esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 4 Results  
### Dataset  
To evaluate our framework model, we built a question-to-Cyber dataset. The dataset includes questions in English, corresponding Cypher queries, and related schema information. It consists of 1240 instances in the test set and 4921 instances in the training set. The dataset was created using three Neo4j graphs: The Movie graph, the Norwin graph, and a modified Movie graph.  
The Movie graph is a common example dataset provided by Neo4j to demonstrate its capabilities and features. It represents a simplified movie database containing information about movies, actors, directors, and their relationships. Here is an overview of its structure: Nodes:  
* Movie: represents movies, with properties like title, released (year), and tagline.
* Person: represents people (actors and directors), with properties like name and born (year). Relationships:
* ACTED_IN: connects a Person node to a Movie node, indicating that the person acted in the movie.
* DIRECTED: connects a Person node to a Movie node, indicating that the person directed the movie.
* FOLLOWS: connects two Person nodes, indicating that one person follows the other (e.g., on social media).  
The modified Movie graph is the more clear version of the Movie graph because it split the People node specifically. In the modified version, we have four nodes: Movie, Actor, Reviewer, and Producer.  
The Northwind graph is another example dataset, inspired by the classic Northwind database often used in relational database tutorials. It represents a simplified version of a business database containing information about customers, orders, products, and suppliers. Nodes:  
* Customer: represents customers, with properties like customerID, companyName, and contactName.
* Order: represents orders, with properties like orderID, orderDate, and shipCity.
* Product: represents products, with properties like productID, productName, and unitPrice.
* Supplier: represents suppliers, with properties like supplierID, companyName, and contactName. Relationships:
* PLACED: connects a Customer node to an Order node, indicating that the customer placed the order.
* CONTAINS: connects an Order node to a Product node, indicating that the order contains the product.
* SUPPLIES: connects a Supplier node to a Product node, indicating that the supplier supplies the product.
# Practical Approach for Developing Lateral Motion Control of Autonomous Lane Change System
### Vehicle Lateral Motion Model  
* \(y:\) distance from the center of gravity (c.g.) to the center of turn in \({}^{\text{\tiny{p}}}\{xyz\}\);
* \(y_{des}:\) distance from the lane center to the center of turn in \({}^{\text{\tiny{p}}}\{xyz\}\);
* \(e_{y}=y-y_{des}:\) lateral lane center offset at c.g.;
* \(L:\) look-ahead distance;
* \(\psi_{des}:\) yaw angle slope of the lane center;
* \(e_{\psi}=\psi_{des}-\psi:\) heading angle error at c.g.;
* \(\dot{\psi}:\) yaw rate;
* \(V:\) velocity of the vehicle at c.g.;
* \(\alpha:\) tire slip angle;
* \(\beta:\) side slip angle at c.g.;
* \(C_{\alpha}:\) cornering stiffness of the tire;
* \(F_{y}:\) lateral tire force;
* \(I_{z}:\) yaw inertia of the vehicle;
* \(M_{z}:\) moment balance of the vehicle;
* \(m:\) total mass of the vehicle;
* \(l:\) distance of the tire from c.g. of the vehicle;
* \(u:\) input (=\(\delta:\) steer angle) of the control system.  
* \(f:\) front;
* \(r:\) rear;
* \(x:\) longitudinal;
* \(y:\) lateral;
* \(des:\) desired.  
We introduce the generally used vehicle model in terms of lateral position and heading errors with respect to the road for lateral control to regulate both lateral position and heading errors. This implies that the vehicle is maintained at the road center. Further, we derive the new vehicle motion model for lateral control systems by considering the jumped state of the lateral offset in a lane-change situation. The new model presents the plane motion of the vehicle in the cylinder domain.
## 2 Governing Equations  
### Mathematical Modeling  
In this study, a viscoelastic unsaturated soil layer of thickness \(H\) with a permeable surface which was subjected to an exponential loading \(q(t)=q_{0}(1-\mathrm{e}^{-bt})\) and an impermeable bottom was adopted, where \(q_{0}\) and \(b\) were the loading parameters determined by the actual loading conditions. As shown in Figure 1, a typical soil element of volume \(\mathrm{d}V=1\times 1\times\mathrm{d}z\) with the 1D flow of water and air phases in the \(z\) direction was investigated in a simplified mathematical model.  
As shown in Figure 2, the Merchant model with elastomer \(L_{0}\) used in this paper consists of a Kelvin body alongside an elastomer \(L_{1}\) and a viscous body \(N\).  
The constitutive equation of the Merchant model can be presented as follows [29]:  
\[\sigma+\frac{\eta}{E_{0}+E_{1}}\frac{\mathrm{d}\sigma}{\mathrm{d}t}=\frac{E_{0 }E_{1}}{E_{0}+E_{1}}\varepsilon+\frac{\eta E_{0}}{E_{0}+E_{1}}\frac{\mathrm{d }\varepsilon}{\mathrm{d}t} \tag{1}\]  
Figure 1: Simplified modeling of 1D consolidation in unsaturated soils.  
Figure 2: Merchant model adopted in this study.  
where \(\sigma\) is the stress; \(\epsilon\) is the strain; \(E_{0}\) and \(E_{1}\) are the stiffness coefficients of the elastic bodies \(L_{0}\) and \(L_{1}\), respectively; and \(\eta\) is the viscous coefficients of the dashpot \(N\).  
Applying the Laplace transform to Equation (1) gives  
\[V(s)=\frac{\widetilde{\epsilon}(s)}{\widetilde{\sigma}(s)}=\frac{1}{E_{0}}+ \frac{1}{E_{1}+\eta s} \tag{2}\]  
where \(s\) represents the Laplace transform of the conjugate variable of \(t\).  
According to [PERSON]'s correspondence principle [37], the solution of the corresponding viscoelastic body can be obtained by using \(V(s)\) instead of the constant \(1/E\) (where \(E\) represents the modulus of elasticity) in the solution of the linearly elastic body.
## 4 Experimental Materials
### Coaja Simulated Scenario  
The second investigation is based on a simulation environment using Cooja simulator to measure the RPL protocol performance in large-scale networks. A large-scale scenario of SG network is simulated using 1000 motes to emulate smart meters that are a small constrained device to report power usage to a central authority. A smart meter is essentially a sensor node in charge of collecting data and finally data routing. Cooja simulator is used to emulate Tmote Sky platform with MSP430 board with a compatible CC2420 radio chip. The Multipath Ray Tracer Medium (MRM) model is considered as the link failure model, which is used to make a realistic interference. This link failure model presents the ray tracing techniques to model different types of radio propagation effects, such as diffraction, multipath, and refraction. Nodes are dispersed randomly and uniformly over an area of 300 \(\times\) 300 m\({}^{2}\) and each node sends a data packet of 200-bytes every 30 s towards the root  
\begin{table}
\begin{tabular}{c c} \hline \hline
**IoT-LAB Experiment Parameters** & **Value** \\ \hline Num. of nodes & 140 nodes M3 hardware \\ Network Topologies & uniform and random over 225 m\({}^{2}\) \\ Testbed site & Inria Lille \\ Experiment duration & 120 min \\ Operating system & Contiki 2.7 \\ Frequency & 2.4 GHz \\ Mac protocol & Contiki-MAC protocol \\ Transmission power & 3 dBm \\ Startup delay & 65 s \\ Data rate & 250 kbps \\ The number of collectors & 7 sniffers \\ The number of the border router & one node \\ DIO Interval Minimum(ms) & 12 (controls the rate of DIO message transmission) \\ DIO Interval Doublings(ms) & 8 (the number of times, the DIO minimum value can be doubled) \\ RPL Mode Operation & 2 (storing mode) \\ DIO Redundancy Constant & 10 \\ Max Rank Increase & 1792 \\ Minimum Hop Rank Increase & 256 (the root rank is 256) \\ DATA packet period & 45 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: IoT-LAB testbed experiment parameters.  
Figure 6: RPL DODAG logical topology using 6 LoWPAN Foren6 diagnostic tool.  
node. We repeat simulation for different values of RX ranging from (30, 40, 50-100%). Traffic logs are extracted from the simulation project using a Perl script. Table 3 summarizes Cooja experiment parameters. The objective of this experiment is to evaluate OF0 and ETX for only the upward traffic defined from client nodes to the root node. We run simulations for different RX values ranging from (30, 40, 50-100%).
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 2 Synthesis of Hydrophobic Catalysts
### Silicas
The two hydrophobic catalysts showed superior catalytic performances in the hydrogenation of methyl acrylate in aqueous solution, in particular, the one impregnated after silanization [32]. The functionalization with organic compounds can be also applied to silica mixed with other oxides. Tetra ethoxy silane combined with methyl triethoxy silane could be used to introduce methyl functionality on TiO\({}_{2}\)-SiO\({}_{2}\) mixed oxide in a controlled way to obtain a more hydrophobic surface useful to improve the catalytic performance in cycloocene epoxydation reaction. However, a too high degree of methylation, and thus hydrophobicity, can be detrimental for the activity of the catalyst due to a lower affinity to H\({}_{2}\)O\({}_{2}\)[33]. [PERSON] et al. showed a method to increase water toleranceof ZrO\({}_{2}\)-SiO\({}_{2}\) acid catalyst used in biphasic esterification of glycerol with oleic acid. The mixed oxide was prepared covering the ZrO\({}_{2}\) with SiO\({}_{2}\), and subsequently, the surface was modified with trimethoxymethylsilane and 2-(4-chlorosulfonylphenyl)ethyltrimethoxysilane tailoring the acidity and surface hydrophobicity [34]. It is not common to find a total synthesis of hydrophobic silica in the literature. One example is the synthesis of superhydrophobic silica aerogels that could be achieved by using methyltrimethoxysilane (MTMS) precursor by a two-step (acid-base) sol-gel process followed by supercritical drying. The obtained hydrophobic aerogels show a water contact angle ranging from 158\({}^{\circ}\) to 164\({}^{\circ}\) and a thermal stability up to 530 \({}^{\circ}\)C [16]. In Table 3, the most common hydrophobic functionalizations of zeolites and silicas are shown.
## 3 Results and Discussion
### Rutting Resistance  
The Wheel Tracking Test was performed at two temperatures (40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C). Figures 8 and 9 show the relationship between the number of passes and the rut depth for all of the samples. The results depict an enhancement in the rut resistance of the asphalt following the addition of the GNPs. The increment in the rut resistance is more pronounced at 55 \({}^{\circ}\)C, where the rut depth decreased from 7.3 mm to 4.7 mm after the addition of 4% of GNPs. At 40 \({}^{\circ}\)C, the rut depth decreased from 2.8 mm to 2.59 mm when 2% of GNPs were added, and further decreased to 2.45 mm following the addition of 4% of GNPs. Table 6 shows the results of the Wheel Tracking Slope. It can be seen that, with the increase in the content of the GNPs, the values of the slope decrease at 55 \({}^{\circ}\)C and remain constant at 40 \({}^{\circ}\)C. A decrease in the slope means that there is an increase in resistance to a permanent deformation [24]. According to the results, the maximum resistance to a permanent deformation can be achieved at 55 \({}^{\circ}\)C by adding 4% of GNPs. Due to the high surface area of the GNPs, they are reactive and form a strong bond with bitumen, imparting strength [25]. The GNPs may also act as filler, increasing the structural asphalt and significantly reducing the temperature susceptibility of asphalt [26].  
Figure 8: The Wheel Tracking Test results at 40 \({}^{\circ}\)C. GNPs = Graphene Nano-Platelets.  
Figure 9: The Wheel Tracking Test results at 55 \({}^{\circ}\)C. GNPs = Graphene Nano-Platelets.
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 2 Materials and Methods
* Is the model able to fulfil the tasks that were placed on it in the project assignment? When the model is fully finished, it is necessary to validate it in one of two available ways. 1. Based on the collected data from previous periods, it is necessary to select a certain period and run the simulation with the same initial parameters as in the selected period. After the simulation run is over, it is necessary to compare the simulation results with the results from the real system. If the results are identical or their difference does not exceed an acceptable limit, the model can be considered valid. 2. If the data were not collected in the required range before the creation of the scheduling system, the test must first be run in the simulation model and then the schedule must be applied in production and the results compared. In essence, this validation logic is the opposite of that in method 1. The statistical module is part of the solution to the short-term planning system of intelligent production. The primary task of the statistical module is to collect data on the production schedule to enable its evaluation and a variant comparison to select the most feasible solution. During the simulation run, all necessary information will be automatically collected, not only to evaluate the production schedule but also to detect bottlenecks, determine resource utilization, and analyze the collected data. It also includes the analysis of areas, storage tank filling, the utilization of machines and logistics resources, and the utilization of operators. To validate the reliability of the proposed scheduling approach, confidence intervals (CIs) were calculated for key performance indicators such as the production cycle time and order completion rates. The significance of the differences between the scheduling scenarios was tested using statistical tests, including Student's _t_-test and ANOVA, to ensure that the observed improvements were statistically significant rather than random variations. The key data for the evaluation of the schedule are as follows. * The makespan--The total time required for all orders to be processed. The goal of production scheduling is to minimize this. * The average time for which the order stays in the system. * An overview of completed and unfinished tasks within the set deadline, information about order delays, etc. A complete statistical evaluation of the schedule is based on selected parameters with the possibility of setting penalty coefficients; see Formula (4). The principle of the coefficients is the same as in Formula (3)--this means that larger coefficients exert a greater impact on the final result of the evaluation.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 2 SAR Interferometric Processing
### Multi-Temporal Interferometric SAR Techniques
Implications of short baselines for the generation of ground displacement products using conventional and new advanced multi-temporal InSAR methods currently represent a very active research topic, particularly with respect to the new generation of SAR systems characterized by improved revisiting times [92]. One of the main limitations of the InSAR methodology is that, for each acquisition geometry, only the one-dimensional sensor-to-target _line-of-sight_ (LOS) projection of the ground deformation vector can be measured. Given the usual near-polar orbits configuration, a simple algebraic combination of data acquired from both ascending and descending orbits can give information on the up-down and east-west components of the deformation while remaining quite blind to the north-south one. On the other hand, more advanced techniques such as pixel offset (PO) tracking [93] and multiple aperture interferometry (MAI) [94] have been extensively used to obtain information also on the north-south component of the displacement. However, they imply the implementation of some additional processing stages such as, for instance, the computation of pixels offsets from coregistered SAR images in the PO method, or the azimuth spectra filtering and the generation of left and right looking multilook interferograms in the MAI approach. The impact on the overall computational load is from moderate to high, depending on the image swath coverage and the number of used interferograms. To reduce the overall computational burden, Kalman-filter-based methods were also developed and tested to obtain updated ground deformation time-series when new SAR acquisitions are added, without the need to re-process the entire datasets [95]. Boosted by successful applications in any area of scientific research, currently, the methodologies based on machine learning paradigms [96] are also being employed for classification, regression, clustering, or dimensionality reduction tasks of large sets of InSAR-driven input data. Research innovation in this field is scientifically valuable, with the potential to open new lines of development for the exploitation of EO data for monitoring, risk management, and forecasting analyses by semi-automatically analyzing large sets of InSAR data and ground displacement InSAR maps [97; 98].
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### Antioxidant Activity Evaluation  
#### 2.5.1 Ferric Ion Reducing Antioxidant Power (FRAP) Microassay  
This test was performed according to [PERSON] et al. with some modifications [29]. FRAP working solution was prepared by mixing 300 mM acetate buffer (pH = 3.6), 10 mM TPTZ solution (i.e., 2, 4, 6-tripyridyl-s-triazine in 40 mM HCl) and 20 mM FeCl\({}_{3}\times\) 6H\({}_{2}\)O at volume ratio 10:1:1. Diluted samples and Trolox solutions (20 \(\mu\)L) were added together with FRAP working solution (280 \(\mu\)L) in 96-well microplate in triplicate. Reaction mixtures were incubated at 37 \({}^{\circ}\)C for 30 min in dark conditions. The absorbance was measured at 630 nm. Antioxidant activity was calculated from calibration curve (y = 1.2368x + 0.0592) with the range 0.1-1 mmol Trolox L\({}^{-1}\) and with linearity (r\({}^{2}\) = 0.9947). Limit of detection (LOD) was 6 \(\mu\)mol L\({}^{-1}\), while limit of quantification (LOQ) was 20 \(\mu\)mol L\({}^{-1}\). Results were expressed as mM Trolox Equivalents (TE) per gram of dry weight (mM TE g\({}^{-1}\) DW).
## 3 Results
### Intraoperative Measurements  
The electrode was inserted slowly; the duration of the insertion was more than 2 min-in 12 (52.2%) patients. In the remaining 11 (47.8%) patients, the electrode was inserted faster. The average depth of the electrode was 415 \(\pm\) 54 degrees, with a minimum of 300 degrees and a maximum of 495 degrees. The insertion depth was not significantly different in the SNHL and otosclerosis groups (\(p\) = 0.099). The average of NRT threshold values across all electrodes was 190.8 \(\pm\) 17.9 microvolts, with a minimum value of 158.4 and a maximum value of 231.2 microvolts (Table 2). The average of NRT threshold values of patients that were diagnosed with otosclerosis was 213.1 \(\pm\) 16.0 (195.4, 231.2) microvolts and the average of all NRT threshold values of patients that were diagnosed with SNHL was 186.3 \(\pm\) 15.0 (158, 210) microvolts (Table 3). Figure 1 shows intraoperative electrode insertion process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & Mean \(\pm\) SD & Median (95\% CI) & Min-Max \\ \hline Age (years) & 63 \(\pm\) 15 & 66 (59, 7) & 22, 9 \\ Electrode insertion depth (degrees) & 415 \(\pm\) 54 & 420 (405, 450) & 300, 495 \\ \hline Time between surgery and & & & \\ audiometry (days) & 542 \(\pm\) 535 & 316 (224, 533) & 163, 2096 \\ \hline Time from surgery to survey & & & \\ completion (days) & 1153 \(\pm\) 810 & 975 (565, 1654) & 199, 2751 \\ \hline Duration of hearing loss before ear & & & \\ surgery with a cochlear & 20 \(\pm\) 12 & 20 (16, 28) & 2, 5 \\ implant (years) & & & \\ \hline Duration of hearing loss before ear & & & \\ surgery without a cochlear & 21 \(\pm\) 12 & 20 (15, 26) & 2, 5 \\ implant (years) & & & \\ \hline External processor wear time & & & \\ (hours per day) & 13.4 \(\pm\) 2.9 & 14 (12, 16) & 6.5, 16 \\ \hline \hline \end{tabular} Legend: UsherSy: Usher syndrome; SNHL: sensorineural hearing loss; SD: standard deviation; CI: confidence interval.  
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean \(\pm\) SD** & **Median (95\% CI)** & **Min, Max** & **N** \\ \hline Average of all NRT thresholds & & & \\ (microvolts) & 190.8 \(\pm\) 17.9 & 191.1 (181.6, 202.9) & 158.4, 231.2 & 23 \\ \hline Postoperative speech audiometry & & & & \\ at 65 dB (\%) & 75.2 \(\pm\) 24.3 & 85 (70, 90) & 20, 100 & 23 \\ \hline Preoperative average tone & & & & \\ audiometry (dB) & 89.0 \(\pm\) 15.8 & 92.5 (81.3, 102.5) & 56.3, 115 & 23 \\ \hline Average postoperative tone & & & & \\ audiometry (dB) & 104.5 \(\pm\) 21.0 & 112.5 (104, 120) & 56.3, 120.0 & 23 \\ \hline The difference between pre and & & & & \\ postoperative tone audiometry (dB) & & & & \\ \hline \hline \end{tabular} Legend: NRT: neural response telemetry; SD: standard deviation; CI: confidence interval.  
\end{table}
Table 2: Average of NRT thresholds and audiometry results.
# Dynamic Characteristic Analysis of Permanent Magnet Brushless DC Motor System with Rolling Rotor
## 2 Dynamic Model of the Rotor-Bearing-Stator System for the BLDCM
### Electric Magnetization Analysis of the BLDCM  
Based on the relationship between the magnetic flux density and the magnetic vector potential, the radial and tangential magnetic flux density in the air gap can be obtained by  
\[\begin{array}{l}B_{r}=\sum\limits_{n=1}^{\infty}\left[P_{1n}sin(n\varphi)+P _{3n}cos(n\varphi)\right]\\ B_{\varphi}=\sum\limits_{n=1}^{\infty}\left[P_{2n}sin(n\varphi)+P_{4n}cos(n \varphi)\right]\end{array} \tag{7}\]  
where  
\[\begin{array}{l}P_{1n}=-\frac{1}{r}[E_{n}R_{1}\frac{S_{n}(r,R_{2})}{T_{n}(R _{1},R_{2})}+F_{n}R_{2}\frac{S_{n}(r,R_{1})}{T_{n}(R_{2},R_{1})}]\\ P_{2n}=-\frac{1}{r}[G_{n}R_{1}\frac{T_{n}(r,R_{2})}{T_{n}(R_{1},R_{2})}+H_{n}R _{2}\frac{T_{n}(r,R_{1})}{T_{n}(R_{2},R_{1})}]\\ P_{3n}=\frac{1}{r}[G_{n}R_{1}\frac{S_{n}(r,R_{3})}{T_{n}(R_{1},R_{2})}+H_{n}R _{2}\frac{S_{n}(r,R_{1})}{T_{n}(R_{2},R_{1})}]\\ P_{4n}=-\frac{1}{r}[E_{n}R_{1}\frac{T_{n}(r,R_{2})}{T_{n}(R_{1},R_{2})}+F_{n} R_{2}\frac{T_{n}(r,R_{1})}{T_{n}(R_{2},R_{1})}]\end{array} \tag{8}\]  
where \(n\) is a positive integer, \(R_{1}\) and \(R_{2}\) are inside and outside radii, respectively, \(E_{n}\), \(F_{tt}\), \(G_{n}\) and \(H_{n}\) refer to the harmonic coefficients of the magnetic vector potential in the air gap, and functions \(S\) and \(T\) are used to deal with the solutions of the magnetic vector potential.  
The electromagnetic torque can be expressed by  
\[T_{e}=\frac{L_{m}r^{2}}{\mu_{0}}\int_{0}^{2\pi}B_{r}(r,\varphi)B_{\varphi}(r, \varphi)d\varphi\ \ \ \ R_{1}<r<R_{2} \tag{9}\]  
where \(L_{m}\) is the axial length of the stator core, and \(\mu_{0}\) is the air gap permeability.  
Substituting Equation (7) into Equation (9), the electromagnetic torque can be described by the Fourier series  
\[T_{e}=\frac{\pi L_{m}r^{2}}{\mu_{0}}\sum\limits_{n=1}^{\infty}\left(P_{1n}P_{ 2n}P_{3n}P_{4n}\right) \tag{10}\]  
Based on the Maxwell tensor formula, the electromagnetic forces along the \(x\) and \(y\) directions can be written as  
\[\begin{array}{l}F_{ex}=\frac{L_{m}r}{2\mu_{0}}\int_{0}^{2\pi}\left[(B_{r}^{ 2}-B_{\varphi}^{2})cos\varphi-2B_{r}B_{\varphi}sin\varphi\right]d\varphi\\ F_{xy}=\frac{L_{m}r}{2\mu_{0}}\int_{0}^{2\pi}\left[(B_{r}^{2}-B_{\varphi}^{2}) sin\varphi+2B_{r}B_{\varphi}cos\varphi\right]d\varphi\end{array} \tag{11}\]  
Substituting Equations (13)-(17) into Equation (18), the differential equations of the motor system can be written in the form of matrix:  
\[\mathbf{M\ddot{X}}+\mathbf{C\dot{X}}+\mathbf{K}\mathbf{X}=\mathbf{F} \tag{19}\]  
where \(\mathbf{M}\), \(\mathbf{C}\) and \(\mathbf{K}\) are the mass matrix, damping matrix and stiffness matrix, respectively, which are shown in Appendix A.