esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of LGE-MRI in Arrhythmia Ablation
###### Abstract  
The use of late gadolinium enhancement magnetic resonance imaging (LGE-MRI) in arrhythmia ablation is increasing due to the capacity to detect, quantify and characterize cardiac fibrosis both in atrium and ventricle. Catheter ablation has become a standard treatment for arrhythmias, and LGE-MRI has demonstrated to be a useful tool to plan and guide ablation. Furthermore, recent studies have proved the usefulness in substrate analysis and postablation evaluation. This review will analyze the application and the current role of LGE-MRI to improve strategies for the two main cardiac arrhythmias: Atrial fibrillation and ventricular tachycardia.  
cardiac magnetic resonance; late gadolinium enhancement; atrial fibrillation; ventricular tachycardia; ablation; fibrosis; electroanatomical mapping
## 3 Results
### Statistical Comparisons  
In the 3D-printed Ti-6 Al-4V sample, the indentation modulus individual values ranged from 138.69 GPa to 153.87 GPa, and in the milled Ti-6 Al-4V sample, the range spanned from 128.02 GPa to 155.22 GPa. The mean value was 3.97 GPa higher in the 3D-printed Ti-6 Al-4V sample than in the milled Ti-6 Al-4V sample, with a statistically significant difference (\(p\) = 0.009).  
The individual indentation hardness varied from 4.12 GPa to 4.85 GPa in the 3D-printed Ti-6 Al-4V specimens and from 3.21 GPa to 4.42 GPa in the milled Ti-6 Al-4V specimens. The mean indentation hardness was 0.79 GPa higher in the 3D-printed Ti-6 Al-4V sample as compared to the milled Ti-6 Al-4V sample, \(p\) < 0.001 (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**95\% CI**} \\  & & & **Difference** & **Lower Upper** & _p_**-Value** \\ \hline \multicolumn{6}{l}{Indentation modulus} \\ \(\bigcirc\) & 3D printed Ti-6 Al-4V & 144.12 & 3.89 & 3.97 & 1.03 to 6.90 & 0.009 \\ \(\bigcirc\) & Milled Ti-6 Al-4V & 140.15 & 5.98 & & \\ \multicolumn{6}{l}{Indentation hardness} \\ \(\bigcirc\) & 3D printed Ti-6 Al-4V & 4.53 & 0.22 & 0.79 & 0.64 to 0.93 & \textless{}0.001 \\ \(\bigcirc\) & Milled Ti-6 Al-4V & 3.74 & 0.26 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results from the \(t\)-test comparison of the 3D-printed Ti-6 Al-4V sample with the milled Ti-6 Al-4V sample.  
Figure 18: Phase images, 2D and 3D AFM images of the samples: milled Ti-6 Al-4V (**a**) and 3D-printed Ti-6 Al-4V (**b**).
## 3 Materials and Methods
### Forecasting Models
#### 3.2.1 AutoArima  
The SARIMA model is one of the most popular models that incorporate seasonality into time series forecasting [7; 8; 9]. It has a limitation--the user must select a proper model using autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, as well as calculate the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). To resolve this problem, the auto_arima method was developed, which automatically selects an optimal model [31]. In our implementation, we used the auto_arima function provided by the Python pmdarima library.  
The auto_arima function fits the best ARIMA model to a time series according to a given information criterion (either AIC, BIC, or the Hannan-Quinn information criterion (HQIC)). The function performs a search (either stepwise or parallelized) for the possible model and the seasonal orders within the constraints provided, then selects the parameters that minimize the given metric. The auto_arima function itself operates a bit like a grid search, as it tries various sets of p and q (also P and Q for seasonal models) parameters, then selects the model that minimizes AIC or BIC. To select the differencing terms, the auto_arima function uses tests of stationarity (such as an augmented Dickey-Fuller test) and seasonality (such as the Canova Hansen test) for seasonal models [31].
## 2 Experimental Setup
Figure 1b illustrates the processed X-ray spectrum obtained with the nominal electron-beam energy of 5.5 keV, which has the longest data accumulation time. The spectra at different energies were calibrated using four strong lines with known wavelengths [6; 16]: 3D, 3F, 3 DNa, and 3 DMg. The latter two lines are satellites of 3D, with the spectator electrons of 3s and 3s\({}^{2}\), respectively. Voigt profile was employed for the fitting of stronger 3D and 3F lines, while Gaussian profile was used for the latter two satellites. A linear dispersive function was established between the central pixels of the four reference lines and their known wavelengths, yielding a large coefficient of determination (\(R^{2}\) > 0.9999). Consequently, the wavelengths of the other four weaker spectral lines are determined and presented in Table 2. The wavelength uncertainty is comprised by statistical error of pixel centroids, invalidation of the dispersive function, and the average uncertainty of the reference lines [73]. It is worth noting that a second polynomial was also attempted as the dispersive function in calibration, yielding wavelength and uncertainty results consistent with those of present linear function. Figure 1: A comparison between calculated (**a**) and observed (**b**) X-ray spectra of L-shell transitions from Ne-like Xe and near charge states. The observed spectrum was measured by a crystal spectrometer installed in the Shanghai-EBIT, utilizing a nominal electron-beam of 5.5 keV. The calculated spectrum was generated by CRM based on FAC package with the electron energy of 5.5 keV and electron density of 1.6 \(\times\) 10\({}^{12}\) cm\({}^{-3}\). The results from each individual charge state are normalized to the brightest lines.
## Materials
### Acquisition of Dataset
#### 2.2.2 Ice-Tethered Profiler 80 (ITP80) Buoy Location Record  
The Ice-Tethered Profilers (ITPs) were contrived and deployed to repeatedly record various properties, e.g., temperature, salinity and measuring location, of the ice-covered Arctic Ocean, with telemetry and underwater instruments, and the data was transferred by Iridium data telemetry [30]. As part of the Office of Naval Research (ONR) Marginal Ice Zone program, the ITP80 buoy was deployed on a 2.6 m thick ice floe in the Beaufort Sea from the IBRV Araon during the field investigation in August 2014 (Table 3).  
The ITP80 data (including its hourly locations) were collected and made available by the Ice-Tethered Profiler Program [31, 32] based at the Woods Hole Oceanographic Institution [30]. The GPS (Global Positioning System) position in the level 3 archive data of the ITP80 was designated as a reference dataset for validating the sea ice motion from the satellite images.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ITP80** & **Specifications** \\ \hline Deployed date & 12 August 2014 \\ Deployed location & 77\({}^{\circ}\)24.2\({}^{\prime}\)N, 146\({}^{\circ}\)10.3\({}^{\prime}\)W \\ Localization method & GPS positioning \\ Location measurement & Hourly \\ Data processing level & Level 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details for the ITP80 deployment and location record.  
Figure 2: Sea ice condition in the study area: (**a**) the sea ice concentration (SIC) around the study area retrieved from the daily SIC data of the National Snow and Ice Data Center (NSIDC) from Nimbus-7 SMMR and DMSP SSM/1-SSMIS Passive Microwave Data [29] on 14 August 2014; and (**b**) an overview of sea ice coverage captured in each satellite image.
## 2 Influence Factors of Service Performance of LSDM
### The Influence Factors of Ground Assembly for Service Performance in Space  
It can be inferred from Equation (1) that, when the \(\alpha\) and \(\beta\) are determined, the pointing angle \(\theta\) of a satellite antenna panel will be uniquely determined. Therefore, during the assembly process, the LSDM can be projected onto the X-O-Z and Y-O-Z planes, respectively. By solving for \(\alpha\) and \(\beta\), the pointing angle \(\theta\), which represents the satellite antenna panel's pointing accuracy, can be calculated.  
Figure 3: Analysis of antenna’s pointing accuracy.  
Figure 2: Service status of an Earth-observation satellite.  
This paper provides a detailed explanation of the modeling of assembly errors and the allocation of tolerances using the LSDM of a satellite's X+ side as an example. Figure 4 shows the schematic projection of an assembly site on the X-O-Z plane, and the letters from \(a\) to \(j\) represent the hinge components.  
Gravity's impact is the main difference in the LSDM's service environment and its on-ground assembly environment. During ground assembly, gravity affects not only the deformation of individual parts but also the interaction between them. To minimize the mutual influence of parts caused by gravity during the assembly process, gravity unloading is implemented for each truss hinge, as shown in Figure 4. However, due to microgravity technology's current limitations, it is not feasible to achieve a microgravity environment for all LSDM components. When gravity compensation is only applied to each truss hinge component, gravity's primary impact on the LSDM assembly can be seen in two aspects:  
1. Deformation of the truss rod. The LSDM's truss rods consist of carbon fiber and have a large length-to-diameter ratio. During ground assembly, except for the rods installed vertically, the other rods experience bending deformation due to gravity's influence. When the LSDM is in the space environment where gravity is absent, the bending deformation tends to recover. However, this deformation recovery will affect the pointing accuracy.
2. Clearance of truss hinge. The shaft and hole of the truss hinge usually have clearances. During ground assembly, the hole and shaft are in contact under gravity. However, when the gravity disappears in space, the contact will be changed, which will also affect the pointing accuracy.  
To analyze the aforementioned factors' impacts, this paper compares the differences between the ground assembly environment and service environment in space, which will provide a basis for constructing an assembly error model in the subsequent section.
## 3 Proposed Method
### Adjustable Subsquares Properties Algorithm
The first one prepares the image to be hidden in a form matching the input of the encoder, and the second one is the encoder, which receives at its input the appropriately transformed, hidden image and the original image in which it is to be embedded. The result of the encoder's operation is an image which is supposed to be, for the human eye, indistinguishable from the original, but which has encoded information. After embedding the hidden image in the input image, the image, so prepared, can be fed into the input of the decoder. The decoder, just like the encoder, also has a network for processing the image. The decoder reads the hidden image from the image and then converts it into text form using the decoder subsystem. The higher the resolution of the image, the more watermark repetitions are read from a single frame. At 2160 \(\times\) 3840 (UHD) resolution, a single frame results in 464 watermark repetitions measuring 128 \(\times\) 128 pixels. All watermark repetitions are subjected to a median function operation that best represents the value of individual pixels for decoding. An example of the results of the described system for watermarking video material is shown in Figure 3. The system user can encode the watermark stored as a text represented as ASCII characters from the range of Arabic numerals (0-9) and the letters of the English alphabet (A-Z), which is a total of 36 possible ASCII characters, or as a binary string of characters stored as shown in Table 2. The total number of characters for a 128 \(\times\) 128 pixel encoder is 16 characters, or 80 bits. To avoid increasing the complexity of the system, and simultaneously the knowledge of the user, the characters whose encoding does not fit into 5 bits (V, W, X, Y, and Z) are not available when reading binary characters from a file, but they are available and work correctly when reading characters from a text file.
Remote Sens._**2014**, \(6\), 1890-1917; doi:10.3390/rs6031890  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product  
**[PERSON] 1-*, [PERSON] 2,3, [PERSON] 4 and [PERSON] 5**  
==WARNING: Truncated because of repetitions==
\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\
+++  
The map of \(\phi_{\text{FRP}}\) at 0.5 \({}^{\circ}\) grid cell resolution (Figure 3) indicates that seasonal sums of coincident SEVIRI and MODIS measurements of FRP agree more closely in the north and east of the CAR compared to the south and west. Since on an instantaneous basis only a small bias exists between coincident SEVIRI and MODIS measurements of the FRP emitted from fire pixel clusters detected by both sensors [17, 33, 59], we hypothesize that (i) ratios of \(\phi_{\text{FRP}}\) are less than one due to SEVIRI omission errors; and (ii) the spatial gradient of \(\phi_{\text{FRP}}\) across the CAR is due to a coupling between the sensitivity of the SEVIRI FTA detection algorithm and a geographic variation in active fire characteristics.  
In an effort to explain the geographical gradient of \(\phi_{\text{FRP}}\) (as shown in Figure 3), the performance of the SEVIRI FTA detection algorithm is henceforth evaluated within four 1.0 \({}^{\circ}\) regions of interest (ROIs) strategically placed across the CAR (Figures 1a and 3). The size of the ROIs was selected to capture a sufficient number of concurrent observations, and the placement of the ROIs was subjectively selected to satisfy two objectives: first, to capture the widest possible range of \(\phi_{\text{FRP}}\) between the ROIs, and second, to minimize the variability of the four 0.5 \({}^{\circ}\) values of \(\phi_{\text{FRP}}\) within each 1.0 \({}^{\circ}\) ROI. Whereas the former objective is intended to sample the broadest possible range of fire behaviour and observation conditions across the CAR, the latter is intended to identify regions of relatively homogeneous fire behaviour and observation conditions. Due to SEVIRI's geostationary projection, there are 1132, 1160, 1228, and 1192 SEVIRI thermal pixels within ROIs 1, 2, 3, and 4, respectively.  
Figure 3: Seasonal SEVIRI-to-MODIS ratios of summed fire radiative power (FRP) concurrently measured by both sensors, \(\phi_{\text{FRP}}\), mapped at 0.5 \({}^{\circ}\) grid cell resolution. The performance of the Fire Thermal Anomaly (FTA) detection algorithm is evaluated in four 1.0 \({}^{\circ}\) regions of interest (ROIs) subjectively located (i) to span the widest possible range of \(\phi_{\text{FRP}}\) across the CAR; and (ii) to circumscribe relatively homogeneous values of \(\phi_{\text{FRP}}\).
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 5 Secure Homomorphic K-Means Clustering Scheme  
### Framework  
This scheme proposes a method for secure K-means clustering and three secure protocols. It consists of preprocessing the data, encrypting each data point, initializing the cluster center with the encrypted value, iteratively updating the cluster and its center while maintaining the encryption, and finally computing the clustering result. This approach allows cluster analysis while protecting sensitive information.The framework of the scheme is shown in Figure 1, and the details of the scheme are as follows.  
1. users Users first encrypt their personal data with the public key, and then upload the ciphertext data to CS for storage. Users can also ask CS to compute the outsourced data in secret state.
2. cloud server CS has \"unlimited\" data storage space and is responsible for storing and managing outsourced data from all registered parties, namely users. In addition, CS can also store all intermediate and final ciphertext results and can perform specific computations on encrypted data.
3. computation service providers CSP provides online computing services for users. In addition, CSP can partially solve the ciphertext sent by CS to perform a specific computation and then re-encrypt the result.
## Abbreviations  
\begin{tabular}{l l} Abbreviation & Definitions \\
2G & Second Generation \\
3G & Third Generation \\
3 GPP & Third-Generation Partnership Project \\
4G & Fourth Generation \\
5G & Fifth Generation \\
6G & Sixth Generation \\ AF & Application Function \\ AI & Artificial Intelligence \\ AAE & Active Antenna Element \\ AMF & Access and Mobility Function \\ AUSF & Authentication Server Function \\ BBU & Base Band Unit \\ BS & Base Station \\ BSS & Business System Support \\ CAPEX & Capital Expenses \\ CN & Core Network \\ CO2 & Carbon Dioxide \\ CU & Central Unit \\ DC & Dana Center \\ DL & Down Link \\ DU & Distributed Unit \\ DV & Data Volume \\ E2E & End to End \\ EC & Energy Consumption \\ EE & Energy Efficiency \\ EEMNDV & Mobile Network Data Energy Efficiency \\ eMBB & enhanced Mobile Broadband Connectivity \\ ES & Energy Saving \\ gNB & Next-Generation Node B \\ HDLLC & High-Data-rate and Low-Latency Communications \\ HMTC & High-performance Machine-Type Communications \\ ICT & Information and Communication Technology \\ ILP & Integer Linear Programming \\ IoT & Internet of Things \\ IMSI & International Mobile Subscriber Identity \\ KPI & Key Performance Indicator \\ LTE & Long-Term Evolution \\ MANO & Management and Orchestration \\ \end{tabular}
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS  
The framework of DTDAS consists of two parts: the physical workshop and the virtual workshop. As shown in Figure 3, the two parts share data. The real-time data in the physical workshop are transmitted to the scheduling model in the virtual space. The scheduling plan generated by the virtual workshop is then sent to the physical workshop. There are four modules in the virtual space: the knowledge support module, the scheduling model module, the scheduling optimization module, and the simulation verification module. In the knowledge support module, the dynamic energy consumption model and the AGV availability prediction provide a decision basis for the scheduling model. The scheduling model includes the allocation of transportation tasks and the allocation of charging tasks. This scheduling model will be solved by the scheduling optimization algorithm. After the simulation verification, the obtained scheduling plan is sent to the execution system, assigning tasks further to the corresponding equipment.  
Figure 3: The framework of DTDAS.  
Figure 2: The layout of the manufacturing workshop.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture  
AI algorithms are essential in converting raw data gathered from IoT sensors into actionable insights, facilitating more efficient and data-informed decision-making in aquaculture. AI improves critical farm management tasks through predictive modelling, automation, and process optimisation, including feeding strategies, water quality control, and overall operational efficiency.  
The combination of computer vision, fuzzy logic, image processing, machine learning, and deep learning (DL) in aquaculture facilitates the analysis of vast and complicated datasets, each offering distinct advantages. ML and DL enable pattern recognition and predictive analytics, allowing for early detection of anomalies in fish behaviour or environmental conditions. Computer vision and image processing support real-time monitoring of fish health, movement, and feeding activity, reducing reliance on manual observation. Fuzzy logic provides a robust framework for handling uncertainties in aquaculture environments, improving decision-making processes in scenarios with variable or imprecise data. These AI-driven approaches enhance precision, efficiency, and sustainability in modern aquaculture management.
## 5 Conclusions  
Different analytical microdosimetric models have been used and combined for calculating the microdosimetric quantity \(y_{D}\) for target spheres of various sizes. The lineal energy spectra were subsequently used to study the variation of the proton \(\overline{Q}\) over the energy range of 1-250 MeV based on the TDRA and ICRU Report 40 microdosimetric methodologies. The microdosimetric-based \(\overline{Q}\) values reveal that the LET-based ICRP Report 60 recommendations may significantly underestimate \(\overline{Q}\) for proton energies below \(\sim\)50-100 MeV. Similar trends were found when MC track-structure literature data were used as input to the calculations. The microdosimetric-based \(\overline{Q}\) values for low-energy protons were also found to be sensitive to the choice of the sphere size. Finally, condensed-phase effects in the analytic model parameters had only a weak influence on \(\overline{Q}\) in the examined range of proton energies and sphere sizes. Although the general trend of the variation of \(\overline{Q}\) with proton energy revealed by the present analytic model calculations is supported by the MC literature data used in this work, future research will include a systematic comparison and validation of lineal energy spectra and subsequent calculations of \(\overline{Q}\) based on new MC data with the latest versions of Geant4-DNA and PHITS track structure codes which make use of different and updated physics models.  
Conceptualization, D.E., I.K., Y.M., S.I. and I.A.D.; methodology, A.P. and D.E.; software, A.P. and Y.M.; validation, I.K. and Y.M.; formal analysis, A.P.; investigation, A.P.; data curation, Y.M.; writing--original draft preparation, A.P.; writing--review and editing, D.E., I.K., Y.M., S.I. and I.A.D.; supervision, D.E., I.K., S.I. and I.A.D.; project administration, I.K. and I.A.D.;funding acquisition, D.E., I.K. and S.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project \"Dioni: Computing Infrastructure for Big-Data Processing and Analysis\". (MIS No. 5047222) which is implemented under the Action \"Reinforcement of the Research and Innovation Infrastructure\", funded by the Operational Programme \"Compentitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund), as well as by the European Space Agency (ESA) under contract \"BioRad III\" 4000132935/21/NL/CRS (2021-2023).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The technique is based on the principle of surface plasmon resonance (SPR), which involves detecting changes in the refractive index near the surface of a sensor chip when target molecules bind to immobilized capture molecules on the chip [19; 20; 21]. The changes in the refractive index (RI) can be monitored over time with SPRi for different ligands simultaneously and various samples consecutively [22]. Images of the charge-coupled device (CCD) camera of the sensor surface provide detailed information on biomolecular interactions, such as affinity and kinetics. Since the detection of protein carbonyls requires a derivatization procedure of the carbonyl group with 2,4-dinitrophenylhydrazine (DNPH) to form dinitrophenyl (DNP) hydrazine products, an ELISA assay was used. ELISA is an immunological assay designed for detecting and quantifying soluble substances such as antibodies, antigens, and proteins in biological samples [23]. In addition, the fluorescence spectra of the body fluids will be measured over time using fluorescence spectroscopy as previously performed by [PERSON] et al. and [PERSON] et al. [11; 12].