esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Delay Inter-Packet Coding in Vehicular Networks
## 1 Introduction  
### Safety Testing of C-ITS  
The emerging concept of Cooperative Intelligent Transportation Systems (C-ITS) suggests a widespread adoption of information and communication technologies in diverse vehicular applications that aim at increasing transport safety, efficiency and comfort. C-ITS vehicles exchange information with each other as well as with roadside infrastructure over vehicular ad-hoc networks (VANETs) [1]. An important milestone for VANETs has been the worldwide allocation of reserved bandwidth for C-ITS in the 5.9 GHz spectrum. The developing communication technology is called as DSRC in USA, and ITS-G5 in Europe [2].  
Intensive testing is being performed so that C-ITS systems could be introduced on public roads. AstaZero in Sweden is an advanced testbed for research and development in the area of active road safety and autonomous transport [3]. The unique capacities of the testbed offer the opportunityto support and accelerate research and development of active safety and C-ITS features through partnerships and close collaboration with vehicle manufacturers, suppliers, legislators, universities and colleges throughout the world. Different traffic and communication environments are available at the facility, and these make it possible to test and analyze systems from function level to vehicle integration--and this in all kinds of traffic and traffic situations (Figure 1).
# Potentials for Pressure Gain Combustion in Advanced Gas Turbine Cycles
## Nomenclature  
**Symbols**  
\(AFR\) & Air to fuel ratio & (-)  
\(C\) & Combustor model parameter & (-)  
\(c\) & Cooling coefficient & (-)  
\(c_{p}\) & Heat capacity at constant pressure & (J/kgK)  
\(c_{p}\) & Heat capacity at constant volume & (J/kgK)  
\(dP_{Loss}\) & Pressure loss & (-)  
\(LHV\) & Lower fuel heating value & (MJ/kg)  
\(\eta\) & Mass flow & (kg/s)  
\(p\) & Pressure & (Pa)  
\(pf\) & Purge fraction & (-)  
\(PW_{output}\) & Output power & (W)  
\(q\) & Specific heat & (J/kg)  
\(Q\) & Heat & (W)  
\(r\) & Combustor model parameter & (-)  
\(R_{g}\) & Specific gas constant & (J/kgK)  
\(T\) & Temperature & (K)  
\(\gamma\) & Ratio of specific heats & (-)  
\(\eta_{th}\) & Thermal efficiency & (%)  
\(\pi\) & Pressure ratio & (-)  
**Subscripts**  
0 & Reference, engine inlet  
B & Station downstream isochoric combustion  
cold & Cooling air  
cp & Station downstream isobaric combustion  
hot & Annulus air  
HPC & High pressure compressor  
in & Inlet  
LPC & Low pressure compressor  
out & Outlet  
**Abbreviations**  
DDT & Deflagration to detonation transition  
HR & Enthalpy ratio  
PGC & Pressure gain combustion/pressure gain combustor  
PR & Pressure ratio  
SAS & Secondary air system  
TET & Turbine entry temperature  
TR & Temperature ratio
## 2 Neural Network-Based Orbit Uncertainty Propagation Orbit Determination Framework
### Orbit Uncertainty Propagation
The other is to approximate the deviation \(\delta\mathbf{x}\) using techniques such as state transition matrix (STM), state transition tensor (STT), or polynomial chaos (PC). Both approaches require numerical integrations, making its computational cost unacceptable for certain onboard applications, especially CubeSat-like platforms with limited computational capacity. Recently, a novel neural network-based method was developed in [46] to significantly improve the computational cost of the OD of an LEO spacecraft. This work extends such an idea to solve the OD problem of an Earth-Jupiter transfer spacecraft with the framework shown in Figure 1. In Figure 1, the orbits propagated using high-fidelity and two-body dynamics (considering only the Sun's gravitational force) are represented by black and blue lines, respectively. In addition, the orbital state propagated by the two-body dynamics is distinguished using a superscript \"2 BP\". For example, \(\mathbf{x}^{\text{2 BP}}\in\mathbb{R}^{6}\) and \(\tilde{\mathbf{x}}^{\text{2 BP}}\in\mathbb{R}^{6}\) represent the orbital states of the nominal and neighboring orbits under the two-body dynamics. Under the two-body dynamics, the orbit deviation in Equation (3) has an analytical solution (i.e., \(\delta\mathbf{x}^{\text{2 BP}}=\tilde{\mathbf{x}}^{\text{2 BP}}-\mathbf{x}^{\text{2 BP}}\) can be analytically computed). The detailed expressions for the orbit deviation propagation under the two-body dynamics are well presented in [46; 48]. As shown in Figure 1, we define \(\mathbf{e}=\delta\mathbf{x}-\delta\mathbf{x}^{\text{2 BP}}\), which represents the residual between the true (under the high-fidelity dynamics) and two-body orbit deviation propagation solutions. If the residual \(\mathbf{e}\) can be predicted using a neural network, then the value of \(\delta\mathbf{x}\) can be obtained without additional numerical integrations, which can significantly reduce the computational burden. In this work, a multivariate attention-based neural network (MANN) is developed to predict the residual \(\mathbf{e}\). The designed structure and the training process of the MANN will be introduced in the next section. Let \(\mathbf{\hat{e}}_{\text{MANN}}\) be the prediction of the MANN; then, the orbit deviation propagation solution can be rewritten as  
\[\delta\mathbf{x}\approx\mathbf{\hat{e}}_{\text{MANN}}+\delta\mathbf{x}^{\text{2 BP}}. \tag{4}\]  
In the following subsection, the solution in Equation (4) is combined with a UT method (denoted as MANN-UT) to improve the OD efficiency of the conventional UKF method. Figure 1: An illustration of the orbit deviation propagation framework.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 4 Experimental Setup
### Dataset  
We conducted experiments on two publicly available datasets, PHEME and RUMDECT, collected from Twitter and Weibo, respectively. These datasets are widely recognized and commonly used in the field of rumor detection.  
As shown in Table 1, the PHEME dataset [13] sourced from Twitter comprises 1972 instances of rumors and 3830 instances of non-rumors. This benchmark contains five newsworthy cases of breaking news that provoked a high number (exceeding 100) of retweets. As these five events propagated through social media, four distinct rumors emerged from the conversations surrounding them. Table 2 illustrates typical examples of rumor and non-rumor tweets from this dataset. Additionally, this benchmark contains rich features, including post text, user information, and release timestamps.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Events** & **Rumor** & **Non-Rumor** \\ \hline \multirow{6}{*}{Ottawa Shooting} & \#Ottawa police confirm shooting at War Memorial. Reports say victim may be a soldier. & \multirow{2}{*}{Canine unit running to Parliament Hill Dawson shootings. Remember, at least half of what you hear about Ottawa shootings will prove untrue.} \\  & One person shot outside Centre Block, a second wounded inside the building in Parliament Hill shooting. & \multirow{2}{*}{I covered Polytechnique, Concordia and Dawson shootings. Remember, at least half of what you hear about Ottawa shootings will prove untrue.} \\ \cline{1-1}  
The RUMDECT [10] consists of data from Weibo and Twitter, although our experiments exclusively focused on the Weibo data. The Weibo dataset comprises 4664 labeled events collected from the Sina Weibo rumor debunking service, encompassing 2313 rumors and 2321 non-rumors. Each event in the Weibo dataset provides text information, related forwarding details, posting and reposting location information, user information of the poster and reposter, and other features. Table 3 presents examples of typical rumor and non-rumor Weibo posts from this dataset.
# Experimental Study on the Current Pretreatment-Assisted Free Bulging of 304 Stainless Steel Sheets
###### Abstract  
In order to further improve the microplastic deformation ability and forming performance of metal sheets, a pulse current pretreatment-assisted micro-forming method was proposed. Firstly, a 304 stainless steel sheet was pretreated with current (0-25 A), and the microstructure changes in the sheet under the action of current were analyzed. Then, a current-assisted bulging experiment was carried out from three aspects as follows: current size, mold structure size, and material properties, to explore the influence of different process parameters on the micro-bulging of the sheet. Finally, the forming quality was analyzed and evaluated from the two perspectives of bulging depth and wall thickness uniformity. The research results show that when the current intensity increases from 0 to 25 A, the fibrous distribution in the thickness direction of the sheet is alleviated, the structure is more uniform, the bulging depth shows an increasing trend, and the thinning rate and wall thickness uniformity are improved. When the current intensity reaches 25 A, the bulging depth increases from the original 463 \(\upmu\)m to 503 \(\upmu\)m, and the thinning rate drops from the most serious 48.52% to 19.4%. At the same time, as the mold size increases, the single-channel aspect ratio (W/H) also increases accordingly. When the mold groove width (W) is 2 mm, the ratio reaches 0.4, the sheet deforms significantly, and the filling effect is better. In addition, the larger the roundness of the convex and concave models, the more uniform the wall thickness distribution of the bulging parts. Under the same experimental conditions, the bulging depth of the 304 stainless steel sheet is higher than that of TC4 titanium alloy, and it is less prone to springback and is more conducive to plastic deformation.  
pulsed current; 304 stainless steel sheet; single channel; free bulging +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Experimental Setup  
The majority of experiments used the train-to-test ratio (TTTR) of 7:3 in each EEG channel, meaning that 70% of the dataset was used for training, and the remaining 30% was used for testing. Many researchers have achieved the estimated performance using a 9:1 ratio, which makes the performance predictably high with such a high percentage of datasets for training. To the best of our knowledge, varying the TTTR merits investigation to test the robustness of the computational performance [6]. Therefore, this experiment set the TTTRs as 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, and 1:9.  
This study implemented the algorithm using the MATLAB (R2017a) software (MathWorks, Natick, Massachusetts, USA) and its toolboxes, on a computer with a 3.20 GHz Intel-Core-i5-3470 CPU (Beijing, China), with 12 GB of RAM and running 64-bit Windows7.  
In order to eliminate the influence of different time-space dimensions, the data had to be processed by normalization methods. After that, the features were of the same order of magnitude and the data were then suitable for comprehensive comparison and evaluation. This experiment was carried out on EEG signals using the following equation, which is usually called the Z-core normalization method:  
\[x_{i}^{*}(t)=\frac{x_{i}(t)-\mu}{\sigma}, \tag{19}\]  
where \(\mu,\sigma\) are the mean and standard deviation of sample EEG segment \(x_{i}(t)\), respectively, and \(x_{i}^{*}(t)\) is the normalized \(x_{i}(t)\).
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 3 V2I Communication  
Efficient data communication between individual traffic entities is one of the basic pillars of Smart Mobility. At the very top of the communication hierarchy is the V2X (Vehicle-to-Everything) communication. Vehicle-to-Everything (V2X) communication is a paradigm of the intelligent transport system (ITS). ITS is used to increase the efficiency and reliability of data transmission. This is achieved by the timely implementation of a complete set of communication mechanisms in all devices and infrastructure involved in traffic management and monitoring [20]. Within V2X we distinguish several subcategories, such as:  
* Vehicle-to-vehicle (V2V).
* Vehicle-to-network (V2N).
* Vehicle-to-pedestrian (V2P).
* Vehicle-to-infrastructure (V2I).
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation  
In the simulation, we set the duplication factors \(\{\tau_{i},i=1,2,3\}\) for spatio-temporal dimension augmentation to \([3,3,10]\), and set the coefficients \(\{a_{j},j=1,2,\cdots,6\}\) for defining the Hankel tensor nuclear norm equally to \(\frac{1}{6}\). We set the penalty constant of the ADMM augmented Lagrangian function (12) to \(\rho=10^{-3}\), and set the threshold of ADMM to \(\xi=10^{-12}\). Since the positions of impaired sensors in the URA are unpredictable in real applications, they are set randomly for each simulated scenario. The root mean square error (RMSE)  
\[\text{RMSE}=\sqrt{\frac{1}{2K\text{N}_{\text{MC}}}\sum_{n_{\text{MC}}=1}^{N_{ \text{MC}}}\sum_{k=1}^{K}\left[(\hat{\theta}_{k,n_{\text{MC}}}-\theta_{k})^{2}+ (\hat{\phi}_{k,n_{\text{MC}}}-\phi_{k})^{2}\right]} \tag{28}\]  
is adopted as the evaluation metric, where \((\hat{\theta}_{k,n_{\text{MC}}},\hat{\phi}_{k,n_{\text{MC}}})\) is the estimate of \((\theta_{k},\phi_{k})\) for the \(n_{\text{MC}}\)-th Monte Carlo trial, and \(N_{\text{MC}}=1000\) Monte Carlo trials are run for each data point to obtain the RMSE curves. Unless otherwise specified, we deploy a URA \(\mathbb{U}\) with \(M=7\) and \(N=7\), and assume \(K=2\) sources from the directions \((\theta_{1},\phi_{1})=(10^{\circ},10^{\circ})\) and \((\theta_{2},\phi_{2})=(25^{\circ},25^{\circ})\), respectively. The proposed algorithm is implemented with Tensorlab 3.0 on MATLAB R2021b, which develops useful tools for implementing the tensor formulation and CPD [57]. Meanwhile, the ADMM for solving the Hankel tensor completion problem can be implemented with well-studied tool in the literature [43].
# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
[PERSON] and [PERSON] [32] applied an approach of [33; 34] to the synthesis of a high-order adaptive algorithm for a multi-agent system control. Quadrotor attitude control was considered as an example. To demonstrate the convergence of trajectories in a system of four agents, a laboratory quadrotor setup [35] was used. Recent results on this topic, including adaptive and variable-structure control approaches for the problem of spacecraft group motion control, can be found in the survey by [36]. The application of an adaptive coding procedure for relative motion control of two satellites over a packet erasure communication channel with a limited transmission rate was discussed in [37]. A discrete-time adaptive controller for attitude control of a single spacecraft using reaction wheels was studied in [38]. Contrary to the habitually adopted approach, when the aircraft model is derived with the help of the Taylor linearization technique in the vicinity of the given flight conditions, the feedback linearization method is applied to the nonlinear aircraft model. A rigorous justification of this approach is obtained through the theory of flat systems; see [39; 40; 41; 21; 42; 43]. The novelty of the present paper's theory is that the uncertain mass is introduced into a linearized system, in contrast to the usual approach in which the feedback-linearized plant dynamics are described by double integrators. This makes it possible to apply adaptation methods by taking into account the presence of the unknown parameter. The remainder of this paper is organized as follows. The point-mass model of UAV dynamics is provided in Section 2. Section 3 is devoted to the adaptive control of a single UAV. Control of a multi-agent UAVs formation is considered in Section 4, and simulation results are presented in Section 5. Section 6 presents a discussion of the obtained results and compares them with other related works. Finally, our concluding remarks and future work intentions are provided in Section 7.
## 3 Proposed Approach
This approach allows the model to learn adaptively and adjust dynamically to different NTN environments, making it more flexible compared to traditional rule-based or value-based handover mechanisms. Through policy gradient updates and adaptive learning, the proposed LBH-PRL approach effectively learns an optimal handover strategy dynamically, improving service continuity and reducing unnecessary handovers in high-mobility NTN environments.
# Multirobot Task Planning Method Based on the Energy Penalty Strategy
###### Abstract  
In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A\({}^{\star}\) (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.  
multirobot; energy penalty; genetic algorithm; task allocation; task sequence planning
# HMM Adaptation for Improving a Human Activity Recognition System
## 4 User Maximum A Posteriori Adaptation  
In order to generate activity HMMs adapted to every user, the system trains general activity HMMs including information from all users. After training these general models, user-adapted activity HMMs are generated by adapting the general models to every user via a Maximum A Posteriori approach (sometimes referred to as Bayesian adaptation). MAP adaptation needs prior knowledge about the model parameter distribution (original HMMs). For MAP adaptation purposes, the informative priors that are generally used are the user-independent model parameters. The updated formula for the \(\mu\) parameter of mixture component \(m\) is:  
\[\mu_{m}=\frac{N_{m}}{N_{m}+MU}\mu_{general\cdot m}+\frac{MU}{N_{m}+MU}\mu_{USER \cdot m} \tag{3}\]  
Figure 3: Search space for activity segmentation.  
The estimated \(\mu_{m}\) is a linear combination of the \(\mu\) parameter of mixture component \(m\) in the original model (\(\mu_{general\cdot m}\)) and the \(\mu\) parameter of mixture component \(m\) obtained considering only observation vectors from the user (\(\mu_{USER\cdot m}\)). \(M\)U is the adaptation coefficient that defines the weighting of user-dependent information compared to general HMMs. \(N_{m}\) is the occupation likelihood of the adaptation data (from the user) along the \(T\) frames, defined as:  
\[N_{m}=\sum_{t=1}^{t=T}N(O;\mu_{general\cdot m},\sum_{general\cdot m}) \tag{4}\]  
After training the adapted activity HMMs for every user, the activity recognition and segmentation process is the same as the method used with user-independent HMMs: the process consists of computing the likelihood of the best model sequence when generating several activities.
## 3 Results and Discussion
### Mechanism Model
#### 3.1.2 Response of Ca2+ and DIC to Toluene Mineralization  
H+ is one of the critical factors affecting carbonate composition. Along with the change in H+, the concentration of other ions in the solution will change accordingly. Dissolution and precipitation are more difficult to observe under field conditions; Ca2+ and dissolved inorganic carbon (DIC) are the most commonly used to detect CaCO3 dissolution. DIC is composed of three dominant species: dissolved CO2 (CO2), bicarbonate (HCO3\({}^{-}\)), and carbonate (CO3\({}^{2-}\)).  
If the amount of hydrogen ions produced by carbon dioxide dissolved in water and then electrolyzed is greater than the amount of hydrogen ions consumed by the reaction,  
Figure 1: Mechanism modeling of the model.  
the CaCO\({}_{3}\) dissolves, which leads to an increase in the concentration of Ca\({}^{2+}\) and CO\({}_{3}\)\({}^{2-}\), accompanied by a corresponding increase in the concentration of H\({}_{2}\)CO\({}_{3}\) and HCO\({}_{3}\)\({}^{-}\). On the other hand, when the solution is saturated with CaCO\({}_{3}\), then the CaCO\({}_{3}\) precipitates, and the concentrations of Ca\({}^{2+}\), CO\({}_{3}\)\({}^{2-}\), and HCO\({}_{3}\)\({}^{-}\) decrease.  
In summary, multiple reactions are involved in toluene mineralization under denitrification conditions, and they are strongly interdependent. Therefore, a mathematical model is required to analyze the impact of toluene mineralization on calcium carbonate in this context.
## 2 Samples and Analytical Procedure
### Aerodyne AMS Measurements
CE values were determined by comparing the AMS mass loading for a given chemical species with mass loading from other particulate chemical measurement techniques such as the particle-into-liquid sampler (PILS) with ion chromatography [40; 61], the AMS total mass loading (sum of all chemical species) with other numbers, and mass-based instrument techniques, such as scanning mobility particle size (SMPS) [62] and tapered element oscillating microbalance (TEOM) [63]. Several earlier field campaigns [64; 65; 66; 67] showed that the mass of sulfate detected by the AMS was often low by a factor of 1.5-2.3, implying an apparent CE value for sulfate between 0.4 and 0.7. CE is also affected by relative humidity (RH) [64], particle acidity ([PERSON] et al., 2006 [62]), and nitrate ([PERSON] et al., 2007 [68]). Other laboratory studies of pure organic species reported that the CE depends on the phase of the particles, with a CE of 1 for liquids and 0.2-0.5 for solids [59]. [PERSON] et al. [42] used an average collection efficiency of 0.7 for total organics in non-refractory-PM\({}_{1.0}\) (CE = 1 for small-mode organics and CE = 0.5 for accumulation-mode organics), based on laboratory experiments performed by [PERSON] et al. [69]. AMS can quantify both organic and inorganic species using a flash vaporization followed by electron impact ionization. The thermal decomposition of organic acids to CO\({}_{2}\) is well known, and m/z 44 fragment can be formed by the decarboxylation of mono-, di-, and poly-carboxylic acids [40; 59; 70; 71]. [PERSON] et al. [40] reported the mass fragmentation patterns for major organic acids using authentic standards. For oxalic acid, m/z 44 peak is the largest, accounting for \(\sim\)34% of the sum of the fragment ions. The values of malonic (C\({}_{3}\)) and succinic (C\({}_{4}\)) acids were 21% and 15%, respectively. On the other hand, those of glutaratic (C\({}_{5}\)), adipic (C\({}_{6}\)), glyoxylic (\(\omega\)C\({}_{2}\)), and phthalic (Ph) acids were 5-7% of the sum of all the fragments. However, other diacids and oxoacids and fatty acids as well as salt forms of organic acids were not examined in [PERSON] et al. [40].