esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Flux Reconstruction  
If one wants to use modern control to synthesize the controls \(v_{sa}\) and \(v_{sb}\) to regulate all the states \([\omega,\psi_{ra},\psi_{rb},i_{sa},i_{sb}]\), then the knowledge of all these state variables must be available. In this work, measurements of the speed rotor \(\omega(t)\) (given by an incremental encoder) and \(I_{sa}\) of the three-phase system (its root mean square value is given by the industrial VFD) are available. It is important to mention that the currents \(i_{sa}\) and \(i_{sb}\) are obtained via the Clarke transformation using the current \(I_{sa}\). \(I_{sb}\) and \(I_{sc}\) are assumed to have the same magnitude as \(I_{sa}\) with a phase change. Furthermore, since the motor is controlled without load torque, \(T_{L}\) could be considered zero. However, measurements of the fluxes \(\psi_{ra}(t)\) and \(\psi_{rb}(t)\) are not available. Then, the reconstruction of the rotor fluxes is based on the estimation of the electrical parameters of the motor, the currents \(i_{sa}\) and \(i_{sb}\), and the angular velocity \(\omega\). Consequently, a state observer is not necessary. Starting from the model (1), the fluxes can be expressed as the sum of the voltage vectors, current vectors, and current derivatives [24] from the fourth and fifth equations. After direct manipulations of these equations, we obtain the following expressions:  
\[\begin{bmatrix}\psi_{ra}(t)\\ \psi_{rb}(t)\end{bmatrix}=\ \ \frac{1}{\beta(\alpha^{2}+\omega^{2})}\begin{bmatrix} \alpha&-\omega\\ \omega&\alpha\end{bmatrix}\begin{bmatrix}\frac{d{i_{sa}}}{dt}+\gamma{i_{sa}}- \frac{\gamma_{sa}}{\omega}\\ \frac{d{i_{sb}}}{dt}+\gamma{i_{sb}}-\frac{\gamma_{sb}}{\omega}\end{bmatrix}. \tag{2}\]  
In these expressions, the fluxes are a function of the available variables.  
As is well known, the approximation must depend mainly on the structure and parameters of the model [29].  
Here, the parameters of the mathematical model corresponding to the induction motor are identified by the recursive least squares matrix inverse (RLS) algorithm [30; 31] due to its simplicity, which requires a relatively low computational cost. Furthermore, in ref. [32], the authors mentioned the feasibility of combining the optimal control approach with the RLS algorithm.  
This algorithm was implemented using LabVIEW software, and the estimated parameters are used to synthesize the nonlinear control. This is described in the following section.
## Appendix B Proof of Proposition 1
### Instability of the Undesired Equilibrium Point  
At the equilibrium point \((\eta,e_{\omega})=(-\eta_{d},0)\), the value of the Lyapunov function is \(V=2k_{p}\). To show that this equilibrium is unstable, it suffices to show that for any neighbourhood \(\mathcal{U}\) around this point, one can find \(\eta^{*},e_{\omega}^{*}\) such that \(V<2k_{p}\). Since \(V\) is non-increasing and all solution converge to either of the two equilibria, any solution starting at \((\eta^{*},e_{\omega}^{*})\) must converge to \((\eta,e_{\omega})=(\eta,0)\). Consider \(\eta^{*}\) arbitrarily close to \(-\eta_{d}\), say at an angle \(e\) away from \(-\eta_{d}\). Then, \(\psi(\eta,\eta_{d})=1-\cos(\pi-\epsilon)\approx 2-\epsilon\) and \(V\approx k_{p}(2-\epsilon)+e_{\omega}^{*\dagger}e_{\omega}^{*}/2\). This means that, if we choose \(e_{\omega}^{*}\) small enough, then \(V<2k_{p}\) and we conclude that the equilibrium point is unstable.  
This line of reasoning parallels that of [PERSON]'s Theorem, for which a version for time-invariant systems is given in Theorem 4.3 in [85].
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
###### Abstract  
Soil conditions can directly influence the inoculum potential of wood decay fungi, which is likely to be a major factor in the premature failure of utility poles across Europe. The objective of our study was to assess the influence of soil pH, humic acid and iron on wood decay. For this purpose, we incubated Fe-impregnated wood specimens on artificial medium to evaluate the influence of the metal on the activity of brown rot fungi. Moreover, the impact of Cu-leaching from impregnated wood specimens that were exposed to humic acid solutions was measured. In addition, weight losses caused by brown rot fungi in impregnated wood pole segments and stiffness (Young's modulus of Elasticity) of Cu-impregnated wood specimens were quantified. The pH measurements showed that the soil samples were slightly acid (pH = 6.7 \(\pm\) 0.7). In comparison to non-impregnated controls, the Fe-impregnated samples significantly increased weight losses by brown rot fungi (>30-40%). In the presence of humic acid the release of copper from chromium-free wood preservatives (up to 143.34 mg L\({}^{-1}\)) was enhanced. Weight losses in impregnated wood segments by brown rot fungi ranged from 5.3 to 20.4%. The recorded reduction in stiffness by brown rot fungi ranged from approximately 3.96 to 55.52% for Cu-impregnated wood specimens after 12 weeks. Our study shows that the pH, humic acid, iron content and selected wood preservatives greatly influence susceptibility of impregnated wood to brown rot fungi during ground contact.  
brown rot fungi; wood preservatives; humic acid; pH; iron; utility poles Article
## 4 Performance Comparison of Observability Indices
### Evaluation Index of Calibration Accuracy  
This paper proposes to evaluate the accuracy of a calibration using the integrated error of the pose of the primary mirror. The integrated error is used to compare the accuracy of multiple calibrations. Preset the 30 target poses of the primary mirror \(\mathbf{Xi}\) (\(i=1,2,\ldots,30\)), \(\mathbf{Xi}\) evenly distributed in the workspace of the six-hardpoint positioning mechanism. Compare  
Figure 4: Process diagram of numerical simulation of kinematic calibration for six-hardpoint positioning mechanisms.  
actual pose \(\textbf{X}i^{\prime}\) with \(\textbf{X}i\), the deviation can indicate the calibration accuracy. Calculate the pose error of the primary mirror \(\textbf{d}\textbf{X}i\):  
\[dXi=\textbf{X}i^{\prime}-\textbf{X}i \tag{39}\]  
where:  
\[dXi=\begin{bmatrix}dx_{i}&dy_{i}&dz_{i}&d\alpha_{i}&d\beta_{i}&d\gamma_{i} \end{bmatrix} \tag{40}\]  
\(\textbf{dX}_{i}\) is composed of position error vector [\(\text{d}x_{i}\)\(\text{d}y_{i}\)\(\text{d}z_{i}\)] and attitude error vector [\(\text{d}a_{i}\)\(\text{d}\beta_{i}\)\(\text{d}\gamma_{i}\)]. Calculate the norms \(\text{d}X\text{n}_{i}\) and \(\text{d}X\text{n}_{i}\) of the two vectors, and then calculate the sum \(\text{d}X\text{zn}_{i}\) (\(i=1,2,\,\ldots,\,30\)) of the two norms:  
\[dX\text{n}_{i}=\sqrt{{dx_{i}}^{2}+{dy_{i}}^{2}+{dz_{i}}^{2}} \tag{41}\]  
\[dX\text{s}n_{i}=\sqrt{{d\alpha_{i}}^{2}+{d\beta_{i}}^{2}+{d\gamma_{i}}^{2}} \tag{42}\]  
\[dX\text{z}n_{i}=dX\text{t}n_{i}+dX\text{s}n_{i} \tag{43}\]  
Finally, calculate the root mean square of \(\text{d}X\text{zn}_{i}\) to obtain the integrated error \(\text{d}X\text{z}\) of the calibration:  
\[dX\text{z}=\sqrt{\frac{\sum\limits_{i=1}^{20}{dX\text{z}n_{i}}^{2}}{30}} \tag{44}\]  
The accuracy of a calibration can be evaluated with the integrated error \(\text{d}X\text{z}\), and it is dimensionless.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## 4 Discussion  
### Empirical Accuracy Assessment of AAT Compared to Traditional Ground Control  
As far as the accuracy of horizontal coordinates is concerned, controlling block deformations with GCP provided the best results, which were on average 0.7 GSD. Dispensing entirely with GCPs (**RTK**) brings the average accuracy to 1.2 GSD, which is a marked decrease relative to **12 GCP**, though not much in absolute terms; adding at least one GCP does not bring on average any real improvement (1.1 GSD) with respect to AAT only. However, the worst result might be well above the average, if no GCP are used.  
Likewise, as far as the vertical accuracy is concerned, using GCP provided the best results, which were on average 1.0 GSD. In the RTK configuration, to the contrary, the average accuracy was 3.1 GSD, which is a dramatic decrease in relative as well as in absolute terms. The reason is a bias that was most likely introduced by the estimation of interior orientation parameters in the self-calibrating BBA (see Section 1.3). Indeed, the mean error in elevation is correlated in excess of 95% with the RMSE Z. As soon as some information on ground elevation is enforced in the BBA, as in **RTK+1 GCP**, the bias is almost entirely or at least greatly reduced, with an RMSE Z of 1.3 GSD; the residual error is still 30% worse than using GCP only, but is acceptable in most cases.
## 2 Results and Discussion
### Volatile Organic Compounds (VOCs) after Fermentation
_cerevisiae_ MERIT.ferm [38], in synthetic grape must fermentation by commercial wine yeast strains [39], in papaya juice fermented by _Williopsis saturnus_ var. _mrakii_[41], and in synthetic grape juice fermented by _Saccharomyces cerevisiae_ var. _bayanus_[42]. The addition of an amino acid mixture (valine (Val), Leu, Ile, and Phe) to soy (tofu) whey has also been reported to increase the amount of phenylethyl alcohol following _Torulspora delbrueckii_ Biodiva fermentation [40]. Benzyl alcohol, which imparts floral notes, can be produced from benzaldehyde via oxidative and non-oxidative pathways [60]. It was detected at the highest peak area in media supplemented with Phe (Figure 3C, medium DMP), where a significantly (\(p<0.05\)) higher amount was obtained with 0.1% acetate compared to 1.2% acetate. A genomic study by [PERSON] et al. [53] reported that _[PERSON]. [PERSON]_ ATCC 367 lacked the gene _araT_ encoding ArAT (putative) enzyme, which initiates the transamination reaction that converts Phe into phenyl pyruvate. However, in the current study, LB672 produced benzaldehyde, phenylethyl alcohol, and benzyl alcohol presumably via the intermediate  
Figure 3: Normalized peak area of benzaldehyde (**3A**), phenylethyl alcohol (**3B**), and benzyl alcohol (**3C**) across different medium compositions (see Table 1) at 0.1% and 1.2% acetate. Values are presented as mean \(\pm\) standard error (\(n=6\)). Different letters represent significant difference between the different medium compositions according to [PERSON]’s test at \(p<0.05\). phenyl pyruvate. [PERSON] and [PERSON] [61] reported that the presence of aspartate aminotransferase (AspAT) in _Brevibacterium linens_ is responsible for aspartate (Asp) transamination and is also active on aromatic amino acids. The gene _aspAT_ encoding for AspAT enzyme has been detected in several _Lev. Brevis_ strains, including _Lev. Brevis_ ATCC 367 [53] and _Lev. Brevis_ CGMCC 1306 [62]. Hence, it seems more likely that, in the current study, AspAT in LB672 carried out the transamination reaction of Phe rather than the ArAT enzyme.
## 3 Results
### Albert Rift Regional Tectonostratigraphy
#### 3.1.1. Tectonostriagraphy of the Pakwach Basin  
The Pakwach Basin, dating from the Early Pliocene, is characterized by four distinct tectonostrigraphic units representing different rifting stages (Figure 3). These events are grounded in the lithostriagraphy, which is outlined in Table 1, along with a record of tectonic events.  
Early Pliocene Rift Onset Phase  
This earliest package marks the onset of rifting, with initial rift subsidence and sedimentation dominated by alluvial fan deposits transitioning to fluvial and alluvial plain systems. The sequence consists of coarse sands and conglomerates in the lower portion, shifting to finer sediments in the upper layers.  
Figure 3. Generalized tectonostriagraphy of the Pakwach Basin.  
Mid-Pliocene Rift Expansion Phase  
Deposition occurred during a phase of rift expansion, with associated slow subsidence across the basin. The sequence is dominated by lacustrine sediments, including shales and fine sands with gastropod shells, reflecting stable tectonic conditions and organic-rich sedimentation.  
Late Pliocene to Early Pleistocene Renewed Rifting Phase  
Renewed rifting and intensified subsidence resulted in the deposition of poorly sorted sediments in deltaic and shallow lacustrine environments. The unit features a mix of gritty sandstones, siltstones, and clays, with evidence of fluctuating lake levels and episodic sedimentation.  
Mid-Pleistocene to Holocene Late Rifting Phase  
The final phase of rifting, marked by significant uplift and subsidence, led to the deposition of high-energy alluvial fans and fluvial systems. The package consists of coarse sand, conglomerates, and gritty clays, indicating localized tectonic adjustments and an aggradational depositional environment.  
Generally, the tectonoststratigraphic evolution of the Pakwach Basin reflects a series of tectonic and depositional phases. The Early Pliocene marks the initiation of rifting, and is characterized by graben and half-graben structures and alluvial fan deposits. In the Mid-Pliocene, rift expansion facilitated lacustrine transgression, with fine-grained, organic-rich sediments found. During the Late Pliocene to Early Pleistocene, renewed tectonic activity resulted in fluvial-deltaic environments transitioning to shallow lacustrine settings. The Mid-Pleistocene to Holocene represents the latest stages of rifting, with pronounced uplift and high-energy alluvial fan and fluvial systems filling the newly created accommodation space. These findings highlight the tectonost stratigraphic evolution of the Pakwach Basin, which has been shaped by dynamic tectonic and sedimentary processes.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Accuracy Assessment of RD Orbits by Using the Three New Methods
#### 3.3.2 Inter-satellite Differential Validation  
The observations of HY-2D satellite-borne GPS were first processed, and then the data of the satellites involved in the validation were selected according to the distribution of the time series of GPS satellites that were observed. The distribution of GPS satellites observed by the HY2 receiver is shown in Figure 5.  
As shown in Figure 5, select data of two or more GPS satellites were observed each hour, so one data set was selected each hour for the experiment, and a total of 24 data sets were selected. Due to the short overlap period between the two GPS satellites, the data of 600 epochs with a sampling interval of 1 s (10 min) in the middle of the overlapping period were taken for experiments.  
Two data sets of GPS satellites were selected for validation each hour and the data of these two GPS satellites were removed from the observations used in POD. A total of 24 sets of experiments were performed, and, as far as possible, two different satellites were selected for validation to ensure the reliability of the inter-satellite differential validation method. The results are shown in Table 8.  
As shown in Table 8, the RMS of the inter-satellite differential validation residuals was better than 1 cm in most periods, with fluctuations in the range of \(-\)0.03 to 0.03 m. The total RMS value of residuals on DOY 266 in 2021 was 0.0106 m, and the residuals of each satellite fluctuated in the same range, with fewer fluctuations, so it can be said that inter-satellite differential validation can be used as a reliable external validation method.  
Figure 5: DOY 266 GPS Satellite distribution overview.
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 4 System Overview  
As shown in Figure 2, the proposed system comprises four core modules: provenance graph construction, abnormal behavioral community detection, hypergraph construction, and attack behaviors detection.  
Provenance Graph Construction. This module transforms kernel logs into a provenance graph, enabling subsequent context feature extraction and method implementation by modeling entities as nodes and their interactions as edges to capture behavioral dependencies and temporal sequences in audit data.  
Abnormal Behavioral Community Detection. It first extracts multi-dimensional node attributes and uses GNNs to learn node representations for classifying abnormal nodes from benign ones. It then detects overlapping communities to model complex multi-entity interactions, leveraging an improved LFM algorithm that starts with abnormal nodes to cluster behaviors.  
Figure 2: Different Modules of the SystemHypergraph Construction. Nodes clustered into abnormal behavior communities serve as the basis for hyperedge construction, resulting hypergraph that captures high-order behavioral correlations that traditional binary graphs cannot represent, providing a rich semantic foundation for subsequent analysis.  
Attack Behaviors Detection. We use a Hypergraph Attention Network to classify attack behaviors. Hyperedges in the hypergraph preserve detailed spatiotemporal and semantic features of entity interactions, which are processed through a two-layer attention mechanism with node-level and edge-level attention.
## Results and Discussions: The Influence and Benefits of PPGIS
### Creating Respect  
As a platform of environmental information gathering, PPGIS largely relies on the translation of stakeholders' environmental experience and knowledge, thus increasing the source and diversity of decision-making information [21; 26]. Living near this refuge for long periods of time, community members have an abundance of lay knowledge about the environment in the refuge, including its hydrology. The PPGIS workshop at the planning stage used the geospatial information platform to gather the community members' lay knowledge about the water surrounding the refuge, and the participants jointly confirmed the scope of monitoring. Surprisingly, community volunteers used their lay knowledge to actively help the academic team resolve difficulties in designing and implementing the monitoring. This reduced the dependence of monitoring on professional equipment and the monitoring cost, thereby increasing the feasibility of subsequent monitoring. PPGIS also played a role in displaying the monitoring results, which allowed the stakeholders participating in the workshop, especially the local residents, to grasp clearly the elevation changes in the wetlands and to be able to compare them with the community members' regular observation of hydrologic variation. These images and discussions also helped to prompt the local community's historical memories of environment changes in the wetland. They shared their memories with other participants (such as the academic team and the public sector) in the PPGIS workshop to help everyone understand the history of wetland development and to raise community awareness of the value of wetlands.  
\"When I first started looking for an outside survey company to come in, as soon as they entered the wetland, they said it would be difficult to perform the measurement. Finally, it was 'A' who thought of some ways to set the water gauge in the refuge using the tools on hand.\" (LC04)  
\"The community volunteers used the buoys of the fishing nets to make a rope that can float on the surface of the water. As long as you measure according to the marked position on the rope, you can systematically obtain the elevation data.\" (AR02)  
\"When the map came out, we thought of the times in our childhood when we would go to the refuge and play in the water... Under the water, there are still old water valves, and sometimes, we step on the gushing springs.\" (LC13)  
\"There are certain situations where there are people who can be called... citizen experts. They may have certain knowledge of the community and may also have a lot of professional knowledge. Spatial information tools such as PPGIS help transform their knowledge into data on the map.\" (AR03)
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 2 Methodology
### Segmentation
#### 2.2.1 Robust Spatial Kernel FCM (RSKFCM)  
Robust Spatial Kernel Fuzzy C-Means (RSKFCM) [28] is the variant of conventional Fuzzy C-Means (FCM). RSKFCM addresses the noise sensitivity and neighborhood information ignorance limitations of FCM. RSKFCM injects the neighborhood information into the FCM objective function and uses the Gaussian Kernel function instead of the Euclidean metric.  
The main aim of the RSKFCM is to minimize the objective function shown in Equation (1)  
\[J=\sum_{i=1}^{c}\sum_{j=1}^{n}w_{ij}^{m}\ \|\Phi(x_{j})-\Phi(v_{i})\|^{2} \tag{1}\]  
where \(c\) is the number of clusters, \(n\) is the number of voxels, \(m\) is a fuzzifier value, which controls the fuzziness of the resulting partition, \(w_{ij}\) is the RSKFCM membership degree of \(x_{j}\) in \(i\)th cluster, \(v_{i}\) is the \(i\)th cluster center, and \(\Phi\) is an implicit nonlinear map which is computed as:  
\[\|\Phi(x_{j})-\Phi(v_{i})\|^{2}=K(x_{j},x_{j})+K(v_{j},v_{j})-2K(x_{j},v_{i}) \tag{2}\]  
where \(K\) is the inner product of kernel function, i.e., \(K(x,y)=\Phi(x)^{T}\Phi(y)\). In this paper, we have adopted the Gaussian kernel function which is defined as:  
\[K(x,y)=\exp\biggl{(}-\|x-y\|^{2}\biggl{/}\sigma^{2}\biggr{)} \tag{3}\]In Gaussian kernel, \(K(x,x)=1\) and \(K(v,v)=1\), hence the kernel function becomes:  
\[\|\Phi(x_{j})-\Phi(v_{i})\|^{2}=2(1-K(x_{j},v_{i})) \tag{4}\]  
Substituting Equation (4) in Equation (1), the objective function becomes:  
\[J=2\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{n}w_{ij}^{m}\left(1-K(x_{j},v_{i})\right) \tag{5}\]  
The RSKFCM membership function \(w_{ij}\) is the combination of the kernel membership function \(u_{ij}\), and the neighbourhood function \(s_{ij}\) and it is computed as.  
\[w_{ij}=\frac{u_{ij}^{p}s_{ij}^{q}}{\sum\limits_{k=1}^{c}u_{kj}^{p}s_{kj}^{q}} \tag{6}\]  
where \(p\) and \(q\) are parameters to control the relative importance of the kernel membership and the neighbourhood membership functions.  
The kernel and neighbourhood membership functions are computed using Equations (7) and (8)  
\[u_{ij}=\frac{\left(1-K\big{(}x_{j},v_{i}\big{)}\right)^{-1/(m-1)}}{\sum\limits _{k=1}^{c}\left(1-K\big{(}x_{j},v_{k}\big{)}\right)^{-1/(m-1)}}\ ; \tag{7}\]  
\[s_{ij}=\sum\limits_{k\in N_{k}(x_{j})}u_{ik} \tag{8}\]  
where \(N_{k}(x_{j})\) represents neighbourhood voxels of \(x_{j}\). This neighbourhood function represents the probability that the voxel \(x_{j}\) belongs to the \(i\)th cluster.  
Similar to FCM, RSKFCM also works in an iterative process to update the membership and cluster center values. The cluster centers are updated using Equation (9)  
\[v_{i}=\frac{\sum\limits_{j=1}^{n}w_{ij}^{m}K(x_{j},v_{i})x_{j}}{\sum\limits_{ j=1}^{n}w_{ij}^{m}K(x_{j},v_{i})} \tag{9}\]  
RSKFCM is an iterative process, and it stops when the stopping criteria is satisfied, i.e., the difference of successive iteration's objective function value is less than the user-specified stopping criteria value.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 4 Antenna Feeding for Fabric Antennas
### Fabrication  
The stable impedance at the feeding section is maintained on the aperture by carefully placing the dielectric section of the cable on the aperture slot with equal widths. The signal wire mesh is fixed to the upper section of the ground (above the slot) while the cable ground mesh is fixed on the lower section as shown in Figure 7. The exposed cables are fixed to the conductive fabric using conductive epoxy with a high curing/adhesion property. After 24 h of curing, the backside is shielded with a low loss of 0.1 mm polytetrafluoroethylene (PTFE) silicone adhesive tape [23]. The resulting antenna is shown in Figure 8a,b, revealing the cable placement with cured epoxy. The assembly of the radiating patch and ground follows the same procedure used in Section III-A.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **BLE Model (mm)** & **LoRa Model (mm)** \\ \hline _Lpatch_ & 43 & 125 \\ \hline _Wpatch_ & 42 & 145 \\ \hline _Lyround_ & 52 & 150 \\ \hline _Wground_ & 42 & 145 \\ \hline \(w\) & 1.0 & 1.2 \\ \hline \(l\) & 25.5 & 72 \\ \hline \(t\) & 0.24 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter List for Aperture-Fed Antenna.  
Figure 8: Photos of fabricated BLE antenna model.