esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Triggered Access for BSM Dissemination in 802.11 bd V2V Networks
## 2 Proposed Scheme
Within the User Info List field of the TF, RU scheduling information for multi-vehicle BSM transmission is included, and the triggering VSTA reserves one of the scheduled RUs for the BSM transmission of itself. If the triggering VSTA does not receive any CTS frame in response to the TF, the VSTA considers the TF transmission has failed and invokes another backoff procedure. In this case, however, the VSTA does not change the current contention window size as the previous backoff procedure was invoked by a broadcast frame. An example of the proposed DTA transmission sequence is described in Figure 3.  
In the proposed BSM dissemination scenario, there exist a few issues that need to be resolved to improve the performance of DTA. First, a triggering VSTA should know which VSTA has a BSM to transmit. If a VSTA with empty buffer is triggered, the scheduled RU might be wasted resulting in poor channel use. However, if a network topology changes rapidly or the number of VSTAs is large, it is difficult for the triggering VSTA to maintain the information on the network traffic consistently. Secondly, the transmitting VSTA does not know if the transmission has succeeded as there is no acknowledgement from the receivers. As soon as a VSTA transmits a BSM, the VSTA removes the transmitted packet from the buffer. If a collision occurs in the channel, the BSM is lost and there is no way to recover it. This can be a very serious problem under high vehicle density since the collision probability increases rapidly as the density increases [35]. To overcome such difficulties, in the proposed scheme, VSTA manages its buffered BSMs with two categories, the active BSM and the non-active BSM. VSTA considers a BSM is active if it has not been transmitted yet. If a VSTA has an active BSM, it performs EDCA backoff procedure to actively acquire the channel. Once the VSTA transmits the active BSM by means of either EDCA or TA, the BSM is no longer active. If the VSTA has no additional active BSM in the buffer, the VSTA keeps the BSM as a non-active BSM, otherwise the BSM is discarded. The VSTA does not initiate an EDCA backoff procedure for the non-active BSM, but it can be transmitted by TA. By doing so, waste of RU can be prevented since all VSTAs always have BSM to transmit. Moreover, since BSMs can be retransmitted by TA, the BSM delivery rate can be improved without increasing the channel contention. As a triggering VSTA does not need to  
Figure 3: An example of the proposed distributed triggered access scheme. consider the buffer status of other VSTAs, in the proposed scheme, we assume that the triggering VSTA performs a round robin scheduling among VSTAs within a predefined triggering range based on the positioning information. In the next section, a mathematical model for the analysis of the proposed scheme is provided.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 4 Resource Consumption and Measurements  
### Resource Consumption  
In this section, we take a \(2\times 2\) MIMO channel as an example to be implemented in one FPGA chip (Virtex-7). It should be noted that a single path generation needs 64 sub-paths or FM signals as shown in (5). Thus, for a single channel with \(M\) multiple paths, the traditional parallel method theoretically needs to prestore 128\(\times M\) cosine tables. In this paper, we implement the channel fading module by adopting a serial scheme or time division idea as shown in Figure 6, which only needs 2\(\times M\) cosine tables. Table 1 compares the hardware resources usages of a \(2\times 2\) MIMO channel emulator in [22] and a \(2\times 2\) MIMO channel emulator generated by the proposed method. It shows that the proposed method is more efficient than the one in [22]. The selected FPGA (Xilinx XC7 VX690 TFFG1927-2) consists of about 433,200 Slice LUTs, 1470 Block RAMs, and 3600 digital signal processors (DSPs). Considering the resource consumption of other modules and the efficiency of FPGA layout, it can be estimated that a \(32\times 32\) MIMO channel can be emulated on this single chip.
## 4 Experiment  
A principal prototype of a pulsed CDWL was designed and constructed on the basis of the aforementioned window function theory study. The system structure is illustrated in Figure 8.  
The structure of the CDWL can be divided into four main parts: transmitter, receiver, upper computer, and fiber optic connection devices. The transmitter is mainly composed of a laser source, acousto-optic modulator (AOM), erbium-doped fiber amplifier (EDFA), and other devices. The pulsed laser beam emitted by it is transmitted to the target wind field area through a fiber ring resonator and four optical antennas controlled by optical switches. The receiver is mainly composed of a balanced detector, an analog-to-digital converter (ADC), and a Field-Programmable Gate Array (FPGA). The wind field echo signal is coherently mixed with the local oscillator optical signal through a 2 \(\times\) 2 fiber coupler, and then received by a balanced detector. The wind field echo optical signal is converted into an electrical signal and quantitatively collected through an ADC. The FPGA processes the collected signal by using algorithms to obtain the wind speed and direction results of the target area, which are then displayed on the upper computer. The specific parameters are shown in Table 1.  
Figure 8: CDWL system architecture diagram.
# Quantum Photonic Simulation of Spin-Magnetic Field Coupling and Atom-Optical Field Interaction
## 4 Quantum Geometric Phases  
So far, we have simulated the dynamics of the spin-magnetic and light-matter interaction systems with a photonic device. However, these quantum devices also generate topological or geometric phases besides the dynamic phases. Geometric phases are precisely due to geometric properties as was originally proven by [PERSON] in his seminal work about an adiabatic quantum system [26]. Later, these geometric phases were generalized by [PERSON] and [PERSON] [27] to non-adiabatic processes, and their calculation is made by using the geometric properties of the projective Hilbert space. Finally, a useful extension to geometric phases associated with non-cyclic circuits on the projective Hilbert space was also proposed [28]. On the other hand, topological phases in optics have also been extensively studied in bulk devices with polarization modes [29] and also in integrated optics with spatial modes [30]. At present, geometrical phases have regained interest for their possible application to geometric quantum computation [31; 32]. Non-adiabatic spatial propagation on the Hilbert space generate the geometric phase known as the Aharonov-Anandan (AA) phase. It is well known that the spin-magnetic field interaction, as for example NMR, produces AA phases. We must stress that the geometric phase for a two-dimensional projective Hilbert space can be calculated in a geometric way as \(\phi_{\rm g}=(1/2)\mathbf{\Omega}(\mathcal{C})\), where \(\mathbf{\Omega}(\mathcal{C})\) is the solid angle subtended by the circuit \(\mathcal{C}\) followed by the quantum state on the Bloch sphere. In this section, we prove that quantum geometrical phases can be obtained by an integrated photonic grating, and therefore, it simulates the geometric phases produced by both a spin-magnetic field system and an atom-optical field system.
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 3 Architecture  
The structure of SATFuzz is shown in Figure 1 and mainly includes three parts: a state selector, test sequence selector, and test case filter.  
First, the state selector randomly selects one state to test among the high-priority states. Second, the test sequence selector selects the optimal test sequence that can reach the state from the queue according to the test file. Then, the old test case in the optimal test sequence is mutated by Mutator, while the minimum pre-lead sequence and the fittest post-end sequence remain unchanged. Next, the test case filter determines the validity of the new test case and only inputs the optimal test sequence, including the valid test case, to the protocol entity. Finally, SATFuzz updates the test file with feedback and prepares for the next testing step.
## 2 Mathematical Model of the Process
### Electromagnetic Submodel  
The electromagnetic problem was solved using finite element harmonic analysis at a frequency of 50 Hz. For the numerical domain, the magnetic vector potential equation was applied in the following form:  
\[\
abla\times\left(\frac{1}{\mu}\
abla\times\mathbf{A}\right)+j\omega\sigma \mathbf{A}=\mathbf{J_{s}} \tag{1}\]  
where \(\mu\) is the magnetic permeability, \(\sigma\) is the electric conductivity, \(\omega\) is the angular frequency, and \(\mathbf{J_{s}}\) is the current density source.  
Knowing the distribution of the magnetic vector potential \(\mathbf{A}\), both the magnetic induction \(\mathbf{B}\) and the eddy current density \(\mathbf{J}\) can be determined as follows:  
\[\mathbf{B}=\
abla\times\mathbf{A}\;, \tag{2}\]  
\[\mathbf{J}=-j\omega\sigma\mathbf{A}\;. \tag{3}\]  
Figure 2: One-way coupling between EM and HD submodels.  
With the current density, one can obtain the volume density of the Joule heat power generated in the bath processed in the furnace:  
\[q_{\rm JH}=\frac{1}{2}\frac{|\textbf{J}|^{2}}{\sigma}. \tag{4}\]  
This quantity is of key importance for the analyzed process because, in addition to heating the molten material, it causes natural convection, the leading mechanism of stirring and homogenizing the bath. The second mechanism, which also mixes the bath to a very small extent (as the later results will show), is convection forced by Lorentz forces:  
\[\textbf{f}_{\rm LF}=\frac{1}{2}Re(\textbf{J}\times\textbf{B}^{*}) \tag{5}\]  
where \(\textbf{B}^{*}\) is the complex conjugate of **B**.  
The spatial distributions of Joule heat sources and Lorentz force densities calculated in the EM model are transferred to the hydrodynamic submodel via one-way coupling (Figure 2).  
Figure 3 shows the geometry of the EM submodel used for the example of a basic furnace. At the boundary of the computational domain, the zero value of the magnetic vector potential **A** was assumed. On the electrode terminators, the current forcing was assumed with a value selected depending on the electrode geometry and ensuring constant power supplied to the device for comparison purposes.  
The geometry and mesh of the electromagnetic submodel were generated in Gmsh software 4.13.1 [26]. The electromagnetic calculations were carried out based on the GetDP software 3.5.0. integrated with Gmsh [27]. The advantage of using the Gmsh and GetDP packages is that they can be fully controlled by scripts, which significantly facilitates their use in optimization systems.
## 2 Materials and Methods
### Equipment  
Scanning electron microscopy (SEM) was performed on a FEI Quanta FEG 250 ESEM (Thermo Scientific, Waltham, Massachusetts, United States) variable pressure and environmental scanning instrument, housed in the Centre for Nanostructured Imaging at the University of Toronto. SEM was performed in a low vacuum 70-130 Pa, imaging at 5-10 eV. Energy-dispersive X-ray spectroscopy (EDX) was performed on the same FEI Quanta FEG 250 ESEM instrument under equivalent conditions. Post-processing EDX analysis was performed using TEAM: Texture & Elemental Analytical Microscopy software. Transmission Electron Microscopy (TEM) was performed on a H-7000 TEM (Hitachi, Chiyoda, Tokyo, Japan), housed in the Microscopy Imaging Laboratories at the University of Toronto. TEM was performed under high vacuum at 75.0 kV. No coating methods were employed in performing TEM or SEM imaging. X-ray diffraction (XRD) was performed on a D2 Phaser (Bruker, Billerica, Massachusetts, United States) powder X-ray diffractometer, housed at Exciton Technologies Inc. in Edmonton Alberta. XRD was performed using Cu K\(\alpha\) 1.54060 A, divergence slit 0.6 mm, air scatter shield 3 mm, air scatter slit 8 mm, step size 0.010\({}^{\circ}\), step time 42 sec. XRD data analysis, including peak area and full width half max (FWHM) calculations was performed using DIFFRAC EVA V4.1.1 software, post-processing including stripping Cu K\(\alpha\)\({}_{2}\).
## 3 CLIK Algorithms for UAM Control
### Velocity-Level CLIK Algorithm for UAMs
However, a minimum joint velocity solution can be found replacing the inverse of the Generalized Jacobian with its pseudoinverse in Equation (19). In this case, it is also possible to adopt the method of the Extended Generalized Jacobian or the null-space projection method developed in [9] and [8], respectively. In order to calculate the task-space error and thus the feedback term, it is necessary to know the actual position, \(\mathbf{x}_{e}\), of the end-effector, which must be compared with the desired position, \(\mathbf{x}_{e,des}\). In the controller implemented in the simulator, \(\mathbf{x}_{e}\) is computed through forward kinematics. In real experiments, it is better to obtain \(\mathbf{x}_{e}\) through sensor measurements (e.g., through a vision system) so that the algorithm is not influenced by uncertainties in the kinematic model of the system. By comparing this algorithm with the algorithms proposed in [12; 15], it is worth noticing that our method includes the dynamics of the system in the inverse kinematics equation through the use of the Generalized Jacobian and updating the linear and angular momenta with known generalized forces (see Equation (14)). On the other hand, the previous methods only include the effect of the UAV state on the Jacobian [15], or they compute the Jacobian for fixed-base manipulators [12].
## 2 Materials and Methods  
### Bed Material  
The LD-slag was prepared by SSAB Merox AB at the integrated steel mill in Oxelosund, Sweden and provided to the research boiler by pressure discharge truck. The production procedure involved drying, crushing, grinding and sieving of roughly 180 metric tonnes of LD-slag taken from an open storage pile. The target size of the particles was 100-400 \(\upmu\)m. The yield in the production process with respect to particles in the desired size interval was reportedly 20-25%. This number could likely be considerably higher in future efforts, since the production method used was rather ad-hoc and subject to some trial-and-error. Production of large batches of oxygen carriers from minerals have previously resulted in yields of about 50% [14]. The amount of LD-slag received was 38 metric tonnes. Neither sieving nor density measurements of random samples provided totally consistent results. This suggests that the LD-slag in the storage pile was not homogeneous. Further, as pointed out above, the production itself proceeded on a trial-and-error basis. Notably, a sieving pan was changed from 100 \(\upmu\)m to 150 \(\upmu\)m during the production process in an attempt to reduce the very time-consuming sieving effort. Nevertheless, the sieving was imperfect and analysis of small samples suggests that 10-15 wt% of the received batch consisted of particles smaller than 90 \(\upmu\)m. During operation this had some minor consequences with respect to elutriation since smaller particles are more likely to be elutriated than larger ones, but this is not believed to have influenced the conclusions of the study. Sand was used as reference material and occasionally for mixing together with LD-slag. Standard silica sand provided by Sibelco Nordic AB (Baskarp B28) was used for this purpose. A summary of the properties of the bed material can be found in Table 1. Note that the composition, while based on elemental analysis, should be treated as approximate since the batch was inhomogeneous. For sand, the composition reported is the one provided by the supplier.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 3 Modeling
### Notation  
The variables in the planning and layout model of the air-ground cooperative co-distribution center are defined in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol Type** & **Notation Expression** & **Meaning** \\ \hline \multirow{4}{*}{Collections} & \(I=\{1,2,3,\ldots.\,n\}\) & Collection of an alternative co-distribution center \\ \cline{2-3}  & \(J=\{1,2,3,\ldots.\,m\}\) & Collection of customer demand points \\ \cline{2-3}  & \(S=\{1,2\}\) & Categorized collection of co-distribution centers \\ \cline{2-3}  & \(U=\{1,2,3,\ldots.\,u\}\) & Collection of UAVs \\ \cline{2-3}  & \(K[1,2,3,\ldots.\,k]\) & Collection of vehicles \\ \hline \multirow{4}{*}{Subscripts} & \(i\in I\) & Subscript of alternative co-distribution centers \\ \cline{2-3}  & \(j\in J\) & Subscript of customer demand points \\ \cline{2-3}  & \(s\in S\) & Subscript of co-distribution center types \\ \cline{2-3}  & \(u\in U\) & Subscript of UAVs \\ \cline{2-3}  & \(k\in K\) & Subscript of vehicles \\ \hline \multirow{4}{*}{Subscripts} & \(\alpha\) & Time function weights \\ \cline{2-3}  & \(\beta\) & Cost function weights \\ \cline{2-3}  & \(h_{j}\) & Distribution demand at demand point j \\ \cline{2-3}  & \(E_{s}\) & Service capacity of the co-distribution centers \\ \cline{2-3}  & \(Q_{u}\) & Maximum load capacity of the UAV (kg) \\ \cline{2-3}  & \(q^{u}{}_{ij}\) & Single-delivery capacity of the UAV (kg) \\ \cline{2-3}  & \(Q_{k}\) & Maximum load capacity of the vehicle (kg) \\ \cline{2-3}  & \(q^{k}{}_{ij}\) & Single-delivery capacity of the vehicle (kg) \\ \cline{2-3}  & \(R_{u}\) & Maximum service distance of UAV co-distribution centers (km) \\ \cline{2-3}  & \(R_{k}\) & Maximum service distance of vehicle co-distribution centers (km) \\ \cline{2-3}  & \(D_{u}\) & Maximum range of the UAV (km) \\ \cline{2-3}  & \(D_{k}\) & Maximum range of the vehicle (km) \\ \cline{2-3}  & \(v_{u}\) & Average delivery speed of the UAV (km/h) \\ \cline{2-3}  & \(v_{k}\) & Average delivery speed of the vehicle (km/h) \\ \cline{2-3}  & \(\text{C}_{s}\) & Construction cost of the co-distribution center (CNY) \\ \hline \multirow{2}{*}{\(d^{u}{}_{ij}\)} & Flight distance of the UAV from co-distribution center i to customer demand point j (km) \\ \cline{2-3}  & \(d^{k}{}_{ij}\) & Distance of the vehicle from co-distribution center i to customer demand point j (km) \\ \cline{2-3}  & \(\text{C}^{u}{}_{v}\) & Distribution and operating costs of the UAV co-distribution center (CNY/pc) \\ \cline{2-3}  & \(\text{C}^{k}{}_{v}\) & Distribution and operating costs of the UAV co-distribution center (CNY/pc) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Explanation of symbols related to the planning and layout model of the air–ground cooperative co-distribution center.
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 3 Preliminary
### Meta-Path-Based Views  
In heterogeneous graphs, a meta-path is defined as a sequence of relations connecting different types of entities. Formally, a meta-path is represented as \(v_{1}\to v_{2}\rightarrow\cdots\to v_{n}\), where \(v_{i}\) denotes a node type, and the arrows denote specific relations. Meta-paths encapsulate the composite relations between the starting node \(v_{1}\) and the ending node \(v_{n}\), providing a structured representation of semantic interactions.  
Each meta-path is treated as a unique meta-path view, containing the semantic information specific to that relationship sequence. For instance, for detecting Ponzi schemes in smart contracts, meta-paths can capture critical interaction patterns indicative of fraudulent behavior. Examples include  
1. \(EOA\to CA\): Externally Owned Accounts (EOA) to Contract Accounts (CA), representing fund inflows.
2. \(CA\to EOA\): CA to EOA, representing fund outflows or transfers.  
Constructing views based on these meta-paths yields a multi-view representation of the data. Each meta-path view constructed from these relations provides a distinct perspective on the data, capturing different semantic aspects that are crucial for identifying Ponzi schemes.
## 1 Introduction
In addition, machine learning is applied to the physical layer processing such as antenna selection and beamforming design in MIMO systems [23; 24], and channel estimation and hybrid precoding for massive MIMO systems [25; 26]. In particular, deep learning [27], which is one of key machine learning techniques, is tackling complicated nonlinear problems and high-computation issues in many areas [22] and overwhelms many existing schemes [22; 24; 25; 26]. In this paper, we develop our initial work [19] on the dimension optimization for the outer precoder design with the machine learning framework. Our contributions can be summarized as follows:  
* We introduce our two-stage precoder design with limited feedback, where the quantized CDI of the dimension-reduced effective channel is only fed back to the BS. We first derive a lower bound of the average sum rate and then optimize the dimension of the outer precoder to maximize average sum rate. * We propose the machine learning framework for the dimension optimization based on a deep neural network (DNN); we determine the DNN architecture of the input, the hidden, and the output layers as well as training procedure. Our DNN architecture takes eigenvalues of covariance matrices of user groups as inputs and returns the structure of outer precoder, which represents the allocated dimensions for all user groups. * We evaluate our DNN model and show that our proposed machine learning based outer precoder dimension optimization improves the average sum-rate and achieves near-optimal performance. The rest of this paper is organized as follows. We introduces our system model in Section 2 and describe our problem in Section 3. We introduce our previous work on the lower bound of the achievable sum rate in Section 4 and propose a machine learning framework for the dimension optimization in Section 5. We evaluate our proposed DNN model in Section 6 and conclude our paper in Section 7. _Notations_: We use upper and lower case boldfaces to denote matrices and vectors, respectively. The notations \((\cdot)^{T}\) and \((\cdot)^{\dagger}\) represent the transpose and complex conjugate transpose, respectively. In addition, the notations \(\mathbb{E}[\cdot]\) and \(\Pr[\cdot]\) denote the expectation and the probability, respectively.