esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 3 Task Planning Model of Space-Robot Cluster
### Objective Function  
The task planning of space-robot cluster mainly considers the benefits and costs of completing the tasks of each node with limited energy. For simplicity, we only consider the moving distance of space robots and the cost of repairing solar panels. Assuming that the damage degree of solar panel is divided into three levels, with damage level I for 10-40%, II for 40-70% and III for 70-100% and the maintenance benefit increases with the increase of damage degree.  
The goal of space-robot cluster task-planning is to maximize the overall benefit  
\[J=\max(\omega_{1}\cdot\sum_{k=1}^{N}\sum_{i=1}^{m}benefit_{i}^{k}+\omega_{2} \cdot\sum_{k=1}^{N}Consumption_{k}) \tag{16}\]  
subject to  
\[benefit_{i}^{k}=gain\cdot\sum_{j=1}^{num_{i}^{k}}damage_{j} \tag{17}\]  
\[Consumption_{k}<\xi\cdot Energy_{k}\ \ \forall\ k\in\{1,2,\cdots,N\} \tag{18}\]  
\[damage_{j}\in\{1,2,3\} \tag{19}\]  
where \(\omega_{1}\) and \(\omega_{2}\) respectively represent the weight coefficients of maintenance revenue and maintenance cost, \(benefit_{i}^{k}\) is the maintenance revenue in the \(i^{\prime}th\) cluster node of the \(k^{\prime}th\) robot, \(damage_{j}\) is the damage level and \(gain\) is the profit coefficient.  
In this model, the objective function (16) aims to maximize the value of the profit and the maximum revenue and the minimum energy consumption of completing the task must be considered. Moreover, the energy consumption includes the maintenance path consumption and the maintenance node consumption; constraint (18) represents the energy limit of each space robot.  
According to the research on constraint-processing methods, constraint-processing methods are mainly divided into three categories: penalty-function methods, feasible-rule methods and multi-objective methods. The penalty-function method is to add a penalty function to the objective function. Because of its simple principle and easy implementation, it is one of the most widely used constraint-processing methods. In this paper, by analyzing the task planning model of space robot, the concept of penalty function is introduced to transform the constraint problem into an unconstrained problem.  
By introducing the penalty function, the objective function is  
\[max\;\;J=\omega_{1}\cdot Profit+\omega_{2}\cdot Cost+Penalty \tag{20}\]  
In which  
\[\begin{array}{c}Profit=\sum\limits_{k=1}^{N}\sum\limits_{i=1}^{m}benefit_{i} ^{k}\\ Cost=\sum\limits_{k=1}^{N}Consumption_{k}\\ Penalty=\varphi\cdot\sum\limits_{k=1}^{N}(Consumption_{k}-\xi\cdot Energy_{k} )\end{array} \tag{21}\]  
In which: \(\omega_{1}\), \(\omega_{2}\) respectively represent the maintenance revenue and the maintenance cost, \(\varphi\) represents the penalty coefficient.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 5 Effect on Tracking Process for BeiDou Receiver
### Determining PLL Variance for the BeiDou B11 Signal
All the calculations above ignore the signal fading, but when it comes to calculating the loss of lock probability, it is necessary to take fading into account, which could consequently lead to a degradation of SNR at the receiver. According to [7], Equation (44) could be changed into:  
\[\sigma_{\Phi T}^{2}=\frac{B_{n}}{(C/N_{0})F}(1+\frac{1}{2t_{0}(C/N_{0})F}) \tag{46}\]  
In Equation (46), \(F\) is the amplitude scintillation intensity randomly distributed with parameter \(S_{4}\). Based on 100 times of simulation, the distribution of normalized amplitude scintillation under different \(S_{4}\) corresponds to a Nakagami-m distribution, so the probability density function of \(F\) could be expressed in terms of a Nakagami-m fading model:  
\[P(z)=\frac{2m^{m}z^{2m-1}}{\Omega^{m}\Gamma(m)}\exp(-\frac{mz^{2}}{\Omega}) \tag{47}\]  
Figure 9: PLL standard deviation. In Equation (47), \(\Omega\) is a scale factor, equal to the mean power of the normalized intensity, \(m=1/S_{4}^{2}\) is the shape factor, \(\Gamma(\cdot)\) is a gamma function. For a given \(C/N_{0}\), \(F\) must be below a threshold to meet the requirement that \(\sigma_{\Phi}\) be above \(15^{\circ}\). Then the probability of event \"F < threshold\" can be evaluated, which represents the loss of lock probability. Figure 10 presents this probability at different given values of the \(C/N_{0}\) for the BeiDou signal. From the figure, we can see that under the same \(C/N_{0}\), the loss of lock probability increases with the increase of scintillation intensity and the probability increases slower as the \(C/N_{0}\) decreases. Under high scintillation intensity and low \(C/N_{0}\) level, the loss of lock probability could be 0.11 to 0.26. In some extreme cases (e.g., \(S_{4}>0.9\), \(C/N_{0}<20\) dB), the loss of lock probability could reach 0.3 or even more. It is also can noticed that, under high scintillation intensity, links with high SNR are quite robust. On the contrary, links with low values of SNR are likely to be lost.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 2 Related Works
In large language models (LLMs), [PERSON] and [PERSON] [31] investigated the effect of unigram [10] and and BPE [32] subtokenization algorithms on the performance of LLMs trained on source code using the PLBART model [33] as a testing ground. The study revealed that reducing the subtokenization length up to 17% could improve the model training efficiency. Additionally, using the right subtokenizer (in this case: unigram) improved the quality of the trained LLM by 0.5-2%. Similarly, [PERSON] and [PERSON] [13] conducted a study to evaluate the performance of pretrained language models using BPE and unigram tokenizers. They pretrained four RoBERTa-base models: two on an English text corpus of 3 billion tokens and two on a Japanese text corpus of 0.6 billion tokens. Each language group had one model pretrained with BPE tokenization and another with unigram tokenization. They concluded that the English model that used the unigram tokenizer slightly outperformed its counterpart model pretrained with a BPE tokenizer on various NLP tasks. However, the unigram Japanese language model showed a more than 10% improvement over its BPE counterpart in the Japanese QA task. Furthermore, [PERSON] and [PERSON] [34] analyzed the relationship between the output of the tokenizer and the time and accuracy of human performance on a lexical decision task. The results showed that the Unigram produced outputs that were less aligned with human cognition than BPE and WordPiece. Compared to current related works, there has been limited investigation into the impact of subword tokenizers on the Arabic language. Additionally, there has been limited exploration of how different tokenizers affect LLMs like BERT, particularly for the Arabic language. Thus, this paper aims to bridge this gap by comparing various tokenization algorithms for the Arabic language. We evaluated three subword tokenizers: WordPiece, Byte-Level BPE, and SentencePiece. We pretrained three BERT models using each tokenizer and then evaluated their performance on range of NLP tasks. The outcomes of this paper will assist researchers in developing better models, thus making better decisions in selecting the appropriate tokenizer for their needs, improving feature engineering, and making models more efficient, which will ultimately lead to advancements in various NLP applications.
###### Abstract  
In this study, a kinematic calibration method is proposed. The method selects the optimal measurement poses based on the observability index \(O_{1}\), and is used for six-hardpoint positioning mechanisms, which can identify and compensate for kinematic parameter errors of the mechanism. The calibration method is based on the derived error model. In order to improve the problem that measurement noise affects calibration accuracy, the proposed calibration method selects specific poses for calibration based on the observability index \(O_{1}\), effectively improving the calibration accuracy. The reason for using \(O_{1}\) is that we compared the performance of the five observability indices through simulation, and the results show that the calibration based on \(O_{1}\) has the highest accuracy. In order to achieve pose selection based on observability indices, the DETMAX algorithm has been improved to achieve better performance of selection. Finally, an overall evaluation of the proposed calibration method is conducted, and the results show that the method can accurately identify kinematic parameter errors, with a fast error convergence speed. Compared with the traditional method, the proposed method has higher accuracy, reducing the mean position error by 78.4% and the mean attitude error by 70.6%. The proposed method is instrumental in the accurate kinematic calibration of six-hardpoint positioning mechanisms, and can effectively improve the accuracy of the mechanism, thus improving the pose accuracy of the primary mirror of the ground-based large aperture telescope.  
Six-hardpoint positioning mechanism; parallel mechanism; kinematic calibration; observability index; optimal measurement pose +
Footnote †: journal: _aciences_  
0023  
Kinetic Calibration Method for Six-Hardpoint Positioning Mechanisms Using Optimal Measurement Pose. _Appl. Sci._**2023**, _13_, 4824. [[https://doi.org/10.3390/app13084824](https://doi.org/10.3390/app13084824)]([https://doi.org/10.3390/app13084824](https://doi.org/10.3390/app13084824))
## 3 Overview Communication Technology for Smart Grid
### Cellular Networks  
Most countries are working toward the deployment of a mobile network and the establishment of infrastructure. Cellular networks enable high-speed data communication of up to 100 Mbps and they can be used in the interaction between various components of the network and devices of a smart grid. WiMAX cellular communication technology is the most remarkable application of smart grid deployment [21]. Working at a data exchange frequency of 2.5, 3.5 and 70 Mbps and coverage of up to 50 km, WiMAX is integrated into the smart meter (smart grid) through deployment. A mobile network has a wide area deployment, high data transmission and secure communication. The main drawback of this system is that it is shared with other systems that are not fully dedicated to a smart grid and can cause a serious emergency situation [22; 23].
# A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexopod Robots
## 5 The Three-Element Trajectory Determination Method in Triangle Gait
### Start Point and End Point Selection  
After the stable point \(P\) is determined, what also needs to be performed is to select the start point \(P_{\rm s}\) and the end point \(P_{\rm e}\) of the support phase. For a hexapod robot, it is symmetric, and the triangle gait is simultaneously repetitive. Thus, the stable point during movement will be the middle point of the trajectory in the support phase. Supposing that the target moving direction is \(\theta_{a}\), the trajectory in the support phase is along the line \(\overline{P_{0}P_{1}}\), as shown in Figure 10. Ideally, \(|PP_{0}|=|PP_{1}|=\frac{D}{2}\), and the coordinates of \(P_{0}\) and \(P_{1}\) can be computed. In reality, the feet can only reach a limited range (as in Figure 10, the foot of _Leg6_ can only arrive at the points between the two dotted arc lines); thus, the selection of \(P_{\rm s}\) and \(P_{\rm e}\) should depend on specific conditions.  
Extend \(PP_{0}\) and intersect it with the outer arc at point \(H\). Extend \(PP_{1}\) and intersect it with the inner arc at point \(G\). If \(\min\{|PH|,|PG|\}\geq\frac{D}{2}\), this means both of the points \(P_{0}\) and \(P_{1}\) can be reached, and they can be the selected points. Then, the priority is given to movement stability, and all feet move equidistant around their respective stable points. If \(|PH|\geq\frac{D}{2},|PG|<\frac{D}{2}\), this means the point \(P_{1}\) cannot be reached, and the selected points are \(P_{0}\) and \(G\). If \(|PH|<\frac{D}{2}\), \(|PG|\geq\frac{D}{2}\), this means the point \(P_{0}\) cannot be reached, then the selected points are \(H\) and \(P_{1}\). If \(\max\{|PH|,|PG|\}<\frac{D}{2}\), then the trajectory will be the line \(\overline{HG}\). Thus, we have the following.  
\[\left\{\begin{array}{ll}\text{If}\min\{|PH|,|PG|\}\geq\frac{D}{2},&P_{\rm s }=P_{0}\text{ and }P_{\rm e}=P_{1},\\ \text{If}|PH|\geq\frac{D}{2},&|PG|<\frac{D}{2},&P_{\rm s}=P_{0}\text{ and }P_{\rm e}=G,\\ \text{If}|PH|<\frac{D}{2},&|PG|\geq\frac{D}{2},&P_{\rm s}=H\text{ and }P_{\rm e}=P_{1},\\ \text{If}\max\{|PH|,|PG|\}<\frac{D}{2},&P_{\rm s}=H\text{ and }P_{\rm e}=G.\end{array}\right. \tag{24}\]  
Transform the start point \((x_{R},y_{R},z_{R})\) and the end point \((x_{R},y_{R},z_{R})\) that were obtained above under the world coordinate to the coordinate \(\{O_{i0}\}\). Then, the trajectory of each foot can be planned by using the method in Section IV.
# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 4 Implementation and Results
### Results of Applying the LSTM Model for SOC Inference

Table 5: LSTM model performance across epochs. at 0.2331 and RMSE at 0.5793, further indicating ongoing improvements in performance. Finally, at epoch 60, the predicted SOC stabilized at 12.4968%, with MAE at 0.2333 and RMSE at 0.5682, suggesting that the model was stabilizing and the margin of error was consistently decreasing. Overall, as the number of epochs increased, the predicted SOC values gradually stabilized, and both MAE and RMSE showed a declining trend. This indicates that the model was training effectively and could make increasingly accurate predictions over time. The continuous decrease in MAE and RMSE reflects a reduction in the model's prediction error, signifying steady improvement in performance.
## 4 Results and Discussion  
### Model Calibration with Field Measured Yields at Pixel Level  
We used the measured yields to calibrate the maize and sunflower RF models driven by different predictors, respectively. The calibration performance of RF models was shown in Figure 3. Comparedwith previous model calibration at county scale [7; 11; 41], the model calibration in this study was based on pixel scale, which could improve the performance of crop yield estimation model at finer resolution. No matter for maize or sunflower, the \(R^{2}\) and adjusted \(R^{2}\) of all RF models were between 0.80 and 0.90, and the difference between \(R^{2}\) and adjusted \(R^{2}\) was very small due to the number of calibration data (272 for maize and 432 for sunflower) was far more than the number of predictors, which indicated that there was no over fitting phenomenon in our calibrated models. The RMSE of maize ranged from 0.85 to 1.12 t/ha, while the RMSE of sunflower were lower than the maize and ranged from 0.34 to 0.47 t/ha. For maize, models 1, 2, 4, 5 and 8 had relatively lower RMSE and higher \(R^{2}\) than the other three models. While for sunflower, models 1, 2, 5, 6 and 8 had relatively lower RMSE and higher \(R^{2}\) than the other three models.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 4 Nonconvex Nonlinear Transformation for Tensor Completion
### Optimization of Parallel Computing
Under the condition that other variables remain constant, the minimization subproblem pertaining to \(\mathcal{A}\) is derived from Equation (30) as follows:  
\[\mathbf{A}^{k+1}= \arg\underset{\mathbf{A}}{\operatorname{min}}\frac{\theta}{2} \Big{\|}\mathbf{G}_{(3)}-\mathbf{B}^{k}\mathbf{A}\Big{\|}_{\mathrm{F}}^{2}+ \frac{\rho}{2}\Big{\|}\mathbf{A}-\frac{\mathbf{J}^{k+1}-\Gamma^{\mathbf{A}}/ \mu+\mathbf{A}^{k}}{2}\Bigg{\|}_{\mathrm{F}}^{2}. \tag{34}\]  
These subproblems are convex and have closed-form solutions provided by:  
\[\mathbf{A}^{k+1}= \Big{(}2\theta\mathbf{B}^{T}\mathbf{B}^{k}+2\rho\mathbf{I} \Big{)}^{-1}\Big{[}2\theta\mathbf{B}^{T}\mathbf{G}_{(3)}+\mu\Big{(}\mathbf{J}^ {k+1}-\Gamma^{k}/\mu+\mathbf{A}^{k}\Big{)}\Big{]}. \tag{35}\]  
The update of the Lagrangian multiplier \(\Gamma^{\mathbf{A}}\) is obtained through the following equation:  
\[\Gamma^{\mathbf{A}}=\Gamma^{\mathbf{A}}+\mathbf{A}-\mathbf{J}. \tag{36}\]  
The update for \(\mathbf{B}\) is similar to \(\mathbf{A}\), and it is omitted here.
## 2 Materials and Methods  
### Specim Manufacturing  
This study investigated the influence of the printing process parameters on the elastic material properties of a printed PLA material. For this purpose, 38 cubic PLA specimens with dimensions of 20 \(\times\) 20 \(\times\) 20 mm\({}^{3}\) were printed using a Creality Ender 6 FDM 3D printer (Shenzhen, China) (Figure 1). Markforged PLA white filament with diameter of 1.75 mm was utilized. The initial process parameters chosen for printing the experimental specimens included a nozzle temperature of 220 \({}^{\circ}\)C, a nozzle speed of 50 mm/s, a build plate temperature of 50 \({}^{\circ}\)C, and a layer height of 0.2 mm. In order to analyze the influence of the nozzle temperature, nozzle speed, and layer height on the elastic properties of the printed material, three sets of cubic specimens were printed, changing the parameter under investigation with respect to the starting point (Table 1). The initial set of specimens, as shown in Figure 1, included a larger number of specimens. However, due to high ultrasonic wave attenuation in some specimens, which complicated the experiments and prevented accurate data collection, these specimens were excluded from the final analysis. Consequently, the final set of specimens reported in Table 1 represents those used in the research. To improve the poor quality of the ultrasonic signal due to surface roughness [39], all specimens were polished using P220 and P400 grade sandpaper. The results for the unpolished specimens are not presented because the ultrasonic wave attenuated very rapidly on these specimens. This high attenuation made it impractical to process and use such data for analysis. Therefore, polishing was necessary to ensure reliable and accurate ultrasonic measurements.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Parameter** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** & **S11** & **S12** & **S13** \\ \hline Nozzle speed, mm/s (nozzle temperature: 220 \({}^{\circ}\)C; layer height: 0.2 mm) & 50 & 60 & 70 & 80 & 90 & 100 & 105 & 110 & 115 & 120 & 130 & 140 & 150 \\ \hline
**Parameter** & **T1** & **T2** & **T3** & **T4** & **T5** & **T6** & **T7** & **T8** & **T9** & **T10** & **T11** & **T12** & **T13** \\ \hline Nozzle temperature, \({}^{\circ}\)C (nozzle speed: 50 mm/s; layer height: 0.2 mm) & 200 & 201 & 203 & 204 & 205 & 206 & 207 & 208 & 209 & 210 & 212 & 215 & 220 \\ \hline
**Parameter** & **TH1** & **TH2** & **TH3** & **TH4** & **TH5** & **TH6** & **TH7** & **TH8** & **TH9** & **TH10** & **TH11** & **TH12** \\ \hline Layer height, mm (nozzle temperature: 220 \({}^{\circ}\)C; nozzle speed: 50 mm/s) & 0.04 & 0.05 & 0.07 & 0.1 & 0.13 & 0.18 & 0.2 & 0.23 & 0.25 & 0.27 & 0.3 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing process parameters used for the specimen manufacturing.
## 2 Scenario Background  
The Smart City vision can be achieved through a proper integration of several key technologies (e.g., ML, EC). In [22], the authors have surveyed different EC technologies that can be useful for realizing the Smart City vision. Various Smart City scenarios and corresponding challenges are reported. Furthermore, in [23], the influence of big data and ML technologies in Smart City developments is presented. Developing a proper ITS is a key to creating sustainable Smart City environments. Different technologies are merging with the goal of forming sustainable, safe, and intelligent VNs for serving VUs. In [24], the authors surveyed several ML-based solutions employed in VNs communication and networking parts. Differently, the importance of a network softwerization technology in the VN and corresponding challenges is surveyed in [25]. In [26], the authors have proposed a software-defined collaborative EC platform for the vehicular scenario. They have mainly focused on ground-based EC technologies, including mobile edge computing (MEC), fog computing, cloudlet, etc. However, limited capacity and coverage issues of ground-based EC platforms limit the performance of traditional VNs.  
Recently, the importance of NTN platforms in wireless communication is highlighted in different projects and research works. Several ongoing projects including EdgeSAT [27], SATis5 [28], Expense [29] are aimed at integration (with terrestrial networks (TNs)), soft-warization, and expansion of EC facilities over NTN platforms. Several study items such as IoT over NTN, new radio (NR) over NTN, satellite components in 5G architecture, and unmanned aerial systems are part of 3 rd generation partnership project (3 GPPPs) Release 17 studies, which will be finalized at the end of the first quarter of 2022 [30]. It is also expected that 3 GPP will reconsider many of these items in an upcoming release (i.e., Release 18) [31].  
In [32], the authors presented the new opportunities and challenges ahead for integrating the NTN technologies into upcoming 6G networks. The work in [33] analyzes the importance and challenges of integrating NTN networks into VN. A space-air-ground integrated VN architecture for supporting different vehicular services in diverse scenarios is proposed. Furthermore, in [34], the authors have presented a space-air ground integrated VN architecture highlighting the key features of each platform layer. It is possible to enable several EC services over different NTN layers by deploying proper computing resources. In [12], authors proposed EC-enabled UAV platforms for improving computation performance and reducing the execution latency of MEC systems. Recently in [35], authors studied the energy performance of an offloading strategy designed over EC-enabled satellites and HAP networks for ground-based users. In [36], the importance of the UAV-assisted VEC system and corresponding implementation issues are highlighted.