esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 3 Results
### Nearly Open-Sky Conditions  
In the next stage of the tests, measurement stations were located in places with one side of the horizon obscured. Those sides were the southern, eastern, and northern for Pt 2, Pt 3 and Pt 4 stations, respectively. Tall trees limited the horizon. The results for each station, in turn, are presented below (Tables 3-5).  
Below (Figures 7-9) the cumulative graphs showing the results are presented. It can be noticed that the horizon obscuration from one side adversely influenced the values of standard deviations, especially of the vertical component. The eastern horizon obscuration at station Pt 3 influenced the Y coordinate error (running along the west-east direction).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Receiver** & & **Coordinate** & **Ref. Difference** & **St. Deviation** \\ \hline \multirow{4}{*}{GS18T} & X [m] & 5,547,998.879 & 0.000 & 0.009 \\  & Y [m] & 7,422,277.461 & 0.027 & 0.009 \\  & H [m] & 204.318 & 0.014 & 0.034 \\ \hline \multirow{4}{*}{ZED-F9P + AS10} & X [m] & 5,547,998.883 & \(-\)0.004 & 0.006 \\  & Y [m] & 7,422,277.467 & 0.020 & 0.010 \\  & H [m] & 204.321 & 0.011 & 0.023 \\ \hline \multirow{4}{*}{ZED-F9P + ANN-MB-00} & X [m] & 5,547,998.890 & \(-\)0.011 & 0.009 \\  & Y [m] & 7,422,277.468 & 0.020 & 0.010 \\ \cline{1-1}  & H [m] & 204.301 & 0.030 & 0.030 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement results on Pt 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Receiver** & & **Coordinate** & **Ref. Difference** & **St. Deviation** \\ \hline \multirow{4}{*}{GS18T} & X [m] & 5,547,978.150 & \(-\)0.001 & 0.007 \\  & Y [m] & 7,422,301.262 & 0.011 & 0.006 \\  & H [m] & 203.921 & 0.005 & 0.029 \\ \hline \multirow{4}{*}{ZED-F9P + AS10} & X [m] & 5,547,978.146 & 0.003 & 0.014 \\  & Y [m] & 7,422,301.263 & 0.010 & 0.008 \\  & H [m] & 203.927 & \(-\)0.001 & 0.031 \\ \hline \multirow{4}{*}{ZED-F9P + ANN-MB-00} & X [m] & 5,547,978.142 & 0.008 & 0.005 \\  & Y [m] & 7,422,301.265 & 0.008 & 0.006 \\ \cline{1-1}  & H [m] & 203.923 & 0.004 & 0.036 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measurement results on Pt 4.  
Figure 8: Cumulative diagrams with results of measurements at the Pt 3 station (obscuring the horizon from the east; differences in relation to the reference points with standard deviations [m]).  
Figure 7: Cumulative diagrams with results of measurements at the Pt 2 station (horizon obscuration from the south; differences in relation to the reference points with standard deviations [m]).  
Figure 9: Cumulated diagrams with results of measurements at the Pt 4 station (obscured horizon from the north; differences in relation to the reference points with standard deviations [m]).  
The results on all stations are satisfactory, although the Leica GS18T stays slightly behind the u-blox ZED-F9P + Leica AS10 antenna set in terms of differences to reference coordinates. Once again, there is also an increase in positioning accuracy favoring the Leica AS10 antenna over the u-blox ANN-MB-00.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 4 Parameter Conversion of the SINS/SRS/CNS Multi-Information Fusion Autonomous Navigation System  
### Navigation Parameter Conversion  
Different navigation coordinate frames are adopted for polar regions and nonpolar regions. Therefore, the navigation parameters need to be switched between the \(g\) frame and the \(G\) frame during long-duration and transport flights.  
\(\mathbf{C}_{g}^{G}\) is the direction cosine matrix from the \(g\) frame to the \(G\) frame, and its transpose matrix \(\mathbf{C}_{G}^{S}\) is the direction cosine matrix from the \(G\) frame to the \(g\) frame:  
\[\mathbf{C}_{g}^{G}=\begin{bmatrix}\cos{\sigma}&-\sin{\sigma}&0\\ \sin{\sigma}&\cos{\sigma}&0\\ 0&0&1\end{bmatrix} \tag{25}\]  
When the UAV enters the polar regions, the navigation coordinate frame switches from the \(g\) frame to the \(G\) frame. The polar attitude matrix, \(\mathbf{C}_{b}^{G}\), and the polar velocity, \(\mathbf{v}^{G}\), can be obtained by \(\mathbf{C}_{g}^{G}\):  
\[\begin{cases}\mathbf{C}_{b}^{G}=\mathbf{C}_{g}^{G}\mathbf{C}_{b}^{G}\\ \mathbf{v}^{G}=\mathbf{C}_{g}^{G}\mathbf{v}^{G}\end{cases} \tag{26}\]  
On the other hand, when the UAV leaves the polar regions, the navigation coordinate frame switches from the \(G\) frame to the \(g\) frame. The nonpolar attitude matrix, \(\mathbf{C}_{b}^{g}\), and the nonpolar velocity, \(\mathbf{v}^{g}\), can be obtained by \(\mathbf{C}_{G}^{g}\):  
\[\begin{cases}\mathbf{C}_{b}^{g}=\mathbf{C}_{G}^{g}\mathbf{C}_{b}^{G}\\ \mathbf{v}^{g}=\mathbf{C}_{g}^{G}\mathbf{v}^{G}\end{cases} \tag{27}\]  
The conversion relationship between the position coordinates in the \(e\) frame and latitude, longitude, and altitude can be obtained from Equation (23).
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Prediction Model
#### 4.3.1 Deep Learning Model  
In this paper, we propose a deep learning model for rock-fall occurrence prediction. It is part of a machine learning process. It is a complex mathematical model that simulates the biological neuron structure and self-learning function. It uses mathematical methods that are based on the idea of linked layers of nodes. [30]. As seen in Figure 3, it has an output layer, three hidden layers, and a three-parameter input layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Domain** & **Frequency Spectrum** & **R** \\ \hline first domain & 100 Hz–1000 Hz & 1.5 \(\pm\) 0.08 \\ \hline second domain & 500 Hz–1000 Hz & 2.7 \(\pm\) 0.32 \\ \hline third domain & 100 Hz–500 Hz & 7.1 \(\pm\) 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average spectral amplitude ratio R.  
Figure 3: Deep learning model design.  
As shown in Figure 4, each node in this neural network is a neuron, and each neuron has six major components, including inputs (\(x_{i}\)), biases (\(b_{j}\)), weights (\(w_{ij}\)), sum functions (\(u_{j}\)), activation functions (\(f\)), and outputs (\(y_{j}\)). The information from neurons or the outside world that is used as a decision variable is referred to as an input. Weights are values that translate the influence of inputs on one another. The sum function (Equation (3)) is an operation that takes into account a bias value and reflects the impacts of inputs and weights [31].  
\[(u)_{j}=\sum\
olimits_{i=1}^{n}w_{ij}x_{i}+b_{j} \tag{3}\]  
where:  
\(i\) is the \(i\)th input neuron;  
\(j\) is the \(j\)th output neuron;  
\(n\) is the number of elements in the \(i\)th input vector;  
\(b_{j}\) is the bias value (also known as the activation threshold) connected to the \(j\)th node.  
According to the equation in Equation (4), the activation function is in charge of converting the node's summed weighted input into the activation of the node or output for that input.  
\[y_{j}=f(u_{j})=\frac{1}{1+e^{-\cos(u_{j})}} \tag{4}\]  
where \(y_{i}\) represents the output of the \(j\)th neuron, controls the slope of the rectified linear activation function, and is typically equal to 1.
## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.5 Multiple Aircraft  
Multiple aircraft path planning deals with both centralized and decentralized coordinated mission efforts of multiple vehicles. Significant research is currently being performed in this area and could constitute a full survey paper on its own. The goal of this section is to highlight some of the primary methods being proposed for single and multiple task allocation that show significant capabilities of interest. Many of the previously discussed methods incorporated multiple vehicle controls into their algorithms, and are not repeated here. Specifically, the algorithms discussed in Section 4.1.4 also supported multiple-aircraft control.  
Swarming is not considered in detail for this review as the controls for swarming have been extensively reviewed [87; 88] and are generally focused on multiple vehicles working towards a single task while acting in a more biological-system manner. The term _swarm_ is used in multiple ways currently to describe different operations. For this paper, a swarm is a group of vehicles working towards a common task in a group manner. This section focuses on multiple-UASs performing unique cooperative single and multiple-tasks in a controlled but decentralized environment.  
One significant area of research has been at MIT under Professor [PERSON]. An unbiased Kalman consensus algorithm was proposed by [PERSON] and [PERSON] [89]. Consensus-based algorithms proposed by [PERSON] and associates consider both a decentralized consensus-based auction algorithm (CBAA) and the consensus-based bundle algorithm (CBBA). The CBAA is used for single-assignment tasking of single agents using an auction with greedy heuristics and a conflict-resolution protocol for consensus on winning bids for allocation [90]. The CBBA algorithm allows each agent to bundle assignments awarded as with CBAA but enables the system to collect and perform multiple assignments [91; 92]. Both algorithms show significant value in allocation of tasks and the overall performance of the system of UASs to complete missions in complex environments.  
A survey of early consensus problems for multi-agent coordination and development of consensus seeking algorithms was completed by [PERSON] and [PERSON] [93; 94; 95]. Extensions of some of this work have included forest fire monitoring using multiple UASs [96] and perimeter surveillance using teams of UASs [97].  
[PERSON] and [PERSON] [22] propose task clustering as a means to divide a large portion of tasks among multiple UASs. [PERSON] _et al._[98] propose a chaos optimization algorithm for task assignment to multiple-UASs. [PERSON] _et al._[99] propose a cooperative and geometric learning path-planning algorithm for single and multiple UASs that attempts to minimize both the risk and length of the path flown by the vehicle(s).
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
###### Abstract  
Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning. In label distribution learning, the associated labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms cannot be directly applied to label distribution learning. Discretizing label distribution data into multi-label data will cause part of the supervision information to be lost. In most practical applications of label distribution learning, the feature space is undefined, and the features are in the form of flow features. To solve this problem, this paper applies fuzzy rough set theory and applies the flow feature framework to propose a dynamic label distribution feature selection algorithm that handles flow features. Experimental results show that the proposed method is more effective than six state-of-the-art feature selection algorithms on 12 datasets with respect to six representative evaluation metrics.  
label distribution; online feature selection; fuzzy rough set; label importance 2020 IEEEexample:BSTcontrol
## 4 Test Scenarios
### Scenario IV: Maritime Navigation under the Influence of Jamming on Lower and Upper L-Bands  
In this experiment, both L1 and L5 wideband jammers are applied to interfere with the lower and upper L-bands. A separate mixer is utilized to integrate the jamming signals from both jammers, which are then mixed with the clean GNSS signal. The jamming profile stays unchanged from earlier experiments (i.e., Scenarios II and III). A full ramp jamming profile is used, starting at the 90 th second and ending at the 150 th second, with a 10 dB step. As illustrated in Figure 9, all signals begin to experience degraded C/N\({}_{0}\) at the 90 th second and keep following the whole ramp pattern until the 150 th second. For all signals around the 120 th second, the C/N\({}_{0}\) degradation is very significant. All signals are recovered almost instantly once the jamming signals are removed.
# Concept of a 3D-Printed Voronoi Egg-Shaped Habitat for Permanent Lunar Outpost
## 3 Materials and Methods  
### Materials  
Because of transportation issues between the Earth and the Moon, building Lunar habitats should be solely based on an in situ resource utilization (ISRU) approach. Thanks to the Apollo missions, the composition of Lunar regolith is fairly well known. The fifth and last lunar mission (Apollo 17) brought back 111 kg of lunar soil [30]. On Earth, on the basis of samples brought from the Moon, materials called lunar simulants (imitating the properties of lunar regolith) were created [12]. This enables an understanding of the behaviour of material similar to the lunar regolith under different conditions [9]. The result was the development of regolith mining equipment [10; 11]. On the other hand, ISRU should provide materials for construction, life support, and energy. Lunar regolith contains ferromagnetic fractions which, after separation, could be used as the protective layer against meteorites and radiation and as an aggregate of 3D printable geopolymer concrete.
## 4 Results
### Modeling and Validation of ET and NEE Estimation
#### 4.1.2 The Performance of Different Regression Methods  
The accuracy levels of the three algorithms differed significantly, whether for ET or NEE estimation (Figure 7). Based on variance analysis, Random Forest Regression (RFR) demonstrated superior accuracy for both ET (R\({}^{2}\) = 0.19-0.81, RMSE = 1.13-2.42 mm) and NEE (R\({}^{2}\) = 0.03-0.83, RMSE = 2.83-6.63 gC/m\({}^{2}\)) estimation compared to the other algorithms. Conversely, Backpropagation Neural Network (BPNN) and Multiple Linear  
Figure 5: The flowchart of the data preprocessing, model building and validation.  
Considering only the optimal cases for each algorithm (i.e., four types of variables combinedly input), RFR achieved an R\({}^{2}\) of 0.81 and an RMSE of 1.13 mm for ET estimation, and an R\({}^{2}\) of 0.83 and an RMSE of 2.83 gC/m\({}^{2}\) for NEE estimation. There are differences in the accuracy of ET and NEE estimation between the BPNN and MLR algorithms. For ET estimation, the highest accuracy of the MLR algorithm (R\({}^{2}\) = 0.78, RMSE = 1.29 mm) is slightly better than that of BPNN (R\({}^{2}\) = 0.66, RMSE = 1.49 mm), while for NEE estimation, the highest accuracy of the MLR algorithm (R\({}^{2}\) = 0.52, RMSE = 3.38 gC/m\({}^{2}\)) is significantly lower (\(p\) < 0.05) than that of BPNN (R\({}^{2}\) = 0.78, RMSE = 3.20 gC/m\({}^{2}\)).
## 3 Methodology  
In this section, we introduce the DI-MDE framework, which tackles the main challenges of monocular depth estimation in dynamic scenes. It does so by combining iterative depth refinement with scale alignment between dynamic and static regions. The DI-MDE framework operates in a self-supervised setting, utilizing monocular video sequences for training without requiring ground truth depth labels Figure 1. The key innovations of our approach are the Dynamic Scene Alignment (DSA) module, which resolves scale ambiguities between dynamic and static regions, and the iterative elastic bin (IEBins) refinement process. The IEBins process progressively improves depth predictions by adjusting bin widths based on uncertainty estimates. This section is organized as follows: First, we outline the overall architecture of the DI-MDE framework. Next, we describe the depth estimation process for static and dynamic regions. We then detail the iterative depth refinement using elastic bins. Finally, we explain the scale-alignment mechanism for dynamic objects and the associated loss functions used during training.  
Figure 1: Overview of the DI-MDE framework. The input frames (reference and source) are processed to generate a motion mask, which isolates dynamic regions for targeted depth estimation. The static and dynamic depth estimation networks operate concurrently, with results refined iteratively by the IEBin module and aligned using the DSA module.
## 5 Threat to Validity
### External Validity  
PROMISE datasets used for validation are open-source software project data. If our proposed approach were built on closed software projects developed under different environments, it might produce better/worse performance.
## 2 Materials and Methods
### Antioxidant Activity Measurements  
For the antioxidant properties of the CPC-based beverages, ABTS, DPPH radicals scavenging activities test as well as FRAP test were chosen. Additionally, the reducing power was evaluated.  
Briefly, DPPH radical scavenging activity was determined by mixing 1 mL of extracts with 1 mL of 0.01 mM DPPH methanolic solution. Absorbance was determined at 517 nm. Three mL of ABTS\({}^{+}\) solution were mixed with 50 \(\upmu\)L of extracts and absorbance was measured at 734 nm [4]. For FRAP assessment, 25 mL of acetate buffer (300 mM), 2.5 mL of 2,4,6-tripyrid-s-triazine (TPTZ) solution (10 mM in 40 mM HCl) and 2.5 mL of ferric chloride hexahydrate aqueous solution (20 mM) were mixed. To 300 \(\upmu\)L of FRAP reagent in a microcentrifuge tube, 10 \(\upmu\)L of extracts were added and vortexed for 10 s. Absorbance was measured at 593 nm [29]. For the determination of reducing power, the extracts (500 \(\upmu\)L) were placed in tubes to which 1.25 mL of phosphate buffer solution (0.2 M, pH 6.6) and 1.25 mL of 1% potassium ferric cyanide solution were added. After incubation at 50 \({}^{\circ}\)C for 20 min, 1.25 mL of trichloroacetic acid solution was added to the tubes. The 1.25 mL of supernatant obtained by centrifugation at 3000 rpm for 10 min was diluted with 1.25 mL of deionized water. Finally, 0.25 mL of 0.1% ferric chloride solution was added to complete the determination the reduction of ferric ion (Fe\({}^{3+}\)). The reducing power was determined as absorbance at 700 nm [4].
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 4 Controller Design
### Controller Design  
First, we define an error as:  
\[\mathbf{e}=\mathbf{q}-\mathbf{q}_{d} \tag{20}\]  
where \(\mathbf{q}_{d}\) is the desired value of the satellite attitude angle and telescope angle, \(\mathbf{q}\) is the actual value of the satellite attitude angle and telescope angle.  
We assume a reference denoted as \(\dot{\mathbf{q}}_{r}\), the control goal is \(\dot{\mathbf{q}}\rightarrow\dot{\mathbf{q}}_{r}\). We design the reference as:  
\[\dot{\mathbf{q}}_{r}=\dot{\mathbf{q}}_{d}-\alpha\mathbf{e} \tag{21}\]  
where \(\alpha>0\) is a gain parameter, it is obvious that if \(\dot{\mathbf{q}}=\dot{\mathbf{q}}_{r}\) this will result in \(\mathbf{q}\rightarrow\mathbf{q}_{d}\). Moreover, we assume that \(\mathbf{q}_{d}\) is second-order differentiable under any conditions.  
The extended error can be denoted as:  
\[\mathbf{S}_{r}=\dot{\mathbf{q}}-\dot{\mathbf{q}}_{r} \tag{22}\]  
We have:  
\[\mathbf{Y}_{r}=\mathbf{H}(\mathbf{q})\ddot{\mathbf{q}}_{r}+\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}} \big{)}\dot{\mathbf{q}}_{r}-\mathbf{d} \tag{23}\]  
Then the dynamic equation becomes:  
\[\mathbf{H}(\mathbf{q})\dot{\mathbf{S}}_{r}+\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\mathbf{S}_{ r}=\mathbf{\tau}-\mathbf{Y}_{r} \tag{24}\]  
We choose the Lyapunov function as [37]:  
\[V= \frac{1}{2}\mathbf{S}_{r}{}^{T}\mathbf{H}(\mathbf{q})\mathbf{S}_{r} \tag{25}\]  
We design the control torque as:  
\[\mathbf{\tau}=-\frac{\pi}{2\eta\gamma\beta T_{c}}\Big{(}\gamma^{2}V^{-\
icefrac{{ 9}}{{2}}}+\beta^{2}V^{+\
icefrac{{9}}{{2}}}\Big{)}\mathbf{H}(\mathbf{q})\mathbf{S}_{r}+\bm {Y}_{r} \tag{26}\]  
Derivative \(V\) yields:\[\begin{array}{ll}\dot{V}&=S_{r}^{T}H(q)\dot{S}_{r}+\frac{1}{2}S_{r}^{T}H(q)S_{r} \\ &=S_{r}^{T}\big{[}\tau-Y_{r}-C\big{(}q,\dot{q}\big{)}S_{r}\big{]}+S_{r}^{T}C \big{(}q,\dot{q}\big{)}S_{r}\\ &=S_{r}^{T}\big{(}\tau-Y_{r}\big{)}\\ &=S_{r}^{T}\Big{[}-\frac{\pi}{2\gamma\gamma\beta T_{c}}\Big{(}\gamma^{2}V^{- \eta/2}+\beta^{2}V^{+\eta/2}\Big{)}H(q)S_{r}\Big{]}\\ &=-\frac{\pi}{2\gamma\gamma\beta T_{c}}\Big{(}\gamma^{2}V^{-\eta/2}+\beta^{2} V^{+\eta/2}\Big{)}S_{r}^{T}H(q)S_{r}\\ &=-\frac{\pi}{2\gamma\gamma\beta T_{c}}\Big{(}\gamma^{2}V^{-\eta/2}+\beta^{2} V^{+\eta/2}\Big{)}\\ &=-\frac{\pi}{\gamma\gamma\beta T_{c}}\Big{(}\gamma^{2}V^{1-\eta/2}+\beta^{2} V^{1+\eta/2}\Big{)}\end{array} \tag{27}\]  
This indicates that the controller is predefined-time stable.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 1 Introduction and Related Work
In the meticulous survey [16] of existing theoretical and practical filesystem synchronizers it has been observed that \"resolving conflicts is an open problem where [...] most academic works present arbitrary resolution methods that lack a rationale for their decisions\" (page i), and that \"a file system can be affected by more than one conflict at once, which has not been discussed in related academic works\" (page 65). By presenting, for the first time, a complete analysis of the synchronization process for two replicas of a theoretical filesystem model, we fill these gaps.