esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this study, we have designed a distributed multi-hop clustering routing protocol ES-MUCRP for RF EH-CRSNs. The protocol incorporates downlink RF EH, intra-cluster and inter-cluster SWIPT technologies, and energy status control mechanisms to extend network lifespan, improve the overall balance of network energy consumption among nodes, and enhance effective network monitoring capabilities. Simulation results reveal that ES-MUCRP significantly surpasses existing clustering routing protocols for CRSNs in terms of surviving node count and the number of nodes effectively collecting data. In particular, compared to S-MUCRP protocol, ES-MUCRP protocol improves the balance degree in network energy consumption among nodes by 17.69% and enhances the average number of effective data-collecting nodes by 6.77%.  
Focusing on the limitations of ES-MUCRP, we plan to investigate the design of CRSNs clustering routing protocols based on imperfect spectrum sensing in our future work. Additionally, communication is restricted to direct links between transmitters and receivers in EH-CRSNs, where substantial path loss due to large Euclidean distance can deplete the limited battery energy of nodes. Therefore, we intend to introduce an intelligent reflecting surface (IRS) to create additional paths for EH or information transmission, further enhancing the energy-saving effects of clustering routing protocols for EH-CRSNs. This will involve channel modeling, optimal placement of IRS, and the design of detailed clustering protocols. ES-MUCRP protocol addresses the issue of ensuring powerful data collection capabilities over an extended lifetime of CRSN nodes. It provides foundational data for solving predictive maintenance issues. Therefore, we will further study the key aspects of predictive maintenance issues, including effective analysis of the collected data, fault diagnosis, prediction, early warning, and maintenance decision-making, and eventually solve the issues.  
Conceptualization, J.W.; methodology, J.W. and Z.W.; software, Z.W. and L.Z.; validation, Z.W. and L.Z.; writing--original draft preparation, J.W. and Z.W.; supervision, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61901102.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
### Reactive Power Support for Voltage Regulation
#### 3.4.2 Measurement and Quantification  
Considering the aforementioned issues, it is suggested in this paper that only the reactive power exchanged through the positive sequence and fundamental frequency voltages and currents should be considered for the voltage regulation AS. Thus, the contribution of DRES in reactive power should be quantified by:  
\[\begin{array}{l}E_{RP}=\int_{T}\big{|}Q_{1}^{+}(t)\big{|}\cdot dt\\ Q_{1}^{+}(t)=\sqrt{3}\cdot V_{1}^{+}(t)\cdot I_{1}^{+}(t)\cdot\sin\!\left[ \varphi_{1}^{+}(t)\right]\end{array} \tag{15}\]  
where, \(V_{1}^{+}(t)\), \(I_{1}^{+}(t)\) are the instantaneous RMS values of the positive sequence and fundamental frequency of the line voltage and current at the DRES terminals, while \(\varphi_{1}^{+}(t)\) is the phase difference between \(V_{1}^{+}(t)\), \(I_{1}^{+}(t)\). It is noted that \(V_{1}^{+}(t)\), \(I_{1}^{+}(t)\) and \(\varphi_{1}^{+}(t)\) can be instantaneously measured using already developed methods for the operation and control of active harmonic filters [84].  
It is evident that \(E_{RP}\) in (15) represents the reactive \"energy\" (in kVArh) exchanged over a period \(T\). Since voltage regulation is a steady-state or quasi steady-state process, the specifications mentioned in [86] with respect to \(T\) can be used: initially a digital value of reactive power is measured with equal to 10 cycles (200 ms). Next, these values will be aggregated over a: 3-s period (15 of 10-cycles values); 10 min (200 of 3-s values) and; 24-h period (144 of 10-min values) [27]. The aggregated data are referred to an absolute time stamp corresponding to the final moment of the aggregation interval. The accuracy in the measurement of voltage and currents should be 1% of their nominal values while for reactive power 5% of the rated DRES apparent power. Such an accuracy can be achieved with a sampling rate of at least 6.4 kHz (128 samples/period).  
The exchange of reactive power increases the losses within the DRES converters. The additional losses can be evaluated by  
\[\begin{split} E_{L-RP}=\int_{T}\Delta P_{loss}(t)\cdot dt\\ \Delta P_{loss}(t)=P_{out}(t)\left[\frac{1}{\eta(P_{out},Q_{1}^ {+})}-\frac{1}{\eta(P_{out},0)}\right]\end{split} \tag{16}\]  
where, \(E_{L-RP}\) (expressed in kWh) is actually the operational cost for providing reactive power. The efficiency (\(\eta\)) of the DRES converter is a function of the converter active (\(P_{out}\)) and reactive power (\(Q_{1}^{+}\)) loading and can be analytically evaluated according to [21].
## 3 The Application Results and Discussion
### Comparison and Analysis of Convergence Speed  
The hybrid improved SVR and PSO-SVR utilize PSO to optimize the SVR kernel parameters; thus, the convergence speeds of the two models are compared with each other. In this paper, \(R^{2}\) is defined as the fitness of PSO to change the combination of \(\sigma\) and \(C\) for the optimal solution in the iteration. The fitness change from the initial age to the end is displayed in Figure 8. It can be observed that the fitness value at the beginning stage is approximately 0.87 and 0.857 for hybrid improved SVR and PSO-SVR. The higher fitness value of hybrid improved SVR at the first iteration age is due to the calibration process of RSM that increases the number of input variables by polynomial expression. As the iteration continues, the two methods both see a rise of fitness value with the functionality of PSO. Around the age of 20, the two methods attain their optimal fitness, and successive minor change is observed before that because of the slight adjustment of self-adaptive evolution strategy. This means that the similar convergence speed is obtained after PSO is combined with RSM to calibrate SVR, compared with PSO-SVR.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{4}{c}{**Train Set**} & \multicolumn{4}{c}{**Test Set**} \\ \cline{2-10}  & \multirow{2}{*}{**SVR**} & \multirow{2}{*}{**PSO-SVR**} & \multirow{2}{*}{**RSM-SVR**} & \multirow{2}{*}{**Hybrid**} & \multirow{2}{*}{**SVR**} & \multirow{2}{*}{**PSO-SVR**} & \multirow{2}{*}{**RSM-SVR**} & \multirow{2}{*}{**Hybrid**} \\  & & & & & & & & **Dproved** \\  & & & & & & & & **SVR** \\ \hline MSE & 10.7087 & 9.9797 & 7.4895 & 3.4849 & 9.8709 & 9.2145 & 8.2087 & 4.5519 \\ MAE & 2.6735 & 2.5443 & 2.2990 & 1.3437 & 2.5748 & 2.3709 & 2.2472 & 1.7646 \\ MAPE & 0.2116 & 0.2003 & 0.1833 & 0.1032 & 0.2038 & 0.1713 & 0.1642 & 0.1233 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy data of three models.  
Figure 6: The comparison of accuracy.  
Figure 7: The prediction capability of different models.
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
## 4 Discussion
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Type** & **Example** & **Method** \\ \hline \multirow{3}{*}{Non-fractal signals} & Stationary series & Random spatial processes & Conventional spatial statistics \\ \cline{2-4}  & Trend series with & Exponential distribution of urban & Conventional mathematical methods \\  & characteristic scale & population density & and wave-spectrum scaling \\ \hline \multirow{3}{*}{Fractal signals} & Self-similar series & Isotropic growing fractal networks & Wave-spectrum scaling based on \\  & correlation function \\ \cline{1-1} \cline{2-4}  & Self-affine series & Anisotropic growing fractal networks & Wave-spectrum scaling relation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different types of spatial signals and corresponding analytical methods. theoretical research on self-affine fractal signals of geographical systems, many principles on self-affine fractal dimension are not yet clear; Thirdly, this paper is mainly devoted to single scaling analysis, while urban systems and traffic networks bear multifractal scaling property. The spatial signals of multiscaling processes can be analyzed by wavelet transform instead of the Fourier transform [47; 48]. Wavelet analysis is one of powerful tools for multiscale signal analysis [13; 49]. In the future, three respects of research will be carried out. One is to generate the 1-dimensional spatial signal analysis to 2-dimensional spatial signal analysis, the other is to make theoretical and positive studies on self-affine fractal signals in complex systems, and the third is to make wavelet analysis on geographical signals based on multiscaling spatial processes.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 4 Case Studies of Multi-Channel and Multi-Hop QoS
### Single-Channel, Three-Hop Network  
A more realistic example is a network that employs several intermediary nodes to forward data, as shown in Figure 4. For this example, two intermediary nodes relay the data, making it a four-node network, with three independent links between the end points. Each section of the communication network, \(\langle\textbf{A-B},\textbf{B-C},\textbf{C-D}\rangle\), may have different characteristics because they may traverse different interconnecting channels or they may travel different distances. Those characteristics are also shown in Figure 4, with notionally assigned values for illustration. To calculate the QoS for each link between hops, the values may be handled in the same manner as the single channel in Figure 3. However, to calculate the equivalent single channel point-to-point network, as shown in Figure 5, the equivalent \(P(\mathcal{C})\) and \(P(I)\) are simply calculated by multiplying each of the component probabilities of the confidentiality or probability of integrity values, respectively. This final value of the equivalent single-channel, point-to-point network is shown in Figure 5.  
Figure 3: A Single-Channel, Point-to-Point Network and QoS Characteristics.
## 1 Introduction
[15] tackle the difficulties in infrared image object detection, including small object dimensions, insufficient feature representation, and background noise, through targeted enhancements to the Single Shot MultiBox Detector (SSD) framework [16]. This is achieved by optimizing the anchor boxes and applying lightweight processing to the network, successfully enhancing detection accuracy and precision. In response to similar challenges in small target infrared image detection, [PERSON] et al. [17] proposes a method that removes low-resolution layers from the network, enhances high-resolution layers, and introduces an adaptive pipe filter (APF), combined with a proposed two-stage detection approach. Experimental results in complex scenarios demonstrate that this method possesses high recall rates and accuracy for detecting and tracking infrared small targets. The above studies indicate that deep learning-based methods are highly feasible for the detection and recognition of infrared images in electrical inspection. However, several important issues remain to be explored in depth. For instance, how to achieve model lightweighting while ensuring detection accuracy to accommodate the hardware limitations of inspection drones. Moreover, addressing the issue of real-time performance and minimizing network parameters efficiently to enhance detection speed remains a significant challenge. To tackle these issues, this study introduces the enhanced YOLOv8_Adv model designed for the intelligent detection and recognition of infrared substation electrical equipment images. Firstly, the model introduces the FasterNet module with partial convolution (PConv) into the YOLOv8 backbone, significantly reducing computation and memory access, achieving model lightweighting. Secondly, generalized-sparse convolution (GSConv) and VoVNet with GSConv and CSPNet (VoVGSCSP) modules are added to the neck, facilitating the multi-stage fusion of feature maps and integrating the EMA attention mechanism to improve the model's capability in capturing and representing essential features. Lastly, an additional upsampling layer is introduced in the head, with a detection scale of \(160\times 160\times 32\), to improve small target detection accuracy in infrared backgrounds. The improved YOLOv8_Adv model achieves lightweight design and high accuracy in infrared image detection and recognition for electrical equipment. It effectively replaces traditional manual inspections, better meets real-time requirements, and is suitable for deployment on mobile drone devices.
# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
## 2 Multi-Scale-SE Recognition Methods  
### Multi-Scale-SE Recognition Models  
To solve the problem of losing details of Chinese characters due to deep convolution and pooling operations, this paper designs an attention-based bottleneck layer structure and incorporates multi-scale fusion to better capture the overall features of Chinese characters. A handwritten Chinese character recognition model combining the Squeeze-and-Excitation (SE) attention mechanism and multi-scale fusion is finally constructed (Multi-scale SE, MSSE), and its structure is shown in Figure 1.  
The MSSE model consists of two convolutional layers, four bottleneck layers, four pooling layers, and one regression layer. Firstly, the Chinese character images within the dataset are preprocessed, and the input grey scale image size is adjusted to 96 \(\times\) 96. The convolution kernels of both convolutional layers in the network are set to 3 \(\times\) 3 because 3 \(\times\) 3 convolution kernels not only capture local spatial information but also have a lower computational cost. Compared to 5 \(\times\) 5 convolutional kernels, a similar sense field can be achieved using a combination of two layers of 3 \(\times\) 3 convolutional kernels, progressively capturing a wider range of spatial information while reducing the number of parameters.  
Batch normalization [21] has been widely used in the field of HCCR, which can significantly improve the training efficiency and stability of the model. Therefore, a batch normalization layer is added after each convolutional layer of this model, and a ReLU activation function [22] is added to increase the non-linearity. In order to further improve the feature extraction capability of the model, MSSE designs a bottleneck layer that combines the attention mechanism and multi-scale feature fusion to replace the traditional convolutional operation to obtain the detailed information and global features of Chinese characters more comprehensively. The specific structure of the network is shown in Table 1.
## 3 Simulation Analysis Using SSA for GREATEM Signal De-Noising  
In order to verify the effectiveness and correctness of the SSA algorithm for GREATEM detection data de-noising, we calculated the electromagnetic response of a uniform half-space using a GREATEM system with an electrical source. The specific calculation parameters are that the length of the long wire is \(L=1000\) m, the transmission current is \(I=10\) A, the effective area of the receiving coil is \(S=10{,}000\) m\({}^{2}\), and the time series length of the synthetic signal is 5001. For the simulation calculation process, refer to reference [PERSON] et al. [13]. Simulation of GREATEM signals: a long grounded wire source is used, the coordinate origin is set in the middle of the wire, the length of the half wire is L, and the vertical component of the induced electromotive force is expressed in the layered earth model as Equation (11) shown  
\[V=-i\omega I\frac{t_{0}S}{4\pi}\int_{-L}^{L}\frac{y}{R_{d}}\int_{0}^{\infty}(1 +r_{TE})e^{\omega_{0}2}\frac{\lambda^{2}}{u_{0}}J_{1}(\lambda R)d\lambda dx^{\prime} \tag{11}\]  
where \(i\) is an imaginary unit, where \(f\) is the frequency (Hz), \(I\) is the emission current, representing the air permeability, \(S\) is the equivalent area of the receiving coil, \(R_{d}=\left[(x-x^{\prime})^{2}+y^{2}\right]^{\frac{1}{2}}\) is the distance between the transmitting source and the receiving coil, and \(\lambda\) is the Hankel transform the integral variable. \(r_{TE}\) is the reflection coefficient, and \(J_{1}\) is the first-order Bessel function. The resistivity of uniform half-space is 100 \(\Omega\cdot\)m.  
Calculate the electromagnetic response of the uniform half-space of the electrical source, and set the simulation parameters as follows: the emission current \(I=10\) A, the source length of the half-length wire is 500 m, the equivalent area of the receiving coil is \(S=10{,}000\) m\({}^{2}\), and the ideal ground-air transient electromagnetic signal curve can be obtained, whose time track length is 5001. Figure 1 shows the calculated GREATEM response of uniform half-space.
# Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data
###### Abstract  
We propose the use of Sentinel-1 Synthetic Aperture Radar (SAR) to provide a continuous and global monitoring of Radio Frequency Interferences (RFI) in C-band. We take advantage of the first 8-10 echo measures at the beginning of each burst, a 50-70 MHz wide bandwidth and a ground beam coverage of \(\sim\)25 km (azimuth) by 70 km (range). Such observations can be repeated with a frequency better than three days, by considering two satellites and both ascending and descending passes. These measures can be used to qualify the same Sentinel-1 (S1) dataset as well as to monitor the availability and the use of radio frequency spectrum for present and future spaceborne imagers and for policy makers. In the paper we investigate the feasibility and the limits of this approach, and we provide a first Radio Frequency Interference (RFI) map with continental coverage over Europe.  
SAR; Radio Frequency Interferences; Synthetic Aperture Radar; Geosynchronous SAR 201791118303390/rs9111830390/rs911183
## 1 Introduction
This study uses the capacity degradation method to estimate SOH and the contributions are as follows:  
* The experimental data of the battery cycle life and open circuit voltage (OCV) are studied. Upon analyzing the data, it is concluded that the degradation of the battery causes OCV variation and the instantaneous voltage drop of the terminal voltage rises along with the increasing cycle numbers. * A novel health factor extracted from the terminal voltage drop is proposed to estimate the available capacity of the battery that can indicate the SOH of the battery. Furthermore, a support vector machine (SVM) approach is used to build the relationship between the health factor and available capacity of the battery. The experimental result showed that the method has high precision. * The reason for selecting the terminal voltage drop information as the health factor is analyzed and its performance is verified. The study explained why charging or discharging a battery to a certain voltage is superior to charging or discharging the battery to a certain SOC. Moreover, the SOH estimating results showed that the method is not limited to the working conditions of the battery. * The modeling precision using different features of the terminal voltage drop as the health factor is compared. Furthermore, the effectiveness of the model is verified and analyzed. The remainder of the study is organized as follows: an overview of the capacity estimation methods is shown in Section 2. In Section 3, the design of the experiments for the cycling test of the battery is introduced and the results are analyzed. In Section 4, a SVM is used to establish the relationship between the available capacity of the battery and its terminal voltage drop. Thereafter, in Section 5, the results of the prediction method based on the SVM are showed and analyzed. Finally, this work is summarized.
## 2 Data Description
### COSMIC/FORMOSAT--3 Radio Occultation Observations  
COSMIC/FORMOSAT--3 (COSMIC--1 in brief) was a six LEO-microatellites constellation launched on the 15 th of April 2006 and decommissioned in 2020 [27]. COSMIC--1 satellites had circular orbits with an inclination of 72\({}^{\circ}\), an altitude of about 800 km, and a longitudinal separation between neighboring satellites of about 30\({}^{\circ}\). Each satellite was equipped with a Global Positioning System (GPS) RO receiver capable of measuring the phase delay of radio waves from GPS satellites as they are occulted by the Earth's atmosphere. Through data inversion, RO measurements provide ionospheric \(N_{\rm e}\) values at the tangent points of the radio occultation--from the ground to the COSMIC--1 satellites altitude. COSMIC--1 RO data are based on the Abel inversion technique [45; 46], which assumes a spherical symmetry in the ionosphere. It is worth noting that this working hypothesis can be violated in locations where strong horizontal electron density gradients are present. The equatorial ionization anomaly region, geomagnetically disturbed conditions, and the hours around the solar terminator are the most affected by such an issue [47; 48].  
COSMIC--1 RO _ionprf_ files containing \(N_{\rm e}\) quasi-vertical profiles are stored in the COSMIC Data Analysis and Archive Center (CDAAC, [[http://cdaac-www.cosmic.ucar.edu/cdaac/products.html](http://cdaac-www.cosmic.ucar.edu/cdaac/products.html)]([http://cdaac-www.cosmic.ucar.edu/cdaac/products.html](http://cdaac-www.cosmic.ucar.edu/cdaac/products.html)), accessed on 7 April 2023) [49].  
In this work, to be consistent with the CSES--01 dataset, we considered COSMIC--1 RO observations in the range 00:00 \(\leq\) LT \(<\) 04:00 for the nighttime sector and in the range 12:00 \(\leq\) LT \(<\) 16:00 for the daytime sector. The LT sectors in which we selected COSMIC--1 RO observations are wider than those used in the Swarm B LT selection (Section 2.2) to ensure the consideration of a statistically significant number of RO profiles. Moreover, only COSMIC--1 RO data measured when the 81-day running mean (F10.7\({}_{81}\)) of the solar flux index F10.7 [50] was lower or equal than 85 sfu (solar flux unit) has been considered, i.e., for the same low solar activity conditions of the CSES--01 dataset.