esa-sceva/satcom-chunk-collection huggingface dataset:
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 4 SOM Neural Network Fault Diagnosis Model  
### SOM Neural Network Structure  
The SOM neural network is used for regional division of data and regional classification of input variables, so as to study the distribution characteristics and topological relations of input variables. The principle is as follows: SOM neural network is an unsupervised clustering algorithm. Any data input to the SOM neural network forms a one-dimensional or two-dimensional planar topological structure array under its action, and then it is mapped into a one-dimensional or two-dimensional discrete graph. Finally, the result is output at the competitive layer, and the characteristic structure of the input mode remains unchanged. The input layer is used to receive the input mode and the contention layer is used to display the output mode. The neurons in the competition layer and the neurons in the input layer are fully connected, and the neurons in the competition layer are connected to each other to complete the function of pattern clustering.  
SOM neural network is an unsupervised learning network that maps similar sample points in high-dimensional space to adjacent neurons in the network input layer. The nodes around the winning node respond to each other due to mutual influence. Therefore, the weight vectors connected by the winning node and all nodes in the winning field adjust to the input direction, gradually reducing the distance between them. Then through self-organizing learning and repeated learning. The spatial distribution density of the connection weights is consistent with the probability distribution of the input mode, and finally the feature map is formed at the input layer. The structure diagram of the SOM neural network is shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Gear Fault Type** & **Sample Number** & **K1** & **K2** & **K3** & **K4** \\ \hline \multirow{4}{*}{Normal gear} & 1 & 0.4373 & 0.1021 & 0.2595 & 0.2011 \\  & 2 & 0.4370 & 0.1026 & 0.2590 & 0.2014 \\  & 3 & 0.4370 & 0.1025 & 0.2592 & 0.2014 \\  & 4 & 0.4374 & 0.1020 & 0.2591 & 0.2015 \\ \hline \multirow{4}{*}{Gear with tooth wear} & 5 & 0.3738 & 0.1601 & 0.2725 & 0.1936 \\  & 6 & 0.3767 & 0.1579 & 0.2763 & 0.1891 \\  & 7 & 0.3730 & 0.1560 & 0.2771 & 0.1939 \\  & 8 & 0.3746 & 0.1600 & 0.2735 & 0.1919 \\ \hline \multirow{4}{*}{Gear with tooth crack} & 9 & 0.3337 & 0.1498 & 0.2995 & 0.2170 \\  & 10 & 0.3343 & 0.1499 & 0.2978 & 0.2180 \\  & 11 & 0.3351 & 0.1497 & 0.2992 & 0.2160 \\  & 12 & 0.3338 & 0.1496 & 0.2995 & 0.2171 \\ \hline \multirow{4}{*}{Gear with tooth break} & 13 & 0.2863 & 0.2491 & 0.3151 & 0.1494 \\  & 14 & 0.2859 & 0.2489 & 0.3149 & 0.1503 \\ \cline{1-1}  & 15 & 0.2880 & 0.2479 & 0.3163 & 0.1477 \\ \cline{1-1}  & 16 & 0.2856 & 0.2482 & 0.3163 & 0.1499 \\ \hline \end{tabular}
\end{table}
Table 2: The kurtosis values of thelMFs are normalized.  
Figure 14: SOM neural network structure.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 3 Link Budget Analysis
### Link Margin  
The link margin represents the excess power available, calculated as the difference between the received power \((P_{RX})\) and the minimum power required for the receiver's reliable communication, known as receiver sensitivity (RS). A positive link margin is essential for the effective establishment of a communication system. The formula for determining the link margin is outlined in Equation (1)  
\[LM=P_{RX}-RS \tag{1}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System Parameters** & **Specification** \\ \hline Operating (center) frequency & 3.5 GHz \\ Antenna gain (drone-mounted) & 10 dBi \\ UAM vehicle velocity & 45 m/s \\ Polarization & VV (Vertical) \\ Transmitter cable and connector loss & 3 dB \\ Receiver antenna diameter & 1.5 m \\ Antenna efficiency & 75\% \\ Base station power & 38 dBm (6G ground station) \\ Noise temperature & 300 K \\ Rain rate & 80 mm/h \\ Specific attenuation (k) & 2 dB/km \\ Polarization constant (\(\alpha\)) (for vertical polarization) & 0.07 \\ Specific attenuation for dry air (\(\gamma_{0}\)) & 0.08 dB/km \\ Specific attenuation for water vapor (\(\gamma_{nx}\)) & 0.5 dB/km \\ Minimum required C/N & \(-\)120 dB (for 6G) \\ Satellite altitude & 800 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.
## 4 Calibration and Application Experiments  
The prototype of the manufactured dual-axis precision level based on the measurement principle proposed in Section 2 is shown in Figure 5. The level body and the electronic box are separate so that they can be mounted together or separately according to the installation space. The laser diode has a wavelength of 635 nm (model DI635, Huanic, Xian, China,). The micro-autocollimator set, consisting of a high precision QPD (QP5.8-6-TO5, First Sensor, Berlin, Germany) and a focus lens (FL1, \(\varphi\)10, Tokyo, Hitachi, Japan), was constructed to detect 2D angle changes. The signal acquisition electronic device is to acquire and process the signal of the QPD using an analog-to-digital converter (ADC 7606, Analog Devices, Norwood. USA) and a micro-control unit (MCU, ARM SAMD21 Cortex-M0+, Microchip Technology). The measurement data is wirelessly transmitted to the computer via Bluetooth to avoid the influence of pulling the data cable when moving the level.
## 3 Cross-Layer Controller Tasking Scheme
### Data Collection  
A scheduling optimization dataset ([[https://zenodo.org/records/4106746](https://zenodo.org/records/4106746)]([https://zenodo.org/records/4106746](https://zenodo.org/records/4106746)), accessed on 15 December 2024) is utilized in the article to correlate the CLCT scheme with the performance metrics and factors. This dataset includes the work schedule of a textile industry operating under three (major) layers with individual machines for 16 h of tasks/day. The average time slot of a machine varies between 20 and 300 with 60 tasks/min (max). The dataset is represented in Figure 1.  
The above machinery representations are presented for hardening, ironing, and printing with independent controllers. The edge devices are connected to six controllers (three for the above process + categorization, cut, and stripping). The actual data is categorized (re-organized) to further suit the process flow of the proposed scheme. As mentioned, the dataset contains 256 tasks assigned with similarities and dissimilarities linking the 3 layers.  
The design goal of CLCTS is to maximize the productivity rate of the smart industry by reducing task acceptance error (TAE) in the IIoT combined smart industrial platform. Smart industry layers are used to gather data from multiple sources and processed through IIoT experiences of different types of machinery and edge devices that are deployed for improving productivity. The proposed scheme is capable of providing better productivity  
Figure 1: Dataset representation for CLCTS from the Source Industry Scene.  
and industrial management towards automation for all the layers in the smart industry. In particular, the EC intelligence and graph-combined learning using IIoT are prevented from TAE to achieve the performance improvement of smart factories.  
The function of CLCTS (Figure 2) is to provide reliable task-grouping and cross-layer control for avoiding time delay systems. Data gathered from the multiple source layers is stored and distributed for analysis to a centralized processing unit in the smart industry. The industrial environment and edge devices are connected through IIoT. The machinery and edge devices are regulated by the centralized controller in the smart industry. Accurate task allocation and distribution are administered to avoid the mis-scheduling of edge devices/machinery information. The IIoT technology enhances the production, maintenance, and outcome to ensure unchangeable task distribution between the smart industry's processing units. The function of task grouping and cross-layer control in the IIoT is used for task allocation, distribution, and similarity verification. The time delay and scheduling mismatches in devices in the smart industry are identified to prevent TAE from using edge computing and graph learning. The aforementioned processes are discussed in the following sections. Before the discussion, the symbols used throughout the article are described in Table 1.
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP2  
#### 3.3.1 Test Results and Analysis in Two-Dimensional Planes  
In this section, the PFG to each CIV at SCP2 will also be analyzed in planes similar as before.  
It can be seen in (a) of Figure 4, the PFG to CIV1 at SCP1 almost present the same changing rules as that at SCP1. In (b) and (c) of Figure 4, the curves of APFG and RPFG to CIV2 at SCP2 present similar shapes analogous to sinusoids. The curves in the normal operation conditions by Reg.1 and Reg.2 modes almost focus within range of \(-0.2\) p.u. to \(0.2\) p.u., while those in the severe operation condition by Reg.2 mode are two to four times larger, indicating that the PFG to CIV2 by Reg.2 mode are have more efficiencies and redundancies adapting to the severe operation conditions, but on this occasion, the resulting impulses or shocks to the power system during the regulation process may also be more intense, and the requirements for suppressing the resulting shocks are more rigorous at the same time. In (d) of Figure 4, the curves of APFG and RPFG regulated in the four regulation scenarios of CIV3 present sinusoidal shapes with different magnitudes and phases, revealing that the PFG to CIV3 can be regulated by various efficiencies in the four regulation scenarios, so as to adapt to all the operation conditions.  
As the same as before, the pictures (e)-(f) of Figure 4 also reflect the principles of PFG to CIV from another viewpoint. In (e) of Figure 4, the curves of APFG to CIV2 against RPFG to CIV2 at SCP2 by Reg.1 and Reg.2 modes also form symmetrical closed shapes, the regulation ranges and areas by Reg.2 mode in the third and fourth operation condition are much larger than others. Referring to (f) of  
Figure 3: Spatial surfaces of PFG to CIV2 at SCP1 in spaces: (**a–d**) Spatial surfaces of PFG to CIV2 by Reg.1 mode of output series inserted voltage (OSIV) in four operation conditions; (**e–h**) Spatial surfaces of PFG to CIV2 by Reg.2 mode of OSIV in four operation conditions.  
Figure 4, the curves of APFG against RPFG to CIV3 at SCP2 still form circular closed loops, and the areas of the loops can increase or decrease in different regulation scenarios.
## 3 The Presented New Method for Noise-Robust Feature Extraction
### The Characteristics of the Proposed Method  
Compared with some DNN-based speech feature extraction methods using MSE as the loss function [36], the newly presented method has the following potential advantages:  
1. Our method aims to maximize the state posterior accuracies of multi-condition acoustic models given the noise-corrupted data, which implicitly reduces the word error rates (WERs) for noisy speech recognition. In contrast, those methods minimizing the MSE among the clean-noisy speech feature pairs in the training set have little to do with the subsequent acoustic models and thus are not guaranteed to improve the recognition accuracy for noisy situations.
2. Our method employs the multi-condition training set to obtain GMM-HMM and DNN-HMM acoustic models in turn, which serve to train the denoising feature extraction network. However, we expect such a denoising framework to work well when using clean noise-free training data. The underlying reason is that the framework adapts the original speech features to fit the subsequent acoustic models. In the later section, we observe and discuss the corresponding results from the evaluation experiments.
# Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams
###### Abstract  
To investigate the anchorage loss mechanism of externally prestressed CFRP tendons in concrete beams, this study introduces a novel theoretical calculation system (TCS) developed through both the finite element method (FEM) and experimental validation. Firstly, the FEM and the proposed TCS were employed based on the mechanism of anchorage loss to compute the deformation of each part of the prestressed tendon-main beam connection system, ensuring result accuracy through mutual validation. Subsequently, field tests, designed according to FEM guidelines, measured the anchorage loss in externally prestressed CFRP tendons, with long-term monitoring included. Finally, experimental data were then used to refine the TCS. The results indicate that deformation at the connecting screw and the front end of the steel reaction frame constitutes approximately 95% of the total deformation, with theoretical calculations aligning closely with the FEM results. The field tests revealed that the anchorage loss of a 12 m long CFRP tendon under 950 MPa prestress accounted for about 35% of the total prestress loss. The discrepancy in deformation compared with the theoretical results was due to a gap of approximately 0.4 mm between the two threaded connections, which can be minimized by improving construction techniques. After correction, the calculation error was reduced to about 5%. Control variable studies confirmed that anchorage loss is influenced by the prestress level, the dimensions of the steel reaction frame front end, the connecting screw length, and the number of thread gaps. This study provides a comprehensive approach for accurately predicting and mitigating anchorage loss in externally prestressed CFRP tendons, with significant implications for future engineering applications.  
b +
Footnote †: journal: Applied sciences  
0000-0001-4146-246
## 3 Application to Field Data
### Multi-Mode Dispersion Curve Comparison  
Figure 5 shows the dispersion energy spectrum calculated by the working stations, including the spatial autocorrelation method and the frequency-Bessel function transform. There are some differences in dispersion modes between the two methods. The first higher-mode curve calculated by the frequency-Bessel transform method for partial frequencies above 10 Hz is more obvious, while by the spatial autocorrelation method the higher-mode data are not detected and a phenomenon of mode crossover with fundamental dispersion is exhibited. The frequency-Bessel transform method has a larger dispersion distribution range of the fundamental mode in frequencies below 10 Hz than the spectral range calculated by the spatial autocorrelation method. The signal-to-noise ratio of dispersion energy is also significantly high, and the dispersion energy is continuous, which makes it easier to identify and extract the dispersion points. When the frequency is higher than 10 Hz, the energy carried by the fundamental mode is much lower than that of the higher-mode mode. When the fundamental signal is disturbed by the higher-mode dispersion or body wave, it is necessary to use the higher-mode mode information to calculate the shear wave velocity structure.  
Figure 5: Comparison diagram of dispersion energy spectrum of measured data. **(a)** Dispersion spectrum of the spatial autocorrelation method. **(b)** Dispersion spectrum of the frequency-Bessel transform method.  
Therefore, the comparison diagram of the dispersion energy spectrum also indicates that the higher-mode dispersion curve cannot be extracted from background noise only by the spatial autocorrelation method, resulting in multiple solutions and instability of inversion by using only the fundamental mode.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
###### Abstract  
This paper proposes a method whereby a device can transmit and receive information using a beacon, and also describes application scenarios for the proposed method. In a multi-device to multi-device (MD2 MD) content-centric networking (CCN) environment, the main issue involves searching for and connecting to nearby devices. However, if a device can't find another device that satisfies its requirements, the connection is delayed due to the repetition of processes. It is possible to rapidly connect to a device without repetition through the selection of the optimal device using the proposed method. Consequently, the proposed method and scenarios are advantageous in that they enable efficient content identification and delivery in a content-centric Internet of Things (IoT) environment, in which multiple mobile devices coexist.  
multi-device to multi-device (MD2 MD); content-centric networking (CCN); Multiple Radio Access Technology (Multi-RAT); Internet of Things (IoT) Article
## 3 Materials and Methods
### Architecture of RED/BLACK Communication  
The architecture that physically realizes RED/BLACK communications encompasses specialized infrastructure and protocols aligned to securely segregate sensitive and public data functions in aviation systems. The foundation lies in physical separation through dedicated RED and BLACK hardware--distinct sets of radios, antennas, cabling, routing equipment, and control systems provisioned for each domain. It is important that the terminal devices--radio stations--are identical in both domains and differ only in tuning frequencies.  
Network architectures implement logical controls and monitoring systems to sustain electronic separation. RED networks are further hardened through encryption, access restrictions, and data leakage countermeasures integrated through secure gateways for external interfaces. BLACK networks leverage standard cybersecurity protections.  
A hallmark of the RED/BLACK environment is sustained redundancy within each domain ensuring maximum information assurance. RED systems mandate comprehensive redundancy with dedicated failover infrastructure guaranteeing near-continuous functionality. BLACK implements cost-optimized redundancy suiting commercial reliability parameters.  
The specified architecture, divided into two domains, creates high security for the operation of the RED domain, but at the same time requires a certain redundancy due to the allocation of communication resources, which generally reduces the efficiency of the use of communication equipment.
## 3 Application for Target Tracking Problem
Since little research is going on in the case of the FMS smoother for nonlinear systems, the proposed unified algorithm is currently difficult to use this type of state space model. Thus, this paper consider only the linear measurement equation. The application of the nonlinear measurement equation can be a future work of the current paper. Using (13) with (15) and (16), the proposed unified algorithm of a FMS filtering and smoothing-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Via extensive computer simulations, the performance of the proposed unified algorithm-based target tracking scheme is evaluated and compared with the existing IMS filtering-based scheme of [20; 21; 22]. Computer simulations are performed for moving target's accelerations of four kinds of shapes. The first and second scenarios consider that the target moves with suddenly changing acceleration such as step-type and ramp-type. The third case considers that the target moves with slowly changing acceleration such as triangle-type. The last case considers that the target moves with randomly changing acceleration. The sampling period is taken by \(T=2\). The covariance of the system noise is \(Q=diag(0.173^{2}\ \ 0.01^{2})\) and the covariance of measurement noise is \(R=0.05^{2}\). The window length is taken by \(M=15\). The lagged length is taken by \(d=5\). Simulations of 30 runs are performed using different system and measurement noises to make the comparison clearer. Each single simulation run lasts 800. Figure 3: Simulation result for slowly changing acceleration(triangle-type). Figure 2: Simulation result for suddenly changing acceleration(ramp-type).
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 3 Results
### Storms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Date Range**} & \multicolumn{2}{c}{**Dates of Available Image**} & \multicolumn{2}{c}{**Average DNB**} & \multirow{2}{*}{**PNL**} & \multirow{2}{*}{**P\({}_{\rm nopower}\)**} \\  & & & & **nW-cm\({}^{-2}\)sr\({}^{-1}\)** & & & \\ \hline \multirow{3}{*}{Before Storm} & \multirow{3}{*}{20 August –20 September} & 22 August–24 August, 28 August, & & & \\  & & 30 August, 1 September, & & & \\  & & 18 September & & & \\ \hline \multirow{6}{*}{After Storm} & \multirow{3}{*}{21 September –10 October} & 27 September –29 September, & & & \\  & & 7 October, 8 October & & & \\  & 13 October, 15 October, 18 October, & & & & \\  & 21 October –30 October & 22 October –27 October & 1.57 & 34.8\% & 77.7\% \\  & 31 October –9 November & 31 October, 1 November, & & & \\  & 03 January –12 January & 3 January, 4 January, 10 January & 3.20 & 70.7\% & 41.7\% \\  & 13 January –15 January, & & & & \\  & 13 January –22 January & 3.16 & 69.9\% & 36.0\% \\  & 23 January –1 February & 4 February, 9 February, & & & \\  & 10 February, 11 February & & & & \\  & 22 February –3 March & 22 February & -24 February, & & \\  & 27 February, 3 March & & & & \\  & 4 March –13 March & 4 March, 7 March, 11 March, & & & \\  & 12 March, 13 March & 4.09 & 90.4\% & 11.0\% \\  & 14 March –23 March & 14 March, 15 March, 17 March, & & & \\  & 19 March, 20 March & 3.76 & 83.2\% & 7.6\% \\  & 24 March –2 April & 24 March, 28 March & 4.32 & 95.5\% & 5.8\% \\ \hline \end{tabular}
\end{table} 
Table 7: The PNL and the percent of customers without power (P\({}_{\rm nopower}\)) values of Puerto Rico after Hurricane Maria. \begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline  & \multicolumn{3}{c|}{**Pre-Storm**} & \multicolumn{3}{c}{**Post-Storm**} \\ \hline Date & 27 September & 28 September & 29 September & 14 October & 15 October & 16 October & 19 October \\ \hline Days Before (After) & & & & & & \\ the Disaster & 15 & 14 & 13 & 2 & 3 & 4 & 7 \\ Cloud Coverage (\%) & 15.6 & 6.6 & 0.0 & 19.8 & 4.9 & 8.6 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The dates and cloud coverages of pre- and post-storm DNB images of the Cyclone Hudhud. However, there were also some places showing an increase in light after the cyclone. We identified these locations as Points of Interest (POIs) in the Bing high-resolution satellite map (Figure 7c, \(P_{1}\)-\(P_{5}\)). \(P_{1}\)_to_\(P_{4}\) are industrial sites (Power Grid, Thermal Power Plant, Petro Chemicals Limited, and Container Terminal), so the increase in light is attributable to the increased production of support power restoration. However, we did not note any abnormality at \(P_{5}\), so we can only suspect this may have been related to fire or some transient lighting for facility repairs. Figure 7: **(a)** The pre- and (b) post-storm averaging composite DNB images, (c) PNL image and Bing satellite images for the brighter region of Cyclone Hudhud.