esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison with Existing Methods  
Existing methods for localizing the EZ in epilepsy surgery planning include localization of the SOZ via visual scrutiny of the patient's ictal EEG data as well as localization of the irritative zone or high-frequency-oscillation zone via visual scrutiny and marking of interictal data. The approach for iEEG analysis proposed here is particularly advantageous when compared to such current methods since it provides a high localization accuracy through a fully automated analysis of very brief data as opposed to the SOZ estimation (which is highly dependent on seizure capturing and human interpretation) or to the irritative or high-frequency-oscillation zone estimation (which is also dependent on the human scrutiny of portions of data that are long enough to capture the patient's representative spike or high-frequency-oscillation populations). Our innovative approach could complement existing methods, particularly in cases where the SOZ cannot be estimated (e.g., the patient does not seize during the monitoring, or their seizures are not of easy interpretation) or where the patient presents without any interictal discharges in the iEEG traces; in these cases, the iEEG would fail to derive a clear hypothesis about the location of the EZ, and thus alternative approaches would be extremely beneficial. Even though the proposed method reduces human effort, there are certain limitations associated with using this technique, which are not encountered when applying traditional clinical methods. Primarily, the deep learning method requires the conversion of the iEEG signal into time-frequency images, which require more space and require additional computational effort to load, process, and save. This means that additional efforts need to be taken for memory management, computational resource management, and garbage collection. Further, this method might be difficult to implement in long-term EEG recordings due to the above-mentioned complexities.
## Abbreviations  
Abbreviation Full Name  
CWT Continuous Wavelet Transform  
DWT Discrete Wavelet Transform  
GFT Generalized Fourier Transform  
iGFT Inverse generalized Fourier Transform  
GMT Gear meshing frequency  
GST Generalized Wavelet-Based Synchrosqueezing Transform  
ICA Independent Component Analysis  
multi-wavelet Multiple Wavelet Functions  
PCA Principal Component Analysis  
SVM Support Vector Machine  
TSA Time Synchronous Averaging  
TFR Time-Frequency Represented  
WPT Wavelet Packet Transform  
WSST Wavelet-Based Synchrosqueezing Transform
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 3 Design Architecture
### Checker Module  
The first step during the VDCLS implementation was to develop the Checker Module (Figure 4). The Checker Module receives the main outputs from the master and slave cores, optionally calculates output signature and buffers the results. The main core outputs consist of the current core program counter, executed instruction, instruction result and stored data. In the classic approach, when the slave core is delayed, e.g., by two clock cycles, the comparison is straightforward as it is made between the master core results and the slave core results delayed by a constant number of clock cycles. In the discussed case, the Checker Module constantly tracks the delay between the two cores and selects the appropriate slave core results for the comparison.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
###### Abstract  
Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.  
traffic speed multi-step prediction; direct strategy; speed detrending; ensemble learning 2021  
11 May 2021
## 5 Experiments and Discussions
### Comparison of DNST Feature  
Numerous experiments on each method were carried out and took the best value and average value as the objective basis of the comparison. The experimental results are shown in Figure 4. From Figure 4, the DNST feature achieves the highest classification results. The best accuracy is 89.92%, and the average accuracy is 89.36%. The Contourlet and DST schemes obtained the same recognition result. The average accuracy of DNST is 1.34% higher than that of DST and Contourlet, 3.15% higher than that of wavelet, and 5.97% higher than that of GLCM. This is due to the fact that DNST has excellent spatial localization properties and directional selectivity, and it can capture defects features more accurately for continuous casting slabs. The classification accuracy obtained by GLCM is the lowest, which indicates that only extracting texture information is not sufficient to represent features of the continuous casting slabs. Figure 4 also shows that the four multi-scale multi-directional feature extraction methods are superior to GLCM. Besides, the difference between the best accuracy and the average accuracy of the wavelet is 88.31% - 86.21% = 2.1%, which indicates that the wavelet is less robust to parameter changes. The difference between the best accuracy and average accuracy of DNSTis \(89.92\%-89.36\%=0.56\%\), the value is lowest, which shows the DNST has better robustness for parameter changes.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 4 Comparison: Single Transmission from the Single Base Station in the URN  
Simultaneous transmissions from multiple BSs (i.e., mBS and sBS) to UAVs can guarantee a seamless relay transmission to \(U_{o}\) and maximize the throughput of the UE in the URN with the placement of the UAV to a region where the UAV can receive signals from both. To prove the throughput enhancement of simultaneous transmissions in the URN, we consider a single transmission from a single BS (i.e., mBS or sBS) in the URN. It can also provide data to \(U_{o}\); however, interference from an adjacent BS exists, thereby resulting in the outage of \(U_{o}\) and the performance degradation of other UE in the URN. In addition, an outage can occur depending on the SINRs of wireless backhaul and access links during two time slots, which are dependent on the position of the UAV.
## 4 Application Characteristics  
HSC focuses on Hainan Province, which is surrounded by the largest ocean area in China. Specific application scenarios determined the composition of HSC, sensor selection, and sensor technical specifications, including spectral band, spatial resolution, swath width, and swaying capacity. These parameters were designed to overcome the limited regional observation coverage, ensuring the constellation provides high-frequency and high-coverage observations of the province.
## 1 Introduction
The method needs alkaline conditions for oxidizing the hydroxyl groups of CDs to carboxyl groups which interact with the surface of AuNPs, decorating and stabilizing them. In this widely used method, the excess of CDs ensures the stability and suitability of AuNPs@CDs for various applications, such as SERS [25; 26; 27; 28]. Ultrasonic and microwave-assisted synthesis are physical methods that use ultrasonic waves or microwaves to reduce gold salts in the presence of CDs, resulting in the rapid synthesis of monodispersed AuNPs@CDs with high crystallinity [29; 30; 31]. The physical methods for the synthesis of CDs-capped AuNPs are based mainly on laser ablation, which involves the use of laser pulses to ablate Au targets in the presence of CD solutions, forming AuNPs that are immediately capped by CDs [32; 33]. CDs have also been exploited for the synthesis of AgNPs for different potential applications [4; 34; 35]. For instance, \(\beta\)-CDs are ideal reducing and stabilizing agents in the synthesis of AgNPs, catalyzing the reduction of Ag\({}^{+}\) to metallic Ag\({}^{0}\), thus preventing agglomeration of AgNPs, mainly under alkaline conditions [36; 37; 38; 39] and probably through the same mechanism as in the case of Au, even at room temperature [34]. In most cases, the reaction is driven at higher temperatures [40], thus shortening the time needed for synthesis. Based on our previous results related to the microwave-assisted one-pot synthesis of AuNPs [30], in this paper, we present the results obtained in the synthesis of both Au and Ag NPs using CDs as both reducing and capping agents in an alkaline environment. Moreover, both noble metal NPs were synthesized, either at room temperature or using microwave or ultrasound methods. The synthesized NPs were tested for their SERS enhancement. In a relatively recent review, it was pointed out that there is controversial information about CD efficiency and further experimental investigations have to be conducted in order to estimate the real potential of CDs in SERS-based analysis. Among other advantages, the CD capping produces an orientation regulation and improves the interaction with certain compounds. We demonstrate for Ag NPs that the CD capping triggers the detection of aromatic compounds, having a higher affinity for the hydrophobic cavity of CDs, which are difficult to detect with citrate-reduced NPs. Also, we demonstrate that CD capping stabilizes the geometry of the interaction of analytes with the plasmonic NPs, strongly reducing the influence of the pH on their SERS spectra. We also show how CD capping can induce a dramatic change in the spectra of analytes knowing to form inclusion complexes with CDs.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 2 Materials and Methods
### Data Segment Length  
In the diagnosis of vibration faults, determining the appropriate length of signal segments is crucial. Evaluation involving different segment lengths revealed that these significantly influenced the model performance. To effectively capture vibration signal features across different sampling rates, this study categorized the acquired signals into two lengths: 2400 and 12,000 data points.  
A 2400-point segment represents approximately 5-6 rotational cycles at a 12 kHz sampling rate and about 1.5 cycles at 48 kHz. In comparison, a 12,000-point segment corresponds to approximately 29 cycles at 12 kHz and 7 cycles at 48 kHz. This dual-segment strategy ensured the extraction of vibration features over both short and extended time durations.  
The 2400-point segment focused on capturing high-frequency variations within a shorter duration, making it suitable for real-time monitoring and detection of high-frequency fault features. This is particularly advantageous at the higher sampling rate (48 kHz), where computational efficiency is a critical factor. Conversely, the 12,000-point segment spanned more rotational cycles, allowing the effective analysis of low-frequency periodic variations in the frequency domain, making it ideal for identifying deeper fault patterns.  
By adopting this dual-segment strategy, the current study achieved a balance between high-resolution time-domain features and comprehensive frequency-domain analysis, leveraging the strengths of both short- and long-duration signal segments.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 6 Future Work  
While the present study demonstrates the effectiveness of deep learning models--particularly the proposed PELM architecture--in detecting pneumonia from chest X-rays, several directions remain for future exploration to strengthen clinical utility and translational impact.  
Integration with Clinical Workflows: A key area for future development is the seamless integration of AI-driven diagnostic tools into real-world hospital environments. This includes compatibility with hospital information systems (HIS), picture archiving and communication systems, and electronic health records. Embedding AI outputs directly into these systems will enable radiologists and clinicians to access model predictions during routine workflows, thereby supporting faster, more informed clinical decision-making with minimal operational disruption.  
Real-World Validation: To ensure robust generalizability and clinical relevance, future studies should focus on large-scale, prospective validation across diverse institutions and patient populations. Evaluating model performance under real-world conditions--including variations in imaging equipment, acquisition settings, and patient comorbidities--will be essential for establishing trust and determining deployment readiness. Such validation will also help identify edge cases and ensure equitable model performance across demographic groups.  
Interpretability and Explainability: Despite high predictive accuracy, deep learning models are often criticized for their lack of transparency. Enhancing model interpretability remains a crucial challenge, especially in high-stakes domains like medical imaging. Future work should incorporate explainable AI techniques, Layer-wise Relevance Propagation, or counterfactual reasoning to provide intuitive visual and textual explanations for each prediction. These tools can improve clinician trust, support regulatory approval, and facilitate collaborative human-AI decision-making.  
Toward Clinical Translation: Addressing the above areas will be instrumental in closing the gap between research and clinical deployment. Another important direction involves conducting systematic ablation studies to quantify the specific contribution of each data augmentation technique to overall model performance. Such experiments will support evidence-based refinement of preprocessing strategies, helping optimize model generalization across diverse imaging conditions. The successful transfer of AI to radiology applications depends on algorithmic accuracy as well as reliability, usability, and system-level integration. By focusing on workflow alignment, real-world validation, and interpretability, future efforts can enable the adoption of scalable, reliable, and clinically meaningful AI tools for pneumonia detection and beyond.
# Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis
## 3 Results
### Short-Term Riverbed Change
The current burial depth standard, as shown in Figure 1, applies a uniform depth without considering the tributary nature of the watercourse, regardless of the spatial context of where the pipeline is buried. For example, a narrow stream classified as part of a larger river system may not always require the same burial depth standard as wider rivers. The current classification system may not fully account for local variations in flow velocity, sediment transport, and riverbed dynamics. This suggests that future safety standards should adopt more flexible and adaptive guidelines that consider the specific hydrological and geological conditions of each site. Furthermore, as concerns about climate change grow and the likelihood of more frequent and intense floods increases, it may become necessary to incorporate larger safety margins and a more regular monitoring of riverbed changes. The findings of this study provide valuable insights for improving pipeline safety management, advocating for adaptive standards that can respond to both long-term and short-term riverbed changes.
# A Programmable ROADM System for SDM/WDM Networks
## 3 Design of Programmable SDM/WDM ROADM System
### Hardware Requirements in Different Topology  
In ROADM systems, switching devices, such as WSSs, contribute to most parts of the cost. The cost depends on the number of WSSs as well as the total number of port count in WSSs. Based on the 6-node topology, the total system cost is estimated by calculating the required number and the total port count of WSSs. With the bypass connection, the required WSSs and total WSSs ports are reduced dramatically. To roughly get the hardware saving, the WSS number and total port number in different topologies should be calculated as shown in Table 1. Firstly, the number of cores used for bypass connection in three different virtual topologies has been calculated. After that, the WSS number and the total ports number are presented. As shown in Table 1, comparing with the fully flexible/basic topology, topology 1 could save 7.7% WSSs while the total port number saves 12.6%. Meanwhile, topology 2 and 3 save a little more than topology 1 which has been given the exact percentage in the table. Furthermore, as shown in the last column, the maximum port number in WSSs was calculated in each topology. Comparing with the other three proposed topologies, the fully flexible topology needs an unpractical large-scale WSSs. As mentioned above, the maximum total port count of practical WSSs is limited around 30 so that under the existing conditions, the fully flexible topology cannot be achieved. Besides, the cost of WSSs will be rapidly increased with the port number increasing. In practical networks with more nodes and large core number MCFs, more hardware can be saved based on the proposed SDM/WDM ROADM system.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## Appendix A Common Vulnerability Scoring System
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Possible Values** & **Value Description** \\ \hline \multirow{4}{*}{Attack Vector (AV)} & \multirow{2}{*}{Network (N)} & The vulnerable component is bound to the network stack, and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. \\ \cline{2-3}  & & The vulnerable component is bound to the network stack, but the attack is limited at the protocol level to a logically adjacent topology. \\ \cline{2-3}  & Local (L) & The vulnerable component is not bound to the network stack, and the attacker’s path is via read/write/execute capabilities. \\ \cline{2-3}  & Physical (P) & The attack requires the attacker to physically touch or manipulate the vulnerable component. \\ \hline \multirow{4}{*}{Attack Complexity (AC)} & Low (L) & Specialized access conditions or extenuating circumstances do not exist. \\ \cline{2-3}  & & A successful attack depends on conditions beyond the attacker’s control. That is, a successful attack cannot be accomplished at will but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected. \\ \cline{2-3}  & \multirow{2}{*}{None (N)} & The attacker is unauthorized prior to attack and therefore does not require any access to settings or files of the vulnerable system to carry out an attack. \\ \cline{2-3}  & & Low (L) & The attacker requires privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with low privileges has the ability to access only non-sensitive resources. \\ \cline{2-3}  & \multirow{2}{*}{High (H)} & The attacker requires privileges that provide significant (e.g., administrative) control over the vulnerable component, allowing access to component-wide settings and files. \\ \hline \multirow{2}{*}{User Interaction (UI)} & None (N) & The vulnerable system can be exploited without interaction from any user. \\ \cline{2-3}  & Required (R) & Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited. \\ \hline \multirow{3}{*}{Scope (S)} & \multirow{2}{*}{Unchanged (U)} & An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same or both are managed by the same security authority. \\ \cline{2-3}  & \multirow{2}{*}{Changed (C)} & An exploited vulnerability can affect resources beyond the security scope managed by the security authority of the vulnerable component. \\ \hline \hline \end{tabular}
\end{table} 
Table A1: Possible values of the CVSS Base–Exploitability metrics.
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 2 Key-Enabled Technologies for Future Networks
### Software Defined Networking  
Software Defined Networking [10; 17] is a novel architecture in network communications. SDN proposes the separation between data and control planes in network devices and a centralized control of the network. In SDN, three well defined layers are established: infrastructure layer, control layer and application layer Figure 1.  
Figure 1: SDN vs. Traditional Architecture.  
Infrastructure layer (data plane) refers to the physical hardware and/or basic software components of the network devices responsible for sending the packets through the network. In other words, this component does not make logical decisions about the network behavior. For its part, the control plane analyzes the information received by the data plane and makes decisions about the network behavior. The control plane offers functionalities to the application layer, which uses these functions to establish high level network policies or services. To connect hardware devices with a SDN controller, a standardized protocol is needed. In practice, OpenFlow is the first SDN Open Protocol initiative adopted by the research community [18]. OpenFlow uses the most common hardware capabilities of network devices (flow tables) and opens those up. With this approach, the whole network behavior can be controlled and dynamically adapted according to user needs. Figure 1 shows the principal differences between traditional and the SDN architectures and their components. It is important to note that SDN also eliminates the complexity and the closed nature of traditional networks. Future Networks can take advantage of the main characteristics of SDN in order to enhance the control, management and customization of the network services. In the context of mobile networks, SDN will allow the management of mobile resources in an easy manner, providing a better QoS in an open environment.