esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 2 Materials and Methods
### Reference Wind Speed Products  
#### 2.2.1 Buoys  
Surface wind and rain observations from several networks of tropical moored buoy arrays (PIRATA [17], RAMA [18], and TAO/TRITON [19]--see Figure 2) were processed and used as reference data for the CYGNSS wind validation. The validation analysis involves averaging buoy measurements over 60 min intervals and screening them to exclude instances of missing data and data flagged for low quality or sensor failure, which follows the methodology described in [4; 20]. The buoy wind speed accuracy is \(\pm\)0.3 m/s or 3%, with further details available at [[https://tao.ndbc.noaa.gov/proj_overview/sensors_ndbc.shtml](https://tao.ndbc.noaa.gov/proj_overview/sensors_ndbc.shtml)]([https://tao.ndbc.noaa.gov/proj_overview/sensors_ndbc.shtml](https://tao.ndbc.noaa.gov/proj_overview/sensors_ndbc.shtml)) (accessed on 31 January 2024).  
Rain gauges are subject to undercatchment errors, particularly in windy conditions. These errors can be substantial, reaching up to 50% at wind speeds of 12 m/s [21]. The data retrieved from the NOAA/PMEL FTP site lack direct corrections for undercatchment. To mitigate this, we employed an approach based on [22] and subsequently by [20], applying a third-order polynomial fit to the dataset to correct for undercatchment in the rain measurements.  
Given that CYGNSS winds were reported at a height of 10 m, adjustments were made to buoy wind measurements to align them with this height. The height adjustment was made by applying Monin-Obukhov similarity theory using the COARE (Coupled Ocean-Atmosphere Response Experiment) version 3.6 algorithm, as described by [23; 24]. Additionally, buoy wind speeds were converted to equivalent neutral winds to facilitate a direct comparison with the CYGNSS measurements [25].  
In the collocation process, CYGNSS winds were matched within a radius of 25 km and \(\pm\)30 min of buoy locations and timestamps, using an inverse-distance-weighting (IDW) approach that incorporates both spatial and temporal proximity [4; 20; 26]. During the matchup process, daily GPS data are utilized to track buoy position over time. A recent observation has identified a notable abrupt temporal instability in the TAO buoy wind time series, characterized by fluctuations of 0.5-0.8 m/s (10%) since mid-2020 [27]. As a result, our buoy CYGNSS verification analysis is limited to observations prior to June 2020. Moreover, the matchups were based on identical sample points between FDS and NOAA to ensure a common set of comparison samples. This results in an aggregated sample size of 21,029 time-space matchup points.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 5 Conclusions  
To address the problem of degraded navigation accuracy caused by GNSS signal blockage in obstructed spaces and insufficient feature points and lighting changes in VNS, this paper proposed an improved AFKF INS/GNSS/VNS integrated navigation algorithm. The algorithm uses INS as the common reference system and constructs sub-filters 1 and 2 by utilizing the position and velocity information of GNSS and the attitude information of VNS as measurement vectors, respectively. A fault detection method combining the proposed residual chi-square test and sliding-window average technique was employed to effectively avoid false alarms and missed detections, thereby improving the fault tolerance of the integrated navigation system. An adaptive information sharing factor was proposed to adaptively allocate the information contribution of each sub-filter according to their respective state covariance matrices, which enhanced the positioning accuracy of the integrated navigation algorithm. Finally, simulations were conducted to evaluate the performance of the improved AFKF algorithm in the presence of obstructed GNSS signals and disturbances affecting the VNS and GNSS sensors separately. Two scenarios were considered where the level of interference varied for the GNSS signals. In both scenarios, the improved AFKF algorithm demonstrated a significant reduction in the position and velocity errors compared to the classic FKF algorithm. Although the improved AFKF algorithm had increased computational requirements, it still met the real-time navigation requirements for unmanned ground vehicles operating in obstructed environments. To further validate the effectiveness of the algorithm, we also conducted simulation experiments on the Gazebo platform. The results of this study are of significance for the practical implementation of navigation systems in obstructed environments.  
All named authors initially contributed a significant part to the paper. Conceptualization, X.W. and Z.S.; methodology, X.W.; software, X.W.; validation, X.W. and L.L.; formal analysis, X.W.; investigation, X.W. and Z.B.; resources, X.W. and L.L.; data curation, X.W. and X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; visualization, X.W.; supervision, Z.S.; project administration, Z.S.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant no. 2020 YFC1511702, 2022 YFF0607400), the National Natural Science Foundation of China (61971048), the Beijing Science and Technology Project (Z221100005222024), the Beijing Scholars Program, and the Key Laboratory of Modern Measurement & Control Technology, Ministry of Education.  
Figure 9: Comparison of navigation errors between improved AFKF and FKF: (**a**) Comparison of position errors. (**b**) Comparison of velocity errors.
## 1 Introduction
### Aim, Objectives and Data Sources  
The aim of this research was to generate a fire risk map on the scale of 1:100,000 for Son La Province and to develop a real-time early warning system. This was achieved through the application of multi-criteria analysis (MCA) and GIS tools to analyze the relationship between natural and social factors and the risk of forest fire. The data sources included the following: (1) 1:100,000 topographic background dataset from the Ministry of Natural Resources and Environment; (2) data on forest cover, population and the current status of resource exploitation from the Department of Agriculture and Rural Development, Department of Natural Resources and Environment of Son La Province, edited and supplemented by Landsat imagery; (3) the meteorological data extracted from the Central Meteorological Office of Vietnam supplemented by local station records (seven stations). In order to make more practical use of the forest fire risk map, we also developed an early warning system that the authorities can use to obtain information in advance that forest fire risk is high in a particular locality.
## 3 Materials and Methods
### Scanning Electron Microscopy (SEM)  
The selected samples were milled using an Ar-ion beam in a Leica EM TIC 3X device (Leica Microsystems GmbH, Wetzlar, Hessen, German), and imaged using a Zeiss Merlin field-emission scanning electron microscopy (FE-SEM) (Carl Zeiss AG, Jena, Thuringia, German) at 1.2 kV at the Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences. The organic matter (OM) and minerals were identified using energy-dispersive spectroscopy (EDS) with a Bruker Quan tax system equipped with the Esprit 1.9 software (Bruker AXS GmbH, Karlsruhe, Baden-Wurttemberg, German). Pore sizes were measured on SEM images using Image-Pro Plus 6.0 software [66; 67].  
The areas of primary POM and MSB were measured based on SEM images using Image-Pro Plus 6.0 software, and the proportions of POM and MSB areas to the total organic matter area were calculated by Equations (1) and (2).  
\[\mathrm{R_{POM}}=\mathrm{S_{POM}}/(\mathrm{S_{POM}}+\mathrm{S_{MSB}})\times 100 \tag{1}\]  
\[\mathrm{R_{MSB}}=\mathrm{S_{MSB}}/(\mathrm{S_{POM}}+\mathrm{S_{MSB}})\times 100 \tag{2}\]Here, S\({}_{\rm POM}\) represents the total pixel area of POM, S\({}_{\rm MSB}\) represents the total pixel area of MSB, R\({}_{\rm POM}\) is the proportion of the POM area to the total organic matter area (%), and R\({}_{\rm MSB}\) is the proportion of the MSB area to the total organic matter area (%).
## Abbreviations  
\begin{tabular}{l l} \(\upmu\)P & Micro-processor \\ AES & Advanced Encryption Standard \\ ASIC & Application-Specific Integrated Circuit \\ BB84 & A Quantum Key Distribution Scheme by Bennett and Brassard in 1984 \\ CAM & Content-Addressable Memory \\ CoAP & Constrained Application Protocol \\ CSR & Configuration Status Registers \\ DBS & Data Block Size \\ DES & Data Encryption Standard \\ DH & Diffie-Hellman \\ D-OTP & Double OTP \\ DTLS & Datagram TLS \\ EPCS & Serial Configuration Device \\ FIFO & First-In First-Out \\ FPGA & Field-Programmable Gate Array \\ gNB & Basestation of 5G \\ GPU & Graphics Processing Unit \\ HW & Hardware \\ ICS & Industrial Control System \\ IETF & Internet Engineering Task Force \\ IIS & Industrial Information System \\ IoT & Internet of Things \\ IP & Internet Protocol \\ JPL & Jet Propulsion Laboratory \\ KS-Generator & Key Stream Generator \\ M2M & Machine-to-Machine \\ MEC & Multiaccess Edge Computing \\ MEM-CAM & RAM-based Content-Addressable Memory \\ mMTC & Massive Machine-Type Communications \\ NASA & National Aeronautics and Space Administration \\ NB-IoT & Narrow Band Internet of Things \\ NIC & Network Interface Controller \\ NIST & National Institute of Standards and Technology \\ NR-KB & Non-Repeating Key Block \\ NR-PRNG & Non-Repeating PRNG \\ \end{tabular}
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 2 Preliminaries
### Sets, Numbers, and Graph Theory
A matching \(M\) of \(G\) is said to _saturate_\(A\) if \(A\subseteq V(M)\). A matching is said to be _perfect matching_ if all vertices are matched by \(M\). A cycle \(C\) in a graph is said to be an _induced cycle_ if \(G[C]\) has no edge other than the edges of the cycle. A graph is said to be a _chordal graph_ if it has no induced cycle of a length of at least four. An _out-tree_ is a directed graph where each vertex has an in-degree of (at most) \(1\), and the underlying (undirected) graph is a tree. An _out-forest_ is a disjoint union of out-trees. A graph is said to be _\(k\)-colorable_ if there is a function \(\lambda:V(G)\rightarrow[k]\) for some \(k\in\mathbb{N}\) such that for every \(uv\in E(G)\), \(\lambda(u)\
eq\lambda(v)\). Such a function \(\lambda\) is called a _proper coloring_ of \(G\). It is well-known that a bipartite graph is \(2\)-colorable. Graph \(G\) is said to be _factor-critical_ if for every \(u\in V(G)\), the graph \(G-u\) has a perfect matching. For other terminologies of the graph theory, we use standard graph-theoretic notations by Diestel [12]. We use the following well-known proposition several times in the paper. **Proposition 1** (Hall's Theorem [13]). : _Let \(G=(V=A\uplus B,E)\) be a bipartite graph. Then, \(G\) has a matching saturating \(A\) if and only if for every \(X\subseteq A\), \(|N(X)|\geq|X|\)._  
Given a bipartite graph \(G=(A\uplus B,E)\), a set \(X\subseteq A\) is said to be a _hall set_ if \(|N(X)|<|X|\). A hall set is said to be _minimal_ if none of its proper subsets is a hall set. Given a bipartite graph \(G=(A\uplus B,E)\), a minimal hall set can be computed in polynomial time using Proposition 1. Before we state the following proposition, we need a few notations. For a graph \(G=(V,E)\), suppose that \(S\subseteq V(G)\) and a set \(\mathcal{C}\) of vertex-disjoint subgraphs of \(G-S\), let \(\mathcal{N}_{G}(S,\mathcal{C})=\{H\in\mathcal{C}\mid E(S,V(H))\
eq\mathcal{O}\}\). **Proposition 2** (Gallai-Edmonds structure theorem [14; 15]). : _For every graph \(G=(V,E)\), there are disjoint subsets \(X,Y,Z\subseteq V(G)\), such that_  
1. \(X\cup Y\cup Z=V(G)\)_;_
2. \(Y=N_{G}(X)\)_;_
3. _Every connected component of_ \(G[X]\) _is factor-critical;_
4. \(G[Z]\) _has perfect matching; and_
5. _For every nonempty subset_ \(Y^{\prime}\subseteq Y\)_, it holds that_ \(|\mathcal{N}_{G}(Y^{\prime},\mathcal{C})|\geq|Y^{\prime}|\)_._  
_Furthermore, such \(X,Y,Z\) can be computed in polynomial time._
## 1 Introduction
In particular, each parallel unit of the proposed method uses one coordinate rotation digital computer (CORDIC) period per iteration, while more are required by the traditional counterparts, such that the eigenvalue decomposition of the MUSIC algorithm. In [15] the authors use Cyclic Jacobi method to implement EVD processor, which can achieve hardware simplification. To reduce the latency, this paper proposes using the neural network model to calculate the values of arctangent, sine and cosine function instead of using the traditional CORDIC (Coordinate Rotation Digital Computer) method. The proposed NN-based Cyclic Jacobi EVD processor was operated at 250 MHz in TSMC 90 nm CMOS technology. This paper consists of a simulation phase and a prototyping phase. The simulation will make it possible to develop the MUSIC algorithm and validate its operating principle with the Xilinx Vivado software. Prototyping will consist of migrating this algorithm to a platform based on an FPGA circuit to verify its technical feasibility and evaluate its performance. It is in this prototyping phase that various constraints may arise, related to the precision of the calculations, the speed of execution (real-time operation) and the testability of the system. The implementation of the MUSIC algorithm for real-time processing will exploit several advanced techniques, including frequency sub-band averaging which will artificially reduce the level of inter-correlation between echoes. These techniques will increase execution speed and push memory limits. One of our contributions in this research concerns the covariance matrix. Exploiting the Hermitian symmetry characteristic of the covariance matrix, the multiplication operations affect only half of the matrix, the other half is deduced by a simple sign change. Another contribution concerns the choice of an EVD decomposition technique with a minimum computational burden while maintaining good precision. The selected candidate is the Jacobi method. The remainder of this paper is organized as follows. In Section 2, we present signal representation and covariance matrix. In Section 3, we handle the hardware architecture design based on Cyclic Jacobi Method. In Section 4 we provide a comparison and a discussion of this work with other research. Finally, Section 5 concludes the paper.
# Determination of Kinematic and Dynamic Characteristics of Oscillating Conveyor Mechanism
## 2 Materials and Methods
### Differential Equations of Motion of the Joints of an Oscillating Conveyor Mechanism
The study highlights the sensitivity of the oscillating conveyor mechanism to initial conditions, but a more detailed sensitivity analysis of key parameters such as driving torque (\(M_{d}\)), resistance coefficient (\(K\)), and moment of inertia \(I_{d}(\varphi_{1})\) is necessary to fully understand the system's oscillatory behavior and stability margins. This section presents an in-depth investigation of how variations in these parameters affect the oscillation frequency, amplitude, and energy dissipation, which are critical for ensuring the system operates efficiently under different conditions. 1. Influence of driving torque (\(M_{d}\)) on system behavior Driving torque plays a crucial role in determining the oscillation intensity and stability of the conveyor system. Changes in \(M_{d}\) directly influence: - higher \(M_{d}\) values lead to increased energy input, causing larger oscillation amplitudes and potential instability; - higher torque may shift the system's natural frequency, leading to resonance risks. - if \(M_{d}\) is too low, the conveyor may experience damped oscillations, limiting material transport efficiency. 2. Effect of resistance coefficient (\(K\)) on damping and stability The resistance coefficient governs energy dissipation and influences how quickly oscillations decay. It primarily affects: - a higher \(K\) results in faster energy dissipation, reducing oscillation persistence; - if \(K\) is too low, oscillations may persist longer than desired, potentially affecting material transport consistency; - excessively high \(K\) values could lead to overdamping, preventing efficient oscillatory motion. 3. Influence of moment of inertia \(I_{d}(\varphi_{1})\) on system response Moment of inertia determines how the system reacts to applied forces. It affects: - higher \(I_{d}(\varphi_{1})\) reduces acceleration, slowing oscillation growth; - lower \(I_{d}(\varphi_{1})\) increases system reactivity, which may lead to excessive motion variations; - higher inertia requires more energy to maintain oscillations.
## 2 Basic Measurement Techniques for Localization in WSNs
### Distance Related Measurement
#### 2.2.2 Received Signal Strength (RSS) Based Measurement  
Received signal strength measurement estimates the distance between two sensor nodes from the received signal strength of the signal [52; 53]. Most sensors have the capability to measure the RSS. The distance estimated from the RSS is a monotonically decreasing function. The relation is modeled by the following log-normal model:  
\[P_{r}(d)[dBm]=P_{0}(d_{0})[dBm]-10n_{p}log_{10}\left(\frac{d}{d_{0}}\right)+X_ {\sigma} \tag{1}\]  
where \(P_{0}(d_{0})[dBm]\) is a reference power in dB milliwatts at a reference distance \(d_{0}\) from the transmitter, \(n_{p}\) is the path loss exponent that measures the rate at which the received signal strength decreases with the distance, \(X_{\sigma}\) is a zero mean Gaussian random variable with standard deviation \(\sigma\) and it accounts for the random effect caused by shadowing. Both \(n_{p}\) and \(\sigma\) are environment dependent. Given the model and the model parameters, which are known via a priori measurements, the distance between two sensor nodes can be obtained from the RSS measurements. Localization algorithm can then be applied to use this distance and estimate the position using multilateration technique.  
Another interesting technique to measure distance between an optical transmitter and an optical receiver is the lighthouse approach [54]. In this approach, the distance is measured by estimating the time duration that the receiver dwells in the optical beam. The advantage is that the optical receiver is of small size and low cost. However, it requires line of sight between the transmitter and the receiver.
## 2 Materials and Methods
### Study Selection Process and Data Extraction  
All retrieved references were imported into the Mendeley Desktop software, Version 1.19.8, and checked for duplicates by one author (NPR). This author (NPR) screened the titles and abstracts of all citations according to the predefined study-selection criteria. Then, the full texts of potentially relevant articles were retrieved and independently assessed by two randomly chosen authors from a set of three authors (AGS, AIM, and NPR), to verify if the inclusion and exclusion criteria were met. If a consensus could not be reached between the two authors, the third author was consulted.  
Data from included studies were extracted by two authors (AIM and MM) using an electronic form developed for this purpose. The extracted information was revised and discussed with the other two authors (AGS and NPR). The information extracted from each study was: the author(s) and year of publication; the sample sizes and characteristics (e.g., sex, age); the type and name of the digital solution (index test); the type and name of the reference standard test; and the diagnostic accuracy property (e.g., estimates of sensitivity and specificity). For each study, the information used to construct a two-by-two contingency table for each index test, including the number of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) results was also extracted. When these counts were missing from the study, the data needed (e.g., sample size, number of participants with the target condition, estimates of sensitivity and specificity, and estimates of PPV and NPV) were extracted.  
The results presented in this review consider the best cut-off reported for each index test in each study for achieving the best diagnostic ability to screen for cognitive impairment.  
If more than one index test result was presented (e.g., different thresholds), we chose the results given by the better cut-off reported, considering the reference standard test. Sensitivity and specificity depend on the cut-off value considered positive to identify the target condition (i.e., generally, the higher the sensitivity, the lower the specificity, and the higher the specificity, the lower the sensitivity) [43; 44].
# Off-Axis Tension Behaviour of Unidirectional PEEK/AS4 Thermoplastic Composites
## 4 Conclusions  
This paper presents an experimental study on the unidirectional PEEK/AS4 thermoplastic composites, subjected to off-axis tension loads, using a full-field digital image correlation measurement technique. Conclusions are drawn as follows:  
1. Finite element simulation and experiments validate that, by using oblique end-tabs with angles designed according to the material properties, stress concentration and shear-induced bending moment could be eliminated.
2. The mechanical response of the material exhibits a linear-elastic relationship at the first stage, and nonlinear behaviour at the strain-hardening stage. The combined tension and shear stress-induced tension/shear coupling effect promotes a higher level of nonlinearity and produces inhomogeneous strain fields. The strength and stiffness decline rapidly when the off-axis angle increases from 15\({}^{\circ}\) to 30\({}^{\circ}\), and then decreases gradually.
3. The primary failure mode for small off-axis angle specimens is matrix shear failure. The tension/shear coupling effect accelerates the damage process of the matrix resulting in tension/shear combined ductile fracture mode. Specimens with large off-axis angles exhibit matrix tension failure feature.  
Figure 13: Fracture surface morphology of different off-axis tension specimens: (**a**) 75\({}^{\circ}\); (**b**) 90\({}^{\circ}\).  
**Author Contributions:** Conceptualisation, Y.M., Y.L. and L.L.; methodology, Y.M. and L.L.; software, Y.M.; validation, Y.L.; formal analysis, L.L.; investigation, Y.M. and L.L.; resources, Y.L.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, Y.L.; visualisation, L.L.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.