esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis
### Displacement Ductility  
The average displacement ductility values of each specimen at ultimate load and at failure load are summarized in Table 3. Based on the generalized envelope curves of the specimens, the ductility was calculated as the ratio of a specific displacement (\(\Delta\)) to the yield displacement (\(\Delta_{y}\)), where in this study \(\Delta\) was chosen to be the displacement at the ultimate load (\(Q_{u}\)) and at the failure load (\(Q_{0.85u}\)), respectively. The yielding displacement (\(\Delta_{y}\)) was determined by the method shown in Figure 10 in Section 4.1.2.  
Table 3 shows that the ductility value of the specimens at failure load varied, in a range between 4.5 and 6.2, and were, on average, 73% larger than the values at the ultimate load, which proved the favorable ductile behavior of the PRMSW failing in flexure. Additionally, the test walls with evenly distributed vertical rebar exhibited a 21% larger ductility value at failure than the specimens with local concentrated reinforcement. The increase of the compression ratio from 0.10 to 0.15 resulted in an approximately 16% decrease in the ductility value of the shear wall. The compression load had a negative influence on the shear walls' ductility, and similar results were reported by [PERSON] [30] and [PERSON] [31].
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation
### Deformation Model  
Figure 12 depicts the relationship between the location of the maximum surface deformation (\(d_{5 vm}\)) and the depth of excavation. Figure 12 shows that the position of \(d_{5 vm}\) moves far away from the edge as the excavation depth increases. However, it is concentrated in areas of 0.42\(H\)-0.62\(H\).  
Figure 11: Effect of support system stiffness on the normalized maximum surface settlement.  
Figure 12: Relationship between the position of \(d_{5 vm}\) and the excavation depth (\(H\)).  
Normalized ground deformation, as well as the trends that have been compared with other cases, are shown in Figure 13. The ground deformation varies from \(0.28 vm\) to \(-0.78 vm\), and it is mainly distributed around the wall. The maximum \(\delta v\) exists in areas that are \(0.5H\)-\(0.75H\) off the wall. Similar to previous studies, a boundary of deformation distribution is proposed for excavations in Beijing, and the concave deformation profile is described using a polynomial curve. Downward vertical displacement can be observed up to approximately \(3H\) outside the edge, but beyond a distance of \(2.5H\), the vertical displacement can become small. On the basis of [PERSON]'s principle, the envelope can be divided into two zones, as follows: the primary zone (\(0\leq d/H\leq 2.5\)), wherein ground movement is influenced by excavating and supporting procedures, appears to be moderate; the secondary zone (\(2.5\leq d/H\leq 3\)), wherein the influence on ground movement is relatively mild, seems to be negligible. The primary zone in Beijing aligns with other corresponding mixed ground profiles [2; 15]. It is slightly wider than the trapezoidal boundaries found in soft to medium clay and in sand [7]. However, the secondary zone is more convergent compared with the trapezoidal boundaries observed in stiff to very hard clay, resembling that of soft soils [1].
## 6 Discussion
### Future Work  
Future research may explore migrating the agent from LEO SOP to MEO or GEO SOP scenarios, necessitating adaptation to altered observation characteristics. In MEO and GEO environments, geometric configuration changes will substantially reduce Doppler frequency fluctuations while moderating SNR variations. Consequently, the agent must undergo retraining using MEO/GEO-specific signals. Through this process, the agent will recalibrate its network parameters by enhancing Doppler frequency sensitivity while desensitizing to SNR changes, thereby maintaining effective collaboration with RLS within the AWR-PF. This adaptive retraining ensures optimal performance across distinct orbital regimes despite fundamental differences in signal dynamics.  
The reward function in this study relies on positioning error, which poses implementation challenges in GNSS-denied environments, where true position is typically unavailable. This limitation necessitates developing truth-position-independent reward mechanisms for practical deployment. Notably, GDOP offers a significant advantage: Calculated solely from satellite geometry without requiring true position, it quantifies positioning solution sensitivity to measurement errors. This characteristic establishes GDOP as an effective indirect quality metric for epoch assessment. Empirical evidence further indicates that SNR and elevation angle critically influence epoch quality, with both being directly measurable from non-cooperative SOP. Consequently, future research should focus on designing reward mechanisms based on GDOP, SNR, and elevation angle--for instance, assigning higher rewards to epochs exhibiting lower GDOP, higher SNR, and larger elevation angles.  
To enhance the agent's versatility, future work could involve multi-constellation training across diverse locations and receivers. Our trained agent currently supports real-time positioning using Iridium signals with RLS at any time near the training location via corresponding receivers. However, significant changes in location (beyond 500 km), receiver hardware, or satellite constellations (e.g., switching to Starlink or OneWeb) necessitate agent retraining. Three primary factors challenge generalization: Location shifts (>500 km) alter ionospheric/tropospheric conditions, receiver variations introduce antenna gain/phase response discrepancies, and constellation differences fundamentally change geometric configurations. Achieving universal positioning--any location, any receiver, any constellation--requires more extensive and diverse training data. This expansion would simultaneously pose greater demands on MDP design and DRL algorithm performance to maintain robustness across such heterogeneous operational environments. Future implementations must address these scalability challenges to realize truly adaptable navigation agents.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
###### Abstract  
Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.  
Keywords:individual tree detection; 3D clustering; airborne laser scanning; point cloud +
Footnote †: journal: Remote Sensing, 11
## 2 Related Works  
### Original ELM and Kernel ELM  
Original ELM stems from single layer feed-forward neural network (SLFN) [10]. In ELM, the input weights \(w\) and biases \(b\) in hidden layers are stochastically determined.  
The output function of ELM can be expressed in Equation (1):  
Figure 1: The structure of the ELM network.  
\[f(x)=\sum_{i=1}^{L}G(w_{i},b_{i},x)\beta=\sum_{i=1}^{L}\beta_{i}h_{i}(x)=h(x)\beta \tag{1}\]  
where \(h(x)\) is the activation function in the hidden layer, and \(h(x)=[h_{1}(x),\dots,h_{L}(x)]\) is the row output vector of hidden layer. \(\beta=[\beta_{1},\dots,\beta_{L}]^{T}\) is the column vector of the output weights between nodes in the hidden layer and output layer. \(L\) is the output dimension. These output weights are generated by linear calculation.  
In order to minimize the training error, the training purpose of ELM is illustrated in Equation (2):  
\[Minimize:\|H\beta-T\|^{2}\text{ }and\ \|\beta\| \tag{2}\]  
where \(H\) is the output matrix of hidden layer as shown in Equation (3), and \(T\) is the output label matrix:  
\[H=\begin{bmatrix}h(x_{1})\\ \vdots\\ h(x_{n})\end{bmatrix}=\begin{bmatrix}h_{1}(x_{1})&\dots&h_{L}(x_{1})\\ &\vdots\\ h_{1}(x_{n})&\dots&h_{L}(x_{n})\end{bmatrix} \tag{3}\]  
Supposing the training error \(\|H\beta-T\|^{2}=0\), then the training work of this model is to find the least-squares solution of a linear system: \(H\beta=T\), as Equation (4):  
\[\beta=H^{+}T \tag{4}\]  
where \(H^{+}\) is the Moore-Penrose pseudo-inverse of \(H\).  
According to the Constrained-Optimization theorem, Equation (4) can be improved into Equation (5) (when the number of input samples is not huge):  
\[\beta=H^{T}(I/C+HH^{T})^{-1}T \tag{5}\]  
where \(I\) is n-order unit matrix and \(C\) is regular cost parameter.  
In ref. [17], [PERSON] et al. demonstrated the fundamentals of ELM and proved that ELM can approximate most of the continuous function. Researchers proposed several modified models such as ensemble ELM (E-ELM) [18], bi-directional ELM (B-ELM) [19], and Meta-ELM [20]. Among these methods, kernel ELM (K-ELM) has enhanced the performance of ELM remarkably [21]. In recent years, many researchers have combined ELM models with deep learning [22], transfer learning [23], and random forests [24].  
K-ELM [21] defines kernel matrix for ELM, as Equation (6):  
\[\Omega_{ELM}=HH^{T}:\Omega_{ELM;j}=h(x_{i})h(x_{j})=K(x_{i},x_{j}) \tag{6}\]  
It should be mentioned, in K-ELM [21], if kernel function is determined, the vectors of input samples will be transferred from feature space to kernel space.
## 4 Results and Discussion
### The Results of the ADI Mitigation Models  
#### 4.2.1 The Results of the GB Classifier-Based ADI Mitigation System  
The evaluation parameters of the GB classifier-based ADI mitigation models are given in Table 19. The results are given for both scenarios one and two. The BER and SNR of the GB-based ADI mitigation systems were evaluated at the receiver side for different learning rates. The results are presented in Table 20.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Seenario One**} & \multicolumn{3}{c}{**Scenarios Two**} \\ \hline \multirow{3}{*}{**Classifier**} & \multirow{3}{*}{**Learning Rate**} & \multirow{3}{*}{**Conventional Configuration**} & \multirow{3}{*}{**Extended**} & \multirow{3}{*}{**Learning Rate**} & \multirow{3}{*}{**Conventional Configuration**} & \multirow{3}{*}{**Extended Configuration**} & \multirow{3}{*}{**Configuration**} \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\  & & & & & &  
In Table 19, individual models (Model 1, 2, and 3) and model ensembles (Model 1 and 2, and Model 2 and 3) were tested. In Scenario One (data from all base stations), Model 2 achieved the highest precision (0.75) and F1 score (0.36), indicating better mitigation effectiveness, while Model 1 and Model 2 had the best recall (0.68). In Scenario Two (data from a single base station), Model 3 performed slightly better in F1 score (0.41), showing its adaptability to more localized conditions.  
Table 20 presents the BER and SNR for both conventional and extended configuration approaches across different learning rates. The extended configuration consistently yielded lower BER and higher SNR, indicating better signal quality and error resilience. For instance, Model 3 with a 0.024 learning rate showed the lowest BER (0.002-0.003) and the highest SNR (up to \(-\)13.5 dB). This suggests that model ensembling and configuration extension enhance ADI mitigation performance, especially under the varied channel conditions represented in the two scenarios.
## 3 Control Strategies
### DC-AC Four-Leg Converter Control  
The DC-AC power stage is responsible for operating the AC side in the two operation modes defined in Section 2.1: grid-connected and grid-disconnected. In this paper, and following the operation philosophy of ATENEA's operation, a strategy based on maintaining the voltage source behaviour in both modes has been adopted. However, and as a difference from the solutions proposed in the literature review of Section 1, the grid-connected operation adopts an AC droop control, but, in grid-disconnected operation, the voltage behaviour is maintained disabling the external droop loop. In this last case, the voltage/frequency control is assumed by the inverter being the voltage master of the microgrid. In this scenario, the obtained dynamics are less limited in time-response due to the absence of droop constraints.  
As defined in Section 2, the inverter is based on a three-phase four-leg topology to be fully compatible with ATENEA four-wire microgrid. This allows the inverter to be controlled by means of three independent single phase systems in order to provide direct, indirect and homopolar sequence control capability. Each phase has its own master AC droop control (only for grid-connected mode) and two inner cascaded stationary frame controllers [51] for the voltage (\(u_{C_{cur}}\)) and current (\(i_{L_{1x}}\)) loops, \(x\) phase being \(u\), \(v\) or \(w\). The inner loops are tuned considering [49, 52] and the tuning values are presented in Section 4.
## 3 Results and Discussion
### Selection of the Crucial Operational Extraction Process Variables
A possible explanation for these results might be the polarity and viscosity of the used extracting solvent [52]. In addition, the extractability of bioactive antioxidants compounds from the tested coffee by-product could be associated with the phenomenon that these compounds are frequently more soluble in organic chemicals less polar than water [53]. From experimental data collected in Table 3, it can be seen that there is a clear decreasing trend of antioxidant activity, as well as TPC of CS extracts prepared by using the absolute pure organic solvents. Similarly, [PERSON] et al. [54] highlighted that methanol was considered to be a less powerful organic solvent of recovering bioactive antioxidant compounds from the most common coffee by-products, such as coffee silver-skin and spent coffee grounds, in comparison with its aqueous solutions. Additionally, the authors of the aforementioned article indicated that coffee by-product extracts can be ranked in the following order EtOH:H\({}_{2}\)O \(>\) MeOH:H\({}_{2}\)O \(>\) MeOH \(>\) H\({}_{2}\)O. Our results are in agreement with this described trend in solvent efficiency of antioxidants extracting process. It is worth emphasizing that methanolic and ethanolic CS extracts are characterized by relatively high antioxidant activity values, which are comparable to the antioxidant activity of fresh fruits such as raspberries, blackberries or grapre fruits [55; 56]. The TPC was in the range of 15.5-46.4 \(\upmu\)mol GAE/g d.m., 17.1-48.4 \(\upmu\)mol GAE/g d.m., 13.1-27.4 \(\upmu\)mol GAE/g d.m. for methanol, ethanol, and aqueous extracts, respectively. As in the case of antioxidant activity, the TPC in CS extracts can be compared to the content of these compounds in fresh fruit extracts such as western raspberries or _Parkia speci
## 5 Experimental Testing (Setup)

Table 4: Specification of the sensed object. Figure 4: Realized technical solution experimental measurement stand with CCD cameras. Suppose we need to obtain relatively high accuracy of the scanned object position. In that case, we need to eliminate the contrast factor of the object within the measured scene as much as possible. Thus, poorly sensed objects would hardly be identifiable, and the system would be unstable to accurately determine their shape, as shown in the left part of Figure 5.  
Obtaining the \"\(x\)\" and \"\(y\)\" coordinates is dependent on the calibration of the CCD cameras. Their calibration is necessary and required, especially in the usage of the triangular method. Their aim covers the reconstruction of sensed objects in order to obtain their shape properties. The example of non-calibrated CCD cameras can be seen in the right part of Figure 5. In the case of a sensed object moving, CCD cameras cannot determine the object's correct position. Therefore, it is appropriate to use the information in the form of optical calibration tag markers that identify the sensed scene. Markers should be identifiable in the measurement scene by their brightness profile related to the correlation factor. Using the simple thresholding method, we assume the intensity of the marker contrast will be higher than the contrast of all other points on the background of the measured scene [30]. A certain degree of change in the area of the marker depending on its position is natural and results from the physical nature of the method used. Our presented solution uses a 1D photogrammetric calibration method from two known points at a stable point field, as shown in Figure 6 (left part of the picture with two white pins).
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 4 Discussion
### Protocols Designed for Hierarchical Networks  
A hierarchical network is a network consisting of peers and super peers. A peer is connected only to a super peer while super peers communicate between them [28]. In this network architecture, if a peer sends a message to a peer in another group, it sends a message to its super peers. A super peer forwards this message to a super peer connected to a destination peer, and then the message is forwarded to its destination.
## 1 Introduction
In such instances, vibrations are induced in the valve control element, such as the plug, ball, or spool [21]. Regarding this aspect, the work performed by [PERSON] et al. [22] focuses on reducing the impact of external mechanical vibrations on hydraulic valves within various military hydraulic drive systems. The research explores the potential reduction of vibrations on the valve casing by incorporating flexibility into the valve installation on vibrating surfaces. This involves introducing materials with known stiffness and damping characteristics, such as steel spring packages or special cushions made of elastomer or oil-resistant rubber. Additionally, the study suggests that elastomer cushions within the valve casing, positioned between the casing and centering springs, can serve as a supplementary or alternative solution for dampening vibrations. The primary focus of the present study centers on exploring the influence of four distinct seat-valve distances and three adjustment nozzle positions on flow characteristics within the valve. The novelty of this paper lies in its comprehensive investigation of the impact of seat-valve distances and adjustment nozzle positions on flow characteristics within the valve. The performed investigation explores parameters such as velocity distribution, pressure profiles, tangential stresses, and turbulent kinetic energy, providing a detailed understanding of how various operational conditions affect the valve's performance.The practical application of this research is significant, as it offers valuable insights into optimizing the design and operation of these valves, the obtained results can be used to make informed decisions regarding seat-valve distances and adjustment nozzle positions, ultimately improving the efficiency and reliability of valves in real-world applications.
## 2 Materials and Methods
### Reliability Analysis  
The reliability analysis results, as displayed in Table 1, evaluate the consistency of the subjective survey data. For the physical hazard factors, the Cronbach's alpha value was found to be 0.797. In the case of biochemical hazard factors, the items 'fumes and dust' and 'infection' were excluded from the analysis, and Cronbach's alpha was 0.913. For ergonomic hazardous factors, the items 'patient lifting/carrying' and'sitting posture' were also removed, yielding a Cronbach's alpha of 0.593.
## 2 Materials and Methods
### Determination of Adsorption Isotherms  
The Langmuir and Freundlich isothermal models were used to analyze the experimental equilibrium measurements for the adsorption of ions of the studied metals in the bio-adsorbent material. The Langmuir model describes the adsorption process when a monolayer of contaminant is adsorbed onto a uniform adsorbent surface. The equilibrium constant controls the distribution of the compounds between the two phases; therefore, both adsorption and desorption rates are equal at equilibrium [82; 83; 84]. The estimation of the Langmuir model is carried out using the relationship between the equilibrium concentration (\(\mathrm{C_{e}}\), mg/L) and the equilibrium adsorbent capacity (q, mg/g), considering the maximum adsorption capacity (q\(\mathrm{q_{max}}\), mg/g) and the Langmuir constant (\(\mathrm{K_{L}}\), \(\mathrm{L/mg}\), Equation (1)).  
\[\frac{\mathrm{C_{e}}}{\mathrm{q}}=\frac{1}{\mathrm{q_{max}}}\mathrm{C_{e}}+ \frac{1}{\mathrm{K_{L}*q_{max}}}, \tag{1}\]  
The Langmuir model can be evaluated from a dimensionless factor (separation factor, \(\mathrm{R_{L}}\)) to show that the experimental data fully belong to this model. The \(\mathrm{R_{L}}\) is calculated using the initial adsorbate concentration (\(\mathrm{C_{i}}\), mg/L) and the \(\mathrm{K_{L}}\) (Equation (2)).  
\[\mathrm{R_{L}}=\frac{1}{1+\mathrm{K_{L}}\times\mathrm{C_{i}}} \tag{2}\]  
An \(\mathrm{R_{L}}\) equal to zero demonstrates an irreversible adsorption. In the case that \(0<\mathrm{R_{L}}<1\), it belongs to a favorable isothermal system. If \(\mathrm{R_{L}}\) is equal to one, it means linear desorption. Finally, \(1<\mathrm{R_{L}}\) corresponds to an unfavorable isothermal system [85].  
The Freundlich model, unlike the Langmuir model, is not restricted to monolayer adsorption and can also be applied to multilayer adsorption, where the adsorbent has a heterogeneous surface composed of several types of adsorption sites, and the adsorbed amount is the sum of adsorption on all sites until the adsorption energy decreases exponentially at the end of the adsorption process [83; 86].  
In the linear form of the Freundlich isotherm model, the constant \(\mathrm{K_{f}}\) is an approximate indicator of adsorption capacity, while \(1/\mathrm{n}\) is a function of the strength of adsorption in the adsorption process [84] (Equation (3)).  
\[\mathrm{log}\;\mathrm{q}=\frac{1}{\mathrm{n}}\mathrm{log}\;\mathrm{log}\; \mathrm{C_{e}}+\mathrm{log}\;\mathrm{log}\;\mathrm{K_{f}} \tag{3}\]  
The values of \(\mathrm{n}\) and \(1/\mathrm{n}\) depend on temperature and indicate adsorption conditions such as adsorption intensity or surface heterogeneity [83]. The magnitude \(1/\mathrm{n}\) is a measure of the adsorption intensity directly associated with the adsorption efficiency; \(0<1/\mathrm{n}<1\) indicates favorable adsorption, \(1/\mathrm{n}>1\) unfavorable adsorption, and \(1/\mathrm{n}=1\) irreversible adsorption. The removal efficiency is expressed as the percent of sorption [87], according to Equation (4):  
\[\%\;\mathrm{Sorption}\frac{\mathrm{C_{i}-C_{e}}}{\mathrm{C_{i}}}\times 100 \tag{4}\]