esa-sceva/satcom-chunk-collection huggingface dataset:
# Arabic Gloss WSD Using BERT
## 8 Discussion  
The results above show that our approach outperforms two of the recent gloss-based models and that the reported results are super-passed previously reported results of knowledge-based WSD. We believe that the reason behind this improvement lies in the use of a pretrained BERT. Indeed, BERT models are trained on a very large dataset to perform masked language modeling and, next, sentence prediction tasks. In order to perform well  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Testset** & **Precision** & **Recall** & **F1** \\ \hline Model & 3601 ambiguous & 92\% & **87\%** & **89\%** \\ II-AraBERTv2 & words & 96\% & 67\% & 79\% \\ \hline Model & 3601 ambiguous & 89\% & 73\% & 80\% \\ II-ArBERT & words & 73\% & - & - \\ \hline [PERSON] [21] & 50 ambiguous & 59\% & - & - \\ \hline [PERSON] [33] & - & 79\% & 63\% & 70\% \\ \hline [PERSON] [24] & 1217 text & - & - & 85\% \\ \hline [PERSON] [34] & manually collected text & 74\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model II versus other knowledge-based models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Embedding Model** & **Result Source** & **Precision** & **Recall** & **F1** \\ \hline Model I & AraBERTv2 & Our benchmark & 69\% & 78\% & 74\% \\ \hline Model I & AraBERTv2 & Our benchmark & 67\% & 78\% & 72\% \\ \hline Model I & AraBERTv2 & Our benchmark & 50\% & 90\% & 64\% \\ \hline Model II & AraBERTv2 & our benchmark & 92\% & **87\%** & **89\%** \\ \hline Model II & AraBERTv2 & Our benchmark & **96\%** & 67\% & 79\% \\ \hline Model II & AraBERT & Our benchmark & 89\% & 73\% & 83\% \\ \hline Laatar [25] & FLAIR & Our benchmark & 63\% & 63\% & 63\% \\ \hline Laatar [25] & FLAIR & Original paper result (100 words) & 67\% & 67\% & 67\% \\ \hline Laatar [23] & word2 vec & Our benchmarke & 45\% & 45\% & 45\% \\ \hline Laatar [23] & word2 vec & Original paper test (100 words) & 78\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 9: Embedding-based models vs. our models.  
on these tasks, it has been shown in [11] that BERT models learned both syntactic and semantic features at the middle layers while, at the top layers, BERT learned long-distance dependency information. Therefore, using these pretrained models resulted in transferring this knowledge to Arabic WSD and hence improved the performance and decreased the amount of data needed for these models to learn. Moreover, we exploited BERT's ability to perform well in sentence pair classification tasks by posing the WSD as a sentence pair classification problem. Finally, we used our new benchmark to perform supervised fine-tuning; we believe that this is one of the main drivers to reach these results.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 4 Theoretical Analysis
### The Bound of Assisted Nodes Number  
Nodes communicate with each other is the prerequisite of constructing DT, based on the triangular set we know that the number of assisted nodes is related to the relationship between \(R_{s}\) and \(R_{c}\). In the following theorem, we present the theoretical bound of assisted nodes numbers. In order to normalize the problem, we suppose that \(R_{c}=kR_{s}\) (\(k\geq 2\)), if and only if nodes can communicate with each other i.e., \(d(s_{1},s_{2})=d(s_{2},s_{3})=d(s_{1},s_{3})=R_{c}\), now \(\theta=\frac{\pi}{3}\) which is one of special scenario from general condition (5), and can provide the upper bound of assisted nodes numbers. Thus, we have the bound of assisted nodes number:  
Figure 5: Coverage holes estimated.  
**Theorem 2**.: _If and only if nodes can communicate with each other, the bound of assisted sensors number (\(N_{bound}\)) in the triangle can be derived as follow:_  
\[0\leq N_{bound}\leq N_{mb}=\begin{cases}[\frac{\sqrt{3}}{4\pi}k^{2}-\frac{1}{2} ]+1,&\rho-[\rho]\geq\mu\\ \parbox[0.0 pt]{170.71654 pt}{$\frac{\sqrt{3}}{4\pi}k^{2}-\frac{1}{2}$},&\text{ else}\end{cases}\]  
_where \(\rho\)= \(\frac{\sqrt{3}}{4\pi}k^{2}-\frac{1}{2}\) and \(0<\mu<1\)._  
Proof of Theorem 2.: When \(S_{1}\), \(S_{2}\) and \(S_{3}\) are far from each other with the distance \(R_{c}\), which means \(S_{\Delta s_{1}s_{2}s_{3}}\) is a regular triangle with side length \(R_{c}\). Easily we can get \(\theta=\frac{\pi}{3}\), from (5) we can get \(\rho=\frac{\sqrt{3}}{4\pi}k^{2}-\frac{1}{2}\), then we can get \(N_{mb}\). When the \(S_{ uncovered}\) is zero then the lower bound of assisted sensors is zero, so the bound of assisted sensors can be derived: \(0<N_{bound}\leq N_{mb}\)
## 3 Results and Discussions
(**c**) Dielectric breakdown strengths of reticulated porous diatomic-kaolin composite and reticulated porous alumina specimens, with pore densities of 45, 60, and 80 PPI. \(<\) 30%) [42], which have low porosity, have been investigated Considering that the porosities of the reticulated porous diatomite-kaolin composite prepared in this study, with pore densities of 45, 60, and 80 PPI, were 89.1%, 87.4%, and 83.9%, respectively, (in addition, the porosities of the reticulated porous alumina prepared in this study, with pore densities of 45, 60, and 80 PPI, were 88.6%, 82.6%, and 81.3%, respectively), the measurements are required to investigate the effect of high porosity on the dielectric breakdown strength. It is evident that the dielectric breakdown strengths of the reticulated porous diatomite-kaolin composite, sintered at 1200 \({}^{\circ}\)C, and reticulated porous alumina specimens, sintered at 1600 \({}^{\circ}\)C were approximately 15 KV/cm, which are similar each other, irrespective of the value of PPI. It should be mentioned that the dielectric breakdown strengths of reticulated porous ceramics, with pore densities of 10 and 25 PPI, could not be measured owing to the arc-generation induced by the highly inter-connected pores between the electrodes. Therefore, by compromising the all above-mentioned experimental data, a reticulated porous diatomite-kaolin composite can be act as a potential dielectric barrier, insulating material, or filter, with a low density, good dielectric breakdown strength compared to reticulated porous alumina, and an acceptable mechanical strength, which can be prepared with low processing cost, if it is used under non-hard loading conditions.
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 1 Introduction
In contrast, image-space methods use mulitple transformation models for the registration of normalized inner CCD images. The authors of [12] used an image-space transformation model for the calibration of stitched images. In 2010, ref. [24] proposed a structure-based image-matching method to build a mosaic of multiple CCD CBERS-02B satellite images. In [25], the authors analyzed the impact of the correction method on integration time. The authors of [26] proposed a method for sift-based stitching of image-space inner CCD images using TH-1 strip imagery. They were able to improve accuracy by introducing a piece-wise linear stitching and logically adaptive image enhancement method for the stitching area. In most of the above-mentioned cases, stitched images are produced using a designed line-time, which is inconsistent with the actual imaging state. This approach introduces distortion during image-space resampling. Nowadays, most researchers are interested in optimizing the pseudo-rigorous model to obtain better stitching accuracy; image-space line-time normalization methods are usually included in this process to produce normalized adjacent inner CCD images. This kind of method eliminate geometric distortions based on reprojected object-space coordinates and the calibration of internal and external parameters. However, in a rigorous model the calibration of these parameters depends on ground control information, and the generality of these parameters needs to be checked periodically. Moreover, errors in external DEMs can introduce extra fitting errors. To obtain stitched images with high geometric performance with lower computational complexity, in this paper we analyze deficiencies in the stitching accuracy of adjacent CCD images caused by the misalignment of non-uniform sampling. We investigate different line-time normalization methods developed using the actual line-time, namely, scene-based, block-based, and line-based line-time normalization methods, in order to eliminate stitching errors between adjacent inner CCD images. Experiments with multi-spectral images obtained from the SDGSAT-1 satellite are used to verify the stitching accuracy based on automatically extracted tie-points between adjacent inner CCD images and on the visual effect. The main contributions of this paper are:  
* A close analysis of misalignment between designed and actual line-times;
* The introduction of three fine actual line-time normalization methods to overcome geometric distortions caused by the fitting error of designed line-times;
* A practical demonstration of block-based actual line-time normalization methods to achieve a balance between running time and stitching accuracy.
## 2 Materials and Methods
### Voice Discrimination Threshold (VDT)  
A three-interval, three-alternative forced-choice methodology was employed to assess the difference limens (DL) for F0 cues, formant cues, and the combined F0 + formant cues, following previous approaches [7; 8; 50]. In this procedure, each trial consisted of two reference sentences and one comparison sentence. Upon presentation of a sentence, a selection square on the computer monitor was illuminated, corresponding to the respective sentence. Participants were instructed to indicate the sentence that exhibited a 'different sound' by clicking the corresponding square using a mouse. Consistency was maintained in using the same sentence for each measurement of the VD threshold, with alterations being limited to the tested vocal cue (F0, formants, or F0 + formants). No immediate feedback was provided, and participants were not constrained by time for their responses. To ascertain the thresholds at which a detection rate of 70.7% was achieved on the psychometric function [58], a two-down, one-up adaptive tracking technique was adopted. Initially, the difference between stimuli was halved until the first reversal point, subsequently being incrementally decreased, or augmented by a factor of \(\surd 2\) until the sixth reversal point. The calculation of the DLs was based on the geometric mean of the last four reversal points.
# VNF Placement Optimization at the Edge and Cloud +
###### Abstract  
Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers' goal of low deployment costs.  
NFV; cloud computing; MEC; IoT; VNF placement; service chain Article
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 5 Visual Adaptivity by Eye-in-Hand System
### Motion Planning
#### 5.3.3 Image-Based Visual Servo Control  
In this study, the target object was placed on a conveyor belt traveling at a constant speed \(V_{conveyor}\). The speed of the conveyor belt can be pre-set by the user or be calculated by the target's movement in serval frames as:  
\[V_{conveyor}~{}=~{}\frac{\sqrt{(\widetilde{x}_{2}-\widetilde{x}_{1})^{2}+( \widetilde{y}_{2}-\widetilde{y}_{1})^{2}}}{\Delta T_{frame}} \tag{27}\]  
Figure 26: Parameters of heights for different frames in the system.  
where \(\widetilde{x}\) and \(\widetilde{y}\) are the real position of the object, subscripts 1 and 2 indicate two incremental time indexes, and \(\Delta T_{frame}\) is the period of the camera's update rate. It was observed that once the eye-in-hand gripper system received a command to move the manipulator to a desired position, owing to the required computation overhead time, the target had already moved to a new position. Therefore, it is imperative to calculate the movement speed of the conveyor and account for its motion when moving the manipulator. However, the estimation of the target object's moving speed is required for the gripper to successfully track and grasp the object. Mathematically, the area under the velocity-time diagram is equivalent to the distance between the gripper (camera) and the object centers, the function of which is given by the following equation:  
\[V_{t}\ =\ (V_{t-1}+aT)\pm\sqrt{a^{2}T^{2}+2a(V_{t-1}T-X)} \tag{28}\]  
where \(V_{t}\) is the output speed, \(V_{t-1}\) is the speed of the previous iteration of the manipulator, \(T\) is the time of each iteration, \(a\) is the acceleration, and \(X\) is the distance between the gripper and the object centers. To determine the output speed, the sign of the acceleration is first determined. The acceleration is positive when \(X\) is greater than \(V_{t-1}\times T\). Otherwise, it is negative. However, the value in the square root must be positive. If \(a^{2}T^{2}+2a(V_{t-1}T-X)\) is less than zero, then \(X\) is too large under this acceleration. In this case, \(X\) must be modified to satisfy these conditions. As the speed of the conveyor is constant, the speed of the manipulator can be similar in each iteration. This speed can be assumed to be the speed of the conveyor so that the eye-in-hand gripper system can compensate for the object's motion at the last iteration because of the known speed of the conveyor. Therefore, the total speed is given by:  
\[V_{total}\ =\ V_{t}+V_{conveyor} \tag{29}\]
# Gut Microbiome in Patients with Obstructive Sleep Apnoea
###### Abstract  
Background: Obstructive sleep apnoea (OSA) is a risk factor for cardiovascular disease. Alterations in the gut microbiome have been implicated in the development of cardiovascular disease and may potentially link OSA to its cardiovascular consequences. However, only one study to date has investigated gut microbiomes in adult patients with OSA. Methods: 19 patients with OSA and 20 non-OSA controls participated in the study. Following a diagnostic sleep study, blood was collected for metabolic profiling, and the subjects provided a stool sample for microbiome analysis. The gut microbiome was investigated using the 165 ribosomal RNA method. Results: Patients with OSA had a higher relative abundance of the _Proteobacteria_ phylum (\(p\) = 0.03), _Gammaproteobacteria_ class (\(p\) = 0.01), _Lactobacillus_ family (\(p\) = 0.02), _Lactobacillus_ (\(p\) = 0.03), and _Roseburia_ genus (\(p\) = 0.03), and a lower abundance of the _Actinobacteria_ phylum (\(p\) = 0.03). The abundance of _Proteobacteria_, _Gammaproteobacteria_, _Lactobacillus_, and _Lactobacillus_ were related to disease severity and dyslipidemia (all \(p\) < 0.05), whilst the abundance of _Proteobacteria_ and _Gammaproteobacteria_ was also related to hypertension and cardiovascular disease (all \(p\) < 0.05). However, following adjustment for relevant confounders only the association between OSA and _Actinobacteria_ remained significant (\(p\) = 0.04). Conclusions: Obstructive sleep apnoea is associated with only subtle changes in gut microbiome. Further studies should investigate gut dysbiosis in OSA.  
Keywords: gut microbiome; dysbiosis; cardiovascular; comorbidity; sleep apnoea; sleep disorder +
Footnote †: journal: _Sci._  
_Applied_  
_Acticle_
## 4 Energy Efficiency  
The detectors introduced in Section 3 have different error rate performance and complexity, which in turn affects the EE of large-scale MIMO systems with low-resolution ADCs. In this section, we first analyze the transmitted power required to achieve a fixed BER under different detection algorithms. We then analyze the corresponding receiver power consumption. Finally, we summarize the EE.
## 5 Results
### Influence of Uncertain Parameters
#### 5.1.2 Failure Probability Analysis  
Combining the fragility function with the joint probability distribution of IT-WS and WS-WD, the failure probability density of transmission tower collapse under combined ice and wind loads is calculated by Equation (2). Figure 12 presents the failure probability density surfaces for the certain model C1 model and the uncertain model U21 model, which exhibit noticeable differences. Table 6 presents the cumulative failure probability of all models under combined ice and wind loads. Compared with the certain model, considering the parameter uncertainty, the cumulative failure probability of the tower-line system changes between 0 and 47%, and the ratio of the maximum to the minimum cumulative failure probability is approximately 1.84. This phenomenon indicates that uncertain parameters can lead to significant fluctuations in the failure probability of the tower-line system under combined ice and wind loads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Number** & **Probability** & **Model Number** & **Probability** & **Model Number** & **Probability** \\ \hline U1 & 2.0090 & U11 & 1.4269 & U21 & 2.6304 \\ U2 & 2.3373 & U12 & 1.7708 & U22 & 2.4706 \\ U3 & 1.5998 & U13 & 1.7197 & U23 & 1.7259 \\ U4 & 2.3103 & U14 & 1.6195 & U24 & 2.1272 \\ U5 & 2.3031 & U15 & 2.1515 & U25 & 2.0025 \\ U6 & 1.5366 & U16 & 1.7122 & U26 & 2.0077 \\ U7 & 2.4650 & U17 & 1.7350 & U27 & 1.8653 \\ U8 & 2.0692 & U18 & 2.1401 & U28 & 1.7766 \\ U9 & 2.0545 & U19 & 2.2018 & U29 & 1.8129 \\ U10 & 1.8713 & U20 & 1.6213 & U30 & 1.4414 \\ C1 & 1.8628 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The cumulative failure probability (unit: \(10^{-5}\)).  
Figure 11: The fragility curves under different ice thicknesses: (**a**) 5-mm ice thickness; (**b**) 10-mm ice thickness; (**c**) 15-mm ice thickness; (**d**) 20-mm ice thickness; (**e**) 25-mm ice thickness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ice** & \multicolumn{2}{c}{**Wind Speed 10 m/s**} & \multicolumn{2}{c}{**Wind Speed 15 m/s**} & \multicolumn{2}{c}{**Wind Speed 24 m/s**} \\ \cline{2-6}
**Thickness** & **Maximum** & **Minimum** & **Maximum** & **Maximum** & **Minimum** & **Maximum** \\ \hline
5 mm & 0.11\% & 0.04\% & 2.98\% & 1.33\% & 30.69\% & 19.32\% \\
10 mm & 0.44\% & 0.19\% & 7.59\% & 4.2\% & 47.85\% & 35.34\% \\
15 mm & 1.13\% & 0.59\% & 13.77\% & 8.86\% & 61.36\% & 50.12\% \\
20 mm & 2.42\% & 1.
## 3 Vehicle-Bridge Interaction Model
### Vehicle Model  
Figure 1 depicts the planar 3-axle rigid truck model employed in the simulations.  
The truck model has 5 DoFs, namely the vertical displacements of the front axle, rear tandem and body masses (labeled in the figure as \(w_{v1}\), \(w_{v2}\) and \(w_{v3}\), respectively) and the rotations of the rear tandem mass (\(\theta_{1}\)) and the body mass (\(\theta_{2}\)). The mass \([M_{v}]\), damping \([C_{v}]\), and stiffness \([K_{v}]\) matrices used in Equation (2) are given in Equations (9)-(11).  
\[[M_{v}]=\begin{bmatrix}m_{1}&0&0&0&0\\ 0&m_{2}&0&0&0\\ 0&0&I_{1}&0&0\\ 0&0&0&m_{3}&0\\ 0&0&0&0&I_{2}\end{bmatrix} \tag{9}\]  
\[[C_{v}]=\begin{bmatrix}C_{11}+C_{12}&0&0&-C_{11}&C_{11}L_{v3}\\ 0&C_{21}+C_{22}+C_{32}&\frac{(-C_{22}+C_{32})L_{v2}}{2}&-C_{21}&-C_{21}L_{v4}\\ 0&\frac{(-C_{22}+C_{32})L_{v2}}{2}&\frac{(C_{22}+C_{32})L_{v2}^{2}}{4}&0&0\\ -C_{11}&-C_{21}&0&C_{11}+C_{21}&-C_{11}L_{v3}+C_{21}L_{v4}\\ C_{11}L_{v3}&-C_{21}L_{v4}&0&-C_{11}L_{v3}+C_{21}L_{v4}&C_{11}L_{v3}^{2}+C_{ 21}L_{v4}^{2}\end{bmatrix} \tag{10}\]  
\[[K_{v}]=\begin{bmatrix}K_{11}+K_{12}&0&0&-K_{11}&K_{11}L_{v3}\\ 0&K_{21}+K_{22}+K_{32}&\frac{(-K_{22}+K_{32})L_{v2}}{2}&-K_{21}&-K_{21}L_{v4}\\ 0&\frac{(-K_{22}+K_{32})L_{v2}}{2}&\frac{(K_{22}+K_{32})L_{v2}^{2}}{4}&0&0\\ -K_{11}&-K_{21}&0&K_{11}+K_{21}&-K_{11}L_{v3}+K_{21}L_{v4}\\ K_{11}L_{v3}&-K_{21}L_{v4}&0&-K_{11}L_{v3}+K_{21}L_{v4}&K_{11}L_{v3}^{2}+K_{ 21}L_{v4}^{2}\end{bmatrix} \tag{11}\]  
where \(C_{ij}\) represents the damping values; \(K_{ij}\), stiffness values; \(L_{vi}\), distances; \(m_{i}\), masses; and \(I_{i}\), mass inertias of the vehicle.  
Table 2 lists typical values of properties for a 3-axle truck, calibrated experimentally [8; 43]. These values are assumed to be the mean values for a truck fleet that will be defined further on. An eigenvalue analysis led to five vehicle frequencies, 2.153, 2.671, 14.806, 15.572 and 15.827 Hz, representing the body mass bounce, the body mass pitch, the rear axle pitch, the front axle hop and the rear axle hop, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Unit** \\ \hline Geometry & & \\ Spacing between front and 1 st rear axle, \(L_{v1}\) & 5.40 & m \\ Spacing between 1 st and 2 nd rear axle, \(L_{v2}\) & 1.20 & m \\ Distance between \(m_{1}\) and \(m_{3}\), \(L_{v3}\) & 4.40 & m \\ Distance between \(m_{2}\) and \(m_{3}\), \(L_{v4}\) & 1.60 & m \\ Weights & & \\ Gross Vehicle Weight, \(GVW\) & 241.00 & kN \\ Front axle, \(P_{1}\) & 64.11 & kN \\
1 st Rear axle, \(P_{2}\) & 88.45 & kN \\
2 nd Rear axle, \(P_{3}\) & 88.45 & kN \\ Masses & & \\ Front axle unsprung, \(m_{1}\) & 500 & kg \\ Rear tandem unsprung, \(m_{2}\) & 1450 & kg \\ Body sprung, \(m_{3}\) & & 22,625 & kg \\ Mass moments of inertia & & \\ Rear tandem unsprung mass, \(I_{1}\) & 732 & kg\(\cdot\)m\({}^{2}\) \\ Body sprung mass, \(I_{2}\) & 162,162 & kg\(\cdot\)m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the vehicle model.