esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Co-Citation and Cluster Analysis  
#### 4.3.1 Overview of Clusters  
CiteSpace is employed to organize the references of all literature and group them into clusters based on shared keywords in the titles. Literature entitled with the same or similar keywords is assigned to the same cluster, and then the frequency of citations in that cluster as well as the citation coverage of the cluster are counted. In this way, researchers can easily recognize important articles in their field of study and gain a perspective on what other scholars have conducted or are doing. Each cluster contains a cited reference list. The most cited literature represents the research base in that cluster, and citing literature represents the research frontier. The collection of most frequently cited references can be considered a key research base in the field of road safety issues for ICV, whereas the aggregation of citing articles is not sorted by publication year and does not equal the overall research frontier in the field of study.  
Taking an overall perspective on the top five co-citation clusters, it is evident that the literature in the field primarily revolves around vehicular network security, machine learning, and computing techniques. The largest cluster is cluster 0, with 114 articles, whereas the other four clusters are about the same size, with no more than 67 articles each. Article types are mainly research papers (research on improving vehicular networking techniques) and surveys. There are also some review articles. From the perspective of publication year, cluster 0 contains articles published in 2014 and is mostly cited by articles published in 2015 and 2018, indicating the topic related to vehicular cloud computing, using VANET, and data management has been developed for a long time, as the citation burst and centrality betweenness verify. Cluster 1 and Cluster 2 are mainly articles from 2018-2019 and are cited by articles published in 2021. The two clusters are emerging, containing topics such as machine learning, blockchain, security privacy, etc., indicating scholars are interested in network security protection for ICVs. Cluster 3 consists mainly of articles published around 2017 and is cited by articles published in 2019 and 2021, which are relatively new. The most cited articles in cluster 4 were published in 2013-2014, and articles cited in this cluster were published in 2011, indicating VANETs back-off mechanism is out of date. Scholars have moved to an in-depth study of vehicular networking.  
As of the completion of this paper, the topics of trust management, security privacy, learning-based recourse allocation, and machine learning are the most recent concerns in the study of road safety issues for ICV. Scholars attempt to address road safety problems by improving intelligent technologies on ICVs, even though their keywords do not contain 'traffic accident'. Such literature could be overlooked when using traditional review methods.  
Cluster 0: vehicular cloud computing; using VANET; data management perspective.
## 2 Materials and Methods  
### Normalized Difference Vegetation Index (NDVI)  
The 25-km Normalized Difference Vegetation Index (NDVI) data were used to examine the long-term (1982-2017) changes in plant growth over the TP. To obtain the NDVI product, we merged the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI, the University of Arizona Vegetation Index and Phenology Lab (VIP) NDVI, and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI using a sophisticated statistical technique known as the Equidistant Cumulative Distribution Function (EDCDF) matching method, which was developed by [PERSON] et al. [22] The fusion method that was utilized was successful in adjusting the GIMMS NDVI and the VIP NDVI in order to match with the MODIS NDVI. Further details of data fusion and its evaluation are provided by [PERSON] et al. [22] The data range excludes urban regions and non-vegetated regions, and all datasets were resampled to the 25 km spatial resolution using bilinear interpolation.
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Results
#### 3.4.1 Training with Canonical Speech Only  
The evaluation of the base Wav2 Vec2.0 model [14] is provided in the **Base model** scenario in Table 3, which yields high-PER results. This is due to the different phone vocabulary and phonetic transcriptions used during the base model's training, which were derived from a different toolkit than the one we used in this work. In scenario **E2**, simply adapting the output layer of the base model to our new phone vocabulary improves the PER, as it aligns the transcription target with our setup. Further fine-tuning the model jointly with the output layer (**E3**) led to additional gains, as the multilingual base model was adapted to the narrower five-language phone set. Utilizing only the canonical speech of the target language (**E4**) further reduced the PER, as the model was trained with data closely relevant to the target dysarthric speech.  
Incorporating in-domain canonical speech (**C_indm**) into the model training (**E5-E7**) significantly enhanced performance, showing that the knowledge of vocabulary is well learned and relevant for the test speech, despite pronunciation differences between canonical and dysarthric speech. Pretraining the model on rich-resource Common Voice data before training on **C_indm** (**E6-E7**) resulted in the best recognition accuracy on target dysarthric speech, as the rich-resource data provides a wide vocabulary base. These training strategies are optimal in the absence of dysarthric speech.
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 6 Conclusions  
In this paper, we study the problem of how to improve the schedulability of communication resources in industrial WSANs. Existing centralized approaches for multipath retransmission scheme do not consider schedulability in the coexistence of diverse industrial applications. In this paper, we propose a resources-scheduling algorithm based on the multipath retransmission scheme in the WirelessHART standard. We use the approaches of RM policy, CES, and multiple sinks to reduce the negative impact of diverse periods and to avoid node interference as far as possible. By simulation and implementation in hardware, we compare our proposed CEM-RM algorithm with M-LLF and M-RM scheduling policies. The achieved results show that our proposed solution outperforms the aforementioned scheduling policies in terms of less use of communication resources, end-to-end delay, and increase in schedulable ratio without jeopardizing network reliability and timeliness of data packets.  
Conceptualization, H.W. and D.Y.; methodology, H.W. and J.M.; software, H.W. and J.M.; validation, H.W. and D.Y.; formal analysis, J.M.; investigation, J.M.; resources, H.W. and D.Y.; data curation, J.M.; writing--original draft preparation, J.M.; writing--review and editing, H.W., D.Y., and M.G.; visualization, H.W. and J.M.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W.  
This research was partially funded by The National Key Research and Development Program of China grant number 2018 YFB1702001 and by the National Natural Science Foundation of China grant numbers 61701018 and 61771040.  
The authors declare no conflict of interest.
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.6 Discussion  
Considering the temporal and spatial properties of radio links, as well as the link asymmetry, our results conclude to a deteriorated radio link quality when nodes are deployed at ground level. The relatively poor quality of wireless links in such deployment is due to the proximity of the ground, which creates multiple signal reflections and can even act as an obstacle to radio wave propagation most of the case. Since reflected and original signal are summed at the receiver, this can produce distortion in the resulting signal and then negatively impact link quality [30].  
The temperature at ground level can also be an explanation for this result. Indeed, several studies [21; 22; 23] show that, the higher is the temperature, the lower is the quality of the link, because of the thermal noise. In our case, with sensors deployed on a road, often in full sunlight, the temperature at ground level was significantly higher than the temperature for the deployment at height.  
Figure 6: CDF of PRR and RSSI for distances 3 m and 6 m and for all links in the network in two different deployments: sensors deployed at ground level and sensors deployed on a support of height 57 cm.  
Figure 7: Link quality classification for a deployment at ground level and at height.  
Although it is well known [14; 15; 20; 21; 22; 23] that the radio link in low-power WSN is very unstable because of many factors such as the radio properties or the propagation environment, the results presented in this section show that, at the ground level, the radio links quality (measured by the PRR and RSSI values) are more severely affected by such environment. This must be taken into account, not only during the deployment of such networks, but also for designing communication protocols.  
On the good side, our results demonstrate that communication between off-the-shelf sensor nodes is possible at ground level. Thus, by using relaying capability of sensors, the door is opened to the numerous applicative use-cases discussed in Section 1. However, results presented in this section only consider one transmission power (0 dBm), one application payload size (82 bytes), one communication channel (number 26), and one deployment area (a relatively flat area). All these parameters may have an impact on the link quality. Therefore, in the following, except the transmission power, which is kept to 0 dBm, we present the analysis of the impact of these parameters on the link properties.
## 4 Discussion
### Minimum Maxwell Stiffness  
As indicated above, several authors have proposed an upper limit to the ratio \(k_{s}/k_{d}\) as a mean to obtain a value of stiffness that allegedly allows the Maxwell system to be assimilated to a pure viscous damper. Limiting ratios of 0.1-0.2 have been proposed [35; 36]. The data of this study are used to assess the validity of this approach. Figure 15 shows the scatter plot of eight relevant variables (drift \(\Delta\), residual drift \(\Delta_{r}\), total acceleration \(a_{t}\), energy dissipated by damper \(W_{damper}\), storey shear \(V_{s}\), damper shear \(V_{d}\), beam moment \(M_{b}\), column moment \(M_{c}\)) versus \(k_{s}/k_{d}\), using the analysis data for Ground Motion Set ESB at intensity level C; the \(y\)-axis shows the ratio of results for models type HB, MB or LB over results for model RB for each variable; limits for \(k_{s}/k_{d}\) suggested in the literature have been marked with a solid (\(k_{s}/k_{d}\) = 0.1) and dashed (\(k_{s}/k_{d}\) = 0.2) lines; the value of \(k_{d}\) used to calculate \(k_{s}/k_{d}\) is the reduced value used in analysis. The plots show a poor global correlation of the ratio \(k_{s}/k_{d}\) with the variability with stiffness for all variables. Many cases with high ratios \(k_{s}/k_{d}\) present a variable value close to 1 (indicating an almost pure viscous behavior); conversely, cases with low ratios \(k_{s}/k_{d}\) present high values of the variables (indicating large influence of the Maxwell stiffness). The condition \(k_{s}/k_{d}\) < 0.1 (marked in solid line) is sufficient but not necessary: when imposed, it bounds the value of the variables, but a majority of cases with small values of the variables are discarded; additionally, if the flexibility of auxiliary elements is accounted for (cases MB, LB) this limit may not be attainable even with very stiff bracing, because the overall stiffness will be dominated by the weaker element. Thus, limiting the \(k_{s}/k_{d}\) ratio does not appear as a convenient condition for the practical choice of brace stiffness if the auxiliary elements are considered. From data presented above it is clear that a condition based solely on damper and storey stiffness does not capture the dependency of the variables on the Maxwell stiffness. As an example, considering drift \(\Delta\) for ADS R.2.5 at design level, the ratio MB/HB is 1.04 and 1.23 for storeys 1 and 5, respectively, with \(k_{s}/k_{d}\) 0.44 and 0.17; it is clear that larger \(k_{s}/k_{d}\) ratios are acceptable at first storey than at roof. Thus, existing recommendations for damper stiffness based on SDOF behavior cannot be directly applied to MDOF systems. It is also apparent that the rule to determine \(k_{d}\) simply as larger than the first storey stiffness (\(k_{d}\geq k_{s,1}\)) proposed by [PERSON] and [PERSON] [37] does not constitute a valid condition to determine a satisfactory Maxwell stiffness (from the point of view of damage control).
###### Abstract
RQ3: What are the key performance differences between ML and DL models in terms of accuracy, robustness, and generalizability for phishing email detection? RQ4: Which DL models demonstrate superiority over ML models in phishing detection, and why? Key Contributions. To answer these questions, this study presents the following contributions:  
1. We develop a novel framework for implementing and evaluating 14 ML and DL models for phishing email detection, ensuring a standardized and comprehensive assessment of their effectiveness. 2. The study utilizes nine publicly available datasets and a merged dataset, addressing the limitations of previous research that relied on single or limited datasets, thereby enhancing the robustness and generalizability of the findings. 3. Through an extensive comparison using various evaluation metrics (e.g., accuracy, precision, recall, F1-score), we demonstrate that deep learning models, particularly transformer-based architectures like BERT [21] and RoBERTa [30], consistently outperform traditional ML models, reinforcing the potential of AI-driven phishing detection. The remainder of this paper is organized as follows: Section 2 provides an overview of related work. Sections 3 and 4 describe the methodology employed and discuss the experimental results. Finally, Section 5 concludes the study and highlights future research directions.
## 4 Formal Description of Timed Colored PNs
### RTA Module and Unit ID Sub-Module  
Each RTA is assigned to a single task \(z_{k}\), and is associated with the corresponding BOM module. Essentially, RTA is a multifunctional unit's time counter, and includes several Unit ID sub-modules (Figure 3). The structure of the Unit ID module is depicted in Figure 11. The meanings of the module's places and transitions are briefly explained in Tables 12 and 13.  
The primary operations of the Unit ID module are:  
* Selecting a unit with a given ID = j from place UPZ and moving to place UPZ j;
* Setting in place E the status \"Unit activity\" and starting the internal time counter;
* Checking the conditions \(n\geq\) rl (RUL) and \(n\geq\) mt (time before maintenance) performed on the output arcs of the transitions T2 and T3 in each time step;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transitions** & **Description of Transitions** \\ \hline T0 & Selecting a unit from the general set of available units \\ T1 & Start task execution \\ T2 & Returning units to AU after completing a task \\ T3, T4 & Selecting the unit model for replacement \\ T5, T6 & Replacing units in UPZ \\ T7 & Unlocking task execution \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transitions of ZExec module.  
Figure 6: The three-phase TASK sub-module.  
* If at least one condition is satisfied, the unit is transferred to the MR module by transition Tout2;
* Generating a predictive maintenance request on the transition T4 output arc according to the Poisson distribution, with a given rate \(\lambda_{\text{M}}\), and moving the unit to the place UM;
* Generating a failure event on the transition T5 output arc according to the Poisson distribution, with a given rate \(\lambda_{\text{F}}\), and moving the unit to the place UF;
* Removing the unit from the place UPZ j, if it becomes inactive;
* Setting new values of unit accumulated operating time \(t_{tu}\) and \(t_{rt}=0\) using the code segments of function newRTLM (u);
* Resetting the module marking to its initial state upon work completion on a signal in the place EndZ.  
The researcher can study the influence of stochastic processes in the simulated equipment. Tokens in the FE and PrME positions of the Unit ID module allow the firing of transitions T4 and T5. The output arc expressions for these transitions generate random times of failure or requests from the predictive maintenance system. CPN Tools provides random distribution functions: normal, Poisson, Weibull, gamma, exponential, and others. The example module in Figure 11 shows Poisson failure flow generation with a rate of \(\lambda=3\). It should be noted that the values of the random distribution parameters depend on the ratio of the model and the real time of the technological process.
## 4 Results and Discussion
### Precision-Recall Curves
Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) joy, (**b**) surprise, and (**c**) negative. These precision-recall curves demonstrate that the emotion 'joy' tends to be more accurately identified by the models. A plausible explanation is that 'joy' is the only explicitly positive emotion in the [PERSON] emotion system, which also categorizes four distinct negative emotions: anger, disgust, fear, and sadness. Additionally, the emotion'surprise' can be either positive or negative. This distribution could lead to a more straightforward identification process for 'joy', as it does not compete or is related to other positive categories. Figure 11: Precision–recall curves for three different emotions analyzed using the XED Aggregated distilled lexicon and applied to the XED Aggregated dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) joy, (**b**) surprise, and (**c**) negative. Figure 12: Precision–recall curves for six different emotions analyzed using the XED distilled lexicon and applied to the GoEmotions dataset. Each graphic represents the precision–recall curve for a specific emotion, with points on the curve representing different thresholds. Emotions include (**a**) anger, (**b**) disgust, (**c**) fear, (**d**) joy, (**e**) sadness, and (**f**) surprise. gories, unlike the negative emotions, which might share overlapping expressive features, potentially confusing the model.
## 3 Materials and Methods  
### Dataset Collection  
In this study, constructing a dataset that includes both textual and image data from the financial domain is a core component of the experimental design. Given the diversity and complexity of the financial industry, diversified data sources were meticulously selected to ensure comprehensiveness and representativeness. Financial textual data were primarily collected from several channels. First, information was obtained from public financial news websites and e-magazines such as Bloomberg and Reuters as well as public reports released by major banks and consulting firms. These sources provided rich information on market dynamics, policy changes, company financial statements, and economic forecasts. Additionally, to capture market sentiment and investor behavior, multiple online financial forums and social media platforms such as StockTwits and Reddit's WallStreetBets were monitored with relevant data acquired through APIs. Furthermore, various publicly available financial statements and annual reports, often in PDF format, were included in the textual dataset and converted using OCR technology. For financial image data, two main areas were focused on. First, stock charts and financial indicator charts were obtained from major financial analysis platforms such as TradingView and Yahoo Finance. These datasets included not only historical price charts but also charts of technical indicators like MACD and RSI. Second, advertisements and promotional materials for financial products were collected to analyze the impact of marketing strategies on consumer behavior.
# Resource Allocation of UAV-Assisted IoT Node Secure Communication System
## 3 Proposed Optimization Algorithm
### Optimal Power Splitting Ratio  
To optimize the PSR with the given workable transmit power and trajectory of the UAV, the problem \(P_{1,1}\) becomes  
\[P_{4}:\max_{\Xi} \sum_{n=1}^{N}\left[R_{SD}[n]-\max_{k\in K}R_{SE,k}[n]\right]\] (17) s.t. \[C1,C2,C6,C8\]  
Let \(c_{n}=\frac{p[n]\tilde{\beta}}{\delta^{2}\left(\left\|\mathbf{q}[n]-\mathbf{w }_{SD}\right\|^{2}+H^{2}\right)^{\frac{\delta}{2}}}\), \(d_{n,k}=\frac{p[n]\tilde{\beta}}{\delta^{2}\left(\left\|\mathbf{q}[n]-\mathbf{ w}_{SE,k}\right\|^{2}+H^{2}\right)^{\frac{\delta}{2}}}\).  
Then the objective function is given by  
\[R_{\text{sec}}[n]=\log_{2}(1+c_{n}\xi[n])-\log_{2}(1+d_{n,k^{*}} \xi[n]) \tag{18}\]  
where \(d_{n,k^{*}}=\arg\max_{k\in K}d_{n,k}\). Similarly, with \(P_{2}\), only if \(d_{n,k^{*}}\leq c_{n}\), the confidential information cannot be decoded. Then \(P_{4}\) can be solved by a Lagrangian dual method. While \(d_{n,k^{*}}>c_{n}\), we set \(\xi[n]=0\) to prevent \(ENs\) decoding. Therefore, the solution of \(P_{4}\) is given as  
\[\xi_{\text{opt}}\left[n\right]=\begin{cases}\min(\xi^{*}[n],1)&c_{n}\geq d_{n,k^{*}}\\ 0&c_{n}<d_{n,k^{*}}\end{cases} \tag{19}\]  
By the same method as \(P_{2}\), we can have  
\[\xi^{*}[n]=\sqrt{\left(\frac{1}{2d_{n,k^{*}}}-\frac{1}{2c_{n}} \right)^{2}+\frac{N}{\eta T\delta^{2}\ln 2}\biggl{(}\frac{1}{d_{n,k^{*}}}-\frac{1}{c_{n}} \biggr{)}}-\left(\frac{1}{2d_{n,k^{*}}}+\frac{1}{2c_{n}}\right) \tag{20}\]where \(T=(l_{n,k^{*}}+m_{n,k^{*}})d_{n,k^{*}}+v_{n}c_{n}\) and \(l_{n,k^{*}}\geq 0,v_{n}\geq 0\), \(m_{n,k^{*}}\geq 0\) are the dual variables associated with the constraints C8, C6, and C1, respectively. Then, \(l_{n,k^{*}},v_{n}\), and \(m_{n,k^{*}}\) can be updated by the subgradient method:  
\[\begin{split} l_{k}(i)&=\Bigg{[}l_{k}(i-1)-\tau_{5} \Bigg{(}E_{sen}-\frac{1}{N}\sum_{n=1}^{N}E_{IT,k}[n]\Bigg{)}\Bigg{]}^{+},\forall k \\ v(i)&=\Bigg{[}v(i-1)-\tau_{6}\Bigg{(}\frac{1}{N} \sum_{n=1}^{N}E_{SD}[n]-E_{\min}\Bigg{)}\Bigg{]}^{+},\forall k\\ m_{k}(i)&=\Bigg{[}m_{k}(i-1)-\tau_{7}\Bigg{(}\frac {1}{N}\sum_{n=1}^{N}E_{SE,k}[n]-E_{min}\Bigg{)}\Bigg{]}^{+},\forall k\end{split} \tag{21}\]