esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Methods
### Self-Consistent Ab Initio Lattice Dynamics Method (SCAILD)  
The approach is described in detail [63] and not repeated here, but the general idea is to account for thermal vibrations of the atoms and their interactions. This is accomplished by (i) calculating the forces on atoms, displaced from the ideal positions according to the temperature, and (ii) computing the phonon density of states (DOS). As the forces, displacements, and the phonon DOS depend on each other the scheme is set up to self-consistently determine the temperature dependence of the lattice dynamics. We conducted SCAILD calculations, utilizing a 54-atom cell for UN, for five temperatures, T = 1000, 1500, 2000, 2500, and 3000 K. In addition, we considered three strategically chosen atomic volumes for a total of 15 simulations. The lattice free energy is well converged with 105 SCAILD iterations for each simulation and the result was used to evaluate the total free energy (also adding DFT electronic contributions) as a function of temperature at ambient pressure. In Figure 1, we illustrate these lattice vibration energies (here at constant atomic volume, 14.85 A\({}^{3}\)) as functions of number of SCAILD iterations. The method was also used to evaluate, from the phonon DOS, the heat capacity that can be compared to experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **V** & **B** & **\(\Delta\)E** & **\(\Delta\)E** & **\(\Delta\)E** & **M** & **\(\Delta_{\text{H}}\)** & **\(\Delta_{\text{H}}\)** \\  & **(Å\({}^{3}\))** & **(GPa)** & **B\({}^{\prime}\)** & **(mRy/at)** & **(kJ/mol)** & **(μ\({}_{\text{Hg}}\))** & **(mRy/at)** & **(kJ/mol)** \\ \hline DFT-FM & 14.85 & 182 & 5.2 & 0 & 0 & 0.78 (1.56) & \(-\)226.6 & \(-\)297.5 \\ DFT-AF & 14.85 & 183 & 5.0 & 1.5 & 1.97 & 0.78 & n/a & n/a \\ DFT-NM & 14.75 & 232 & 2.0 & 2.3 & 3.02 & 0 & n/a & n/a \\ Expt & 14.62 & 191–206 & 6.3 & n/a & n/a & 0.75 & \(-\)223.6 & \(-\)293.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uranium mononitride (UN) results from density-functional-theory (DFT) ferromagnetic (FM), anti-ferromagnetic (AF), non-magnetic (NM) calculations and experimental data [3; 20; 21]. V, B, and B’ are the atomic volume, bulk modulus, and the bulk modulus pressure derivative (B’ = dB/dP), respectively. \(\Delta\)E is the relative total energy and M the total (spin and orbital) magnetic moment on the uranium atom. All results are obtained from calculations that include orbital polarization except for the magnetic moment in parenthesis that refers to a treatment with spin-orbit coupling only. \(\Delta_{\text{H}}\)H is the formation enthalpy.  
Figure 1: Self-Consistent Ab Initio Lattice Dynamics (SCAILD) lattice vibration energies for five temperatures, at constant atomic volume (14.85 Å\({}^{3}\)), as functions of number of SCAILD iterations.
## 3 Transfer Learning
### Cascade Transfer Learning  
We used the cascade transfer learning strategy to classify the five input channels from the RxRx19a dataset to mock cells and active SARS-CoV-2 viral cells, as shown in Figure 4. First, the pre-trained DenseNet was trained with the siRNA images, as shown at the top of the figure. From the figure, it can be seen that the DenseNet acted as a feature extractor. Next, the last three layers of the trained DenseNet, a batch normalization (BN) layer, a fully connected (FC) layer, and a SoftMax layer (shown in the top-right of the figure), were replaced by a FC layer, a BN layer, another FC layer, another BN layer, a third FC layer, and then finally a SoftMax layer (shown in the bottom-right of the figure). The number of neurons in the added layers was mainly constrained by the input and output sizes. It must be noted that the size of the SARS-CoV-2 images in the RxRx19a dataset was \(1024\times 1024\), therefore, a down-sampling procedure was applied to these images so they can be processed by the feature extraction model, i.e., the trained DenseNet in the first stage. The scoring model shown at the bottom of the figure was then retrained with the RxRx19a dataset. After the layers in the scoring model were trained with the COVID-19 dataset, we fine-tuned the entire classification model by unfreezing the weights of the DenseNet. The Adam Optimizer, an optimization algorithm for stochastic gradient descent provided by MATLAB, was used to train the deep learning model in both stages. Additional illustration of the idea is given in Figure 4.  
The data from the RxRx1 dataset shared many characteristics with the RxRx19a dataset, which was suitable for transfer learning. As such, we pre-trained the DenseNet model for feature extraction and dropped the classification layers, instead opting for classification layers that would classify the cells into \"healthy\" and \"viral/active,\" with a normalized exponential function to provide an efficacy score for each of the candidate compounds.  
Figure 4: Architecture of the COVID-19 drug development model.  
A healthy cell is assigned a score of \"0\", and a viral cell with no treatment is assigned a score of \"1\". Therefore, mock cells are labeled \"0,\" and active viral cells are labeled \"1\". A viral cell treated by a compound is scored between 0 and 1, indicating the degree of effectiveness of the compound in treating COVID-19. Thus, with our method, we could take the mean of the outputs for each compound, which could give us a more informative measure for the perceived impact of each compound instead of a binary \"healthy/active\" output. This method allows us to select the compounds with the most promising leads for further investigation.
## 1 Introduction
_Reference evapotranspiration (ET\({}_{0}\)):_ Collect climate data and choose the method for calculating ET\({}_{0}\) for each 30 or 10 day period using the average climate data. 2. _Crop coefficient (kc):_ Determine the timing of planting or sowing, the rate of crop development, the duration of crop development stages and the growing season. Choose the kc for a given crop plan and crop development stage under prevailing climatic conditions, and prepare a crop coefficient curve for each one. 3. _Crop evapotranspiration (ETcrop):_ Calculate ETcrop for each 30- or 10-day period:\[ETc=Kc*ET_{0}\]  
Irrigation requirements: Part of the crop water requirements is met by rainfall (_Pe_), groundwater (_Ge_) and stored soil water (_Wb_); or  
\[Irr.Req=ETc-Pe-Ge-Wb\]  
and is determined on a monthly basis. The specific objectives of this work were to (i) use high spatial resolution Sentinel-2 images to map crop types in the Boufakrane watershed; (ii) evaluate machine learning (ML) methods such as support vector machine (SVM) classifier and random forest (RF) in crop species' mapping; (iii) use the CROPWAT 8.0 model to estimate water demand for agriculture in the study area through the calculation of potential evaporation and effective rainfall. Finally, irrigation water requirements were estimated.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 5 Experiments in the Linux Shell Environment
### Parameter Server Subroutine  
In the parameter server approach, the actions of broadcasting and gathering (allgather) together make up one cycle or operation. To put it simply, when you add the time it takes for broadcasting and gathering (allgather) in this cycle, you obtain the total latency in the parameter server method. In our study, we broke down and separately tested each of these sub-tasks. As explained in Section 2, MPI used the gather function, while NCCL used the allgather function for these tasks.
# A Termination Criterion for Probabilistic Point Clouds Registration
## 5 Conclusions  
We introduced the usage of the relative cost drop as termination criterion for the Probabilistic Point Clouds Registration Algorithm. We tested this criterion on different datasets and on a comprehensive benchmark for point clouds registration algorithms [6], which is composed of several registration problems, with different degrees of overlap and initial misalignment. The experiments prove that the cost drop is effective at stopping the algorithm at the right iteration, that is, when the algorithm has converged to a good solution that cannot be improved substantially anymore. Moreover, it stops the algorithm very early when solving problems that are not going to converge to the right solution even when using more iterations, which is a very desirable behaviour. While it requires two  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sequence** & **Median** & **0.75 Quantile** & **0.95 Quantile** & **Iterations** \\ \hline box\_met & 1.28 & 2.49 & 4.27 & 39.40 \\ hauptgebaude & 0.01 & 0.02 & 0.77 & 42.35 \\ pioneer\_slam3 & 0.15 & 0.34 & 0.75 & 36.34 \\ urban05 & 0.44 & 0.65 & 4.31 & 39.65 \\ gazebo\_winter & 0.02 & 0.03 & 0.05 & 41.09 \\ planetary\_map & 0.54 & 1.08 & 2.07 & 24.89 \\ long\_office\_household & 0.17 & 0.63 & 1.99 & 35.20 \\ plain & 0.17 & 0.46 & 0.93 & 31.78 \\ pioneer\_slam & 0.18 & 0.44 & 3.64 & 38.13 \\ stairs & 0.03 & 0.09 & 0.23 & 34.08 \\ gazebo\_summer & 0.05 & 0.19 & 0.74 & 35.96 \\ wood\_autumn & 0.02 & 0.03 & 0.06 & 40.41 \\ apartment & 0.06 & 0.28 & 1.50 & 33.36 \\ wood\_summer & 0.01 & 0.02 & 0.03 & 42.44 \\ p2 at\_met & 0.47 & 0.97 & 1.96 & 29.75 \\ \hline total & 0.10 & 0.45 & 1.86 & 29.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of PPCR on a comprehensive benchmark [6], using the cost drop with 1% and 20 iterations as thresholds.  
Figure 7: Histograms of the median results of PPCR on a comprehensive benchmark [6] with two different termination criterion (100 iterations and the cost drop).  
parameters, we propose values that are effective on a wide range of registration problems and do not need any further fine-tuning.  
Conceptualization, methodology, software and writing--original draft preparation S.F.; writing--review, editing, supervision and project administration, D.G.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study is available at [[https://github.com/iralabdisco/point_clouds_registration_benchmark](https://github.com/iralabdisco/point_clouds_registration_benchmark)]([https://github.com/iralabdisco/point_clouds_registration_benchmark](https://github.com/iralabdisco/point_clouds_registration_benchmark)) (accessed on 17 March 2021).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PPCR & Probabilistic Point Clouds Registration \\ ICP & Iterative Closest Point \\ MSE & Mean Squared Error \\ \end{tabular}
# Spare Parts Made by Additive Manufacturing to Improve Preventive Maintenance
## 3 Results: Case Study
### Selected Lists
Another example could be a tool head that is used to transfer loads or to transmit rotations;
* Small and medium size: most of the time they are small and hard parts and sometimes medium-sized parts that do not reach more than a cube volume of \(150\times 150\times 150\) mm\({}^{3}\);
* Fourth list (4): this category includes large-volume individual parts made of metallic material. All parts selected in this category are similar to the ones in category 2, but have a larger volume. Most of these parts are of mechanical construction with large diameters (more than 500 mm) and shaft lengths of more than 800 mm;
* Fifth list (5): this category groups assembled parts containing two or more pieces. They are parts made of multiple materials (polymers and metals) and have a small or medium volume, not exceeding \(500\times 500\times 500\) mm. They are parts manufactured by combining a metal and a flexible polymer such as elastomer that will protect the metal by absorbing impacts or other loads;
* Sixth List (6): this list includes assemblies made from a single family of materials. Category 6A group assemblies are made of polymers only and category 6B group assemblies are made of metals. When roughness is not a constraint, some assemblies can be printed by SLS technique;
* External list: it is a list of pieces selected by technical criteria which are in a relationship with 3D printing parameters and characteristics, and that are available and suitable for the technology; the criteria were as follows:
* Metallic materials: for example, stainless steel, aluminum and titanium, available in 3D printing technology;
* Polymeric materials: for example, thermoplastic and thermosets like Epoxy, ABS (Acrylonitrile Butadiene Styrene) and PA (Polyamide);
* Pieces that do not exceed a volume dimension of \(500\times 500\times 500\) mm\({}^{3}\) to ensure their entire volume printability in the 3D printer without dividing them into parts and joining them together to conserve the same properties and the geometry of the original part. Figure 5: Examples of classified spare parts: (**a**) from list 1A (polymer), (**b**) from list 2B (metal), (**c**) from list 3A (polymer), (**d**) from list 4 (large part), (**e**) from list 5 and (**f**) from list 6.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
###### Abstract  
Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap in field of Text-to-Cypher queries, focusing on Neo4j. Using the Design Science Research Methodology, we developed a Natural Language Interface which can receive user queries in real time, convert them into Cypher Query Language (CQL), and perform targeted queries, allowing users to choose from different graph databases. In addition, the user interaction is expanded by an additional chat function based on the chat history, as well as an error correction module, which elevates the precision of the generated Cypher statements. Our findings show that the chatbot is able to accurately and efficiently solve the tasks of database selection, chat history referencing, and CQL query generation. The developed system therefore makes an important contribution to enhanced interaction with graph databases, and provides a basis for the integration of further and multiple database technologies and LLMs, due to its modular pipeline architecture.  
chatbot; ChatGPT; cypher language; graph database; knowledge graphs; LLM; natural language interface; Neo4j; question answering +
Footnote †: journal:  
0000-0001-5000-0001-5000]M.[PERSON]; [PERSON]; [PERSON]; [PERSON] Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases. _Future Internet_**2020**, _16_, 488. [[https://doi.org/10.3900/s16120438](https://doi.org/10.3900/s16120438)]([https://doi.org/10.3900/s16120438](https://doi.org/10.3900/s16120438))
## 4 Experimental Results
### Error Rate  
The error rate is the measure that indicates the number of incorrectly recovered bits in the extracted secret information. Here, the RDH scheme is able to recover with a zero-bit error rate, which implies that it extracts the embedded data error-free.  
The details of the embedding capacity and the bit error rate of the proposed RDH scheme are represented in Table 2.
## 3 Results
### Quantification Speed Test  
Table 1 summarizes the processing time for quantifying optical properties and perfusion properties using both GPU- and CPU-based computation. It is clear that with the GPU acceleration, the processing time of three types of quantification were reduced. Note that the LUT access time (processing time in the parentheses) accounts for a smaller portion of the overall processing time in GPU-based processing than that in CPU-based processing.  
Figure 4: Two-stage LUTs for stage-1 optical (**A**,**B**) and stage-2 perfusion (**C**,**D**) property mapping. Reflectance and modulation in the stage-1 LUT, and absorption coefficients at 660 and 850 nm in the stage-2 LUT, were converted into search indices.  
Figure 3: Flowchart of in silico quantification and validation of 30 digital phantoms.  
Although the quantifications for the perfusion property with both the stage-2 LUT and the Beer-Lambert Law were accelerated with a GPU, the stage-2 LUT clearly showed a much higher efficiency. The stage-2 LUT is approximately 5.5 and 13.5 times faster for small and large sized images, respectively. Without GPU acceleration, the stage-2 LUT still showed about 90% higher efficiency than the Beer-Lambert Law method.  
It is worth noting that a large image size imposes a much higher penalty on CPU-based processing than on GPU-based processing. The CPU-based processing time is proportional (1\(\times\), 4\(\times\), and 16\(\times\)) to the image sizes for all three types of quantifications. On the other hand, the GPU-based processing time is less affected by the image sizes, especially for the LUT-based quantifications  
Converting the processing time to processing speed (frames per second/FPS) revealed the GPU-based two-stage LUT (sum of processing time I and II) was capable of achieving a processing speed of 266, 174, and 74 FPS for small, median, and large sized images, respectively, which is 2.1, 3.9, and 6.5 times faster than that of the Beer-Lambert Law-based processing (sum of processing time I and III) (Figure 5A). On the other hand, CPU-based quantification was slower, but it still achieved approximately 65, 16, and 4 FPS with the two-stage LUT for three image sizes, which is about 30% faster than Beer-Lambert Law-based processing (Figure 5B).
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology  
This paper improves a one-stage object detection algorithm and a vessel trajectory tracking algorithm to achieve accurate vessel detection and tracking along a waterway. Additionally, a trajectory prediction algorithm is employed to forecast vessel AIS data, facilitating the integration of waterway video imagery and AIS data. Finally, to enhance the accuracy of inland waterway traffic situation awareness, we have developed an augmented reality (AR) framework for traffic situation perception.  
The overall framework of our waterway traffic situation awareness architecture is shown in Figure 2 below:
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
## 5 Conclusions  
In this paper, we developed a VSP-driven local full waveform inversion. First of all, we demonstrate the local FWI based on the standard Marchenko redatuming. We then introduced the Marchenko source-receiver redatuming method with VSP data to estimate the subsurface redatumed wavefield containing more accurate laterally propagating waves. A numerical experiment was carried out to investigate the sensitivities of the VSP Marchenko source-receiver redatuming method and the standard Marchenko redatuming method to laterally propagating waves. The VSP-driven local FWI can be performed by simply reforming the objective function of the local FWI based on standard Marchenko redatuming.  
The VSP and surface data jointed local FWI was applied to a synthetic data set of the Benchmark model of the strike-slip faulted and fracture-vuggy carbonate reservoir in Tarim Basin. As a result of the inclusion of laterally propagating waves in the observed data, the VSP and surface data jointed local FWI has a significant improvement in the lateral resolution of small structures compared to local FWI based on standard Marchenko redatuming.  
Conceptualization, X.H. and K.L.; methodology, K.L.; software, K.L.; validation, Y.H., K.L. and X.C.; formal analysis, X.H.; investigation, K.L. and K.C.; data curation, K.L.; writing--original draft preparation, K.L.; writing--review and editing, X.H., Y.H. and J.T.; visualization, K.L. and K.C.; supervision, X.H.; project administration, Y.H.; funding acquisition, X.H. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 41904130, Grant 42241206 and Grant U20B2016.  
Not applicable.  
The authors would like to thank the Tarim Oilfield Exploration and Development Research Institute for the data support. We would also like to thank [PERSON] for reviewing and improving the manuscript.  
Figure 14: The common source gather with a column of virtual receivers x\({}_{\text{pv}}\) = (X: 2155 m, Z: 5555–6185 m) corresponding to a virtual source at x\({}_{\text{sep}}\) = (X: 2500 m, Z: 6600 m) obtained by the VSP Marchenko source-receiver redatuming with a small aperture.
## 2 Materials and Methods
### Phase Field Model
#### 2.2.2 Navier-Stokes Equations  
The Navier-Stokes equation replaces [PERSON]'s law to describe the fluid flow in porous media and calculates the average linear velocity of fluid, so as to obtain accurate results on the pore scale, and describes the mass transfer and momentum transfer of the two-phase fluid. Surface tension is added to the model to explain the capillary effect [26]. The Navier-Stokes equation is:  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho(\mathbf{u}\cdot\
abla)\mathbf{u}=\
abla[-p \cdot\mathbf{I}+\mu(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})]+\mathbf{F}+\rho g \tag{2}\]  
Because the model is in a two-dimensional horizontal plane, ignoring the gravity, that is, \(\rho g=0\), the formula is obtained:  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho(\mathbf{u}\cdot\
abla)\mathbf{u}=\
abla[-p \cdot\mathbf{I}+\mu(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})]+\mathbf{F} \tag{3}\]  
\(\rho\)--The mixed fluid density in the two-phase transition interface, kg/m\({}^{3}\);  
\(\mathbf{u}\)--Fluid velocity, m/s;  
\(t\)--Time, \(s\);  
\(p\)--Pressure, Pa;  
\(\mathbf{I}\)--Unit matrix, dimensionless;  
\(\mu\)--Mixed fluid viscosity, Pa\(\cdot\)s;  
\(T\)--Temperature, K;  
\(\mathbf{F}\)--Surface tension, N;  
\(\mathbf{g}\)--Gravity acceleration, kg/s\({}^{2}\).  
\(\mathbf{F}\) can be expressed by Formula (4):  
\[\mathbf{F}=G\
abla\varphi, \tag{4}\]  
\(G\) is a chemical potential, J/m\({}^{3}\), expressed by Formula (5):  
\[G=\lambda\left[-\
abla^{2}\cdot\varphi+\frac{\varphi\big{(}\varphi^{2}-1 \big{)}}{\varepsilon^{2}}\right]=\frac{\lambda}{\varepsilon^{2}}\cdot\psi \tag{5}\]  
\(\varepsilon\)--Interface thickness control parameter, m;  
\(\lambda\)--Mixed energy density, N;  
\(\varphi\)--Phase field variables, dimensionless;  
\(\psi\)--Phase field covariates, dimensionless.  
Fluid viscosity \(\mu\) and mixed fluid density \(\rho\) in the two-phase transition interface are expressed as follows:  
\[\left\{\begin{array}{l}\rho=\rho_{1}+(\rho_{2}-\rho_{1})V_{f2}\\ \mu=\mu_{1}+(\mu_{2}-\mu_{1})V_{f2}\end{array}\right. \tag{6}\]  
\(\rho_{1}\)--Density of fluid 1, kg/m\({}^{3}\);  
\(\rho_{2}\)--Density of fluid 2, kg/m\({}^{3}\);  
\(\mu_{1}\)--The viscosity of fluid 1, Pa\(\cdot\)s;  
\(\mu_{2}\)--The viscosity of fluid 2, Pa\(\cdot\)s;  
\(V_{f2}\)--Volume fraction of fluid 2, dimensionless.  
\[\left\{\begin{array}{l}V_{f1}=\frac{1-\varphi}{2}\\ V_{f2}=\frac{1+\varphi}{2}\end{array}\right., \tag{7}\]  
The range of phase field variable \(\varphi\) is [\(-\)1,1]. In the homogeneous phase region, the value of \(\varphi\) is \(-\)1 or 1. When \(\varphi=1\), the phase region is pure fluid 2, and when \(\varphi=-1\), the phase region is pure fluid 1. In the transition zone between fluid 1 and fluid 2, when \(\varphi\)changes uniformly, \(-1<\varphi<1\). In this oil-water two-phase flow simulation, the oil phase as the displaced phase is fluid 1, namely \(\varphi=-1\), and the water phase as the displaced phase is fluid 2, namely \(\varphi=1\).