esa-sceva/satcom-chunk-collection huggingface dataset:
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 3 Research Methodology
### Formulation of Research Questions  
Oriented by the framework in Figure 1, this phase aimed to define the analytical scope by formulating precise research questions. The process began with a review of the existing literature to identify open problems in the security of DMSs, including architectural blind spots, risk exposure, and under-addressed attack surfaces. The main objective was to select a representative DMS architecture that encapsulates critical safety functionalities (e.g., distraction detection and biometric analysis) and to assess its exposure to cybersecurity risks. The research questions serve as the conceptual foundation of this study. They define the investigative scope and guide the systematic evaluation of DMS architectures, component integration, and associated security risks. Each question addresses a specific dimension relevant to the secure design and assessment of driver monitoring technologies:  
* **RQ1:** Which components and functionalities are commonly integrated into contemporary Driver Monitoring Systems?
* **RQ2:** What are the communication mechanisms between these components within the broader vehicle architecture?
* **RQ3:** What architectural variations exist among current DMS implementations, and how do they differ in terms of performance, integration, and reliability?
* **RQ4:** What cybersecurity risks are associated with different DMS configurations, and how can these threats be systematically addressed?
* **RQ5:** Which DMS architecture demonstrates the most favorable balance among functionality, security, and integration with vehicle systems?  
These questions provide a structured framework for assessing DMS solutions in terms of design robustness, architectural soundness, and threat mitigation potential.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 3 Target Detection and Damage Level Assessment Based on YOLO-VGGNet
### Target Damage Level Assessment Based on VGG-1
To speed up the learning process and ensure the classification accuracy of the trained model under limited samples, only the parameters of partial layers are updated in the training process, which is usually called _fine-tuning_. As for the dataset, targets with four different damage levels are collected from the simulation environment. The dataset contains 1000 images with 4 damage levels. For each type of target, 250 images are collected, of which 200 are used as training and validation samples, and 50 are used as test samples. In the training process, the epoch of the training dataset is 20, the batch size is 32, the learning rate is 0.001, and the output of the final fully connected layer is modified to 4.
###### Abstract  
The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil-pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response.  
steel pipeline; longitudinal ground movement; fragility surfaces; Monte Carlo Simulation (MCS); Global Sensitivity Analysis (GSA); uncertainty quantification; operational loads +
Footnote †: journal: Applied Sciences
## 3 Results
In general, closely related species emit a similar blend of VOCs (with some species-specific metabolites) compared to distantly related species [52]. Using SPME-GC-TOF-MS to profile the VOCs produced by the four PGPR strains (_Ps. fluorescens_ (N04), _Ps. koreensis_ (N19), _Pa. alvei_ (T19) and _L. sphaericus_ (T22)), a total of 121 VOCs (Table S1) were identified. The annotated VOCs generally belong to the following classes: aldehydes, alcohols, ketones, acids, alkanes, alkenes, amines, derivatives of salicylic acid, pyrazines, furans, sulfides and terpenoids (Table S1), and most have been previously reported. The two Pseudomonas had 28 VOCs in common (Figure 3), whereas the four strains were found to share only a small number of overlapping molecular patterns among a large pool of different VOCs (Figure 4). These included 2-tridecanol, 2-decanone, 2-dodecanone, nonane, decanal, dodecanal, tetradecanal, isoamyl salicate, benzene (2-methyloctyl), pyrazine (2,5-dimethyl), pyrazine (trimethyl), pyrazine (3-butyl-2,5-dimethyl) and 8-ocimene. The observed variation in VOC profiles could suggest that these strains promote plant growth and inhibit pathogen growth in different ways. Figure 3: Venn diagram comparing the volatile organic compounds of the two _Pseudomonas_ strains. The diagram shows shared and distinct volatile organic compounds from _Pseudomonas koreensis_ (N19) and _Pseudomonas fluorescens_ (N04), as indicated by the numbers in the intersections and ellipses, respectively.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 1 Introduction
The channel condition is changing all the time and its variations are complex and unpredictable due to motion and shadowing effects of the human body. We use Lyapunov optimization theory and propose a resource allocation algorithm to achieve close-to-optimal time average user utility. The contributions of this paper are the following:  
1. By using energy harvesting technology, the lifetime of the system is greatly prolonged. Energy harvesting technology is very important to the BAN system. Sensors harvest energy from the renewable sources. We propose a framework to characterize the stochastic nature of the energy harvesting and energy consumption, we do not need any a priori knowledge of these stochastic processes. 2. We propose an optimization problem to maximize the user utility. We use Lyapunov optimization approach to decompose the user utility optimization problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an optimal online resource allocation algorithm that makes decisions at the beginning of each time slot and then updates queues, and achieves close-to-optimal time average user utility. The remaining parts of the paper are organized as follows. Related works are reviewed in Section 2. The system model is presented in Section 3. A resource allocation algorithm is proposed in Section 4. We analyze the performance of the proposed algorithm in Section 5. Simulation results are provided to evaluate the performance of the proposed algorithm in Section 6. Section 7 concludes this paper and future works.
## 3 Numerical Simulation
### Model Parameters  
The model for the strata utilizes the modified Mohr\(-\)Coulomb model, which is an optimized version of the traditional Mohr\(-\)Coulomb model incorporating soil hardening characteristics. This model allows the elastic modulus to vary based on loading and unloading conditions, making it highly suitable for simulating foundation pit excavations [23]. The parameters of the soil used in this study are detailed in Table 2 and all derived from laboratory tests. Due to the relatively minor presence of groundwater at the site and preemptive waterproofing measures, groundwater seepage was not incorporated into the numerical model.  
Considering the substantially higher strength of concrete and steel compared to soil materials, using elastic materials [15; 24] for the simulation is justified. Both the diaphragm wall and internal supports are modeled using elastic parameters based on their actual properties. The tunnel lining is represented as an orthotropic linear elastic material, with detailed parameters being listed in Table 3. The geometric shape of the grid columns is complex; therefore, equivalent cross-sectional dimensions were calculated based on stiffness equivalency. Considering the decrease in stiffness resulting from the joints [25], the rigidity ratios for the lining were adjusted to 0.7 [15; 26] in the circumferential direction and 0.1 [27] in the longitudinal direction, resulting in elastic moduli of 24.15 GPa circumferentially and 3.45 GPa longitudinally. Table 3 displays the parameters of the structure and support models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stratigraphic Sequence** & **Unit Weight (kN/m3)** &
\begin{tabular}{c} **Compression Modulus** \\ **(MPa)** \\ \end{tabular} & \multicolumn{2}{c}{**Direct Shear**} \\ \cline{3-4}  & **Friction Angle (\({}^{\circ}\))** & **Cohesion (kPa)** \\ \hline Stockpile soil & 18.2 & 5 & 15.0 & 12.0 \\ Gravelly clay & 18.1 & 6 & 23.0 & 22.0 \\ Strongly weathered granite & 18.9 & 12 & 27.0 & 25.5 \\ Fully weathered granite & 18.6 & 8 & 24.0 & 24.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical properties of soil.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Basic Size/mm** & \
\begin{tabular}{c} **Unit Weight** \\ **(kN/m3)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(GPa)** \\ \end{tabular} & **Poisson’s Ratio** & **Element Type** \\ \hline lining & Diameter 6000 & 24 & 34.5 & 0.1 & Plate \\ diaphragm wall & 1000/1200 & 24 & 31.5 & 0.2 & Plate \\ Bracing I & 600 \(\times\) 600 & 24 & 31.5 & 0.2 & implantable truss \\ Bracing II & 1000 \(\times\) 1200 & 24 & 31.5 & 0.2 & implantable truss \\ pipe support & Diameter 800 & 76 & 250 & 0.25 & implantable truss \\ Thickness 20 & & 76 & 250 & 0.25 & implantable beam \\ lattice column & 400 \(\times\) 400 hollow & 76 & 250 & 0.25 & implantable beam \\ pile & 1200 & 24 & 31.5 & 0.2 & implantable truss \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation model parameter description.
## 4 Results
### Classification Performance  
For the classification task, accuracy, precision, and AUC were employed as evaluation metrics. The comparative results are shown in Figure 7, and detailed numerical values are provided in Table 4.  
BiCA-LI outperforms both baselines, achieving nearly perfect classification results. While Informer-only also performs well in this task, LSTM-only shows clear limitations, highlighting the importance of long-term sequence modeling and feature fusion.
## 4 Simulations and Results
### Identification Parameters of Simplified SFR after Islanding for Small Receiving-End System  
The general structure of the simplified SFR model is depicted in Figure 1. This model is widely applied in UFLS scheme designs for small power systems.  
Nominal parameters of the generator and governor model in the practical small receiving-end power system are listed in Table 1. In the full scale system simulation, the composite load model (Power System Simulator/Engineering (PSS/E) model) is employed to represent the dynamic characteristics of loads.  
Figure 9: Schematic of the Da Dan power grid.  
Ten independent runs using PSO were simulated to collect the statistics including the standard deviation, mean, and best and worst optimum results, which are shown in Table 2. Figure 10 shows the optimization process of the presented model for two sample data sets derived from the full scale power system simulation after islanding. The simulation results show that the proposed model converges within 100 generations. The efficiency and accuracy of the proposed model make it useful for applications in parameter identification for the simplified SFR model.  
The identified parameters are listed in Table 3. At the same time, the parameters obtained by using the static equivalent (SE) and least square (LS) method proposed in [14] are also listed in Table 3 (The system power base \(S\) is the sum of all loads).  
For thoroughly testing the advantages of the proposed PSO-based parameter identification model, the experimental results in Figure 11 compare the performance of the identified model and the static equivalent model with that of the full scale power system. Figure 11a shows  
\begin{table}
\begin{tabular}{c c c c} \hline
**Power Plant** & **MVA** & **Inertia Constant** & **Governor Model in PSS/E** \\ \hline A & \(2\times 150\) & 14.1 & HYGOV \\ B & \(1\times 330\) & 4.59 & GAST2A \\ C & \(2\times 600\) & 2.98 & IEEEG1 \\ D & \(2\times 600\) & 2.39 & TGOV5 \\ \hline HYGOV: Hydro Turbine-Governor; GAST2A: Gas Turbine Model Type 2; IEEEG1: IEEE Type 1 Speed-Governing Model; TGOV5: IEEE Type 1 Speed-Governing Model Modified to Include Boiler Controls. & \\ \end{tabular}
\end{table}
Table 1: Generator data.  
Figure 10: PSO optimization process.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Best** & **Worst** & **Mean** & **Standard Deviation** & **Average Run Time (s)** \\ \hline
0.1046 & 0.2118 & 0.1307 & 0.0334 & 1.27 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of the optimization results for parameter identification.  
under 1000 MW imported active power and Figure 11 under the 800 MW imported condition. Because the performance of the model with the identified parameters matches the results of the full scale system quite well, the advantages of the proposed PSO-based model parameters identification technique are demonstrated. Compared with the equivalent model obtained by using the static equivalent and least square methods, the dynamic frequency responses of the small receiving-end power system can be presented accurately and precisely after islanding.
## 4 Conclusions  
This study describes using plant extracts to successfully prepare ZrO\({}_{2}\)-CdWO\({}_{4}\), a heterojunction material. A heterojunction was made by depositing tetragonal zirconium dioxide over the monoclinic cadmium tungstate. The 2.66 eV energy band gap was reported for this photocatalyst, which was lower than the bare oxide (ZrO\({}_{2}\) and CdWO\({}_{4}\)) as well as various reported CdWO\({}_{4}\)-based nanocomposites (ZnO-CdWO\({}_{4}\) and CuO-ZnO-CdWO\({}_{4}\)). A ZrO\({}_{2}\) heterojunction with CdWO\({}_{4}\) may lower the band gap energy and aid visible light photon absorption. Critically, it was reported that heterojunction formation forms a Schottky barrier, which increases charge separation and prevents electron-hole combinations. This mechanism exhibits a complete distribution of electrons on the semiconductor surface, which is important for photocatalytic applications. In this study, hydrogen production was enabled via water splitting to understand photocatalytic application. The heterojunction material resulted in improved hydrogen production. After a 5 h reaction at 27 \({}^{\circ}\)C, pH 7, and using 30 vol. % of methanol, 1574 umol/g hydrogen gas was produced using 250 mg of ZrO\({}_{2}\)-CdWO\({}_{4}\). This is a useful study on heterojunction materials being used for hydrogen production.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The author declares no conflict of interest.
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### Algorithm Implementation
#### 3.4.3 Feature Engineering  
Feature engineering is a vital part of data analysis. The quality of results of deep learning algorithms heavily relies on the quality of the input features. For the traditional train dynamic model, as can be seen in Figure 1, with the inputs of \(v_{m-1}\), \(F_{m-1}\) and \(B_{m-1}\), the current speed \(v_{m}\) can be calculated. To be consistent with the previous model, in the proposed dynamic model, the raw features, i.e., analog output\({}_{t-1}\), traction brake\({}_{t-1}\), train speed\({}_{t-1}\) and slope\({}_{t-1}\), are directly fed into the LSTM cells after removing the missing values. To facilitate understanding, in this paper, we do not use abbreviations for these features.  
Considering the problem of train speed prediction, since we expect to obtain the train speeds for next \(n\) time steps, more delicate descriptions of the features should be made to capture more information from previous data. For this reason, we establish a different set of features as inputs for train speed prediction. The set contains three types of features, i.e., lagged features, crossed features and statistical features. All features are produced by analog output, traction brake, train speed and slope, which are listed above.  
The lagged features imply the instantaneous values of the previous time steps, which are defined as:  
* At time iteration \(t\), the train speed of \(k\)th step earlier: train speed\({}_{t-k}\), where \(k\in\{1\sim 10,20,30,50,80\}\).  
* At time iteration \(t\), the analog output of \(k\)th step earlier: \(\text{analog output}_{t-k}\), where \(k\in\{1,3,5,20,60\}\). The statistical features capture the changes of the input values in the previous period:
* The average value of train speed of past \(k\) steps: train speed\({}_{k}^{mean}\), where \(k\in\{5,10,20\}\).
* The standard deviation of train speed of past \(k\) steps: train speed\({}_{k}^{std}\), where \(k\in\{5,10,20\}\).
* The difference of train speed between the previous \(k\)th step and the current step: train speed\({}_{k}^{diff}\), where \(k\in\{5,10,20\}\).
* The average value of analog output of past \(k\) steps: analog output\({}_{k}^{mean}\), where \(k\in\{5,10,20\}\).
* The standard deviation of analog output of past \(k\) steps: analog output\({}_{k}^{std}\), where \(k\in\{5,10,20\}\).
* The difference of analog output between the previous \(k\)th step and the current step: analog output\({}_{k}^{diff}\), where \(k\in\{5,10,20\}\).  
Since recent data play a more important role for a time series problem, the crossed features are designed as:  
* The product of train speed of past \(k\) steps: train speed\({}_{k}^{\otimes}\), where \(k\in\{1,2,3,4,5\}\). For example, train speed\({}_{4}^{\otimes}=\text{train speed}_{t}\times\text{train speed}_{t-1}\times\text{train speed}_{t-2}\times\text{train speed}_{t-3}\times\text{train speed}_{t-4}\). In total, 43 features are extracted for the train speed prediction task.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### Modulation Waveforms
#### 2.1.2 Fbmc  
The FBMC modulation technique, as one of the waveform candidates in 5G communication networks, has attracted a great amount of research attention. In the 1960s, the concept of FBMC modulation was firstly proposed by [PERSON] and [PERSON] [92]. However, it did not receive much attention by researchers due to its complexity. The well-known discrete multi-tone (DMT) modulation and discrete wavelet multi-tone (DWMT) modulation reported in the 1990s are two specific cases of FBMC modulation [93]. Currently, FBMC based systems have been extensively studied from the aspects of spectrum efficiency analysis [94; 95; 96], system complexity analysis [97], prototype filter design [98; 99; 100], frequency offset estimation [102; 103], MIMO [104; 105; 106], and so forth. Ever-emerging research projects investigate the application of FBMC modulation in practical scenarios, for example, PHYDYAS [107], METIS [108], 5 GNOW [109]. FBMC is robust against frequency offset because of its negligible spectrum leakage. Furthermore, FBMC does not need the guard band in frequency domain, which greatly improves the spectrum efficiency, thus FBMC can flexibly control the interference between adjacent subcarriers, and the synchronization requirement among subcarriers is relaxed [36].  
Simultaneously, there are still some underlying issues of FBMC, for instance, high PAPR, which may well result in signal nonlinearities. Though many existing methods have been dedicated to lowing PAPR of FBMC [110; 111; 112; 113], it is actually a trade-off between low PAPR and other characteristics, that is, in Reference [110], the PAPR reduction is at the expense of higher computation complexity. In spite of this, advantages above still make FBMC popular in the academic field, and FBMC has already been considered as the waveform candidate in future MCM networks [33].