esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Case Study 2: Addressing Pigment Variability
There is no change in \(D_{eff}\), both are 12 \(\upmu\)m. The non-algal optical constituents are modelled with \(a_{\beta}(400)=0.07*[Ch_{a}]^{0.75}\), and \(b_{\textit{bmap}}(550)=0.005\) m\({}^{-1}\). This applies equally to accessory pigments other than phycoerythrin absorbing in spectral regions affected by phytoplankton scatter and/or by variability in non-algal scatter. This speaks to the importance of both the magnitude of the change in \(b_{b}\phi(\lambda)/a\phi(\lambda)\) and the proportional contribution of \(b_{b}\phi(\lambda)/a\phi(\lambda)\) to the total \(b_{b}(\lambda)/a(\lambda)\) when evaluating the potential for accessing these signals from \(R_{rs}\) (see Figure 2). This result also implies that there are cases where the \(\delta D_{eff}\) signal is augmented by pigment changes, for example when moving from small fucoxanthin- or peridinin-dominated cells to large phycoerythrin-dominated cells, as the optical effect of reduction in backscatter around 560-570 nm by large cells will be enhanced by additional pigment absorption in the \(\delta R_{rs}\) signal. However, there remains a complex optical relationship with biomass, and this effect needs to be properly accounted for in order to accurately detect the augmented signal. The EAP approach to pigments does not address the extent to which fine spectral resolution accessory pigment absorption features persist in \(R_{rs}\), nor their retrieval from hyperspectral radiometry. The intention with the EAP model is to demonstrate the dependence on biomass to retrieve absorption features, and the inherent signal ambiguity as the contrasting optical effects of \(b_{b}\phi(\lambda)\) and \(a\phi(\lambda)\) interact to form the phytoplankton signal within the \(R_{rs}\). It is worth noting that hyperspectral radiometry will not overcome the inherent signal-related constraints identified in this study.
## 2 Materials and Methods
### Performance Evaluation of SM-OGFC
#### 2.4.5 Permeability Test and Scanning Electronic Microscopy Test  
OGFC mixture is a special type of hot mix asphalt characterized by high interconnected air voids and a coarse, granular skeleton, with proper stone-on-stone contact. The presence of a largerpercentage of internal air voids leads to a relatively highly porous structure of OGFC mixes, which helps in quick and effective removal of surface water from the pavement. Thus, the permeability capacity of SM-OGFC determines the performance of permeable pavements. The permeability capacity of SM-OGFC is evaluated via permeability test. The laboratory permeability test is divided into constant head permeability test and falling head permeability test based on Darcy's law. The constant head permeability test is generally applied to measure the permeability coefficient of high permeability material and the falling head permeability test is applicable to low permeability material [20; 21]. Therefore, the constant head permeability test was conducted to evaluate the permeability coefficient of SM-OGFC, in accordance with the Specifications for Design of Highway Asphalt Pavement (JTG D50-2006), and the permeability coefficient (\(K\)) is obtained by the Equation (1):  
\[K_{20^{\circ}\mathrm{C}}=\frac{QL}{At\Delta t}. \tag{1}\]  
\(Q\): The quality of the water permeating through the specimen during \(t\) time (cm\({}^{3}\))  
\(L\): Infiltration length (cm)  
\(A\): Cross-sectional area of the specimen (cm\({}^{2}\))  
\(t\): Infiltration time (s)  
\(\Delta t\): Water head difference (cm)  
Scanning Electron Microscopy test (SEM) was used to magnify the sample and observe its microscopic structure. In this research study, Hitachi SU8000 (Tianmei.co, Tokyo, Japan) scanning electronic microscopy was utilized to observe the micro-structure of OSW particles.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 5 Experimental Results
### Stacking Ensemble with Feature Selection  
The stacking algorithm described in this study uses DT, RF, and KNN as regression models. Hence, the top 5 or 10 features were input into the stacking algorithm by counting the frequency of the features selected for each classification algorithm through feature selection methods such as feature importance, RFE, RFECV, and SFS. A list of the 5 or 10 features used in the stacking algorithm is presented in Table 9. This shows that the features present in the five-feature subset were also present in the 10-feature subset. Table 10 shows the multiclass classification evaluation results using the five or ten features shown in Table 9. Based on three evaluation metrics (i.e., accuracy, precision, and F1-score), the performance was better when using 10 features than when using 5 features. When using 10 features, the accuracy decreased by 0.08%, whereas the number of features decreased by 82.82%. This indicates that the feature selection method based on the frequency of features selected for each classification algorithm used as the regression model is meaningful in the stacking algorithm.
# Risk Assessment of Fixed Obstacles along Roads
## 1 Basic Knowledge Related to the Issue
One similar topic related to the risk assessment of fixed obstacles, specifically trees, was involved in research at the at the Gdansk University of Technology, where they demonstrated the correlation between roadways and accidents using an accident database [15; 16]. It was found that the density of road casualties decreases when there are crash barriers and hard shoulders on the section, while trees placed within 3.5 m of the road also have a crucial influence. The use of mathematical models in safety assessment is shown, for example, in an article related to the assessment of traffic accidents near crash barriers. The output of this work is the determination of the risk scale of barrier deficits and the proof of the extent to which traffic deficits affect traffic accidents [17]. Within the Czech Republic road safety inspection methodology certified by the Ministry of Transport of the Czech Republic [18], identified transport safety deficits are assigned a severity on a three-level scale (low, medium, and high risk of deficit severity). However, the evaluation of safety deficits related to the safety inspections carried out is highly influenced by the subjectivity of the evaluators. The auditor, as a person creating an expert assessment of road safety, determines the level of risk based on his expert opinion, without the possibility of verifying his judgment with a validated mathematical tool. The evaluator does not have a detailed standardized metric. On the basis of such an assessment of the level of road safety, it is not possible to make a fully objective decision on the riskiness of a given deficit. For this reason, it is not even possible for road managers to focus on eliminating the riskiest deficits and thus to use the economic and human resources at their disposal more efficiently for the benefit of road safety. The aim of this research is to develop and validate a methodological approach that will enable the standardized evaluation of the level of severity of specific exposures of fixed obstacles in road traffic located in rural area sections along the most important roads in the Czech Republic. The risk will be determined for the crew of a passenger vehicle. In calculating the risk level, only parameters related to the traffic infrastructure will be analyzed with respect to its construction design. The effects of other road safety factors (e.g., human factors, technical condition of the vehicle, weather conditions or traffic density) will not be included in the calculations. At the same time, this work will only consider fixed obstacles whose shape and structure are only subject to elastic deformation after a vehicle impact (the effect of plastic deformation is considered only in the form of surface damage; however, there will be no significant disruption of the structure supporting the object).
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Methodology
In addition to generating mean value maps for 6 h, maps were also constructed for 5-min duration during scintillation period. Maps containing data for five minutes with scintillation are generated in order to compare parameters at more specific time.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 1 Introduction
The term long-term dependency refers to the sequence, whose desired/current output values (prediction results) depend on long-sequence of previous input values rather than the only single previous input value. Motivated by the success of RNN to learn and predict long-term dependent sequence values in various applications [11; 14; 16], we use RNN to extract the pattern of packet arrival time from real wireless traffic traces and to predict the values of the next packet arrival time. Based on the prediction results, we propose an AI-DRX algorithm that works on a ten-state DRX model to enable energy saving. AI-DRX for multiple beam communications in 5G network saves the UE energy by enabling dynamic short and long sleep cycles, respectively. To be more specific, the following are our key contributions to save UE energy in multiple beam communications scenario of 5G networks. * We perform the training of the LSTM neural network on wireless traffic. During training, LSTM network extracts the packet arrival time pattern from the wireless traffic trace. The prediction results show that the trained model predicts with minimum RMSE of 5 ms on random test set from trace 1 and 6 ms on random test set from trace 2, respectively. * We devise DRX as a ten-state model. * We propose an artificial intelligence based DRX mechanism for multiple beam communications in 5G networks. We suggest AI-DRX algorithm using ten state model to enable dynamic short or long sleep cycles, depending on the prediction results. * We evaluate the performance of AI-DRX in terms of energy efficiency and mean delay. AI-DRX achieves the energy efficiency of 59% on trace 1 and 95% on trace 2, respectively; while considering the mean delay requirements of different services. The remaining paper is organized as follows. In Section 2 we present an overview of existing DRX mechanism and introduce the RNN with focus on LSTM neural network. Section 3 proposes AI-DRX algorithm with a ten-state DRX model, to enable dynamic short or long sleep cycles in multiple beams communications of the 5G network. Section 4 presents the performance analysis of AI-DRX in terms of energy efficiency and mean delay. Finally, we conclude our work in Section 5.
# Structural Evaluation of Cable Bolts under Static Loading
## 2 Literature Review
### Testing of Ground Support Systems
#### 2.3.2 Drop Test  
The drop test is another dynamic experimental test method, which simulates dynamic loading patterns by dropping a known mass (with controlled drop speed and height) onto the support elements under testing [13] In general, drop tests are relatively simple to perform, provide the opportunity to obtain repeatable results and are suitable for quality control and comparative testing. However, impact and direct loading do not represent the true nature of rock burst loading. The lateral continuity of reinforcement support cannot be appropriately represented and in situ stresses that occur in burst-prone grounds cannot be accounted for accurately [11]. Globally, many different drop test facilities exist which all employ varying dynamic loading mechanisms.  
The SRK Drop Weight Test Facility was developed to determine surface support dynamic loading capacities in South African mines, with the capability of inputting 70 kJ of energy [35]. The advantages of this facility include its relatively inexpensive set-up cost and its ability to undergo consistent and repeatable tests due to its configuration. However, after further analysis, it was concluded that transmission losses of up to 50% input energy occurred [36]. The facility is no longer in operation.  
The CANMET Drop Test Facility was developed in Canada, which functions by dropping a hammer, that can have a weight of up to three tonnes, from a height of two metres onto the test element, directly applying a dynamic load [36]. The testing rig can dynamically input up to 60 kJ of energy, and thus is used by suppliers to test existing and experiment new products dynamically.  
The WASM Dynamic Testing Facility was developed in Western Australia and involves dropping integrated rock mass and supporting elements on buffers to generate momentum energy transfer; Ref. [37] verified that it can be utilised to determine the energy absorptioncapacity of support elements. The WASM Testing rig is well instrumented as it is equipped with load cells, motion sensors and high-speed digital video cameras utilised for post processing. Due to its ability to test reinforcement and support systems unified, the WASM Testing facility is very active and used in the more recent publications [35].
## 3 Results and Discussion
### Medical Aspects
#### 3.3.2. Biofunctionalization Approaches in Foot Arthrodesis
Analyzing the general literature in this field has shown that surface modifications, such as micro-arc oxidation combined with hydrothermal treatment, significantly improve the cytocompatibility of 3D-printed titanium implants [55]. Additionally, mechanical surface treatment methods, chemical modification approaches, or bioconvergence modification technologies can be applied in practice to change the morphology of the implant, making it conducive to cell attachment and proliferation and fostering better integration with the surrounding bone tissue [56; 57]. However, none of the reviewed papers specifically discussed the use of coatings or additional surface treatments for the used 3D-printed implants. Instead, as previously mentioned, they focused on the porous design [17; 21; 37], integration with biologics like bone grafts [15; 38; 40], and bone morphogenetic proteins (BMP) [27].
# Detecting Underground Geospatial Features for Protection of Rights in 3D Space: Korean Cases
## 4 Discussion
### Building Data on the Rights Relation between Ground and Underground Space
However, in the case of individual stores, a separate rights information item was added as shown in Figure 10 for efficient information management, since the rights relation varies according to the lease agreement for each store, and it is common for such information to change constantly. To systematically manage underground space registration information through public ledgers in the future, data modeling and standardization of registration information are required. Therefore, in this study, a data model for underground space registration was built using the Unified Modeling Language (UML), as shown in Figure 11. The underground shopping center class is the superclass of the feature model for registering underground spaces, and the store and management facility classes are the subclasses of the underground shopping center class. The underground shopping center class consists of the object code, shopping center administrative information, shopping center operation information, and shopping center spatial information; the store class consists of the store administrative information, store operation information, store spatial information, and store rights information; the management facility class consists of the management facility code and management facility information. Among these, object code and management facility code are defined in the code lists, whereas the rest are defined in the data type lists. The object code and management facility code are managed by classifying each item (management operation facility, convenience facility, and other facilities) by the code number. The attribute information defined in the data type list defines the items required for registration and operation of each underground shopping center and individual stores by dividing them into administrative information, operation information, spatial information, and rights information. The attribute specification that describes each item is presented in Appendix A.  
Figure 9: Rights status information generation for underground shopping center. Figure 11: Data model for underground space registration information. Figure 10: Rights status information generation for individual stores.
## 4 Results
### Dataset Description  
**RoEduNet-SIMARGL2021 Dataset [35]:** Sourced from the SIMARGL project funded by the European Union, this dataset comprises realistic network traffic and features derived from actual traffic flows. It adheres to the Netflow protocol for organization.  
The dataset follows a structured data schema based on Netflow [65], a network protocol devised by CISCO for the collection and analysis of network flows.  
**CICIDS-2017 Dataset [33]:** Developed by the Canadian Institute for Cybersecurity, this benchmark dataset for intrusion detection includes six distinct attack scenarios: Brute Force, heartbleed, botnet, DoS, PortScan, Web attack, and Infiltration. It simulates realistic background traffic using the B-Profile system [66].  
**NSL-KDD Dataset [34]:** An improvement on the original KDD dataset, created by the University of New Brunswick and the National Research Council of Canada, the NSL-KDD dataset features a diverse array of attack types and normal instances, organized according to a data schema that processes raw traffic. This dataset is extensively utilized for testing intrusion detection systems [67,68].  
**Summary and Statistics of the Datasets:** Detailed statistics for each dataset, including its size, the variety of attack types, and the distribution of attack labels, can be found in Table 5. This table offers a comprehensive view of the datasets' compositions and features.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 3 Conditional Volatility Models
### Generalized Autoregressive Conditional Heteroscedasticity Models
Some weaknesses of an ARCH(\(p\)) model are that the order \(p\) may be high (leading to the need for estimating many parameters), that it is likely to overpredict the volatility, that it does not capture the excess kurtosis seen in financial time series, and that it assumes that positive and negative shocks produce the same effect on volatility [68]. ARCH(\(p\)) models also have high volatility in short bursts, or short volatility persistence, while GARCH(\(p,q\)) models allow for more persistent volatility [56]. GARCH processes also tend to have heavy tails and GARCH models also tend to be more parsimonious, and the simplest form, GARCH(\(1,1\)), has been successful in predicting conditional volatility [56; 69]. GARCH models may be used to model the innovations of ARMA models (ARMA-GARCH) as well. For a GARCH(\(1,1\)) model, values of \(b_{1}\) closer to \(1\) produce a high correlation between \(\sigma_{t}^{2}\) and \(\sigma_{t-1}^{2}\) which allows for longer persistence of volatility than an ARCH(1) model [68]. The speed with which the process reverts to the unconditional variance (persistence) is governed by the magnitude of \(a_{1}+b_{1}\)[63]. The half-life (HL) of a volatility shock, a measure of the length of time it takes for a shock to decrease by one-half, is given by \(HL=\ln(0.5)/\ln(a_{1}+b_{1})\), and as \(a_{1}+b_{1}\) approaches \(1\), the larger the half-life [63]. Models for which \(a_{1}<b_{1}\) also imply longer volatility persistence [56]. These results can be extended to higher order GARCH(\(p,q\)) models [63].
# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
###### Abstract
The imbalance problem in the data was processed by the SMOTE algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, the XGBoost algorithm was used to solve this multi-classification problem. When the steam turbines were detected by the trained model in this paper, the prediction information fed back to the thermal power plant immediately. This early warning information for a predictive failure will give the thermal power plant enough time to deal with the problems in advance. During this time, the plant could use other methods to reasonably determine when to take action. Compared with the above literature, the differences between the proposed method and other studies are shown in Table 1. The main objective of the proposed method was to develop a novel procedure for actual power plant data. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Method** & **Other Literatures** \\ \hline Data set source & Actual data from the actual plant & Experimental data or numerical simulation data \\ Data length & Larger (months or even years) & Smaller (hours or days) \\ Fault label & Partly missing or being blurred & Identified by the experiment \\ Fault verification & Based on real faults in the plant & Based on simulated faults \\ Iterative strategy for research & Determined by the actual operation of the plant & Unable to iterate \\ Significance of research & Solving practical problems & Continuous improvement of research algorithms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research comparison of the proposed method. The rest of this paper is organized as follows. Section 2 discusses methods. Section 2.1 introduces and discusses the performance indicator extraction based on t-SNE and K-means. Section 2.2 introduces the imbalanced data recognition model based on SMOTE and XGBoost. A model evaluation method is presented in Section 2.3. Section 3 presents the data experiment and results and discussion of the proposed method. Finally, conclusions are drawn in Section 4.
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 3 Assessment Criteria Employed to Judge the Effectiveness of MCM Approaches  
The evaluation of MCM schemes involves an analysis of factors such as the data arrangement, which directly influences spectral efficiency (SE), in addition to power efficiency, computational complexity, BER, and PAPR [2; 36; 37; 38; 39].