esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 4 Consensus on \(Tso(4)^{n}\) with Reshaping
### Illustrative Examples
#### 4.2.2 Five-Agent Ring Graph with Initial Conditions Away from Consensus  
Now, we reconsider the ring graph with the \(N=5\) agent configuration in Section 3.3.2. The base case protocol has been simulated using the consensus protocol derived in Equation (50) and is shown in Figure 8a. The reshaping Case 1 (\(q=3\)) consensus protocol  
Figure 7: Simulations on \(TSO(4)^{N}\) for (**a**) base case protocol on a 5-agent ring graph and (**b**) base case protocol on a 9-agent ring graph. Initial conditions are chosen within the region of attraction of the consensus manifold. Both plots demonstrate consensus.  
has been simulated for the protocol derived in Equation (50), with the appropriate \(b_{n}\) coefficients. These results are shown in Figure 8b.  
The results in Figure 8a show that the closed loop using the base case consensus protocol converges to a stable, balanced non-consensus equilibrium. As in the \(SO(4)\) simulation, there are two clusters of relative configurations denoted by relative PRAs \(\theta_{ij1}\), which converge towards \(72^{\circ}=2\pi/N\) and \(\theta_{ij2}\), driven to zero. Similarly to consensus on a circle, there are known stable equilibria on a ring graph [20] with \(n=5\) agents at \(2\pi p/N\), \(p=1,2\) relative rotation angles on the same axis.  
Figure 8b shows the relative configurations converging to zero for both \(\theta_{ijk}\) as desired. The two PRAs for each agent converge to the same steady-state limits, although they do not equal each other. As in the \(SO(4)\) example, this is expected when not enforcing an isoclinic rotation. For the five-agent ring topology and initial conditions chosen, the reshaping Case 1 protocol achieves the desired outcome. The reshaping consensus protocol implemented in the simulation has destabilized the stable non-consensus equilibrium and resulted in the AGAS of the synchronized state.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results
### Characteristics of Sprayed Galinstan Films
#### 3.1.2 Conductivity of LM Films  
To estimate the amount of gallium oxide in the sprayed LM films, the average diameters of LM droplets were measured using a laser scanning microscope. The average droplet diameters were 2.435 \(\upmu\)m at 100 kPa, 1.507 \(\upmu\)m at 300 kPa, and 0.715 \(\upmu\)m at 500 kPa, and the standard deviation of the diameters were 0.2546 \(\upmu\)m, 0.1773 \(\upmu\)m, and 0.1295 \(\upmu\)m, respectively. The thickness of the native gallium oxide of Galinstan was estimated to be approximately 1 to 3 nm [19]. Assuming that the thickness is about 3 nm, the volume fractions of the oxide layer in the LM films sprayed at 100 kPa, 300 kPa, and 500 kPa were 0.37%, 060%, and 1.25%, respectively. Thus, LM films sprayed under higher pressures contain a larger fraction of oxidized LM versus films sprayed at lower pressures, resulting in lower electrical conductivity.  
As shown in Figure 4, the conductivity decreases as the thickness of LM film increases. The conductivity per unit thickness of the films should be the same for all sprayed film. However, it decreased due to the increase of oxide formation inside the film. The pressurized air is strong enough to break the outer oxide shell of the LM already deposited on the surface, so the LM that is now exposed to air is rapidly oxidized. Thus, the amount of oxide in the film increases, decreasing the conductivity of thicker LM films with longer spraying times.  
LM was sprayed on a nitrile surface to quantify the effect of deformability on the resistance of stretched LM films. As the strain was increased, the resistance of the liquid-metal film also increased (Figure 5) due to the decrease in the thickness of the liquid metal. The resistance steadily increased up to a strain of 80%. When the sample was stretched beyond 80%, the resistance abruptly increased by three orders of magnitude. The same dramatic change in resistance was reported for a metal film under a strain of only 10% [20], and for Ag-Au nanowire electrodes on a PDMS substrate under a strain of 30% [21].  
Figure 4: Conductivity of LM film sprayed at a distance of (**a**) 5 cm and (**b**) 10 cm from the substrate for 1 s, 3 s, and 5 s, under various spraying pressures.  
Figure 5: Change in normalized measured resistance versus uniaxial strain for a sprayed LM pattern on a stretchable nitrile surface.
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
Sediment observation entails monitoring suspended sediment concentration (SSC), sediment constituents, etc., by collecting water samples at sediment stations in the river channel and conducting laboratory analyses on sediment samples to obtain information on sediment content and particle size [19; 20]. Meteorological monitoring involves setting up meteorological stations to monitor meteorological elements such as temperature, precipitation, and wind direction to assess the impact of weather changes on water and sediment in the river [21]. Geomorphological monitoring evaluates the influence of terrain and vegetation in the basin on water and sediment yield, as well as potential events like debris flows and flood-prone areas, using on-site and remote sensing methods [22; 23]. The technical means of water and sediment monitoring in the Yellow River mainly include (1) Traditional manual monitoring: Collecting water samples on-site (as shown in Figure 1) and conducting further analysis in the laboratory using instruments; (2) Sensor monitoring: Various sensor devices, such as water quality sensors and sediment sensors, are widely used in modern hydrological monitoring [24]. They can be directly installed in the river to monitor water quality and sediment conditions in real-time and to transmit monitoring data to the monitoring center through data transmission channels, with the characteristics of unmanned automatic operation; (3) Satellite remote sensing monitoring: Utilizing satellite remote sensing allows for the comprehensive and rapid monitoring of river hydrology, sediment, and terrain features [25; 26]. High-resolution image data can be obtained through satellite sensors, combined with remote sensing image interpretation and digital image processing techniques to extract and analyze information on water and sediment in the river. Given the changing climate and hydrological situations and the construction of major projects on the Yellow River [27], the development of relevant technologies for the digital governance of the Yellow River is imperative. Building on the current status of water and sediment monitoring technology, this article proposes an integrated remote sensing monitoring network for water and sediment covering the sky, land, and sea using satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment throughout the entire Yellow River basin from the upper reaches to the Bohai Sea estuary. This article introduces the construction of in situ hyperspectral stations from the upstream sediment source to the Qingshui River section and remote sensing monitoring, as well as some multi-source satellite monitoring demonstrations of water and sediment transport processes in the downstream areas.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 1 Introduction
[24] applied the acceleration, magnetic field strength in QS, and the outputs of gyroscope to estimate the heading by extended Kalman filter (EKF). The linearization of the coefficient matrix by EKF was unstable, and the accuracy of linearization was perhaps not high enough. An unscented Kalman filter (UKF) does not require linearization of the coefficient matrix, and has at least second-order approximation accuracy. Therefore, UKF has a better result than EKF. [PERSON] et al. [25] used UKF for pedestrian navigation, and the mean heading error was less than 10\({}^{\circ}\). Although UKF can achieve relatively high accuracy, it requires a certain amount of computation. The magnetic field is susceptible to contamination in complex indoor environments, and this can cause deviation of heading estimation [26]. Due to the accumulation of errors, a gyroscope is insufficient for long-term heading estimation [27]. Therefore, an accurate heading is still a challenge for pedestrian navigation with a smartphone. Inaccurate prior knowledge of stochastic models and model function error will affect the accuracy of KF, and the adaptive Kalman filter (AKF) can address this problem. [PERSON] et al. [28] used the innovation-based adaptive estimation (IAE) to adjust the state noise matrix or measurement noise matrix, showing that an AKF was better than KF. [PERSON] et al. [29] applied the square of the Mahalanobis distance to detect model function error, and a fading factor was introduced to inflate the prior covariance to reduce the influence of inaccurate model functions. [PERSON] et al. [30] used two factors to adjust the measurement noise matrix, and a hypothesis test was introduced to avoid interference from abnormal states. [PERSON] et al. [31] applied a two-stage function to obtain an adaptive factor, and a robust-adaptive KF was used to estimate the pedestrian heading. [PERSON] et al. [32] adaptively adjusted the measurement noise matrix according to the level of acceleration. Currently, AKF has been widely used in various fields of navigation, but is rarely applied in PDR. This paper proposes a method of adaptive heading estimation. A novel heading prediction based on an autoregressive model (AR) prediction model is introduced to KF, and the current heading--determinedby fusing these headings calculated by the gyroscope, obtained by the magnetometer and stored by the previous estimate--is used as the measurement of KF. Taking into account model function error and inaccurate prior noise, AKF is used to improve the accuracy of heading estimation. The change rate of the total magnetic field is used to detect its QS, and the heading calculated by gyroscope is corrected by the feedback of the estimated heading by AKF in QS. In order to intuitively and precisely express the accuracy of heading estimation, the estimated heading was used for the position estimation of PDR. Two kinematic experiments are used to verify this algorithm.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Noise Decorrelation (\(\delta_{SNR}\))  
Noise decorrelation is related to the signal-to-noise Ratio (SNR) [32]. The SNR is calculated by the ratio of the target signal (\(S\)) to noise power (\(N\), Equation (27)) [32]. The noise contribution of this term mainly comes from receiver thermal noise and jammer noise [151]. Receiver thermal noise is formed when the temperature of the internal radar receiver interferes with the target signal. Jammer noise introduces noise into the radar receiver through the antenna and interferes with the target signal [151; 152]. Both receiver thermal noise and jammer noise reduce SNR and cause difficulty in detecting target signals. Low SNR values result in decreased coherence or high decorrelation (Equation (28)). SNR over the entire radar image is not necessarily uniform and is spatially variable over the scene. This variability is dependent on the strength of the backscattered signal. For example, noise decorrelation is dominant in radar shadow areas as no signal is returned from these areas. Therefore, the correlation in shadow areas is zero.  
\[SNR=\frac{S}{N} \tag{27}\]  
\[\gamma_{SNR}=\frac{1}{1+SNR^{-1}}=1-\frac{1}{1+\frac{N}{S}} \tag{28}\]
## 7 Results Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**True Class** & **Predicted Class** & **Normal** & **Suspicious** & **Danger** \\ \hline
**Normal** & 99.1\% & 0.7\% & 0.2\% \\
**Suspicious** & 1.1\% & 98.4\% & 0.5\% \\
**Danger** & 0.2\% & 0.3\% & 99.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for the optimized decision tree. and SMV gently stabilize. In turn, the start of the sitting down activity causes the mild change in acceleration in the \(z\)-axis, and at the end of the activity, both acceleration in the \(z\)-axis and SMV change quickly. Based on appropriate thresholds, the identification for both of these changes allows one to distinguish between those two activities. Similar analysis can be used to distinguish between lying down and rising activities. When lying down, due to an orientation change of the accelerometer causing a shift of the axis that the gravity force influences the most, the acceleration in the \(y\)-axis rapidly decreases, and acceleration of the \(z\)-axis sharply increases; while when rising, the acceleration curves of the \(y\)- and \(z\)-axes behave conversely; the \(y\)-axis rapidly increases; and acceleration of the \(z\)-axis sharply decreases. These phenomena are used to distinguish these two activities. The proposed activity recognition method was validated based on tests consisting of 350 falls, 200 lying down, 200 standing up, 400 sitting down and 200 walks, and the results are summarized in Table 3. The overall validity of the system activity recognition was 95.5%, where 2.5% of activities were recognized incorrectly and 2.0% not detected at all. The activity categorization in class of behavior as normal, suspicious or dangerous is based on the analysis of five components: time of day, section of apartment, zone of activity, form of activity and duration of activity. The classification procedure is grounded on the comparison of current measures with the existing behavior database. The behavior database was created from regular three-day observation of three volunteers' activities. From six evaluated machine learning techniques, the decision trees II, KNN I and KNN II showed the highest classification validity of 94.8%, 96.0% and 95.8%, respectively. The conducted optimization of the three best methods indicated that only the tuned decision trees II increased classification accuracy up to 99.1%. The confusion matrix for the tuned decision tree indicates that only 0.2% of the tested dangerous behaviors were classified as normal, which can be treated as a critical mistake, and 0.3% of them were classified as suspicious, which is less critical since the suspicious behavior should be verified anyway. The chosen behavior classification technique of highest validity was verified with a set of 25 normal, 7 suspicious and 18 dangerous behaviors of random timeframes, and as a result, 100% of them were classified correctly.
###### Abstract  
This paper presents a novel approach to enhance pose estimation accuracy and precision in mobile robotics using multiple graphical markers. While traditional single-marker methods using graphical markers such as ArtUco offer simple implementation, their performance is susceptible to environmental variations and measurement errors. To address these limitations, we propose a robust pose estimation algorithm that leverages multiple markers simultaneously. Our approach incorporates two key mechanisms: spatial consistency verification to detect invalid markers within the marker array, and temporal stability analysis to identify and exclude outlier measurements. The algorithm enhances pose estimation accuracy by averaging measurements from valid markers while preventing any single marker from dominating the estimation process. The effectiveness of our approach was validated through experiments using both fixed and drone-mounted camera configurations. The results demonstrated that the pose estimation using multiple markers significantly improved both accuracy and precision compared with single-marker approaches. In fixed-camera experiments, the proposed method showed reduced mean errors and standard deviations in both position and orientation measurements across various camera poses. Similarly, in drone-mounted camera experiments, our approach exhibited superior stability with significantly lower measurement variations during hovering maneuvers. These improvements were particularly pronounced in challenging scenarios, such as when the camera was tilted at large angles relative to the marker plane. This research contributes to the advancement of reliable pose estimation methodologies in mobile robotics and autonomous systems, with potential applications across diverse environments where precise position and orientation measurements are crucial.  
pose estimation; graphical markers; multiple-marker systems; mobile robotics Article
## 3 Laser Diode Structure Optimization  
Based on the band engineering and carrier transportation mechanism [9; 10; 11], a optimized 2 \(\upmu\)m InGaAsSb/AlGaAsSb laser diode with graded carrier blocking layer(CBL) is proposed. The CBL is inserted between the waveguide and cladding layers, which can provide adequate confinement of carriers in the active region. The schematics of proposed design with CBL are given in Figure 2.  
As shown in Figure 2, the core region of this laser is constructed by three In\({}_{0.25}\)G\({}_{0.75}\)A\({}_{80.02}\)Sb\({}_{0.75}\) undoped wells sandwiched between Al\({}_{0.25}\)Ga\({}_{0.75}\)As\({}_{0.02}\)Sb\({}_{0.98}\) barriers. The thickness of wells and barriers are 10 nm and 30 nm respectively. The core region is incorporated in undoped 0.45 \(\upmu\)m Al\({}_{0.25}\)Ga\({}_{0.75}\)As\({}_{0.02}\)Sb\({}_{0.98}\) broadened waveguide layers. The graded carrier blocking layer(CBL) AlGaAsSb is followed by the waveguide layer, and the next is the cladding layer of Al\({}_{0.8}\)Ga\({}_{0.2}\)As\({}_{0.08}\)Sb\({}_{0.92}\) with 1.2 \(\upmu\)m in thickness. The n-cladding layer is Te-doped to \(n\) = 4 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), the p-cladding layer is Be doped to \(p\) = 5 \(\times\) 10\({}^{-18}\) cm\({}^{-3}\). The GaSb buffer is Te-doped with \(n\) = 1 \(\times\) 10\({}^{-18}\) cm\({}^{-3}\) and the substrate is oriented Te doped GaSb with \(n\) = 1 \(\times\) 10\({}^{-19}\) cm\({}^{-3}\).  
The CBL with graded Aluminum compositions of x = 0.8-0.25(Al-rich) and x = 0.25-0.8 (Ga-rich) is designed not only to reduce the voltage drop at the interface of heterojunction but also to improve the quality of interface. It is also designed as undoped, taking into account internal factors caused by the impurity scattering and non-radiative recombination. The thickness of CBL is set to be 50 nm to prevent the minority carriers from overflowing to the cladding layers, considering the thermal resistance of the laser diode.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## The UNSW-NB15 Dataset  
The lack of labeled TCP/IP data for research and algorithm testing is a classic problem in network security. The KDD family (KDD Cup'98, KDD Cup'99 and NSL-KDD) is formed by perhaps the most popular datasets in this regard. They have been widely used for the last 20 years, in spite of the received criticisms. [PERSON] et al. [17] proposes the NSL-KDD dataset to partially overcome the defects of the previous versions, yet it is still based on the same old captures. The KDD family is therefore severely outdated and, 20 years later, hardly represents current communications and attacks.  
More recently, [PERSON] and [PERSON] [5; 6] presented the UNSW-NB15 dataset to be \"a new benchmark dataset for evaluating NIDSs\". The UNSW-NB15 is formed by legitimate traffic and attacks split into nine different big categories: Fuzzers, analysis, backdoors, DoS, exploits, generic, reconnaissance, shellcode, and worms. Datasets are provided in raw captures (_pcaps_, 100 GB) supported by a ground truth CSV file, as well as already preprocessed and separated into training and test datasets in the Argus-Bro format. In terms of flows, the UNSW-NB15 total figures are shown in Table 2.  
Traffic data for this dataset were originally generated by an IXIA PerfectStorm platform: A powerful commercial solution designed to simulate large networks and evaluate security aspects ([[http://www.ixiacom.com/products/perfectstorm](http://www.ixiacom.com/products/perfectstorm)]([http://www.ixiacom.com/products/perfectstorm](http://www.ixiacom.com/products/perfectstorm))). The tool generated 100 GB of both legitimate communications and attacks, collected in two simulated periods: 16 h corresponding to 22 January 2015 and 16 h for 17 February 2015. We joined both periods in our experiments. For the attack generation, the platform obtained information related to novel malware from a CVE (Common Vulnerabilities and Exposures (CVE)) site, which is continuously updated ([[https://cve.mitre.org/](https://cve.mitre.org/)]([https://cve.mitre.org/](https://cve.mitre.org/))). In the cited papers, [PERSON] and [PERSON] [5; 6] developed a deep study about the statistical and practical suitability of the dataset by itself and when compared with its predecessors. The UNSW-NB15 dataset is publicly available and can be downloaded from the authors' website ([[https://www.unsw.adfa.edu.au/austalian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/](https://www.unsw.adfa.edu.au/austalian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/)]([https://www.unsw.adfa.edu.au/austalian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/](https://www.unsw.adfa.edu.au/austalian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/))).