esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mid-Layer Software EMIDE for User-Friendly Application Development Environment
### Performance Evaluation
#### 4.4.1 Features Comparison  
The comparison of the features in the different mechanisms is shown in Table 6.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \multirow{2}{*}{**Features**} & **EJVM** & **DLM** & \multirow{2}{*}{**EMIDE**} & **Atmel** \\  & **(simpleRTJ, nanoVM)** & **(TinyLD, ContikiBL)** & & **OTAU** \\ \hline APP decoupled from & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} \\ system & & & & \\ \hline APP programming & \multirow{2}{*}{Java} & \multirow{2}{*}{C} & \multirow{2}{*}{C} & \multirow{2}{*}{N/A} \\ language & & & & \\ \hline APP image format & Pre-linked byte code & \begin{tabular}{c} CELF and \\ microExe \\ \end{tabular} & \begin{tabular}{c} Pre-linked machine \\ code \\ \end{tabular} & \begin{tabular}{c} N/A \\ \end{tabular} \\ \hline Flexibility of APP image & \multirow{2}{*}{Medium} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{\begin{tabular}{c} Subported, by the \\ registration mechanism \\ \end{tabular} } & \multirow{2}{*}{N/A} \\ \hline \begin{tabular}{c} APP multitasking \\ programming \\ \end{tabular} & \begin{tabular}{c} Supported, by Java \\ threads \\ \end{tabular} & \multirow{2}{*}{N/A} & \multirow{2}{*}{\begin{tabular}{c} Supported, by the \\ registration mechanism \\ \end{tabular} } & \multirow{2}{*}{N/A} \\ \hline Mechanism to call from the & \begin{tabular}{c} By Java native \\ mechanism \\ \end{tabular} & By dynamic linking & By pre-linking & N/A \\ \hline Mechanism to callback & By the VM byte code & \multirow{2}{*}{N/A} & \multirow{2}{*}{\begin{tabular}{c} By the callback \\ registration mechanism \\ \end{tabular} } & \multirow{2}{*}{N/A} \\ from the system to the APP & \multirow{2}{*}{\begin{tabular}{c} Supported in \\ simpleRTJ \\ \end{tabular} } & \multirow{2}{*}{N/A} & \multirow{2}{*}{\
\begin{tabular}{c} Fault prevention \\ mechanism is \\ implemented \\ \end{tabular} } & \multirow{2}{*}{N/A} \\ \hline
\begin{tabular}{c} Garbage collection \\ \end{tabular} & Yes & N/A & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 6: Feature comparison of the EJVM, the DLM, the EMIDE and the Atmel OTAU.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 3 Conventional Relay Selection Scheme  
This section describes the conventional relay selection schemes. In the multi-hop transmission system, the relay selection scheme is essential. Especially, the relay selection scheme is very important in the DF scheme based multi-hop transmission system. If an appropriate relay is not selected, the error propagation problem can degrade the wireless communication reliability of the multi-hop transmission system. An alternative solution to the error propagation problem is to use more power at the base station. This alternative solution can increase the received SNR at the relay and reduce detection or decoding error. However, this method is very limited in cost because it requires an expensive amplifier. Therefore, the relay selection scheme is very important in the many studies related to the relay selection scheme.
## 1 Introduction
### Design Consideration of the Duty Cycle D  
Using the volt-second balance theorem, the voltage across the magnetizing inductor \(L_{M}\) during the switch's on-time, multiplied by the duration of the on-time, is equal to the voltage across \(L_{M}\) during the switch's off-time, multiplied by the duration of the off-time. This relationship can be expressed by the following equation:  
\[\left(V_{IN}-\frac{V_{OUT}}{2}\right)\cdot DT_{S}=\frac{N_{1}}{N_{2}}\cdot \frac{V_{OUT}}{2}\cdot(1-D)T_{S} \tag{1}\]  
where \(D\) is the duty cycle of the power switch and \(T_{S}\) is the switching period. The output voltage \(V_{OUT}\) is related to the input voltage \(V_{IN}\), and the duty cycle \(D\) as well as the turns ratio of \(N_{1}/N_{2}\), and can be expressed as  
\[V_{OUT}=\frac{2D\cdot V_{IN}}{D+(1-D)\cdot\frac{N_{1}}{N_{2}}} \tag{2}\]  
In accordance with Equation (2), Figure 8 demonstrates the correlation between the output voltage \(V_{O}\) and the duty cycle \(D\) for various turns ratios \(N_{1}/N_{2}\). Substituting the circuit parameters into Equation (2), with a \(V_{IN}\) of 18 V, a \(V_{OUT}\) of 10 V, an \(N_{1}\) of 1, and an of 1, a value of 0.278 can be obtained for the duty cycle \(D\). In addition, when implementing the prototype of the UV-C LED lamp driver circuit, the duty cycle \(D\) is given a value of 0.3.
## 3 Overview and Theoretical Foundations
### Big Spatial Multidimensional Data Analytics
#### 3.2.1 Spatial Data Models  
Geospatial data differs from basic data types in the sense that they have locational information attached to them, such as city, zip code, GPS coordinates, and geotagging. While the attributes are standard parametrized features that are processed by and stored in relational databases, the locational attributes require dedicated Geographical Information System (GIS) libraries and data structures to be processed. Spatial data exist in various representational models, from which the most common are vector and raster data.  
The vector spatial data model needs a Cartesian coordinate system (e.g., perpendicular x, y) with Euclidean metrics. Point is the core element, while lines and areas are represented as sequence of points, non-closed or closed with no inner boundaries (e.g., line) or closed and boundaries (e.g., polygon). This representation is characterized by loss of accuracy, but lower memory consumption and computation time.  
Raster data model applies non-overlapping polygons (pixels) to represent spatial objects such as points, lines, and areas. Lines and areas are represented by sequence of adjacent connected pixels, where in the line case, we take all pixels where part of the line passes through.  
In geospatial data analytics, what need to be modeled includes spatial objects such as streets, people, vehicles, cities, etc., in addition to the embedding space where spatial objects reside (typically administrative divisions of a city such as neighborhoods, districts, boroughs, etc.,). Objects include: (1) points which are object locations without their extent (e.g., schools, restaurants), (2) lines, a trajectory of moving spatial object or a line connecting multiple points (e.g., streets, moving vehicle trajectory), (3) polygons (i.e., regions, areas), those spatial objects with extents (e.g., cities, countries).  
Typically, working on rasterized spatial objects is computationally more expensive than vectorized counterparts. The same applies to data transfer via communication networks, where raster formats induce additional transfer costs attributable to bigger data sizes, basically due to the sophisticated data representation. Having said that, most georeferenced data are collected and stored in vector representations, which then need to pass through rasterization to reconstruct them in raster representation and render them on computer screens. This kind of data representation and transmission is better known as parametrization, where geolocation data attached to objects represent coordinates on either a projected or geographic coordinate system (PCS or GCS, respectively). Spatial data in such models loses its shape and costly geometric operations on the receiver's side need to be applied to reconstruct them into their original shapes. Among those operations, spatial join remains the most applied and expensive essential operation for spatial data analytics, which is discussed in detail in the next subsection.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 4 Discussion
### Possible Applications  
One natural application for the model is for showing special places in museums. PAR models, especially when using wood cutting technology, are a cost effective method to add value to any exhibition. For example, they can be used to exhibit interactive recreations of historical places or remote places, on Earth or other planets. The models can be created for cities and places of the past, as well as for scenarios predicted for the distant future.  
In PARIMC, the physical model can be examined by the visitors, who can even walk around it, to see from different perspectives what the 3D structure of the place is like. Using the mobile application, users can also make changes to the model, simulating different scenarios or recreating different situations, as they travel around the model. Using the game engine, it is possible to add or remove features from the scene. So, in the future it could be possible to show or hide features like cars, people, trees, animals, water or boats, for example.  
Another very important possible application of the technology is to model scenarios of catastrophes. In disaster situations, such as floods, fires, earthquakes or even battles, the commanding officers need a picture as clear as possible of the situation on the terrain. Using a model such as the one proposed, it is possible to recreate in near real time the advance of the flames of a fire, the advance (real or expected) of water in a situation of flood or other other situations. The fact that the model has precise geographical information is very important, for example to predict the advance of water in flooding situations. The 3D model offers more information than just 2D projections on the computer screen. In the proposed model, the results have also shown that it is possible to communicate and make simulations on the model with ease, using the mobile application.
## 3 Methods
### Data Analysis
#### 3.6.2 Similarity Calculation  
We used two metrics to compute the similarity between attention maps: the Dice score and cross-correlation. The Dice score [65] was used for binary masks and specifies the overlapping area relative to the total attended area. It is calculated by taking two times the area highlighted in both maps to be compared (intersection) and dividing it by the sum of the two individual areas. If the areas have the same size (as it is the case in the present study), the Dice score simplifies to the size of the intersection divided by our chosen area size of 5%. This procedure creates values between 0 and 1, with 0 indicating no overlap and 1 indicating complete overlap. This metric has already been used in previous studies to compare human and CNN attention [45; 56]. It is not only easy to compute but also easy to interpret because the numerical value directly corresponds to the share of overlapping area. As all maps are reduced to the same size, it also allows for a straightforward comparison of the overlap in different conditions (e.g., tasks or image types), even when the total attended areas systematically differ between them. However, this simplicity can be considered a limitation as well because comparing binary masks eliminates the rich information available in gradual density maps. Therefore, we additionally compared the gradual maps using cross-correlation (Pearson), which has been deemed one of the most suitable metrics for purposes similar to ours [66]. Pearson's correlation coefficient treats two given density maps as variables and describes their linear correlation. We calculated the value for two density maps as proposed by [PERSON] et al. [66] by dividing the covariance matrix for both maps by the product of the covariance of each map itself.
## 3 Data and Methodology
### Ground Motion  
In November 2022, the Copernicus Land Monitoring Service released a new Pan-European product, EGMS, which is based on the elaboration of Synthetic Aperture Radar (SAR) data acquired by Sentinel-1 constellation satellites [24]. EGMS allows users to investigate ground motion (both vertical and horizontal) with unprecedented accuracy, over spatial scales ranging from that of a nation or a city to that of a single infrastructure [104]. Both maps and time series are provided; the latter allow for the computation of mean annual vertical velocities in the regions of interest. The current dataset, which will be updated at yearly frequency, covers the period January 2016-December 2021, with a time step of one datum every 12 days until October 2016 and one datum every 6 days from October 2016 to December 2020, thus collecting almost 250 records over 6 years (time interval 2016-2021).  
EGMS offers three different map products which are described in [PERSON] et al. (2020) [23]. In the present work, the Ortho L3 product was used: it is an update of the calibrated product where data are collected in a square area of 100 \(\times\) 100 m in ETRS89-LAEA coordinates for the whole European territory. It combines different satellite view angles to provide calibrated motions, in two separate layers, purely vertical and purely horizontal (E-W), resulting in the direction of any observed displacement [23; 24].  
The ground motion component of our GIS tool uses EGMS data (n. 8607 cells). Within the DTM area, the available data of ground motion were acquired directly from the Copernicus system raster with a spatial resolution of 100 \(\times\) 100 m. The EGMS raster points do not cover the entire study area. So, the GRASS _rfillnulls_ interpolation algorithm [105; 106; 107]was used to produce information where needed. The algorithm used retains the original data and fills in the gaps using spline interpolation [108; 109]. The result of the interpolation procedure is shown in the first two panels of Figure 2 for the study area of Rome. The upper panels provide a comparison of the grid covered by the Copernicus Land Monitoring Service (left) and our interpolation to have displacement values for each 100 \(\times\) 100 m grid within the study areas. The lower panel shows the ground motion displacement (mm) measured by the Sentinel satellite (blue) and the interpolated values (brown) along the A-B transect.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
## 2 Materials and Methods  
### Data Acquisition  
This project included data from a patient affected by aortic stenosis awaiting aortic valve replacement surgery.  
The dataset used for the acquired heart valve surgery use case included CT images with a slice thickness of 1 mm, a spacing between slices of 0.5, a spiral acquisition, and an in-plane acquisition matrix of 512/512 pixels. The Iodine contrast agent enhanced the phases acquisition, and the resulting CTDI value was 41.5.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 5 Evaluation Results
### Satellite Clock Offset Fitting Residuals  
The satellite clock offset fitting residual is calculated through second-order polynomial fitting relative to the clock offset of the autonomous time scale. This residual is compared with the fitting residual error of the satellite relative to the clock offset of the anchor station. The comparison results for some satellites are shown in Figure 2.  
Results for 7 days are shown in Figure 2, where red represents the residual error of the clock offset fitting of the satellite relative to the anchor station, and blue represents the residual error relative to the autonomous time scale. The C27 satellite is equipped with a hydrogen clock, while the C36 satellite has a rubidium clock. Different space-borne clocks exhibit improved accuracy in the clock offset relative to the autonomous time scale, with hydrogen clocks showing a more significant improvement.  
Quantitatively, the Root-Mean-Square (RMS) statistics of the clock offset fitting residuals are shown in Table 1. The table indicates that when converting the reference from anchor station clocks to autonomous time scales, the 7-day clock offset residual error for hydrogen clocks decreases from 0.47 ns to 0.20 ns, and for rubidium clocks, it decreases from 0.50 ns to 0.38 ns.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 6 Conclusions  
In this paper, a gait phase detection method based on the LSTM-DNN algorithm was presented. Compared with the commonly used LSTM, SVM, and KNN algorithms, the recognition accuracy and F-score of the LSTM-DNN algorithm for gait phase detection are as high as 91.8% and 92%, respectively,demonstrating the superiority of the LSTM-DNN algorithm. The experimental results showed that the LSTM-DNN algorithm was effective for distinguishing the two gait phases of the lower limb.  
In this study, wearable acceleration devices were used to collect the acceleration data of the lower limb and the gait phase of test subjects was automatically detected and classified. Although the LSTM-DNN algorithm was effective for gait phase detection, further optimization of the model is required. Future studies will focus on improving the accuracy of the gait classification and investigating factors that may affect the gait. In this study, the use of three acceleration sensors was considered acceptable. However, the presence of the sensors may have a potential impact on people's gait.  
The LSTM-DNN algorithm is a promising approach to process acceleration signals for gait recognition at different walking speeds. In the future, the LSTM-DNN algorithm will be used to analyze more complex gait patterns and other applications will be investigated. Gait phase recognition technology plays an important role in the human-machine coupling of the human lower extremity exoskeleton assisting robot, and is of great significance to the development of the human lower extremity exoskeleton assisting robot. In addition, gait phase recognition technology has great potential in pathological gait recognition and diagnosis.  
Writing-review & editing, T.Z. and L.Y.; investigation, P.Y.; funding acquisition, L.Y.  
This research was funded by the Fundamental Research Funds for the Central Universities (No. 2015 ZCQ-GX-03).  
The authors declare no conflict of interest.
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
## 4 Simulation of Nonlinear Ultrasonic Guided Waves Using Finite Element Methods
### Detection and Analysis of Overall Weakening in Adhesive Layers  
This article focuses on the geometric nonlinearity caused by the weakening of the adhesive layer (such as interface debonding), rather than material constitutive nonlinearity. The assumption of linear elasticity is applicable to the simulation of guided wave propagation under small strain conditions [22]. This study uses, for reference, the method employed by [PERSON] et al. [30] in finite element simulation modeling of ultrasonic guided waves to detect early overall weakening in the adhesive layer and analyze its impact on nonlinear coefficients. [PERSON] et al. characterized local performance degradation by increasing the third-order elastic modulus of local materials to simulate the weakening effect. This paper adopts a comparable approach by altering the overall third-order elastic modulus of the adhesive layer to represent early weakening and conducts related simulations.  
With all other conditions remaining constant, the third-order elastic modulus l, m, and n of the adhesive layer are increased by one-third to simulate an overall weakening, and the waveforms and frequency spectrum from the overall weakening are presented in Figure 10. To further analyze the influence of weakening on the nonlinear coefficient, the second-harmonic amplitudes are extracted for both the weakened and non-weakened layer. A comparison of the second-order harmonic amplitude and nonlinear coefficient as functions of propagation distance is constructed, as shown in Figure 11. Compared to the non-weakened adhesive layer, the change in the second-harmonic amplitude and nonlinear coefficient with propagation distance is more pronounced for the overall weakened adhesive layer. This demonstrates that nonlinear guided wave detection has a significant effect on detecting overall weakening in the adhesive layer of the composite structure.  
Figure 10: Waveforms and frequency spectrum from the overall weakening of the adhesive layer: (**a**) Waveforms; (**b**) frequency spectrum.  
Figure 9: Changes in Fourier coefficient and nonlinear coefficient over propagation distance.
## 4 Discussion
V-EPS and T-EPS are rich in mannose, followed by glucose, xylose, and galactose, and based on the FT-IR analysis, it was suggested that both samples contained heteromannans and \(\beta\)-glucans. It was found that mannose in heteromannans from other yeast species can be in \(\alpha\)-anomeric configuration as 1,2-, 1,3-, and 1,6-linked or in \(\beta\)-anomeric configuration as 1,4,6-linked [43; 44]. The uronic acid content in V-EPS and T-EPS was low, which meant that uronic acids were not the main structural components in the EPSs. It was assumed that both samples contained glucuronic acid, as it has already been found in other yeast EPSs, where it was terminally linked [43; 45]. It is worth mentioning that both EPSs did not contain _O_-acetyl groups, which was in contrast to a previous study that reported the presence of acidic glucuronoxylomanan carrying _O_-acetyl groups (3-10%) [46]. The molecular weight distribution analysis revealed that both EPSs consisted mainly of high molecular weight populations (Figure 3). However, the molecular weight of yeast EPSs can reach several million g/mol, which considerably affects their hydrodynamic properties [45; 46]. From a purely technological point of view, it could be speculated that EPSs with higher molecular weights can express better film-forming, stabilizing, and gelling properties, which are absolutely necessary for the development of food, nutraceuticalil, cosmetic, or pharmaceutical products. A previously done study on the yeast _Rhodotorula acheniorum_ MC and _S. salmonicolor_ AL(1) revealed the mannan and glucomannan nature of their EPSs. Moreover, EPSs have shown good emulsifying properties [10]. Furthermore, EPSs from psychrophilic strains of _Cryptococcus laurentii_ and _Sporobolomyces salmonicolor_ have been used to develop cosmetic creams with UV protection [47]. Interestingly, a 1,2,6-linked \(\alpha\)-mannan with in vitro anti-inflammatory activity has been isolated from Antarctic bacteria _Sphingobacterium_ sp., and an anti-tumor 1,3,6-\(\beta\)-glucan has been obtained from the Antarctic fungus _Thelebolus_ sp. [48; 49; 50]. Therefore, it is worth further studying the functional properties and biological activity of V-EPS and T-EPS, which will be our next task.