esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Framework to Understand Tables in Engineering Specification Documents
## 6 Domain Knowledge Map Extension
### Mapping Labels to Concepts and Finding Relationships  
An ontology has concepts and relationships, and it can explicitly represent meanings of them. Terms in labels for engineering specification tables should express some concepts related to engineering specification. Let us assume that a domain ontology was developed and used to represent product specification as shown in Figure 9.  
Rules were developed to find mapping relationships between labels and concepts in the domain ontology. The rules are applied to POS-tagged labels, and the rules determine whether or not a tagged label is a sub-concept of \"Object\", \"Attribute\", or \"Unit\". The rules reused existing concepts and their relationships to introduce a new term as a concept so that they can extend an existing domain ontology. The rules are shown in the followings:  
1. If [NN]* exists in the ontology, then its concept is the existing one. For example, \"pump\" term is the concept \"pump\" which is an object and a sub-concept of \"Equipment\".
2. If a label is [NN1]*+ [NN2]*, and [NN1]* includes an object, and [NN2]* is an attribute, then the object in [NN1]* and [NN2]* attribute are linked with \"has_attr.\" For example, \"central pump max speed\" means \"pump\" has a \"max speed\" attribute.
3. If a label is [NN1]*+ [NN2]*, and [NN1]* does not include an object, and [NN2]* is an attribute, then the [NN1]*+ [NN2]* becomes a sub-concept of [NN2]* attribute. For example, \"stroke length\" becomes a sub-concept of \"length\" attribute.  
Figure 8: A pre-processing example for the table in Figure 1.  
Figure 9: A partial domain ontology example for product specification.  
4. If the label is [NN1] *+ \"of\" + [NN2] * and [NN1] * is an attribute, then [NN2] * is an object and [NN1] * is an attribute of [NN2] *. For example, \"total weight of central pump\" means that \"central pump\" has an attribute \"total weight.\"
5. If the label is [NN] * + [object] and the object exists in the ontology, then the [NN] * + [object] becomes a sub-concept of the object. For example, \"Single-acting plunger-type pump\" becomes a sub-concept of \"pump\".  
The rules defined above discover whether classified and tagged labels in a minimal table are an object, an attribute or a unit, and further the relationships that exist between them. Figure 10 shows the concepts (object, attribute, and unit) that matched with the labels (pump, stroke length, speed rating, rpm, m/s, m, and mm), and how they are related to each other. Two new concepts \"single-acting plunger-type pump\" and \"double-acting piston-type pump\" are defined as a sub-concept of \"pump\" because of rule 5. By rule 3, \"stroke length\" becomes a sub-concept of \"length.\"
# Skin Absorbed Dose Coefficients for Human Legs from Beta Radiation as a Function of Height
## 4 Conclusions  
Beta particles in accident sites are known to be one of the main contributors to skin burns and other health issues for personnel. In this paper, we concluded that it is necessary to expand the previous calculations for contaminated soil with beta particle decay for skin-absorbed doses to other heights above the ground. The energy deposited to the leg dosimeter near the ground from the beta decay is much higher than the deposited energy to the leg at 100 cm height. Thus, the actual absorbed dose by the skin can be higher than using the previous calculations, which makes it necessary to consider these findings in radiation protection measurements. Furthermore, the comparison of the deposited energy between primary electrons and secondary electrons and the generated photons showed the primary electrons make almost all the deposited energy in the phantom cells (>99%) for energy sources greater than 0.4 MeV. However, for lower energy sources, the secondary electrons and photons made up most of the energy deposited in the phantom cells. This validated the simulation results. To further evaluate the results, statistical analysis was conducted, which proved the statistical significance of the results from this paper. The results from the Geant4 at 1 m height were compared with the SADC published by FGR 12, and the results were matched perfectly well with a significant \(p\)-value. Thus, we concluded that the results obtained from Geant4 are valid. Since there is not much experimental data available, the results of this paper can be used in radiation protection measurements. It provides an excellent dataset for an unfortunate nuclear accident to help the power plant staff and the civilians around the contaminated area, as it is an expansion of the FGR 12 report, which only contains the skin-absorbed dose data for the height of 1 m. Thus, the provided results can be used to prepare for nuclear accidents and improve radiation safety measurements for such incidents. Figure 10: Skin absorbed dose comparison between FGR and Geant4. **Author Contributions:** Conceptualization, H.M.; methodology, M.Y. and H.M.; software, M.Y., validation, A.N. and H.M.; formal analysis, M.Y. and R.D. ; investigation, M.Y. and R.D. ; resources, H.M.; data curation, M.Y. and R.D. ; writing--original draft preparation, M.Y. ; writing--review and editing, R.D., A.N. and H.M.; visualization, M.Y. and A.N. ; supervision, H.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Any data request should be directed to the corresponding author, [PERSON] ([EMAIL_ADDRESS]). **Conflicts of Interest:** The authors declare no conflicts of interest.
# Conditional Dependencies and Position Fixing
## 7 Normalizing the Pool of Data
The constituent \(a\) is the \(j\)-th indication support that the \(i\)-th point is a true position, and it is a product of the density function as a diagram of the converted (X and Y) histograms, as well as a unary constant that defines the scope of the assumed neighbourhood. Constituent \(b\) is the mass of uncertainty that is imbedded in the \(j\)-th observation. The sum of both elements (\(a\) and \(b\)), as specified by Equation (8), must not be greater than one. A proportional reduction in the excess amount is suggested if necessary. The combination of all the assignments that involve a given point and all the observations provides a supporting measure that the location represents the true position of the observer. The presented rational enables us to evaluate the conditional dependencies (\(P(H_{i}.x\big{|}\ o_{j})\) and \(P(H_{i}.y\big{|}\ o_{j})\)) once we have obtained the nautical aid indication. We mine the knowledge on their distribution from the raw experimental datasets. The converted histograms \(g_{Xi}\) and \(g_{Yi}\) are the outcomes of the preliminary computation made for the two-dimensional dispersion. An adequate probability is the area in the vicinity of any point of discernment. The density function is used to estimate the probability of a unary range around a given location. To avoid misunderstandings in the results, one should keep the proposed assumption in mind.
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
## 2 Materials and Methods
### Field Tests
#### 2.2.2 Plate Load Test  
The plate load test was carried out using the ground anchor method, as shown in Figure 5. The bearing plate is a rigid circular steel plate with the diameter of 300 mm and the area of 0.07 m\({}^{2}\). The reaction force required for the test is provided by the ground anchors, which are anchored into coal rock. The loading device is a hydraulic jack. The load applied is calculated by the pressure gauge at the hydraulic pressure controller, and the settlement of the bearing plate is measured by the displacement indicator [26].  
Figure 4: Layout of test points.  
Figure 5: Sketch of plate load test: (**a**) Front view; (**b**) Floor plan.  
The surface layer of the coal rock may be disturbed during excavation. In order to eliminate the influences, a test pit with the depth of 1 m was excavated downward from the working plane, and the fresh coal rock was exposed. Thus, the elevation of the test plane was about 1627 m. After leveling the test plane with coarse sand, the bearing plate was placed for testing.  
The plate load test adopts the single-cycle loading method. The load is gradually increased until failure, and then unloaded in stages. When loading, the loading amount of the first level shall be 1/5 of the estimated bearing capacity, and the loading amount of each subsequent level shall be 1/10 of the estimated bearing capacity. The settlement is measured immediately after the application of each level of load, and then every 10 min thereafter. When the difference between three consecutive readings is not greater than 0.01 mm, the settlement convergence criteria are met, and the next level of load can be applied. When the pressure cannot be applied, or barely applied but cannot be maintained, it can be considered that the ultimate bearing capacity has been reached, and the loading process shall be terminated. When unloading, the unloading amount of each level is twice the loading amount. The settlement of each level of load is measured at an interval of 10 min, and the load can be removed after three times of reading. After unloading to zero, the settlement is measured at intervals of 10 min, until the rebound amount of 30 min is less than 0.01 mm [26].  
The loading conditions of each plate load test are shown in Table 1. The loading amount of the first level is 280 kPa, and the load increment of each level is 140 kPa. When PLT1 is loaded to the 14 level, it is damaged, and the maximum loading value is 2100 kPa; the loading level and the maximum loading value are 13 and 1960 kPa for PLT2 and 15 and 2240 kPa for PLT3, respectively.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Data Collection Environment and Procedures
#### 2.1.2 Driving Behavior Data Collection  
To obtain the behavioral data of drivers on sharp turns, a driving simulation test was conducted using the driving simulator developed by Beihang University. Firstly, based on the measured road profile data of the X412 County Road test section, a virtual road scene model with the same height as the actual road section was built in the driving simulator, and parameters such as traffic flow, weather, and vehicle conditions matching the actual conditions were set. Then, 30 drivers were recruited as participants for the experiment, covering different genders, ages, and levels of driving experience to ensure representativeness and diversity of samples. During the experiment process, each driver had to complete two simulated driving tasks from Damao Village to Lishu Village with a time interval of no less than 7 days to eliminate the impact of learning effects. The drivers wore SMI eye-tracking glasses to record their visual attention behavior data. The driving simulator was equipped with a steering wheel, a pedal-force feedback device, and a multi-channel data acquisition system to collect the drivers' speed, acceleration, manipulation amount, and other driving behavior data at a sampling frequency of 60 Hz. Through high-fidelity road scene reproduction and detailed collection of driving behavior data, rich  
Figure 2: Location of the experimental section.  
behavioral data samples of drivers on sharp turns were obtained, laying the foundation for subsequent analysis. The instruments used in this experiment are shown in Figure 3.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity
### Two-Dimensional (2D) VISAR
#### 3.1.7. **Mavis**  
The MAVIS is shown in Figure 18. The permanent magnets are toroidal in shape; the coils are wound in the same direction; a capacitor is connected across the coil output for noise reduction; and a resistor on the output side of the coil is necessary for impedance matching. Eddy currents are induced in the metallic impactor as the projectile enters the field of the permanent magnet. The currents flowing in the impactor set up their own magnetic field, which interacts with the field of the permanent magnet. A current is therefore induced in the stationary pickup coil due to the approaching impactor field. This process is repeated with each pickup coil station. After the test, measurements are taken at or near the zero crossover points of the records to obtain the time interval needed to calculate the velocity.  
This measurement method belongs to the non-contact measurement category and has high accuracy. When measuring the projectile speed of non-metallic flyers, it is necessary to add metal rings to realize magnetic induction, but this will increase the projectile mass, thus affecting the acquisition of high projectile speed. In addition, the structure and installation of the measuring device are relatively complex.  
[PERSON] et al. developed a magnet-induced system capable of measuring speeds ranging from 1 to 6 km/s. The system was used to monitor the projectile velocity on a two-stage light gas gun. Time observation and velocity measurement accuracy are 0.03% and 0.10%, respectively [49]. [PERSON] et al. used a magnetic induction approach to measure projectile velocities on the two-stage light gas gun. The device is designed to allow projectile speeds ranging from 3.5 to 6.5 km/s [50].
## 3 Results
### Hybrid Models  
Hybrid models combine statistical and RTM input and output data to generate a simulation dataset which includes spectral data, vegetation indices and biophysical and biochemical properties of vegetation [49]. There are different hybrid model types, which include an iterative approach, optimisation method, look up tables (LUT) and artificial neutral networks. In terms of machine learning algorithms, ANN was most prominent type of hybrid model in the reviewed literature; this gained popularity because of its computational speed and retrieval performance [50]. ANN was reported in the literature as more accurate than empirical models for estimating LAI [51]. A number of studies applied hybrid models and achieved satisfactory results, and these include [40; 42; 52].  
The R\({}^{2}\) range of hybrid models was 0.21-0.99 while the RMSE range was 0.26-1.13. The study by [53] compared a hybrid PROSAIL model and empirical model based on NDVI and simple ratio (SR). It found that PROSAIL performed better in terms of RMSE (0.38), while the RMSE of NDVI was 2.28 and 0.88 for SR. Hybrid models can be used to calibrate traditional vegetation indices such as NDVI, develop new vegetation indices and analyse the performance of vegetation indices.
# Measurement of Wheel Radius in an Automated Guided Vehicle
## 4 Research Outcomes and Their Analysis  
### Research Object  
The research object shown in Figure 11 was built at the Rzeszow University of Technology. It is intended for transporting goods. The vehicle has three wheels. The front wheel is a drive and steering wheel. This wheel is connected via gears with two DC electric motors. One of them built inside the wheel is used to drive the vehicle. The second engine is located on the vehicle frame and, through a worm gear, regulates the steering angle around the vertical axis of rotation. The rear wheels are support wheels. These wheels are connected with encoders (Figures 11b and 2a). Signals from encoders are used to control the movement of vehicles.  
All vehicle wheels have an outer diameter of 200 mm and a width of 50 mm. There are polyurethane belts around the wheel circumferences. Compared to rubber, polyurethane is characterized by greater stiffness and therefore less elasticity. Wheels with such belting can be used for loads of 8500 N.  
In order to ensure the possibility of autonomous operation, the vehicle is equipped with an onboard computer. All data necessary to control the movement of the vehicle was supplied to the onboard computer using the National Instruments NI USB-6343 measuring acquisition card. The scheme of the measuring system is shown in Figure 11b. Encoders were used to measure angular velocity and distance traveled data of individual wheels.  
In the case of rear wheels, these were MHK40 encoders from Automatics Company. Encoders integrated with electric motors were used to measure the angular position, speed and traveled distance of the front wheel. The vehicle uses additional laser sensors to determine the position of the vehicle relative to the adopted reference surface. DT50-P2113 lasers were used to measure the distance perpendicular to the longitudinal axis of the vehicle, e.g., from the corridor wall. The DT500-A611 laser was used to measure the distance in the longitudinal direction.  
Depending on the batteries used, the vehicle in working order had different weights. For low capacity batteries, the weight was 100 kg. With larger batteries enabling several hours of operation, the weight was 160 kg. The maximum speed of movement was 1 m/s. The expected maximum load is 2500 N.  
In the vehicle from Figure 11, odometry was the primary navigation system. While the vehicle was moving, fixed rolling radius values were used in the calculation algorithm. These values were introduced into the calculation system at the beginning of the study. Additional measuring devices installed, such as laser rangefinders, allowed the current position of the vehicle to be determined in relation to the surroundings. Using the appropriate calculation systems [26] based on the measurements obtained, it is possible to correct errors [10; 11].
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results
### Evaluation of the Proposed Algorithms on the MRIs from the BrainWeb Dataset
#### 3.1.2 Convergence in the Presence of Bias Field  
Next, we investigate the registration of the MR images in the presence of the bias field noise. This setting is particularly of interest to us because intensity-based algorithms often fail to converge when images are distorted by the bias field noise. Even converging algorithms have less accuracy compared to the registration of the same image without any bias field noise. To study the convergence of registration algorithms when images are noisy, we manually added Gaussian functions to both floating and reference images according to Equation (1). Then, we performed image registration using LR-EMD, AFR-EMD and the intensity-based benchmark algorithm. An algorithm is considered converged when the T-RMSE error is lower than four pixels. For each image in the BrainWeb dataset, we repeated the registration process for 15 random selection of the Gaussian kernel centers. The convergence rate is then defined as the number of converged algorithms divided by 15 (times 100 to get the percentage).  
Figure 4 shows the convergence rate for each of the four similarity measures (SSD, CC, RC and MI). The number of Gaussian kernels are varied between \(K=0\) (no bias field) to \(K=4\) (four Gaussian kernels in each of the floating and reference images). For SSD, CC, and MI similarity measures, adding one Gaussian function remarkably degrades the convergence rate for the intensity-based algorithm (red curves in Figure 4). In particular, SSD never converged with one Gaussian kernel. This confirms previous observations that the intensity-based registration is not robust to the bias field noise. RC is the most robust similarity measure for the intensity-based registration with 80% convergence rate in the presence of one Gaussian kernel and 100% for two to four Gaussian kernels. Moving  
Figure 3: Registration performance when there is no bias field. Mean and variance of T-RMSE and I-RMSE for images in the BrainWeb dataset are shown in (**A,B**) panels. (**C**) panel compares the running time for all methods.  
to our proposed EMD-based algorithms, we observe that both LR-EMD and AFR-EMD improve the convergence rate for almost all of the cases. Using MI or CC as the similarity measure, both LR-EMD and AFR-EMD converge 100% of the times while intensity-based registration converges in 20% to 60% of the times for MI and 25% to 80% of the times for CC. For SSD, both LR-EMD and AFR-EMD have higher convergence rate compared to benchmark registration for one to three Gaussian kernels. When four Gaussian kernels are present, LR-EMD has a lower convergence rate compared to the other two algorithms. This is primarily because of the low SNR in images. Overall, our results suggest that both EMD-based algorithms as well as the intensity-based registration with the RC similarity measure have a higher convergence rate compared to other similarity measures.
# Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition
## 2 Materials and Methods
### EEG Detection
#### 2.3.1 PSD Features Extraction  
The raw electrode data were filtered using a wavelet transform to obtain the PSD features. The wavelet transform is appropriate for multiresolution analysis, in which the signal can be examined at different frequencies and time scales. We used Daubechies' wavelet transform coefficients for feature extraction because EEG signals contain information at various frequency bands. The wavelet, which is a mathematical transformation function, divided the data into diverse frequency components. With the resolution matched to the scale, these components were analyzed separately. A wavelet transform is the representation of a function by mother wavelets. The one-dimensional sustained wavelet transform denoted by \(w_{f}(s,\tau)\) of a one-dimensional function \(f(t)\) is defined in (5) [19]:  
\[w_{f}(s,\tau)=\int_{-\infty}^{\infty}f(t)\varphi_{st}(t)dt, \tag{5}\]  
where \(\varphi\) is the mother wavelet function, \(s\) is the scale parameter, and \(t\) is the translation parameter. To reconstruct the original signal from the transformed signal, the inverse sustained wavelet transform is defined in (6):  
\[f(t)=\frac{1}{C_{\varphi}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}W_{f} (s,\tau)\varphi_{st}(t)\frac{dtds}{s^{2}}, \tag{6}\]  
such that \(C_{\varphi}=\int_{-\infty}^{\infty}\left(\left|\varphi(u)\right|^{2}/|u|du\right)\), where \(\varphi(u)\) is the Fourier transform of \(\varphi(t)\)[20].  
For the experiment run on offline dataset MAHNOB-HCI and DEAP, we chose only 14 electrodes (Fp1, T7, CP1, Oz, Fp2, F8, FC6, FC2, Cz, C4, T8, CP6, CP2, and PO4) and three symmetric pairs (i.e., T7-T8, Fp1-Fp2, and CP1-CP2) for the EEG feature extraction. The logarithms of the PSD from the theta (4 Hz \(<\) f \(<\) 8 Hz), slow alpha (8 Hz \(<\) f \(<\) 10 Hz), alpha (8 Hz \(<\) f \(<\) 12 Hz), beta (12 Hz \(<\) f \(<\) 30 Hz), and gamma (30 Hz \(<\) f \(<\) 45 Hz) bands were extracted from all 14 electrodes and three symmetric pairs to serve as features. The total number of EEG features in the offline dataset using 14 electrodes and three corresponding asymmetric features were \(14\times 5+3\times 5=85\).  
For the online experiment, all five electrodes (AF3, AF4, T7, T8, and Pz) provided by the device were used. The logarithms of the PSD from the theta (4 Hz \(<\) f \(<\) 8 Hz), slow alpha (8 Hz \(<\) f \(<\) 10 Hz), alpha (8 Hz \(<\) f \(<\) 12 Hz), beta (12 Hz \(<\) f \(<\) 30 Hz), and gamma (30 Hz \(<\) f \(<\) 45 Hz) bands were extracted from all five electrodes. The total number of EEG features in the online experiment were \(5\times 5=25\).