esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Research Method and Process
#### 2.3.5 Accuracy Verification of Desertification Classification  
This study employs confusion matrix analysis to determine the classification accuracy and reliability, which can indicate not only the total error for each category, but also the category misclassification. The error matrix is composed of the number of image elements classified into a category and a proportional array containing the high-resolution relative test truth value for that category [48; 49]. The confusion matrix can be used to calculate the overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA), and Kappa coefficient evaluation metrics, which are used to compare various aspects of desertification recognition accuracy. The PA is the probability of a sample point being correctly classified, which is a measure of missing error. Additionally, UA quantifies the inclusion error by comparing the ratio of the correctly classified sample points in each row to the total sample points in that row. Theoretically, its formula is expressed as follows:  
\[\text{OA}=\sum_{i=1}^{i=5}X_{ii}/N \tag{7}\]  
\[\text{PA}=X_{ii}/X_{i+} \tag{8}\]  
\[\text{UA}=X_{ii}/X_{+i} \tag{9}\]  
\[\text{Kappa}=\frac{\left[\text{N}\sum_{i=1}^{i=5}X_{ii}-\left(\sum_{i=1}^{i=5 }X_{i+}+X_{i+}\right)\right]}{N^{2}-\sum_{i=1}^{i=5}X_{+i}+X_{+i}} \tag{10}\]  
where \(N\) indicates the total sample count, \(X_{ii}\) denotes the sample count in row \(i\) and column \(i\), and \(X_{i+}\), and \(X_{+i}\) represent the total sample counts of row \(i\) and column \(i\), respectively.  
To ensure the accuracy of the results, 100 validation points were distributed uniformly and randomly throughout the experimental area. The validation points were visually interpreted based on Landsat 8 true color images and Google Earth maps. The resulting interpretation data were compiled to construct a confusion matrix and solve for the Kappa coefficient.
# Wavelet-Based Multi-Class Seizure Type Classification System
###### Abstract  
Epilepsy is one of the most common brain diseases that affects more than 1% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures' EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset.  
diagnostics; dual-tree complex wavelet transform (DTCWT); electroencephalography (EEG); epilepsy; LightGBM; seizure type classification Article
# Three-Dimensional Formation Control for Robot Swarms
## 5 Covering the Sensing Holes Inside the User-Specified Shape
### MATLAB Simulation of Algorithm 1
#### 6.1.2 Change the User-Specified 3D Shape  
There may be a case in which the networked ARs entirely cover the user-specified 3D shape, but we want to change the shape to a new shape. We next address how to change the user-specified 3D shape from a sphere to a box shape. The user-specified shape is a box, which has height (z) as \(200/3\), width (y) as \(200/3\), and length (x) as \(300\) in meters.  
The sensing range \(r_{sc}\) is set as \(50\) m. Utilizing the rendezvous controls in Section 4.2, we first make all ARs rendezvous at the leader located at the origin. Thereafter, Algorithm 1 is applied to make the ARs cover the user-specified 3D box, while setting \(r_{sc}\) as \(50\) m.  
Figure 6 shows the path of each maneuvering AR until the user-specified box is entirely covered. Here, Algorithm 1 is utilized for formation controls. The path of a maneuvering AR is depicted with magenta line segments. The final position of a maneuvering AR is  
Figure 4: The path of each maneuvering AR until the 3D sphere is entirely covered (\(r_{sc}=70\) m). The path of a maneuvering AR is depicted with green line segments. The final position of a maneuvering AR is depicted as a blue diamond. See that blue diamonds are marked at end points of each line segment.  
Figure 5: The final position of each AR under our formation controls (Algorithm 1) for covering the user-specified sphere shape. We use \(r_{sc}=70\) m.  
depicted as a blue diamond. See that blue diamonds are marked at the end points of each line segment.  
Figure 7 presents the ARs' final positions under Algorithm 1. Among 100 ARs, 32 ARs maneuver for covering the user-specified box shape, while (\(100-32\)) ARs are located at the leader. In other words, Algorithm 1 is finished as \(i=100-32\), since no frontierPt is detected at this instant. The ARs consume 1689 s until the 3D shape is entirely covered. MATLAB consumes 16 s to perform the simulation.
# Metadata Quality for Biobanks
## 6 Metadata Quality Characteristics and Metrics
### Metadata Consistency
#### 6.5.1 Related Work on Metadata Consistency  
The following authors address metadata consistency in their papers:  
1. [PERSON] et al. [19] defined metadata consistency (1) with respect to the current collection or to the different versions the current collection (is all the data consistent?); (2) with respect to the data from other collections or the publicly available (community) data;
2. [PERSON] et al. [22] defined the degree of consistency for the metadata values;
3. [PERSON] et al. [17] defined consistency as (1) lack of duplication w.r.t data sources and w.r.t real world (2) non-ambiguity w.r.t data sources (3) consistency in compliance to ontologies;
4. [PERSON] et al. [25] defined consistency (1) for the metadata records--as conformance to the schema (i.e., the records are supposed to have types defined by the schema) (2) for the metadata catalog--as the degree of having the same metadata specifications assigned to multiple digital objects;
5. [PERSON] et al. [16] defined metadata consistency mostly in linked data context, also as metadata compliance.
6. [PERSON] and [PERSON] [26] defined consistency as the degree of matching the standard definition for the metadata element measured by counting the number of violations of the standard, whereas coherence is measured as a semantic distance between different fields belonging to the same metadata element, e.g., between title and description of the data item.
7. [PERSON] et al. [52] identified inconsistency as the main problem in sharing the metadata describing microbiological datasets related to food safety and proposes using standards and common ontologies as a means of harmonization of such metadata.
8. In the medical domain, [PERSON] et al. [53] dealt with quality assessment of biomedical metadata, on the example of Gene Expression Omnibus (GEO) database, which defines metadata as key-value pairs. The authors identified the most frequent quality problems for both keys and values being ambiguity and lack of consistency.  
In the survey of opinions by metadata practitioners regarding the relative importance of metadata quality characteristics conducted by [PERSON] et al. [39], consistency was distinguished as the most important characteristic.
## 4 Network and Service Modeling
\begin{table}
\begin{tabular}{l l} \hline \hline
**Set or Parameter** & **Description** \\ \hline \(\mathcal{G}=(\mathcal{N},\mathcal{E})\) & graph made by node set \(\mathcal{N}\) and edge set \(\mathcal{E}\) \\ \(T_{c}\) & cyclostationary period for both topology and service generation \\ \(\tau\) & discrete time-cycle duration \\ \(T\) & number of time cycle of duration \(\tau\) in a cyclostationary period \\ \(N_{S}\) & number of physical nodes (either satellites or \(vGS\)) \\ \(i\) & index in \([0,\ldots,N_{S}-1]\) representing the physical node \\ \(t\) & index in \([0,\ldots,T-1]\) representing the time cycle \\ \(p\) & index in \([0,1]\) representing the processing state (0 standing for \\ \(n_{i}^{t,p}\) & unprocessed, 1 processed) \\ \(e^{t}_{\{i_{s},p_{s}\},\{i_{t},p_{d}\}}\) & node in the set \(\mathcal{N}\) \\ \(\Gamma_{i}\) & processing capacity associated to the \(i\)-th physical node (in Mbps) \\ \(\gamma_{i_{s},p_{s}}^{i_{s},p_{d}}\) & energy cost to be paid for a Mb of data crossing \(e^{t}_{\{i_{s},p_{s}\},\{i_{t},p_{d}\}}\) edge (in \\ \(vGS\) & virtual ground station represented by the \(i=N_{S}-1\) physical node \\ \(\mathbf{C}^{t}_{\{i_{s},p_{s}\},\{i_{t},p_{d}\}}\) & capacity associated to the \(e^{t}_{\{i_{s},p_{s}\},\{i_{t},p_{d}\}}\) edge \\ \(M_{i}\) & data storage capacity associated to the \(i\)-th physical node (in Mb) \\ \(\varepsilon_{i}\) & energy available at the beginning of each time cycle on the \(i\)-th physical node (in J) \\ \(\hat{\tau}^{t}_{i_{s},j_{d}}\) & actual visibility time between \(i_{s}\)-th and \(i_{d}\)-th physical nodes during \(t\)-th \\ \(k\) & processor effective capacitance coefficient \\ \(n_{cyc}\) & number of CPU cycles to process 1 bit \\ \(v_{i}\) & clock frequency of the processor on the \(i\)-th physical node \\ \(\Sigma\) & set of all generated services \\ \(f^{h}\) & service in \(\Sigma\), with \(h\in[0,\ldots,N_{T}-1]\) \\ \(N_{T}\) & total number of generated services \\ \(f^{h}_{s}\) & \(f^{h}\) service source satellite index \\ \(f^{h}_{0}\) & \(f^{h}\) service pre-processing size \\ \(f^{h}_{1}\) & \(f^{h}\) service post-processing size \\ \(f^{h}_{t}\) & \(f^{h}\) service generation time cycle \\ \(f^{h}_{d}\) & number of time cycles after generation within which \(f^{h}\) service shall be delivered to the \(vGS\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network and service model sets and parameter
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
## 4 UAV Flight Control
### Successive Loop Closure Flight Control  
#### 4.4.1 Longitudinal Control Law  
The mission environment of MP Solar UAV was cruising at a speed of 10 m/s on a plateau at an altitude of 4500 m. The longitudinal control input of the UAV was all-moving elevator and throttle, and the longitudinal dynamic state space, which is shown in Formula (23), was extended to consider the influence of elevator and throttle.  
\[\begin{split}\dot{\mathbf{x}}_{lon}&=\mathbf{A}_{lon }\mathbf{x}_{lon}+\mathbf{B}_{lon}\mathbf{u}_{lon}\\ \mathbf{x}_{lon}&=\left[\begin{array}{cccc}u&w&q& \theta\end{array}\right]^{T}\end{split} \tag{23}\]  
Where \(u\), \(w\), \(q\), and \(\theta\) correspond to \(x\)-, \(z\)- axis velocity component, pitch angular rate, and pitch angle. The control law of longitudinal inner loop was altitude hold (Formula (24)), and the output was command signal of deflection angle of all-moving elevator and throttle.  
\[\begin{split} q_{c}&=k_{q}(\theta_{c}-\theta)\\ \delta_{e}&=(k_{p}+k_{p_{i}}\cdot\frac{1}{s}+k_{p_{d}}\cdot s)(q_{c}-q) \end{split} \tag{24}\]  
The outer loop was the height and airspeed hold control law, which are shown in Formula (25), and the simulation results of the UAV's pitch angle and altitude command tracking are shown in the Figure 13. Both the pitch angle and the altitude can well realize the command tracking without static error or overshoot, and the response time of the pitch angle command was about 1.1 s.  
\[\begin{split}\theta^{c}&=k_{p_{h}}(h^{c}-h)+\frac{k_ {p_{h}}}{s}(h^{c}-h)\\ \delta_{t}&=k_{p_{V}}(V_{a}^{c}-V_{a})+\frac{k_{p_{V} }}{s}(V_{a}^{c}-V_{a})\end{split} \tag{25}\]  
Figure 12: Free response of rudder deflection at low altitude and high altitude: (**a**) Rudder deflect 20 degree. (**b**) rudder deflect 1 degree.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Initial Partitioning Algorithm Based on Partition Connectivity Clustering
In Figure 5d, the number of clustered vertices has exceeded the threshold, and the clustering process ends. The time complexity of Algorithm 2 is primarily determined by the random vertex selection, the execution of the ClusterSearch function, and the outer loop. In each iteration, Algorithm 2 randomly selects a vertex and generates a subgraph through the ClusterSearch function. During the ClusterSearch process, Algorithm 2 traverses the neighboring vertices of the current subgraph and calculates the connectivity degree, selecting the vertex with the highest connectivity to add to the subgraph until the weight of the subgraph exceeds the threshold \(LR\). The time complexity of the ClusterSearch function consists of operations such as updating the candidate vertex set, calculating connectivity, selecting the vertex with the maximum connectivity, and updating the subgraph. The time complexity for updating the candidate vertex set is \(O(d)\), where \(d\) is the average degree of the vertices in the graph. The time complexity for calculating connectivity is \(O(|E|)\), where \(|E|\) is the number of edges in the graph, and selecting the vertex with the maximum connectivity has a time complexity of \(O(|V|)\), where \(|V|\) is the number of vertices in the graph. The operation of updating the subgraph has a time complexity of \(O(1)\). Therefore, the total time complexity of the ClusterSearch function is \(O(|V|\cdot(d+|E|))\). Since Algorithm 2 needs to execute \(k\) ClusterSearch operations, the overall time complexity is \(O(k\cdot|V|\cdot(d+|E|))\), where \(k\) is the number of partitions. Hence, the time complexity of Algorithm 2 is proportional to the number of vertices, edges, and partitions in the graph.
## 2 Materials and Methods
### Density Test  
A density test of the cut disk-shaped specimens S1, S1-HT, S2, and S2-HT with dimensions of -\(\mathcal{O}\)25 \(\times\) 5 mm followed the Archimedes principle by measuring the masses of these specimens at room temperature (23 \({}^{\circ}\)C) using an electronic densitometer MH-300A (Shenzhen Omena Technology Co., Ltd., Shenzhen, China). Figure 2 illustrates the measurement process.  
Figure 2: Illustration of the specimen mass measurement process in the MH-300A densitometer for experimental density determination.  
The masses of the specimens were measured in grams in air (m\({}_{\text{a}}\)) and distilled water (m\({}_{\text{w}}\)). The density of distilled water (\(\rho_{\text{w}}\)) at 23 \({}^{\circ}\)C is \(\rho_{\text{w}}\) = 0.9975412 g/cm\({}^{3}\)[32]. The experimental density (\(\rho_{\text{exp}}\), g/cm\({}^{3}\)) and theoretical density (\(\rho_{\text{th}}\), g/cm\({}^{3}\)), and porosity (%) of the specimen assessment followed the methodology given in [33]. Theoretical density is calculated using the rule of mixture as shown in Equation (1).  
\[\rho_{\text{th}}=\rho_{\text{AI}}\times\text{wt}_{\text{AI}}+\rho_{\text{B4C}} \times\text{wt}_{\text{B4C}} \tag{1}\]  
Experimental density was calculated using the relation shown in Equation (2).  
\[\rho_{\text{exp}}=(\text{m}_{\text{a}}/(\text{m}_{\text{a}}-\text{m}_{\text{w} }))\times\rho_{\text{w}} \tag{2}\]  
Porosity of the composites was estimated by the following equation:  
\[\text{Porosity}=(\rho_{\text{th}}-\rho_{\text{exp}})/\rho_{\text{th}} \tag{3}\]  
Theoretical calculations used commercial densities of matrix Al (\(\rho_{\text{AI}}\)) and B\({}_{4}\)C powder (\(\rho_{\text{B4C}}\)) equal to 2.68 g/cm\({}^{3}\) and 2.52 g/cm\({}^{3}\), respectively.
## 3 Experiments and Results
### Experiments Setup
#### 3.1.3 Estimate Metrics  
To assess the computational feasibility of MSACN in the domain of cloud extraction, we adopted a variety of metrics, including P (Precision), R (Recall), F1 score based on confusion matrix, accuracy and cross-union ratio (IoU), expressed as follows.  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1}=2\times\frac{\text{P}\times\text{R}}{\text{P}+\text{R}} \tag{4}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{5}\]  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{6}\]  
In the case of cloud extraction, P (precision) stands for the ratio of true cloud pixels in the output results, while recall refers to how many pixels are detected as clouds out of all cloud pixels in the image. Both are important metrics for evaluating the performance of classification models. TP means True Cases, which is the part of pixels correctly labeled as clouds; FN stands for False Negatives, which is the number of cloud pixels incorrectly marked as belonging to non-clouds; FP represents False Positives, which is the number of cloud pixels identified instead of labeled, TN represents true negative examples, the pixels of non-clouds marked as non-clouds. For F1, it better reflects multifaceted cloud extraction errors. The F1 score is figured by precision and recall, and is a comprehensive indicator considering P and R. The accuracy rate is expressed as the ratio of cloud pixels correctly detected as clouds and non-clouds to the total number of pixels, which can reflect  
Figure 6: Loss function Plot in the ZY-3 satellite cloud dataset.  
the ability of MSACN in correctly classifying pixels. IoU is used to measure the ratio of the overlapping area between the model-predicted cloud area and the truth cloud area.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 5 Conclusions  
In this study, a laboratory investigation of different soil types treated with 1% and 3% lignin solutions was conducted to understand the effect of lignin on soil strength and seed growth. Based on the obtained data, the following conclusions can be drawn:  
* The application of lignin biopolymer increased the strength of all three tested soil types. Both pocket penetrometer and vane shear test results indicated that the soil treated with lignin produced greater strength, especially when a 3% lignin solution was used. For high-plasticity Soil 1, the maximum increase in the strength, compared to the untreated control soil sample, occurred after the first 10 days, while for low-plasticity Soil 2, the maximum increase in the strength occurred later, within 20-30 days.
* The increase in soil strength was greater when lignin solutions were mixed with soil (Method 1) compared to the other method (Method 2) when lignin solutions were sprayed on the surface of the soil mass.
* The lignin-treated soil could retain more water over the long term compared to the untreated soil. This was more pronounced for very plastic soil (Soil 1) and non-plastic sand (Soil 3) than for Soil 2. This extra moisture could contribute to better vegetation growth, leading to greater resistance to soil erosion.
* Both lignin-treated and untreated soils produced similar results on seed germination and growth, suggesting that lignin does not have a negative effect on vegetation. The outcomes of this study indicate that the addition of lignin to soil can improve soil strength without negative effects on the environment and vegetation growth, which is important for alternative slope bioengineering methods.  
Conceptualization, I.G. and J.S.; methodology, I.G. and Q.T.; investigation, I.G. and Q.T.; writing--original draft preparation, I.G. and S.A.; writing--review and editing, I.G., S.A. and J.S.; supervision, I.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. This research was performed with the financial assistance of the Griffith University Postgraduate Research Scholarship, GUPRS.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 4 Sparse Signal Transmission Framework  
In this section, we first present the background, which shows the characteristic of a lossy wireless link and the ubiquity of a sparse signal in sensor networks. Then, we reveal the process of sparse signal transmission and propose the problem formulation.
## 1 Introduction
Furthermore, fixing ambiguities with respect to nearby reference stations is in principle easier to PPP ambiguity fixing with possibly contaminated UPDs. Therefore, this approach can achieve a higher positioning performance as well as ambiguity fixing compared to PPP when processing the BDS observations. In this contribution, we concentrate on the comprehensive study of PPP and POP with aspects of ambiguity fixing and multi-GNSS impact and their improvements in positioning performance for their applications in the polar regions. The rest of the paper is organized as follows. After the introduction of the basic observation model of multi-GNSS data processing, algorithms of double-difference (DD) ambiguity fixing is introduced for complexity with some special analyses for GLONASS and BDS. Afterwards, with data from the IGS MGEX network and data from a real flight experiment over Antarctica, the performance of single-, dual- and four-system PPP and POP ambiguity fixing, as well as kinematic positioning, are analyzed and investigated. The results, conclusions and perspectives are finally summarized.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 1 Introduction
Besides this, the ionosphere-free combination of global positioning system (GPS) L1 C/A and L2 signals is used in the proposed system to further reduce the ionospheric influence. Finally, a hardware simulator is used and semiphysical experiments are conducted to assess the proposed system's performance.