esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)
#### 3.5.1 RL Framework  
In our second approach, the set of parameters used in the fuzzy system, and thus provided to the RL agent, may vary depending on the active profile. Specifically, instead of fixed triple parameters \(\{NN,\,SS,\,CT\}\), any subset of parameters can be included, such as \(\{NN,\,E_{\text{res}}\}\) for energy-based profiles or \(\{D_{\text{bol}},\,CT\}\) for Delay-sensitive profiles. The RL framework, therefore, adapts accordingly:  
* State \((S)\) describes the current network condition via a (possibly changing) set of metrics. A fuzzy layer translates these raw metrics into a fuzzy state, capturing the overall broadcasting context (e.g., \"Many Neighbors & High Energy\" or \"Long Connection & Strict Delay\"). For example,
* In a densely populated profile, the RL state might emphasize \((NN)\) and \((SS)\).
* In an energy-constrained profile, the RL state might include \((E_{\text{res}})\) instead of \((SS)\).
* In a Delay-sensitive profile, the RL state could rely on \((D_{\text{tol}})\) and \((CT)\). Regardless of which parameters are active, the fuzzy membership functions produce a unified fuzzy state representation (e.g., \"Many Neighbors & High Energy\" or \"Long Connection & Strict Delay\").
* Action \((A)\): The RL agent's decision space remains BC, DNB, reflecting whether a given node will broadcast or refrain from broadcasting under the current fuzzy-state conditions.
* Reward \((R)\): At each time step, after performing the chosen action, the agent receives a scalar reward that balances Coverage quality and Overhead reduction. As in our previous work, we encourage Coverage and penalize redundant transmissions. This reward function remains consistent across all profiles, even though the state representation changes with the profile.
## 4 Proposed MiniatureVQNet Model  
This study's proposed models have two parts: speech signal feature extraction and quality prediction. The feature extraction part is a modified standard DeepSpeech ASR system, and the prediction part is a stacked layer of two bidirectional GRUs followed by a fully connected layer. DeepSpeech is an open-source speech-to-text engine that uses a model trained by machine learning techniques based on Baidu's study [33]. The DeepSpeech-based technique does not require hand-designed features to model the background noise, reverberation, or phoneme dictionary. Instead, it depends on large amounts of data for training. This was the motivation behind modifying the DeepSpeech model for feature extraction to avoid hand-designed features, as there are no well-known speech features for capturing transmission network distortions. The major obstacles to implementing deep neural networks (DNNs) on embedded systems are the large model size and many operations needed for inference. However, several model optimization techniques can be applied to DNN models to deploy the models on resource-constrained systems. The commonly used techniques are weight sharing, network pruning, knowledge distillation, quantization, and designing compact network architectures. This study applied two optimization techniques: designing a compact network architecture and reducing the model precision (model parameters quantization). As a result, we achieved a very light model weight in the proposed network configuration, with only 5689 trainable parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distortion** & **Parameter** & **Values** \\ \hline \multirow{3}{*}{Network} & Packet loss (\%) & 0, 10, 15, 20, 25, 30, 35 \\ \cline{2-3}  & Delay (ms) & 0, 100, 200, 300, 500 \\ \cline{2-3}  & Jitter (\% delay) & 0, 10, 20, 30, 40 \\ \cline{2-3}  & Codec & G722 \\ \hline \multirow{3}{*}{Noise} & Noise type & Babble, car, exhibition hall, restaurant, street, airport, train station, train \\ \cline{2-3}  & SNR (dB) & 0, 5, 10, 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Combined noise and network distortion parameters.
## 2 Materials and Methods  
### Theoretical Background  
To accurately impose realistic inter-variable relationships in the simulated cohort, the method applies Cholesky decomposition to a correlation matrix rather than a covariance matrix. This decision is rooted in both practical and methodological necessity. In most real-world settings, especially in the absence of access to raw patient-level data, it is not feasible to derive a full covariance matrix due to the lack of information on variable units and scale. However, correlation coefficients are often reported in the literature or can be reasonably estimated through expert input, making the construction of a correlation matrix far more attainable. To ensure compatibility with this matrix, all variables are first standardized into Z-scores, transforming them to have a mean of zero and a standard deviation of one. This standardization step is essential, as Cholesky decomposition applied to a correlation matrix requires the input variables to be on a common scale, which would not be possible if values were retained in their original units [13,14]. In contrast to methods that infer dependency structures from raw data, our approach assumes that these relationships are known or can be estimated and enforces them explicitly.  
Our approach conceptually parallels extensions of the bootstrap method designed for correlated data, where observations are first transformed into uncorrelated forms, resampled, and then transformed back. Such transformation-based strategies preserve underlying dependency structures while allowing flexible resampling or simulation. Similarly, our method ensures that the synthetic cohort retains realistic inter-variable dependencies while enabling data generation under limited or aggregate statistical inputs [15].  
The simulation process proceeds in a structured sequence. First, independent random numbers are generated for each parameter-patient combination. These are transformed into sampled values using the inverse cumulative distribution function (CDF) corresponding to each variable's assigned distribution; special care is taken to ensure that inputs are appropriately clamped to a safe interval. The sampled values are then standardized into Z-scores. Next, to impose the desired correlation structure, the algorithm multiplies the standardized matrix by the Cholesky decomposition of the input correlation matrix. The resulting correlated Z-scores are transformed back into their original scale. Finally, the model applies logical constraints, including hard limits and deterministic formulas, to enforce clinical plausibility. This structured pipeline ensures that the generated data respects user-defined statistical properties and inter-variable relationships.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Datasets and Preprocessing
#### 2.2.2 Satellite Data  
The VI for the growing season is closely related to photosynthesis and the biomass of crops. We obtained the MODIS NDVI data from the GEE platform, using the latest version (Collection 6.1), for the summer maize-growing season, from 2000 to 2019. The Savitzky-Golay (S-G) filter, which has the potential to remove random noise, was used to smooth the NDVI series in accordance with the \"Quality reliability of VI pixels\" metric, which describes the quality of the pixels in the data. Then, the high-quality pixels from the original sequence were used to replace the pixels at corresponding locations in the new sequence [39]. To calculate the vapor pressure deficit (VPD), we also downloaded the DEM data (digital elevation model), with a 90 m \(\times\) 90 m spatial resolution, from the SRTM digital elevation dataset ([[http://strtm.csi.cgiar.org](http://strtm.csi.cgiar.org)]([http://strtm.csi.cgiar.org](http://strtm.csi.cgiar.org)), accessed on 30 October 2022).
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 4 Parameters Selection and Empirical Design
### The Compared Model Setting and the Steps of Forecasting Model  
The innovation of the SVR forecasting model in this paper is that the bat algorithm parameters of the radial basis sum function are invoked to optimize the bat algorithm. In order to verify the accuracy of the SVR model after the bat algorithm optimization search when forecasting stock index prices, the polynomial kernel function SVR and the sigmoid kernel function SVR with default parameters are selected as the control group in this paper. The full sample interval used by these two types of comparison models are the same as those defined in the previous paper for the BA-SVR hybrid model, and the same last 20 days, 60 days and 250 days of data as the short-term, mid-term and long-term forecast groups of each index, respectively. The rest of the forecasting steps are the same as the BA-SVR described in the previous section. The difference is that the polynomial kernel function SVR model and the sigmoid kernel function SVR model use default initial parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Index Name** & **Abbreviation** & **Index Code** & **Start Time** \\ \hline \multirow{8}{*}{The Shanghai Composite Index} & Shanghai Composite Index & SSE & 000001.SH & 15 July 1991 \\ \cline{2-5}  & Shanghai A-Share Index & SSE A shares & 000002.SH & 21 February 1992 \\ \cline{2-5}  & Shanghai Stock Exchange B-Share Index & SSE B shares & 000003.SH & 17 August 1992 \\ \cline{2-5}  & Shanghai Stock Exchange 380 & SSE 380 & 000009.SH & 29 November 2010 \\ \cline{2-5}  & Shanghai Stock Exchange 180 Index & SSE 180 & 000010.SH & 1 July 2002 \\ \cline{2-5}  & Shanghai Stock Exchange 50 Index & SSE 50 & 000016.SH & 2 January 2004 \\ \hline \multirow{8}{*}{China Securities Index} & China Securities Index 300 Index & CSI 300 & 000300.SH & 8 April 2005 \\ \cline{2-5}  & China Securities Index 1000 Index & CSI 1000 & 000852.SH & 17 October 2014 \\ \cline{2-5}  & China Securities Index 100 Index & CSI 100 & 000903.SH & 29 May 2006 \\ \cline{2-5}  & China Securities Index 500 Index & CSI 500 & 000905.SH & 15 January 2007 \\ \cline{2-5}  & China Securities Index 800 Index & CSI 800 & 000906.SH & 15 January 2007 \\ \hline \multirow{8}{*}{Shenzhen Stock Exchange Index} & Shenzhen Stock Exchange Component Index (Price) & SZCI & 399001.SZ & 23 January 1995 \\ \cline{2-5}  & Shenzhen Stock Exchange Constituent B-Share Index & SZSE B shares & 399003.SZ & 23 January 1995 \\ \cline{2-5}  & Shenzhen Stock Exchange Composite Index & SZSE & 399106.SZ & 4 April 1991 \\ \cline{2-5}  & Shenzhen Constituent A Share Index & SZSE A shares & 399107.SZ & 4 April 1991 \\ \hline \multirow{8}{*}{Small and Medium Board Index} & SME & 399005.SZ & 24 January 2006 \\ \cline{2-5}  & SME 300 Index & SME 300 & 399008.SZ & 22 March 2010 \\ \cline{1-1} \cline{2-5}  & SME Composite Index & SME CI & 399101.SZ & 1 December 2005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The name and code of the selected stock index.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 4 System Model and Settings  
A system-level simulator has been developed by the authors in this work using the Matlab platform to run, test, and compare the system performance of FFR, PFR with centralized resource allocation of C-V2X, and the system with a conventional single band (SB) operation. Similar simulators were applied in the investigations reported in [23; 24]. The bandwidth has been divided in the frequency domain between RSUs with the FFR technique and further between inner and outer vehicles as assumed in the PFR technique. The Poisson distribution has been proposed (see Equations (5) and (6)) to distribute the vehicles along the highway with three lanes in each direction, different vehicle speeds, and distances in each lane according to [6]. The total number of sub-channels in \(T_{\textit{cycle}}\) is given by Equation (4).  
A 100 ms interval with the RC range [5; 15] is proposed as the simulation time step (CAM interval) and RC range according to standard [36], which is proposed for Mode 4 as the maximum allowed latency. Each vehicle in the network receives packets from all vehicles before specifying which of them are located inside its coverage area. Each vehicle transmits its packet to all vehicles. In other words, all vehicles transmit/receive packets to/from all vehicles in each CAM interval (i.e., if the number of vehicles is equal to 150 and the simulation step is equal to 100 ms, each vehicle will test 150 packets from all vehicles in the network in each simulation step). The vehicles' positions are updated according to their direction and speed in each simulation time step. \(\gamma_{min}\) is obtained by Equation (11). Each technique has been tested with different vehicle densities in the network (determined by the varying scalar factor) to investigate the impact of the vehicles' number on the performance of the called techniques. Each simulation run is equivalent to  
Figure 3: Example of PFR for three RSUs.  
a 5-s time interval to test the packet reception ratio for all vehicles, minimum frequency reuse distance, average frequency reuse distance, and interference impact in the network in every \(T_{cycle}\) period. Five connected RSUs have been placed within an equal distance of each other to cover five kilometers along the highway. The main parameters of simulations for both FFR and PFR are the same and described in Table 1. The WINNER+B1 model has been assumed as a propagation model [37]. To show the gains obtained thanks to the both bandwidth partitioning methods, we compare their performance with the system applying the conventional single band (SB) transmission.
## 4 Results
### Security Analysis
#### 4.1.6 Architecture Analysis  
The solution proposed in this study is based on the FIDO standard, which offers several advantages. As shown in Table 11, both FIDO2 and this study demonstrate excellent performance in many aspects. They both support the convenience of two-factor authentication and can be applied to various platforms and devices. Additionally, they can withstand a brute force attack, phishing attack, and man-in-the-middle attack. However, they differ in their ability to defend against DDoS attacks. The FIDO standard itself does not provide a specific solution for DDoS defense. Nevertheless, when the FIDO standard is combined with blockchain technology, it can offer a certain level of defense capability, along with the advantages of decentralized identity verification and traceability. This combination significantly enhances the security resilience of FIDO. Furthermore, this study goes beyond identity verification and provides access control mechanisms, allowing only authenticated users with appropriate permissions to access specific resources.
## 3 Model Architecture
### Encoder Layer  
The encoder layer is used to extract local and global information between words at different positions. It is composed of positional encoding, depthwise separable convolution, a self-attention mechanism, and a feed-forward network. Layer normalization is applied for each layer, and residual connections are used between layers. First, a position function calculates the encoded positional information, which is further weighted and summed with the word embedding of the previous layer to form the input of this layer--in the convolutional layer, a depthwise separable convolution network with fewer parameters and stronger generalization ability to obtain more contextual information, specifically, with a kernel size of 7, 128 filters, and 4 convolutional layers. The self-attention layer adopts a multi-head attention mechanism with eight heads to calculate the connection between each word and the input texts to obtain the global contextual information. Finally, the feed-forward network is used to generate the contextual vector representation of the context \(H\in R^{d\times n}\) and the query \(V\in R^{d\times m}\) as the output, where \(d\) is the matrix dimension.
###### Abstract
The main contributions of the paper are as follows:  
* It presents the VEC system of a fault-tolerant environment, including the service migration model, and edge server fault model for the first time. * Propose a path selection algorithm that combines the sliding window model to evaluate the time-varying failure rate of ESs and obtain the high-quality migration path. * Propose a service migration algorithm. To cope with edge node failure in the VEC environment, this paper uses the advantages of replication and resubmission strategies to ensure the reliability of service migration. * To verify the performance of the DRPS approach, we also conducted simulation experiments on two real edge computing datasets. The experimental results demonstrate that, compared with the traditional method, our proposed DRPS approach has a better task completion rate and average task completion time. The remaining chapters of this paper are structured as follows. In Section 2, we review related work and analyze its limitations. The system model and problem description are presented in Section 3. In Section 4, we describe the proposed DRPS approach in detail. We describe experiments based on the real mobile edge computing dataset and analyze the results in Section 5. Finally, Section 6 presents the main conclusions and future work.
## 1 Introduction
This mechanism is likely associated with alterations in ionic homeostasis, membrane permeability and metabolic stress, which can enhance pathogen susceptibility to antimicrobial agents.Furthermore, the efficacy of antibiotics is influenced by environmental factors, including pH, metabolite availability and oxygen tension [20,23]. This highlights the importance of further research into the potential synergistic role of vitamin C in modulating environmental conditions that enhance antimicrobial efficacy. The selection of _Escherichia coli_, _Staphylococcus aureus_ and _Candida albicans_ in this study was based on their high clinical relevance and increasing resistance to commonly used antimicrobial agents. _E. coli_, while naturally susceptible to many antibiotics, has become a major reservoir of resistance genes through horizontal gene transfer, including genes encoding extended-spectrum \(\beta\)-lactamases (ESBLs), carbapenemases and polymycin resistance (e.g., mcr genes), making it a critical priority pathogen [24]. _S. aureus_ represents a major cause of both hospital- and community-acquired infections and exhibits complex resistance mechanisms to \(\beta\)-lactams (notably methicillin), glycopeptides and other last-line antibiotics, posing significant treatment challenges [25]. _C. albicans_, the most prevalent cause of candidiasis and fungal bloodstream infections, demonstrates adaptive mechanisms such as biofilm formation and efflux pump overexpression, contributing to antifungal resistance, particularly against azoles like funconazole [26]. These pathogens are frequently associated with high morbidity and mortality, and their inclusion allows for a representative evaluation of antimicrobial efficacy across both bacterial and fungal species of major public health concern. Alternative treatment strategies, such as the use of chlorine dioxide, have shown promise in vitro for eradicating various antibiotic-resistant bacteria, including _E. coli_ and _S. aureus_[27]. This study aims to investigate the synergistic antimicrobial activity of carvacrol and thymol--key components of _Origanum vulgare_ and _Thymus vulgaris_ essential oils--in combination with vitamin C against _Escherichia coli_, _Staphylococcus aureus_ and _Candida albicans_. In addition, the study explores the potential involvement of host-related molecular targets associated with inflammation, oxidative stress and immune modulation, in order to better understand the mechanisms and broader therapeutic potential of these compounds beyond their direct antimicrobial effects.
## 4 Results
### Prediction with Machine Learning Models
#### 4.5.3. Feature Interpretation with SHAP Values  
SHAP values were analyzed for NGBoost, Random Forest, and XGBoost models to understand feature importance (see Figures 5-7, respectively). The SVM model was omitted because of its low performance. NGBoost placed significant emphasis on Velocity (Min), Happy (Std), and Facial Valence (Median), indicating that this model values both physical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Count** & **Count** & **Count** & **Mean** & **Median** \\  & **Control** & **Treatment** & **Performance** & **Performance** \\ \hline
0 & 29 & 14 & 15 & 0.0128 & 0.0942 \\
1 & 21 & 6 & 15 & 0.4652 & 0.1445 \\
2 & 19 & 13 & 6 & \(-0.5169\) & \(-0.5585\) \\
3 & 15 & 9 & 6 & \(-0.1256\) & \(-0.1201\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overview of K-Means Cluster and their Performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **R\({}^{2}\)** \\ \hline Random Forest & 0.6894 & 0.6289 & 0.2853 \\ XGBoost & 0.3123 & 0.3522 & 0.6762 \\ NGBoost & 0.4144 & 0.4888 & 0.5704 \\ SVM & 1.0111 & 0.5549 & \(-0.0483\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of Machine Learning Models.  
movement and emotional variability. This suggests NGBoost's sensitivity to changes in emotional expressions and their potential impact on outcomes.  
Figure 5: SHAP Analysis—Beeswarm Plot for NGBoost model.  
Figure 6: SHAP Analysis—Beeswarm Plot for Random Forest model.  
Random Forest and XGBoost, on the other hand, prioritized Vocal Arousal (Slope), Velocity (Min), Neutral (Std), and Brightness (Slope), albeit with different weights. XGBoost particularly emphasized Vocal Arousal (Slope), highlighting its sensitivity to changes in vocal pitch over time, which could signal team enthusiasm, stress, or agreement. Random Forest additionally emphasized Fear (Min), while XGBoost also considered Sad (Median) and Happy (Slope), reflecting a broader approach to capturing emotional health.  
A commonality across all models was the importance of Velocity (Min), underscoring its overall relevance, likely capturing crucial aspects of physical activity or movement pertinent to team performance. However, the models diverged in their prioritization of other features. XGBoost's focus on Happy (Std) contrasts with Random Forest and XGBoost's focus on Vocal Arousal (Slope), illustrating XGBoost's focus on emotional variability while the other models emphasize the temporal dynamics of vocal expressions.