esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotic Assembly for Irregular Shaped Peg-in-Hole with Partial Constraints
## 2 Materials and Methods
### Key-Slot Assembly  
This process comes from a practical project, which aims to design an automatic packaging machine robotic assembly work cell. As shown in Figure 2. there are two arms used to hold a plastic bag during the packaging process.  
Figure 1: The peg with discontinuous curvature.  
The two arms have to rotate to realize the periodic holding and releasing operation. Therefore, the left and right rotational joints have to be installed. Because these joints are coupled with each other, there is a coupling mechanism between them.  
There are three parts involved in the key-slot-shaft assembly process. There is a cylinder hole in the key and the shaft is inserted in this hole, i.e., a typical peg-in-hole assembly problem. The key and slot fit each other and the key can move inside the slot along one direction. To assemble these three parts, two separated sub-assembly operations have to be performed.  
As shown in Figure 3, there are two approaches: key-shaft-first or key-slot-first. The Key-shaft-first approach first installs the key in the shaft by a standard peg-in-hole process, then installs the irregular partially constrained peg hole assembly; The key-slot-first approach first installs the key in the slot then installs the peg by a standard peg-in-hole process. The last step of the Figure 3 shows the ideal key position in the slot. At this time, the key is located in the middle position of the slot \(X\)-direction, there is a large gap along the \(X\)-direction between the key and the slot. The gap between the key and two sides of the slot are 2.5 mm and a tight gap in the \(Y\)-direction.  
Figure 3: Possible assembly sequences for the coupling mechanism.  
Figure 2: The robotic assembly problem for the automatic packaging machine.  
It can be found that besides the common peg-in-hole process, either approach requires a partially constrained assembly process. For the key-shaft-first approach as shown in Figure 4. because the key-shaft unit rotates around the right joint, the position of the key is not fixed. Furthermore, the key can also rotate around the shaft, therefore, the key has two DOFs during the slot-key assembly process; For the key-slot-first approach, the key is placed on the base plate without any constraints, the external force can drive the key along the surface and rotates horizontally, therefore, the key has three DOFs during the slot-key assembly process.  
In this process, only one robot is used to perform the above assembly operation. Another robot could be used to fix the workpiece. However, this requires a specific design of a small gripper to hold the parts while small enough to avoid the potential collision. Therefore, in this paper, an assembly strategy without any fixture is proposed.  
The state of the key can be determined by two angles according to the above definition. The goal is to find an appropriate control policy to robustly and efficiently install the slot into the key.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 6 Uncertainties of Reconstructed Wind Characteristics
### Example for a Two-Beam Nacelle Lidar
#### 6.2.2 Propagating Uncertainties with the GUM  
Applying the law of propagation of uncertainties to the expressions of \(V_{x}\), \(V_{y}\) (Equation (23)), we obtain:  
\[\left\{\begin{array}{l}u_{V_{x}}^{2}=\left(\frac{1}{2\cos\varphi\cos\alpha} \right)^{2}\cdot\left(u_{1}^{2}+u_{2}^{2}+2r_{1,2}u_{1}u_{2}\right)+V_{x}^{2} \left(\tan^{2}\varphi\cdot u_{\varphi}^{2}+\tan^{2}\alpha\cdot u_{\alpha}^{2} \right)\\ u_{V_{y}}^{2}=\left(\frac{1}{2\cos\varphi\sin\alpha}\right)^{2}\cdot\left(u_ {1}^{2}+u_{2}^{2}-2r_{1,2}u_{1}u_{2}\right)+V_{y}^{2}\left(\tan^{2}\varphi \cdot u_{\varphi}^{2}+\frac{u_{\alpha}^{2}}{\tan^{2}\alpha}\right)\end{array}\right. \tag{26}\]  
where \(r_{1,2}\) is the correlation coefficient between the uncertainties \(u_{1}\), \(u_{2}\) on \(V_{r1}\) and \(V_{r2}\). We implicitly assumed that the uncertainties \(u_{\varphi}\) and \(u_{\alpha}\) are correlated neither with \(u_{1}\), \(u_{2}\) nor between each other. Then, the law of propagation is applied to Equation (24) to obtain the wind speed and relative direction uncertainties, respectively denoted \(u_{V_{H}}\) and \(u_{\varphi}\):  
\[\left\{\begin{array}{l}u_{V_{H}}^{2}=\frac{1}{V_{H}^{2}}\cdot\left(V_{x}^{2 }u_{V_{x}}^{2}+V_{y}^{2}u_{V_{y}}^{2}+2r_{xy}V_{x}V_{y}u_{V_{x}}u_{V_{y}} \right)\\ u_{\theta_{r}}^{2}=\frac{1}{v_{H}^{2}}\cdot\left(V_{y}^{2}u_{V_{x}}^{2}+V_{x} ^{2}u_{V_{y}}^{2}-2r_{xy}V_{x}V_{y}u_{V_{x}}u_{V_{y}}\right)\end{array}\right. \tag{27}\]
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 4 Methodology of Bifurcation Tests in Aircraft Flight Dynamics  
MAV motion is described through a system of highly non-linear ordinary differential equations. For a classical model of a non-deformable micro aerial vehicle with movable control surfaces, motion equations are presented by relationships (1), (12), (13), and (14).  
The dynamical system theory enables analysing solutions to a system of highly non-linear ordinary differential equations describing aircraft motion, depending on slow parameter changes (so-called bifurcation parameters). When analysing aircraft flight dynamics, it is assumed that the bifurcation parameters are the control vector components (i.e., deflection angles of the elevator, rudder, ailerons, thrust vector, etc.). The first stage in the analysis of a non-linear system of differential equations in the dynamical system theory is assessing the stability of steady states of a system of differential equations describing aircraft flight dynamics (1), (12), (13), and (14). The steady state is determined by equating the derivatives to zero and solving a system of algebraic Equation (2).  
Given the experience from numerous research (based on the bifurcation analysis and continuation technique), the following, three-stage test diagram for a non-linear aircraft motion was formulated [8; 9; 10; 11; 12; 13; 14; 18; 19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 34; 48; 49; 51; 52; 53; 54; 56]  
1. The first stage involves determining all parameters of a dynamical system. The fundamental task is to study all possible equilibrium states and periodic orbits, and the analysis of their local stability. This test should be very thorough. The outcome of the attempted test should be a determined global structure of the state space (e.g., phase portraits) of all discovered attractors (steady states and closed orbits). Approximated graphic representations of the calculations are crucial in this case, since they enable diagnosing the obtained results.
2. The second stage involves, based on information on the evolution of phase portraits together with parameter changes, predicting dynamical system behaviour. Next,  
Figure 4: Examples of aerodynamic derivative waveforms as function of angle of attack \(\alpha\), and reduced frequency \(f\)[50].  
based on the knowledge on the type of present bifurcations and the current position of system parameters relative to stable areas, further aircraft behaviour is predicted. Information on the range of parameter changes is also important for these analyses and predictions. Rapid parameter changes and higher differences between steady and transient states are also observed.
3. The third step involves a numerical simulation, which enables verification of the expected aircraft behaviour. Waveforms of transient system characteristics for significant state parameter changes upon a dynamical system parameter change are obtained.
# Sound Localization Framework for Construction Site Monitoring
## 3 Literature Review
### Sound-Based Localization and Applications  
Localization methods that use sound data are widely used in several industrial sectors. The automotive sector uses localization methods to analyze and estimate the location of interior and exterior sounds to actively respond to changes in vehicle interiors, engines, and exteriors to manage car maintenance [20; 21].  
The military sector uses localization methods to analyze sounds recorded in open spaces. Researchers have developed algorithms for the armed forces to analyze shooting sounds to verify the type of ammunition and estimate the positions of the shooters by measuring the arrival time and angle [22].  
Sound-based localization systems have been developed for indoor service robots, considering robots that operate indoors must accurately calculate their targets and destinations to provide services. Therefore, sound-based localization systems have been developed to supplement vision-based localization systems and improve robot navigation performance [23].  
In construction, sound-based localization systems are mostly used for the nondestructive inspection of structures. The status of bridge structures can be monitored by performing localization on degraded or damaged wires using sound data generated by pipes at gas facilities to locate leaks and monitor the status of such facilities [6; 24; 25]. However, because they involve the analysis of a single structural component, these acoustic-emission-based monitoring methods are not suitable for monitoring workspaces at construction sites.  
Additionally, algorithms are being developed to classify equipment sounds to monitor the status of civil engineering sites [26; 27; 28]. Equipment sounds are no longer considered noise that should block sound data in the construction field, but instead are necessary data for management. Therefore, this study aimed to develop a system that uses sound data to monitor the work status or detect abnormal signals related to accidents that could occur at construction sites. Compared to sensor-based monitoring systems, sound-based site monitoring systems are easy to install and recover and can monitor a wider area comparedto camera-based monitoring systems, while providing 24 h monitoring. Their usefulness at construction worksites could be very high and could enable more active and trouble-free site supervision by identifying the locations of sound sources in real time.
## 8 Conclusions and Future Work  
This work presented Edge-FLGuard, a privacy-preserving, federated learning framework for real-time anomaly detection in 5G-enabled IoT environments. The system inte  
Figure 5: Scalability and convergence analysis of Edge-FLGuard. The left plot shows the detection accuracy versus increasing numbers of participating clients. The right plot illustrates model convergence over 50 federated learning rounds, indicating stable training dynamics.  
Figure 6: Robustness of Edge-FLGuard to model poisoning. The F1-score gradually declines as the proportion of malicious clients increases, with a sharp performance drop observed beyond the 40% threshold.  
grates lightweight autoencoder and LSTM models deployed at the edge with a decentralized training pipeline to address the limitations of centralized intrusion detection systems.  
Through rigorous experimentation on both public (CICIDS2017, TON_IoT) and synthetic datasets, Edge-FLGuard demonstrated  
* High detection performance, achieving F1-scores \(\geq\) 0.91 and AUC-ROC values up to 0.96;
* Low inference latency (<20 ms), compatible with real-time edge deployments;
* Efficient scalability, maintaining stability with up to 100 participating clients.  
The framework outperformed local-only models and closely matched the accuracy of centralized models, while eliminating raw data transmission and enabling privacy-preserving training. Differential privacy enhancements, including gradient norm clipping and noise injection (NSR = 0.21), resulted in only minor performance degradation. Furthermore, Edge-FLGuard maintained operational resilience in adversarial environments, tolerating up to 40% poisoned clients before performance significantly declined.
# Short-Term Effects of Pine Plantations on Vegetation and Soil in Northern Mongolia
## 3 Results
### Understory Vegetation Composition and Its Change
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Plots** & \multicolumn{3}{c}{**BBS**} & \multicolumn{3}{c}{**MGS**} & \multicolumn{3}{c}{**BDS**} \\ \hline
**Species** & **RE \%** & **RC\%** & **IV\%** & **RF\%** & **RC\%** & **IV\%** & **RE \%** & **RC\%** & **IV\%** & **RE \%** & **RC\%** & **IV\%** \\ \hline _Acillina asiatica_ Serg. & 17 & 0.2 & 8.4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ _Agrimonia pilosa_ Ledeb. & 17 & 2.1 & 9.4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ _Artrenisia commutator_ Bess. & 17 & 0.2 & 8.4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ _Artrenisia interiglia_ L & 17 & 1 & 8.9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ _Crisium esculentum_ ([PERSON].) [PERSON]. & 0 & 0 & 0 & 0 & 0 & 0 & 4.8 & 2.4 & 3.6 \\ _Cleistogenes squarrosa_ ([PERSON]) [PERSON]. & 0 & 0 & 0 & 0 & 0 & 0 & 4.8 & 4.8 & 4.8 \\ _Elymus sibiricus_ L. & 0 & 0 & 0 & 0 & 0 & 0 & 4.8 & 2.4 & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis for IV of herbaceous vegetation in BBS, MGS, and BDS plots. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**BBS**} & \multicolumn{3}{c}{**MGS**} & \multicolumn{3}{c}{**BDS**} \\ \cline{2-13}  & **SR** & **PC** & **AGB** & **BGB** & **SR** & **PC** & **AGB** & **BGB** & **SR** & **PC** & **AGB** & **BGB** \\ \hline DBH & \(-0.14\) & \(-0.11\) & 0.5 & 0.5 & 0.21 & 0.21 & \(-0.5\) & \(-0.51\) & 0.16 & 0.16 & 0.48 & 0.59 & 0.38 & 0.31 & 0.03 & 0.26 \\ Height & \(-0.2\) & \(-0.21\) & 0.45 & 0.56 & 0.28 & 0.28 & \(-0.619\) * & \(-0.59\) & \(-0.1\) & \(-0.1\) & 0.04 & 0.21 & 0.3 & 0.43 & 0.16 & 0.39 \\ BA & \(-0.18\) & \(-0.15\) & 0.48 & 0.49 & 0.18 & 0.18 & \(-0.39\) & \(-0.38\) & 0.2 & 0.2 & 0.46 & 0.57 & 0.47 & 0.23 & \(-0.1\) & 0.17 \\ Vol & \(-0.17\) & \(-0.15\) & 0.53 & 0.57 & 0.19 & 0.19 & \(-0.46\) & \(-0.44\) & 0.06 & 0.06 & 0.45 & 0.6 & 0.43 & 0.28 & \(-0.02\) & 0.25 \\ CL & \(-0.2\) & \(-0.15\) & 0.627 * & 0.653 * & 0.4 & 0.4 & \(-0.57\) & \(-0.56\) & \(-0.33\) & \(-0.34\) & \(-0.02\) & 0.14 & 0.29 & 0.42 & 0.16 & 0.41 \\ CD & 0.18 & 0.23 & \(-0.06\) & \(-0.01\) & 0.33 & 0.33 & \(-0.34\) & \(-0.4\) & 0.27 & 0.27 & 0.33 & 0.52 & 0.15 & 0.18 & 0.09 & 0.37 \\ CPA & 0.14 & 0.19 & \(-0.17\) & \(-0.1\) & \(-0.1\) & \(-0.1\) & \(-0.637\) * & \(-0.781\) ** & 0.49 & 0.48 & 0.1 & 0.29 & 0.22 & 0.27 & 0.08 & 0.35 \\ \hline \hline \end{tabular} Note: * \(p\) \(<\) 0.05, **_p_ \(<\) 0.01, vegetation variables: SR—species richness, PC—plant coverage, AGB—above-ground biomass, BGB—below-ground biomass, tree variables: DBH—diameter at breast height, Height—tree height, BA—tree basal area, Vol—growing stock, CL—tree crown length, CD—tree crown diameter, CPA—ccrown projection area. Plots: BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand, NRS—natural regeneration stand.  
\end{table} 
Table 2: Correlations between tree variables with species richness, plant coverage, and above–below-ground biomass.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion
### Transformation of Soil Element Valance  
XPS was used to analyze the changes in the elemental chemical states of As, S and Fe in the soil before and after phytoremediation in order to further clarify the mechanism of functioning of iron-sulfur-reducing bacteria. The XPS spectra of As 3d, Fe 2p and S 2p are illustrated in Figure 8.  
The peaks at 44.1 eV and 44.8 eV corresponded to As(III) 3d3/2 and 3d5/2, respectively, while the peaks at 46 eV, 46.7 eV, 49.3 eV and 50 eV were assigned to As(V) [45]. After phytoremediation, the proportion of As(III) in the soil decreased from 37.68% to 34.28%. This could be attributed to the higher mobility of As(III), while As(V) readily resorbs on the soil surface. Consequently, the higher levels of As(V) detected on the soil surface by XPS may have influenced this result. Regarding sulfur, the peaks at 164.2 eV and 165.8 eV corresponded to S(II) 2p1/2 and 2p3/2 of FeS, respectively, while the peaks at 167.3 eV and 168.5 eV were assigned to S(II) 2p1/2 and 2p3/2 of FeS. The peaks at 170.2 eV and 171.4 eV were attributed to S(VI) 2p1/2 and 2p3/2 of sulfate. As watering during the experiment did not result in the loss of sulfate, the decrease in sulfate content from 48.56% to 30.27% after remediation suggests that the metabolic activity of iron-sulfur-reducing bacteria was responsible for the reduction in sulfate levels in the soil. In terms of iron, the peaks at 725.0 eV and 711.8 eV corresponded to Fe(III) 2p1/2 and 2p3/2 of Fe\({}_{2}\)O\({}_{3}\) or FeOOH [46; 47], respectively. The peaks at 727.0 eV, 723.5 eV, 713.8 eV and 710.2 eV were attributed to Fe(II) 2p1/2 and 2p3/2, while the peak at 719.0 eV represented the Fe(III) satellite. The proportion of Fe(III) decreased from 42.27% to 40.14% after phytoremediation, indicating that the iron-sulfur-reducing bacteria and plant organic matter (POM) contributed to the reductive dissolution of iron oxides.  
Figure 7: Fractionation of arsenic in soil after phytoremediation.
## 3 System Models
### Multitarget Bayes Filter  
For limited-energy large-scale sensor networks, a method with less computational cost and less complexity for target tracking is anticipated. The target state is \(X_{k}\) (matrices are denoted in italic uppercase nonbold letters in this study) at timestep \(k\) within the state space  
Figure 1: The nodes in the aquaculture network of tracking targets adopted in the environment for our aquaculture sensor network. (**a**) Schematic diagram of the structural representation of nodes. (**b**) Buoys equipped with sensors in this network. (**c**) Layout representation of tracking system in this aquaculture network.  
**X** (sets are denoted in uppercase bold letters), which contains kinematic states (position, velocity, turn rate, orientation, etc.). Measurements at timestep \(k\) are represented as \(M_{k}\) within the measurement space **M**, which denotes all measurement sets from time \(t=1\) to \(k\), including time \(t=k\). The state and measurement set of the targets are represented by \(X_{k}\in F(X)\) and \(M_{k}\in F(M)\), which are the finite subsets of \(X\) and \(M\), respectively. The system model and measurement model can be generally described as follows [4]:  
\[X_{k}=\left\{x_{k}^{1},x_{k}^{2},\ldots,x_{k}^{N_{k}}\right\}\in F(X), \tag{1}\]  
\[M_{k}=\left\{m_{k}^{1},m_{k}^{2},\ldots,m_{k}^{Z_{k}}\right\}\in F(M), \tag{2}\]  
where \(N_{k}\) and \(Z_{k}\) are the number of targets and the number of measurements at timestep \(k\), respectively. Target state \(x_{k}\) at time \(k\) contains three parts, which are targets surviving from the previous timestep \(k-1\), newborn targets spawned from targets at the previous timestep \(k-1\), and spontaneous newborn targets at the timestep \(k\), which are indicated as \(S_{k|k-1}\), \(SP_{k|k-1}\), and \(B_{k}\), respectively; \(X_{k}=S_{k|k-1}\cup SP_{k|k-1}\cup B_{k}\). It is worth noting that newborn targets spawned from targets at the previous timestep \(k-1\) can be considered as newborn targets for classification and modeling simplicity, which is denoted as \(X_{k}=S_{k|k-1}\cup B_{k}\).  
The multitarget state posterior density at time can be denoted as \(p_{k|k}(X_{k}|M_{1:k})\). According to Bayesian estimation theory, multitarget state posterior density can be obtained by optimal Bayes recursion as follows:  
\[p_{k|k-1}(X_{k}|M_{1:k-1})=\int f_{k|k-1}(X_{k}|X_{k-1},M_{1:k-1})p_{k-1|k-1}( X_{k-1}|M_{1:k-1})dX_{k-1}, \tag{3}\]  
\[p_{k|k}(X_{k}|M_{1:k})=\frac{l_{k}(M_{k}|X_{k})p_{k|k-1}(X_{k}|M_{1:k-1})}{ \int l_{k}(M_{k}|X_{k})p_{k|k-1}(X_{k}|M_{1:k-1})dX_{k}}, \tag{4}\]  
where \(f_{k|k-1}(X_{k}|X_{k-1},M_{1:k})\) is the state transition density function based on all measurement sets \(M_{1:k}\) from \(M_{1}\) at time \(t=1\) up to \(M_{k}\) at time \(t=k\) and target state \(X_{k-1}\) at previous time \(t=k-1\), and \(l_{k}(M_{k}|X_{k})\) denotes the target measurement likelihood function.
## 2 Digital Twin
### Architecture
#### 2.2.2 Types of DT  
In addition to the three variants mentioned above, the DT can be categorized into four types: DT prototype (DTP) [43], DT instance (DTI) [44], DT aggregation (DTA) [45], and DT environment (DTE) [44; 45; 46]. Figures 3 and 4 represent the architecture of DT types and DT environment, respectively.  
Figure 4: DT environment.  
Figure 3: DT types.  
The DTP represents a type of DT framework that leverages all the information from the physical system, which is necessary to reproduce the system in the physical world. The data flow transfers from the physical system to the digital system. The purpose of DTP is to improve the time and cost efficiency of the operation of the physical system. DTP can be used in monitoring systems, such as smart grid monitoring systems. However, the DTP data flow is a one-way flow from the physical system to the digital system. It can only monitor the system and cannot manage it according to the incoming data from the physical system.  
The DTI is a type of DT that connects to its corresponding physical target. Unlike the DTP, the DTI handles the data flow from the digital system to the physical system. The data flow contains predictions or guidelines, which assist the physical system to operate simultaneously when the environment changes. With both DTP and DTI, the two-way connection between the physical system and digital system has been established. Nonetheless, the DTI only represents a single data flow from the digital system to the physical system. In this case, another term, called DTA, has been proposed to represent the aggregation of all the DTIs. DTI/DTA can be used to manage systems as data flows from digital systems to physical systems. The operator can operate the physical system according to the prediction result of the physical system. Nonetheless, the DTI/DTA data flow cannot be transferred directly from the physical system to the digital system. Additional mechanisms need to be implemented to retrieve information from the physical system to enable the DT to operate.  
The DTE (DT environment) can be considered as the boundary of the DT application. One DTE can contain multiple DT systems. The DTE is responsible for managing all the DT systems under its coverage to assist in the operation of the relevant physical systems. From the application point of view, DTE can be used to manage large systems. At the same time, a synchronization mechanism needs to be implemented to speed up the query processing among multiple digital systems.