esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The technique was further developed in [6] for spinning reserve assessment in a composite power system evaluation. A hybrid method proposed in [7] presents a probabilistic framework to evaluate the probabilities of finding the system in \"healthy\", \"marginal\", and \"at risk\" operating states that are classified using the N-1 deterministic criterion. This technique is utilized to assess spinning reserve requirements in wind connected power systems in [8]. The hybrid method is further modified in [9] for spinning reserve evaluation in a deregulated market. Probabilistic methods have also been developed to assess the risk associated with the allocation of the spinning reserve to the committed units with varying ramp rates and capacity constraints. The probability of failing to generate sufficient response capacity within the specified margin time to satisfy the load following a disturbance is termed as the response risk (RR) [10]. This index is utilized in [11] for advanced dispatch of spinning reserve. A hybrid method is presented in [12] for optimal distribution of spinning reserve in a generating system. Unit commitment and load dispatch are treated as two distinct operating tasks with separate risk criteria for decision making in published probabilistic methods for operational reliability. A UCR criterion-based unit commitment may restrict a desired dispatch with non-deployable spinning reserve due to ramp rate constraints of the generating units that are not considered in UCR evaluation. As the probability of power outage following a disturbance depends on risks associated with both of these tasks, a comprehensive risk index is necessary, and introducing such an index and developing a methodology for its application in operating decisions are the major contributions of this paper. A comprehensive response risk of committed generating units for a specified lead time is introduced in this paper and is designated as the committed generators' response risk (CGRR). An analytical method to evaluate the CGRR is presented and validated using a Monte Carlo simulation technique. An application of the new index and methodology is illustrated using the IEEE Reliability Test System (IEEE-RTS) [13], and the impact on operating reliability of important operating variables, such as number and size of committed units, unit ramp rates, regulating margin, and reserve distribution, are analyzed. The CGRR quantifies the comprehensive operating risk of the scheduled generation, considering all possible disturbances until further assistance is available to the system, and therefore provides a single reliability index to help operators in both the unit commitment and dispatch decisions of the generating units to meet the projected load in the short future time.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 5 Experimental Analysis
### Identification of Regimes
#### 5.1.2 Identification Results Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station ID** & **States** & **Regimes** & **Flow Rate (veh/5-min)** & **Occupancy (\%)** & **Speed (mi/h)** \\ \hline \multirow{4}{*}{400976} & State 1 & Regime 2 & 378.3687 & 0.0782 & 65.2341 \\  & State 2 & Regime 1 & 120.1265 & 0.0224 & 69.4739 \\  & State 3 & Regime 3 & 485.8626 & 0.1272 & 50.7289 \\  & State 4 & Regime 4 & 475.2812 & 0.2121 & 32.3619 \\  & State 5 & Regime 5 & 393.6340 & 0.3277 & 19.2915 \\ \hline \multirow{4}{*}{400081} & State 1 & Regime 2 & 391.3933 & 0.0774 & 68.2787 \\  & State 2 & Regime 1 & 103.8473 & 0.0214 & 69.3800 \\  & State 3 & Regime 3 & 543.7127 & 0.1219 & 59.3171 \\  & State 4 & Regime 4 & 504.4221 & 0.1744 & 43.5848 \\  & State 5 & Regime 5 & 334.6128 & 0.4000 & 17.5781 \\ \hline \multirow{4}{*}{400329} & State 1 & Regime 1 & 113.3446 & 0.0215 & 69.4238 \\  & State 2 & Regime 2 & 375.6891 & 0.0690 & 67.4715 \\  & State 3 & Regime 5 & 397.1494 & 0.2806 & 26.6234 \\  & State 4 & Regime 3 & 506.9161 & 0.1728 & 40.1897 \\  & State 5 & Regime 4 & 468.1100 & 0.2012 & 34.8338 \\ \hline \multirow{4}{*}{400825} & State 1 & Regime 1 & 104.1068 & 0.0226 & 72.2015 \\  & State 2 & Regime 2 & 371.7557 & 0.0727 & 69.6464 \\ \cline{1-1}  & State 3 & Regime 3 & 539.3112 & 0.1592 & 49.1119 \\ \cline{1-1}  & State 4 & Regime 5 & 397.3773 & 0.3206 & 19.9382 \\ \cline{1-1}  & State 5 & Regime 4 & 520.8873 & 0.2234 & 32.9221 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical means of traffic measures for each identified regime. Figure 4: Regime identification results associated with the four study stations.
## 3 Results and Discussion
### Adsorption Isotherns, Kinetic Study and Rate Law  
To analyze the adsorption isotherm associated with the metal uptake onto the adsorbent, the results presented in Section 3.1.5 were fitted to various adsorption isotherms using Equations (2)-(5). The results of these fits, as shown in Figure 7, indicate that the highest correlation coefficient (R\({}^{2}\) = 0.9667) was obtained for the Langmuir isotherm Type-II, being K\({}_{\rm L}\) estimated as 0.01 L/mg. Moreover, R\({}_{\rm L}\) is defined as:  
\[\rm R_{L}=\frac{1}{1+K_{L}[Cr]_{aq,0}} \tag{15}\]  
Figure 5: Effect of solution ionic strength on the Cr(VI) adsorption process. Aqueous phase: 0.01 g/L Cr(VI) at pH 3. Carbon dosage: 0.5 g/L. Temperature: 20 \({}^{\circ}\)C. Time: 5 h. Stirring speed: 1000 min\({}^{-1}\).  
Figure 6: Effect of the adsorbent dosage on the Cr(VI) adsorption process. Aqueous phase: 0.01 g/L Cr(VI) at pH 3. Temperature: 20 \({}^{\circ}\)C Time: 5 h. Stirring speed: 1000 min\({}^{-1}\).  
To determine the probable rate law governing the Cr(VI) adsorption, the experimental data were fitted to Equations (8)-(10), as shown in Figure 9. The best fit (R\({}^{2}\) = 0.9969) was found within the film-diffusion model, with a calculated rate constant of 0.0473 min\({}^{-1}\).  
Finally, from the results presented in Section 3.1.3, the thermodynamic parameters were calculated using Equations (14) and (15). The negative value obtained for the standard enthalpy (-71 kJ/mol) was indicative of the exothermic nature of the adsorption process. The negative calculated value for the entropy (-13 J/mol-K) indicated a decrease of the randomness in the Cr(VI) uptake onto the carbon. Despite this, in all cases, the calculated Gibbs free energy change was negative, revealing that the adsorption is a spontaneous process.
## 3 UAV 6G Networks
### 6G Application Requirements
#### 3.2.4 Aircraft Identification and Regulation  
An automated dependent surveillance broadcast (ADS-B) system is utilized for the identification of commercial aircrafts. Because of the increased number of UAVs in future applications, the usage of ADS-B may inundate the designated frequency band [58]. In view of this fact, a new scheme for the identification of commercial flights is deemed necessary. Radio waves are used to convey the remote ID. Registration, identification, tracking and regulation of aircraft all require reliable cellular network connectivity. In the same way, remote ID data can be utilized in the integration with future 6G networks. By effective monitoring of UAVs status data, positioning and related information, early warning along with subsequent remedial measures can be executed on the basis of monitoring parameters. Probable threats can also be contained after due analysis of the received data [76]. Low altitude authorization and notification capability (LAANC) for UAVs allows UAV operators to approach controlled airspace near the landing strips, air bases, Commercial  
Figure 10: Multimedia application of UAVs.  
airports through valid authorization based on real-time system for management of active geofencing [77]. The prominent UAV Regulations Domains are depicted in Figure 11.
# Current Context and Research Trends in Linear DC-DC Converters
## 8 Future Prospects for LDO Regulators in Power Supplies
### Supercapacitors as Lossless Voltage Droppers  
The SCALDO technique works on the simple principle that an ideal capacitor provides a voltage drop across its terminals, with the option to reuse that stored energy later. However, a small-value capacitor, such as an electrolytic of the order of microfarads, will fully charge in a short time if placed in series with an LDO and will block the current path.  
Within the past two decades, supercapacitors (SCs) with capacitance values from 1 to 100,000 F in a single cell have entered the market [69]. With the same canister size as electrolytic and film capacitors, SCs offer at least a million times bigger capacitance while their ESR is in the range of 0.1 m\(\Omega\) to 100 m\(\Omega\). [60]. With the capacitance in the order of farads, when they are placed in series with a resistive load such as a loaded LDO, they create charging circuits with a very long time constant, allowing the designer to use very slow speed switches for transferring the energy back to the LDO with ease. Commercial supercapacitor families today come in four different sub-categories, namely, (i) symmetrical supercapacitors, (ii) hybrid supercapacitors, (iii) battery capacitors, and (iv) Li supercapacitors. More details are given by [PERSON] [12]. Out of these types, symmetrical SCs come in values varying from 0.1 F to over 3000 F, and their ESR values vary from 100 m\(\Omega\) to 0.1 m\(\Omega\), respectively [54]. Table 5 provides a generalised range of specifications for symmetrical-type SCs, available in the commercial market [69; 70; 71].  
Typically, supercapacitors having a capacitance of 50 F or less have an ESR value between 20 m\(\Omega\) and 100 m\(\Omega\)[54]. If we compare these values with the MOSFETS used inside an LDO IC, they are comparable to, or lower than, the ON resistance of the MOSFET, and hence useful in connecting in series with an LDO as a lossless dropper.  
Figure 10: LTM8001 operated as a (**a**) single output [68]; and (**b**) dual outputs [68].
# Design of Power Splitters Based on Hybrid Plasmonic Waveguides
## 4 Conclusions  
We presented several new power splitters based on five HPWs. The propagation behaviors of SPPs in five HPWs were numerically simulated by the 3D FDTD method. The incident field was transferred from the middle waveguide to the waveguides on both sides due to the coupling between adjacent waveguides. The intensity distributions could be explained by the multimode interference of SPPs supermodes. According to the field intensity distribution of five HPWs, we reduced the length of some specific waveguides and designed a 1 \(\times\) 3 power splitter and a 1 \(\times\) 2 power splitter, which the ratios of the output power being 1:1:1 and 1:1, respectively.  
Conceptualization, X.S. and W.Y.; methodology, H.X.; software, X.C.; validation, W.Y., H.X. and X.C.; formal analysis, X.S.; investigation, X.S.; resources, H.X.; data curation, X.C.; writing--original draft preparation, X.S.; writing--review and editing, W.Y.; visualization, W.Y.; supervision, H.X.; project administration, X.C.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Doctoral Foundation of Henan University of Technology (2016 BS010), the Fundamental Research Funds for Henan Provincial Colleges and Universities in Henan University of Technology (2016 QNJH13), the Key Scientific and Technological Research Projects in Henan Province (192102210077), and the Natural Science Foundation of Shanghai (21 ZR1402200).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A New Image Oversampling Method Based on Influence Functions and Weights
## 4 Results and Discussion
### ImageNet-LT Dataset Experimental Results and Analysis  
In order to verify the performance of our scheme in more categories of imbalanced datasets, we conduct experiments on the ImageNet-LT dataset with SMOTE, OLTR, FSLwF, mixSMOTE, mixSMOTE+GRM, and the results of the comparison experiments are shown in Figure 8.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{Imbalanced CIFAR-10} \\ \hline Class & Airplane & Auto & Bird & Cat & Deer & Dog & Frog & Horse & Ship & Truck \\ \hline Number & 5000 & 3237 & 2096 & 1357 & 878 & 568 & 368 & 238 & 154 & 100 \\ \hline CB [24] & 92.92 & 96.30 & 79.20 & 75.13 & 82.46 & 69.95 & 75.06 & 69.18 & 73.62 & 66.81 \\ LDAM [25] & **96.97** & 98.51 & 82.94 & 74.75 & 82.88 & 69.08 & 78.57 & 69.94 & 65.35 & 66.04 \\ LDAM-DRW [25] & 94.83 & 97.84 & 82.61 & 72.32 & 85.30 & 73.00 & 82.03 & 76.71 & 75.86 & 72.48 \\ IB-Loss [19] & 92.20 & 96.26 & 81.35 & 66.61 & 85.72 & 76.46 & 81.76 & 75.92 & 79.97 & 81.14 \\ IB-Loss+Facal [19] & 90.96 & 96.13 & **91.72** & 69.08 & 82.00 & 75.73 & **85.28** & 77.53 & 80.23 & 78.65 \\ \hline DeepSMOTE [12] & 87.64 & 71.78 & 73.58 & 69.14 & 67.83 & 67.20 & 64.75 & 57.57 & 43.95 & 51.86 \\ MC-CCR [31] & 89.72 & 81.74 & 87.79 & 72.35 & 61.50 & 56.98 & 63.93 & 58.46 & 59.06 & 46.83 \\ MC-RBO [32] & 91.85 & 83.15 & 81.97 & 72.42 & 70.68 & 73.14 & 63.02 & 46.60 & 54.84 & 46.47 \\ \hline Ours method & 94.98 & **98.57** & 86.01 & **79.92** & **86.62** & **78.52** & 82.46 & **78.89** & **82.44** & **81.67** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy rates of various categories in the CIFAR-10 dataset of long-tail distribution for different schemes (%).  
The superiority of our scheme can be seen in Figure 8. The accuracy of the traditional SMOTE scheme on the ImageNet-LT dataset is only 34.1%, and the accuracy of the OLTR scheme on the dataset is only 35.6%. One reason is because the method is sensitive to hyperparameters, and an unreasonable hyperparameter will lead to low accuracy. In addition, since the memory module stores the features of the minority class samples, the model may overfit on the minority class samples, especially when the minority class data are limited. The accuracy of the mixSMOTE scheme, which improves on the SMOTE algorithm, is 40.2% because the method first removes a portion of the data in the majority class and then oversamples the minority class using a GAN-based method. This method removes a part of the data and reduces the diversity of the data, so it leads to lower accuracy. In contrast, the accuracy of our scheme is 44.35%, and the increase in accuracy is due to our deletion of noise and the increase in the diversity of the synthesized samples by a random function in the process of synthesizing the data.
# Evaluation of Factors Affecting the MgO-C Refractory Lining Degradation in a Basic Oxygen Furnace
## 3 Results
### Identification and Analysis of Factors Influencing the Wear of the MgO-C Refractory Lining of BOFs
#### 3.2.4. Slag Line Area (S9 Zone)  
Table 4 shows the most important factors that cause the periclase-carbon refractory lining in the slag line area (S9 zone) to wear.  
The effect of lime on wear in this area of the BOF refractory lining must be seen primarily in terms of the formation of excessive skulls, which must be removed with a 20-hole oxygen lance, causing oxidation of the MgO-C lining and thus excessive lining wear (Figure 15a,b). Lime consumption thus has an indirect effect on the wear of MgO-C in BOFs.  
Silicon is the acidic component of molten pig iron, which, after oxidation by oxygen, passes as an oxide into the early BOF slag, where it causes an increase in slag viscosity, resulting in increased slag clogging in the pores and cracks of the refractory lining (Figure 16a,b). The effect of SiO\({}_{2}\) on the slag is only temporary, as the addition of basic flux additives, such as dolomitic lime and lime, eliminates the effect of silicon and its compounds. As in the previous cases, the increased oxygen consumption causes oxidation of the refractory components (Figure 17a,b). In the case of the slag line area (S9 zone), oxygen acts on the refractory material mainly in the form of metal oxides.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Factor** & **Factor** & **Correlation** & **Factor during** & **Influence on MgO–C** \\
**Rank** & **Factor** & **Coefficient R** & **Campaign** & **Refactory Lining** \\ \hline
1 & Lime consumption & \(-0.2370\) & Figure 15a & Figure 15b \\
2 & Silicon in pig iron & \(-0.1811\) & Figure 16a & Figure 16b \\
3 & Overall oxygen consumption & \(-0.1795\) & Figure 17a & Figure 17b \\ \hline \end{tabular}
\end{table}
Table 4: Most important causes of BOF refractory lining wear in the slag line area (S9 zone).  
Figure 15: Graphical expression of the lime consumption influence. (**a**) Variation of the BOF refractory lining thickness in the slag line area (S9 zone) as a function of the lime consumption during the campaign. (**b**) Dependence of lime consumption on the refractory lining thickness in the slag line area (S9 zone).
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 4 Data Source and Preprocessing
### Evaluation Metrics  
The predictive performance of the model is evaluated using the following statistical metrics:  
Mean Absolute Error: As defined in Equation (19):  
\[MAE=\sum_{i=1}^{n}\left|y_{i}-\widehat{y}_{i}\right|/n \tag{19}\]  
where \(y_{i}\) is the actual value, \(\widehat{y}_{i}\) is the predicted value after denormalization, and \(n\) is the number of observations. A lower MAE indicates a model with higher predictive accuracy.  
Mean Squared Error: As defined in Equation (20):  
\[MSE=\sum_{i=1}^{n}\left(y_{i}-\widehat{y}_{i}\right)^{2} \tag{20}\]  
MSE emphasizes larger errors more than smaller ones because it squares each difference, providing a stringent metric of accuracy.  
Coefficient of Determination (R\({}^{2}\)): As described in Equation (21):  
\[R^{2}=1-\left(\sum_{i=1}^{n}\left(y_{i}-\widehat{y}_{i}\right)^{2}/\sum_{i=1} ^{n}\left(y_{i}-\overline{y}_{i}\right)^{2}\right) \tag{21}\]  
where \(\overline{y}_{i}\) is the mean of the observed data. The values of \(R^{2}\) range from 0 to 1. Values closer to 1 indicate that the model can effectively explain the variance observed in the data, signifying a high degree of fit between the model predictions and the actual observations.  
Pearson Correlation Coefficient (r): As described in Equation (22):  
\[\text{r}=\frac{\sum_{i=1}^{n}\left(y_{i}-\overline{y}_{i}\right)\cdot\left( \widehat{y}_{i}-\overline{\widehat{y}_{i}}\right)}{\sqrt{\sum_{i=1}^{n}\left( y_{i}-\overline{y}_{i}\right)^{2}}\cdot\sqrt{\sum_{i=1}^{n}\left( \widehat{y}_{i}-\overline{\widehat{y}_{i}}\right)^{2}}} \tag{22}\]  
where \(\overline{\widehat{y}_{i}}\) represents the means of the predicted data after denormalization. The values of \(r\) range from \(-1\) to \(1\). A value of \(r\) close to \(1\) indicates a strong positive linear relationship between the predictions and actual outcomes, while a value close to \(-1\) indicates a strong negative linear relationship. A value around 0 suggests no linear correlation between the predictions and the actual outcomes.
## 2 Background and Related Work
### Related Work  
#### 2.2.1 V2X Simulation Research  
[PERSON] et al. introduce the C-ITS SIM developed by the Czech Technical University (CTU) in Prague [28]. C-ITS infrastructures are under expansion in Europe as a part of the C-Roads project in Europe, and [PERSON] et al. propose simulations for testing the interoperability between the C-ITS infrastructure and vehicles and testing the validity of messages. A C-ITS unit that can communicate with the C-ITS infrastructure from the driving vehicle is installed, and transmitted/received data can be checked using the user interface on a laptop. Tests are provided through the receiving mode, in which messages are received from the surrounding C-ITS infrastructure, and the broadcasting mode sends messages to the surrounding C-ITS infrastructure. In all communications, the ITS-G5 standard [16], which is Europe's V2X standard, is used, with ETSI TS 103 097 [29] and ETSI 102 941 [30], which are standards for security. However, since this simulation tests only the interoperability and the validity of messages, it is close to a conformance test, and it is not appropriate to perform tests for security threats while driving at the application level.  
Figure 6: Environment configuration for parallel testing proposed by [PERSON] et al. Application level security evaluation should be performed in an environment where a real device is mapped to a virtual test environment, as shown the actual test vehicle in this figure.  
Veins is an open-source vehicle network simulator [31]. Veins performs simulations based on the event-based network simulator OMNeT++ and the SUMO road traffic simulator. In particular, Veins discloses a misbehavior dataset, VeReMi (Vehicular Reference Misbehavior Dataset), using Veins simulations [32]. The authors of [32] state that misbehavior detection aims to discard malicious messages for other vehicles by analyzing application data. However, the authors of [32] present the limitations of VeReMi: the implemented misbehavior type cannot represent all possible attack types in V2X communication. Therefore, it is impossible to analyze the impact of various attack types in a single simulation data of VeReMi. Furthermore, since attack detection is performed only to detect V2X communications that are not interactive, it is difficult to apply a solution for this problem and perform a security evaluation. This can be solved by implementing a message handler for the V2X messages received. If there is data with which misbehavior detection has been performed, a handler that can process them is implemented, and a security evaluation of the handler can be performed.
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
## 3 Performance Analysis
### Statistical Properties of Channels
Through utilizing [37], we can obtain the confluent hyper-geometric function  
\[{}_{1}F_{1}\left(m_{q};1;\frac{\delta_{q}x}{\bar{\gamma}_{q}}\right)=e^{\frac{ \delta_{q}x}{\bar{\gamma}_{q}}}\sum_{i=0}^{m_{q}-1}\frac{\left(1-m_{q}\right)_ {i}\left(-\delta_{q}\right)^{i}}{\left(i\right)^{2}\left(\bar{\gamma}_{q} \right)^{i}}x^{i}. \tag{13}\]  
Thus, the above Formula (12) is rewritten as  
\[f_{\gamma_{q}}(x)=\alpha_{q}\sum_{i=0}^{m_{q}-1}\frac{\left(1-m_{q}\right)_{i }\left(-\delta_{q}\right)^{i}}{\left(i!\right)^{2}\left(\bar{\gamma}_{q} \right)^{i+1}}x^{i}e^{\left(-\Delta_{q}x\right)}, \tag{14}\]  
and  
\[F_{\gamma_{q}}(x)=1-\alpha_{q}\sum_{i=0}^{m_{q}-1}\sum_{t=0}^{i}\frac{\left(1 -m_{q}\right)_{i}\left(-\delta_{q}\right)^{i}}{i!\left(\bar{\gamma}_{q} \right)^{i+1}}t!\Delta_{q}{}^{i-t+1}x^{i}e^{-\Delta_{q}x}, \tag{15}\]  
where \(\left(\cdot\right)_{i}\) is a Pochhammer function [37], and \(\Delta_{q}=\left(\frac{\beta_{q}-\delta_{q}}{\bar{\gamma}_{q}}\right)\).