esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Heatwave Responses of VODs and NDVI  
In our previous work (Kazakhstan, 2016; Kazakhstan, 2016), we have noted the 2010 heatwave affecting Russia and Kazakhstan (Kazakhstan, 2016; Kazakhstan, 2016) and the 2007 heatwave affecting Ukraine (Ukraine, 2016). Here, the heatwave effects on croplands were revealed by the time series of VODs and NDVIs, the relative comparison of the two LSP measures, and the model derived phenometrics PH\({}_{\text{NDVI}}\) and NAHT. During the heatwave years, the VODs and NDVIs were well below the average VOD and NDVI for non-heatwave years, as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **n** & **Intercept** & **Slope** & **R\({}^{2}\)** & **SEE \({}^{*}\)** \\ \hline
6.925 & 48 & \(-\)0.1065 & 1.7352 & 0.770 & 0.1401 \\
10.65 & 48 & \(-\)0.0988 & 2.0079 & 0.844 & 0.1275 \\
18.7 & 48 & 0.1728 & 1.6652 & 0.781 & 0.1298 \\ \hline \hline \end{tabular} \({}^{*}\) indicates standard error of the estimate.  
\end{table}
Table 2: Analysis of covariance (ANCOVA) to test whether slopes resulting from linear fit of PH\({}_{\text{NDVI}}\) and PH\({}_{\text{VDO}}\) differed significantly by microwave frequency. With Bonferroni adjustment, the p\({}_{\text{CRIT}}\) is 0.0167 (=0.05/3).  
Figure 13: Scatterplot and linear fit between the PH\({}_{\text{NDVI}}\) derived from the CxQ model and the PH\({}_{\text{NDVI}}\) determined by the maximum-value method from the same MODIS dataset for 2003–2010. The linear fit for the PH\({}_{\text{NDVI}}\) from the two approaches of the same dataset was high with R\({}^{2}\) = 0.86.  
croplands were negatively impacted by the excess heatwave (Figure 14). Heatwave years were more evident in the VODs compared to their counterpart NDVIs, since VOD strongly responds to vegetation water content. The difference of the VOD data during the heatwave years to the average non-heatwave years VOD was found significantly different (\(p<0.001\)) from that of the similarly analyzed NDVI data (Figure 14). The CxQ model derived NAHT as a function of PH\({}_{\text{NDVI}}\) for sites affected by the 2010 heatwave was lower relative to the other non-heatwave years (Figure 15a). The difference between the PH\({}_{\text{NDVI}}\) and NAHT for the 2010 data was found significantly different (\(p<0.001\)) from the average of the 2003-2009 data (Figure 15b). The marker in the upper-right corner of figure 15a is the mixed forest site in Mari El, Russia, showing little effect of the heatwave at that site.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 4 Simulation Results  
The simulation results presented in this section demonstrate the effectiveness of UAV trajectory optimization in enhancing the secrecy rate of the UAV-aided IoT secure communication system. The simulation consists of two parts. First, we present several UAVtrajectories under different \(v_{max}\) and show how the UAV's instantaneous speed changes over time. In the second part, we analyze the impact of UAV mobility constraints on the secrecy rate and validate the convergence of the secrecy rate.  
In the simulation, the UAV's transmit power is set to \(p=$20\,\mathrm{d}\mathrm{B}\mathrm{m}$\) and the power spectral density of the AWGN is fixed at \(-$174\,\mathrm{d}\mathrm{B}\mathrm{m}$/\mathrm{H}\mathrm{z}$\). The communication bandwidth is defined as \(1\,\mathrm{M}\mathrm{H}\mathrm{z}$\). The UAV operates at a constant altitude of \(100\,\mathrm{m}\) throughout a total flight duration of \(100\,\mathrm{s}\). Additionally, the UAV's acceleration is assumed to be \(2\,\mathrm{m}\mathrm{/}\mathrm{s}^{2}\). The horizontal coordinates of the three eavesdroppers are fixed at \(\mathbf{W}_{1}=[300,50]^{T}\), \(\mathbf{W}_{2}=[50,100]^{T}\), and \(\mathbf{W}_{3}=[20,250]^{T}\), respectively. The communication destination is fixed at \(\mathbf{W}_{g}=[200,200]^{T}\), and UAV's initial and final horizontal positions are specified as \(\mathbf{U}_{q}[I]=[0,0]^{T}\) and \(\mathbf{U}_{q}[F]=[400,0]^{T}\), respectively.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 3 Related Works
### Identification of the NLOS and MP Conditions in the Literature Based on Machine Learning Techniques
In contrast with the above-mentioned approaches, we performed a direct identification of the multi-class classification for UWB ranging systems in this paper. The classified classes were the LOS, NLOS, and MP conditions. Based on the measurement data, we performed three ML models (Section 5),namely SVM, RF, and MLP, to compare their performances (Sections 6 and 7). The experimental research data utilized in this paper [25] are provided on a public archive for reproducible results and further exploration.
## 2 Methods  
During offshore oceanic sailing, GPS measurements cannot be corrected using on land reference receivers, or a GNSS (Global Navigation Satellite System) network, since the distance between rovers and reference station should not be larger than few tens of kilometers. To solve this problem, in the present work, two GPS data processing techniques have been applied, by using a novel approach. The processed data provided by the GPS receivers have been used to compute the boat motion and to estimate the waves properties faced by the sailboat during its navigation around the world. This sections aims at detailing the proposed methods.
## 4 Blackout Analysis
### Scenarios with Different Initial Condition before the Event on 8 January 2021
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **How the Incident on 8 January 2021 affected the Greek Grid** \\ \hline
**Stage** & **Location** & **Events** \\ \hline
**Preconditions** & & Stable operating conditions that adhere to the predefined grid security constraints (N-1 criterion) \\
**Preconditions** & & Total load: 5500 MW \\
**Preconditions** & & Total scheduled export (AC interconnections): 1000 MW (without 400 MW of the DC interconnection with Italy, depicted with green vector) \\
**Normal cross-border power flows from south to north \\ \hline
**Cascade of events** & & Coupler tripping in Substation Ernestinovo (Croatia) Continental Europe has been split into two different areas Greece is in the region with over-frequency \\
**(steady state)** & & Total import (AC interconnections): 800 MW (change of the \\
**(steady state)** & & direction of the power flow to the neighboring countries) \\ \hline
**Restoration** & & Stable operating conditions after synchronization of the two European regions \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline
**Scenario: Split of Europe in two regions with different initial conditions in the Greek Power Transmission System than these on 8 January 2021** \\ \hline
**Stage** & **Location** & **Events** \\ \hline
**Preconditions** & & Stable operating conditions that adhere to the predefined grid security constraints (N-1 criterion) \\
**Preconditions** & & Total load: 8500 MW \\
**Preconditions** & & Total scheduled import (AC interconnections): 2500 MW (without \\  & & the amount of the DC interconnection with Italy, depicted with green vector) \\
**Normal power flows from north to south \\ \hline
**Cascade of events** & & Coupler tripping in Substation Ernestinovo (Croatia) Continental Europe has been split into two different areas Greece is in the region with over-frequency \\
**(steady state)** & & Total import (AC interconnections): 3500 MW (change of the power flow direction to the neighboring countries) \\ \hline
**Cascading line** & & Trip of the heavy loaded 400 kV line Greece–Macedonia (at 80\% of the maximum capacity) Recclosing without success because of a permanent error on the power line \\
**(steady state)** & & The power flow is taken over by the neighboring lines and become overloaded (over 110\% of its maximum capacity) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sequence of events during the European split in Greece on 8 January 2021 and a hypothetical scenario.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Feature Extraction  
It has been found that both time and frequency analysis provide informative features for speech recognition [16]. Two of the widely used time and frequency ways audio signals are processed are the Mel spectrogram [17] and Mel frequency cepstral coefficients [18]. These features that are extracted from the audio signals will eventually feed the convolutional neural networks to conduct the classification task. In the following, we provide a brief explanation of how these features are computed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Samples** & **Percentage** \\ \hline BN & 20,645 & 82.04\% \\ \hline SN & 1228 & 4.88\% \\ \hline AN & 1771 & 7.04\% \\ \hline HN & 1521 & 6.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Class distribution of the samples in the database.  
Figure 2: Diagram for data collection points spatial distribution.
# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
## 6 Conclusions  
The RV retarder is the core part of an industrial robot. With the increasing demand for industrial robot, it is very important to predict the failure of RV retarders in actual production. To solve the above problems, a fault diagnosis model of an RV reducer based on EEMD-MPA-KELM was proposed in this paper. After modeling and research, the following conclusions were obtained:  
(1) The data measured on the RV reducer test platform is decomposed by EEMD to obtain the optimal special collection. The regularization coefficient C of KELM and the kernel function parameter \(\sigma\) are optimized with MPA to improve the prediction accuracy and stability of the KELM. The feature set is imported into the MPA-KELM model as input for fault identification. Compared with ELM, KEML, EEMD-GA-KELM and EEMD-PSO  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Accuracy Rate** & **Time** \\ \hline KELM & 44.67\% & 6.69 s \\ EEMD-KELM & 52.33\% & 6.58 s \\ EEMD-GA-KELM & 59.33\% & 5.62 s \\ EEMD-PSO-KELM & 78.67\% & 4.84 s \\ EEMD-MPA-KELM & 90.33\% & 4.58 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy and running time statistics of each model after 30 runs.  
Figure 12: Comparison of accuracy of 30 runs of various models.  
KELM models, EEMD-MPA-KELM's RV retarder fault diagnosis and classification model, this model is faster, more accurate and more stable.  
(2) Timely and accurate judgment of the operation state of the RV reducer of industrial robots is conducive to the timely maintenance of the RV reducer, providing guarantee for the accuracy of the RV reducer test data, so as to help the RV reducer manufacturers to improve production efficiency and reduce economic losses.  
Conceptualization, Z.T.; Data curation, L.G.; Formal analysis, Y.L.; Funding acquisition, L.G. and Z.Z.; Investigation, Z.T.; Methodology, Z.T.; Project administration, Y.L.; Software, X.W.; Supervision, Y.L.; Validation, X.W. and Z.Z.; Visualization, Z.T.; Writing--review & editing, X.W.; Writing--original draft, X.W. and Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key research projects supported by the National Natural Science Foundation of China, grant number No. 92067205; Key Research and Development Project of Anhui Province, grant number No. 2022a05020035; Major science and technology project of Anhui Province, grant number No. 202103a05020022; HFIPS Director's Fund, grant number No. YZJJQY202305; Supported by Anhui Province Intelligent Mine Technology and Equipment Engineering Laboratory Open Fund, grant number No. AIMTEEL202201; Key Project of Scientific Research of Anhui Provincial Education Department, China, grant number No. 2022 AH050995.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The design of the proposed protocol is carried out by first representing the WSN that consists of a set of sensor nodes and a sink as a weighted graph. After the network initialization, the sink constructs the possible MSTs that are used as the routing tables (RTs) using a GA. The construction of the MSTs is repeated by the sink after the death of sensor node(s) during the round of data transmission until the network graph is disconnected. The sink then uses LSPI to learn the optimal or near-optimal MST(s) during the round of data transmission so as to maximize the network lifetime while minimizing the network energy consumption. The performance analysis of the CRPLEOAGASPI is carried out by means of a simulation using the network lifetime, the number of alive nodes (NAN), network energy consumption, and computation time as performance metrics. The novelty of this paper is as follows:  
1. Formulation of a reward function for the joint optimization of the lifetime and energy consumption for WSNs. 2. Design of a centralized routing protocol using a GA and an LSPI for WSNs to improve their lifetimes and energy consumption performances. The remaining of the paper is presented as follows: Section 2 is the literature review. Section 3 explains the design of the CRPLEOAGASPI. Section 4 is the simulation and discussion of results, and Section 5 is the conclusion.
## 3 Results and Discussion  
### Mechanism of Interaction of Dislocation with Inclusion in MD  
Displacement of the upper boundary of the system along the \(x\)-axis leads to the appearance of shear stresses (Figure 4) at the level of several tens of MPa (this fact was described in detail in the previous work [34]). The occurrence of shear stresses in the system brings the dislocation into motion. After a sufficiently short free slip, the interaction between dislocation and \(\theta^{\prime}\) precipitate begins, and dislocation stops on the precipitate. A sharp decrease in the rate of plastic deformation provokes the essential increment in shear stresses in the system up to the level of 300-800 MPa, depending on the temperature and inter-precipitate distance. The details of the process of the first dislocation-precipitate interaction are shown in Figure 5. After approaching the inclusion by dislocation (Figure 5, 20 ps), the dislocation is pulled into the elastic field of inclusion that can be seen as the bending of dislocation toward the inclusion at the moment of 30 ps (Figure 5). With an increment in shear stresses in the region, the dislocation moves along the inclusion rim, significantly bending at the same time (Figure 5, 100-200 ps). At the moment of 200 ps (Figure 5, 200 ps), the formation of the screw segment on the more elongated parts of the dislocation is visible. After formation, this segment experiences a cross-slip, shifts from the initial slip plane and forms a stacking fault in the slip plane rotated relative to the initial one. After the dislocation segments adjacent to the edges of precipitate are so elongated that they are directed along the \(x\)-axis (Figure 5, 270 ps), they are closed; and the dislocation separates from the inclusion. The separated dislocation increases its speed, that provokes an increase in the rate of plastic relaxation; and shear stresses in the region temporally decrease (Figure 4). Further, the dislocation crosses the periodic boundary; and a new dislocation-precipitate interaction begins that leads to formation of the second maximum on the dependence of stresses on time (Figure 4). Multiple repeating of this process leads to many peaks on the time dependence of stress corresponding to successive interactions of the dislocation with inclusion. In relation to the plastic deformation of real alloy, this multiple interaction of dislocation with \(\theta^{\prime}\) phase inclusion and the resulting evolution of the inclusion structure and properties can be interpreted as the successive action of different dislocations lying in the same or neighboring slip planes on the same precipitate.
## 1 Introduction
* Establishment of this validated EV model as a core component within a digital twin framework, providing an essential physical representation for advanced AI-based energy management in V2G systems. * Demonstration of the model's accuracy and reliability in predicting EV energy consumption and SoC under diverse real urban driving conditions, which is crucial for intelligent V2G optimization. The remainder of this paper is organized as follows: Section 2 details a new digital twin architecture suitable for exploiting the advantages of the developed multi-physics EV model to optimise V2G systems. Section 3 outlines the main model features and the validation methodology. Section 4 presents the results and discusses the model's performance. Finally, Section 5 concludes the paper and highlights future research directions. Into detail, Section 2 provides a detailed theoretical background for the proposed intelligent Vehicle-to-Grid (V2G) system, focusing on its digital twin architecture and the underlying multi-physics electric vehicle (EV) model. The overall digital twin architecture is described through its distinct layers, clarifying how real-world data is integrated and processed to enable predictive capabilities. Furthermore, the intricate structure of the multi-physics EV model is thoroughly explained, detailing its sub-models and their interactions that capture electrical, thermal, and mechanical dynamics. The data flow within the entire system, from sensor inputs to model outputs and their use in energy management, is also comprehensively elaborated to ensure a clear understanding of the framework's operation.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Proposed Short-Term Frequency Support Method of a DFIG
#### 4.2.1 Power Reference in the Deceleration Period of Short-Term Frequency Support Method  
To provide the approximate performance of frequency nadir improvement with less released energy from a DFIG, \(P_{STFS}\) is defined as:  
\[P_{STFS}=P_{MPT}+\Delta P_{\text{pro}}=P_{MPPT}+\Delta P_{\text{con}}(\frac{ \omega_{r}-\omega_{\text{min}}}{\omega_{0}-\omega_{\text{min}}}), \tag{13}\]  
where \(\Delta P_{\text{pro}}\) is the incremental power of the proposed method and is a function of \(\Delta P_{\text{con}}\) and the rotor speed; \(\omega_{0}\) and \(\omega_{\text{min}}\) are the rotor speed prior to disturbance and minimum value of the rotor speed, respectively.  
Note that \(\Delta P_{\text{pro}}\) in the proposed method varies with the rotor speed, whereas \(\Delta P_{\text{con}}\) in the conventional method is fixed during the deceleration period. As indicated in Equation (13), \(\Delta P_{\text{pro}}\) equals to \(\Delta P_{\text{con}}\) at instant of \(t_{0}\) because the value of the rotor speed is the same as \(\omega_{0}\). As the rotor speed decreases, \(\Delta P_{\text{pro}}\) decreases until \(P_{e}=P_{m}\) at Point C (see in Figure 4). As a result, the power injection in the proposed method during the beginning stage of a disturbance is slightly smaller than the conventional strategy. Thus, the proposed strategy can provide the approximate performance of the system frequency nadir improvement. As a comparison between Figures 3 and 4a, \(P_{m}-P_{e}\) rapidly reduces to zero compared to the conventional strategy. This is because the second part of Equation (13) leads \(P_{e}\) to rapidly intersect \(P_{m}\) curve. Accordingly, the rotor speed for the proposed method rapidly converges to \(\omega_{c}\), which is higher than that of the conventional strategy. Hence, less rotating kinetic energy is released after supporting the system frequency during the deceleration period.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Ablation Experiments  
To evaluate their effects on model performance, ablation experiments were performed for the RG-C2f module, DySample upsampling operator, and inner-WIoU loss function. In this paper, we first use YOLOv8 as the baseline model and evaluate their effects on model performance by gradually removing or replacing key modules. The experiments were trained using the TILDA dataset, the symbol \"\(\surd\)\" indicates that the method was used, and the results are shown in Table 1.  
According to the table, the baseline model records a mAP of 0.756 and an FPS of 103.5. The addition of the RG-C2f module results in a slight mAP improvement to 0.761, and the FPS is increased to 119.3, while the number of FLOPs and parametric quantities is significantly reduced, suggesting that RG-C2f optimizes the computational paths and improves the inference efficiency. After adding DySample, the mAP improves to 0.763, but the FPS decreases to 95.4, indicating that it increases the computational overhead while improving the detection performance. After the introduction of inner-WIoU, the accuracy and mAP are improved to 0.783 and 0.772, respectively, and the FPS is basically unchanged, indicating that it enhances the localization accuracy without significantly affecting the inference speed. When RG-C2f, DySample, and inner-WIoU are introduced at the same time, the model performance reaches optimization, with the mAP boosted to 0.82, the FPS is 114.7, and the FLOPs remain low at 7.9G. The experimental findings verify the improved model's effectiveness.  
A comparison of detection results between this model and YOLOv8 on the TILDA dataset is illustrated in Figure 8, which shows the detection results of four categories, namely holes, foreign objects, oil contamination, and line errors. It can be seen that in the original YOLOv8 in the category of foreign objects, there is a leakage of detection, and other categories of accuracy are also low. This paper's model, in terms of accuracy and leakage of detection rate, has been improved, showing its good performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**YOLOv8** & **RG-C2f** & **DySample** & **Inner-WIoU** & **P** & **R** & **mAP** & **FLOPs** & **FPS** & **Params** \\ \hline \(\surd\) & & & & 0.743 & 0.750 & 0.756 & 8.1G & 103.5 & 3,006,428 \\ \(\surd\) & \(\surd\) & & & 0.727 & 0.725 & 0.761 & 7.9G & 119.3 & 2,801,431 \\ \(\surd\) & & \(\surd\) & & 0.751 & 0.716 & 0.763 & 8.1G & 95.4 & 3,018,780 \\ \(\surd\) & & & \(\surd\) & 0.783 & 0.740 & 0.772 & 8.1G & 104.0 & 3,006,428 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.844 & 0.710 & 0.788 & 7.9G & 115.5 & 2,915,341 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.824 & 0.744 & 0.82 & 7.9G & 114.7 & 2,915,341 \\ \hline \end{tabular}
\end{table}
Table 1: Results of ablation experiments.