esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodologies
### Impulse Detection Using the Constant False Alarm Rate (CFAR) Algorithm  
In this study, impulse detection is performed with the CFAR algorithm. CFAR is a popular data-based target-detecting technique that excels in environments where background noise and interference are varying, especially for radar systems. A threshold based on power is determined so that when a signal segment exceeds it, this segment can be considered a \"hit\" on the target.  
Most CFAR schemes utilize a power threshold calculated from the noise floor of the cell blocks surrounding the cell under test (CUT). Several cells adjacent to the CUT are neglected to protect the training cells from signal leaking, which can negatively influence the noise estimation. Figure 6 describes a simplified CFAR scheme.  
Figure 5: Training procedure of an SAE.  
In this study, the cell average CFAR with acknowledged stability and robustness [30] is chosen for implementation. The threshold for detection \(T\) is first computed as follows:  
\[T_{i}=\alpha P_{i} \tag{5}\]  
with \(\alpha\) as the threshold factor and \(P_{i}\) being the estimated noise power, as calculated:  
\[P_{i}=\frac{1}{2N}\sum_{\ell=G+1}^{G+N}|C_{i+\ell}|^{2}+|C_{i-\ell}|^{2} \tag{6}\]  
where \(N\) and \(G\) are the number of training cells and guard cells, respectively. Generally, the number of leading and lagging cells are set as equal. The threshold factor \(\alpha\) is harnessed to control the number of detected targets in a directly proportional relationship:  
\[\alpha=N(P_{fa}^{-1/N}-1) \tag{7}\]  
with \(P_{fa}\) being the desired false alarm rate. This parameter should be chosen with caution, since it is a trade-off between the number of detected targets and the number of detected false targets.  
This study deals with signals in one-second segments, each of which is then divided into 2000 cells for analysis. Both leading and lagging blocks contain 30 cells, in which 20 are used for training and the rest are guard cells. Equations (5)-(7) are used to calculate the adaptive threshold of each CUT. A target is detected when the power of the CUT exceeds its threshold. The concept and results of this algorithm are shown in Figure 7.  
Figure 6: Diagram of a CFAR detection scheme.  
Figure 7: The application of CFAR detection for impulse detection. (**a**) Power of cells versus adaptive threshold, (**b**) AE impulse automatically picked during 1-second length.
## 2 Methods and Materials
### External Load Data Collection  
During all the training sessions, a global positioning system (18 Hz, GPEXE, Exelio SRL, Udine, Italy) was used to monitor the EXLD. The device was placed inside a pocket (between the scapulae) on a specific sports vest. [17]. This equipment is reliable for determining movement patterns [53; 54; 55]. To establish connection with the satellites, the GPS units were activated 15 min before the start of the training session [56]. Each device needed a minimum of 8 satellites and the players always wore the same device (to avoid inter-device variability from affecting the collected data) [57]. The analysis was performed using the variables listed below: training duration (DUR), acceleration actions of 2-3 m/s\({}^{2}\) (ACm), high-acceleration actions > 3 m/s\({}^{2}\) (ACh), deceleration actions of \(-\)2--3 m/s\({}^{2}\) (DCm), high-deceleration actions < \(-\)3 m/s\({}^{2}\) (DCh), total distance (TD), distance in speed zone 14.4-19.7 km/h (SPm), distance in speed zone 19.8-25.2 km/h (SPh), distance in speed zone > 25.2 km/h (SPvh), number of actions > 19.8 km/h (SPA), maximal speed (MSP), metabolic power distance 15-25.5 w/kg (PWm), metabolic power distance 25.5-50 w/kg (PWh), metabolic power distance > 50 w/kg (PWvh), number of metabolic power actions > 25.5 w/kg (PWA) [19; 58].
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.9. Russian Forest Biomass Map  
[PERSON] et al. [71] combined field data with MODIS reflectance data using the Random Forest model, and mapped the spatial distribution of living forest biomass in Russia at 500 m spatial resolution for the year 2000. The model underestimated regions with high biomass and overestimated regions with low biomass, with an overall biomass data error of circa 40%.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Date** & **M** & **Causative Source(s)** \\ \hline
1\_CNAFS & 14 January 1703 & 6.9 & The southern half of the ITCS028 \\ \hline
2\_CNAFS & 3 June 1781 & 6.5 & The northern 30\% of the ITCS129 \\ \hline
3\_CNAFS & 7 September1920 & 6.5 & The northern half of the ITCS083 \\ \hline
4\_CNAFS & 30 October 2016 & 6.6 & The southern half of the ITCS127 \\ \hline
1\_NMTFS & 17 December 1096 & 8.4 & CD/CDE \\ \hline
2\_NMTFS & 22 February 1099 & 8.4 & AB \\ \hline
3\_NMTFS & 3 August 1361 & 8.4 & AB/ABCD/ABCD \\ \hline
4\_NMTFS & 20 September 1498 & 8.6 & CDE/ABCD \\ \hline
5\_NMTFS & 3 February 1605 & 7.9 & ABCD \\ \hline
6\_NMTFS & 28 October 1707 & 8.4 & ABCDE \\ \hline
7\_NMTFS & 23 December 1854 & 8.4 & CDE \\ \hline
8\_NMTFS & 24 December 1854 & 8.4 & AB \\ \hline
9\_NMTFS & 7 December 1944 & 8.2 & CD \\ \hline
10\_NMTFS & 20 December 1946 & 8.2 & AB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Historical and instrumental earthquakes that ruptured segments from the year 1000 of the CNAFS (having a magnitude equal to or larger than 6.5; DISS Working Group 2021 [25]; [PERSON] et al., 2022 [19]; Table S1), and the NMTFS (having a magnitude equal to or larger than 7.9; [PERSON] et al., 2012 [20] and references therein; Table S2). For CNAFS, magnitudes refer to Mw (converted from macrosiexia data for historical earthquakes or measured for the 2016 event). For NMTFS, magnitudes were mostly obtained from tsunami records and possibly referable to Mw. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **CNAFS** & **NMTFS** \\ \hline
**Region** & Italy, central-northern Apennines & Japan, Nankai \\ \hline
**Historical earthquakes** & More than ten centuries of records available & More than ten centuries of records available \\ \hline
**Largest earthquake** & 14 January 1703, M 6.9 & 20 September 1498, M 8.6 \\ \hline
**Fault system** & 43, Crustal & 1, Subduction \\ \hline
**Horizontal velocity** & About 3 mm/yr & About 60 mm/yr \\ \hline
**Maximum slip rates** & 1.35 mm/yr & 60 mm/yr \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the main characteristics of the two geodynamic contexts.
###### Abstract  
Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.  
stereo matching; cost measurement; adaptive shape guided filter; census transform; textureless regions +
Footnote †: journal: Applied Sciences
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Acoustic Sensors
#### 4.3.1 PD Detection in Gas-Insulated Switchgear  
The GIS is designed as a sealed structure, filled with pressurized gas. As an insulator, SF6 gas is more effective than air because it exhibits higher dielectric strength, acts as an electronegative medium to capture free electrons and prevent electrical discharges, is non-flammable, possesses an extremely stable molecular structure, and serves as a cooling medium. The acoustic emission (AE) reflected off the metal walls of the GIS can be used to discern PD. The acoustic waves created by PD have a wide frequency range (20 kHz-250 kHz) and a radical symmetry. The PD measurement at GIS can be conducted using a touch sensor, as shown in Figure 15.  
Figure 14: The two most common chemical methods for detecting PD, including (**a**) hydrogen-in-oil detector and (**b**) continuous monitoring [127].
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Performance of the Link Discovery (OFDP)
#### 2.6.1 OFDP in Huge and Dynamic Environments  
However, there is not much research analyzing the OFDP performance on the SDN networks and most of the researchers in OpenFlow performance analysis have concentrated on SDN-Controller types such in [55; 72; 73; 74]. Therefore, OFDP performance analysis would be appropriate to open up this issue to the researchers to search in these domains.  
In this work, we will discuss the performance issues of topology discovery in the OpenFlow protocol and its impact on the SDN-Controller, where OFDP will have a major role.  
In [69], the authors investigated the OFDP performance for transport networks (i.e., backbone service provider networks) and found that when carrier rank requirements are met, the transport networks should recover from link failure within a maximum of 50 ms. Thus, to achieve a 50 ms recovery time for OpenFlow-based carriers, the topology discovery service at the SDN-Controller should run approximately every 10 ms. Therefore, the SDN-Controller must check hundreds of LLDP Packet_Out messages per active SDN-Switch port every second to detect one direction per link. Moreover, it also must receive and process two hundred LLDP Packet_In messages every second for each link and end-to-end tunnel. In addition, transport networks have hundreds of links and thousands of tunnels. This means that the SDN-Controller has to handle millions of messages per second just to monitor the health of the network. This undoubtedly imposes a large load on the SDN-Controller and also a large overhead on the control network, especially for in-band control channels. In addition, another type of network environment where OFDP shows performance issues is multi-tenant cloud data centers. In such environments, the network topology is dynamic, because the tenant can build and modify their network; They can add and remove SDN-Switches or links at any time. This means that the topology can dynamically and continuously change [38]. Hence, the SDN-Controller must be efficient enough to maintain an updated network topology. However, for OpenFlow networks where OFDP is the link discovery protocol, the SDN-Controller only discovers the topology at periodic, constant, and relatively long intervals (floodlight controller for example every 15 s). Therefore, the SDN-Controller only realizes the new topology changes in each discovery round, and if an error occurs, the error correction needs to wait for the next topology discovery round which is too long [75]. As a result, application-level network applications such as routing will use the wrong network configuration until at least the next topology round.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 3 Materials and Methods
### Yolo  
The YOLO algorithm is a popular object detection method that comprises a backbone network for feature extraction, a neck network for feature aggregation, and predictive heads. Originally created by [PERSON] et al. in 2015, the architecture makes use of a single network for bounding box predictions and class probabilities [24]. By the introduction of YOLO, many new architectures have been proposed building upon the foundation of YOLO and improving detection accuracy and speed, such as V5 [25], V7 [26], V8 [27], and V9 [28]. Each of these architectures has its own merits and drawbacks depending on the application in terms of speed versus accuracy. Our focus in this paper is on YOLOv5, YOLOv8, and YOLOv9 due to their performance compared to the other architectures.  
YOLOv5 modifies the backbone and neck, improving both the accuracy of the model and the real-time performance [25]. The modifications made to the backbone architecture include the design and implementation of Cross Stage Partial (CSP)-Darknet53 and modifications to the neck include Cross Stage Partial Path Aggregation network (CSP-PAN) and Spatial Pyramid Pooling Layer (SPPF) structures [25].  
YOLOv7 proposes a trainable bag-of-freebies to allow for the optimization of the training process in which accuracy can be improved without high inference computational costs. The key proposed features include model re-parameterization to help with gradient flow and dynamic label assignment to better handle multi-head training, among other reductions in model size through proposed scaling systems.  
YOLOv8's proposed architecture enhancements include improvements to the backbone, neck, head, and anchor proposal system [27]. An overview of the architecture can be seen in Figure 1.  
YOLOv9 is one of the most recent iterations of the YOLO family of architectures [28]. Their work addresses the data loss of traditional deep networks through the proposed Programmable Gradient Information (PGI) structure. This structure comprises three main components, the main branch, auxiliary reversible branch, and multi-level auxiliary information [28]. The main branch is responsible for inference, while the two auxiliary branches feed information to the main branch assisting in learning. The auxiliary branches are illustrated in Figure 2.
## 5 Conclusions  
In this paper, a novel approach of using LEO satellite microwave signal links for cloud measurements is proposed. The feasibility of the approach is investigated using simulations, in which a synthetic cloud attenuation field is generated and the SNR estimation at the ground receivers is simulated. We use two types of basis functions to define the targeted field and employ a constrained least-squares algorithm to retrieve it. We also propose a piecewise linear (PL) function to quickly correct the overestimation issue due to the cloud LWC induced sky noise in the initial least-squares computing. Furthermore, partial retrieval can be achieved when only a limited number of receivers with restricted locations are available. The fact that the retrieval is satisfactory for both thin and heavy cloud areas suggests that the proposed approach has great potential. Its high resolution and low level of error imply that it would be very useful to the observations of the micro- and macro-physical details of nonprecipitating clouds.  
Many aspects of the proposed approach still need further investigations. Testing the model with other cloud types, especially the ones primarily in the lower troposphere, is underway. Moreover, studies on precipitating clouds will be carried out, in which the main challenge is to distinguish the attenuation field induced by cloud liquid water from that induced by the rain water.  
Conceptualization, D.D.H. and X.S.; methodology and simulation, X.S.; cloud field data generation, A.F.P. and W.W.; writing, X.S.; supervision, D.D.H. and R.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by Australian Research Council (ARC) under Grant DP190100786, was also supported in part by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under Grant No. DE-SC0020050. NCAR is sponsored by the National Science Foundation.  
We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6 RX99 HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation.  
The authors declare no conflict of interest.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 3 Semiconductor Switch Technologies  
### Well-Established Si IGBT and MOSFET Transistors  
The beginnings of the use of today's well-established power switch components, such as Si MOSFET and IGBT transistors in applications with switching frequencies in the range of a few kHz to several tens of kHz, can be traced back to the early 1980s. They enabled significant progress in efficiency, voltage and current rating, and reliability of existing electrical power converters, as well as development of various new industrial and consumer electronics devices [10].  
Recently, the \"super junction\" technology of MOSFETs (e.g., CoolMOS) has enabled such advances in the reduction in \(R_{DS,on}\) values that in the range of rated voltages between 600 V and 900 V IGBTs are increasingly being replaced by suitable MOSFETs. On the other hand, however, the development of IGBTs has not stalled. Recent research results show that with a suitable modification of the geometry of the so-called \"mesa\" structure, the saturation voltage (\(U_{CE,sat}\)) can be reduced to only 80% of the value of conventional IGBTs [118]. While on the one hand IGBTs are replaced by MOSFETs in the lower-voltage and lower-power applications systematically [119], IGBTs are on the other hand successfully replacing GTO byristors in the field of high-power electric traction with voltages of up to 6.5 kV (Figure 13) [120]. In this respect, Hitachi Energy (ABB) offers solutions with the most powerful elements for 6.5 kV/1000 A (e.g., HiPak IGBT module 5 SNA 1000G650300 [121]), 5.2 kV/3000 A (e.g., StakPak BIGT module 5 SJA 3000L520300 [122]), etc.  
The latter is currently the most powerful commercially available semiconductor module in StakPak housing. It is the result of a new development in Reverse Conducting IGBT (RC-IGBT) technology, in which the integration of the anti-parallel diode into the IGBT structure has been optimized. A single silicon chip is used for both forward and reverse conduction. This technology is called Bi-mode Insulated Gate Transistor (BIGT); the technology makes it possible to combine the properties of modern low-loss IGBTs with the ability of the diode to withstand large short-circuit currents [123].  
Not so long ago, semiconductor-based converters with efficiencies of 96% to 98% were considered as approaching the border of impossible [124]. It seemed that efforts to further improve efficiency did not make sense. However, sometime in the 2000s, the industry started to develop wide bandgap (WBG) components [1]. In 2001, the first silicon carbide (SiC) diode was introduced, followed by the first SiC JFET in 2006. The first gallium nitride (GaN) components were introduced in 2010 [125; 126].
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Pipeline  
Figure 10 depicts the processing pipeline. The supervised pipeline was first introduced by [PERSON] et al. [36] and [PERSON] et al. [57]. Data flows from the left (input to the model) to the right (predictions of the model). The listeners (i.e., the sensor nodes) receive the signals (with or without interference) in real-time. The signals are collected, synchronized, and stored (i.e., snapshots from multiple listeners) in the database in the cloud.  
The multi-stage framework pre-processes features from different (multi-modal) input streams and fuses them (Section 5.2). Entropy, kurtosis, CN0, AGC, and elevation features are extracted from data from deterministic and realistic measurement campaigns. High-rate (1.0 Hz) sampled or interpolated snapshots of both spatial features and time series data thereof are fed to the detection and classification components as a spectrogram (i.e., image) or as time series (i.e., array). From there, the database pushes new signals to the detection component. The classification component further categorizes only the detected interference signals.  
In a post-processing step, Monte Carlo dropout (MCD) [98] is applied to the fused components to assess the uncertainty of each estimate (see Section 5.4). The \(F_{\beta=2}\) score evaluates the performance and efficiency of the framework (see Section 5.4). Next, a detection event is stored (states: interference and no interference), along with the category, the uncertainty values for both the detection and each category, and a compressed version of samples used in the database.  
Figure 9: System usage with all modules running according to Profile 1.  
From there, the database handles requests for the visualization component (see Section 5.4.1). The graphical user interface (GUI) visualizes selected sensor nodes at known locations (see the blue antenna symbols in Figure 10), and the appearance of interference signals (see the red antenna symbol), when a listener detects an interference. The GUI also handles the history of events, thus visualizing a heat-map of interference signals along fixed deterministic trajectories (roads). Red indicates a high amount of detected interference, and the green represents no detected interference. For details on the visualization component, see Section 5.4.1.