esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Uniform PAN polymer nanofiber mats were fabricated using the electrospinning method. We investigated the effects of precursor concentration of PAN, thermal stabilization and carbonization temperature on the diameter, surface morphology, conductivity and flexibility of the carbon nanofibers. It is found that a precursor concentration of 12 wt% PAN, a thermal stabilization temperature of 270 \({}^{\circ}\)C and a carbonization temperature of 900 \({}^{\circ}\)C are the optimized experimental factors in this work. Furthermore, the electrical and mechanical properties of the carbon nanofibers could be improved by the addition of MWCNTs, most of which are embedded in the nanofibers. The optimal mass fraction of MWCNTs is 2.4 wt%, leading to a decrease in sheet resistance of the co-spun nanofiber mat to 60 \(\Omega\)/sq. Finally, different activation strategies were explored to improve the electrochemical performances of the carbon nanofiber mats. The results suggest that the specific capacitance of the carbon nanofibers could be greatly increased from 18.8 F/g to 113.5 F/g with an activation treatment of 1:3 HNO\({}_{3}\):H\({}_{2}\)SO\({}_{4}\)  
Figure 7: SEM images of the sample PMC-3 activated by 20 wt% HNO\({}_{3}\) (**a**), 1:3 HNO\({}_{3}\): H\({}_{2}\)SO\({}_{4}\) (**b**) and 1:1 HNO\({}_{3}\): H\({}_{2}\)SO\({}_{4}\) (**c**), respectively; electrochemical performance comparison of the activated carbon nanofiber mats: (**d**) CV curves at a scan rate of 5 mV/s in potential window of \(-\)0.8–0 V; (**e**) GCD curves at a current density of 0.1 mA/cm\({}^{2}\).  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/18/3683/s1](http://www.mdpi.com/2076-3417/9/18/3683/s1)]([http://www.mdpi.com/2076-3417/9/18/3683/s1](http://www.mdpi.com/2076-3417/9/18/3683/s1)), Figure S1: SEM image of PAN-derived carbon nanofiber mats of the sample C-1 with a high magnification, Figure S2: SEM images of PAN-derived carbon nanofiber mats of the sample C-2 (a), C-3 (b) and C-4 (c) with a high magnification, Figure S3: SEM images of PAN-derived carbon nanofiber mats of the sample C-5 (a) and C-6 (b) with a high magnification.  
**Author Contributions:** Conceptualization, X.H.; Data curation, Q.L.; Formal analysis, S.Z. and M.C.; Funding acquisition, X.H.; Investigation, J.L. and B.C.; Methodology, C.Z.; Writing--original draft, X.H.; Writing--review & editing, C.Z. and M.S.  
**Funding:** This research was funded by Science Foundation for Young Teachers Projects of Wuyi University (2018 td03); Science and Technology Projects of Jiangmen ((2017)307, (2017)149, (2018)352, (2018)359); Innovative Leading Talents of Jiangmen (Jiangmen(2019)7); Cooperative education platform of Guangdong Province ((2016)31); Key Laboratory of Optoelectronic Materials and Applications in Guangdong Higher Education (2017 KSYS011); College Students Maker Space Project of Wuyi University (18 KWL01).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results of the Assessment Process
### Electric Field Intensity Assessment Process  
The maximum measured values obtained for the 15 critical set points are presented in Table 1. All of them were performed on the 24 July 2019, during the morning (8:00-10:00 a.m.), in excellent sunny weather conditions, with no rain or fog at temperatures from 24 up to 28 \({}^{\circ}\)C, and a relative humidity from 40 up to 46%, and an air pressure of 738 mmHg.  
Figure 5: The Measuring Set Points for the Autotransformer area.  
Figure 6: The Measuring Set Points for the Distribution Substation.  
All measured values were located below the high action level, and, if limited exposure measures are applied, they are compliant with the safety working regulations. There are no human operators who must stay in the measured area for more than a few hours, except the command and control room where there are no risks at all (values around 2 kV/m).  
The highest values are obtained for the transformers and autotransformers areas, but, as previously explained, there are no permanent human operators in those areas, at 3 m from the power transformers, in service mode.  
Human staff in the commercial area reported frequent electric discharges at different levels, touching metallic or non-metallic objects, in conditions of high humidity, which can be explained by the electric charge on their bodies.  
Electric discharges were observed between the joints of an umbrella with imperfect metal links, but also on hand-contact with the wet surface of fruit or vegetables, which reinforces the idea of the existence of a potentially harmful field in the analyzed area.  
After the assessment processes, by considering more measurements than these presented, we observed that the first phase line has slightly higher values for the field intensity E, in most of the considered points. These values were obtained in excellent summer day conditions. During rain or higher humidity days, values could increase by 10 to 20% or, in worst cases, by 40%, but without concerns for human health (no maintenance operation is scheduled during heavy rains or extremely humid or foggy days).
## 2 Theoretical Framework
### Reliability Improvement Strategies for Analyzed Medium-Voltage Networks  
Various strategies were explored in the analyzed MV semi-urban and rural distribution networks to enhance reliability performance while considering financial constraints. These strategies are categorized into three main groups:  
1. Installation of new components, which includes: * Construction of new power lines to form semi-rings on single-fed lines; * Installation of new reclosers (REC).
2. Replacement of outdated components with new ones, ensuring that aging infrastructure does not compromise reliability.
3. Upgrading both components and network operations, including: * Replacement of overhead lines (OHLs) with underground cable lines (UCLs); * Installation of remotely operated disconnectors (RODs) with fault indicators to enable faster fault detection and sectionalizing. This upgrade improves response time.  
For each analyzed network, specific investment options and the necessary financial resources for their implementation were defined. By selecting the most effective measures from the defined options, investments would be directed toward maximizing improvements in reliability indicators, significantly enhancing the overall stability and efficiency of the distribution system.
## 3 Results and Discussion
### FTIR Spectra  
For all tested treatment times, the obtained FTIR spectrum does not differ significantly from the spectrum for the control sample, as shown in the example in Figure 4 for a treatment time of 30 min. The spectrum shows peaks characteristic of diatomaceous earth. The broad band between 3100 and 3600 cm\({}^{-1}\) is due to H-O-H stretching vibrations of absorbed water, whereas the band at 1640 cm\({}^{-1}\) may be related to the presence of water H-O-H bending vibrations from the absorbed water in opal present in the sample [51]. The very strong absorption peak at 1070 could be related to Si-O stretching vibrations from Si-O-Si [53]. For all treatment times, slightly reduced absorbances were observed for the H-O-H band with a slight increase in the Si-O band, which may be related to the evaporation of water during treatment. The obtained spectra were consistent with those reported in the literature; no formation of new functional groups was observed for any of the conditions.  
Figure 3: Microscopic imaging of diatomaceous earth before and after non-equilibrium plasma treatment—KEYENCE VHV-5000 optical microscope, magnification 800\(\times\).
## 2 Study Area and Data
### Data  
Sentinel-2 is a twin satellites constellation consisting of Sentinal-2A launched on 23 June 2015 and Sentinal-2B followed on 7 March 2017. The two satellites flying in the same orbit but phased at 180\({}^{\circ}\) to provide a high revisit frequency of five days with a constant view angle to the same target area. The onboard Multispectral Imager Instruments (MSI) covers a field of view of 290 km and provides thirteen spectral bands from visible near-infrared (VNIR) to SWIR, with the ground sampling distance (GSD) of four bands at 10 m, six bands at 20 m and three bands at 60 m [50]. The coastal band (band 1), water vapor band (band 9) and cirrus band (band 10) are at the same GSD of 60 m. Three red edge bands (i.e., bands 5, 6, and 7), an NIR band (band 8a), and two SWIR bands (bands 11 and 12) are the same at 20 m. Bands 2, 3, and 4 are three visible bands (R/G/B) at a 10-m GSD as the left NIR band (band 8). Sentinel-2 standard level-1C (L1C) products are available freely from the Copernicus Scientific Data Hub website as Top-of-Atmosphere (TOA) reflectance ortho-images in terms of elementary granules of fixed size. The granules, also called tiles, are the minimum indivisible partition of a product in 100 \(\times\) 100 km\({}^{2}\) with UTM/WGS84 projection [7]. The whole watershed is over the coverage of one tile, and two tiles acquired on 28 February 2017 with the center position coordinates of 31\({}^{\circ}\)6\({}^{\prime}\)45\({}^{\prime\prime}\)N, 119\({}^{\circ}\)40\({}^{\prime}\)20\({}^{\prime\prime}\)E and 30\({}^{\circ}\)12\({}^{\prime}\)39\({}^{\prime\prime}\)N, 119\({}^{\circ}\)38\({}^{\prime}\)52\({}^{\prime\prime}\)E, respectively, were used for subsequent processing.  
Figure 1: Location of the Xitiaoxi watershed and Taihu Basin. The Sentinel-2 Multispectral Instrument (MSI) imagery is shown with true-color composite of Red, Green, and Blue bands in Bottom-of-Atmosphere reflectance data.
## 2 Materials and Methods
### Preparation of the Electrospun NFs  
The synthetic route followed to produce the electrospun NFs (Figure S1) was described in detail in previous papers [36; 39; 42]. Concisely, the spinnable solutions were prepared via sol-gel method. In a typical procedure, 0.325 g polyacrylonitrile (PAN) was dissolved in 4.550 g \(N\),\(N\)-dimethylformamide (DMF) and a homogeneous solution was obtained after magnetic stirring at room temperature (RT) for 2 h. Then, 0.389 g iron (II) acetate (FeAc\({}_{2}\)), corresponding to a Fe concentration of 38.5% relative to PAN, was added to prepare the (reference) iron oxide NFs. The Ge-doped iron oxide NFs were prepared by the addition of 0.350 g FeAc\({}_{2}\) and 0.0531 g germanium (IV) isopropoxide (GeIPO), corresponding to a (Fe + Ge) concentration of 38.5% relative to the polymer and to a Ge:Fe mass ratio of 1:9 (corresponding to 0.085 Ge:Fe molar ratio). After 2 h magnetic stirring at RT, the as-prepared solution was loaded into a 20 mL glass syringe equipped with 40 mm long 0.8 gauge stainless-steel needle and electrospun at a DC voltage of 17 kV via a CH-01 Electro-spinner 2.0 (Linari Engineering s.r.l.). Solution was fed at a rate of 23.5 \(\upmu\)L min\({}^{-1}\). The electrospun NFs were collected on a grounded aluminum foil placed at a distance of 11 cm from the tip of the needle. After drying at RT overnight to remove the DMF residuals, the as-spun fibrous membranes were peeled from the collector and calcined in air for 2 h through a single-step process at 600 \({}^{\circ}\)C (heating rate: 5 \({}^{\circ}\)C min\({}^{-1}\)). Upon calcination, the organic components were completely removed from the FeAc\({}_{2}\)-PAN and GeIPO-FeAc\({}_{2}\)-PAN NFs and the iron oxide was generated. For further details on the syntheses and experimental setup utilized see refs [36; 39; 42].
## 3 Moving Shadow Detection  
Recently, various shadow identification techniques have been announced in science-related literature. They can be divided into two areas. The first area generally concerns static images, while the second concerns image sequences and specifically video contents ([PERSON] et al., 2016). Static shadows are shadows cast by immobile objects such as buildings, parked vehicles, and trees. In that regard, moving object identification methods do not suffer from static shadows because these shadows are classified as a piece of background. In contrast, dynamic shadows, the subject of interest for this manuscript, are harmful to moving object recognition algorithms. Shadows can be smoothly stitched to an object in action or can be disconnected from it. In the first case, the shadows usually cause the shape of the original object to look different, making the utilization of subsequent shape recognition strategies less reliable. In the second case, the shadows might be incorrectly categorized as an object in the scene. The work performed in this study concerns the second case, where, in this way, it addresses the issue of the detection and removal of moving shadows cast from objects in video surveillance. This is done to improve the process of moving object identification. Moving shadows are often recognized as foreground objects, and this degrades the expected execution of object tracking and accurate segmentation, as depicted in Figure 1.
## 3 Congestion-Aware EFL-Based Model Aggregation Policy in Edge Computing
### MLSTM-CP and Orchestration of EFL Model Aggregation
The proposed EFL model aggregation policies consider the congestion rate of the current time slot by evaluating the total incoming model services and prioritizing the optimal path for each level of criticality. To create a forwarding graph, the SDN primary controller is used to determine whether to alter or maintain the current edge aggregator destination. To configure instance properties, the NFV MANO-enabled interface facilitates interactivity from the outputs of the predictive module towards virtual resource placement policies. The proposed MLSTM-CEFL is achieved through its capabilities of adjusting virtual resources in the edge aggregator based on congestion prediction, scheduling the long-term forwarding path, and modifying the offloading decision of FL local-edge communications. The predicted congestion rates \(Y_{M_{n(t)}}\) provide insights into IIoT-EFL network performance and enable QoS enhancements. By anticipating potential traffic bottlenecks, QoS in EFL model update processes can be predicted as \(\tau_{pre}(t+1)\) to compare with the upper-bound \(\tau_{m}(t+1)\) for dynamically scheduling flow paths and allocating edge resources where needed to prevent congestion. The overall process ensures optimal model update flow, minimizes delays, and enhances learning experience, which harmonizes network resources with demand and maintains reliable service delivery.
## 3 Improved DBSCAN Algorithm Based on Sparrow Search Algorithm  
### Basic Idea  
The DBSCAN algorithm can identify clusters of arbitrary shapes and effectively handle outliers. However, the algorithm's efficacy is substantially shaped by the chosen parameters, namely Eps and MinPts, which often require a lot of anthropogenic experimentation to determine. This manual tuning typically involves extensive experimentation. Moreover, for different datasets, the DBSCAN algorithm requires different Eps and MinPts parameters, which limits its applicability.  
To solve this problem, the number of clusters in the dataset is automatically determined by the density peak. Utilizing the local fast search capability of Sparrow Search Algorithm, combined with contour coefficient as fitness function, iteratively selecting the optimal solution of the two input parameters of DBSCAN to avoid the adverse effects of manual input parameters on clustering results, thus finding the best clustering solution and achieving adaptive clustering of SSA-DBSCAN.
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 5 Simulation Results
Figure 10: NMSE performance: (a) scalar parameterization; (b) vector parameterization. In terms of BER values, in scalar parameterization, DU-TCG and LCG have similar detection performance. Additionally, LCG-V outperforms the DU-TCG-S. However, when we employ the vector parameterization with Tikhonov regularization in DU-TCG, more than \(1\) dB SNR gain is achieved at the \(10^{-3}\) BER value compared to LCG-V.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### Keypoint Cloud Extraction  
In the field of pose estimation, relying solely on image-based detection is insufficient for obtaining the 3D information for target objects. On the other hand, point cloud data, though rich in 3D information, are challenging to process due to their sparse, irregular, and unordered nature. To address these challenges, a hybrid approach that leverages the strengths of both image and point cloud data is often adopted by researchers.  
There is a subtle relationship between images and point clouds: the projection of 3D keypoint cloud onto a 2D image plane typically corresponds to the edges of the target object. This allows for the conversion of 2D images to 3D point clouds through coordinate system transformation. To further enhance the internal feature information of point clouds, the neighborhood relationships in 2D images can be utilized to define neighborhood relationships in 3D space. By selecting keypoints based on the curvature of point cloud neighbors, the extraction of internal keypoints within the point cloud can be improved.  
Based on this understanding, we propose a novel keypoint cloud extraction algorithm that combines edge detection and differential curvature analysis. This approach aims to effectively extract keypoints from point clouds, addressing the limitations of using either image or point cloud information alone.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Computation Time
#### 4.1.3 Performance Analysis Comparison  
Figure 15 reports the median security CT results for DLAPP and MFSPV in OBU and smartphone X. MFSPV outperforms DLAPP in both nodes. Analysing this difference from the perspective of operations per second, MFSPV allows the protection of 3248 and 1016 more messages on the OBU and smartphone, respectively; plus 3461 and 3178 message de-protections. This translates into a performance increase between \(\sim\)16% to 113%, depending on the node and type of operation. MFSPV achieves this performance advantage due to the exclusive use of hashes, which are computationally lighter than the HMAC operation. Despite this, as both protocols were designed to be lightweight, the magnitude of the times involved is minimal--in the order of tenths of milliseconds.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 3 Methodology
### Structure of the Proposed CEM
\begin{table}
\begin{tabular}{c c} \hline
**Layer** & **Number of Kernels** \\ \hline
1,2,3 & 64 \\
4,5,6, & 128 \\
7,8,9 & 256 \\
10,11,12,13,14,15 & 512 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of convolution kernels for each convolutional layer. Figure 5: Loss rate curve. In Equation (1), f denotes the fused feature map, d5 denotes the output of the D5 unit, and e4 denotes the output of the E4 unit; a and b are fusion coefficients, and the values of a and b are determined after training. The convolution layer of D4 adjusts the depth of the fused feature map to 256. The D3 unit is composed of an up-sampling layer and a convolutional layer. The up-sampling layer is used to up-sample the output of the D4 unit to match the size of output of the E3 unit; Equation (1) is then used for fusion, but the fusion coefficients are different from those used in D4. The fused features are also adjusted through the convolutional layer. The structures of the D2 and D1 units are the same as that of the D3 unit, and the same working mode is adopted. Table 2 presents the depth of the feature map generated by each decoder unit. Finally, the semantic features generated by the D1 unit are concatenated with the spectral features outputted by E0 to form a feature map. The CEM model uses the SoftMax model as a classifier. The SoftMax model exploits the fused feature map generated by the feature extractor to produce the category probability vector for each pixel, and then uses the category corresponding to the maximum value of the probability vector as the category of the pixel.
## 2 System Model
### Receiver  
The signal at the receiver input can be presented as  
\[s_{0}=r\,\mathrm{e}^{\mathrm{j}(\phi_{n}+\theta)}+n, \tag{9}\]  
where \(r\) is the fading envelope, \(\theta\) is a random signal phase due to multipath propagation, and \(n\) represents narrowband Gaussian noise. The in-phase and quadrature components of noise \(n\) are random signals with Gaussian PDF whose mean values are zero, and standard deviations are identical and denoted by \(\sigma\).  
The received signal random phase variations due to multipath fading should be detected and removed during the demodulation process. Actually, PLL has the role to follow these phase variations. In practice, the phase extractor in the receiver is not ideal, and there is a difference between received signal phase and estimated phase. The difference between these two phases is called the phase error, which is a random variable. It is well known that in the case when there is only AWGN in the loop circuit, this phase error is a random variable with Tikhonov PDF [3; 4; 5; 6]. In this paper, we assume that the phase error can be modeled by Tikhonov distribution because the reference signal recovery is performed from pilot signal, and the PLL bandwidth is much lesser than the channel bandwidth. It means that the PDF of the phase error can be written in the form of [3; 4; 5; 6]  
\[p_{\varphi}(\varphi)=\frac{\mathrm{e}^{\rho_{\mathrm{PD}}\cos\varphi}}{2\pi \,\mathrm{I}_{0}(\rho_{\mathrm{PD}})},\quad|\varphi|\leq\pi, \tag{10}\]  
where \(\rho_{\mathrm{PD}}\) is SNR in the PLL circuit. This \(\rho_{\mathrm{PD}}\) can be linked to the phase noise standard deviation as \(\rho_{\mathrm{PD}}=1/\sigma_{\varphi}^{2}\)[3; 4; 5; 6]. Formula (10) can be presented in the form of [3; 4]  
\[p_{\varphi}(\varphi)=\frac{1}{\pi}+\sum_{n=0}^{+\infty}c_{n}\cos(n\varphi), \quad|\varphi|\leq\pi, \tag{11}\]  
where coefficients \(c_{n}\) are defined as \(c_{n}=\mathrm{I}_{n}(\rho_{\mathrm{PD}})/(\pi\mathrm{I}_{0}(\rho_{\mathrm{PD} }))\).  
After signal demodulation, the in-phase and quadrature signal components in the upper and lower branches of the receiver can be presented as  
\[s_{x} =r\cos(\phi_{n}+\varphi)+n_{x},\] \[s_{y} =r\sin(\phi_{n}+\varphi)+n_{y}, \tag{12}\]  
where \(n_{x}\) and \(n_{y}\) have zero mean value and standard deviation denoted by \(\sigma\). The detection is performed based on the value of  
\[\tan\delta=s_{y}/s_{x}. \tag{13}\]