esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Military Software Defined Networks
## 6 Statistical Analysis and Metrics
### Citations and Publication Year  
All reviewed articles were organized with respect to the year of publication. Table 7 shows that most of the significant articles within military SDN are published after 2015. The year of publication table shows an exponential growth of articles for the last four years. It is not clearly known why there is a drop in the number of articles for 2017, but this can be attributed to the time of the literature review and a possible delay in publishing processes. However, the increasing number of articles indicates the relevance and requirements for a systematic literature review on the topic.  
Registered citations from Google Scholar were used as the data source for the number of citations. The distribution of the number of citations (Table 8) shows that the number of the most cited articles is low for the significant articles. This also reflects the increase in significant research contributions in recent years and indicates that the reason for a low citation number can be due to the publication year. Accordingly, it becomes clear that an initial knowledge base has already been established in the field, allowing for crucial contributions to emerge.  
Tables 9 and 10 show the most cited articles collected in the review. It is noted that the significant research contributions concern primarily SDN in wireless and mobile networks (Table 10). Furthermore, the additional research contributions also relate mostly to wireless technologies, potentially indicating a lack of research contributions on military network control applications with SDN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Number of Publications** \\ \hline
2018 & 4 (Until 23 August 2018) \\
2017 & 34 \\
2016 & 39 \\
2015 & 24 \\
2014 & 17 \\
2013 & 2 \\
2012 & 2 \\
2011 & 3 \\
2010 & 4 \\ -2009 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Year of publication.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Citations** & **Significant Contributions** & **Additional Contributions** \\ \hline
0 & 13 & 13 \\
1–2 & 13 & 14 \\
3–4 & 12 & 16 \\
5–9 & 5 & 13 \\
10–14 & 0 & 9 \\
15–29 & 0 & 8 \\
30–49 & 0 & 10 \\
50–99 & 0 & 5 \\
100+ & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of citations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Article** & **Citations** \\ \hline
1 & Toward software-defined battlefield networking [15] & 8 \\
2 & Enhancing wireless communications with software defined networking [17] & 6 \\
3 & Distributed SDN for Mission-Critical Networks [33] & 6 \\
4 & Distributed flow controller for mobile ad hoc networks [32] & 6 \\
5 & Software Defined naval network for satellite communications (SDN-SAT) [45] & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Top five citations’ significant contributions.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 2 Study Area: Mainland China  
Our study area is mainland, or continental, China and extends from 73\({}^{\circ}\)-135\({}^{\circ}\)E and 18\({}^{\circ}\)-54\({}^{\circ}\)N. It covers an area exceeding 9 million km\({}^{2}\) and, due to its size and topography, a diverse range of climates from the desserts in the west, the winter-cold regions with fertile soils in the north, to the hilly regions with high temperatures and favorable precipitation in the south and southeast. Throughout these diverse regions, China has about 120 million ha of agriculturally-used land of which 20%-30% is used for paddy rice production [2; 50; 51; 52]. Paddy rice cultivation is practiced in all provinces, except Qinghai, with Hunan, Heilongjiang and Jiangxi being the biggest producers of rice and contributing 33% to the total production of 208 million tonnes for the year 2014 (see Figure 1) [2]. Most of the paddy rice areas are located along large fluvial plains and river deltas due to their fertile soils, flat topography and access to enough water to regularly flood the paddies. Prominent rice regions are the Sanjiang Plain in Heilongjiang, the Yangtze River Delta, the plains surrounding Dongting Lake, but also the hilly regions of the Sichuan, Jiangxi and Guangxi provinces, where rice terraces with limited access to farm machinery are actively cultivated. In provinces with favorable climate multi-cropping, the growing of multiple crops in succession throughout one year is practiced. The cropping intensity gradually increases from one crop in the northern provinces to up to three crops per year in the southern provinces [53], with the boundaries closely resembling China's different climate zones. North of the Yangtze River and in the westernmost provinces, only one season of rice per year is cultivated. South of the Yangtze is a zone of transition with one long or two short rice seasons. Further south, starting around the Nanjing Mountains, mostly two rice crops are cultivated per year due to an abundance of rainwater, moist soils and high temperatures year-round [32; 54]. Rice can be planted in a variety of cropping schemes, such as rice-rice, rice-vegetables, rice-soybean, winter wheat-rice or rice-rice-alfalfa [31].
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 2 Process Semantics of Rainstorms  
According to geographical process semantics [21; 22; 23], a rainstorm process consists of one or more evolution sequences, each of which includes one or more instantaneous objects. The related concepts are listed as follows, shown in Figure 1.  
* A rainstorm process: A rainstorm covers a specified spatial region and evolves from production through development to termination.
* A rainstorm sequence: A successive series of rainstorm objects which have similar spatial structures and thematic features.
* An instantaneous rainstorm object: A rainstorm region which has a clearly defined spatial range in a given snapshot (i.e., at a given time).
* A rainstorm region: Rainstorm cells which connect with each other in spatial neighborhoods.
* A rainstorm cell: A grid cell in a raster dataset which exceeds a rainfall threshold.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 6 Conclusions  
Loose formations of small satellites open new paths towards higher performances allowing major scalability, less exposure to single point failure and reduced costs. In this paper, it has been shown that along the track formation of small satellites, even loosely controlled in their position, can provide high resolution and SNR, wide swath coverage, and good ambiguity suppression. Simulations have been used to demonstrate the goodness of the recombination and mitigation methods since, currently, data from formations of mini-satellites are not available yet. To show results on real data, it would need to make modification of real datasets, in order to emulate the acquisition by the different viewpoints of a formation. This requires a very detailed software simulator and will be a matter of successive work.  
Degradation of performance when sensors lose their optimal position along the track has been analyzed also from a probabilistic point of view. PRF tuning, especially when combined with antenna splitting, have been shown to offer superior performance, avoiding a too frequent control of the sensors' position in order to increase the operative lifetime of the mission. Anyway, for few configuration cases and longer formation extents, the mitigation methods may not guarantee a sufficient recover of the performances.  
It appears that formations are better fit for lower frequencies, whereas, for higher frequencies, constellations of small independent satellites would be simpler to adopt. In fact, the ambiguities suppression necessary for a wider swath that would motivate the formation is limited by the very narrow orbital tube needed. In this view and according to the needs, the same formation may be alternatively used for one-pass tomographic applications exploiting the non-zero across-track baseline. A phase term correction, derived from a possible autofocus method, could be considered as a mitigation scheme to counteract the baselines effect. This topic may be a matter of future investigation.  
We finally remark that, since such formations are stochastic in nature, design them to achieve always predictable and optimal results would lead to costs likely higher than those encountered with large satellites, contradicting the initial motivations. Rather, these systems are adaptive in nature, yielding quite interesting results, but never guaranteeing perfection and working at all times. The definition of quality of SAR images could be revised to explore costs and benefits of these stochastic  
Figure 10: Eigenvalues of \(A\) (in dB) for a tube diameter from 100 to 1000 m, with an \(H_{max}=50\) m and \(N_{H}=4\). Little tubes have the greatest dynamics between minimum and maximum eigenvalues.  
solutions. The next step should be a thorough analysis of the propulsion technologies to be used that indeed could determine the possibilities and the limits of this concept, now ready to be explored in practice.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 8 Conclusions  
In this article, blind detection methods for spoofing signals that exploit the spatial diversity of an antenna array were presented. The detection method was implemented in a snapshot receiver and evaluated using open-sky data, recorded with a six-element array. Additionally, a spoofing attack emulation using a splitter instead of an array was used. First, only a pair-wise spoofing detector was presented. Next, several metrics were developed, implemented, and analyzed which consider the entire system of received satellites. These metrics include multiple detectors, Eigenvalue based methods, and clustering methods.  
The results have shown that observing a system-level based detector could have significant benefits to spoofing detectors. Three detection metrics resulted in perfect detection capabilities; however, some concerns to why this is not necessarily achievable for all scenarios and experimental setups are highlighted and discussed. This indicates the need for further research in this area. Nevertheless, it is shown in this article that blind spoofing detection methods could yield adequate spoofing detection capabilities and initial results are promising.  
For future research, it is suggested to repeat the tests with different satellite constellations to isolate and quantify some effects and variables which can degrade the system. Further, it would be beneficial to validate the performance inside an anechoic chamber or real live tests, as this would improve the legitimacy of a spoofing attack and also allow a mixed spoofing-real satellite set to be recorded. This would also further challenge the existing detection algorithms. Lastly, the identification of the current metrics paths the way for future statistical methods, such as machine learning.  
Conceptualization, J.R.v.d.M. and A.R.; methodology, J.R.v.d.M.; software, J.R.v.d.M.; validation, J.R.v.d.M. and A.R.; formal analysis, J.R.v.d.M.; investigation, J.R.v.d.M.; resources, J.R.v.d.M. and A.R.; data curation, J.R.v.d.M.; writing--original draft preparation, J.R.v.d.M.; writing--review and editing, J.R.v.d.M. and A.R.; visualization, J.R.v.d.M.; supervision, A.R.; project administration, A.R. and W.F.; funding acquisition, W.F.  
The authors declare no conflict of interest.
# Map Matching Based on Seq2 Seq with Topology Information
## 4 Experiment
### Evaluation Index  
Since the problem of road network matching is the matching of noise track sequence to road segment sequence, the absolute accuracy of the model cannot be simply used as the evaluation criterion. In this paper, the accuracy evaluation method of road section mentioned in article [15] is followed, as shown in Equation (10).  
\[acc=\frac{len(true_{tra}\cap pre_{tra})}{\max(len(true_{tra}),len(pre_{tra}))}\;. \tag{10}\]  
\(len(true_{tra})\) is the total length of the standard trajectory of the section, \(len(pre_{tra})\) is the total length of the matched trajectory, and \(len(true_{tra}\cap pre_{tra})\) is the total length of the intersection between the correct trajectory and the matched trajectory. In this article, the Havresine formula is used to calculate the distance between latitude and longitude points, as shown in Equation (11).  
\[d=2r\;\sin^{-1}\left(\sqrt{\sin^{2}\left(\frac{\theta_{2}-\theta_{1}}{2} \right)+\cos(\alpha_{1})\cos(\alpha_{2})\sin^{2}\left(\frac{\alpha_{2}-\alpha _{1}}{2}\right)}\right)\;, \tag{11}\]  
where \(r\) is the radius of the Earth (6371 km), \(\theta_{1}\) and \(\theta_{2}\) are the latitude between two points, and \(\alpha_{1}\) and \(\alpha_{2}\) are the longitude between two points.
## Nomenclature  
\begin{tabular}{l l} _Indexes and Sets_ \\ \(i\) & Index for the dispatchable unit \(i\) \\ \(k\) & Index for the transmission line \(k\) \\ \(a\) & Index for the AGC unit \(a\) \\ \(r\) & Index for the dispatchable (but non-AGC) unit \(r\) \\ \(j\) & Index for the load site \(j\) \\ \(l_{k}\) & Index for the transmission line \(k\) \\ \(m\) & Index for the wind farm \(m\) \\ \(\Omega_{g}\) & Set of all generator sites \\ \(\Omega_{d}\) & Set of all load sites \\ \(\Omega_{w}\) & Set of all wind farms \\ \(\Omega_{a}\) & Set of all AGC units \\ \(\Omega_{k}\) & Set of all transmission lines \\ _Variables_ \\ \(\mathbf{p}_{i}^{sch}/\mathbf{p}_{a}^{sch}\) & Scheduled generation of the dispatchable unit \(i\)/the AGC unit \(a\) \\ \(\mathbf{p}_{a}^{act}\) & Actual generation of the AGC unit \(a\) \\ \(A_{a}^{+}/A_{a}^{-}\) & Purchased capacities of up/down regulating reserve of the AGC unit \(a\) \\ \(R_{i}\) & Spinning reserve of the dispatchable unit \(i\) \\ \(P_{l_{k}}\) & Power flow on the transmission line \(k\) \\ \(P_{0i}\) & Scheduled energy dispatch for the generator \(i\) \\ \(E_{m}\) & Forecasting error of each wind farm \(m\) \\ _Parameters_ \\ \(e_{i}\) & Bidding price of energy of the dispatchable unit \(i\) \\ \(b_{a}\) & Bidding prices of regulating reserve of the AGC unit \(a\) \\ \(g_{i}\) & Bidding prices of spinning reserve of the dispatchable unit \(i\) \\ \(D_{j}\) & Forecasted load demand of the site \(j\) \\ \(N_{d}\) & Total number of load sites \\ \(N_{a}\) & Total number of AGC units \\ \(N_{g}\) & Total number of dispatchable units \\ \(N_{w}\) & Total number of wind farms \\ \(\beta_{a}\) & Participation factor of the AGC unit \(a\) \\ \(P_{imax}/P_{imin}\) & Maximum/minimum output of the dispatchable unit \(i\) \\ \(A_{amax}/A_{amain}\) & Maximum/minimum AGC limit of the AGC unit \(a\) \\ \(r_{a}^{+}/r_{a}^{-}\) & Up/down regulating speed of the AGC unit \(a\) \\ \(t_{1}/t_{2}\) & Response time of the regulating reserve/spinning reserve \\ \(\mathbf{D}_{R}^{s}\) & Minimum system spinning reserve requirements \\ \(D_{R}^{z}\) & Minimum zonal spinning reserve requirements \\ \(N_{g}^{z}\) & Number of dispatchable units in zone \(z\). \\ \(P_{l_{k}\max}\) & Transmission capacity limit of the transmission line \(k\) \\ \(\mathbf{E}_{m}^{\min}\) & Lower bound of the variable \(E_{m}\) \\ \(E_{m}^{\max}\) & Upper bound of the variable \(E_{m}\) \\ \(\mathbf{W}_{m}^{f}\) & Forecasted wind power generation output of the wind farm \(m\) \\ \(H\) & Shift distribution factor matrix \\ \(H^{s}\) & Shift distribution factor matrix under the outages of the \(s\)-th line outage \\ \(H^{u}\) & Shift distribution factor matrix under the outages of the \(u\)-th generator outage \\ \(\mathbf{H}_{ki}\) & Element located in (\(k\), \(i\)) of the shift distribution factor matrix \(H\) \\ \(\mathbf{H}_{ki}^{s}\) & Element located in (\(k\), \(i\)) of the shift distribution factor matrix \(H^{u}\) \\ \(\mathbf{H}_{ki}^{u}\) & Element located in (\(k\), \(i\)) of the shift distribution factor matrix \(H^{u}\) \\ \end{tabular}
## 3 Results
### Response of SWUE to Phenological Variation  
#### 3.5.1 Spatial Distribution of SOS, LOS, and EOS  
Figure 7 shows the pixel-scale spatial distribution of land surface phenological parameters (SOS, EOS, and LOS) in northeast China from 2007 to 2015. It was found that the average SOS mainly occurred between May and early June (120-155 th day of the year (DOY)) in the middle of the Songnen Plain and the Liaohe Plain. In the Changbai Mountains, Greater Khingan Mountains, and Lesser Khingan Mountains, vegetation had early SOS dates (95-120 th DOY, mainly in April). The spatial distribution of EOS dates was significantly different from that of SOS and gradually became later from south to north. In the Greater Khingan Mountains, Lesser Khingan Mountains, Changbai Mountains, and the north of the Songnen Plain, the average EOS dates were mainly between the end of October and November (300-340 th DOY). However, the EOS of vegetation in the central and southern part of  
Figure 6: The spatial patterns of correlation coefficients between soil water use efficiency (SWUE) and NDVI (**a**), land surface temperature (LST) (**b**), and precipitation (**c**).  
Songnen Plain and Liaohe Plain was early in October (280-300 th DOY). During the past nine years, the average LOS was 223 days in northeast China, with a similar spatial distribution to that of EOS. The LOS (<200 days) was observed in the cropland of the south of Songnen Plain and Liaohe Plain, while the LOS in other areas was mostly between 200 and 275 days.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 1 Introduction
In our procedure, the projection parameters of the eye-display model are estimated by means of standard camera calibration and computer vision techniques. To this aim, the OST HMD must be mounted over a rigid and adjustable holder equipped with a camera that replaces the user's eye. The main contributions of our work are as follows:  
1. An off-line calibration procedure that is easy to replicate in both laboratory environments and real-world settings. 2. A calibration procedure that can work with any type of OST HMD: with finite and infinite focal distances, with complex or simple optical combiners, featuring inside-out or outside-in tracking mechanisms. 3. A calibration procedure that is completely automated. 4. A calibration procedure that entails a simple on-line refinement step to account for the user's eye position. 5. A detailed formulation of all the mathematical steps involved in the procedure. The remainder of this paper is structured as follows: Section 2 surveys different two phase approaches for OST HMD calibration. Section 3 provides a general overview of our camera based OST HMD calibration procedure together with its mathematical formulation starting from the standard projection matrix of the off-axis pinhole camera model of the eye-display. Section 4 illustrates the technical implementation of the calibration procedure. Section 5 describes the experiments and discusses the results. Finally, Section 6 concludes with a summary and future work directions.
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods  
The overall system for Wi-Fi-based positioning and localization consists of fixed WAP's, mobile devices, and positioning services. The stages of this general method are shown in Figure 1. To start with, new features are generated from the dataset. The generated dataset is split into two categories, namely training and test. This process is carried out randomly and the ratio of training data and test data is 70% and 30%, respectively. In the next stage, training data are presented to Gaussian naive Bayes (NB), decision tree (DT), K-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) algorithms. These learning algorithms were obtained from Scikit-learn library [25; 26; 27; 28; 29]. They are then compared according to the classification and regression performance metrics, which are explained in the ML section, while the most suitable algorithms are selected for positioning and localization. In addition, the hierarchical fusing ML (HF-ML) algorithm is shown in Figure 2. In this algorithm, the dataset is primarily used for the training of building classification. Results of the building prediction are then combined with the input dataset. Consequently, prediction results and the input dataset are used for the training of floor prediction. After the training, floor predictions are combined with the previous dataset. This dataset contains the input dataset, building predictions, and floor predictions. The resulting dataset of these combined hierarchical prediction procedures is used in the training of longitude and latitude regression. The selected learning models are fused hierarchically using deductive reasoning. According to HF-ML, there are two more types of input data, namely building information and floor information. Since two new variables were introduced, HF-ML was trained with the training data and the performance was evaluated with the test data. This obtained model can produce location and position data after applying the feature generation process to the new data. See the supplementary materials for the codes and the data.  
Figure 1: Positioning and localization diagram.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 4 Multi-Modal Data Collection Analysis  
When performing multi-modal data collection via AUV, the freshness of the collected data and the energy consumption of the network nodes need to be fully considered. The choice of communication options fundamentally affects the data collection efficiency. Among the various communication options, the UOC is capable of transmitting a large volume of data rapidly to reduce transmission latency but increases the navigation time and energy consumption of the AUV. Meanwhile, the UAC has a lower bandwidth but can collect small volume of data over long distances to reduce the travel time of the AUV. Consequently, to collect data in a timely and efficient manner, several key factors, such as the data collection option, data type, packet size and AoI requirement, should be fully analyzed and integrated into the optimal path-planning scheme for data collection.