esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 1 Introduction
We compared our proposed algorithm and the original K-LEACH algorithm, and we proved its impact in increasing the lifetime of the full network. We tested our modified K-LEACH algorithm on an application related to water pollution in different scenarios to study the effect of efficient hierarchical data-clustering approaches on optimizing data routing, forwarding processes, and reducing energy consumption in static and dynamic contexts, and further adapted the algorithm to consider different levels of pollution. We proved its effect by measuring average residual energy, node death, and throughput in both dynamic and static contexts with different pollution levels. The K-means algorithm forms clusters based on calculating the minimal distance between nodes and CH and based on residual energy level [2; 7; 11; 25; 26]. Hence, this approach helps in reducing sensor-node-consumed energy in sending data to the CH in their cluster, which in turn will ensure an efficient and alive network for as long as possible [18; 25]. In this paper, we present a smart lightweight content-aware data-clustering approach for the monitoring of water pollution levels that helps in increasing network lifetime due to the usage of a modified K-means clustering algorithm in conjunction with the LEACH protocol. One selected parameter of water quality monitoring is the detection of hydrogen peroxide pollutants in water, which is a measure of free radical formation in water. Additionally, our protocol is applied along with experimental sensing measurements using cerium oxide (ceria) nanoparticles as an active, static sensing media for pollutants, such as hydrogen peroxide, through a visible fluorescence quenching mechanism excited by a violet optical source [28]. Our main contribution in this paper is to provide an efficient hierarchical content-aware data-clustering and routing method for enhanced water quality monitoring operations with increased network lifetime and optimized network energy consumption. The rest of this paper is organized as follows. Section 2 discusses different routing protocols used for efficient water quality monitoring and their impact on prolonging network lifetime. The proposed K-means LEACH algorithm is presented in Section 3. Section 4 shows the physics setup for sensing water pollutants to feed the simulation studies. Section 5 presents the simulation studies and scenarios in various operating contexts and discusses the obtained results. Section 6 summarizes the work and findings.
## 4 Vulnerability Assessment and Simulation Platform
### Simulation Platform  
In this section, we build a MATLAB-based platform for the offline simulation and analysis of the vulnerability of ECPSs from the five aspects in Section 4.1. The detailed system configuration we used is listed in Table 2.  
Three necessary settings are required before the simulation: (i) input the topology of a power grid and related electrical information; (ii) input the locations of sensors, routers, and actuators; and (iii) specify the initial tripped branch (or the amount of power injection and the ID of a power node) and the protection algorithms. Then the proposed program will give vulnerability assessments of the system based on the simulation results.  
In the simulation, the IEEE 39-bus system (including 10 generators, 21 loads, and 46 branches) is used as an example (Figure 3). We assume that the sensors, routers, and actuators are located at each bus and branch.  
Figure 4 shows the flowchart of the platform for the offline simulation of the performance of ECPSs under different faults. The program begins with a situation selection. In CASE 1, the program user randomly selects a power node and sets the amount of the power injection and the program searches for the overloaded lines at first. In CASE 2, the user randomly selects a branch \((i,j)\) as the initial triggering event and trip the line in CASE 2, the ECPS will suffer a protection and control procedures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Content** \\ \hline Operating system & Windows 7/10, Mac OS X 10.11 \\ CPU & Intel (R) Core (TM) i7-4790, 3.60 GHz \\ RAM & 8.00 GB \\ System Type & 64-bit \\ Software & MATLAB 2012a \\ \hline \hline \end{tabular}
\end{table}
Table 2: The system configuration.  
Figure 3: The topology of the IEEE 39-bus system.
## 3 Algorithm Flow
### Infrared Polarization Image Fusion  
The spectral and polarization information of the target can be demodulated through a polarization interference image array. However, infrared images have inherent disadvantages such as large noise and low resolution, so the imaging quality of infrared images is also an important indicator of the system. Polarization image fusion can make full use of polarization information, make up for the disadvantage that a single intensity image cannot provide sufficient information under certain scene conditions, and further improve the amount of information obtained by optical detection. Therefore, the research on infrared polarization image fusion is carried out. Figure 5 is the overall flow chart of polarization image fusion.
## 3 UT NDE and Nuclear Fuel Channel Pressure Tubes  
A typical Canada Deuterium Uranium (CANDU) reactor contains 480 zirconium alloy Pressure Tubes (PT). Each is approximately 560 mm long, with a diameter of 100 mm and wall thickness around five millimeters. During reactor operation the PT house the reactor fuel. PT operate under high pressure and temperature and experience significant neutron flux. These factors cause the PT to geometrically deform and make thinning and weakening of the pipe wall possible over time. PT are also subject to chemical processes that increase the risk of catastrophic failure due to embrittlement; this is especially relevant in and around evolving cracks, scratches, and fissures in the PT material [18].  
The confirmation of PT integrity by way of physical inspection is one risk-mitigation strategy employed by nuclear owner operators to ensure continued safe operation. And so during planned maintenance outages PT are emptied of their fuel and inspected with UT NDE tooling. A primary goal of inspections is to locate and characterize material flaws. Flaws are sudden changes in pipe wall geometry such as those caused by abrasion and wear of reactor fuel bundles and debris interacting with the PT inner diameter surface. Flaws are of interest primarily because regions at the bounding points of their geometry are under increased mechanical stress which enhances the risk of catastrophic failure [18]. Inorder to monitor and mitigate the risk of such an outcome PT flaws are characterized and a set of criteria are used to determine the risk of each to continued safe reactor operation.
## 2 Theoretical Framework and Methods
### Subway Station Flood Resilience System
#### 2.1.2 Flood Resilience Process for Subway Stations  
The resilience process is comprised of three primary components: Resistance, Recovery, and Adaptation [9]. Correspondingly, resilience is expressed through three distinct capacities: resistance capacity, recovery capacity, and adaptation capacity. \"Resistance capacity\" pertains to the system's ability to withstand adverse effects resulting from disturbances, thereby safeguarding its essential functions from total disruption. In the context of this study, this concept pertains to the subway station's defensive mechanisms against heavy rainfall disturbances, which include factors such as the elevation of flood walls, the height of flood barriers, the topography of access points, and the accessibility of flood-related information. \"Recovery capacity\" is a critical element of resilience, denoting the system's capability to swiftly restore its compromised components to their intended operational state following disturbances, such as natural disasters. This aspect primarily encompasses the efficiency of drainage systems, the density of drainage networks, the ratio of trained rescue personnel, and the availability of emergency supplies. \"Adaptation capacity\" refers to the system's ability to modify its structure through either active or passive learning processes to better address future uncertainties. This includes the development of flood emergency response plans, the execution of flood preparedness drills, the conduct of safety inspections and training, and the improvement of flood information management systems.  
The processes of resistance and recovery in response to each disturbance create opportunities for adaptation. The enhancement of adaptive capacity, in turn, reinforces both resistance and recovery capabilities when confronted with disturbances. Collectively, resistance, recovery, and adaptation facilitate the system's continuous dynamic evolution.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 4 Experiments
### Scenario  
The OTFS wireless transmission and sensing measurements have been carried out in the laboratory environment, with the measurement test-bed placed on the table and the transmitting and receiving antennas mounted on tripods. This configuration allows easy control of the height and tilt of the antennas. The antenna position was inspired by previous work on person counting [31], with antennas mounted at a height of 2.5 m with a downward tilt of 30 degrees. The underlying idea is to sense the environment from above persons, while at the same time limiting the effects of reflections from the ground.  
As the main intention of the experiment is to demonstrate the function of the persistent homology detector in realistic conditions, we limited the investigations to the scenario of only one person present and relatively directional horn antennas; so, in ideal conditions, only one target would be detected. We have also conducted experiments with multiple persons, but since individual persons can be obscured by each other, the data measured in this way are not very suitable for validating the detector. The photo, together with a sketch of physical dimensions, is shown in Figure 8. The monitored area, in which the person can move, is limited by the laboratory tables on the left and right-hand sides.  
To generate a variety of different delay and Doppler profiles of a person's movement, we considered several simple scenarios of their presence and activity, such as:  
1. A person standing still in front of antennas;
2. A person randomly walking in the monitored area;
3. A person running out from the monitored area;
4. A person in front of the antennas jumping/dancing.  
Figure 7: Frequency plan for up- and down-conversion. The useful signal is centered at 60.2 GHz, its partially compensated image at 59.0 GHz, the receiving image at 57.8 GHz does not influence the useful signal.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 2 Data and Methods
### Development of Stochastic Model
#### 2.3.2 Sisre  
It is a common practice to down-weight observations from other GNSS when compared to GPS due to mis-modelling of satellite orbits and clocks. The contribution of orbit and clock error to range measurements can be expressed as signal-in-space ranging errors (SISRE) [40] using the following formula:  
\[SISRE=\sqrt{\left[RMS(w_{R}\cdot\Delta r_{R}-\Delta cdt)\right]^{2}+w_{A,C}^{ 2}\cdot(A^{2}+C^{2})}, \tag{3}\]  
where \(w_{R}\) and \(w_{A,C}^{2}\) are GNSS-dependent SISRE weight factors for statistical contribution of radial (\({}_{R}\)), along-track (\({}_{A}\)), and cross-track (\({}_{C}\)) errors to the line-of-sight ranging error, \(\Delta r_{R}\) and \(\Delta cdt\) are the residuals for radial and clock components, respectively, \(A\) and \(C\) are the RMS of along- and cross-track orbit components, respectively. We use orbit and clock residuals from the comparison of real-time multi-GNSS CNES products with final products [31]. Values of all the parameters required in SISRE  
Figure 2: Dependence on a posteriori error of unit weight \(m0\) (**a**) from a-priori error of GPS raw pseudorange in the GPS code-based only solution for each test station and (**b**) from a-priori errors of GPS raw pseudorange and raw carrier phase in GPS PPP solution averaged for all stations.  
computation are presented in Table 3. Figure 3 presents SISRE computed individually for each satellite, as well as a single RMS of SISRE for each GNSS, with an additional division to MEO and IGSO satellites for BeiDou. We found that only for some GLONASS and BeiDou IGSO satellites individual SISRE values significantly differ from the system SISRE, and therefore we use system values for simplicity. For missing BeiDou GEO satellites, we use BeiDou MEO SISRE multiplied by a factor of 10, following the fact that RMS of the BeiDou GEO orbit differences between IGS analysis centers show up to a few meters in the along-track direction, whereas for BeiDou MEO the RMS is at the level of a few decimeters [41].
## 5 Results on ML/DL-Based Modulation Recognition  
This section discusses the results of ML and DL techniques for modulation recognition in UWA communication systems. First, Section 5.1 outlines the methods used across selected studies. Second, Section 5.2 elaborates system characteristics for each approach. Subsequently, Section 5.3 compares their performance to show strengths and limitations. Finally, Section 5.4 discusses the contributions and impacts of these studies on modulation recognition.
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 5 Conclusions  
Numerical modeling of coastal hydrodynamics constantly gains importance in the world of coastal engineering due to the rapid development in computing technology. The modeling techniques have become more sophisticated and a large number of models can now be employed in a variety of coastal hydrodynamic problems. Accordingly, the performance assessment of numerical codes becomes crucial. The credibility of a numerical model has a direct relationship with its performance, which can be evaluated by indicating the level of agreement between observation and prediction. NAMI DANCE is one of many coastal models that solve two-dimensional depth-averaged NSW equations. This study investigated the performance of these equations in the estimation of long wave evolution, propagation, amplification, and long wave currents using NAMI DANCE. In this respect, the numerical code was applied to a benchmark problem which describes a series of experiments that analyze the transformation of a single solitary wave as it propagates up a triangular shaped shelf with an island feature located at the offshore point of the shelf. The results of this study indicate that depth-averaged NSW equations satisfactorily predict the evolution, propagation, and amplification of long waves over complex bathymetry when turbulence effect is negligible. On the other hand, NSW equations are not able to produce accurate results when the turbulence effects become dominant. Depth-averaged NSW equations may still be preferred while developing tsunami mitigation strategies. However, further studies on different benchmark problems are necessary to identify the performance and validity of depth-averaged NSW equations when the current behavior of long waves is investigated.  
This work is a part of the doctoral dissertation of [PERSON] who analyzed the benchmark data and ran the numerical simulations. [PERSON] contributed to the analysis of the model outcomes. [PERSON] and [PERSON] took part in the development of the numerical tool NAMI DANCE. All authors contributed to the study. The authors declare no conflict of interest.
(2018) applied 30 m Sentinel-2 data from the Harmonized Landsat and Sentinel-2 product to estimate the GPP (R\({}^{2}\) = 0.77 and RMSE = 0.81 g C m\({}^{-2}\) day\({}^{-1}\) comparing to EC GPP) of three evergreen broadleaf forest sites and two grassland sites in southeast Australia. In the study by [PERSON] et al. (2018), Sentinel-2 and Landsat 8 data were used to estimate GPP (R\({}^{2}\) = 0.92 and RMSE = 1.38 g C m\({}^{-2}\) day\({}^{-1}\)) on five crop fields (four in the USA and one in Germany). Given that Sentinel-2 has been shown to be capable of accurate representation of phenology ([PERSON], 2018; [PERSON], 2018) and has the capability to provide detailed estimates of biophysical and biochemical vegetation parameters ([PERSON], 2018; [PERSON], 2018) it can be hypothesized that Sentinel-2 MSI data has the potential to enhance the performance of GPP models also in northern landscapes. However, the impact of the lower temporal frequency of Sentinel-2 compared to MODIS in these high-cloudiness areas is a factor that could have negative impact. In this study, we analyze eight study sites within the Nordic region (Denmark, Finland and Sweden, Figure 1) to investigate the accuracy of GPP estimates from Sentinel-2 data in comparison with MODIS data. We base the comparison on empirical regression models as they are commonly used for estimating GPP, and are functionally similar to the LUE type models. Following this, we examine the relationships between EC-derived GPP and EVI2-derived GPP using EVI2 and meteorological variables. In investigating the potential of Sentinel-2 vegetation index data for modeling carbon uptake, the study aims to (1) examine the effects of using Sentinel-2 MSI 10-m data compared to MODIS 250 m and 500 m data on modeling GPP from EC measurements, and (2) determine the importance of the input data for modeling accuracy in comparison to the choice of environmental driving variables. Figure 1: Study area with location of flux sites and corresponding Sentinel-2 tiles.
## 8 Results
### Distribution of the Detected Effects in Fault Blocks  
In Cephalonia, the effects were generated and distributed to all fault blocks of the island comprising the Erissos, Paliki and Argostoli peninsulas, the Aenos Mt and the southeastern Cephalonia. Particularly,oseismic uplift was generated along the eastern coastal part of the fault block of Aenos Mt, along the northern coastal part of Argostoli peninsula, at the northwestern coastal end of the transition zone from the Erissos peninsula to the Aenos Mt fault block and at the northern coastal part of Paliki peninsula. Slope failures were mainly triggered along steep slopes, which are characterized by instability conditions favorable for the triggering of slides and rockfalls, in the case of strong earthquake ground motion. In Cephalonia, these zones include the transition zone from Eriksos peninsula to the Aenos Mt fault block, the transition zone from the Aenos Mt fault block to Paliki peninsula, the southern slopes of Aenos Mt, which are characterized by considerable uplift and intense incision, as well as along steep slopes within the aforementioned fault blocks. Hydrological anomalies were detected in residential areas located within the Aenos fault block and in the northwestern part of Argostoli peninsula, where wells and springs were used for water supply. Ground cracks were mainly formed close to sites affected by slope failures and liquefaction phenomena. Anomalous waves/tsunami mainly affected funnel-shaped bays, including Argostoli bay in Cephalonia. Liquefaction was limited to port areas and sites close to the seashore, which are composed of late Holocene deposits susceptible to liquefaction. The other effects comprising dust clouds, vegetation effects and hydrocarbon-related phenomena were also observed in areas strongly affected by the earthquake ground motion.  
As regards Ithaki, the induced effects were distributed to the central and the southern fault blocks of the island. Slope failures and ground cracks were generated in the central and the southern fault blocks, while anomalous sea waves/tsunami and hydrological anomalies in the southern fault block. In Zakynthos, the triggered effects were limited to the coastal Zakynthos town and the adjacent Skopos Mt.  
Far-fields effects were also triggered in the adjacent Aetoloakamania and the Peloponnese and comprised hydrological anomalies and tsunami in Aetoloakamania, slope failures and ground cracks in Achaia and slope failures, ground cracks and hydrocarbon-related phenomena in Elis.
## 2 Materials and Methods
Responses within the range of 10-13 h for 3-4-year-olds, 9-11 h for 5-13-year-olds, and 8-10 h for 14-17-year-olds were categorised as \"meeting sleep guidelines,\" and participants out of these ranges were classified as \"not meeting sleep guidelines\", based on WHO international guidelines for early-years [19] and child and adolescent 24 h movement behaviour sleep recommendations [3]. The HRQoL was assessed with the EQ-5D-Y (proxy version 1). This instrument has been developed to assess HRQoL in children and adolescents [21]. The five different dimensions ('mobility', 'looking after myself', 'doing usual activities', 'having pain or discomfort', and 'feeling worried, sad or unhappy') were assessed with three levels of severity ('no problems','some problems', and 'a lot of problems'). A three-digit code was used to represent health profiles (each digit represented the level of severity in each dimension). Furthermore, the EQ-5D-Y instrument contained a visual analogue scale, with a score ranging from zero (worst) to 100 (best). Evidence concerning the reliability and validity of this instrument has been previously published [22]. For our purpose, the different dimensions of HRQoL were categorized into: 0--'no problems'; 1--'some problems'; and 2--'a lot of problems'. Diet was assessed as the adherence to the Mediterranean Diet, and for this purpose, the Mediterranean Diet Quality Index (KIDMED) questionnaire was used [23]. The following variables were considered as covariates in the model: age, sex (male/female), nationality (Spain/Brazil), breadwinner's educational level (no university studies or university studies), socioeconomic status (SES; low SES, medium SES or high SES) [14], BMI (z-score) [15; 16], and Mediterranean Diet [23]. These covariates were identified as correlates in the scientific literature [24]. Moreover, the association between the covariates and the outcomes is shown in Table S1. The results of the categorical variables are presented as absolute and relative frequencies and mean and standard deviation were provided for continuous variables. An analysis of covariance (ANCOVA) was performed to assess mean differences in HRQoL scores according to the meeting of all three 24 h movement guidelines. As no interaction was shown between a higher prevalence of meeting the 24 h movement guidelines (i.e., for each of the behaviours: PA, SB, and sleep) and sex in relation to HRQoL (\(p\) = 0.724), all the analyses were conducted including males and females together. A binary logistic regression analysis was performed to verify associations between meeting all three 24 h movement guidelines and the different categorical dimensions of HRQoL. The software Statistical Package for Social Sciences (SPSS, IBM Corp., Armonk, NY, USA) was used to perform the analysis. Statistical significance was set at \(p<0.05\).