esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Image Processing
#### 4.2.2 Target Image Preprocessing  
The color balance of the cucumber target image is adjusted by performing contrast stretching on its different color components. The upper limit of each channel is defined as maxG (\(\leq\)255), and the LUT lookup table function is employed to convert the color gradient of a color channel from 0-255 to 0-maxG, thereby producing a reduction in color intensity of the color channel. Similarly, inside the HSV color space, the contrast of a particular channel can be expanded by utilizing the Look-Up Table (LUT) to modify the saturation, brightness, and other picture attributes.  
We decrease the intensity of the green channel by 50% in the RGB color model and increase the saturation of the image in the HSV color model.  
Following the color adjustment, the color contrast is intensified in the HSV color space. Consequently, the image with improved saturation is divided into segments inside the HSV color space. The H and S components are utilized for color distance representation, and distinct regions of varying colors are subjected to thresholding and blending for segmentation. The preliminary segmentation is set with a H range of 78 to 99 and a S range of 43 to 255 [35; 36].  
The image that was first segmented is converted into binary form and then subjected to morphological processing in order to eliminate smaller, unconnected sections. The processed image is superimposed onto the original image to get the segmented image, followed by the execution of edge extraction, as depicted in Figure 8.  
Figure 8: The target image preparation has four steps: (**a**) color segmentation, (**b**) binarized morphology, (**c**) image mask, (**d**) edge extraction.  
Figure 7: Preprocessing of images: (**a**) the initial image; (**b**) thresholding; (**c**) hole filling; (**d**) image mirroring; (**e**) edge extraction.  
After applying the Sobel operator for edge detection on the original image, there are 4939 edge points. Following color segmentation, the image contains 116 edge points. The template matching algorithm complexity is O(kn), resulting in 73 edge points in the template image. Using only one template reduces the algorithm's matching of the target image after color segmentation by 352,079 times, significantly enhancing the algorithm's efficiency.
# Parametric Optimization of a New Gear Pump Casing Based on Weight Using a Finite Element Method
## 2 Materials and Methods
### Creating a Model of the Research Object  
The casing calculation was performed by FEM in ABAQUS.  
The choice of material for the casing of a new gear pump (Table 2) should be justified by strength calculations. The most common materials for such pumps were chosen for the pump casing of the new design: aluminum (Europe), cast iron (Kazakhstan), and a composite material, polycarbonate (China).  
Figure 4: Diagram of fluid flow in the gear pump with 5 gears.  
Figure 3: Design of a gear pump with 5 assembled gears: 1—front cover; 2—casing; 3, 6—bearings; 4—driven gears; 5—drive gear on the drive shaft; 7—flange; 8—seal; 9—retainer ring; 10—back cover.  
These materials have the isotropic behavior of the material, which is characterized by the Young modulus and the Poisson ratio [38].  
This study applies the von Mises yield criterion and the Prandtl-Reuss flow rule to create a stress-strain equation for an elasto-plastic material with isotropic properties [39].  
The simulation was carried out for three parts of the pump: the casing, the back, and the front cover. The dimensions of the new gear pump parts were calculated analytically using machine theory.  
To model each part, a solid model was made, which was then divided into elements. Figure 5 shows an example of a solid model of a gear pump casing with back and front covers. Figure 6 shows an example of a finite element model of a hull.  
When creating a computational model for analysis, a standard procedure is performed to apply a finite element mesh. In the meshing process, three-dimensional second-order HEXA FEMs (hexa elements) were used. HEXA FEM (hexa elements) is the best element for modeling; in turn, when modeling, the use of TETRA FEM is avoided, since this type of element concentrates stress in the structure of the model. To accurately represent the distribution of internal stresses in samples under the influence of external forces, it is necessary to pay due attention to the distribution of the finite element mesh. The validity of the results depends on the quality of the network. To achieve optimal mesh quality, quality criteria for a finite element mesh were used (Figure 7). The number of elements that do not pass the quality criteria is less than 1% of the total number of elements (Figure 8), which is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Yield Strength,** & **Tensile Strength,** & **Tensile** & & \\
**MPa** & **MPa** & **Modulus, GPa** & **Poisson’s Ratio** & **Density, kg/m3** \\ \hline Aluminum (PA6 alloy) & 250 & 390 & 72.5 & 0.33 & 2790 \\ Cast iron (QT450-10) & 310 & 450 & 120 & 0.257 & 7060 \\ Polycarbonate & 280 & 300 & 16.5 & 0.46 & 2074 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
Figure 5: Solid model of the casing with front and back covers.  
Figure 6: Finite element model of the hull.  
the optimal value. The total number of elements of the model is 163,292, of which 12 are tetras; this is less than 1% and is acceptable for this model (Figure 8).
# Vibration Compensation of the Frequency-Scanning-Interferometry-Based Absolute Ranging System
Moreover, we use an auxiliary interferometer and digital signal processing to reduce the influence of laser frequency nonlinear tuning. The advantage of the proposed method is that we do not need to add additional devices in the FSI-based absolute ranging system, but only use an algorithm to reduce the impact of vibration on the ranging system.
## 5 Discussion and Summary  
In this work, we have considered the cooperation issues in UASNs that would degrade the performance of the network which is mainly attributed by the presence of malicious or misbehaving nodes in the routing vector (source-destination path). We conducted a comprehensive review for investigating different factors affecting cooperation in UASNs. We study various techniques used in UASNs from different perspectives and present a taxonomy for underwater cooperation. We further analyzed different game methods and compared their performance using different metrics, in which our analysis shows that cooperative game theoretic-based approaches are the most suitable and promising method for providing cooperation in UASNs. A comprehensive summary of different game approaches is presented in Table 2, which summarizes the addressed problems, the applied methods, the main contributions and their limitations. In summary, based on our study there is no work that has high performance in all the metrics we have analyzed as shown in Table 3. In Table 3, a Yes means the paper considers that issue and solves it to a certain level (degree), and No means that the paper does not, in any way, solve that issue.
## 4 Incorporation of Wearable Sensors to Measure OA Risks
### Laboratory-Based 3D Motion Capture Systems  
The gold standard for biomechanical assessments are highly reliable data obtained from marker-based 3D motion capture systems such as Optotrak, Vicon and Optitrak. More practical but less precise markerless systems, such as Kinect Azure, are available, which can provide acceptable accuracy for some applications [49]. All these motion capture systems are designed to sample raw position-time data from which information, such as knee joint angles, velocity and acceleration, can be computed from noise-filtered coordinate data. When this kinematic information is used in combination with 3D foot-ground reaction forces, the computational technique \"inverse dynamics\" allows for estimation of joint kinetics to compute adduction moment and compression forces [19,50]. It is difficult to obtain sufficient kinetic data, because in the usual biomechanical laboratory setup, each walkthrough trial can provide only limited GRF data depending on the number of force plates installed. Data volume can be considerably increased, using continuous sampling when treadmill walking, but there is debate as to whether it represents natural, everyday locomotion [51]. While 3D motion capture and force plates are the laboratory standard, wearable systems will be needed to assess OA effects outside the laboratory. Althoughthe types of data obtained by wearable systems are limited (joint angle, acceleration, etc.), in principle, machine learning algorithms can be further developed to estimate knee OA-related mechanics with sufficient reliability [13; 48]. Below we consider wearable systems, and the data obtainable from them with application to knee OA.
## 4 Parameters Affecting Nano-EOR
### NP Concentration  
The concentration of injected NPs is one of the key parameters that determine the EOR process. According to [PERSON] [151], disjoining pressure will increase with increasing concentration, which also increases the repulsion forces. Increasing concentration will also improve the displacement efficiency due to the enhanced viscosity of the nanofluid and the spreading of NPs on the grain surface [89]. The interfacial tension between reservoir fluids can be decreased significantly by increasing the concentration of injected NPs [152]. A high concentration also leads to a higher wettability alteration effect. In light of this, a higher concentration is favorable for higher oil recovery.  
However, there is a limit to the concentration of injected NPs, and above that limit, NPs will tend to block the pore throat and reduce the ultimate oil recovery. A higher concentration will improve the displacement efficiency, wettability alteration, and IFT reduction. However, when the concentration is too high, the aggregated NPs will accumulate around the inlet and reduce the displacement efficiency [109]. [PERSON] et al. [152] reported that the permeability and porosity of a Berea core was impaired by about 2% after injecting silica NPs with a concentration above 0.5 wt %. Thus, an optimum injected concentration is necessary to get maximum oil recovery. However, this optimum concentration varies depending on the type of nanoparticle, porous medium, and environmental conditions.
# The Effect of Retinal Eccentricity on Visually Induced Motion Sickness and Postural Control
###### Abstract  
The present study investigated the effect of retinal eccentricity on visually induced motion sickness (VIMS) and postural control. Participants wore a head-mounted display masked for the central \(10^{\circ}\) (peripheral vision), the peripheral except for the central \(10^{\circ}\) (central vision), or unmasked (control) to watch a highly immersive 3D virtual reality (VR) ride along China's Great Wall. The Simulator Sickness Questionnaire was administered to assess VIMS symptoms before and after the VR exposure. In addition, postural sway data were collected via sensors attached to each participant's head, torso, and hip. Results demonstrated that peripheral vision triggered the most severe symptoms of motion sickness, whereas full vision most perturbed posture. The latter finding contradicts previous research findings demonstrating the peripheral advantage of postural control. Although the source of compromised postural control under peripheral stimulation is not clear, the provocative nature of visual stimulation depicting a roller-coaster ride along a rugged path likely contributed to the contradictory findings. In contrast, motion sickness symptoms were least severe, and posture was most stable, under central vision. These findings provide empirical support for the tactic assumed by VR engineers who reduce the size of the field of view to ameliorate the symptoms of motion sickness.  
virtual reality; visually induced motion sickness; retinal eccentricity; head mounted display; simulation sickness questionnaire; postural control +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion  
### Formation of PP and PPTC  
Figure 1 schematically shows the processes for preparing PP and PPTC as well as their use for photocatalytic degradation of various organic pollutants. Briefly, DVB and BMI monomers are initiated by AIBN to participate in radical polymerization reaction, thereby affording PP. Then TBT and CuSO\({}_{4}\) are adsorbed into the pores of PP, followed by hydrothermal reaction forming TiO\({}_{2}\)/Cu composite (i.e., TC) in the pores, thereby yielding PPTC with stable structure and excellent adsorbing performance. The as-obtained PPTC is further used as the photocatalyst to degrade organic pollutants such as DMF,methyl orange, phenol, and methylene blue under the irradiation of simulated sunlight or nature sunlight, thereby even achieving mineralization.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 2 Magnetic Field Measurements at Low Earth Orbit Altitudes  
The scalar- and vector-magnetic field measurements are acquired from two low Earth orbit (LEO) missions around 500 km altitude with their respective instruments operating in the ionospheric F-region, CSES and the three _Swarm_ satellites.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
###### Abstract  
Although entity and relation joint extraction can obtain relational triples efficiently and accurately, there are a number of problems; for instance, the information between entity relations could be transferred better, entity extraction based on span is inefficient, and it is difficult to identify nested entities. In this paper, a joint entity and relation extraction model based on an Enhanced Span and Gate Mechanism (ESGM) is proposed to solve the above problems. We design a new span device to solve the problem of entity nesting and inefficiency. We use the pointer network method to predict the beginning and end of the span, and combine them through the one-to-many matching principle. A binary classification model is then trained to predict whether the span of the combination is the subject. In the object prediction stage, a gating unit is added to fuse the subject information with the sentence information and strengthen the information transfer between the entity and the relationship. Finally, the relationship is used as the mapping function to predict the tail entity related to the head entity. Our experimental results prove the effectiveness of this model. The precision of the proposed model reached 93.8% on the NYT dataset, which was 0.4% higher than that of the comparison model. Moreover, when the same experiment was conducted in a nested entity scenario, the accuracy of the proposed model was 4.4% higher than that of the comparison model.  
nested entities; relation overlap; joint extraction; span; gate unit +
Footnote †: journal: Applied Sciences
## 4 Discussion  
### Cocoa Data Refinement  
The quality refinement of the cocoa data had a beneficial impact on the classification accuracy. The mean OA increased by 1.7%; the mean F1 by 1.6%; and the mean UA and PA by 2.5% and 0.7%, respectively, when using the best-performing random forest. An accuracy gain was achieved in almost all input configurations we examined, yet this gain was varied (Table 4). Narrower one standard deviation intervals were achieved for the majority of the scores after the refinement, indicating less uncertainty associated with the accuracy. Even though the refinement resulted in the removal of almost half of the initial cocoa data (Table 1), the size reduction had no negative impact on the performance of the random forest. Instead, the enhancement of the cocoa data quality contributed to the improvement of the model's discrimination capacity.  
Other classification studies at larger geographical scales performed more sophisticated data refinement frameworks, which included hierarchically organised datasets based on the reliability level [117], or photointerpretation was undertaken by multiple analysts for increased confidence [22]. In our study, we implemented a simple approach of refining the initial cocoa dataset of uncertain quality by retaining the purest pixels possible that included full-sun cocoa trees. The refinement approach involved manually digitising on Google Earth images of very high-spatial-resolution since the 10 m resolution of the Sentinel-2 image was not sufficient. Our manual labelling was carried out meticulously, yet subjective and biased interpretation could be expected to some extent since the refinement was performed by one photo interpreter [26]. However, our cocoa refinement method was used as a complementary tool to the field measurements and not as a surrogate.  
The purpose of the quality refinement was to ensure thematic homogeneity in the cocoa dataset and to mitigate intra-class variability. Nonetheless, it is important to acknowledge that this objective might not be entirely feasible in practice. Elements belonging to the same land-use/cover class may not have homogeneous spectral responses due to, e.g., the health state, the phenology, or the spatial arrangement within the pixel area. This issue can be more challenging in heterogeneous and complex landscapes [118]. When considering solely the spectral behaviour, a pixel's response may resemble more than one land-use/cover class, and, similarly, different classes may have spectral characteristics in common [119]. The Earth's surface is a continuum of various land-use and land-cover classes. However, in hard classification tasks, only one class is allocated to each pixel, as the underlying assumption is that image pixels are pure and represent a discrete land-use/cover class [28].
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### Performance Measurements of BERT, ELMO, and GPT-2  
Every model in the deep learning pipeline was assessed based on performance metrics. The model initially trains or fine-tunes the text dataset for sentence similarity, and, after testing, it validates the similarity results. There are three main functions of any similarity model: create contextualized relationships, predict relevant embeddings, and make appropriate decisions. In this section, we have identified the most used \(\bigvee^{\prime}\) and unused \(\bigvee^{\prime}\) measurements used by the researchers for short-sentence similarity tasks presented in Table 11. We targeted three models: the BERT, ELMO, and GPT-2. Approx. 24 studies were included. In the performance measurements, we noted that the parameters of confusion matrices are still widely used by many researchers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BERTBase**}
## 5 Simulation Results
### Outage Probability  
Let \(R_{m}=\log(1+\Gamma_{m})\) and \(R_{k}=\log(1+\Gamma_{k})\) denote the achievable rate of beam-center UT and beam-edge UT at thresholds \(\Gamma_{m}\) and \(\Gamma_{k}\), respectively. We provide simulation  
Figure 6: Ergodic sum-rate/rate with \(\tau=1\) ms and \(\alpha=0.2\).  
Figure 7: Ergodic sum-rate/rate with \(M=9\), \(\tau=1\) ms and \(\alpha=0.2\).  
results with \(R_{m}=0.4\) bps and \(R_{k}=0.3\) bps, shown in Figure 8. As expected, the curves of analytical expressions closely follow with those of numerical simulations. It can be clearly observed that the co-carrier-based SC can provide a promising outage probability performance compared with the co-carrier-based DC method since the SC strategy is able to choose the best signal path. Specifically, we can find out that the diversity order of the co-carrier-based DC is zero which illustrates the DC method could not offer additional gain in high SNR regions.  
Next, we set \(M=9\) and study the benefit of \(M\) for both methods. It can be clearly noticed in Figure 9 that the outage performance for the co-carrier-based SC method can be significantly improved with more optional LEO satellites. Unfortunately, the increase in \(M\) could not provide extra diversity gain for the co-carrier-based DC scheme. Since no selection is involved in the demodulation of beam-center UTs, the outage performance could not be relieved either.  
Figure 10 shows the outage probability with a lower power factor, \(\alpha=0.2\). It is observed that the performance of the beam-edge UT has been improved in both methods.  
Figure 8: Outage probability with \(R_{m}=0.4\) bps, \(R_{k}=0.3\) bps, and \(\alpha=0.4\).  
Figure 9: Outage probability with \(M=9\) and \(\alpha=0.4\).  
As a consequence, the results of the beam-center UT degrade. Again, the superiority of the SC scheme is obvious and the DC method works well in low SNR regions.
## 2 Materials and Methods
### Pull Stem Roll Design
#### 2.4.2 Length of the Stem-Pulling Roller  
The length design of the stem-pulling roller needs to meet two conditions, adapting to different earning heights and ensuring that multiple corn plants are picked at the same time. The working diagram for this is shown in Figure 4.  
In order to ensure that the highest and lowest corn ears and multiple corn plants are picked at the same time, the feeding amount of the header should be increased, while the working length L of the stem-pulling roller should be satisfied:  
\[L\geq L_{g}\sin\beta \tag{9}\]  
\[L\geq\frac{n_{y}d}{\cos\beta} \tag{10}\]  
In the formula, \(L_{g}\) is the height difference between the highest ear and the lowest ear, mm; \(\beta\) is the horizontal inclination angle of the stem roller, \({}^{\circ}\); \(n_{y}\) is the number of corn plants; and \(d\) is corn plant spacing, mm.  
As shown in Figure 5, the length comparison diagram of the stem-pulling roller is shown. According to Formula (6), the greater the feeding angle of the corn plant, the lower the energy consumed by grain shedding and crushing. When the feeding angle increases from 30\({}^{\circ}\) to 60\({}^{\circ}\), the energy consumed by grain shedding and crushing decreases from 2.3 J to 0.8 J. From Figure 6, it can be seen that when the fixed point and the stem-pulling roller are at a certain height from the ground, increasing the length of the lifting roller can increase the feeding angle. This can reduce the energy consumption of grain shedding and damage and decrease the losses in the header.  
Figure 4: Stem-pulling roller work diagram: A, B, C and D are corn ears; \(L_{\text{min}}\) is the shortest working length of stem-pulling roller, mm; and \(v_{z}\) is the relative speed of corn ear and stem-pulling roller, m/s.  
Figure 5: Comparison of stem-pulling roller length.  
In order to improve the picking performance of lodged plants and reduce collision losses, the length of the stem-pulling roller for this machine was ultimately determined to be 1200 mm.