esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We achieved the utilization of the AMPCVD to decompose ethanol precursor by microwave plasma and the controllable synthesis of nanomaterial in a high-temperature tube furnace separately. The emission spectrum of the Ar-ethanol-H\({}_{2}\) microwave plasma shows that the plasma decomposed the ethanol precursor into CH, C\({}_{2}\), and OH groups. The results of the ethanol contents in the exhausted gas at the temperature of 400, 500, 600, 700, 800, 900, and 1000 \({}^{\circ}\)C, with and without plasma, also show that the decomposition capacity of 200 W atmospheric pressure microwave plasma is powerful. The CNTs  
Figure 6: HRTEM images of CNTs prepared by APMCVD at the temperature of 500, 600, 700, and 800 \({}^{\circ}\)C. (**a–d**): low magnification images; (**e–h**): high magnification images; (**i–k**): specific structure of the CNTs grown at 800 \({}^{\circ}\)C.  
Figure 7: XPS and Raman spectra of CNTs prepared at 800\({}^{\circ}\)C. (**a**): the global spectra; (**b**): the magnification view of **C1s**.  
were synthesized by the AMPCVD using ethanol vapor as the carbon source, with a temperature of 500, 600, 700, and 800 \({}^{\circ}\)C at atmospheric pressure. The tube furnace temperature controls the growth of CNTs. The Raman spectra show that the defect concentration of CNTs decreases with the rise of furnace temperature, and the quality of CNTs obtained at 800 \({}^{\circ}\)C is relatively high. The prepared CNTs, characterized by SEM, HRTEM, and XPS, show that the CNTs are multiwalled, tangled with each other, bamboo-shaped structured, and have a large amount of oxygen-containing functional groups on the surface, especially aldehydes. Compared with CVD, the CNTs synthesized by AMPCVD have a higher growth rate and lower defect concentration with the same substrate. Compared with DC-PECVD, RF-PECVD, and MPCVD, the quality of the CNTs synthesized by AMPCVD is similar, but with no vacuum equipment required. This indicates AMPCVD has excellent potential in nanomaterial synthesis, and further research is needed.  
Conceptualization and supervision, L.T., and D.L.; methodology, D.L.; validation, L.T., and B.G.; formal analysis, D.L., and L.T.; resources, D.L.; data curation, D.L.; writing--original draft preparation, D.L. and L.T.; writing--review and editing, D.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2016 YFF0102100) and the Pre-research Project of Civil Aerospace Technology of China (D040109).  
The high temperature tube furnace and sealing technology are supported by Tianjin ZHONGHUAN Electric Furnace Co. Ltd.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
## 4 Field Test Results
### Dynamic Test Results  
Digital filtering, average processing and discrete Fourier transform of time domain signals were carried out to get the amplitude spectrum (or power spectrum) and phase spectrum of each pick-up point. Then, the multiple natural frequencies can be obtained in each direction of the structure. As shown in Figure 7, the experimental results of the transverse and vertical frequency peaks are 0.769 Hz and 1.567 Hz, respectively. The calculated first transverse and vertical frequencies are 0.735 Hz and 1.489 Hz, respectively. The maximum discrepancy between the calculated and experimental results of the fundamental frequencies is less than 5%, suggesting that the finite element model provide reasonable prediction of the frequencies.  
Figure 5: (a) Maximum moment of each section under the most unfavorable loading case; (b) Experimental results of the load efficiency factor.  
Figure 6: Load efficiency factors of Sections A-A and C-C in different loading cases.  
The first five mode shapes are shown in Figure 8. The first order vibration mode is the longitudinal bending vibration of the fixed pier, and the first natural frequency is 0.433 Hz, which indicates that the bending stiffness of the main pier is low. The second and third order modes are the transverse bending vibration of the main girder, while the vertical bending vibration lag appears in the fourth order mode, which indicates that the transverse stiffness of the bridge is weaker than the vertical stiffness. Based on the vibration attenuation curve measured by driving test, the damping ratio of the bridge structure can be calculated according to the following formula:  
\[D_{r}\ =\ \frac{1}{2m\pi}ln\frac{A_{i}}{A_{i+\ m}} \tag{2}\]  
where \(D_{r}\) denotes the damping ratio; \(m\) denotes the number of cycles; \(A_{i}\) denotes the amplitude of the \(i\)th waveform of the vibration attenuation curve. The damping ratios for the first transverse and vertical modes are 0.0015 and 0.0036, respectively.  
Figure 8: Natural frequencies and corresponding vibration modes: (**a**) Longitudinal bending of fixed pier (f = 0.433 Hz); (**b**) 1 st transverse bending (f = 0.769 Hz); (**c**) 2 nd transverse bending (f = 1.026 Hz); (**d**) Longitudinal bending of pier 2\(\#\) (f = 1.191 Hz); (**e**) 1 st vertical bending (f =1.567 Hz).  
Figure 7: Frequency spectrum of the bridge structure: (**a**) transverse vibration; (**b**) vertical vibration.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.4 Map Classification  
After training DL models, the resulting models are used to classify each pixel from satellite imagery, where the perceptual field (fov) for each image is assumed as a sliding window of the input size expected by each DL model (see Figure 6). This process ended when all pixels are classified.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Dynamic Node Scoring with Temporal Decay
#### 4.1.1 Node Scoring Function  
Traditional offline scoring functions, such as HITS (Hyperlink-Induced Topic Search) [39] and centrality measures (e.g., Betweenness Centrality) [14], are effective at identifying degree centrality, influential nodes, or ego nodes in static graphs [2]. However, in an online setting, where graphs evolve over time, capturing dynamic fluctuations becomes essential.  
In our previous work, the DecayRank algorithm [38] was introduced to assign importance scores to individual nodes by considering neighboring edges and evolving graph properties. Based on the iterative formula in Equation (2), DecayRank incorporates a **fixed decay factor** to dynamically adjust scores at every iteration. While effective, this fixed decay factor posed limitations in adapting to varying graph dynamics, an issue addressed by the proposed Adaptive-DecayRank algorithm. The modified node score vector, \(v_{u}\), for DecayRank is defined as  
\[v_{u}=c\hat{P}v_{u}+(1-c)h_{u}e^{-\delta\cdot(t-A_{u}^{(T)})} \tag{4}\]  
Here, \(\hat{P}\) is the row-normalized adjacency matrix, and the other parameters follow the notation in Equation (2). The temporal decay function, \(e^{-\delta\cdot(t-A_{u}^{(T)})}\), reduces the influence of older interactions, with the decay factor, \(\delta\), controlling the rate of decay. The term \(A_{u}^{(T)}\) represents the most recent timestamp when node \(u\) was last updated in the adjacency structure of the graph. Specifically, it is derived from the adjacency structure, \(A_{u}\), which maintains historical interaction records of node \(u\). This ensures that more recent interactions contribute more significantly to the node score, while older interactions decay exponentially over time.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 3 Experiments and Results
### Parameter Estimation  
As described in Section 2.1, each sequence from images in training set consists of \(K\) patches with size of \(s\times s\). The CNN module in our SCNN-LSTM is trained by fine-tuning the CNN with an input of \(11\times 11\) patch in [33]. Such a size has also shown good performance in work [33], so the patch size \(s\) for SCNN-LSTM is fixed to 11. The length of the sequence is important to achieve high verification accuracy. If \(K\) is too small, more detailed vein patterns are extracted but including more noise. Matching pixels in noisy regions can create errors which result in verification accuracy reduction. On the contrary, sequences with large \(K\) will suppress vein feature details, leading to smooth vein features, which also degrades the verification accuracy. Therefore, we determine the appropriate size of sequence for SCNN-LSTM experimentally. Firstly, we train the proposed SCNN-LSTM model to extract the vein feature of the finger-vein images in the training and validation at different lengths of sequence. To reduce the redundant information, we obtain patches with sampling intervals of one pixel to create training sequences. Secondly, the first 6 images acquired at the first session are employed as registration templates and the remaining as testing images. Therefore, there are 300 (50 \(\times\) 6) genuine scores and 14,700 (50 \(\times\) 49 \(\times\) 6) impostor scores. The False Rejection Rate (FRR) is computed by the genuine scores and the False Acceptance Rate is computed by impostor scores. The Equal Error Rate (EER) is the error rate when FAR is equal to FRR. Figure 7 illustrates the relationships between length of sequence and EER, and the results are obtained by using only the validation data. From Figure 7, we can see that a smaller equal error rate is achieved at a sequence of length 11 and 13. With increasing the length \(K\), the computation time will be increased. Therefore, we fix the length of a sequence to 11 in our experiments.  
To verify over-fitting of our model, we shows learning curves in Figure 8. Figure 8a,b show the accuracy on the validation dataset and loss on the training dataset. From Figure 8, we can observe that the accuracy of validation dataset increases to about 65% and the loss decreases slowly after 2000 backpropagations. When the number of iteration steps is between 5000 and 10,000, the accuracy increases to more than 90% and the loss dramatically reduces. After 10,000 iterative steps, the loss fluctuates but it still decreases slowly. Therefore, our SCNN-LSTM model has good convergence for finger-vein segmentation.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### DoS Effect Duration  
The \"DoS Effect Duration\" classification in PDoS attacks characterizes the longevity of an attack's impact on the targeted system. The duration of an effect can be classified into two categories: temporary and permanent.  
Temporary DoS Effect: Attacks classified under this category cause disruptions that, while potentially severe, do not inflict lasting damage on the system's hardware or software. Recovery actions typically involve system restarts, reconfigurations, or software reinstalls. The temporary nature of these attacks means that the core functionality of the hardware is retained post recovery. For instance, a malware like BlackEnergy, which temporarily disrupts ICS but can be resolved through device replacement, would fall under this category. Temporary DoS attacks, despite their reversible nature, can still have significant operational and financial repercussions.  
Permanent DoS Effect: This category includes the most severe PDoS attacks, where the inflicted damage is irreparable, necessitating the complete replacement of the affected systems or components. Permanent attacks are often strategically planned and executed with the intent to cause irreversible damage to critical infrastructure or data. Malware like USB Killer, which physically damages the hardware, or PaperW8, which permanently corrupts the bootloader, create permanent DoS effects. Such attacks require extensive recovery efforts or full-device replacement.
## 5 Results  
The objective of this study was to develop a framework that improves the accuracy of existing methods for detecting helmet use violations, which often rely on manual inspection and can be prone to error. Three different object detection models were considered, namely YOLOv5, YOLOv7, and YOLOv8. Additionally, augmented and synthetic images were generated using DCGANs to further improve performance on under-represented classes. Each of the techniques was then applied and their performance was evaluated to identify the best-performing detector. TTA was applied during the inference phase to further improve the model performance. The models were trained and tested on a dataset of 100 traffic videos collected from the AI City Challenge Dataset (data from India). This dataset presents a wide variety of different challenges common to the transportation domain, including varying image quality, lighting, and occlusion. The DCGAN model was trained on a device equipped with an NVIDIA RTX3080 Ti graphics card and 12 GB GDDR6X RAM. Its run-time was approximately twelve hours per class, for a total of 1000 epochs using an early stopping window of 30 epochs. The YOLO detector models, trained on the same device, had a run-time of approximately three days for a total of 300 epochs, using an early stopping window of 20 epochs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline batch\_size & 8 \\ imgsz & 1088 \\ optimizer & Adam \\ learning\_rate & 0.001 \\ dropout & 0.1 \\ iou & 0.7 \\ momentum & 0.937 \\ weight\_decay & 0.0005 \\ warmup\_bias\_lr & 0.1 \\ fl\_gamma & 2.0 \\ label\_smoothing & 0.1 \\ mosaic & 0.1 \\ close\_mosaic & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters for the trained YOLO Models.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Data Analysis  
Convolutional neural networks (CNN) are an example of specialized neural network architecture, which has been proven a successful method in many vision classification applications [16]. In this work, _CNN_ was adopted for HLB disease classification. Our _CNN_ framework shared the basic architecture of the classic LeNet-5 [16], while some adjustments were carried out to fit our data. This network architecture is composed of three modules: (1) Preprocessing; (2) Feature extraction; (3) Classification, as shown in Figure 5.
## 7 Metabolic Adapations of _Lactobacillus_ spp. to PPCs
### Sugar and Energy Metabolism  
_L. plantarum_ altered the expression of genes or proteins involved in the transport and metabolism of different carbon sources in response to PPCs.  
_L. plantarum_ decreased the expression of glucose permease and increased the expression of genes involved in the malolactic fermentation (MLF) pathway in the presence of _p_-CA. This metabolic adaptation generates energy via chemiosmotic mechanisms and increases the intracellular pH to reduce acid stress. A similar transcriptomic reprogramming was observed during the fermentation by _L. plantarum_ of vegetable and fruit juices rich in hydroxcinnamic acids [59; 60]. Upregulation of genes involved in the MLF pathway has also been observed during the response of _L. brevis_ to ferulic acid [23].  
Genes encoding transketolase (_kta_), phosphoglycerate mutase (_pgm_), and phosphode-tolase (_xpkA_) were upregulated under _p_-CA stress [25]. This profile suggests accumulation of glycolytic intermediates such as 3-phosphoglycerate and 2-phosphoglycerate, a metabolic adaptation characteristic of carbohydrate starvation conditions that is supported by _uspA_ induction, which encodes a universal stress protein leading to a continuous growth-arrest  
Figure 3: Venn diagram assembled from _Lactiplantiobacillus plantarum_ WCFS1 GlnR regulon genes involved in nitrogen metabolism that are differentially downregulated in response to individual plant phenolic compounds. _p_-CA, _p_-cumaric acid; RSV, resveratrol; HXT, hydroxyytrosol; GA, gallic acid.  
state [61]. The Pgm and UspA proteins were also induced under tannic acid stress [37], suggesting similar metabolic adaptations.  
OLE transcriptionally reprogrammed the expression of transporters from the phosphotransferase (PTS) system of _L. plantarum_[28] including the induction of the mannitol and fructose PTS systems which are the main soluble components of olive source and sink tissues. This expression profile would promote sugar transfer from the host and support the proposed role of OLE as a signaling molecule in the association between _Lactobacillus_ and the plant host [28].  
_L. plantarum_ WCFS1 encodes an \(N\)-acetyl glucosamine (GlcNAcAc) transporter (_tagE_) that is downregulated in presence of \(p\)-CA, OLE, and RSV [25, 27, 28]. However, the expression of _nag_ genes involved in GlcNAc catabolism was not affected by these PPCs. The concurrent downregulation of genes involved in the production of cell-wall and capsular polysaccharide constituents suggests that _nagE_ downregulation is related to cell-wall remodeling, as GlcNAc may act as a building block for biosynthesis of these cellular components.  
Unlike \(p\)-CA, OLE, and RSV, the transcriptomic signature of GA showed increased expression of genes required for GlcNAc utilization, including those coding for glucosamine-6-P isomerase (_nagB_) and a putative PTS transporter of the [GlcNAc]\({}_{2}\) disaccharide (_lp_2954_) suggesting that GA promotes GlcNAc utilization by _L. plantarum_ WCFS1 [26].
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 4 The Galileo HAS User Algorithm for HAS Initial Service  
The Galileo HAS User Algorithm is a real-time precise positioning algorithm that processes the corrections provided by the Galileo HAS Initial Service (orbit and clock corrections and code biases) and calculates position and velocity referred to the GTRF (Galileo Terrestrial Reference Frame), time-stamped with respect to the GST (Galileo System Time) using Galileo L-Band observables and, in the case of a multi-constellation configuration, GPS L-Band observables as well. The User Algorithm was designed to work in all of the following dual- and triple-frequency modes:  
* Dual-frequency modes:
* Galileo only: E1-E5a/E1-E5b/E1-E5 AltROC/E1-E6B.
* Galileo and GPS: E1-E5a/E1-E5b/E1-E5 AltROC/E1-E6B + L1-L2P/L1-L2C/L1-L5.
* Triple-frequency modes:
* Galileo only: E1-E5a-E6B.
* Galileo and GPS: E1-E5a-E6B + L1-L2C-L5.  
The Galileo HAS User Algorithm used to characterize the typical positioning performance of the Galileo HAS described in the HAS SDD was configured for kinematic operation.
## 7 Conclusions  
This paper presents the cluster formation and dynamic broker selection, which provides security. The services are transferred among nodes in a secure environment, with the aid of a broker that is selected based on the QoS metrics among the mobile nodes. We have analyzed the performance of the AODV routing protocol in MANET with secured and unsecured conditions, with the effects of the QoS metrics. Our proposed model provides a better QoS and scalability with the help of the cluster formation and a number of brokers to avoid congestion. By the results shown, it is proven that bandwidth, end-to-end delay, throughput, delivery ratio, routing-control overhead, and network lifetime outperform better with our proposed model. Finally, our simulation results show better performance with the AODV routing protocol in the dynamic secured environment. The delay and packet-drop ratio scales down in the proposed secured model. Thus, the proposed architecture reduces the network load for the single mobile node and achieves more scalability. In the future, the optimization algorithm can be incorporated into our current proposed architecture to select the optimal broker node to improve the efficacy in a large mobile area network. In addition, the same can be evaluated with different QoS metrics for a large mobile area network.  
Conceptualization, R.R., R.M. and A.D.; methodology, D.P.S., H.G.M. and R.S.; validation, R.S.; formal analysis, D.A. and I.D.N.; investigation, R.S. and R.R.; writing--original draft preparation, R.R., R.M. and A.D.; writing--review and editing, D.P.S., H.G.M. and R.S.; supervision, D.A. and I.D.N.; project administration, D.A. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022 TR140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
Figure 9: Throughput vs. delay.
## 4 Evaluation Metrics
### Mobility Models' Metrics
#### 4.1.4 Average Clustering Coefficient  
The clustering coefficient is the ratio of radio links among neighbors and the number of neighbors. It is calculated for each node by determining how many links there are between neighbors of the node, divided by the number of neighbors it has at that point in time. More formally [42], if \(N(u)\) is the set of neighbors of node \(u\), then the clustering coefficient is defined as:  
\[\frac{\sum_{v\in N(u)}\lvert\{x\in N(v)\}\rvert}{N(u)} \tag{2}\]  
This metric is a measure of the network redundancy. When a node presents a high clustering coefficient, this means that its neighbors have many radio links with each other. Therefore, the node is said to be redundant if a route can, with a high probability, pass through its neighbors without a path length increase.
## 3 System Architecture
### Concept Overview  
Figure 2 describes the proposed concept with use cases. In this figure, our target scenario includes use cases such as stadiums, campuses, workplaces, and real estate agencies/post offices with local bases in various places and their own space. The users of MEC purchase subscriptions via the control plane in lower frequency bands that have comprehensive coverage, such as LTE, and acquire computing resources that they can use to obtain several kinds of applications (e.g., mandatory, frequent use). It can be used in a form that suits each scenario, such as cache content and offloading processing. Furthermore, the allocated computing resources can be used locally via the user plane in a closed state to receive the service at a higher data rate and lower latency. Therefore, it is possible to obtain lower-cost service, because it does not use network facilities such as backhaul networks and Internet connection. In addition, applications and data are automatically taken over as one in order to more seamlessly share information or virtual space with others. On the other hand, it is possible to receive local-specific services (e.g., VR attractions, AR autonomous driving support). An MEC/Cloud Orchestrator can control MEC or cloud by receiving and responding to requests from users and determining the resources required to realize the above services.