esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Alternative Illustrative Example
### Adaptive Control Scheme Without a Feedback Filter (\(aL=0\)) and Without a Fixed Gain \((K=0)\)  
The control scheme illustrated in Figure 1 is implemented without a fixed gain \((K=0)\) and the adaptive law without the feedback filter \((aL=0)\), as described in Equation (56). Additionally, the interaction turning term \((\gamma_{y})\) and the input \((u)\) remain the same from the previous example.  
As illustrated in Figures 1 and 2, both internal and external errors converge to a radius around zero. Due to the theorem only guaranteeing asymptotic convergence andthe relatively large discrepancy between the initial model and the true system, it does take a relatively significant time for the error states to approach zero. Nonetheless, the model is updated so the error states converge to a radius around zero.  
As discussed in the main text, if there is a large discrepancy between the model and the true system, this theorem may not be realistic in an online setting for fast dynamic systems. This is imparted by the time required for the error states to approach zero can be substantial. Conversely, the theorem could be beneficial offline, where extended time frames can be simulated quickly.  
Furthermore, due to the true system having eigenvalues with large negative real parts, the true system response has a smaller magnitude than the previous example using the same input. Consequently, applying the same input leads to slower observable error decay. For this specific case, increasing the magnitude of the input scales the true system's response accordingly, allowing the adaptive estimator to adjust rapidly and causing the error to subside faster.  
Although there exists variability in the estimated \(L\)'s data as time approaches infinity, as shown in Figure A3, the last 20 s of simulated data was averaged, denoted by \(L_{avg}\), resulting in the following:  
\[L_{avg}\approx\begin{bmatrix}-74.05&19.96\\ -4.64&-48.45\end{bmatrix}.\] (A3)  
Therefore, the estimated plant \((A_{avg})\) can be expressed as follows:  
\[A_{avg}\approx\begin{bmatrix}-67.45&2&-12.75\\ -11.16&-1&-46.34\\ -28.16&2&-139.13\end{bmatrix},\] (A4)  
where the eigenvalues of \(A_{avg}\) follows: \(\sigma\{A_{avg}\}=\{-1.72,-62.91,-142.95\}\). Compare the similarities between the true and estimated plant, denoted by Equations (A2) and (A4), respectively.  
This example illustrates that model uncertainty can be either local or global. As long as the uncertainty adheres to the specified decomposition, the error states can be shown to converge to the neighborhood around zero. Furthermore, the decay of the error states is influenced by the true system input, emphasizing the role of excitation in the adaptation process.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis
### Error Sources
#### 3.3.5 Multipath Error
If \(R(\tau)\) denotes the correlation function of the GNSS signal's spreading code, then the multipath delay error in the tracked code phase, relative to unobstructed LOS, is given as the solution to [52]  
\[0=S_{\text{coh}}(\tau)=\sum_{i=0}^{N+2}A_{i}\cos(\theta_{i}- \theta_{c})\] \[\times\left[R\bigg{(}\tau-\tau_{i}+\frac{d}{2}\bigg{)}-R\bigg{(} \tau-\tau_{i}-\frac{d}{2}\bigg{)}\right],\]  
where \(\theta_{c}\) is the tracked carrier-phase of the combined signal:  
Figure 3:0  
The paramter \(d\) is the early-to-late correlator spacing in the receiver. It is well-known that a wide-bandwidth receiver with narrow correlator spacing mitigates the effect of multipath [52]. To this end, the receiver considered in this simulation implements \(d=0.1\). It must be mentioned that \(R(\tau)\) was implemented as the correlation function for GPS L1 C/A identically for all the simulated signals. Modernized GNSS signals have better multipath mitigation characteristics [11], but this behavior was not included in the simulation. Another important observation is that when the LOS signal is strong as compared to the echo signals, the time derivative of the tracked carrier-phase is equal to the Doppler frequency of the LOS signal, which changes smoothly in accordance with the motion between the satellite and the receiver. However, when the LOS signal is comparable to or weaker than other rapidly-decorrelating echoes, the combined carrier-phase is uniformly random. In a GNSS receiver, the phase lock loop's phase-lock indicator indicates whether a sufficiently strong LOS signal is available, enabling carrier lock [6]. The simulator's phase-lock indicator is asserted only if (1) the tracked Doppler frequency does not deviate significantly from a second-order polynomial, and (2) the strongest received echo is attenuated by more than 25 dB with respect to an unattenuated signal.
## 1 Introduction
Enabling cooperation among VLC and RF systems, the concept of hybrid VLC/RF networks is introduced and the recent works in the field are analyzed. * In Section 6, remaining challenges and open issues for cooperative and precoding schemes in VLC networks subject to shadowing are outlined. These challenges comprise adapting the transmission schemes to limited backhaul links, modeling the channel state information (CSI) prediction for users mobility, uncorrelating the channel responses among users, novel user-centric clustering schemes or the application of the massive MIMO concept to VLC. It is worth noting that there are several surveys in the field of VLC networks, e.g., [6; 11; 41; 42; 43; 44]. However, none of these articles are focused on managing the blocking and shadowing effects, which indeed is one of the main VLC limitations. With this work, our goal is to present a clear and comprehensive picture of the cooperative and precoding schemes for VLC and how they are useful for overcoming the harmful blocking and shadowing effects. They invoke techniques that exploit multiple rays, arriving at the destination, either by reflection [45] or by using different transmission points [31], the cooperation among APs [46; 47], and signal processing techniques associated to reduce or manage interference [48].
## 2 Methods
### Computational Complexity  
#### 2.5.1 Time Complexity Analysis  
The improved FMT algorithm proposed in this article first uses standard FMT* to generate feasible paths. For the standard FMT*, suppose the number of sampling points is \(n\), and if the connection radius is set to the maximum distance between sample points, the time complexity is \(O(n\log(n))\). Then, in the second sampling step, it is necessary to segment and sort the feasible paths, and expand the width of the feasible paths. The time complexity of this step is linearly related to the number of path points, so its time complexity can be seen as \(O(n)\). In the subsequent collision detection phase, each path point needs to be filtered using a cycle, so its time complexity is also \(O(n)\). For the secondary sampling path expansion, compared with the path expansion of the original FMT* algorithm, the improved FMT* uses a singularity avoidance cost function based on geometric perception, which needs to calculate the inverse kinematics solution of the inner point of the connection radius in each iteration cycle, so its time complexity is \(O(n^{2})\). Therefore, the total time complexity of improving FMT is as Equation (19):  
\[O(n\log(n))+O(n)+O(n)+O(n^{2})\approx O(n^{2}) \tag{19}\]
## 3 Results and Discussion
### The Stratification Effects of Rock's Splitting Characteristics
#### 3.1.2 Contribution of AE Characteristic Parameters
Through the kernel density cloud shown in Figure 10, it was observed that the most densely distributed RA-AF values were concentrated along the vertical axis. This indicates that most AE events exhibit low RA values and high AF characteristics, suggesting that tensile failure predominates over shear failure during the fracture process. This distribution characteristic was found to have little relationship with the number of stratification layers and was more influenced by the stratification angle. Figure 10: Kernel cloud density plots of two-stratification specimens vary with angle: (**a**) 2-0-2, (**b**) 2-30-1, (**c**) 2-45-1, and (**d**) 2-60-1. Figure 9: The RA and AF values of 2-45-1 vary with time. Figure 10 presents the kernel density cloud plots for specimens with two stratification numbers as a function of the angle. The plots indicate that, as the stratification angle increases from 0\({}^{\circ}\)to 60\({}^{\circ}\), the densest data region shifts downward along the vertical axis. This suggests that, with an increasing stratification angle, there is a higher occurrence of AE events characterized by high RA values and low AF values during the rock fracture process. Consequently, the fracture mode of layered rock transitions from tensile failure to shear failure as the stratification angle increases. This observation aligns with the changes in the fracture modes of layered rock discussed in Section 3.1.1 (Tensile Strength and Failure Mode Analysis).
## 2 Related Work  
### Skeleton-Based Action Recognition  
[PERSON] et al. [16] proposed a CNN-based co-occurrence feature learning method, where they employed a hierarchical aggregation approach to learn point-frame-level skeleton information. However, CNN-based methods do not consider the natural spatial structure of the skeleton. Inspired by methods based on GNNs, [PERSON] et al. [9] first proposed a ST-GCN to model the spatial and temporal sequences of skeleton data simultaneously. Subsequent to this research, [PERSON] et al. [11] utilized the potential physical dependencies between joints to model the global relationships within the skeleton. [PERSON] et al. [12] introduced a two-stream adaptive GCN model, utilizing bone length and orientation information to improve the model's feature discrimination ability. [PERSON] et al. [17] proposed a novel GNN based on context encoding to model global dependencies for the automatic learning of skeleton topology. [PERSON] et al. [15] designed a large-scale multi-scale context aggregation and spatio-temporal dependency complex model (MS-G3D). [PERSON] et al. [18] adopted a residual GNN to hierarchically capture the dependency information between body joints, and combined it with multiple skip-clip LSTMs to model the dynamic changes of skeleton sequences. Subsequently, [PERSON] et al. [19] proposed a novel attention-enhanced graph convolution LSTM network to extract discriminative features. To enhance the robustness of action recognition models against noisy/incomplete skeleton data, references [20,21] extracted non-local features from the skeleton joint position data. [PERSON] et al. [10,13] proposed a multi-stream Graph Convolution Network to explore more discriminative features, achieving more robust action recognition with incomplete skeletons. [PERSON] et al. [22] introduced a pose refinement module to recover joints/pose that are occluded or inaccurately detected.  
In recent years, transformer-based methods have received extensive attention due to their effectiveness in capturing long-range dependencies. [PERSON] et al. [23] proposed a two-stream transformer self-attention network to separately model the spatio-temporal correlations of joints. [PERSON] et al. [24] combined a GCN and transformer operations to capture the spatial-temporal features of joints. Additionally, multi-modal learning has achieved mutual complementation between different types of data, enabling a more comprehensive understanding of the context of actions. [PERSON] et al. [25] integrated the features of RGB and pose streams through the idea of pose-driven, achieving good recognition accuracy. [PERSON] et al. [26] proposed an asynchronous fusion strategy to address the issues of temporal consistency and spatial complementarity in multi-modal fusion. However, existing algorithms improve model performance while introducing a large number of learnable parameters, leading to more difficult model training and higher computational complexity.
## 2 Materials and Methods
### Data Analysis  
The experimental data were quantitatively analyzed by the curve fitting of the far-IR spectrum as a linear combination of individual Gaussian component bands with an iterative adjustment of their width, position, and relative weights (value of peak area), as described in our previous investigations [6]. The process was iterated until an agreement between the fit and experimental data was obtained. The minimum number of component bands was used to obtain the best acceptable fit. To do so, at first, the initial peak positions were determined by a visual inspection of the spectrum in the dry samples. Six components for the DMPC samples and seven components for the DPDE samples were used to initialize the fit. After the convergence of the fit with the minimum number of band components, additional components were added in the frequency regions with the larger residuals with respect to the fitting curve. The evaluation of the number of components that could minimize the residual sum of squares (RSS) allowed us to determine the minimum number of Gaussian functions necessary for the best data analysis. In agreement with our previous work, we found that eight components were needed to accurately fit the DMPC samples independently of their hydration degree [6], while ten components were necessary for all of the DPDE samples. Notably, for all of the samples, a full study of the RSS that involved varying the number of component bands was performed. Additional considerations regarding the data analysis can be found in the results section.
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 5 Evaluation of Simplification Effect
### Building Geometry Dataset  
As the simplification method proposed in this study does not consider building materials or semantic information (e.g., relationships and hierarchies of components), the 3D geometry can be extracted and applied regardless of the data format of the building model. To evaluate the proposed method for models with various geometric characteristics, the building models were obtained from the BIM library available on the web. Two or three models have been selected from every three libraries as follows: _Academic Advance Sample_, _Academic Kingo_, _Haus30_ models from [35], _Medical Clinic_, _Duplex Apartment_, _Office Building_ models from [36], and _OTC Conference Center_, _West Riverside Hospital Parking Garage_ from [37]. The domain for building environment simulations, such as CFD simulations and fire safety assessments, is usually an interior space, rather than a structural part of the building. As such, the geometry of the indoor space (_IfcSpace_ class) was selected for evaluation from among the attributes of the BIM model. To set the boundary conditions for the indoor environmental analysis model, the information of the door and window was required; in consideration of this, the geometries of the openings in doors and windows (_IfcOpeningElement_ class) adjacent to the target space were simultaneously extracted. Of the total 40 spaces that were selected, five were from each respective BIM model, including spaces with complex geometries that could be simplified, such as curved surfaces, walls with different depths, and interior walls. To evaluate the general effect ofthe geometry simplification, geometries of various geometrical complexities were selected (Table 1). The characteristic length, which is an index that represents the hydrodynamic properties of geometry, was calculated as follows:  
\[L_{\text{C}}=V/A \tag{10}\]  
where \(L_{\text{C}}\) is the characteristic length (m), \(V\) is the volume (m\({}^{3}\)), and \(A\) is surface area (m\({}^{2}\)) of the target geometry.
## 5 Deterioration Model for Wire Load-Bearing Strength
### Analysis of the Simulation Results  
Figure 8 shows the simulated stress-strain curves of the worn wire-groove under different contact loads and different fatigue loads. When the number of fatigues is \(1\times 10^{5}\), the load-bearing strength of the steel wire decreases very little because the damage to the steel wire is small in the early stage of the tribo-corrosion-fatigue test. The load-bearing strength of the steel wire gradually decreases with increasing fatigue cycles. When the number of cycles is \(5\times 10^{5}\), the load-bearing strength of the steel wire decreases from the initial 1650 MPa to 1622 MPa, with a small degree of reduction. When the contact load \(F_{n}\) is 60 N, 80 N, and 100 N, the load-bearing strength of the steel wire is 1622 MPa, 1619 MPa, and 1610 MPa, respectively. It shows that with the increase in contact load, the load-bearing strength of steel wire decreases gradually, and the increasing contact load can promote damage to the steel wire. By comparing the stress-strain curves under different fatigue loads, when the fatigue load peak \(F_{max}\) is 1000 N, 1100 N, and 1200 N, the load-bearing strength of the steel wire is 1622 MPa, 1617 MPa, and 1612 MPa, respectively. It shows that the load-bearing strength of the wire decreases gradually with increasing fatigue load, and the increase in fatigue load aggravates the damage to steel wire. Figure 9 shows the simulated stress-strain curves of the worn wire-wire under different contact loads and different fatigue loads. The changing trend of the curves is consistent with the change in the wire groove. The load-bearing strength of the fatigue wire decreases from the initial 1650 MPa to 1629 MPa with increasing fatigue cycles. The load-bearing strength of the wire decreases significantly with the increase in contact load and increasing fatigue load. The influence of the contact load and fatigue load on the load-bearing strength of the wire creates almost no difference.  
Figure 7: Comparison between simulation and experiment. (**a**) Tensile curves; (**b**) tensile fracture.  
Figure 8: Simulated stress-strain curves of worn wire and saddle groove: **(a)**\(F_{n}\) = 60 N and \(F_{max}\) = 1000 N; **(b)**\(F_{n}\) = 80 N; **(c)**\(F_{n}\) = 100 N; **(d)**\(F_{max}\) = 1100 N; **(e)**\(F_{max}\) = 1200 N.  
Figure 9: Simulated stress–strain curves of damaged wire and wire: (**a**) \(F_{n}=60\) N and \(F_{max}=1000\) N\({}_{2}\) (**b**) \(F_{n}=80\) N; (**c**) \(F_{n}=100\) N; (**d**) \(F_{max}=1100\) N; (**e**) \(F_{max}=1200\) N.
## 4 Results of the Experiment
### Analysis of Spatial Variability in Drought
#### 4.3.2 Seasonal Changes in Vegetation in Laibin  
Based on the month-by-month drought results of the vegetation areas in Laibin City from 2021 to 2023, the mean values were taken to synthesise a quarterly drought rating results map of the vegetation in Laibin City for each quarter, which is detailed in Figure 14.  
Figure 14: Map of quarterly drought level results of vegetation areas in Laibin in the last three years.  
Figure 13: Map of changes in the percentage of drought area of vegetation in Laibin by month in 2023.  
Between 2021 and 2023, Laibin City experienced varying drought conditions. For specific information, refer to Figure 15. Moderate drought prevailed during summer and autumn, while winter was typically the wettest season. Instances of three consecutive years of moderate or higher drought were common in summer. In spring 2021, moderate drought affected the southwestern and east-central regions of Laibin City's vegetation area, with the rest experiencing mostly light drought. About 39.44% of the area was affected by moderate drought. In summer, severe drought hit the east-central part, covering 9.73% of the area, while moderate drought affected 64.15%. Autumn saw severe drought in some central and western areas, with most of the region experiencing moderate drought, totaling 75.59%. Winter brought relief with mostly mild conditions, and normal to wet areas covered 36.53%, the highest proportion in three years. In 2022, over 70% of the area faced moderate drought during summer and autumn, with severe drought in the east-central region. Summer 2023 was the most extensive drought period in three years, with 86.2% experiencing moderate drought and severe drought in the east-central area. However, autumn 2023 had the least drought in three years, with normal and wet conditions prevailing in the east. Summer has consistently been the most drought-prone season, especially in the east-central part of Laibin City's vegetation area.
## 4 Integrated Framework for Construction Lifecycle Optimization
### Application Recommendations of the Framework
#### 4.2.1 Plan Phase  
This subsection presents the recommended applications of the proposed framework for the plan phase. Below is one example of the first row of the framework that is shown in Table 7, and the rest are provided in Appendix A.1.  
VSM in Capturing Existing Condition  
Figure 2 provides a detailed visualization of the implementation process of the first step in the plan phase, which is using VSM in capturing existing conditions.  
The diagram illustrates integrating Value Stream Mapping (VSM) with advanced technologies, laser scanning, photogrammetry, and drones during construction planning. VSM streamlines workflow by identifying inefficiencies, reducing redundant processes, and enhancing collaboration between data collection and BIM, resulting in reduced cycle time, improved accuracy, productivity, and cost savings [70].  
The first step, Mapping the Current State, involves deploying drones, laser scanners, and photogrammetry to gather essential site data (aerial images, point clouds, and topographical details) converted into BIM-compatible 3D models. Common challenges include manual data validation, delays in processing data, and coordination inefficiencies [71]. The second step, Identifying Waste, categorizes workflow inefficiencies such as overproduction (excessive data), waiting times (processing delays), defects and rework (measurement errors), and underutilized talent (poor team allocation) [72].  
In the third step, Designing the Future State, workflow optimizations include automating data processing using AI tools, establishing standardized protocols (SOPs) for  
Figure 2: Example for the application of VSM in capturing existing conditions and using different technologies (Laser Scanning, Photogrammetry, Reality Capture, and Drones/UAVs).  
consistent data collection, promoting cross-functional collaboration through Common Data Environments (CDEs) and implementing IoT-enabled drones for real-time quality control. Focus is placed on collecting project-critical data and automating error checks.  
The final step, Implementation and Monitoring, involves training programs on VSM and technologies, real-time dashboards to monitor progress, and iterative feedback loops to continuously refine processes.  
Direct impacts on KPIs include cost savings via reduced redundancies; improved time efficiency through automation; enhanced productivity by streamlined workflows; waste reduction by eliminating unnecessary data collection; improved quality and safety from standardized protocols; higher stakeholder satisfaction due to transparency; and process optimization leveraging AI and IoT, minimizing manual interventions.