esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Freeboard  
Sea ice freeboard estimates are used to assess the potential to retrieve sea ice thickness from altimeter data over small-scale sea ice features. The procedure to convert the measured altimeter waveform to surface elevations is called retracking and an overview of commonly applied methods can be found in [27]. Common to all these methods is to identify the point on the leading edge of the waveform that corresponds to the nadir return. Freeboard estimates used in this study are solely from the provided CryoSat-2 level 2 product that uses a threshold retracker referenced to the first peak of a smoothed waveform [28]. Distortions of the leading edge, e.g., by off-nadir specular reflectors or mixtures of ice types, can cause false elevation estimates [12; 29].  
Figure 3 shows an example of waveform evolution over a multiyear sea ice floe taken on 18 February 2018. Time separation between the SAR image and altimeter measurement is just a few minutes. The circular, darker SAR signature between samples 502 and 505 probably represents a smoother ice surface. The highly negative freeboard for sample 502 is related to the deformed, slightly kinked leading edge. The strong peak can no longer be distinguished from the first peak as in the waveforms before and after. While this is an exaggerated example with highly negative freeboard, this can also cause an underestimation of sea ice thickness in less affected cases. Freeboard measurements represent radar freeboard, i.e., the scattering horizon of the radar waves that usually falls somewhere between the air-snow and snow-ice interface [26]. Sea ice thickness is of primary interest but estimation of it requires auxiliary information about snow load and physical and electromagnetic properties of the sea ice, which is beyond the scope of this study.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
###### Abstract  
_Origanum vulgarae_, belonging to the Lamiaceae family, is a principal culinary herb used worldwide which possesses great antioxidant and antibacterial properties corresponding to various volatile organic components (VOCs). However, the metabolite profiles and underlying biosynthesis mechanisms of elaborate tissues (stems, leaves, bracts, sepals) of _Origanum vulgarae_ have seldom been reported. Here, solid-phase microextraction-gas chromatography/mass spectrometry results showed that _Origanum vulgarae_ 'Hot and Spicy' (_O. vulgarae_ 'HS') was extremely rich in carvacrol and had the tissue dependence characteristic. Moreover, a full-length transcriptome analysis revealed carvacrol biosynthesis and its tissue-specific expression patterns of 'upstream' MVA/MEP pathway genes and 'downstream' modifier genes of TPSs, CYPs, and SDRs. Furthermore, the systems biology method of modular organization analysis was applied to cluster 16,341 differently expressed genes into nine modules and to identify significant carvacrol- and peltale glandular trichome-correlated modules. In terms of these positive and negative modules, weighted gene co-expression network analysis results showed that carvacrol biosynthetic pathway genes are highly co-expressed with TF genes, such as _ZIPs_ and _bHLHs_, indicating their involvement in regulating the biosynthesis of carvacrol. Our findings shed light on the tissue specificity of VOC accumulation in _O. vulgarae_ 'HS' and identified key candidate genes for carvacrol biosynthesis, which would allow metabolic engineering and breeding of _Origanum_ cultivars.  
_Origanum vulgarae_; carvacrol; tissue dependence; SMRT; WGCNA
# Crack Growth and Delamination Analysis in GFR Composite Materials
## 2 Homogenization Procedure
### Homogenization of Viscoelastic Materials  
In the present study, the viscous effect of the matrix is manifested employing a viscoelastic homogenization scheme that takes into account prony series viscoelasticity. On the other hand, the glass fibers are considered as linear elastic for normal temperatures. The material model of the viscoelastic matrix medium follows the following constitutive equation:  
\[\sigma(t)=G(t):\varepsilon(0)+\int G(t-\tau):\dot{\varepsilon}^{(ve)}(\tau)d\tau \tag{9}\]  
\[\varepsilon(0)=\lim_{t\to 0^{+}}\varepsilon(t) \tag{10}\]  
\[G(t)=2G_{R}(t)\ I^{dev}+K_{R}(t)\ \hat{I}\otimes\hat{I} \tag{11}\]  
where the stress (\(\sigma\)) and strain (\(\varepsilon\)) second order tensors are invoked, \(G(t)\) is the relaxation tensor, \(\dot{\varepsilon}^{(ve)}\) the viscoelastic (indicated by the ve superscript) strain rate tensor, \(I^{dev}\) is the deviatoric part of the fourth order unit tensor, and \(\hat{I}\) is the second order unit tensor. The juxtaposition symbol \(\otimes\) represents the dyadic product. The prony series expression of the bulk and shear modulus is given by:  
\[G_{R}(t)=G(0)[1-\sum_{i=1}^{n}w_{i}(1-e^{-t/\tau_{i}})] \tag{12}\]  
\[K_{R}(t)=K(0)[1-\sum_{i=1}^{n^{\prime}}w_{i}^{*}(1-e^{-t/\tau_{i}^{*}})] \tag{13}\]  
where \(w_{i},w_{i}^{*}\) are the weights for the respective relaxation times \(\tau_{i},\tau_{i}^{*}\).  
The relation \(\vartheta(s)=\hat{E}(s)\dot{\varepsilon}(s)\) is the elastic analogous of Equation (9) for isotropic materials in the Laplace-Carson domain. The quantities invoked \(\vartheta\), \(\hat{E}\) and \(\dot{\varepsilon}\) are the transformed stress, stiffness and strain tensor in the Laplace domain, while \(s\) is the complex variable. This leads to a fictitious RVE in the Laplace-Carson domain [15], and the homogenization results are expressed as functions of \(s\). Inevitably, an inverse Laplace-Carson transformation is required to calculate the results in the time domain [16]. The prony series values for the viscoelastic homogenization are taken from the literature [17].  
Figure 1: Steps used during homogenization procedure: (i) pseudo-grain discretization, (ii) homogenization of each pseudo-grain, (iii) homogenization of all pseudo-grains with Voigt approach.
## 3 Results and Discussion
### Quasi-Static Compression Testing
This is because the BCC structure was characterised by a bending-dominated structure that collapsed solely, rendering it unfeasible to observe the modulus series. Conversely, the other structure was stretching-dominated, meaning that the combination of BCC and CP might exhibit an indeterminate fluctuation in collapsing due to the arbitrary breaking of the studs. Table 2 shows a response for the lattice structure with a solid sample. It is clearly seen that the lattice structures yield strength was not linearly reduced with the weight. The CP structure had maximum yield stress with no mean difference with the BCC structures4 and these CP and BCC structures had the lowest yield stress in the test matrix. Compared to the reduction of yield stress with the weight saving, the CP was the lightest lattice structure, at approximately 94 per cent lighter than the solid, but it could withstand less than 1 per cent of the yield stress of solid. The BCC + CP and FCC + CP could benefit from their weight reduction as this reduction would increase yield stress more than twice. The most robust lattice structure was the FCC + CP sample, which had a weight saving of close to 15.8 per cent of the solid weight, but it performed better in terms of the relative stress at roughly 30 per cent. As the stress property dramatically decreases, most lattices are carefully selected for suitable applications. From this test, it can be seen that the mechanical properties of the lattice structures were not able to compete with their original solid state except for the weight. Because all of the mechanical properties of the solid state were highly different from the lattice, those properties could be located in the outlier and had a discontinuous response; it would be better to track the data by normalising those solid properties. All lattice structures can perform much better than a solid in an elastic modulus. For example, although the BCC is the weakest in stress, in this matrix, it can achieve excellence in an elastic modulus with a relative elastic modulus at 0.29 \({}^{\text{a}}\) per cent. Figure 7 represents an energy absorption per unit mass. Most of the force may transmitted to substrate in solid, thus its energy absorption was closely with FCC. Among these lattice structures, the highest energy absorption was the BCC + CP, then the FCC + CP. On the other hand, the lowest energy absorption was in the CP structure. The summation between the CP's energy absorption and either the BCC or FCC did not agree with the BCC + CP and FCC + CP. This is evidence to deny the concept of superposition within a lattice structure.
# Fishman Replacement with Animal Protein Source
## 2 Materials and Methods
### Feeding Experiment
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Fry Diets (D1-D3)** & \multicolumn{4}{c}{**Fingerlings, Sub-Adult, and Adult Fish (D4-D6)**} \\ \hline
**Ingredients (g/kg)** & **D1 (0\% CM)** & **D2 (05\%/50\%, FM/CM)** & **D3 (100\% CM)** & **D4 (0\% CM)** & **D5 (05\%/50\%/FM/CM)** & **D6 (100\% CM)** \\ \hline Fishman\({}^{1}\) & 25.000 & 12.500 & - & 15.000 & 7.500 & - \\ Maize\({}^{2}\) & 20.000 & 20.139 & 20.279 & 30.000 & 30.000 & 30.000 \\ Corcolo mea\({}^{3}\) & - & 9.022 & 18.063 & - & 8.406 & 16.812 \\ Soybean meal 46 4  & 15.000 & 15.000 & 15.000 & 15.000 & 15.000 \\ Cancho seed meal 5  & 15.000 & 15.000 & 15.000 & 15.000 & 15.000 \\ Maize gluten 60 6  & 10.000 & 10.000 & 8.324 & 6.436 & 4.548 \\ Wheat bran 7  & 8.601 & 9.174 & 9.746 & 4.496 & 4.264 & 3.592 \\ Canrola oil 8  & 3.531 & 4.309 & 5.087 & 4.356 & 4.774 & 5.191 \\ Monocalcium phosphate 9  & 1.719 & 2.940 & 4.161 & 1.031 & 4.565 & 8.099 \\ Vitamin Premier 10  & 0.800 & 0.800 & 0.800 & 0.800 & 0.800 \\ Limestone 11  & - & - & 3.528 & 2.244 & 0.959 \\ L-lysine HCL 12  & 0.349 & 1.107 & 1.864 & 2.024 & 1.012 & \\ \hline
**Total** & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline
**Proximate Composition (%)** & & & & & & \\ \hline Moisture ** & 8.995 & 8.895 & 8.994 & 8.627 & 8.517 & 8.746 \\ Crude protein & 38 & 38 & 38 & 32 & 32 & 32 \\ Crude H 4  & 8.842 & 8.090 & 6.408 & 8.344 & 8.935 & 5.446 \\ Ash Ash\({}^{**}\) & 7.536 & 7.632 & 7.413 & 9.185 & 9.674 & 10.949 \\ Eb (M/kg) ** & 13.426 & 11.707 & 9.988 & 12.840 & 11.320 & 9.800 \\ \hline
**Essential Amino Acids (g/100 g dry matter)** & & & & & & \\ \hline Argin ** & 2.82 & 2.87 & 3.05 & 2.36 & 3.31 & 2.83 \\ Histidine ** & 2.03 & 1.94 & 1.90 & 1.63 & 2.27 & 1.61 \\ Isolecuire ** & 1.53 & 1.56 & 1.63 & 1.30 & 1.45 & 1.45 \\ Leucine ** & 3.36 & 3.54 & 3.79 & 3.00 & 3.13 & 2.99 \\ Lysine ** & 2.04 & 3.24 & 4.13 & 3.02 & 1.52 & 2.11 \\ Methionine ** & 1.26 & 1.22 & 1.32 & 0.97 & 1.29 & 1.12 \\ Prephalamine ** & 3.82 & 3.87 & 4.04 & 3.11 & 3.88 & 2.89 \\ Threonine ** & 2.35 & 2.39 & 2.50 & 2.00 & 2.64 & 2.24 \\ Valine ** ** & 1.93 & 1.93 & 1.94 & 1.67 & 1.82 & 1.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main ingredients and proximate composition of the experimental diets. \begin{table} \begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fry Dies (D1-D3)**} & \multicolumn{2}{c}{**Fingerlings, Sub-Adult, and Adult Fish (D4-D6)**} \\ \hline \multicolumn{6}{l}{**Non-Essential Amino Acids (g/100 g dry matted)**} \\ \hline Alanine ** & 2.33 & 2.54 & 2.65 & 2.12 & 2.02 & 2.05 \\ Asparagine ** & 3.24 & 3.85 & 4.14 & 3.08 & 3.34 & 3.21 \\ Glutamic acid ** & 6.34 & 7.62 & 8.45 & 6.23 & 6.86 & 6.32 \\ Glycine ** & 2.65 & 2.59 & 2.62 & 2.04 & 2.66 & 2.43 \\ Proline ** & 2.16 & 2.28 & 2.47 & 1.95 & 1.99 & 1.90 \\ Serine ** & 2.12 & 2.16 & 2.24 & 1.88 & 2.39 & 1.95 \\ Tyrosine ** & 3.27 & 2.32 & & 3.15 & 2.33 \\ \hline \end{tabular} 
\begin{tabular}{l c c c c} \hline \hline  & FM
# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 5 Conclusions  
Route optimization in autonomous vehicles is crucial for various applications. This study analyzed the current status and challenges of improving efficiency and adaptability in dynamic environments. The key techniques, methods and tools for integrating spatiotemporal and curvature constraints were identified. Flexibility and adaptability are essential for real-world applications. By offering workable and efficient alternatives, the research findings further the development of autonomous route planning.  
The systematic literature review revealed limited areas of research. There is a need to integrate spatiotemporal constraints in dynamic environments. It is also crucial to develop techniques that consider the dynamic aspects of the environment. The application of heuristic algorithms for real-time multi-objective optimization is critical. The design of adaptive control systems that minimize distance and time simultaneously requires attention.  
The emerging fields of research include adaptive path planning. Algorithms need to be developed that allow vehicles to adjust their routes in real time. Multi-objective optimization, integrating criteria such as distance, time and energy, is essential. Systems must operate efficiently in environments with moving obstacles and unpredictable changes. These interdependent challenges require a balance between theoretical efficiency and real-time adaptability.  
The computational power of algorithms and the accuracy of sensors must be considered. Vehicle energy efficiency is also a critical factor to consider. Organizations such as the International Federation of Robotics (IFR) promote the development of route planning technologies. Their goal is to encourage the adoption of autonomous systems in various sectors. These initiatives seek to improve efficiency and reduce operating costs.  
Finally, the study highlights the importance of route optimization in autonomous vehicles. The key areas for future research and development were identified. The integration of dynamic constraints and multi-objective optimization is essential. The promotion of these technologies is greatly aided by international organizations. Autonomous route planning is expected to become much more efficient and flexible in the future.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 3 GNSS Receiver Signal Processing
### GNSS Code and Phase Observables  
From the individual satellite delay and phase MLEs and after demodulating the navigation data message, the receiver obtains a set of code and phase observables for each satellite in view. Disregarding ionospheric and tropospheric delays, as well as other sources of error, for the ith satellite, we write,  
\[\widehat{o}_{i} = ||\mathbf{p}_{T_{i}}-\mathbf{p}_{R}||\;+\;c\;(\delta t_{r}- \delta t_{i})\;+\;e_{\varrho,i}, \tag{14}\] \[\widehat{\Phi}_{i} = ||\mathbf{p}_{T_{i}}-\mathbf{p}_{R}||\;+\;c\;(\delta t_{r}- \delta t_{i})\;+\;\lambda_{c}N_{i}\;+\;e_{\Phi,i}, \tag{15}\]  
where \(||\mathbf{p}_{T_{i}}-\mathbf{p}_{R}||=\sqrt{(x_{i}-x_{R})^{2}\;+\;(y_{i}-y_{R })^{2}\;+\;(z_{i}-z_{R})^{2}}\) is the geometrical distance between the receiver and the ith satellite; \(\mathbf{p}_{T}^{\top}=[x_{R},y_{R},z_{R}]\) and \(\mathbf{p}_{T_{i}}^{\top}=[x_{i},y_{i},z_{i}]\) are the position coordinates of the receiver and the ith satellite, respectively; \(\delta t_{r}\) and \(\delta t_{i}\) are the receiver and satellite clock offsets w.r.t. the GNSS time. Since in the asymptotic region, the MLE becomes unbiased, efficient, and Gaussian distributed [36], \(e_{\varrho,i}\) and \(e_{\Phi,i}\) are zero-mean white Gaussian noise terms, and their variance is driven by the performance of \(\widehat{\tau}\) and \(\widehat{\varphi}(\widehat{\tau})\), respectively. \(N_{i}\) is an ambiguous term related to the (unknown) number of phase cycles. The latter has a fractional part \(B_{i}\), which depends on the initial phase of the ith satellite clock, a fractional part \(B_{r}\) due to the initial phase at the receiver, and an integer part \(N_{\text{int},i}\), which is related to the satellite to receiver distance, then \(N_{i}=B_{i}\;+\;B_{r}\;+\;N_{\text{int},i}\).
# Influence of Headlight Level on Object Detection in Urban Traffic at Night
## Materials and Methods
### Test Roads  
The study was conducted on three test roads located in Darmstadt, Germany, images of which are shown in Figure 1. The test roads have LED street lighting systems of different lighting classes from M4 to M6 according to EN 13201-2 [40].  
The pictures of the test roads shown in Figure 1 serve as an overview of the test roads and the test setup. Photometric analysis of the test roads was performed using luminance images, which are shown schematically in Figure 2. The average roadway luminance \(L_{m}\) varies from 0.91 cd m\({}^{-2}\) for test road 1 to 0.53 cd m\({}^{-2}\) for test road 2 and 0.38 cd m\({}^{-2}\) for test road 3; see Figure 2. From the luminance images, the differences in the homogeneity of the street lighting are evident, as are the different roadway brightnesses. The luminance recordings were carried out with a luminance camera (TechnoTeam LMK 5 color) attached to the rearview mirror of the test vehicle in order to realize the measurement from the driver's point of view.  
Further parameters, such as the overall uniformity \(U_{0}\) and longitudinal uniformity \(U_{1}\), can be found in Table 1.  
From Table 1, it can be seen that the requirements for overall uniformity \(U_{0}\) are not met by the three test roads. Nevertheless, these test roads were used for the investigation because the aim of this work is to consider the low beam influence on object detection at different street illumination levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Road** & \(L_{m}\) **in cd m \({}^{-2}\)** & \(U_{0}\) & \(U_{1}\) & **Class** \\ \hline
1 & 0.91 & 0.35 & 0.66 & M4 \\
2 & 0.53 & 0.30 & 0.42 & M5 \\
3 & 0.38 & 0.32 & 0.40 & M6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the test roads.  
Figure 1: Test roads on which the study was conducted: (**top left**) test road 1 (M4 class); (**top right**) test road 2 (M5 class); (**bottom**) test Road 3 (M6 class).  
Figure 2: Luminance images of the test roads on which the study was conducted: (**top left**) test road 1 (0.91 cd m\({}^{-2}\), M4 class); (**top right**) test road 2 (0.53 cd m\({}^{-2}\), M5 class); (**bottom**) test Road 3 (0.38 cd m\({}^{-2}\), M6 class). From the luminance images, the homogeneity differences are obvious.
# Addressing the Potential of L5/E5a Signals for Road ITS Applications in GNSS-Harsh Environments +
## 2 Methods and Materials
### Methods
To estimate the PVT solution, the smartphone-based pseudorange and Doppler measurements are processed through an Extended Kalman Filter (EKF) [10]. The EKF algorithm was developed as an additional capability to the existing open-source software _PANGNAV_[10]. The EKF is described by  
\[\mathbf{x}_{k|k-1}=f\Big{(}\mathbf{x}_{k-1|k-1}\Big{)} \tag{7}\]  
\[\mathbf{P}_{k|k-1}=\mathbf{F}\mathbf{P}_{k-1|k-1}\mathbf{F}^{T}+\mathbf{Q} \tag{8}\]  
\[\mathbf{K}_{k}=\mathbf{P}_{k|k-1}\mathbf{H}^{T}\Big{(}\mathbf{H}\mathbf{P}_{k|k-1}\mathbf{H}^{T}+\mathbf{ R}_{k}\Big{)}^{-1} \tag{9}\]  
\[\mathbf{x}_{k|k}=\mathbf{x}_{k|k-1}+\mathbf{K}_{k}\Big{(}\mathbf{z}_{k}-h\Big{(}\mathbf{x}_{k|k-1} \Big{)}\Big{)} \tag{10}\]  
\[\mathbf{P}_{k|k}=\mathbf{P}_{k|k-1}-\mathbf{K}_{k}\mathbf{H}\mathbf{P}_{k|k-1} \tag{11}\]  
where the left indicator of \(\mathbf{{}_{k|k-1}}\) represents the current time instant whilst the right indicator represents the time instant value for the associated parameter being used, \(\mathbf{x}_{k|k}\) is the state estimate at time k, \(\mathbf{P}_{k|k}\) is the updated (a posteriori) estimate covariance at time k, \(\mathbf{x}_{k|k-1}\) is the prediction of the state at time k, \(\mathbf{P}_{k|k-1}\) is the predicted (a priori) estimate covariance at time k, \(\mathbf{K}_{k}\) is the Kalman Gain at epoch k, \(\mathbf{R}_{k}\) is the covariance of the measurement error at epoch k, \(\mathbf{Q}\) is covariance of the process noise, \(\mathbf{H}\) is the geometric matrix of the measurements (pseudorange and Doppler), \(h\) is the state-to-measurement mapping function, \(\mathbf{F}\) is the state transition matrix, \(f\) is the state transition mapping function and \(\mathbf{Z}_{k}\) is the measurement vector at epoch k. For the EKF, a constant velocity dynamic model was adopted, whilst the ISBs are handled as white noise [11].
Sensors_**2011**, _11_, 10010-10037; doi:10.3390/s1111110010  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceara, CEP 60455-760, Fortaleza, Brazil; E-Mails: [EMAIL_ADDRESS] (D.G.); [EMAIL_ADDRESS] (J.S.)  
LRSM/IBISC Laboratory, University of Evry Val d'Essonne, 91020 Evry Courcouronnes CE 1433, France; E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +55-85-3366-9797; Fax: +55-85-3366-9066.  
_Received: 18 August 2011; in revised form: 13 October 2011 / Accepted: 19 October 2011 / Published: 25 October 2011_  
This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction.  
**Keywords:** wireless sensor networks; multivariate correlation; data reduction
# Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review
## 3 Results
### Result Synthesis  
#### 3.2.1 RQ1. Graph Model Taxonomy  
Table 1 categorizes the different DSLs we reviewed, depending on the graph model they adopt. We note three reoccurring models:  
* The property graph model [50], which associates nodes and edges of the graph with arbitrary properties as key-value pairs. This model was the subject of the ISO standards for SQL/PGQ, which defines a way of building property graphs over relational data, and for GQL, a property graph query language [5].
* The GraphBLAS specification [51], which defines building blocks for graph algorithms in linear algebra.
* The vertex-centric model [52], which considers a local, vertex-oriented view of the graph, as opposed to a global view, and is thus more suitable for writing distributed implementations of graph algorithms. The vertex-centric model was first introduced in a paper on [PERSON] [53].  
Sitxty percent of the reviewed DSLs explicitly adopt the property graph model. It is worth noting that these models are not necessarily mutually exclusive: a property graph DSL can also be vertex-centric or target a GraphBLAS-based platform. With that in mind, we can safely say that the property graph model is indeed prevalent. The remainder of our results focus on this model. A popular counterpart to property graphs, which we do not encounter in our systematic review, is the Resource Description Framework (RDF) standardized by W3C [54], which models data as labeled edge-directed graphs. The standard query language for RDF is SPARQL. Several related works consider property graphs and RDF to be the most widely used graph models [5; 15; 17; 40].