esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
###### Abstract  
With the increasing amount of space debris, it is necessary to develop space debris active cleaning technology. The space netted pocket system is a flexible mechanism system which can be used to capture a non-cooperative target flexibly due to the advantages of having a stable structure, strong adaptability and a large capture range. In this paper, the dynamics of the space netted pocket system capturing a non-cooperative target are investigated. The dynamics model of the space netted pocket system is established based on the absolute nodal coordinate formulation (ANCF). The mathematic model of the closing rope is modeled using an ANCF flexible cable dynamics model. Then, the contact collision force model is presented to describe the collision characteristics during the space netted pocket system capturing the target. Finally, the dynamic characteristics of the space netted pocket system capturing the non-cooperative target are analyzed and discussed for different capturing strategies. The simulation results indicate that the target is captured successfully and the target contacts and collides with the rope net in the capturing process, which is subjected to contact and collision forces.  
space netted pocket system; contact and collision; ANCF; dynamics modeling and simulation +
Footnote †: journal: Applied Sciences  
Article
## 3 Results
### XPS Analysis  
Accordingly, XPS analysis was performed on the semi-coke and tar from the co-pyrolysis of tar-rich coal with NH4 Cl for secondary validation. The XPS plots of tar-rich coal, semi-coke from the pyrolysis of tar-rich coal alone, as well as the semi-coke and tar from the co-pyrolysis of tar-rich coal with NH4 Cl, are shown in Figure 6. As shown in Figure 6I, the spectral trends were basically the same for c and d, indicating that the elemental content did not change much. The c group had a higher content of C 1s than the d group, and the ionic current intensity of O 1s was lower than that of the d, which suggests that the elemental content of C increased while that of O decreased after pyrolysis. This change is related to the volatilization of water from the coal in the pyrolysis process. However, in the coke generated by the co-pyrolysis of tar-rich coal with NH4 Cl (Figure 6I b), there was a peak in the range of 399-404 eV, which was determined to be the N 1s peak after comparison. There were two more distinct peaks around 175 eV with higher ion current intensity than the N 1s peak, which were identified as Cl 2s and Cl 2p peaks by comparison. The possible reason is that the final pyrolysis product is mixed with NH3 and HCl at lower temperatures, which are then re-reacted to generate NH4 Cl. In addition, it is also possible that the Cl radical reacts not only with nitrogen-containing compounds but also with other non-nitrogen compounds during the reaction process. The peaks of N 1s were fitted as shown in Figure 6 II. It was found that N-O was more abundant in the coke after co-pyrolysis. A possible reason may be that a large number of OH radicals were generated during coal dehydration, which promoted the oxidation of the amino groups produced in the co-pyrolysis system to generate N-O. Similarly, the presence of N-Q was partially attributed to the regenerated NH4 Cl mixed with coke produced by pyrolysis. As shown in Figure 7, the color of the pyrolysis product when combined with NH4 Cl changed from black to gray, which also proves that part of the N-Q is derived from NH4 Cl. In the corresponding coke fraction (shown in Figure 6I a), there was a similar N 1s peak in the range of 399-404 eV. After fitting the N 1s peak as shown in Figure 6 III, it was found that the tar mainly contained pyrrole-N, amine-N, imine-N, pyridine-N, and nitrile-N, with pyrroles being the main nitrogen-containing compounds, which was in agreement with the GC/MS results.  
Figure 6: (**I**) shows the XPS results of four substances (a: tar co-pyrolyzed by tar-rich coal and NH4 Cl; b: co-pyrolyzed by tar-rich coal and NH4 Cl; c: co-pyrolyzed by tar-rich coal alone, and d is tar-rich coal); (**II**): the N 1s peak of the b-fit in (**I**); (**III**): the N 1s peak of the a-fit in (**I**).
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 1 Introduction
### Motivation
However, due to inherent mathematical complexity in ML, which increases with the increase in OAM modes and the number of users [4], an additional deep learning-based approach for Successive Interference Cancellation (SIC) is also used at the receiver end to improve the overall performance of the system.
## 2 Biosorbent-Based Extraction Phases
### Cork as a Biosorbent  
Cork is the bark of the cork oak tree (_Quercus super_ L.) and as a lignocellulosic material, it is composed of 40% suberin, 24% lignin, 20% cellulose and hemicellulose and 15% of other extractives [13]. In 2013, [PERSON] et al. [14] proposed, for the first time, the use of a cork-based biosorbent as a coating for the solid phase microextraction technique (SPME) introduced by [PERSON] et al. in 1990 as a miniaturized technique [4]. The procedure to obtain SPME cork fiber involves immobilization of the cork powder (approx 200 mesh) on a nitinol wire of 0.2 mm thickness and approximately 2 cm length. After this, the wires with biosorbent are heated at a temperature of 180 \({}^{\circ}\)C for 90 min. Before use, the cork fibers produced are conditioned at 260 \({}^{\circ}\)C for 60 min in a gas chromatograph (GC) injection port [14]. The fiber characterization conducted using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) showed a heterogeneous chemical composition. Lignin presents several aromatic rings that may allow \(\pi\)-\(\pi\) interactions between sorbent phase and analytes, mainly the non-polar compounds. On the other hand, cellulose and hemicellulose exhibit a number of O-H groups in their structure, allowing for hydrogen-bonding and dipole-dipole interactions with the compounds presenting intermediate polarity. Furthermore, a homogeneously distributed coating and a porous structure are reported for the surface of the fiber. The coating thickness obtained for the proposed fiber was about 55 \(\mu\)m [14]. The SPME biosorbent-based fiber has already been successfully applied for the determination of polycyclic aromatic hydrocarbons (PAH) [14], organochloride pesticides (OCPs) [15] and UV filters such as 3-(4-methylbenzylidene) camphor (4-MBC) and 2-ethylhexyl 4-(dimethylamino) benzoate (OD-PABA) [16] (Table 1). In the work proposed by [PERSON] et al. [14], the cork fiber extracted the PAHs by adsorption through \(\pi\)-\(\pi\) interactions and suberin was reported to play a more important role than lignin, in this case. The cork fiber was compared to commercially available fibers such as polydimethrylsiloxane/divinylbenzene (PMDS/DVB), divinylbenzene/carboxen/polydimethrylsiloxane (DVB/CAR/PDMS) and polydimethrylsiloxane (PDMS), presenting similar or better extraction efficiency for most compounds. An advantage reported by the authors was the lifetime of the coating layer, which was higher than the commercial ones, 50-100 times against 40 times, respectively.
## 5 Experiment Results and Discussion
### Scenario 2: Loading Rate at 0.3 MPa/s
#### 5.2.2 Results on Shear Strain and Y-Direction Strain Dynamics  
Through detailed calculation, it is found that, when the loading speed was at 0.3 MPa/s, the strain values in the shear direction and y-direction were not the main factors affecting sandstone's failure phenomenon, as was found in Scenario 1, where the loading rate was at 0.1 MPa/s.  
Figure 16: X-direction strain–time relationship at point C (loading rate = 0.3 MPa/s).  
Figure 15: X-direction strain–time relationship at point B (loading rate = 0.3 MPa/s).
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### gMIMO
#### 3.1.9 Evaluation  
Gigantic MIMO is a groundbreaking innovation that can potentially revolutionize wireless telecommunications. It expands in the new FR3 band and utilizes the near-field area of EM wave propagation, giving the opportunity for enhanced capacity and network coverage. With new and improved configurations, it can tackle previous challenges and cover different operational scenarios. However, its advantages also come with new challenges, such as near-field channel modeling and noise between the antenna elements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No** & **F (GHz)** & **Number of Antennas** & **Number of Ports** & **Publication Time** \\ \hline
[51] & 2.4 & 64 & 16 & 2012 \\
[52] & 3.7 & 100 & 100 (10) & 2014 \\
[53] & 5.8 & 256 & 64 & 2014 \\
[54] & 3.5 & 128 & 128 (12) & 2016 \\
[55] & 3.5 & 64 & 32 & 2017 \\
[56] & 3.5 & 288 & 72 & 2018 \\
[57] & 5.8 & 120 & 120 & 2019 \\
[58] & 3.5 & 240 & 60 & 2024 \\
[59] & 13 & 4096 & 256 & 2024 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MIMO prototype evolution toward gMIMO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. No** & **BFM** & **\# Beams** & **\# of SD** & **T** & **FBW** & **Antenna Element** & **Antenna Array** \\ \hline
[44] & B: 4 \(\times\) 4 & 2 \(\times\) 16 = 32 & 2 (DBF + ABF) & MS & 0.1 & V, Bow tie & 4 \(\times\) 4 = 16 \\
[45] & A & 8 & 1 & MS & 0.1 & P, Patch & 8 \(\times\) 16 = 128 \\
[46] & MB:4 \(\times\) 8 & 64 & 1 & SIW & 0.17 & V, TSA & 8 \(\times\) 16 \\
[47] & B: 8 \(\times\) 8 & CS & 2 & WG & 0.05 & P, WG-A & 8 \(\times\) 8 \\
[48] & B: 4 \(\times\) 4 & 2 \(\times\) 4 = 8 & 1 & MS & 0.15 & V, Yagi-Uda & 1 \(\times\) 4 \\
[49] & B: 4 \(\times\) 4 & 2 \(\times\) 4 = 8 & 1 & MS & 0.22 & P, Patch & 1 \(\times\) 4 \\
[50] & B: 4 \(\times\) 4 & 2 \(\times\) 8 & 1 & MS & 0.2 & P, Patch & 4 \(\times\) 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Beamformer–antenna array state of the art (all systems operate on parts of FR1 or FR2).  
Therefore, new solutions must be provided in the form of new hardware designs and advanced channel models. Promising designs like QF-UCA, mmMIMO, and UE-CoMIMO demonstrate gMIMO's versatility in addressing high-capacity transmission, localization, and user experience challenges.
## 3 Minimum Expected Delay Based Routing Protocol
### The Link Expiration Time  
Based on our previous assumption, each mobile node can know its location coordination by GPS at any moment, and all the sensor nodes have synchronized clocks. Therefore, each mobile node can conveniently calculate its motion parameters (speed and direction), and broadcasts the parameters to its neighbors by the periodic hello messages. Assume two nodes \(i\) and \(j\) are within the transmission range \(r\) of each other at time \(t\). Let the coordinates of \(i\) and \(j\) be \((x_{i},y_{i})\) and \((x_{j},y_{j})\), the speeds be \(v_{i}\) and \(v_{j}\), and the moving directions be \(\theta_{i}\) and \(\theta_{j}\)\((0\leq\theta_{i},\theta_{j}\leq 2\pi)\), respectively. According to the method in [26], we can calculate the link expiration time between node \(i\) and j, denoted as \(T_{ij}^{E}\):  
\[T_{ij}^{E}=t+\frac{-(ab+cd)+\sqrt{\left(a^{2}+c^{2}\right)r^{2}-\left(ad-bc \right)^{2}}}{a^{2}+c^{2}} \tag{1}\]  
where \(a=v_{i}\cos\theta_{i}-v_{j}\cos\theta_{j}\), \(b=x_{i}-x_{j}\), \(c=v_{i}\sin\theta_{i}-v_{j}\sin\theta_{j}\), \(d=y_{i}-y_{j}\).
# Coverage-Based Classification Using Association Rule Mining +
## 1 Introduction
More precisely, we select the strong class association rules (CARs) according to their contribution to improving the overall coverage of the learning set. Our proposed classifier has an advantage in that it generates a reasonable fewer number of rules on bigger datasets compared to traditional classifiers, because our method has a stopping criterion in the rule-selection process based on the training dataset's coverage; that is, our algorithm is not sensitive to the size of the dataset. Once we build the classifier, we compute the relevance measures such as \"Precision\", \"Recall\" and \"F-measure\", as well as an overall coverage of the classifier to show how overall coverage affects the accuracy of the classifier [38]. We then perform statistical significance testing by using the paired T-test method between our method and other classification methods. We have performed experiments on 12 datasets from the UCI Machine Learning Database Repository [39] and compared the experimental result with 8 well-known rule-based classification algorithms (Decision Table (DT) [40], Simple Associative Classifier (SA) [41], PART (PT) [24], C4.5 [18], FURIA (FR) [42], Decision Table and Naive Bayes (DTNB) [43], Ripple Down Rules (RDR) [44], CBA). The rest of the paper is organized as follows: Section 2 highlights the related work to our research work. The problem statement is given in detail in Section 3. Section 4 presents how to generate class association rules. In Section 5, our proposed algorithm is widely described. Experimental evaluation (obtained results) is discussed in Section 6. Discussion about this research work is presented in Section 7. Conclusions and future plans are given in Section 8. The Acknowledgement and References close the paper.
# Fine-Tuning BERT-Based Pre-Trained Models for Arabic Dependency Parsing
###### Abstract  
With the advent of pre-trained language models, many natural language processing tasks in various languages have achieved great success. Although some research has been conducted on fine-tuning BERT-based models for syntactic parsing, and several Arabic pre-trained models have been developed, no attention has been paid to Arabic dependency parsing. In this study, we attempt to fill this gap and compare nine Arabic models, fine-tuning strategies, and encoding methods for dependency parsing. We evaluated three treebanks to highlight the best options and methods for fine-tuning Arabic BERT-based models to capture syntactic dependencies in the data. Our exploratory results show that the AraBERTv2 model provides the best scores for all treebanks and confirm that fine-tuning to the higher layers of pre-trained models is required. However, adding additional neural network layers to those models drops the accuracy. Additionally, we found that the treebanks have differences in the encoding techniques that give the highest scores. The analysis of the errors obtained by the test examples highlights four issues that have an important effect on the results: parse tree post-processing, contextualized embeddings, erroneous tokenization, and erroneous annotation. This study reveals a direction for future research to achieve enhanced Arabic BERT-based syntactic parsing.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 2 Compartmental Model of the Spread of the COVID-19 Epidemic in Hungary
### Vaccination Model  
For simplicity, our vaccination model assumed that only susceptible and recovered people are eligible for vaccination, and we neglected those rare cases when the shot is given during an unidentified infection. Based on the serological test data [58], our model assumed that a subject becomes immune \(T_{\rm v}=21\) d after the first dose with an average probability of \(\
u=0.75\). Correspondingly, variable \(\mathbf{V}_{k}\) in (1) denotes the number of individuals who received the first dose of vaccine on day \(k-T_{\rm v}\). In our model,  
_an individual is said to be immune to the disease if he/she will not be infected within the modeled time horizon._  
With this simplification, the people in the \(\mathbf{R}\) and \(\mathbf{U}\) compartments do not transmit the disease any more, as well as those who are still in the hospital. It is worth remarking that a positive IgG test does not necessarily ensure immunity in this sense. Serological tests suggest that even a relatively high IgG level does not exclude the possibility of reinfection [59].  
On the other hand, we assumed that the willingness of the susceptible and recovered patients to vaccinate is roughly the same. Namely, the proportion of susceptible and recovered people vaccinated coincides with the proportion of all susceptibles and recovereds on each day. Therefore, the model counts with \(\
u\,\mathbf{S}_{k}\mathbf{V}_{k}/(\mathbf{S}_{k}+\mathbf{R}_{k})\) susceptible and \(\
u\,\mathbf{R}_{k}\mathbf{V}_{k}/(\mathbf{S}_{k}+\mathbf{R}_{k})\) recovered individuals who achieved immunity at time \(k\). It is worth remarking that the value of \(\mathbf{V}_{k}\) is known, but cannot be manipulated as the computations were performed on _past_ data of the epidemic spread. Correspondingly, \(\mathbf{V}:k\mapsto\mathbf{V}_{k}\) can be considered as a preliminarily known input, or a _scheduling variable_[60], or a _measured disturbance_[61; 36]. The official European vaccination data including Hungary are available at [62]. Official Hungarian COVID-related data with additional analyses are also available at [63].
## 3 Results
### Analysis of GPP Estimation Using Multisource SIF Satellite Products
#### 3.2.2 Analysis of Spatial Features  
From Figure 4, it can be seen that there are differences in the spatial distribution characteristics of the annual average values of RTSIF, CSIF, SIF_OCO2_005, and GOSIF satellite products in arid regions. The RTSIF, CSIF, and SIF_OCO2_005 satellite products exhibited reasonably distributed spatial patterns with distinct attribute features. The highest values are predominantly distributed in regions with high vegetation cover, such as the Altai Mountains and the northern and southern slopes of the Tianshan Mountains, whereas the lowest values are found in areas with low vegetation cover, such as the Gurbantunggut Desert and the eastern Gobi Desert. Specifically, in the pasture grassland area, the annual mean values are 0.10 mw/m\({}^{2}\)/nm/sr (RTSIF), 0.12 mw/m\({}^{2}\)/nm/sr (CSIF), and 0.13 mw/m\({}^{2}\)/nm/sr (SIF_OCO2_005). In the cultivated and farmland area, the values are 0.26 mw/m\({}^{2}\)/nm/sr (RTSIF), 0.18 mw/m\({}^{2}\)/nm/sr (CSIF), and 0.24 mw/m\({}^{2}\)/nm/sr (SIF_OCO2_005). In the desert vegetation area, the values are 0.03 mw/m\({}^{2}\)/nm/sr (RTSIF), 0.01 mw/m\({}^{2}\)/nm/sr (CSIF), and 0.04 mw/m\({}^{2}\)/nm/sr (SIF_OCO2_005). The annual mean values of RTSIF satellite products are closer to the observed GPP data at the stations.  
In contrast, the spatial distribution of the GOSIF satellite products is unreasonable in arid regions, with indistinct attribute features and an overall overestimation tendency. The rationality of the attribute features of these products in arid regions is ranked as follows: RTSIF > CSIF > SIF_OCO2_005 > GOSIF. The RTSIF satellite products most effectively reflects the spatial features of GPP in the study area.  
Figure 3: (**a**) The linear regression fitting of 2020 GPP data from three site with corresponding site data of CSIF satellite products. (**b**) The linear regression fitting of 2020 GPP data from three site with corresponding site data of RTSIF satellite products. (**c**) The linear regression fitting of 2020 GPP data from three site with corresponding site data of SIF-OCO-005 satellite products. (**d**) The linear regression fitting of 2020 GPP data from three site with corresponding site data of GOSIF satellite products.
## 4 Experiments
### Approach Application to Two Real-Life Use Cases
#### 4.2.2 Use Case on Crime in Communities
Across all support levels, high confidence levels generate a higher number of regressors (with respect to generated rules) than at low confidence levels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Min** & **1 st Q** & **Mean** & **Median** & **3 rd Q** & **Max** & **sd** \\ \hline \#rul & 1.0 & 722.0 & 2245.0 & 1693.0 & 3153.0 & 6355.0 & 1879.7 \\ \#regr & 9.0 & 108.0 & 157.4 & 167.0 & 205.0 & 210 & 52.1 \\ \#mod & 1.0 & 658.0 & 1721.3 & 1565.0 & 2696.0 & 3570.0 & 1170.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Descriptive statistics of the experiment runs for the Crime and Communities dataset. Figure 10: Rules and regressors for each experimental setting in the Crime and Communities dataset. At a high confidence level, the number of regressors exceeds that of rules by a factor of 2-3 for support levels higher or equal to 0.8, and reduces well below zero at lower confidence levels. This is in line with the observation made for Figure 10 where we pointed out that in some settings, a few rules were obtained, which generated a comparable or higher number of regressors. Figure 12 shows the confidence intervals for the regressors obtained for a support equal to 0.09 and confidence equal to 0.9. Among the 535 models containing the variable racePctBlack, the figure only reports the first 50 models for the sake of readability. These models show common trends among all models, but they also show some notable outliers. First, regressors in the first 42 models show a significant positive impact on the decision variable. Delving into greater detail, we observe that the third line of each model which corresponds to the highest percentage of black people in the community is the one with the largest impact on the class. Therefore, overall these models seem to suggest that if the percentage of black people increases, the possibility of being classified as a dangerous area also increases. However, the magnitude of the impact does seem to change in some models, moving from a strong impact (more than three-fold) to a marginal and even negative impact. For example, a model in Figure 12 shows that the first two values are close to zero; and the third line actually goes below 0, thus pointing out that this value is not significant at all for this model. It is worth noting that in the remaining set of models we analyzed (not reported here for the sake of space), the racePctBlack regressor always has a strong impact with the same dynamics we described above. Starting from these observations, the policy maker can actually recover the corresponding model to investigate the reasons underlying the observed differences.
## 3 Results and Discussion
### Calibration Test
Gross primary productivity (GPP; (**A**)) and net ecosystem CO\({}_{2}\) exchange (NEE; (**B**)). In the Taylor diagram we can observe that simulated LE and H are most sensitive to soil moisture fraction and the coefficient of stomatal conductance, mainly during the dry season (Figure 1, Table 2). For \(\theta\)g/\(\theta\)d = 0.165, the model provides a better fit between simulated LE and H and observed values in the wet season, while setting \(\theta\)g/\(\theta\)d = 0.075 results in a better fit during the dry season (Table 2). On the other hand, the NEE simulated by the model is most sensitive to the \(V_{\rm max}\) parameter (Figure 2; Table 2). When using the lowest \(V_{\rm max}\) value (40 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) the SITE model underestimated NEE, while when using the highest \(V_{\rm max}\) value (120 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) it overestimated NEE. The most appropriated value for \(V_{\rm max}\) was 90 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the wt season and 60 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the dry season (Table 2). After analyzing the calibrated simulations through the Taylor's diagram, we compared the ones with the higher correlation coefficient, lower RMSE and standard deviation for each season (Table 2) with non-calibrated simulations and EC observations. Table 2 shows the calibrated parameters and the best calibrated simulations in the wet season and dry season (represented by uppercase letters V, U, M and G), as analyzed by means of Taylor diagrams. For the analyses of validation, the Pearson correlation coefficient (\(r\)), median absolute error (MAE), root mean square error (RMSE) and Willmott's index of agreement (\(d\)) was included between the modeled data with calibration by SITE model and the observation data for the years 2014 and 2015 (Table 3).