esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
To address these challenges, this study makes the following key contributions:  
1. We propose a novel MHALSTM model: This study integrates multi-head attention mechanisms with Long Short-Term Memory (LSTM) networks to design the Multi-Head Attention Long Short-Term Memory (MHALSTM) model. This model improves the accuracy of passenger flow prediction by capturing temporal dependencies and nuanced patterns in passenger flow, enabling precise forecasting. 2. We establish a dynamic scheduling strategy: Based on the predicted passenger flow, this study introduces a proactive scheduling strategy for security resources. During peak periods, the number of security lanes is increased in advance to enhance operational efficiency. Conversely, during off-peak periods, resources are reduced appropriately to minimize operational costs. 3. We demonstrate the effectiveness of the proposed model: Through comparative experiments using real passenger flow data, this study highlights the advantages of the MHALSTM-MPC model. The results show that the proposed approach significantly reduces average waiting times and operational costs, while improving resource utilization and overall service quality. The remainder of this paper is structured as follows. The next section reviews existing research on resource scheduling and passenger flow prediction in high-speed railway systems, identifying the limitations and research gaps that motivate this study. The proposed MHALSTM-MPC model is then introduced, and we detail its components and operational mechanisms. Subsequently, the experimental setup and data sources are described, followed by a presentation and discussion of experimental results, including comparisons with traditional scheduling methods. Finally, the paper concludes with a summary of the findings and suggestions for future research directions, highlighting the practical implications of this study.
## 3 Results and Discussion
### Evaluation of Acute Toxicity towards Vibrio fischeri  
Another topic of this research was the evaluation of toxicity towards _V. fischeri_ using bioluminescence inhibition of chamomile and sage aqueous extracts performed at the three temperatures. The results are presented in Figure 1.  
The EC\({}_{50}\) of extracts towards the bioluminescence photobacterium _V. fischeri_ ranged between 0.032 and 1.264 mg/mL. Chamomile aqueous extracts showed a higher capacity to inhibit bioluminescence of _V. fischeri_ than sage aqueous extracts. Specifically, the obtained values of EC\({}_{50}\) for chamomile extracts at 80 and 100 \({}^{\circ}\)C indicated high toxicity whereas the aqueous extract at 25 \({}^{\circ}\)C exerted lower toxicity. In the case of sage aqueous extracts, toxicity towards _V. fischeri_ increased as the extraction temperature increased.  
There are no studies reporting bacterial toxicity to the bioluminescent bacteria _V. fischeri_ from chamomile extracts, since most studies examined other herbal species [6; 24]. However, scarce literature exists for toxicity towards _V. fischeri_ of sage aqueous extracts associated with bioluminescence bioassay. In a previous study reporting inhibition of bioluminescence from sage aqueous extracts at different extraction temperatures, the data showed lower values than the obtained values, which could be explained by the differences in growing conditions, regions, and sage plan genotype, but indicated the same trend concerning the temperature [5]. The experimental values of sage infusion at boiled water showed good agreement with those estimated by [PERSON] et al. who reported the EC\({}_{20}\) of inhibition [25].  
Some phenolic compounds are toxic to _V. fischeri_, revealing the bacterial bioassay as sensitive to monitoring their toxicity [4]. In a previous study, the main compound of chamomile extract, ferulic acid, as shown in the Table 2 below, was found to be toxic using the Microtax biotest [26]. Concerning sage infusions, synergistic action between water soluble compounds and volatile compounds towards _V. fischeri_ has been presented. Moreover, this study reported that toxicity cannot be attributed to rosmarinic acid [5], which is the main compound of sage aqueous extract, according to Table 2.  
To conclude, the extraction temperature of herbal aqueous extracts affected the toxicity towards _V. fischeri_. Moreover, besides the different values EC\({}_{50}\) of the examined aqueous extracts, all of them exerted a toxic effect on _V. fischeri_, showing antimicrobial capacity and potent self-defense.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Routing Protocols  
Proper routing is a challenge due to highly dynamic topology, varied network characteristics, and different types of communication (V2V, V2I). The continuous vehicular movement demands a suitable communication link. The primary requirement of an efficient routing protocol is to provide QoS, better traffic and network management, handle mobility and provide delay-tolerant message delivery. The three protocols considered are topology-based, where routing information is mainly stored in the routing table. An efficient routing mechanism is necessary for appropriate route selection from sender to receiver via a proper communication link.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 4 Discussion
Therefore, it is noteworthy that thorough crop phenology enables more precise production estimations to be made on a broader scale [14; 21; 38].
## 4 Experimental Results
### Individual Evaluation
#### 4.2.3 Computation Time  
We also compared the computation time of the proposed pipeline to that with no-selection. It is worth noting that we compared here only the computation time related to the quality prediction without integrating the saliency-based patch selection. As shown in Table 7, the quality of an given image is predicted using 180 patches whatever the dimension of the image, while 625 and 310 patches are used for images of CID:IQ and VDID14 datasets, respectively. The results show that the proposed method based on saliency is faster compared to using all patches.
## 2 Proposed CPM-GFDM Transceiver
### CPM-GFDM Transmitter
For instance, the signal constellation has five possible points if for a binary CPM, \(h=\frac{2}{5}\), as seen below and shown in Figure 2:  
\[\phi_{k}=\left\{0,\frac{2\pi}{5},\frac{4\pi}{5},\frac{6\pi}{5},\frac{8\pi}{5}\right\} \tag{6}\]and eight possible points if \(h=\frac{1}{4}\), as shown below and shown in Figure 2:  
\[\phi_{k}=\ \left\{0,\frac{\pi}{4},\frac{\pi}{2},\frac{3\pi}{4},\pi,\frac{5\pi}{4}, \frac{3\pi}{2},\frac{7\pi}{4}\right\} \tag{7}\]  
Next, a pulse shaping filter--denoted by \(g[n]\)--is applied. This is a circular prototype filter whose length is \(N\times M\) for a \(k\)th subcarrier since each subcarrier carries \(N\) samples per symbol and there are \(M\) symbols at each subcarrier. For each subcarrier \(k\), the filter is delayed by \(mN\) in time corresponding to the \(m\)th symbol where \(m=0,1,\ldots,M-1\). Five pulse shaping circular filters have been investigated in the literature. These are: (1) Root Raised Cosine, (2) Raised Cosine, (3) Xia Pulse, (4) Gaussian Pulse, and (5) Dirichlet Pulse [40]. The pulse shaping filter is followed by IFFT, which is essentially an up-conversion of the complex stream by a complex subcarrier. Hence, the transmitted signal \(x[n]\), without the cyclic prefix, is given by  
\[x[n]=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d_{k,m}g(\left[n-mN\right]_{NM})e^{-j2\pi k \frac{n}{N}} \tag{8}\]  
The above equation can be represented in the form of a matrix as in [36]  
\[\mathbf{x}=\mathbf{A}\mathbf{D} \tag{9}\]  
where  
\[\mathbf{A}=\left[ \begin{array}{c}g[n]\\ g[n]e^{-j2\pi\frac{n}{N}}\\ \vdots\\ g[n]e^{-j2\pi(K-1)\frac{n}{N}}\\ g[n-N]e^{-j2\pi\frac{n}{N}}\\ \vdots\\ \vdots\\ g[n-(M-1)N]\\ g[n-(M-1)N]e^{-j2\pi\frac{n}{N}}\\ \vdots\\ g[n-(M-1)N]e^{-j2\pi(K-1)\frac{n}{N}}\end{array} \right]^{T}\]  
Figure 2: Constellation example for a binary CPM (**a**) \(h=2/5\) (**b**) \(h=1/4\). \[\mathbf{D}=\left[ \begin{array}{c}d_{0,0}\\ d_{1,0}\\ \vdots\\ d_{K-1,0}\\ d_{0,1}\\ d_{1,1}\\ \vdots\\ d_{K-1,1}\\ \vdots\\ \vdots\\ d_{0,M-1}\\ d_{1,M-1}\\ \vdots\\ d_{K-1,M-1}\end{array} \right]\]  
The transmitted signal after adding the CP can be expressed as in [41]  
\[\mathbf{x}_{\mathbf{CP}}=\left[\mathbf{x}\left(NM-N_{CP}+1:NM\right)\qquad\mathbf{x}\right] \tag{10}\]  
where \(N_{CP}\) represents the CP samples.
Impact of Attitude Model, Phase Wind-Up and Phase Center Variation on Precise Orbit and Clock Offset Determination of GRACE-FO and CentiSpace-1  
[PERSON]  
1 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China; yuanjun  
method to bring real-time and precise positioning, navigation and timing (PNT) services, such as shortening the convergence time in precise point positioning (PPP) [9; 10; 11]. In order to achieve this goal with LEO satellites, the first key point is further improving the consistency of LEO satellites' orbit and clock offset.  
Because of the all-weather and high-precision characteristics of global positioning systems (GPSs), the processing of GPS observation data has become a primary method to derive the trajectories of LEO satellites, which can reach a few centimeters of uncertainty [12; 13; 14; 15]. In the context of such high accuracy, it is a challenging task to further improve the orbit accuracy of LEO satellites. The accessibility of the attitude data of GRACE-FO provides a good opportunity to study the orbit difference between the measured and nominal attitude model [16; 17]. The carrier phase wind-up (PWU) errors depend on the relative rotation between the transmitting and receiving antennas [18; 19]. Mismodeled mobile receiver antenna PWU errors can reach the centimeter level [20], especially for high-speed LEO satellites. As important systematic error sources, phase center variation (PCV) errors have been deeply explored [21; 22; 23] and researchers have used direct approaches and residual approaches to obtain PCV maps [24; 25; 26; 27]. Because of the manufacturing cost, LEO satellites often carry simple chip-scale clocks [28], which are inferior to precise GNSS atomic clocks. Any small errors may make a large difference in clock offset [6]. However, the influence of these errors on LEO satellites' clock offset is often ignored because we usually regard LEO satellites' clock offset as a parameter to be solved. Therefore, it is necessary to analyze the impact of the attitude model, PWU and PCV on LEO satellites' orbit and clock offset simultaneously, especially for LEO-based navigation systems.  
Given this, the primary aim of this paper is to assess the impact of the attitude model, PWU and PCV on the orbit and clock offset determination of LEO satellites. Section 2 describes the testing spacecrafts and data quality analysis. The POCD platform and detailed strategy are discussed in Section 3. Then, we elaborate on the causes and influence of these errors in the following section. The discussion associated with the experimental results and analysis is presented in Section 4. Finally, the conclusions are presented.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 3 Blast Loading Prediction Method Based on BDAL  
Common predictive models with uncertainty quantification include Gaussian process regression (GPR), deep ensemble models, and Bayesian neural networks (BNNs). GPR is a non-parametric Bayesian method that models output values as a Gaussian distribution to quantify uncertainty. However, this has limitations regarding data quality and scale, performing better with continuous and Gaussian data; its effectiveness may decline with discrete data or multimodal distributions, and its computational complexity increases significantly with larger datasets, resulting in longer processing times. Deep ensemble models quantify uncertainty by combining predictions from multiple deep learning models, leveraging their diversity. However, integrating multiple models adds complexity and increases computational costs. BNNs treat neural network weights as random variables; through Bayesian inference, they combine prior and posterior distributions from data to quantify uncertainty. Unlike other models, BNNs naturally incorporate regularization during training, which helps reduce overfitting. Although deep neural networks are often viewed as \"black box\" models, analyzing the posterior distribution of weights in BNNs provides valuable insights into model parameters, such as confidence intervals, thereby enhancing interpretability.  
Considering the characteristics of the explosion data obtained from numerical simulations, such as large sample sizes and complex input-output relationships, this study employed a BNN as the predictive model for blast loading, quantifying the uncertainty in the predictions. Building on this, the model was extended to a BDAL method, which actively selects training samples to reduce the required amount of training data without sacrificing predictive accuracy. The framework of the constructed BDAL method is illustrated in Figure 3.  
First, combining the Bayes principle with deep learning, the Bayesian deep learning (BDL) model was constructed by treating the parameters in the neural network as random variables, and the variance of the prediction result of the BDL model was taken as a measure of its uncertainty. Then, the sample uncertainty was quantified based on the BDL model, and the uncertainty quantified by the BDL model was used as the evaluation index of sample selection in active learning. Finally, the BDAL model was constructed by combining the active learning based on an uncertain selection strategy with BDL. By selecting samples with greater uncertainty for model training, the required training data can be reduced while ensuring the prediction accuracy of the model.  
Figure 3: BDAL method framework.  
This section outlines the principles and construction process of the BDAL model, including an introduction to the BNN, the methodology for solving the posterior distribution of Bayesian network parameters using Monte Carlo dropout (MC dropout) [35; 36], and the steps involved in active learning based on uncertainty selection strategies.
## 1 Introduction
### Contribution of this Study  
The existing body of literature in the realm of CAD diagnosis through Machine Learning (ML) techniques, particularly CNNs and FCMs, has made noteworthy advancements. However, these contributions often fall short of addressing the critical issue of explainability, thereby limiting their practical utility in clinical settings. The \"black-box\" nature of such models poses a significant barrier to their adoption by healthcare professionals who require transparent decision-making processes for ethical and practical reasons.  
In light of these limitations, the present study introduces a groundbreaking methodology, termed DeepFCM, which aims to bridge this gap by offering a truly transparent and explainable model for CAD diagnosis. Unlike existing models, DeepFCM is designed to integrate both imaging data, specifically MPI polar maps, and tabular clinical data. This multi-modal approach not only enhances the model's diagnostic accuracy but also its interpretability. The cornerstone of DeepFCM's transparency lies in its three-pronged approach to explainability: (i) Visual explainability: The model incorporates an integratedGradient Class Activation Mapping (Grad-CAM) algorithm, which illuminates the significant regions within the MPI polar maps. This feature provides clinicians with a visual guide to the areas of interest that influenced the model's decision, thereby enhancing its transparency. (ii) Weight disclosure: DeepFCM goes a step further by revealing the internal weights and their corresponding influence on the diagnostic outcome. This level of transparency is instrumental in fostering trust and facilitating the model's adoption in clinical practice. (iii) Textual explanation: To bridge the gap between machine-based reasoning and human comprehension, the model employs the state-of-the-art Generative Pre-trained Transformer (GPT) 3.5 to generate coherent, meaningful, and human-readable explanations. This feature serves as a valuable tool for medical professionals, aiding them in making well-informed clinical decisions.
# Construction of a Chemical Kinetic Mechanism of Five-Component Surrogate Fuel for RP-3 Kerosene
## 1 Introduction
The main objective of this study is to propose and construct a new surrogate fuel mixture model suitable for RP-3 aviation kerosene and develop the corresponding kinetic reaction mechanism, ensuring that the constructed surrogate fuel mixture mechanism can accurately predict the ignition and combustion characteristics of real RP-3 aviation kerosene. Based on the physical and chemical properties of RP-3 aviation kerosene and the current research status of surrogate fuel mixtures, this paper adds n-tetradecane [17; 18; 19] and isododecane [20; 21; 22; 23] in the construction of alternative fuels, and proposes a five-component surrogate fuel mixture model of RP-3 aviation kerosene with physical properties more similar to the actual ones, revealing the oxidation behavior of the low-temperature combustion process of RP-3 surrogate fuels and deeply understanding the alternative fuel mechanism.
## 3 Control of the Inverter  
In this work, the adopted control scheme is a classical cascade control structure. The two inner loops, one for each boost cell, are current control loops. Due to its great flexibility and ease of implementation, a sliding mode control strategy was retained. The outer loops, one for each boost cell, are voltage control loops based on saturated PI controllers whose outputs provide an adequate current reference for the inner loop controllers. The need of saturated PI controllers will be justified later. An important step for the success of the adopted control strategy is the generation of appropriate voltage references considering the performance improvements discussed and proposed in Section 2. These voltage references are determined by a harmonic balance method applied to a power balance which constitutes a constraint imposed by the inverter structure. Figure 3 depicts the overall controlled system. Note that the sliding modes controllers are implemented by using simple hysteresis comparators leading to inner controls expressed as:  
\[u_{x}=\left\{\begin{array}{ll}0&\text{if}\quad S_{x}(x)>\delta\\ 1&\text{if}\quad S_{x}(x)<-\delta\end{array}\right.\quad x=1,2\]  
where \(\delta\) is a small positive number defined to constraint the maximum switching frequency of the converter and \(S_{x}(x)\) are the sliding surfaces introduced in a next paragraph. Note that possible variations of the input source voltage is considered in the proposed strategy to modify the DC-component of the voltage references. This aspect is covered by taking measure of \(v_{in}(t)\) and using a low-pass (LP) filter to smooth the effect of ripple content. The cutoff frequency of the LP filter must be selected accordingly with the output frequency (three or four times is enough).
## 3 Middleware for WSN Virtualization
### Middleware for Multi Radio Support
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Software Architecture Design**} & **Sensor** & **Network** & **Multi Radio** & **Heterogeneity** \\  & & **Virtualization** & **Virtualization** & **Support** & **Support** \\ \hline \multirow{4}{*}{Servilla [93]} & \(\bullet\) & Virtual Machine & & & \\  & \(\bullet\) & Service Oriented Architecture & & & \\  & \(\bullet\) & Platform independent applications & YES & NO & NO & YES \\  & & execution & & & \\ \hline \multirow{4}{*}{Melete [46]} & \(\bullet\) & Virtual machine & & & \\  & \(\bullet\) & Provides instruction set for sensors & & & \\  & \(\bullet\) & programming & YES & NO & NO & NO \\  & \(\bullet\) & Modular system update mechanism & & & \\  & \(\bullet\) & Group-keyed programming model & & & \\ \hline \multirow{4}{*}{Mate [21]} & \(\bullet\) & Virtual machine & & & \\  & \(\bullet\) & Provides instruction set for sensors & & & \\  & \(\bullet\) & programming & YES & NO & NO & NO \\  & \(\bullet\) & 24 Byte instruction-long Instructions & & & \\  & \(\bullet\) & Split Phase non-blocking execution & & & \\ \hline \multirow{4}{*}{SenShare [20]} & \(\bullet\) & Overlay Layer & & & \\  & \(\bullet\) & Hardware Abstraction Layer at & & & \\ \cline{1-1}  & \(\bullet\) & Node & YES & YES & NO & NO \\ \cline{1-1}  & \(\bullet\) & Embedded Linux & & & \\ \cline{1-1}  & \(\bullet\) & Multi-Task OS & & & \\ \cline{1-1}  & \(\bullet\) & Split-Phase access & & & \\ \hline \multirow{4}{*}{VITRO [44]} & \(\bullet\) & Component based Architecture & & & \\ \cline{1-1}  & \(\bullet\) & Software Oriented Architecture & & & \\ \cline{1-1}  & \(\bullet\) & Resource Oriented Architecture & & & \\ \cline{1-1}  & \(\bullet\) & Virtualized modular mechanism & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Middleware architecture design and WSN virtualization support. In the literature most of the traditional middleware designs are targeting some specific problems along with the objective to support a particular aspect of WSN. Furthermore, most of the middleware architectures consider only single application. For example, middleware designed for health care application, _i.e._, I-Living [94] cannot be efficiently used in vehicular networks as each domain and application have their own requirements and limitations.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types  
The latest developments in sensor technologies have supported in-season crop monitoring [57]. Studies performed for over a few years have improved the potential of sensor data acquisition to develop agronomic management decisions. To achieve these decisions, a single sensor is not always able to provide satisfactory results. This could be achieved by multiple sensors such that their functions could improve and provide the required results. This section provides an overview of the most important sensors, such as optical, thermal infrared, multispectral, hyperspectral, LiDAR, and radar (Table 1). Furthermore, their limitations and solutions are also discussed.