{\tau}\)); and (2) the neighbor best (\(Nb^{\tau}\)). Let \(Pb^{\tau}\) indicate the best position of the \(j\)th particle and be the best position in comparison with the neighbors. If we consider the rest of the particles as neighbors, then this term can be called the global best, and it can be represented by \(Gb^{\tau}\). These swarm parameters help with quick convergence towards the possible solution. The velocity and position of individual particles can be updated using the expression given in Equations (6) and (7) as follows:  
\[V_{jd}^{\tau+1}=V_{jd}^{\tau}+\eta_{1}\delta_{1}(Pb^{\tau}-S_{jd}^{\tau})+ \eta_{2}\delta_{2}(Gb^{\tau}-S_{jd}^{\tau}) \tag{6}\]  
\[S_{jd}^{\tau+1}=S_{jd}^{\tau}+V_{jd}^{\tau+1} \tag{7}\]  
where \(\eta_{1}\) and \(\eta_{2}\) are two positive constants termed the learning factor or acceleration coefficients and \(\delta_{1}\) and \(\delta_{2}\) represent the uniform random numbers fully distributed in the range [0, 1]. The terms \(S_{jd}^{\tau+1}\) and \(V_{jd}^{\tau+1}\) represent the updated position and velocity factors for the \(j\)th particle, respectively.
## 2 Results and Discussion
### Experimental and Theoretical NMR Spectra
\begin{table}
\begin{tabular}{c c c c} \hline
**Carbon Atom** & **Experimental (ppm)** & **Calculated, Unscaled (ppm)** & **Calculated, Scaled (ppm)** \\ \hline C24 & 13.1 & 13.5 & 11.4 \\ C22 & 14.8 & 15.5 & 13.2 \\ C4 & 27.3 & 32.8 & 27.8 \\ C23 & 39.9 & 44.7 & 37.9 \\ C21 & 40.2 & 46.0 & 39.0 \\ C17 & 42.0 & 46.8 & 39.7 \\ C8 & 43.9 & 48.5 & 41.1 \\ C7 & 56.0 & 60.8 & 51.5 \\ C5 & 63.2 & 71.2 & 60.3 \\ C14 & 109.7 & 130.9 & 110.9 \\ C3 & 110.8 & 132.6 & 112.4 \\ C12 & 112.6 & 133.4 & 113.2 \\ C2 & 118.3 & 139.3 & 118.0 \\ C9 & 119.7 & 146.2 & 123.9 \\ C13 & 123.3 & 146.4 & 124.1 \\ C16 & 126.3 & 149.0 & 126.3 \\ C11 & 128.1 & 152.4 & 129.2 \\ C15 & 134.0 & 155.4 & 131.8 \\ C10 & 136.3 & 166.3 & 141.0 \\ C18 & 170.9 & 196.4 & 166.5 \\ \hline R & & 0.999 & 0.999 \\ \hline MAE (ppm) & & 14.9 & 2.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental and theoretical (at M05-2X/6-311++G(d,p) level of theory) \({}^{13}\)C chemical shifts of LSD in chloroform.