esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This research focused on developing Arabic word embeddings. Our approach involved creating 108 different models, each with unique combinations of hyperparameters and corpora. To build our corpora, we leveraged available online resources, as well as Arabic Books. We utilized the Word2 Vec and FastText libraries in our modeling process.  
A series of evaluations were conducted to assess the performance of these models. Notably, we achieved a 90% accuracy rate in the analogy test. In the sentiment analysis tests, our models achieved approximately 99% accuracy. Furthermore, in the similarity score test, some of our models attained an average similarity score of 8 for all pairs in the dataset.  
Our observations revealed that the size of the corpus and the values of the hyperparameters had varying impacts on each test:  
* In the analogy test, it has been shown that employing a large vocabulary size had a positive influence on the results. Additionally, the FastText models with skip-gram approaches proved to be effective in solving analogy test questions.
* For sentiment analysis, we discovered that vocabulary size played a crucial role. Furthermore, for the FastText models, applying CBOW architecture led to improved accuracy, whereas for Word2 Vec, applying skip-gram architecture enhanced accuracy. Moreover, smaller window size has a positive effect on accuracy.
* Finally, in the similarity score test, the FastText models delivered the highest scores. Smaller window sizes and smaller vector sizes positively impacted the results.  
Figure 12: Word2 Vec models similarity test results.  
For future work, we will consider building models specifically for different Arabic dialects.  
Conceptualization, A.A. and A.C.; methodology, A.A.; software, A.A.; validation, A.C.; formal analysis, A.A. and A.C.; writing--original draft preparation, A.A.; writing--review and editing, A.C.; visualization, A.A.; supervision, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Laboratory Evaluation of Tensile Creep Behavior of Concrete at Early Ages
## 1 Introduction
Investigations of the creep and relaxation effect on the mechanical responses of pavements are very limited [6; 26; 27; 28]. [PERSON] et al. [26] used the creep coefficient adopted from CEB-FIP Model Code 1990 [23] to analytically calculate the average stress at the mid-depth of a slab. [PERSON] et al. [27] predicted the average stress generated in the pavement using the creep properties measured from a ring test. [PERSON] et al. [6] considered the effect of concrete creep in pavement slab for analyzing moisture warping and warping stresses by using specimens under different drying conditions exposed to drying at the age of 7 days. The creep relaxation effect is most significant in the first 7 days and tends to be stable after 60 days [28]. For the stress analysis of concrete structures, a proper material model should be used that represents a realistic constitutive behavior of early-age concrete. In this study, the early-age concrete is assumed to conform to the linear viscoelastic behavior with a strong aging effect. Short-term uniaxial tensile creep tests were conducted for a minor concrete paving mixture with 25% fly ash at different ages in an effort to characterize early-age concrete as an aging viscoelastic material. Based on the test results, the viscoelastic material properties of the test concrete were characterized in terms of the Dirichlet series creep compliance and relaxation modulus functions. In order to verify the aging creep model and its conversion to an age-dependent relaxation modulus, the tensile creep tests at different ages were simulated using the finite element program, ABAQUS, with the estimated relaxation modulus at each age. This paper presents the test procedures and data analysis with a brief introduction of the theoretical background.
## 3 The Relay Experiment and Parameters Analysis
### The Relay Lifetime Prediction Based on GNN Model  
The relay lifetime experiment is long, so the number of samples is small. The relay lifetime belongs to the case of fewer data and poor information. Thus, the relay lifetime on the initial state is predicted with the GNN model. The process is as follows.  
The original data are accumulated as network input and output parameters. The final output is lifetime and the input variates are the remaining five parameters.  
The network parameters and the value of the parameters are initialized. \(a,\ b_{1},\ b_{2},\ b_{3},\ b_{4},\ b_{5}\) is \(0.3+rand(1)/4\).  
Learning rate is set to \(u_{1}=u_{2}=u_{3}=u_{4}=u_{5}=0.0015\).  
The weights \(\omega\) are calculated according to the definition of the network.  
The output of each layer is calculated based on each input sequence \((t,\ y(t))t=1,2,\cdots,n\).  
L1 layer: \(a=\omega_{11}t\)  
L2 layer: \(b=f(\omega_{11}t)=\frac{1}{1+e^{-\omega_{11}}}\)  
L3 layer: \(c_{1}=b\omega_{21},\ c_{2}=y_{2}(t)\omega_{22},\ c_{3}=y_{3}(t)\omega_{23}, \ \cdots,c_{n}=y_{n}(t)\omega_{2n}\)  
L4 layer: \(d=\omega_{31}c_{1}+\omega_{32}c_{2}+\cdots+\omega_{3n}c_{n}-\theta_{y1}\)  
Then, the weights and thresholds are adjusted according to the error between the prediction value of the network output and the expected output.  
Error of L4 layer: \(\delta=d-y_{1}(t)\)  
Error of L3 layer: \(\delta_{1}=\delta(1+e^{-\omega_{11}t}),\ \delta_{2}=\delta(1+e^{-\omega_{11}t}),\ \cdots,\ \delta_{n}=\delta(1+e^{-\omega_{11}t})\)  
Error of L2 layer: \(\delta_{n+1}=\frac{1}{1+e^{-\omega_{11}t}}(1-\frac{1}{1+e^{-\omega_{11}t}})( \omega_{21}\delta_{1}+\omega_{22}\delta_{2}+\cdots+\omega_{2n}\delta_{n})\)  
Adjust the connection weights of L2 layer to L3 layer:  
\(\omega_{21}=-y_{1}(0),\ \omega_{22}=\omega_{22}-\mu_{1}\delta_{2}b,\ \cdots, \omega_{2n}=\omega_{2n}-\mu_{n-1}\delta_{n}b\)  
Adjust the connection weights of L1 layer to L2 layer:  
\(\omega_{11}=\omega_{11}+at\delta_{n+1}\)  
Adjust threshold: \(\theta=(1+e^{-\omega_{11}t})(\frac{\omega_{22}}{2}y_{2}(t)+\frac{\omega_{22}} {2}y_{3}(t)+\cdots+\frac{\omega_{2n}}{2}y_{n}(t)-y_{1}(0))\).  
The prediction result of GNN model is shown in Figure 6.  
The trend of training error decreasing with the increasing of evolution time is shown in Figure 7.  
Figure 6: The comparison of actual value with prediction value using GNN model.  
Figure 7 presents the convergence rate of the GNN model is fast and the error converges to the minimum at the 12 th time. However, this study presents that the prediction results are not ideal. The prediction accuracy is lower than the multi-dimensional GM because the initial parameters of the GNN model are randomly generated and the premature convergence causes the network to jump into the local optimal solution. The GNN parameters are not optimal. The GNN parameters should be optimized to improve the prediction effect of GNN model.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 2 Multimodal Feature Fusion for Image Preprocessing and Feature Enhancement  
### Image Preprocessing  
In the process of preprocessing and augmenting garbage images, various random transformations were applied to the images in the training set to diversify the scenes encountered during model training, enhance its generalization across different distributions, and effectively mitigate the risk of overfitting. The implemented code encompasses the following data augmentation operations: adjustments of brightness, contrast, saturation, and hue; random grayscaling; Gaussian blurring; random rotations; as well as random horizontal and vertical flipping.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 2 System Uncertainty Analysis and Calibration
### Calibration
#### 2.2.2 Eccentricity Calibration  
The C-axis eccentricity measurement performed by XYC axis synchronous path called BK2 path as ISO 10791-6 standard is recorded every 45 degrees alone the entire trip of 360 degrees, which is repeated three times. The measurement results are shown in Figure 8. The standard deviation before compensation was 1.6, the error of the \(X\)-axis was 21.4 \(\upmu\)m, and \(Y\)-axis was 31.6 \(\upmu\)m; the standard deviation after compensation was 0.6, the error of the \(X\)-axis was 3.8 \(\upmu\)m, and the \(Y\)-axis was 8.9 \(\upmu\)m. The A-axis eccentricity measurement performed by the YZA axis synchronous path called the BK1 path as the ISO 10791-6 standard is recorded every 15 degrees along the entire trip from \(-90\) to \(+30\) degrees, which  
Figure 5: Compensation on the \(X\)-axis: (**a**) results before compensation and (**b**) results after compensation.  
Figure 6: Compensation on the \(Y\)-axis: (**a**) results before compensation and (**b**) results after compensation.  
Figure 7: Compensation on the \(Z\)-axis: (**a**) results before compensation and (**b**) results after compensation.  
is repeated three times, and the measurements results are shown in Figure 9. The standard deviation before compensation was 0.35, the error of the \(Y\)-axis was 26.4 \(\upmu\)m, and the \(Z\)-axis was 44.1 \(\upmu\)m; the standard deviation after compensation was 0.3, the error of the \(Y\)-axis was 9.3 \(\upmu\)m, and the \(Z\)-axis was 10.3 \(\upmu\)m.  
Figure 8: C-axis eccentricity measurement results: (**A**) BK2 path static measurement repeatability before compensation, (**B**) BK2 path static measurement standard deviation before compensation, (**C**) BK2 path static measurement repeatability after compensation, and (**D**) BK2 path static measurement standard deviation after compensation.  
Figure 9: A-axis eccentricity measurement results: (**A**) BK1 path static measurement repeatability before compensation, (**B**) BK1 path static measurement standard deviation before compensation, (**C**) BK1 path static measurement repeatability after compensation, and (**D**) BK1 path static measurement standard deviation after compensation.  
In the above figures, the eccentricity under the influence of error on translational axes is described through these measurement results. On the other hand, before and after the compensation on the translational axes is seen in these results with a significant amount of reduced error.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
###### Abstract  
Due to the short revisit time and large coverage of Geosynchronous synthetic aperture radars (GEO SARs) and the increasing number of low earth orbit synthetic aperture radar (LEO SAR) constellations, radio frequency interference (RFI) between GEO SARs and LEO SARs may occur, deteriorating the quality of SAR images. Traditional methods only simplify RFI to noise-like interference without considering the signal characteristics. In this paper, to accurately evaluate the impacts of GEO-to-LEO RFI and LEO-to-GEO RFI on imaging quantitatively, an RFI-impact quantitative analysis model is established. Taking account of the chirp signal form of SAR systems, the RFI power and image Signal-to-Interference-plus-Noise Ratio (SINR) are theoretically deduced and validated by numerical experiments. Based on the proposed method, the SAR image quality under different system parameters and bistatic configurations is estimated, and the probability of different configurations is also given. The results show that specular bistatic scattering RFI between GEO SARs and LEO SARs has serious effects on imaging, and the probability can approach 2% for certain orbital parameters and will become higher as LEO SAR constellations increase in the future, implying the necessity to suppress the RFI between the GEO SAR and the LEO SAR system.  
RFI; geosynchronous synthetic aperture radar; bistatic synthetic aperture radar; specular scattering 2022143048 [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [20] [21] [23] [26] [24] [  
However, due to its large coverage, GEO SARs are prone to observing the same area as low earth orbit (LEO) SARs simultaneously, potentially resulting in strong main lobe coupling between the two systems. As a result, GEO SAR signals can be received by a LEO SAR. Similarly, LEO SAR signals may be received by a GEO SAR, deteriorating SAR images if they have the same signal frequency. Furthermore, the large coverage and short revisit time of GEO SARs may increase the probability of RFI with LEO SARs.  
Footnote 1: [[https://www.eos.org/](https://www.eos.org/)]([https://www.eos.org/](https://www.eos.org/))  
Footnote 2: [[https://www.eos.org/](https://www.eos.org/)]([https://www.eos.org/](https://www.eos.org/))  
Footnote 3: [[https://www.eos.org/](https://www.eos.org/)]([https://www.eos.org/](https://www.eos.org/))  
Footnote 4: [[https://www.eos.org/](https://www.eos.org/)]([https://www.eos.org/](https://www.eos.org/))  
Footnote 5: [[https://www.eos.org/](https://www.eos.org/)]([https://www.eos.org/](https://www.eos.org/))
## 3 Data Introduction
### Analysis of Raw Data Characteristics  
Considering that the 1:50,000 scale geochemical data has a higher sampling density and is relatively more abundant, but it was collected by different companies, which introduces systematic errors, this data is the primary focus of the study. To compare the data characteristics of the northern and southern regions, exploratory analysis was conducted for a preliminary summary and comparison. The maximum, minimum, percentile values, mean, standard deviation, and coefficient of variation of the raw data are presented. A comparison of each element between the north and south was made. From the Table 3, it can be seen that, except for the Sb element, which has a smaller minimum value and a larger maximum value in the northern region compared to the southern region, the other elements generally show a distribution where the northern region has larger minimum values and smaller maximum values compared to the southern region. By examining the box plots in Figure 4, it is evident that the element data in the northern region is relatively more concentrated, while the element data in the southern region shows stronger dispersion (with the boxes generally being longer). However, the average values of most elements do not differ significantly, and the median (50 th percentile) also shows little difference (except for Cu and Bi elements). At the same time, due to the varying orders of magnitude in the concentrations of different elements, some of the boxes in the box plots, such as those for Ag, Mo, Bi, and Sb, are severely compressed, making it difficult to directly observe the characteristics of these boxes. Additionally, from the kurtosis and skewness revealed in Table 3, it can be observed that all elements do not follow a normal distribution, exhibiting right skewness and positive kurtosis. Therefore, if the raw data were directly used for statistical analysis, it could lead to erroneous results [14]. It is thus necessary to process the raw data.
# Ultra-Thin Metamaterial Beam Splitters
## 6 Conclusions  
In summary, we presented blueprints for three types of ultra-thin beam splitters by changing the geometric parameters of a fishnet metamaterial structure. The achieved wavelength-to-thickness ratio is \(\lambda/a=26\) for the PBS and PPBS and \(\lambda/a=13\) for the NPBS at \(1.55\) um. The designed beam splitters can be integrated with other components for compact optical and photonic devices and systems. The tunability of the designed fishnet based beam splitters can be possible through, for example, voltage-tunable nematic liquid crystals [30], thermally-tunable property of physical systems with nematic liquid crystal mixtures [31], electro-optically tunable liquid crystals [13, 32, 33], and temperature using an external laser [34]. There exist theoretically proposed [35, 36] and experimentally realized [37, 38, 39], even around \(1.55\) um wavelength [38], tunable fishnet metamaterials based on liquid crystals [35, 37, 38, 39] and temperature [36]. Although dielectric metasurfaces were generally proposed for the miniaturization of optical components, it is difficult with dielectric metasurfaces to achieve ultra-thin beam splitters such as those presented in this work. Furthermore, even though, here, we only considered power transmittance and reflectance, plasmonic metamaterials (e.g., fishnet structures) or plasmonic metasurfaces [20, 40, 41] at optical wavelengths may also enable tailoring of complex reflection and transmission coefficients with ultra-thin interaction lengths. This can be especially useful for manipulating coherent optical signals including quantum information processing tasks [42].  
Conceptualization, D.O.G.; formal analysis, D.O.G., X.Z. and J.E.D.; investigation, X.Z. and J.E.D.; methodology, D.O.G., X.Z. and J.E.D.; project administration, D.O.G.; resources, D.O.G. and X.Z.; supervision, D.O.G.; writing--original draft, X.Z.; writing--review & editing, D.O.G. and J.E.D. All authors have read and agreed to the published version of the manuscript.  
Portions of this work were presented in [43].  
The authors declare no conflict of interest.  
Figure 4: R, T, and A spectra versus (**a**) \(w_{x}\), and (**b**) \(\Lambda_{x}\) for TE-polarized and TM-polarized light.
## 3 Geological Setting and Database
### Geological Setting and Data of Telfer Gold Mine  
A large gold deposit occurs within Proterozoic stratigraphy at the Telfer Gold Mine in the Great Sandy Desert of Western Australia. Rock types include calcareous and argillaceous siltstones, sandstones and quartzites. The geological structure is complex and is the primary reason behind the mineralization [54].  
The properties of intact rock mass and rock mass shear strength, and to a lesser extent, bedding shear strength, vary with the degree of weathering and the type of alteration (clay or silica enrichment). Planar sliding along adversely oriented bedding planes within siltstone, sandstone and quartzite are the most common causes of slope instability.  
Similarly to the Hungarian granitic rock masses, two types of data exist for Telfer: field values of GSI and RMR\({}_{89}\), estimated on-site based on observations from 12-24 m high slope faces (Figure 4), and calculated GSI values from drill core estimations of Q and RMR\({}_{89}\)[55].
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion
### Effect of the Volume of Toluene  
Toluene was chosen as the best solvent according to the data of the elemental analysis. The volume of toluene changed from 20 to 40 mL and the degree of grafting was calculated. The results show a decrement in grafting degree from 54.19 to 50.95 % for utilization of 20 and 40 mL of toluene, respectively.
## 3 Structural Vulnerability Theory
### Clustering Process  
The clustering process consists of identifying primitive structural clusters made up of members at the lower level of a structure. The clustering process can be divided into three stages [26]: initial clustering stage, second clustering stage, and reference clustering stage.  
Figure 2: Deformation diagram of CFS shear wall systems under earthquakes (a two-story shear wall system is chosen as an example). (**a**) Traditional type. Reproduced with permission from [6], American Society of Civil Engineers, 2013; (**b**) reinforced type.  
Five typical clustering criteria are proposed in structural vulnerability theory [26; 27; 28; 29]:  
1. Maximum well-formedness (\(Q\));
2. Highest minimum damage demand (\(D_{\rm min}\));
3. Maximum nodal connectivity (\(N\));
4. Maximum distance from the reference (\(D_{\rm is}\));
5. Free choice (\(F_{\rm c}\)).
chloride or betaine, but others such as thymol, menthol, decanoic, or lauric acid have been used [11], acting as hydrogen bond acceptors. As hydrogen bond donors, it is common to use urea, amino acids, sugars, alcohols, amides, amines, or carboxylic acids [12; 13].  
In fact, the components of DESs are directly associated with their properties and thus can be designed by properly combining the components [10]. In general, DESs show several properties such as low preparation costs, environmentally-friendly, wide polarity range, low vapor pressure and volatility, non-inflammatory, thermal and chemical stability, biodegradability, low toxicity, etc. Additionally, they present many possible combinations to obtain different kinds of solvents depending on their uses [14; 15]. Components of DESs can interact through intermolecular forces, not only via covalent or ionic bonds and thus are considered as good candidates to replace ionic liquids or traditional solvents [16].  
A few years ago, a new type of DES arose when [PERSON] et al. [17] noticed that mixtures of natural products such as choline, sugars, and amino acids had a similar behavior to that of a DES when combined properly. These authors named these products as natural deep eutectic solvents (NADESs) [12]. At least 174 DESs have been described in the literature [16].  
Due to the nature and versatility of DESs, these mixtures are attracting attention from the academic and industry sectors. It has been demonstrated that DESs can be used for many applications such as synthesis [18; 19; 20], separation processes [21; 22], extraction [23; 24; 25; 26; 27; 28], biocatalysis [29], nanomaterials, biotechnology [12; 13], electrochemistry [30; 31], food [32], cosmetics, pharmaceuticals [33; 34; 35; 36; 37; 38], or as a biofuel [12; 14; 16; 25; 39]. DESs are also useful in the pharmaceutical industry as recipients for the delivery of hydrophobic drugs or as a vehicle [40]. In fact, the pharmaceutical industry requires the use of organic solvents as media for poor soluble drugs and DESs seem to be promising alternatives in this area [33]. Considering some of these properties, in this work, a revision focused on the health field applications was carried out. Figure 1 shows the most important DESs, properties, and main applications of this type of mixture.