esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 4 Experiments
### Experimentation Scenarios  
The following subsections outline the two experiment scenarios. The former describes an IoT experiment where deployed sensors communicate between the EU and the U.S. The second scenario entails simulating communication among real sensors positioned in Smart Santander and existing testbeds in the EU and the U.S. The actual geographical location of these sensors was incorporated in the configuration file (.ini) of OMNeT++ to guarantee the precision of the results. In both scenarios and their respective experiments, we captured snapshots (refer to Figures 2 and 3) and illustrate diagrams of the RTT over time and ping loss over time.
## 3 Results
### Case Study
#### 3.3.1 Business Process Model of the Example  
From the tutorial in reference [36], we know that teach trace in the event log represents a specific process instance, which starts with an activity to register the telephone, and ends with an activity to archive the instance. By using the \(\alpha\)-algorithm plug-in for ProM to mine the running instance, the business process model, as shown in Figure 9, can be obtained. The first activity of the process is \"Register\", which means registering the telephone sent by the user. The next activity is \"Analyze Defect\", which means analyzing the defect category of the phone. Once the defect category is determined, it needs to be told to the customer through the \"Inform User\" activity, and according to the defect category, the \"Repair(Simple)\" activity or \"Repair(Complex)\", activity on the phone will be carried out. After the repair work is finished, the phone must go through the \"Test Repair\" activity to determine whether it has been actually fixed. If the telephone is fixed, the instance is archived through the \"Archive Repair\" activity and the telephone is sent to the customer; otherwise, the \"Restart Repair\" activity will be launched and the telephone needs to be repaired again.  
Going back to the running example itself, we find that some event attributes in the log are not reflected in the process model. This is because the business process model obtained by mining is the abstraction of all instances with the aim of discovering all the possible execution paths of the process from a global perspective. The business process model shows a series of activities and their sequence in the telephone repair process. In the tutorial, these activities are performed by different departments in the company. If we regard each department as an independent enterprise, then this is a cross enterprise cooperation process.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 6 Conclusions  
The main contributions of this paper are as follows:  
1. Proposed a hybrid spatial indexing method;
2. Proposed a new octree leaf nodes encoding method is proposed;
3. Designed a \(kNN\) searching algorithm that refers to the hybrid structure.  
The hybrid spatial indexing method intends to efficiently organize and manage large-scale Point Cloud. By setting thresholds adaptively and comparing octree and 3D R*-tree performance, the hybrid method performed better than the single octree and 3D R*-tree. Experiment data confirm that 3D R*-tree conduct better in query operation but worse in tree constructing than octree. Therefore, the hybrid method could complement the two methods and improve the performance both in tree construction and query operation to a certain extent. For structure constructing, hybrid spends about the same time as octree but takes about one-fifth of 3D R*-tree. For \(kNN\) searching, hybrid spends less than a half of the 3D R*-tree and about one-fifth of the octree. The \(depth\) of octree is fixed in this paper, although we have discussed the influence of \(depth\).  
The method proposed is just a novel exploration. There are advantages and disadvantages to our approach. Firstly, the octree is constructed with a fixed depth, which implies that the depth will be set differently with different Point Cloud data depth manually. Secondly, 3D R*-tree is sensitive to noisy data because once the noisy data are inserted into the R*-tree, a series of optimization will be conducted to update the R*-tree. While it is meaningful to comprehensively consider the \(leaf\_size\) and the number of nearest neighbors.  
Conceptualization, W.W. and Y.Z.; methodology, W.W.; software, W.W.; validation, G.G., Q.J.; formal analysis, W.W.; investigation, Q.J.; resources, W.W.; data curation, G.G.; writing--original draft preparation, W.W.; writing--review and editing, G.G.; visualization, W.W.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z., Y.W.: Assisted with data collection and analysis. L.H.: Assisted with manuscript writing and revising. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China(NSFC, grant number 61703067, 61803058, 51604056, 51775076), Science and Technology Research Project of Chongqing Education Commission (grant number KJ1704072).  
Not applicable.  
Great appreciation to [PERSON] for their project [[https://github.com/david-moten/rtree-3d](https://github.com/david-moten/rtree-3d)]([https://github.com/david-moten/rtree-3d](https://github.com/david-moten/rtree-3d)), accessed on 1 May 2021.  
The authors declare no conflict of interest.
## 4 Challenges and Future Research Directions
### Energy Consumption and Low-Carbon Emission Challenges  
The deployment of UDNs results in a massive consumption of energy. The mobile network operators face a considerable financial burden, with approximately 30% of their operational expenses (OPEX) being attributed to energy consumption [126]. Moreover, BSs  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Major Applications** & **Techniques** \\ \hline Massive connectivity & D2D/cooperative PD-NOMA with IRS, beamforming, THz, AI-based edge computing, Q-learning \\ Monitoring & PMU/PLeC/AB-PLeC/FIB/BW-PLeC \\ Energized massive connected devices & NOMA-SWIPT \\ Security and privacy & FL and blockchain \\ Reducing vulnerabilities & Edge-cloud-assisted FL, the blockchain, and ISTMXF \\ Energized edge/cloud devices & NOMA-SWIPT \\  & D2D/cooperative PD-NOMA with IRS, beamforming, THz, AI-enabled network slicing, AI-based edge computing \\ Real-time energy load balancing & EDGE algorithm \\ Unsupervised voltage stability & PMU-based index \\ Real-time island detection & Phase-angle based detection \\ Real-time line outage detection and recovery & Learning-to-infer approach \\ Energized edge/cloud devices & NOMA-SWIPT \\ \hline \hline \end{tabular}
\end{table}
Table 4: The major application and respective suitable techniques for 6G-based smart grid communication networks.  
predominantly rely on the electrical national power grid, which, unfortunately, is powered by methods that generate CO\({}_{2}\) emissions, as documented in [127].  
WPT is a viable solution for overcoming these challenges. Moreover, there are other suitable techniques to overcome the energy consumption issues of UDNs, such as optimization of the radio process, radio planning and deployment, hardware solutions, and the BS sleeping strategy [128]. More future research needs to be conducted on the abovementioned AI-based techniques for performance optimization.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation
### Validation Practices for Satellite Precipitation  
The World Climate Research Programme (WCRP) conducted three Algorithm Intercomparison Projects (AIPs) in 1986 [22; 23; 148]. These AIPs aimed to evaluate the performance of the available algorithms for IR, PMW, and MPE. Simultaneously, the WetNet Project organized three Product Intercomparison Projects (PIPs) [149; 150; 151; 152; 153]. These PIPs were designed to assess the accuracy of SPEs and determine the optimal algorithm. Both the AIPs and PIPs evaluated SPEs primarily at relatively low resolutions (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), 2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\), and 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\)) from various perspectives. The AIPs prioritized algorithm performance, while the PIPs focused on accuracy and application. In 2007, the WMO implemented the Program to Evaluate High-Resolution Precipitation Products (PEHRPP) (<3 h, <0.25\({}^{\circ}\)) [55; 154]. The primary objective of PEHRPP was to comprehensively assess the errors in various high-resolution precipitation products across different spatial and temporal scales, as well as diverse underlying surfaces and climatic conditions. The PEHRPP evaluations primarily covered Australia [155], the United States [155; 156], Europe [155], and India [157]. Additionally, the GPM Cold Season Precipitation Experiment (GCPEX), a collaborative effort between NASA's GPM ground validation program and its international partner Environment Canada, aimed to provide new datasets and insights into snowfall processes for enhancing falling-snow retrievals [158; 159].  
Alongside these programs, the International Precipitation Working Group (IPWG), which is jointly sponsored by the Coordination Group for Meteorological Satellites (CGMS) and the WMO, placed significant emphasis on the verification, validation, and intercomparison of SPPs in 2002 [61; 63; 65]. The Precipitation Virtual Constellation (P-VC) within the Committee on Earth Observation Satellites (CEOS) also focused on comparing and evaluating different SPPs in 2019. Furthermore, a Joint IPWG-GDAP Precipitation Assessment report was released by IPWG and GEWEX Data and Analysis Panel (GDAP) to assess precipitation products [57]. Additionally, numerous studies have evaluated the performance of SPPs over the past two decades (Figure 4).  
Figure 4: Number of SPP validation studies published over the last two decades (covered in Web of Science Core Collection). We used the keywords “validation” or “evaluation” or “assessment” for the topic and “IMERG”, “PERSIANN”, “CMORPH”, “GSMaP”, “CMAP and Merged Analysis of Precipitation”, “GPCP” or “TMPA or 3B42” for the abstract, focusing on the period between 2020 and 2024, the period between 2015 and 2019, the period between 2010 and 2014, and the period between 2000 and 2009.
# Tourism-Induced Urbanization in Phuket Island, Thailand (1987-2024): A Spatiotemporal Analysis
## 3 Results
### Analysis of Urban Growth and Densification Patterns  
Phuket has undergone a transformation in its urban development as shown through a series of maps illustrating the changes over time. Different stages of growth, each presenting characteristics and challenges, define this evolution. In the 1990s, urbanization was mainly concentrated around town centers and coastal areas. Over the years, there was a gradual expansion with new urban zones emerging and existing ones extending along major transportation routes. The period around the year 2000-2005 marked a phase of urban growth for Phuket. Between 2000-2005 and 2006-2010, there was development within existing urban areas leading to increased density in city cores. By 2010 the cityscape had become more compact and interconnected due to economic progress and initial signs of urban sprawl.  
Moving into the decade from 2011-2015 to 2016-2020, there was a shift towards consolidating and densifying spaces. High-density projects became more prevalent in central city areas. Suburbs also experienced population densities supported by enhanced infrastructure and transportation systems. The recent period spanning from 2016-2020 to 2021-2024 demonstrates a more balanced approach to urban expansion, focusing on sustainable practices and smart growth concepts. This phase likely incorporates areas and efficient land use strategies to sustain quality living standards while handling continued development. In years past, the coastal and central areas of Phuket have seen a surge in urban development, mainly due to the booming tourism industry and the construction of related infrastructure. This rapid urbanization has resulted in shifts in land usage patterns as natural landscapes and farmlands are transformed into  
Figure 6: Temporal variation in Phuket’s urban expansion.  
bustling urban zones. The swift expansion of areas brings forth various opportunities and challenges for Phuket. While it signifies economic progress, it also raises concerns regarding environmental consequences, infrastructure capacity, and sustainable resource management (Figure 7).
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 5 Evaluation
### Self-Interference Cancellation Algorithms Results  
In this section we evaluate the usage of the LMS, NLMS, RLS and QRD-RLS algorithms for cancelling the SI signal in the fairest scenario of Setup I. For that, we also employ passive suppression (see Section 2.4) by means of fixing the polarity of [PERSON]'s transmit antenna to \(90^{\circ}\) relative to the other antennas for better SI cancellation as shown in Figures 5-7. In all these tests, [PERSON]'s power is fixed at 20 dBm, and [PERSON]'s jamming power varies from 20 dBm to 32 dBm (in increments of 2 dBm).  
The setting up of parameters of the SI cancellation algorithms was made according to [34], with the number of filter taps \(t\) being set to the maximum that the processor could handle considering the maximum sample rate \(S\) attainable. The parameters used are presented in Table 1, where \(\mu\) is the step size of LMS and NLMS algortims [34], and \(\sigma\) and \(\lambda\) are, respectively, the normalization and forgetting factors of the RLS-type algorithms [35].  
The measured EVM for [PERSON] and [PERSON] as a function of the jamming power is presented in Figure 8. The results show evidence of the advantages of using FD jamming and the ability of [PERSON] to cancel his SI. In fact, it can be observed that [PERSON] experiences a much lower EVM than [PERSON], and the LMS algorithm largely outperforms all the others. The significant gap between [PERSON] and [PERSON]'s EVM curves means that it is possible to increase the jamming power to degrade [PERSON] with less harm to [PERSON].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & \(\mu\) & \(\sigma\) & \(\lambda\) & \(t\) & \(S\) \\ \hline LMS & 0.0075 & - & - & 64 & 405 KHz \\ NLMS & 0.0075 & - & - & 32 & 405 KHz \\ RLS & - & 1 & 1 & 12 & 200 KHz \\ QRD-RLS & - & 1 & 1 & 12 & 200 KHz \\ \hline \end{tabular}
\end{table}
Table 1: SI cancellation algorithm’s parameters.
## 4 Algorithms to Determine the Diameter of the DG Tree and Minimum Delay for Data Aggregation
### Algorithm to Determine the Minimum Aggregation Delay of a Data Gathering Tree  
In this subsection, we present the benchmarking algorithm to determine the minimum aggregation delay for a data gathering tree. Algorithm 6 presents the pseudo code that makes use of the diameter of the DG tree and the list of nodes at various levels of the tree to determine the delay for data aggregation. An intermediate node cannot forward an aggregated data to its upstream node unless it gets the aggregated data from all its immediate downstream child nodes. We assume it takes one time unit for a child node to send aggregated data to its parent node. We assume the leaf nodes of the DG tree (at all the levels) to have data readily available at time unit 0 (hence, the delay at any leaf node is 0). The delay at an intermediate node \(u\), \(Delay[u]\), is iteratively computed over all its child nodes as shown in lines 6-8 of Algorithm 6 and explained here: We first sort the delays of the child nodes of \(u\) and maintain _Sorted-Delay-Child-Nodes\([u]\)_. We maintain a temporary estimate of the delay (initialized to zero) at the intermediate node \(u\) using a running variable _Temp-Delay\([u]\)_ that is incremented as follows for each child node of \(u\) considered in the increasing order of their delays: For a child node \(v\in\)_Child-Nodes-List\([u]\)_, _Temp-Delay\([u]=\)_Maximum(_Temp-Delay\([u]+1\)_, _Delay\([v]+1\)_), as we assume it takes one time unit for data to reach from a child node to the immediate parent node. The increment of the _Temp-Delay\([u]\)_ by 1 within the Maximum function takes into consideration scenarios when the delay at the child node \(v\) would be smaller than the _Temp-Delay_ already computed at the parent node due to data aggregation delays involving the siblings of node \(v\) (_i.e._, the other child nodes of node \(u\)). The delay associated with an intermediate node \(u\), \(\textit{Delay}[u]\), is the final value of \(\textit{Temp-Delay}[u]\) after going through the delay-based sorted list of the child nodes of \(u\). The above procedure is repeated at all the intermediate nodes, including the leader node. The aggregation delay at the leader node is considered to be the aggregation delay of the entire DG tree. The overall time complexity of the algorithm to compute the aggregation delay of a DG tree is dominated by the time incurred to sort the delays of the child nodes of the intermediate nodes at the different levels.
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
###### Abstract  
This paper studies the journey planning problem in the context of transit networks. Given the timetable of a schedule-based transportation system (consisting, e.g., of trains, buses, etc.), the problem seeks journeys optimizing some criteria. Specifically, it seeks to answer natural queries such as, for example, \"find a journey starting from a source stop and arriving at a target stop as early as possible\". The fastest approach for answering to these queries, yielding the smallest average query time even on very large networks, is the Public Transit Labeling framework, proposed for the first time in [PERSON] et al., SEA 2015. This method combines three main ingredients: (i) a graph-based representation of the schedule of the transit network; (ii) a labeling of such graph encoding its transitive closure (computed via a time-consuming pre-processing); (iii) an efficient query algorithm exploiting both (i) and (ii) to answer quickly to queries of interest at runtime. Unfortunately, while transit networks' timetables are inherently dynamic (they are often subject to delays or disruptions), ptl is not natively designed to handle updates in the schedule--even after a single change, precomputed data may become outdated and queries can return incorrect results. This is a major limitation, especially when dealing with massively sized inputs (e.g., metropolitan or continental sized networks), as recomputing the labeling from scratch, after each change, yields unsustainable time overheads that are not compatible with interactive applications. In this work, we introduce a new framework that extends ptl to function in delay-prone transit networks. In particular, we provide a new set of algorithms able to update both the graph and the precomputed labeling whenever a delay affects the network, without performing any recomputation from scratch. We demonstrate the effectiveness of our solution through an extensive experimental evaluation conducted on real-world networks. Our experiments show that: (i) the update time required by the new algorithms is, on average, orders of magnitude smaller than that required by the recomputation from scratch via ptl; (ii) the updated graph and labeling induce both query time performance and space overhead that are equivalent to those that are obtained by the recomputation from scratch via ptl. This suggests that our new solution is an effective approach to handling the journey planning problem in delay-prone transit networks.  
Keywords:journey planning; transit networks; dynamic graph algorithms; algorithms engineering; massive datasets; experimental algorithmics +
Footnote †: 29 September 2019; Accepted: 10 December 2019; Published: 18 December 2019
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 3 Results and Discussion
### Results of the Three-Dimensional Wind Field Measurements  
Figure 7 shows the atmospheric vertical wind component information below 1 km observed in the Hefei area from 5 March to 6 March 2021. The horizontal coordinate represents the wind speed, and the vertical coordinate represents the height. Each profile in this figure shows the wind profile retrieved from the Lidar measurements for one scan (2 min) every half hour. It can be seen from Figure 7 that the wind speed did not change significantly below 300 m on 6 March and the average vertical wind speed was about 0.33 m/s. A low-level wind shear occurred below 400 m at 01:30 a.m. due to the turbulence of the low altitude upswing. As shown in Figure 7, the vertical wind direction diagram can more directly illustrate the drastic changes in the wind speed and direction with height perpendicular to the direction of the landmark.  
Figure 6: Beam frequency shift contrast diagram.  
The profiles from four consecutive Lidar volume scans (Figure 8) demonstrate the severe variations in the wind speed and direction with height, which may have been caused by turbulent changes before dawn. Each panel of this figure shows the wind profiles retrieved from Lidar measurements for one scan (2 min) every half hour. Taking into account that these measurements were conducted at a strongly stable stratification, we can assume that the variations in wind profiles for 1 h were most likely caused by the nonstationary process (mesoscale process) than by small-scale turbulence. It can be seen that the wind speed and direction changes were not significant (below 300 m), and the mean horizontal wind speed below 300 m was 11.4 m/s. An important feature of the VAD scanning Lidar analysis is the ability to provide accurate profiles in a short observation time compared with other radar products.  
The experimental results show that the accuracies of the wind speed and direction measurements of the KC-WL-2006 Lidar are better than 0.2 m/s and \(3^{\circ}\), respectively. The wind vectors were retrieved from eight radial wind velocity directions via the sine-fitting method. Thus, the eight-beam method partly overcomes the significant probe volume averaging problem, which is otherwise observed when using the VAD method. Compared with previous techniques, the eight-beam method has a higher wind measurement accuracy and the wind profile is smoother and no longer exhibits an obvious step shape. It has the capacity to detect the horizontal and vertical wind fields and to obtain large-scale, high-precision, high-resolution three-dimensional wind field data in real-time, which can be used for atmospheric boundary layer observations, wind field and flux observations and analysis, and aerosol spatial and temporal distribution analysis.  
Figure 7: Wind profiles obtained for two consecutive days in the vertical direction.
## 4 Enabling Technologies for FSO Communication
### Spatial Diversity  
Spatial diversity is a key technique employed in FSO systems to enhance communication reliability and performance. By utilizing multiple transmission and/or reception points, spatial diversity effectively mitigates the effects of AT, scattering, and signal fading, which are prevalent challenges in FSO communications. This section explores the principles of spatial diversity, its implementation strategies, and the associated benefits,including improved signal quality and increased system robustness. Through a detailed examination of various spatial diversity configurations, we aim to highlight their significance in achieving more resilient FSO links, particularly in dynamic and unpredictable environmental conditions.