esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Methods
#### 2.3.3 Data-Driven Modelling of Selected Variables  
In this study, the monthly data from dryland area in Egypt (mainly the Nile Delta region) during the years 2010 to 2019 were sampled and selected for the SSM and ET model development and evaluation. This region has 50 randomly selected sampling locations with a 10 km scale. A total of 29 parameters, including MODIS SSM and ET (modeling objective), as well as GLDAS and FLDAS outputs (e.g., Heat flux, specific humidity, total precipitation rate, soil temperature, wind speed, etc.) were used in the modelling (see comprehensive list in Table S1). The modelling dataset (\(\sim\)3000 records) was randomly split into a training set and testing set, with a ratio of 8:2, respectively.  
Traditionally, intensive manual experiments are needed for model selection and parameterization, because it is difficult to know which model is most suitable for the problem before researchers try all the models. Therefore, the automatic optimization methods were proposed to overcome this issue, which try to train and evaluate model groups by parameters sequentially, and then include new parameter groups according to the updated results. In this study, automated model selection and tuning techniques were performed using Bayesian parameter optimization on the OptiML platform ([[https://bignml.com/api/optimls](https://bignml.com/api/optimls)]([https://bignml.com/api/optimls](https://bignml.com/api/optimls))), which tried different supervised models (101 in total) for the regression tasks, including one linear regression, 89 tree models, 9 ensembles (up to 256 trees), and two deep neural networks. During the process, the platform used the Monte Carlo cross-validation [65], which iteratively generated new original data sets of training and test segmentation for the upper half of the model, and discarded the remaining half of the model due to poor performance. Therefore, this approach has an advantage over k-fold cross validation for the training and validation subsets, in that it is independent of the number of iterations. For both SSM and ET, deep neural networks outperformed other candidate models with R\({}^{2}\) values of 0.87 and 0.81 (see comprehensive metrics in Table S2), respectively, and thus applied for the simulation.
## 1 Introduction
This article studies the performance of UWA communications, using MIMO communication systems, for different levels of channel correlations between adjacent antenna elements of the MIMO configurations, with different receiver types, using LDPC codes, and using SC-FDE transmission. Instead of studying individual schemes, this article studies such a complex system, also considering impulsive noise, LDPC codes, and channel estimation. This article is organized as follows: Section 2 describes the system and signal characterization, Section 3 analyses the performance results, and Section 4 concludes the article.
Sensors_**2011**, _11_, 10283-10292; doi:10.3390/s111110283  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Skyline Query Processing in Sensor Network Based on Data Centric Storage**  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Department of Computer Engineering, Chungju National University, 72 Daehak-ro, Chungju-si, Chungbuk 380-702, Korea; E-Mail: [EMAIL_ADDRESS]  
2 School of Media Engineering, Dong-Ah Institute of Media and Arts, Dong-Ah yedae Rd. 47 Samjuk-myeon, Anseong-si, Gyeonggi-do 456-717, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-43-841-5349; Fax: +82-43-841-5340.  
_Received: 1 October 2011; in revised form: 27 October 2011 / Accepted: 28 October 2011 / Published: 28 October 2011_  
**Abstract:** Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.  
**Keywords:** skyline query; sensor network; data centric storage
## 2 Materials and Methods
### Bioinformatics Analysis  
A set of phylogenetic trees were constructed suing the _P. jordanii_ strain G34 draft genome and ten closely related _Pseudomonas_ species, each with available published genomes: _P. argenttinensis_ LMG22563, _P. rstaninea_ JCM _2783, P. punonensis_ CECT _8089, P. flavscens LMG 18387, P. daroiniae FRB 228, P. selenipraecipitans LMG 25475, P. drayadis FRB 230, P. yangonensis JCM 33396, P. guguanensis JCM 18416, P. sediminis P111, and _Staphylococcus aureus_ DSM 20231 as an outgroup. Phylogenomic analysis began with the KBase server [26], employing the species tree builder and OrthoANI (Orthologous Average Nucleotide Identity) via the Orthologous Average Nucleotide Identity Tool (OAT 0.93.1) software [28]. The genomic signature dissimilarity phylogenetic tree was constructed based on octanucleotide frequencies using the GScompare server ([[http://gscompare.ehu.eus/](http://gscompare.ehu.eus/)]([http://gscompare.ehu.eus/](http://gscompare.ehu.eus/)) accessed on 9 July 2024).  
Digital DNA-DNA hybridization was performed using the Genome-to-Genome Distance Calculator 3.0 (GGDC) [29] available on the TYGS server ([[https://tygs.dsmz.de/accessed](https://tygs.dsmz.de/accessed)]([https://tygs.dsmz.de/accessed](https://tygs.dsmz.de/accessed)) on 9 July 2024), according to the formula d4. Phylogenetic tree construction utilized FastME 2.1.6. [30], employing Genome Blast Distance Phylogeny (GBDP) with branch lengths scaled according to the GBDP distance formula d5. Branch support values above 60% from 100 replications were included above the branches, with an average branch support of 80.6% [31].  
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRR) prediction was conducted using the CRISPRCasFinder online program, ([[https://crisprcas.i2](https://crisprcas.i2) bc.paris-saclay.fr/CrisprCasFinder/Index]([https://crisprcas.i2](https://crisprcas.i2) bc.paris-saclay.fr/CrisprCasFinder/Index), accessed on 1 March 2024) [32]. A pan-genomic analysis was performed using the KBase Compute Pangenome function and Pangenome Circle Plot [26]. The distribution of genome annotations was visually represented in a circular graphical display using the Comprehensive Genome Analysis function on the PATRIC server [33].
## 3 ECG Signal Processing
### Reduction of Baseline Oscillations  
The first step in the processing of the ECG signal is to use a baseline filter, reducing the variation of the baseline and thus obtaining a better quality and definition of the temporal signal that will result in better characteristics provided by the \(TFR\). This processing consists of the implementation of an 8 th order infinite impulse response filter (IIR) with a Butterworth bandpass type ranging from 1 Hz to 45 Hz [28; 29]. Figure 2 represents the effect of applying this bandpass filter, showing a reduction of the baseline. Thus, all signal contribution not located in the mentioned frequency range, which corresponds to non-ECG source, are eliminated.  
Figure 1: General diagram of the processing steps performed for the detection of Ventricular Fibrillation. After the reduction of the baseline oscillation and the calculation of the reference marks, the Hilbert transform (Ht) and the time-frequency representation (\(TFR\)) for a time window (\(t_{w}\)) is obtained. Optionally, the possibility of obtaining the equivalent image from the \(TFR\) (\(TFRI\)) is used to serve as input to the combined classifier using three cascaded classifiers (\(Cla\), \(Clb\), \(Clc\)) where all of them are the same type (\(Cla=Clb=Clc\)) or combined \(Cla\_Clb\_Clc\) in case at least one classifier among them (\(Cla\), \(Clb\), \(Clc\)) is different.
## 4 Discussion
### Changes in NEE and GPP in Alaska from 1992 to 2014  
From 1992 to 2014, NEE and GPP of ATQ sites showed a trend of drastic fluctuation, which was due to fire disturbance. The ecosystem was disturbed by fires during 1982-2002 and 2003-2011 at the ATQ site, which led to drastic changes in the carbon flux of the ecosystem [42]. At the beginning of the fire period (1982-1995), the disturbance of the ecosystem was remarkable. During this period, the fire led to a decrease in the flux and GPP showing a downward trend, with less GPP in burn sites than in uninterrupted areas [42].  
With the decrease of GPP related to recent disturbances, NEE showed an overall trend of increasing. In the late period of the fire (1995-2002), the vegetation in the burned ecosystem began recovering slowly, and the GPP showed an upward trend while the NEE showed a downward trend [42]. The change trend of NEE and GPP of the PRR site was consistent during the same period, because they were not disturbed.  
From 1992 to 2014, the decreasing NEE values indicated that the carbon uptake rates increased, and the GPP values also increased. Compared with the tundra site, the needle leaf forest site had a faster-increasing rate of carbon uptake and GPP values. Similar findings have also been reported by in situ observation data, i.e., the carbon uptake rates of forest regions have been increasing during the past decades, while the tundra areas showed little change in carbon uptake rates [45]. This can be attributed to the fact that the tundra area is dominated by herbaceous species, which have limited biomass and carbon uptake capacity. It has also been found that Arctic tundra may already be a carbon source because of limited vegetation growth but high soil organic carbon loss [46; 47]. In addition, the NEE and GPP at the tundra sites showed greater fluctuations than those at the needle leaf forest site. This pattern confirmed that the growth of perennial herbs is more sensitive to climate conditions, while woody forests have a stronger adaptative capacity to climate conditions. Consequently, the GPP values for the forest usually showed smaller changes than those in grassland [48].
## 4 Architecture Design
### Emulation Environment
#### 4.2.1 Network and Vehicle Movement Simulation  
R precomputation (implementation with R programming language) produces two main inputs for the execution of emulation scenarios: Path loss matrices (used to determine throughput and connectivity) and vehicle movement (location, speed, and heading at user-defined time intervals). Three inputs are needed: topology specification, access point locations and specifications, and vehicle waypoints and speed profile. The activity diagram in Figure 9 illustrates the creation of network and vehicle movement simulation data for emulation purposes.  
At this phase, our simulation model does not yet contain proper propagation models to compute path loss matrices. Instead, we are using an approximate ad hoc model that should produce results that are sufficiently close to published results from real measurements, e.g., [26]. While such an ad hoc model is of course inaccurate, we believe that it captures the dynamics needed in the evaluation of this proposed emulation system at this early phase of development. Some samples of simulated signal levels are shown in Figure 10. Furthermore, the vehicle mobility model is fully deterministic; that is, vehicles follow the path defined by the waypoints using the given speed profiles.  
Figure 8: ML-based inference for facilitating decision making on multi-access routing.  
Figure 10: Examples of the results from an ad hoc propagation model. On the right-hand side, two dead zones are simulated—one around 175 m and another around 400 m.  
Figure 9: Activity diagram for the creation of network and vehicle movement simulation data for emulation purposes.
## 3 Results and Discussion  
### Penetration Resistance Test  
The penetration resistance test results of all mix designs are shown in Figure 5. It was found that the transfer time between 40 mm and 0 mm of penetration depth is very short for all mix designs in this study. The sudden stiffening phase change may be attributed to the presence of VMA, which could prolong the initial setting time and has a small impact on the final set. Based on this test, it is not possible to measure the initial and final setting time for these mixtures precisely. The initial setting time of mixtures MIX-L, MIX-M and MIX-H are about 52, 78, and 147 min, respectively. The initial setting time was significantly reduced by increasing the content of MK in calcined clay. Without adding any calcined clay, the initial set was about 258 min in the case of mixture MIX-R.
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.7 Fuzzy Region-Based Active Contour Model (Frbacm)  
The FRBACM is driven by weighting global and local fitting energy, wherein fuzzy region energy with local spatial image information is proposed. The segmentation results of this method are independent of initialization. To extract object boundaries while maintaining its distance, an initial evolving curve of pseudo level set function (LSF) followed by the pseudo-LSF and further smoothed by edge energy is proposed. This method consists of fuzzy region energy which is formulated with local spatial image information and edge energy which use the evolving curve to stop the object boundaries. The fuzzy region energy is strictly convex and used to drive the motion of the evolving curves. To minimize the energy functional, the fuzzy region energy directly calculates the change in the fuzzy region energy instead of using the Euler-Lagrange equation [53].  
This proposed model succeeds in extracting objects in different situations such as images with noise and images with intensity inhomogeneity; however, this model fails when the similarity between the object and its background is high.  
2.2.8. Global and Local Signed Energy-Based Pressure Force (Glsepf)  
The GSLEPF uses the energy difference between the inner and outer energies to obtain the contour of the object. This model will improve the initial curve robustness. The local signed energy-based pressure force (LSEPF) is determined by means of the pixel-by-pixel energy difference within the local neighborhood region. This LSEPF can handle images with depth inhomogeneity and noise. Global image information and local energy information are used, respectively, for global and local force propagation functions. Global and local variances are used to balance the weights of GSEPF and LSEPF automatically, which can solve the problem of parameter setting. In the meantime, a regularization term and a penalty term are added in order to prevent re-initialization during iterations and smooth, the level set function [54]. The level set formulation of the GSEPF model is written as:  
\[\begin{split}\text{GSEPF model:}\ \frac{\partial\phi}{\partial t}=& \frac{\Delta E^{\delta}(I(x))}{\max(|\Delta E^{\delta}(I(x))|)} \cdot|c_{1}-c_{2}|\cdot\
abla\phi\\ &+\mu\delta(\phi)\cdot\text{div}\bigg{(}\frac{\
abla\phi}{| \
abla\phi|}\bigg{)}\\ &+v\bigg{(}\
abla^{2}\phi-\text{div}\bigg{(}\frac{\
abla\phi}{| \
abla\phi|}\bigg{)}\bigg{)}\end{split} \tag{17}\]  
where \(\Delta E\delta(I(x))\) is the global energy. Objects in images with noise and intensity can be detected by incorporation of the global and local image information; however, for color images, the segmentation is poor because only the intensity information is used.  
In next subsection, we explain the database that the methods are applied to.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Experiment with Proximity-Based Adaptive Algorithm
#### 4.3.2 Experiment Evaluation in Static State
Positioning in the central region is not close to the AP, so the MP technique is not applied. As a result, the solid red line and solid blue line show the same positioning results. In the case of the experimental results measured along the path in Figure 14b, differences can be seen with or without the MP technique applied. For the AP4 and AP5 regions, the solid blue line shows that when the path is close to the AP, the MP technique is applied, and the positioning is similar to the actual path. However, the solid red line did not reach the region close to the AP, and the positioning continued along the next movement route. This can be seen as a phenomenon that occurs due to the problem of positioning in trilateration in weak signal regions. When approaching an AP, the RSSI of one AP is strong, but the RSSI of the other two APs is weak due to the NLOS environment. Therefore, RSSI is deleted through OR, making positioning impossible at that location. Therefore, the red solid line represents the actual path and the location estimation result with a large error, especially when close to the AP. Figure 15 presents a graph of the position estimation error of the experimental results on the actual path within 5 m of AP1, AP4, AP5, and AP8 in Figure 14b. The solid red line shows the positioning error using trilateration only, and the solid blue line shows the error of the PAP algorithm. It is shown that the error in positioning using the PAP algorithm decreases significantly as it gets closer to the AP. This indicates that the PAP algorithm, where the MP technique is applied when close to the AP, helps improve positioning accuracy. Figure 13: Localization paths: (**a**) straight path; (**b**) moving path. Figure 14: Experimental results by path type: (**a**) for straight path; (**b**) for moving path. Figure 15: Positioning error according to distance from AP: (**a**) AP1; (**b**) AP4; (**c**) AP5; (**d**) AP8; (**e**) average.
# Optical Bullets and Their Modulational Instability Analysis
## 6 Results and Discussion  
According to the obtained solutions of the 3D-NLSS in Section 4, it is worth mentioning that the proposed mathematical tools have been powerful in generating a plethora of exact solutions containing hyperbolic function, trigonometric function, and rational function solutions, which describe various wave types. An optical soliton is one of the extracted wave structures, and it includes the shapes of bright, dark, and singular solitons. Further to these wave forms, periodic waves that have dissimilar profiles are also retrieved.  
To report the behavior of optical bullets and other waves, the graphical representations of some obtained solutions are given. Appropriate values for the model parameters are selected to draw the 3D plot for the absolute of these solutions. Figure 1 illustrates the behavior of solution (35), which describes a dark optical soliton. Figure 2 depicts the plot of solution (44), which characterizes a bright soliton wave. Figure 3 exhibits the evolution of solution (52), where the graph shows a periodic wave structure. Furthermore, we observe that the plot of solution (56) in Figure 4 represents a bright soliton pulse. One can clearly see that the graph in Figure 5 presents a dark soliton profile, which demonstrates the dynamic behavior of solution (62).  
From the presented graphical representations of some obtained solutions, it can be noticed that the optical solitons (bullets) possess various forms of profiles. The rest of the derived solutions may depict miscellaneous soliton structures. In addition, the evolution of optical bullets can be affected by the variations in the physical parameters. In comparison with previous studies [58; 59] that scrutinized the 3D-NLSS (2), the optical bullets solutions revealed in this work have different characteristics and behaviors. Hence, these outcomes are expected to contribute to understanding the nature of optical bullets in multidimensional optical media.
## 2 D2D Communications in LTE-A Standards
### Proximity Discovery Scenarios and Interference  
A feasibility study with regard to proximity-based services (FS-ProSe, TR 22.803) defines proximity-service for UEs. The identified areas included services related to commerce and public safety that would be of interest to both operators and users. In Release 12 of the 3 GPP standards [16], D2D discovery is designed to support different services that are bounded by discovery range. For this reason, D2D communication is also limited according to the D2D discovery boundary. Figure 2 shows the scenario for D2D ProSe where UE1 and UE2 are located in-coverage/out-coverage of a cell [16]. According to D2D connection scenarios, DUEs can be affected from the different interference sources. Table 1 presents network coverage combinations and interference sources for each proximity discovery scenarios depicted in Figure 2. Thus, to mitigate the interferences of D2D link efficiently, an interference mitigation scheme should consider all of the abovementioned scenarios and interference sources.
## 5 The Group-Aware Query Region Tree (GQR-Tree)
### Description
It should be noted that the spatial query range \(q_{1}.R\) is not assigned to \(o_{2}\) because the query identifier of the corresponding query \(q_{1}\) is contained in \(full\_qid\_list\) of the tuple \(\langle g_{1}.bv,full\_qid\_list\rangle\) maintained in \(N_{1}.full\_qid\_table\) (_i.e._, because \(q_{1}.R\) covers \(N_{1}\)). When \(o_{2}\) exits its resident domain \(N_{1}\) as depicted in Figure 6, it sends the \(RequestDomain\) message to the server. Then, the server assigns \(o_{2}\) a new resident domain and additionally checks if \(o_{2}\)'s movement affects the result of \(q_{1}\). Because \(o_{2}\) does not cross the boundary of \(q_{1}.R\), the server need not update the current result of \(q_{1}\). Third, by grouping the issued queries according to their query bit-vectors, GQRT efficiently handles the case, where a leaf node \(N\) overlaps with \(\theta+1\) spatial query ranges as a result of a newly issued query \(q\). In the existing methods (MQM, QRT, and BQRT), without any consideration of non-spatial query values (or intervals), \(N\) is recursively split until all of its descendant nodes (_i.e._, subspaces of \(N\)) overlap with no more than \(\theta\) spatial query ranges. On the other hand, in GQRT, if \(q\) is an element of some query group \(g\in\mathcal{G}\) and \(|g\_N|\leq\theta\), no split process occurs although \(\theta+1\) spatial query ranges overlap with \(N\). In case that \(|g\_N|>\theta\), \(N\) is recursively split until, for each descendant node \(\hat{N}\), \(|g\_\hat{N}|\leq\theta\).