esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Open Issues and Opportunities for Future Research
### Data Sharing across Management Domains  
Data sharing across multiple management domains is another open issue for future research. The data model and the data repository are two important aspects of this problem that deserve thorough investigation. Standard data models play a crucial role in data sharing among different management functions/services within and between management domains. The first question that needs to be answered is if there is a single preferred data model that can be used by all management functions or if multiple data models are needed. Then, for the former case, the common data model needs to be standardized and for the latter case, how different data models can be reconciled must be studied.  
A cross-domain data repository is an important component in the management architecture to allow data sharing across management domains. A centralized data storage suffers scalability issues in a large network; therefore, the structures of distributed storage collaborating for exposing a common data service interface need to be investigated. In addition, the appropriate level of data abstraction for cross-domain data sharing with a trade-off between service performance and system scalability also deserves a more thorough study.
## 3 Methodology
### Detecting Multicollinearity among the Explanatory Variables  
The overall goal of conducting a multicollinearity test is to assess high intercorrelations among the explanatory independent variables and to remove these highly correlated explanatory variables from the model. Addressing multicollinearity is fundamental to accurately estimate the effects of individual explanatory variables in the regression model. Several techniques are available to detect multicollinearity in multiple regression analyses, such as eigensystem analysis [31; 32], condition  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variables** & \multicolumn{2}{c}{**Derived Variables**} \\ \hline  & \(\bullet\) & Elevation \\ Topographical characteristics & \(\bullet\) & Aspect \\  & \(\bullet\) & Slope \\ \hline  & \(\bullet\) & Seasonal precipitation \\ Climate data & \(\bullet\) & Seasonal temperature \\ \hline Vegetation types & \(\bullet\) & Vegetation communities \\ \hline \hline \end{tabular}
\end{table}
Table 1: Explanatory variables used in this study including climate and topography drivers. Climate data were divided into the standard four seasons: winter (JFM: January, February, and March), spring (AM): April, May, and June), summer (JOS: July, August, and September), and fall (OND: October, November, and December).  
Figure 5: Map of annual cumulative NDVI (VI unit) for 2014 showing vegetation productivity distribution.  
number technique, and the examination of correlation matrix. For this study, the examination of correlation matrix of explanatory variables was selected to determine the degree of correlation between the explanatory variables. This method is widely used to detect multicollinearity issues in multiple regression models [33; 34].  
Fourteen explanatory variables were selected for this test: 3 topography-based factors, elevation, slope, and aspect, one vegetation type variable, 10 climate-based variables (8 seasonal precipitation and temperature-based variables, 1 total annual precipitation, and 1 average annual precipitation). Climate data were divided into the standard four seasons. We calculated the seasonal precipitation by accumulating precipitation over the considered period and seasonal temperature by averaging over the same period. Two variables were excluded from the potential explanatory list, annual cumulative and mean annual rainfall, due to their high multicollinearity with other seasonal precipitation variables.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### 4.2. Phased FT for AUVs
#### 4.2.3 FT for Recovery  
The AUV initially ascends to the area surrounding the USV during the recovery phase. When the AUV is close to the USV, it starts to dock with it after receiving navigation control commands via communication. As a result, consideration was given to a propulsion system, a navigation system, a power system, a communication system for exchanging mission and status data, and a controller for docking control. The FTA result for the launch is displayed as shown in Figure 2(c). Similar to the FT for launch, it includes all driving units, control units, and communication units for docking with a USV. However, the majority of mission sensors and charging-related modules are not included.
## 2 Materials and Methods
### Data Preparation
The data from studies that included data from diurnal variations were classified into groups as noon vs. morning and evening (N vs. ME), day vs. night (D vs. N) and forenoon and afternoon vs. dawn and dusk (FA vs. DD) data; FA vs. DD was included to avoid the influence of inverted light-impacted stomatal closures at midday [130]. Data from periods longer a day, such as daily and seasonal records, were employed only for testing. The summer solstice day and the autumnal equinox day (normally on 20 June and 23 September, DOY 171 and 266) were used as the beginning and end of summer, respectively. For comparison, PAR = 600 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) was the criterion of sunny and cloudy conditions when separating the records of the control experiments (Table 1). Notably, different biomes in the same study were classified as separate groups, and data from the same study site recorded at different temporal scales were separated into independent groups. A total of 101 separate studies were used to establish the dataset. The mean value of each variable and its sampling size and standard deviation were also collected. The  
Figure 2: Locations of carbonyl sulfide uptake observation sites in this meta-analysis study. The full name of each biome, as well as its source literature are NLF—needle leaf forest [113; 115; 118; 123; 125; 128; 129], BLF—broad leaf forest [14; 73; 90; 101; 113; 129], MXF—mixed forest [73; 96; 115], GRA—grassland [14; 113; 114; 120; 126], MSH—marshland [127], CPL\({}_{\mathrm{C3}}\)—cropland dominated by C3 species [14; 51; 114; 117; 119; 122; 124] and CPL\({}_{\mathrm{C4}}\)—cropland dominated by C4 species [117; 124; 126]. sample size of each group and each indicator, as well as the characteristics of the variations in environmental conditions, are listed in Table S1.
## 1 Introduction
The proposed method provides greater precision for both overall and for each IoT device identification than conventional single-stage centralized clustering. Moreover, by comparing the accuracies of six different time series representations as datasets, we demonstrated the effectiveness of analyzing IoT traffic over multiple time intervals. The rest of this paper is organized as follows. Section 2 presents related studies on IoT device identification and introduces two-stage clustering [15; 16], which is the basic concept of this paper. Section 3 presents an IoT traffic analysis method based on two-stage clustering. Section 4 presents and evaluates the analytical results of the two-stage clustering in comparison with those of one-stage clustering, which is similar to conventional methods. Section 5 proposes an IoT device identification method based on the analytical results obtained using two-stage clustering and evaluates the proposed method on indices of classification accuracy and efficiency. Finally, we assessed the effectiveness of distributed traffic analysis of IoT traffic using two-stage clustering. Section 6 presents the conclusions of the study and discusses future perspectives.
## 5 Evaluation
### Results and Discussion  
#### 5.2.1 Number of Clusterheads  
In this section, we compare our simulation results with the theoretical bound we achieved using a centralized clustering approach. In practice, using a centralized approach may not be feasible in large sensor networks due to amount of information required regarding state of the network. Our algorithm uses a distributed approach to form clusters aiming to provide a close enough approximation to performance in theory. In Figure 3, through simulations, we compare the performance achieved in proposed HMIMO scheme with theoretical results. We use mean square error (MSE) as a metric to compare the number of clusterheads arrived at through proposed algorithm with the number of clusterheads derived in Equation (17). Our results suggest that, for a given network setting (various instances of uniformly distributed nodes), number of clusters with minimum energy expenditure is close to the values derived in Equation (17). We plot a graph for N = 100 and N = 200 nodes. Figure 3 shows that for 100 nodes, when intercluster range is between 80 m and 120 m, MSE is less than 1, showing that our algorithm performs close to theory. As the network becomes more and more dense, the value of MSE also increases as compared to a sparser network setting, a factor to be kept in consideration to reap benefits of MIMO in an actual network deployment.  
In subsequent experiments, we show that the overall energy consumption of our proposed scheme is less than the previously proposed methods. Since the overall energy consumption in the network and network lifetime are dependent upon the underlying clustering scheme, therefore, through this set of simulations, it is established that the proposed clustering scheme provides results close to our theoretical analysis.
## 2 Minimum Connected Dominating Set Problem  
Given a simple undirected graph \(G=(V,E)\), where \(V(G)\) is the set of vertices (or nodes), these nodes connect by the set of edges \(E(G)\). A path \(P\) from vertex \(u\) to vertex \(v\) is an alternating consecutive sequence of vertices and edges that connects vertices \(u,v\in V(G)\) such that no vertex is repeated in the path from \(u\) to \(v\), where the number of edges in that path represent the length of the path \(P\). In graph \(G\) every vertex \(v\) is said to dominate itself and any vertex adjacent to it. A dominatingset is a set of vertices that collectively dominates all other vertices in the graph \(G\)[8]. The dominating set \(D\) in a connected graph must satisfy two conditions:  
* A path from any vertex in \(D\) to another vertex in \(D\) stays entirely within \(D\). Thus, \(D\) represents a connected subgraph of \(G\);
* Every vertex in \(G\) is either in \(D\) or adjacent to a vertex in \(D\).  
The MCDS problem seeks to find a connected dominating set of minimum cardinality, which is also called the domination number of \(G\) and written as \(\gamma(G)\).  
The MCDS problem is one of the classical MDS problem variations, which is classified as a NP-hard problem. Thus, it is a hard combinatorial problem and cannot exactly be solved in polynomial time. Therefore, acquiring an effective solution for the minimum connected dominating set problem is one of the significant research areas in graph theory [1]. Recently, several researches are moving towards the MCDS problem because of its various applications in wireless networks, facility locations, and operational research in computer science [9; 10; 11; 12].  
The concept of the MCDS problem is significant and useful in wireless networks, particularly for networks with very large scales like a WSN. In such networks, the minimum CDS works as a virtual backbone in the network and dominates all the nodes in the network. Consequently, it is easy to forward the packets from the source node to the destination node through the VB nodes [2].
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 2 Related Work and Materials  
### Deep Models for Object Detection  
Object detection is a longstanding problem which has been witnessing a rapid revolutionary change in the field of computer vision. Its involvement in the combination of object classification as well as object localization makes it one of the most challenging topics. Traditional object detection algorithms generate features by manually selected features and can achieve better results in specific scenes. Compared with traditional methods, algorithms based on deep learning overcome the disadvantages of poor regional selection strategy and poor robustness of extracted features and the detection effect has been significantly improved.  
The current mainstream object detection algorithms can be divided into two types, one is two-stage methods [12; 13; 14; 15], the other is one-stage methods [16; 17; 18; 19; 20; 21]. Two-stage methods follow the traditional object detection pipeline, generating region proposals at first and then classifying each proposal into different object categories. The detection accuracy could be improved to a large degree. However, the detection speed is slow and cannot meet the requirements of real-time detection.  
One-stage methods regard object detection as a regression or classification problem, adopting a unified framework to achieve final results (categories and locations) directly. The typical methods include the series of You Only Look Once (YOLO) methods. With the introduction of the series of PP-YOLO [22], the detection performance of the YOLO-based methods has been improved further. Currently, the newest version PP-YOLOv2 integrates many new technologies to optimize the computational efficiency.  
Additionally, over the past two years, the major advances in object detection academia have focused on anchor-free detectors [23; 24; 25]. The anchor-free methods aim to detect objects by predicting their centroids and sizes which are architecturally simpler and faster.
_Applied_  
_sciences_  
_Article_  
**Dynamic Displacement Forecasting of Dashuitian**  
**Landslide in China Using Variational Mode**  
**Decomposition and Stack Long Short-Term**  
**Memory Network**  
[PERSON] 1\({}^{\copyright}\), [PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 1  
1 College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China  
2 College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China  
3 College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-139-1597-7693  
Received: 11 June 2019; Accepted: 17 July 2019; Published: 24 July 2019  
**Abstract:** In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)-LSTM network, the proposed model has higher forecast accuracy.  
**Keywords:** landslide; displacement forecasting; variational mode decomposition; stack long short-term memory network
## 4 Simulation Analysis
### Experimental Results
#### 4.3.3 Comparison of Algorithm Effects in Different Scenarios  
We analyze them from a quantitative perspective below to compare the algorithm's effect in different scenarios. Without losing generality, we select the case where the switching time is 5 s as an example for comparative analysis of the algorithm in each scenario. The simulation results are displayed in Figure 9. We also count the corresponding error results listed in Tables 6 and 7. M-Imp signifies the maximum improvement and m-imp denotes the minimum improvement, both of which are relative to INS navigation. We use slanted green fonts and red bold fonts to indicate each indicator's minimum and maximum values.  
Under adjacent orbits, various algorithms can also effectively overcome the divergence in the INS. The performance of these algorithms is sorted in descending order as follows: OE/DO > DO > CE/DO; that is, the performance of the integrated navigation algorithm based on INS+2-satellite alternate switching ranging under the LEO 3-satellite is better than that of the INS+LEO3-satellite alternate switching ranging algorithm. The original scenario effect is also apparently better than the comparison scenario, as we can see from the various error curves and indicators in Figure 9 and Tables 6 and 7.
3. In the same orbits and different orbits and two comparison scenarios, the performance of these algorithms in descending order is roughly as follows: OE/DO > DO > OE/SO > SO > CE/DO > CE/SO.  
In general, the 3-satellite alternate switching ranging algorithm based on different orbits and the original scenarios based on the integrated navigation algorithm of INS+2-satellite alternate switching ranging under LEO 3-satellite on different orbits has the best results. It can improve by more than 90% relative to INS in most performance indicators. From the final trajectory curve and parameters, the errors of longitude, latitude and altitude are all very small, which meet the needs of cm level real-time location services. However, the INS + LEO3-satellite alternate switching ranging algorithm based on different orbits requires only one true ranging value simultaneously. This scenario can be regarded as a single-satellite positioning algorithm with certain redundancy. In this case, the requirements for satellite visibility are lower, but the reliability is worse. In the original scenario of the integrated navigation algorithm of INS+2-satellite alternate switching ranging under  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Error Index** & & **EFE (m)** & **NPE (m)** & **UPE** & **EVE** & **NVE** & **UVE  
the LEO 3-satellite on different orbits, under the premise of guaranteeing a real ranging value, an actual ranging value is required to participate in alternate switching ranging. This situation can be regarded as a dual-satellite positioning algorithm with certain redundancy. In this scenario, the reliability is higher, but the requirements for satellite visibility are also higher.
# Phase-Pure Epitaxial b-Axis-Oriented Bronze TiO\({}_{2}\) Films
## 1 Introduction  
Among all transition metal oxides, titanium dioxide is one of the best studied and most used materials in industry [1]. A number of polymorphs of TiO\({}_{2}\) are known that differ in their physical properties and make them appealing for various applications. The three main polymorphs that are frequently encountered in nature are the rutile, anatase and brookite phases. All of these three phases are still heavily investigated in photocatalytic [2; 3] and solar-cell applications [4; 5], and improvement of their performances typically requires an optimized phase mixture. A fourth naturally occurring, far less well studied, polymorph is bronze-type TiO\({}_{2}\). This modification is the least dense of the four natural polymorphs of TiO\({}_{2}\), so it seems to be an excellent host for Li intercalation [6]. The bronze-type structure has one-dimensional open channels along the [010] direction that offer a good path for ion transport [7]. Bronze-TiO\({}_{2}\) has therefore, for example, been considered as a candidate anode material for lithium-ion batteries [8; 9; 10]. Because of its metastable properties, only a few investigations have been carried out in the electrochemistry field. These publications have indicated great potential in applications when several intrinsic drawbacks, such as moderate electrochemical kinetics and unsatisfactory long-cyclic stability, can be resolved [11].  
The preparation of phase-pure crystals with well-defined surfaces is a prerequisite to understanding the potential of bronze-type TiO\({}_{2}\) for future applications. However, the preparation of phase-pure epitaxial films has been a challenge. Coexistence with one of the three more thermodynamically stable polymorphs is commonly observed [12]. Advances have recently been made in thin film heterostructures where the insertion of suitable buffer layers has allowed for the stabilization of epitaxial single-phase bronze films [13; 14; 15]. These films are c-axis oriented, which puts the one-dimensional ion channels in the film plane.  
Here, we show that phase-pure bronze-type TiO\({}_{2}\) films can be prepared using pulsed laser deposition on single-crystal MgAl\({}_{2}\)O\({}_{4}\) substrates without the need of a buffer layer. Our films are b-axis out-of-plane oriented and might thus serve as an ideal system to study fundamental properties of bronze-like TiO\({}_{2}\) related to ion migration.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 7 OCTA Measurements of Hemodynamics  
Based on dynamic changes in intensity, phase, or complex signal, OCTA algorithms can distinguish dynamic tissue from static tissue. Thus, while OCTA can answer the question \"where is there flow?\", it cannot yet reliably answer the question \"how much flow is there?\". In recent years, several attempts have been made to further quantify OCTA signals. Many of these efforts are based on estimating the autocorrelation function. While the autocorrelation function can be estimated, to date, there is no rigorous theory or model for recovering RBC flow or speed from OCTA signals. Here, we summarize some promising work towards these goals.