esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## 3 Results and Discussion
### Experimental Campaign 2: Sensitivity of the Sensor at Different Locations  
In the second campaign, the sensors were tested to differentiate the cracking levels in different areas of a same beam, as depicted in Figure 3. The average values are obtained for the three repetitions for each point. Figure 13 displays these values and their standard deviation.  
Figure 13: Sectorization Id.O\({}_{2}\) results obtained at the HS level in the (**a**) High Performance Concrete HPC) beam, (**b**) UHPFRC beams, (**c**) UHPFRC-n beam.  
The results that were obtained in the HPC beam in uncracked state (Figure 13a, bars labelled as Ref) show very similar Id.O\({}_{2}\) values in the three sensor zones (L, M, H). For the beam pre-cracked at the HS level, higher oxygen availability was detected at the three sensor zones, even in sectors S1 and S4, where minimum cracking was thought to have occurred (probably because of the existence of shear cracks). Sector 2 shows the most relevant increase in oxygen presence in the sensor that was located at the low area (L), indicating the location of a crack produced by bending, while the rest of measures do not show a clear trend.  
The four sectors displayed a similar behavior regarding the results of UHPFRC concretes in cracked state (Figure 13b,c), with the highest oxygen presence in zone L, where the cracks were wider. On the contrary, the lower value was consistently obtained in zone H, and there was a clear trend of decreasing oxygen content going towards the upper part of the beam in all cases. This demonstrates the good sensitivity of this sensor, which was able to detect different oxygen availability levels at the three studied heights, despite UHPFRCs' extremely thin cracks.  
Additionally, the UHPFRC-n mix shows slightly lower values of oxygen availability inside the matrix when compared to UHPFRC, which is especially noted at the zone H. This is a possible indicator of the durability improvement produced by the functionalizing agents added to this mix.  
When comparing the results of HPC and UHPFRCs, it can be concluded that the crack patterns in UHPFRCs appeared to be more distributed and uniform in the different sectors and studied heights, while, in HPC, the relative position of the crack compared to the position of the sensor was unclear. The HPC cracking pattern exhibits wider and longer cracks, which are generally visible to the naked eye, whereas, multiple thin cracks are produced in UHPFRC, which are only visible when using microscopes. These cracks can be even more reduced in the interior of the beam when compared to the surface crack, as reported previously in other works [10; 11]. Subsequently, together with the high content of steel fibers in UHPFRC, and also with nano-fibers in UHPFRC-n concrete types, the oxygen availability can be reduced even further.
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 4 Results and Discussion  
In this section, we analyze the top cluster of administrative dongs, obtained through the earlier k-means clustering process. We then explore the spatial characteristics of emergency bell installations throughout Gwangju Metropolitan City. GIS software was utilized for visualization and elucidates the ideal positions for emergency bells as determined by EBLSCP and EBMCLP.
## 1 Introduction and Related Works
in [23], where the cluster is used for authentication), but none of the protocols seen use cluster-based schemes to build different networks. So, this is the first time that different sensor networks have been built using cluster-based schemes. This paper presents a proposal where nodes from different clusters have to communicate in order to build different wireless sensor networks (such as virtual networks). In order to control and manage the system, some limitation parameters have been added. Connections will be established if they are close enough and only if they are the same type of sensor. The main contribution of our work is the design and verification of a new protocol and its comparison with other protocols in existence. There is no proposal in the literature with a similar purpose to the one presented in this paper. This paper is structured as follows. Section 2 formulates the problem, explains which issue has to be solved and presents some application environments where our proposal can be used. Section 3 describes our proposal. Its scalability is demonstrated in Section 4. Section 5 explains how it operates, how it provides fault tolerance and shows the protocol messages. Section 6 gives the measurements obtained from a test bench and shows the fault tolerant procedure. The comparison of the proposed protocol with other existing cluster-based protocols is shown in Section 7. Finally, Section 8 gives the conclusions and future works.
## 2 Materials and Methods
### Data Processing
#### 2.5.1 Maximum Correlation and Minimum Redundancy Algorithm  
The mRMR algorithm is a feature selection method that maximizes the correlation between feature variables and targets and minimizes the correlation between features.  
Figure 5: Diagram of the proposed MC diagnostic method.  
Mutual information is used as the standard to measure the correlation between features and category variables:  
\[I(x;y)=\iint p(x,y)\log\frac{p(x,y)}{p(x)p(y)}dxdy \tag{1}\]  
where \(I(xy)\) is the mutual information between the feature variables \(x\) and \(y\), and their probability densities and joint probability densities are \(p(x)\), \(p(y)\), and \(p(x\),\(y)\), respectively.  
In order to find the feature subset \(S\) which contains the optimal m features, the maximum correlation principle takes \(I(x_{i};c)\) to search for the m features related to target category \(c\) in the appropriate order, which can be measured and calculated by the following formula:  
\[\max D(S,c);D=\frac{1}{|S|}\sum_{x_{i}\in S}I(x_{i};c) \tag{2}\]  
However, the subset with m features may not necessarily be the perfect feature subset. In particular, if there are two features that are interdependent, the minimum redundancy criterion should be introduced to eliminate them by using Equation (3); then, the operator \(\Phi\) could be used to integrate the maximum correlation coefficient and minimum redundancy by using Equation (4).  
\[\min R(S),R=\frac{1}{|S|^{2}}\sum_{x_{i},x_{j}\in S}I(x_{i};x_{j}) \tag{3}\]  
\[\max\Phi(S,c);\Phi=D-R \tag{4}\]  
Thus, the ranking of each feature variable in the sample set \(S\) can be calculated. In this paper, the time domain parameters of near/far-end AE signals are chosen as the feature vectors, as they have a relatively fast diagnostic speed and a fairly high detection accuracy. After data normalization, mRMR could just be operated for feature dimension reduction.
## 5 Results
### Challenges, Limitations, and Future Issues for WSN Deployment in Smart Cities (RQ5&RQ6)  
Node deployment of WSN can be either indoor or outdoor, depending on the smart city application requirements. Indoor deployment can be applied in closed areas, such as buildings and structures, while outdoor deployment can be applied in open and harsh areas, such as roads, gardens, forests, and volcanoes. So, the area of the monitored region plays a significant role in determining the deployment type to be random, deterministic, or dynamic [108]. The network coverage type also depends on the nature of the monitored area, which can be area, target, or barrier coverage. In addition, some applications require one-connectivity, that is, a single path between sensor and sink node; others require more reliable connectivity, called k-connectivity, with the sink node [109].  
Each study has its own advantages, disadvantages, and limitations, according to the application, environment, sensing model, coverage type, and required objectives, but it has been observed that most of these have the following issues:  
* Most of the research studies deal with homogenous nodes in WSN and use unreal and simplified models.
* Most of the research studies deal with 2D plane deployment, while modern applications require 3D space deployment.
* Security, reliability, scalability, and energy consumption are also important issues that need to be considered with coverage and connectivity when deploying a sensor node [110; 111].
* Most papers do not take the existence of the obstacle into account when determining coverage and connectivity.
* Localization techniques need to be merged with the deployment techniques, in order to increase reliability and robustness.  
In the future, most of these limitations should be taken into account when designing an optimization deployment algorithm for WSN node deployment.
# Surface Topographical Control of a Liquid Crystal Microlens Array Embedded in a Polymer Network
## 2 Materials and Methods
### Sample Preparation  
First, 0.50 g of 5 CB containing 0.10 mol% of TR5, 0.10 g of A6 CB, 17.5 mg of HDDMA, and 2.0 mg of Irgacure 651 were added to a small brown glass vial and dissolved in a THF solution. The mixture was magnetically stirred for 1 h until fully dissolved. The solvent was later evaporated in a vacuum desiccator for 2 h. The chemical composition ratio of 5 CB, A6 CB, HDMMA, Irgacure 651, and TR5 was 84.3, 12.2, 2.87, 0.33, and 0.3 mol%, respectively. The mixture was subjected to a differential scanning calorimeter (DSC, Hitachi High-Tech Corp., DSC7000X, Tokyo, Japan) at a scanning rate of 1 \({}^{\circ}\)C/min, and the third scan was reported and shown in Figure 2. The mixture showed great miscibility with 5 CB. The nematic-to-isotropic phase transition temperatures (\(T_{\rm NL}\)) of the mixture were determined as 37.2 \({}^{\circ}\)C and 37.1 \({}^{\circ}\)C with enthalpy changes (\(\Delta H\)) of 0.17 and 0.18 kJ/mol for the heating and cooling processes, respectively.  
Figure 1: Chemical structures of the compounds used in this study.  
Figure 2: Differential scanning calorimetry (DSC) thermogram of the mixture at the third heating and cooling cycle. Scanning rate of 1 \({}^{\circ}\)C/min.  
Second, LC glass cells were prepared using the following steps. Two commercially available glass substrates (2.5 cm \(\times\) 2.5 cm) were ultrasonically washed in IPA for 30 min. The glass substrates were then treated with a UV-ozone cleaner (NL-UV42, Nippon Laser & Electronics Lab. Co. Ltd., Nagoya, Japan) for 10 min. The cleaned glass substrates were treated with a silane coupling agent by immersing the glass substrates in a 200 mL ethanol solution containing 0.4 g of silane coupling agent for 30 min. The surface-modified glass substrates were then annealed to 120 \({}^{\circ}\)C for 2 h to yield glass substrates modified with a silane coupling agent. To fabricate an LC glass cell, two surface-modified glass substrates were bonded with 100 \(\upmu\)m thick polyimide tapes.
## 5 Packet Length Control for Sparse Signal Transmission
### Relationship between Communication Parameter and Mutual Coherence  
Packet length does not directly act on mutual coherence/signal transmission quality. In fact, its effect combined with the bit error rate (BER) can determine the packet reception rate (PRR) and then influence the signal transmission quality.  
Figure 7: Relationship between mutual coherence and reconstruction error using RCS projection with K = 5.  
BER is a fundamental parameter that can be determined by transmitting power, modulation scheme and transmission distance. In practical applications, transmission distance is fixed once sensor nodes are deployed, as well as the modulation scheme with specific hardware chips, for example chips that offer the IEEE 802.15.4 standard communication adopt the offset-quadrature phase shift keying (O-QPSK) modulation scheme. Without considering transmitting power control in this paper, BER is almost settled. What is more, BER is the key parameter that determines the quality of the wireless channel.  
This part first presents the relationship between BER, packet length and PRR, then shows how these communication parameters affect signal transmission quality.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Data Collection
#### 4.3.4 Dataset Structure and Feature Extraction  
The dataset used in this study consisted of a total of 750 individual shot attempts, with 25 shots recorded per participant. The dataset is provided as Supplementary Materials. These attempts were evenly distributed across three shot types: smashes, drops, and clears. Each shot attempt formed a unique sample, with the dataset organized in a tabular format where rows represented individual shots and columns corresponded to various features.  
The dataset was structured with key variables derived from the raw data, including:  
Quiet Eye (QE) metrics: QE duration, fixation point coordinates (X, Y), saccades, and pupil dilation.  
Biomechanical data: Body posture (joint angles of the shoulder, elbow, wrist, hip, knee, and ankle), racket trajectory (3D path), and shuttlecock trajectory (flight path post-impact).  
Feature extraction was performed using a combination of eye-tracking data (Tobii Pro Glasses 2, 100 Hz) and biomechanical data from motion capture technology (Vicon Motion Systems, 200 Hz). Specifically:  
QE metrics: QE duration was computed as the total time spent fixating on the shuttle-cock before shot execution, while fixation points and saccades were extracted directly from the gaze tracking data.  
Biomechanical metrics: Joint angles and racket trajectory were calculated by processing the motion capture data using a low-pass Butterworth filter (cut-off frequency: 10 Hz). Key moments such as the initiation of backswing, forward swing, and shuttlecock impact were identified to ensure the accurate segmentation of the data for model training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Skill Level** & **Age Range (Years)** & **Experience Range** & **Notable Characteristics** \\ \hline Novice & 18–35 & 1 to 2 years & University students, young professionals \\ Intermediate & 23–35 & 3 to 5 years & Diverse professional backgrounds \\ Elite & 22–31 & 6 to 12 years & National team members, professional players \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of participant demographics.  
This detailed feature extraction process ensured the robustness of the input data for the subsequent neural network model.
# Do Pores Exist?--Foundational Issues in Pore Structural Characterisation
## The Problem of the Definition of a 'Pore'
### Imaging Methods  
Modern tomographic techniques, such as micro-focus X-ray imaging or 3D transmission electron microscopy (3D-TEM), can provide full 3D reconstructions of the void space of a porous medium. For many types of materials, it is possible to resolve the pore space itself. For a certain type of bimodal-pore-sized, alumina, catalyst support pellets, [PERSON] et al. [34] used electron tomography (3D-TEM) to image a sample volume of 304 nm \(\times\) 304 nm \(\times\) 53 nm at a resolution of 7 nm to characterise the mesoporous (modal pore size -14 nm), and synchrotron X-ray nano-CT for a sample volume of characteristic dimension, 203 \(\upmu\)m, at a resolution of 35 nm to map the macroporosity (modal pore size -461 nm). Strictly speaking, these reconstructions of the void space are often still models, rather than being full representations, since they will miss void space features below the resolution limit of the imaging modality [35]. Further, the ubiquitous presence of noise in the raw image data means that these images often need processing, such as passing through filters [36]. A key step in the reconstruction of the void space is the segmentation procedure whereby void space is distinguished from the skeletal solid. Different algorithms for this process can give rise to different results for the finished void space representation, so the presence of noise often means there is not a unique solution that all segmentation algorithms zero in upon [36].  
Once a 3D rendering of the void space has been obtained from the imaging data, then simulations of physical processes, such as diffusion, flow and reaction, can be performed. For example, for single or multiphase flows the following methods are often used: (1) the Lattice Boltzmann method [37; 38; 39; 40; 41]; (2) the finite element, finite volume and volume of fluid methods [42; 43]; (3) smoothed particle hydro-dynamics [44; 45; 46]; (4) the finite-difference and Laplace Stokes equation solvers [47; 48]; (5) level sets [43; 49]; and (6) the phase-field method [50; 51]. However, even for laminar flow, all these methods are computationally expensive, which limits the size of the image-based lattice that can be studied to a volume of, typically, up to \(\sim\)700\({}^{3}\) voxels [52]. Further, the run time required, even with high-power computing resources, can be several weeks because of the requirement for an as large as possible overall lattice size to capture the representative elemental volume (REV) of a porous medium. The REV is the size of the region of the sample above which the volume-averaged characteristic parameters, such as porosity, become constant with increasing length scale. For many heterogeneous porous materials, such as rocks, the REV is so large that feasible computation is not possible in a reasonable time. The correlation length can also exceed the macroscopic size of the porous object [53].
## 2 Analysis of Antenna Pattern Errors
Antenna patterns are measured in an anechoic chamber. To mitigate the influence of the frequency dependence of the antenna voltage patterns, the weighted average of the antenna voltage pattern is adopted [22]. It is evident from Equations (1) and (2) that both the visibility function and the G-matrix are functions of the antenna pattern. Therefore, antenna patterns play an important role in BT reconstruction. In practical application, the antenna pattern will bring a variety of errors to the reconstructed BT, thereby affecting the radiometric accuracy of the system. The subsequent analysis will focus on these antenna pattern errors.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 3 Results
### Wave Separation for the Soft-to-Hard Single-Interface Model  
For the soft-to-hard single-interface model, the full wave and extracted reflected wave for the first receiver of the cross-dipole tool in the COG domain are shown in Figure 8. Figure 8a,b are the full wave and theoretical reflected SH wave, respectively. At 4-5 ms, a very weak disturbance waveform appears in the reflected wave extracted by MC-ConvTasNet (see Figure 8d). The extracted reflected wave closely approximates the theoretical reflected wave in both phase and shape, with the highest R\({}_{\text{std}}\) of 33.4 dB. The reflected waves extracted by parameter estimation, the median filter and the F-K filter are shown in Figure 8c,e,f, respectively. In Figure 8f, the reflected wave is close to the theoretical waveform, except for in the case of interference waveforms on trace 1-trace 2 and trace 12-trace 16. The results of the other two methods are similar to those of the hard-to-hard single-interface model.  
Figure 8: _Cont._  
Figure 7: Full waveforms and extracted reflected waves for the hard-to-hard single-interface model. (**a**,**b**) are, respectively, COG full waveforms and reflected waveforms obtained by forward simulation. (**c**–**f**) are the reflected waves separated by parameter estimation, MC-ConvTasNet, median filter and F-K filter, respectively. (**a**,**c**) are normalized by the maximum amplitude of the flexural wave. The rest are normalized by the maximum amplitude of the reflected wave. The average R\({}_{\text{std}}\) for parameter estimation, MC-ConvTasNet, the median filter and the F-K filter is \(-\)24.9 dB, 34.7 dB, 6.8 dB and 27.9 dB, respectively.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms
#### 2.3.5 Simulated Annealing (SA)  
SA is an algorithm developed by [PERSON] et al. (1983) based on metal annealing processes [52]. It starts with a random solution and iteratively explores neighboring solutions to find the best one. Better and worse solutions are accepted to avoid the trap of local optimal solution, as shown in Figure 5. However, the worse solutions are accepted based on probability. As the algorithm progresses, the chance of accepting worse solutions reduces, enabling it to converge towards the best solution. SA is a flexible algorithm [53] with the capacity to avoid the local optima trap by accepting worse solution [45]. However, slow convergence is required in SA to obtain the actual optimal solution [45; 46; 54]. In addition, it is a single solution-based algorithm, where only one solution is generated and optimized from a local search [45].
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 6 Proposed CCHP System  
Figure 2 depicts the schematic arrangement of the proposed smart CCHP system. The CCHP supplies electricity, heating, and cooling loads. The CCHP system consists of an electric grid, a heating system, and a cooling system, where these systems interact to allow the flow of energy and supply their load in an economical and environmentally friendly framework. The electric system includes a variety of distributed generators (DGs) such as fuel cells (FCs), diesel generators (DEs), microturbine-based CHPs, wind turbines (WTs), and solar panels (PVs) and it has storage batteries (SBs). The heating system includes a gas boiler (GB), electric heater (EH), heat storage tank (HS), and heat recovery (HR) to recover waste heat from the CHP generator. The cooling system encompasses an electric chiller (EC) and an absorption chiller (AC). The CHP is fueled by natural gas to produce  
electric and heat energy simultaneously, whereas FC and GB consume natural gas (NG) to generate electric and heat energy. In addition, the EH transforms electric power to heat. Furthermore, the EC utilizes electricity to supply a cooling load, while the AC employs heat to generate cooling energy. The different loads are satisfied by the exchange of energy between these systems and the utility grids.  
The hourly electrical and thermal energy demands, along with the corresponding energy exchange prices, are detailed in Table 1. The generation profiles of photovoltaic (PV) and wind turbine (WT) systems are illustrated in Figure 3. These data points are sourced from credible studies [33; 51]. The rated delivering and absorbing power of the SB is 90 kWh, while the rated high and low levels of charge of the SB are 300 kWh and 90 kWh, respectively. The rated maximum and minimum stored energy of the HS are 300 kWh and 90 kWh, respectively. The rates of GB and the electric heater are 150 kWh and 500 kWh, respectively. The ramp rates of the CHP, FC, DE, GB, and AC are set at 100 kW, 30 kW, 70 kW, 120 kW, and 160 kW, respectively. The system combines multiple energy generation and storage technologies to balance supply and demand efficiently. The maximum output power in kW of CHP, FC, DG, GB, EH, and grid exchange capacities is 200, 60, 140, 150, 500, 200, 200, 100, and 200, respectively, while the minimum output power is 20, 5, 20, 5, 5, 10, 10, 5, and 5.  
Figure 2: Schematic of the proposed CCHP system.