esa-sceva/satcom-chunk-collection huggingface dataset:
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 2 Proposed Method: MICP
### Factors to Consider in Proposed Method  
#### 2.3.1 Normal Correction  
Fundamentally, the 3D vertex points are the raw data obtained from the 3D scanning and are not positioned regularly due to the geometry of the model. From this point cloud, a mesh model is generated from various meshing algorithms and a surface normal can be calculated. From the near surface normal directions of a point, a vertex normal can be calculated. However, the mesh model generated from raw scan data is not good mesh data because of its point irregularity. There are many long-edge triangle faces on the raw mesh data and the calculated normal data is noisy. The dynamic segmentation that is proposed for MICP is sensitive to the normal direction of the scan data. Using raw scan data works fine but more accurate registration results can be achieved by correcting the normal data. There are two ways to correct normal data, normal smoothing and remeshing [37; 38]. Normal smoothing can remove the high-frequency noise in the normal data and generates more reliable intensity profiles. However, the input normal for smoothing is basically inaccurate because of the irregular mesh data from the raw point cloud data. To overcome the irregularity, we remeshedthe mesh data. After remeshing from the original mesh data with equal edge lengths, points on the surface are realigned and regular mesh data is generated (Figure 4). More accurate face normal data can be calculated from the regular mesh data and more accurate vertex normal data can be obtained sequentially.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 2 Applications for VLC-Based IPS  
VLC-based IPSs have a wide range of applications, as shown in Figure 1. In retail, they can be used to enhance the customer experience by adding location-specific amenities and customized suggestions. In healthcare, VLC-based IPSs can be used to facilitate patient tracking and monitoring, thereby enhancing effectiveness and ensuring well-being. In industrial domains, these systems act as catalysts, enabling seamless asset monitoring and streamlining workflow management. In addition, VLC-based IPSs can be used in transportation networks to provide real-time passenger monitoring and seamless navigation. Taken together, these pioneering systems provide unprecedented indoor positioningaccuracy and adaptability, enabling industries to optimize operational efficiency, enhance security, and deliver customized services.
## 4 Open-Source Libraries
### Arpack  
* _Summary_: ARPACK [40] is a library developed for solving large eigenvalue problems with the IR-Arnoldi method.
* _Eigenvalue Methods_: IR-Arnoldi iteration.
* _Matrix Formats_: ARPACK supports the Reverse Communication Interface (RCI), which provides to the user the freedom to customize the matrix data format as desired. In particular, with RCI, whenever a matrix operation has to take place, control is returned to the calling program with an indication of the task required and the user can, in principle, choose the solver for the specific task independently from the library.
* _Returned Eigenvectors_: only right eigenvectors are calculated.
* _Dependencies_: ARPACK depends on a number of subroutines from LAPACK/BLAS. Moreover, ARPACK requires to be linked to a library that factorizes matrices. This can be either dense or sparse. In the simulations that are described in this paper, we linked ARPACK to the efficient library KLU, which is part of SuiteSparse [61], and that is particularly suited for sparse matrices whose structure originates from an electrical circuit.
* _GPU-based version:_ to the best of the authors' knowledge, a library that provides a functional GPU-based implementation of ARPACK is not available to date.
* _Parallel version_: PARPACK is an implementation of ARPACK for parallel computers. The message parsing layers supported by PARPACK are MPI (Message Passing Interface) [62] and BLACS.
* _Development_: the first version of ARPACK became available on Netlib in 1995. The last few years, ARPACK has stopped getting updated by Rice University. The library has been forked into ARPACK-NG, a collaborative effort among software developers, including Debian, Octave, and Scilab, to put together their own improvements and fixes of ARPACK. The latest version of ARPACK-NG is 3.7.0 and it was released in 2019.
# Cloud Storage Data Verification Using Signcryption Scheme
## 1 Introduction
In addition, separately performing the signature and encryption comes with an expensive cost. Fortunately, [PERSON] came up with a concept called signcryption [9; 10] that performs the dual tasks of public-key encryption and digital signature in a single step. The work shows that the cost (signcryption) \(<\) the cost (signature) + the cost (encryption). However, the original signcryption concept does not allow for public verification; therefore, the only possible verifier is the recipient of the signcryption messages. Refs. [11; 12] proposed an identity-based signcryption, and Ref. [13] proposed a signcryption scheme based on elliptic curve. Unfortunately, those signcryption schemes did not provide important properties needed in data verification: public verifiability, privacy-preserving, and blockless verification. Refs. [14; 15; 16; 17] gives a public verifiability property to their works. However, it lacks privacy-preserving and blockless verification in the verification process. The user must forward the original message to the verifier, threatening the user's data privacy. Furthermore, the verifier needs to download the message first in order to verify the data that is not efficient. Therefore, we propose a data verification protocol using a signcryption scheme based on a short signature by [PERSON], [PERSON], and [PERSON] (ZSS) [18] that supports not only public verifiability but also privacy-preserving to prevent data leaks to the verifier and blockless verification. To support the last two properties, in our proposed scheme, the verifier will not be required to receive the original message to do the verification process. The verifier only requires a mathematically generated proof from the CSP in its replacement. By doing so, the user's data privacy is preserved; furthermore, the verifier will not need to download the original message beforehand, making our proposed scheme a blockless verification protocol. Our contribution:  
* Proposed an improved signcryption scheme that provides three desirable properties in data verification, public verifiability, privacy-preserving, and blockless verification. * Presented a use case of data verification for data stored in cloud storage using a signcryption scheme to ensure data integrity, confidentiality, and non-repudiation. The remainder of the paper is organized as follows. We first describe preliminaries in Section 2. Then, we present the proposed improved signcryption scheme in Section 3 along with the data verification use case using our improved ZSS signcryption scheme. Discussion and security analysis about our equations' correctness, unforgeability from malicious CSP and verifier, computational cost, and time complexity of our proposed scheme will be presented in Section 4. Finally, we conclude the paper in Section 5.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 5 Discussion
### Limitations  
It should be pointed out that this work comes with some limitations. Since we did not have actual ground truth data, we assume the validity of farmers' declarations to train and evaluate our models. Even though the declarations in Navarra are expected to be \(\sim\)97% accurate [19], still this can affect to a small degree the training and evaluation process. Additionally, the proposed methodology has been applied only to a specific area for a specific year and it should be tested in areas of different agroclimatic characteristics and for different inspection years to evaluate its generalization capabilities. Finally, the production of the polarimetric features is a very time-consuming procedure and, therefore, applying  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**No Clouds**} & \multicolumn{3}{c}{**Clouds**} \\ \hline
**Selection Method** & **\#Features** & **OA** & **f1 Macro** & **\#Features** & **OA** & **f1 Macro** \\ \hline MR & 111 & 92.63 & 89.81 & 120 & 87.72 & 83.54 \\ RFE & 111 & 93.54 & 91.19 & 120 & 89.38 & 85.85 \\ Lasso & 111 & 93.83 & 91.60 & 120 & 89.83 & 86.03 \\ RF & 111 & 93.18 & 90.59 & 120 & 88.36 & 83.86 \\ GA & 111 & **94.00** & **91.75** & 120 & **90.66** & **87.80** \\ \hline \end{tabular}
\end{table}
Table 6: Performance of the different feature selection methods. Bold indicates the methods with which the best results are achieved.  
Figure 13: Feature importance of each month with and without clouds.  
the proposed pipeline from scratch in another area requires a lot of computational time. However, we partially mitigate this limitation by proposing to use only a handful of useful polarimetric features, for the task at hand, based on our analysis.
## 3 Results and Discussion
### Comparision of Analysis Methods
Therefore, the obtained results revealed that although univariate imaging is the simplest and most convenient method, it is not applicable when the intense peaks of target adulterants overlap with those of other sample constituents. Moreover, the presence of high-frequency noise, which is common for agricultural and biological materials, can result in the observation of false positive pixels. This problem can be resolved by utilizing bivariate imaging, which ultimately enhances adulterant particle intensities and thus suppresses noise. This approach allows the straightforward and rapid analysis of Raman imaging data due to using only two data points and can thus be considered \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Univariate Analysis**} & \multicolumn{3}{c}{**Bivariate Analysis**} & \multicolumn{3}{c}{**Multivariate Analysis**} \\ \hline \multirow{2}{*}{**Sample**} & **Added** & **Detected Sudan** & **Detected** & **Detected Sudan** & **Detected** & **Detected Sudan** & **Detected** \\  & **value (\%)** & **dye (\%)** & **Congo red (\%)** & **dye (\%)** & **Congo red (\%)** & **dye (\%)** & **Congo red (\%)** \\ \hline Mixture 1 & 0.1 & 0.183 & 0.071 & 0.168 & 0.069 & 0.062 & 0.054 \\ Mixture 2 & 0.25 & 0.26 & 0.217 & 0.279 & 0.21 & 0.225 & 0.212 \\ Mixture 3 & 0.5 & 0.487 & 0.52 & 0.485 & 0.504 & 0.58 & 0.502 \\ Mixture 4 & 0.75 & 0.889 & 1.06 & 0.789 & 1.04 & 1.11 & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of adulterant detection obtained with three different analysis methods. an effective analysis method for the online application of Raman imaging in the agro-food sector. On the other hand, SAM imaging, considering the complete dataset, requires an increased amount of computation time and has not been proven effective for low-concentration samples. It is worth mentioning that binary SAM images featured a lower number of mixed pixels (shown in black color in Figure 6) than those obtained by the other two techniques. Moreover, SAM analysis exhibits the advantage of increased reproducibility due to being the least sensitive to the slightly time-dependent spectral intensity, but being highly sensitive to the spectral pattern. Summarizing the comparison of the data analysis methods, it can be concluded that the bivariate method (band multiplication) is most suitable for the quantitative analysis of two different adulterants (Sudan dye and Congo red dye) in paprika powder. The pixel-based detected concentration of adulterants is almost consistent with the added concentration, unlike SAM analysis, which shows false negative pixels for low concentration samples and over-estimation for high concentration samples. Moreover, bivariate analysis uses a very small portion of data sets (only two band images), hence reducing the computation time and facilitating the real-time visualization of Raman chemical images of adulterants, which is not possible using the SAM analysis method owing to the aforementioned complications in data analysis.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 2 Materials and Methods
### Implementation of the Adding-Doubling (AD) Algorithm  
A GPU-accelerated AD algorithm for single-layer tissue was developed using MATLAB's parallel toolbox. We utilized the library supporting batched functions, which enabled us to perform parallel calculations of several smaller matrices. Initialization of the CUDA library and running the algorithm on GPU required about 3 s; however, this initialization only needed to be performed once. Subsequent memory allocations would have posed an obstacle, potentially leading to time delays. To tackle this issue, we undertook a comprehensive rearrangement of the code to eliminate the need for for loops and enable the application of the code to larger arrays, specifically optimized for the operations with the previously mentioned library. In this way, we avoided the problem of performance degradation, which would have happened when calculating small matrices on GPU, as the matrix operation times would be comparable to data transfer times between GPU and CPU. Figure 3 shows the flowchart of the CUDA implementation of the GPU-accelerated AD algorithm. In the following, we will describe the individual parts of the AD1L class. The algorithm is implemented in a custom class AD1L. The class is initialized by calling the constructor **AD1L**  
\[\text{obj}=\text{AD1L(lam, [dtype])};\]  
where _lam_ represents an array of wavelengths with _Nl_ rows. The _dtype_ parameter is optional and specifies the precision used in the algorithm. There are two options: _single_ and _double_. By default, the precision is set to _single_ as it provides faster computation. An example of the class initialization with double precision is as follows:  
\[\text{A}=\text{AD1L(lam, 'dtype, 'double')};\]  
First, the algorithm is initialized by specifying the properties which remain constant throughout the computation. This includes calculating tissue matrices, such as scattering and reflectance/refraction matrices.
## 2 Materials and Methods
### HPLC-PDA-ESI-tQ-MS Metabolite Quantification  
For quantification of 28 compounds of _M. stylosa_ and _M. serrulata_ herb extracts with HPLC-PDA-ESI-tQ-MS, following reference compounds were used: lycopsamine, lycopsamine \(N\)-oxide, lycopsamine 7-O-acetate, lycopsamine \(N\)-oxide 7-O-acetate, 4-O-caffeoylquinic, 3-O-caffeoylthreonic, 2-O-caffeoylthreonic, 4-O-coumaroylquinic, 5-O-caffeoylquinic, 4-O-feruloylquinic, 2-O-caffeoylglyceric, 3-O-caffeoylquinic, 4-O-caffeoylthreonic, 3-O-caffeoylflyceric, 5-O-coumaroylquinic, 3-O-coumaroylquinic, 5-O-feruloylquinic, 2-O-caffeoylartronic, 3-O-feruloylquinic, caftaric, 3-, 5-, 4-S-di-Ocaffeoylquinic, rosmarinic acids, quercetin-3-\(O\)-rutinoside, quercetin-3-\(O\)-glucose, kaempfer-3-\(O\)-rutinoside, kaempfer-3-\(O\)-rutinoside, kaempfer-3-\(O\)-glucose. For the preparation of stock solutions (1000 ug/mL) applied for the calibration curve building, 10 mg of reference compounds were separately weighted and dissolved in the methanol-DMSO mixture (1:1) in volumetric flasks (10 mL). The calibration solution (1, 10, 25, 50 and 100 ug/mL) was chromatographed in known HPLC-PDA-ESI-tQ-MS conditions (Section 2.3). Determination coefficients (r\({}^{2}\)), standard deviation (S\({}_{\text{YX}}\)), limits of detection (LOD), limits of quantification (LOQ), and linear ranges as the main validation criteria were studied using the previously developed method [19] (Table 1). Five HPLC runs were satisfactory for the quantitative analyses, and the results were expressed as mean value \(\pm\) standard deviation (S.D.).
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 2 Numerical Simulation Theory
### Turbulence Model  
Turbulence is an inevitable problem in the simulation of flow field. The rotor flow field of helicopter is a highly nonlinear turbulent flow, so before conducting the numerical simulation of a rotor flow field, it is necessary to study the turbulence model and add an appropriate turbulence model in the flow-governing equation to obtain a reasonable solution and complete the flow field simulation.  
The \(SST\)\(k-\omega\) two-equation model uses the \(k-\omega\) model in the boundary layer, which has better treatment effect in the viscous sublayer region. It is transformed into a \(k-\varepsilon\) model in the outer layer, which avoids the problem that the \(k-\omega\) model is too sensitive to the turbulent characteristics. The viscous formula is modified by considering the shear stress in the turbulent flow. It is more suitable for high curvature flow, flow with obvious separation and jet flow, and more accurate for the rotor flow field model. The turbulent kinetic energy \(k\), turbulent dissipation rate \(\varepsilon\) and specific dissipation rate \(\omega\) are as follows:  
\[k=\frac{\overline{u_{i}^{\prime}u_{i}^{\prime}}}{2} \tag{9}\]  
\[\varepsilon=\frac{\mu}{\rho}\bigg{(}\frac{\partial u_{i}^{\prime}}{\partial x_ {k}}\bigg{)}\bigg{(}\frac{\partial u_{i}^{\prime}}{\partial x_{k}}\bigg{)} \tag{10}\]  
\[\omega=\frac{\varepsilon}{k} \tag{11}\]
## 4 Discussion
### Practical Implications  
The findings of this study can directly inform construction safety management practices, regulatory enforcement, and urban planning efforts aimed at reducing the potential risk posed to pedestrians near construction sites. For example, as shown in Figure 6a, this study found that when sidewalks are obstructed by construction-related materials or equipment by more than 25%, participants perceive the walking environment as unsafe (i.e., the mean perceived risk exceeds 3). Therefore, policymakers can consider implementing guidelines that impose fines when the obstruction exceeds one-quarter of the available sidewalk space. Furthermore, construction managers or urban planners might consider using the image segmentation model provided in this study as part of advanced surveillance technologies to monitor and minimize encroachments on pedestrian walkways in real time. Such quantitative methods employed in this study demonstrate the potential of data-driven approaches in evaluating and improving pedestrian safety. Ultimately, this study contributes not only to the immediate identification of hazardous areas adjacent to construction sites, but also to the development of long-term strategies for pedestrian-friendly urban infrastructure.
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 1 Introduction
### Automatic Continuous Sign Language Recognition: State of the Art
#### 1.2.1 Specific corpora used for Continuous Lexical Sign Recognition  
Specific annotated SL corpora are used for this task, with lexical annotations only, with three of them standing out:  
The German Sign Language (DGS) SIGNUM Database [10] contains 780 elicited sentences, based on 450 lexical signs. In total, there is approximately five hours of video. Because of the rigorous elicitation procedure, it is safe to say that the level of spontaneity as well as the interpersonal variability in the observed SL are very low. Signers indeed repeat the original reference sentences with no initiative. The elicited gloss sequences have lengths ranging from two to eleven glosses.  
The RWTH-Phoenix-Weather (RWTH-PW) corpus [11; 12; 13] is made from 11 h of live DGS interpretation of weather forecast on German television, with nine interpreters. Conversely to many corpora produced in laboratory conditions and/or strong elicitation rules, RWTH-PW has been described by its authors as real-life data [11]. However, because of the specific topic, the language variability is necessarily limited. Furthermore, it is crucial to note that interpreted SL is a specific type of SL, quite different from  
Figure 3: LSF sequence from Dicta-Sign–LSF–v2 (video reference: S7_T2_A10). Expert annotations for right-handed (RH), two-handed (2H) and left-handed (LH) Fully Lexical Signs (FLSs) and Partially Lexical Signs (PLSs) are given.  
spontaneous SL. There is a good chance that the translation will be strongly influenced by the original speech (in German), especially in terms of syntax, and make little use of the structures typical of SL [14].  
The Continuous SLR100 (CSLR100) dataset [15] is a continuous Chinese Sign Language (ChSL) corpus, with more than 100 h of recordings. However, the level of variability and spontaneity in the produced language is very low: the corpus is based on 100 pre-defined _sentences_, that are repeated five times by 50 signers. In total, 25,000 sentences are thus recorded. The lexicon only amounts to 178 different lexical signs.
# 5G Positioning: An Analysis of Early Datasets
## 4 Results
### Least Squares Solution
#### 4.2.1 Loading Zone Trajectories: Takes 01, 02, and 03  
In the loading zone, only the outdoor stations (7, 8, 9, 10, 11) are used. In this area, a good line of sight (LOS) is expected. The trajectory of Take 01 is analyzed first. The trajectory is repeatedly estimated by using as the \(TDoA\) reference, firstly, station 7, then 8, and so on, up to 10. By a visual check of the results and the residuals, the observations of station 7 are clearly affected by the largest errors, both locally correlated and sparse outliers. This is probably due to the presence of a reflecting surface near the station, and the resulting multipath. By excluding station 7 from the dataset, the results clearly improve, due to a significant reduction in spurious patterns and isolated outliers; compare, for example, Figures 9 and 10. Considering that, in general, station 7 showed the best SNR, this confirms that in our data the SNR does not provide a useful index of quality.  
Figure 9: Take 01, reference station 8: least squares results.  
Note that the exclusion of station 7 locally improves the quality of the results but does not significantly change the general statistics. After removing station 7, the redundancy of the the least squares method estimates in single epochs is very low (one, equal to three \(TDoA\) observations minus two unknowns).