esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The advancement of nanotechnology as a credible, environmentally sustainable technique for producing a broad range of biocompatible materials and nanomaterials, including metal/metal oxide nanomaterials and nanocomposites, depends on the development of eco-friendly green methods for producing nanoparticles. Green synthesis is therefore seen as an essential strategy to reduce the negative impacts associated with the traditional methods of synthesis for nanoparticles that are routinely utilized in laboratories and industry. The particular phytochemicals generated from plant parts such the fruits, leaves, roots, and seeds have been used to synthesize ZnO-NPs. Natural plant extracts provide an inexpensive and environmentally friendly substitute for the use of intermediate base groups. The present study demonstrated the cost-effective and eco-friendly synthesis of ZnO NPs using _C. spinosa_ L. extract as an effective reducing and capping agent. Through UV-Vis spectroscopy, the synthesis of the NPs was confirmed by peaks at 374 nm, whereas DLS and FESEM indicated the size, shape, and stability of the NPs as well as presence of Zn\({}^{2+}\) ions. The capping of the synthesized ZnO NPs with bioactive compounds of C. spinosa L. was confirmed with FTIR analysis. In addition, the crystal structure of the NPs was confirmed by XRD analysis. The ZnO NPs showed no cytotoxic activity against normal cells in a dose-dependent manner. Our data indicated that the synthesized ZnO NPs have an antioxidant effect using DPPH assay. The ZnO NPs have hemolysis activity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **IC50 (\#g/mL)** \\ \hline _C. spinosa fruit extract_ & 81 \(\pm\) 0.054 \\ ZnO-NPs & 43.68 \(\pm\) 0.04 \\ Ascorbic acid & 26.4 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ascorbic acid, ZnO NPs, and plant extract IC50 values.  
Figure 9: Antioxidant activities of _C. spinosa_ L. _Fruit_ extract and bio-synthesized ZnO NPs.  
Zinc oxide nanoparticles (ZnO NPs) biosynthesized by using C. _spinosa_ L. extract exhibited significant antioxidant properties, low cytotoxicity on L929 normal fibroblast cells, and good biocompatibility with RBCs. ZnO NPs did not exhibit any hemolytic reaction at doses ranging from 7.5 to 120 ug/mL; thus, they can be used as a stable and safe substitute for synthetic substances in the pharmaceutical and biomedical research sectors.  
Conceptualization and methodology, S.A.N.; formal analysis, I.Q.F. and Y.H.D.; investigation and data curation, S.A., H.B.M. and I.Q.F.; validation, H.B.M. and Y.H.D.; visualization, original draft preparation, S.A. and S.A.N.; writing--review and editing, S.A. and S.A.N.; supervision, S.A.; project administration, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledge the Researchers Supporting Project number (RSP2023R375), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 4 Conclusions  
From these results, several conclusions could be obtained:  
1. The main variable related to maritime accidents in the SAR region is the enforcement of the minimum crew members and, at a lower level, the ship dimensions, which, indirectly, are associated with the ship type.
2. The results are centred in the SAR region, and this original methodology must be tested in another region where weather conditions and new variables like crew members' experience can affect the selection of the more relevant variables.
3. Three main clusters can be observed in Figures 6-9 based on their three main colours (cluster 1 (in white), cluster 2 (light green) and cluster 3 (dark green), which are associated with the meeting of the minimum crew member requirement and that are differentiated by the year of construction of the ship. At the same time, based on the 3D model, it can be concluded that a more adequate definition of the minimum number of crew members may help to decrease the number of accidents.
4. It is possible to obtain a mathematical model based on feedforward neural networks that let us predict the expected type of accident at any moment based on the year of construction, wind force, nighttime, length, beam and minimum crew with a determination factor over 67%, and new variables associated with the human factor could increase this result in future works.
5. An electronic control system based on this model can be of interest to predict and prevent future maritime accidents in this region.  
Conceptualization, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Methodology, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Software, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Validation, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Formal analysis, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Investigation, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Resources, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Data curation, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Writing--original draft, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Writing--review & editing, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Visualization, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Supervision, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Project administration, C.M., G.C.-F., J.M.P.-C. and J.A.O.; Funding acquisition, C.M., G.C.-F., J.M.P.-C. and J.A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Product Service System Case Study
### PSS Availability Model  
The second step in the overall process is to construct the PSS availability regression model by performing key parameter variation experiments for PSS using the simulation model. A model of PSS availability as a function of input parameters shown in Figure 2 is created. Table 1 shows the key parameters and ranges of the parameter values used for the case study.  
To create a reliable model, 240 different experiment runs were set up and performed using different deterministic parameter values, and resulting PSS availability values were recorded. Table 2 shows results of the 15 deterministic experiment runs out of 240 runs performed. For example, experiment number 2 in Table 2 shows a spare part with a life of 10 million uses (0.1 failure per million use), with only one provided in the field repair kit. When the simulation was performed with PSS monthly usage of 10.2 million, the availability of the PSS overall was 15% due to shortage of the part. Availability for each run was recorded. Once all experiment runs are complete, a regression based PSS availability model was constructed using parameters shown in Table 1. The regression model provides a good surrogate means to calculate PSS availability using spare part failure rate, onsite spare part quantity, and PSS usage per period. One thing to note is that the regression model is valid within parameter value ranges shown in Table 1.
## 1 Introduction
These messages also help the vehicles maintain the list of their neighbours. The congestion information is already available through navigation systems (Sat Nav) but exact real-time position information of other vehicles is not. This is the future that can enable the driver to drive safely even if there is zero visibility or when the driver is unable to drive, _etc._ Therefore, the vision is to create a mutual awareness among all vehicles on the road that leads to driver-less cars. All vehicles can be programmed to transmit their location information whenever they make a sudden change in speed or position. However, the bandwidth in DSRC is quite limited, so we just cannot have vehicles transmitting their position and speed information all the time or we will have a jammed channel. The DSRC band consists of seven bands of 10 MHz each with a 5 MHz guard band in the beginning. One band out of these is reserved for BSM, but even this dedicated channel can be congested very quickly if the traffic is heavy and many vehicles are broadcasting BSMs. The DSRC and 4G/LTE connectivity in vehicles can supplement each other and solve the bandwidth problem to give high quality, reliable, and secure information to VANET users. This combination will make it possible for a wide variety of applications and opportunities that will be discussed in the next section.
# On the Use of Parsing for Named Entity Recognition
## 4 Parsing for NER
### Syntactic Information as a Feature for Sequence Labeling NER
[129] tried to find a better way to incorporate syntactic information into deep learning models for NER. For this purpose, they built BioKMNER, a NER model for biomedical texts based on Key-Value Memory Networks (KVMN) [130]. They parsed biomedical text sentences to extract three types of morpho-syntactic information: PoS tags, syntactic constituents, and dependency relations. The KVMN weighs the corresponding syntactic information (values) according to the importance of context features (keys) and combines the weighted syntactic information with the output of the encoder Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (bioBERT) [131]. Finally, the decoder receives the combined embedding and tags the input sequence accordingly. BioKMNER outperforms baselines without memories and achieves new state-of-the-art results on four biomedical data sets. The system by [PERSON] et al. [129] is based on bioBERT [131], a pre-trained biomedical language model designed for biomedical text mining tasks. It is worth remarking that there is a recent trend in end-to-end NLP systems that use powerful pre-trained language models with huge parameter spaces based on transformers to solve a variety of tasks, as in the case of the Bidirectional Encoder Representations from Transformers (BERT) [132] or Generative Pre-trained Transformer 3 (GPT-3) models [133]. It is important to highlight that it has been shown that explicit syntactic information helps these pre-trained transformer models [134], despite the fact that some authors have questioned the use of explicit syntax in these settings [99].
# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 3 Materials and Methods
### Event Generation Model  
Each pixel of the event camera senses changes in brightness in the scene independently and asynchronously [27]. If a pixel senses a change in brightness that exceeds a predetermined threshold, an event is generated for that pixel. The event is output in the Address Event Representation (AER) format and can be expressed as \(e=(x,y,t,p)^{T}\), where \(x\) and \(y\) represent the coordinate position of the pixel generating the event, \(t\) is the time stamp of the event, and \(p\) is the polarity of the event (i.e., is either 1 or \(-1\)).  
The mathematical model for event generation is shown in Equation (2), where \(\mathbf{L}_{xy}\) is the latent image, representing the instantaneous brightness of the pixel at coordinates \((x,y)\) at the current moment, and \(t_{ref}\) is the instantaneous brightness at the pixel location when the previous event was triggered [28]. The instantaneous brightness is calculated by taking the logarithm of the instantaneous brightness values at both moments. If the intensity of the increase in brightness exceeds the threshold, a positive event is generated and the value of \(p\) is 1. Conversely, if the intensity of the decrease in brightness exceeds the threshold, a negative event is generated and the value of \(p\) is \(-1\), as shown in Equation (3), where \(c\) denotes a predetermined threshold value and \(d\) denotes the brightness change value.  
\[\sigma=\mathcal{T}\Bigg{(}\log\Bigg{(}\frac{\mathbf{L}_{xy}(t)}{\mathbf{L}_{xy} \Big{(}t_{ref}\Big{)}}\Bigg{)},c\Bigg{)} \tag{2}\]  
Figure 2: The pipeline of the proposed corner detector. The double threshold filter is used to filter out redundant or noisy events and template-based pre-detection removes events which can hardly be detected as corners.  
\[p=\mathcal{T}(d,c)=\left\{\begin{array}{ll}+1,&d\geq c\\ 0,&d\in(-c,c)\\ -1,&d\leq-c\end{array}\right. \tag{3}\]  
When a new event is triggered at that pixel, \(\mathbf{L}_{xy}(t_{ref})\) should be updated to the instantaneous brightness at the time of the latest triggered event.
# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 6 Conclusion and Outlook  
In this paper, a vehicular fog computing architecture was proposed. The vehicular fog computing architecture makes full use of the vehicles in the road and has the functions of cloud computing and fog computing. It not only solves the shortage of resources and delay in the vehicle network, but also solves the shortage of communication resources. However, due to the high mobility of the vehicle nodes, it is necessary to model the behavior of the vehicle in order to thoroughly analyze the behavior of the vehicle. Therefore, based on the previous research results of complex networks and hypergraph theory, and based on the vehicle cooperation relationship in the fog computing environment, this paper established a vehicular cooperation network model, established a more general dynamic equation, and then applied Poisson stochastic process theory. The average field method was used to analyze the evolution law of the model, and the theoretical expression of the super-degree distribution of the node was obtained. The corresponding super-degree distribution was shown in Equation (7), where \(m,\varphi\), \(k1\) and \(k2\) are undetermined coefficients. Although the model is proposed for cooperation vehicular network in vehicular fog computing, the model can be adapted to various cooperative complex networks by adjusting parameters such as \(m,\varphi\), \(k1\) and \(k2\), showing the universality of the model.  
Figure 4: The characteristics with different \(m\).  
With the continuous deepening of theoretical research, more and more people have discovered that the intrinsic growth mechanism of actual networks shows great complexity. Accurate mathematical models can describe the common characteristics and intrinsic nature of networks. Problems such as difficulty in obtaining real data need to be solved with the help of advanced technology, and theoretical results should be applied to actual systems to provide reference for decision makers.  
Conceptualization, writing--original draft preparation, software, formal analysis, writing, investigation and validation, M.R.; funding acquisition, supervision, review and editing, methodology, resources and project administration, X.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61661041) and the Graduate Scientific Innovation School Program of Inner Mongolia University (10000-15010109).  
We thank the anonymous reviewers for their very helpful comments, which helped improve the presentation of this paper.  
The authors declare no conflict of interest.
# Development and Control of an Electro-Hydraulic Actuator System for an Exoskeleton Robot
## 2 Design of Axial Piston Pump
### Analysis of Gait and Calculation of the Required Flow Rate
As shown in Figure 5, the radius of the dotted line in the valve plate is \(R\), the cam angle is \(\alpha\), and then a piston stroke length is \(L\) as shown below [23]:  
\[L=2R\tan\alpha. \tag{1}\]  
The cross-section area of the piston is \(A\), the number of the piston is \(z\), and the displacement volume is \(V_{p}\) expressed as follows:  
\[V_{p}=zAL=2 zAR\tan\alpha. \tag{2}\]  
When the piston rotates by \(\theta\) from the bottom dead center (BDC), the piston moving is expressed as:  
\[x=\frac{L}{2}-R\cos\alpha=\frac{L}{2}(1-\cos\theta). \tag{3}\]  
The velocity of the piston \(v\) becomes \(\frac{L\omega}{2}\sin\theta\) in Equation (3). \(\omega\) is the angular velocity of the cylinder block. At this time, the instantaneous discharge amount \(Q_{t}\) of the pump is expressed as follows [23]:  
\[Q_{t}=\sum_{i=1}^{z_{0}}Av_{i}=\frac{AL\omega}{2}\sum_{i=0}^{z_{0}}\sin\theta _{i}=\frac{AL\omega}{2}\sin\Bigl{(}\frac{z_{0}\pi}{z}\Bigr{)}\sin\Bigl{(} \theta+\frac{z_{0}-1}{z}\pi\Bigr{)}/\sin\frac{\pi}{2}. \tag{4}\]  
If the number of pistons \(z\) is an even number, \(z_{0}=z/2\) and the discharge amount \(Q_{t}\) is  
\[Q_{t}=\frac{AL\omega}{2}\cos\Bigl{(}\theta-\frac{\pi}{z}\Bigr{)}/\sin\frac{ \pi}{z}. \tag{5}\]  
If it is an odd number, \(z_{0}=(z\pm 1)/2\) and the discharge amount \(Q_{t}\) is  
\[Q_{t}=\frac{AL\omega}{4}\cos\Bigl{(}\theta-\frac{\pi}{2z}\Bigr{)}/\sin\frac{ \pi}{z}. \tag{6}\]  
Figure 2:0 Structure of an axial piston pump. The pump consists of a drive shaft, a valve plate, a cylinder block, pistons, piston shoes, and a swash plate. If \(z\) is an even number, the cylinder block pulsates \(z\) times when it rotates once, but if it is an odd number, it pulsates 2z times. The pulsation rate \(\varepsilon\) is expressed as \((Q_{\max}-Q_{\min})/Q_{mean}\), and when \(z\) is an even number, it is as follows:  
Figure 2:1  
When it is an odd number, it is as follows:  
Figure 2:2  
The pulsation rate of the discharge amount according to the number of pistons is calculated as shown in Figure 2:3  
For the calculated pulsation rate \(\varepsilon\), as the number of pistons increases, the pulsation rate becomes small. However, if it is an odd number, it becomes much smaller. Therefore, it is common to choose an odd number of pistons, in terms of the pulsation rate. In this study, nine pistons were selected considering the size of the piston pump. The inner diameter of each piston is 6 mm and the cam angle is 12 degrees. At this time, according to Equation (2) and the number of pistons, the displacement volume discharged is about 1.5 cc in one rotation. Considering the maximum speed of the cylinder rod, the required discharge flow rate is about 113 cc/s. At this time, because the discharge amount of the pump in one revolution is about 1.5 cc, the pump speed had to be over 75.3 rev/s. That is, the speed of the motor needed to be 4520 rpm or more.
## 4 Discussion
If the local strengths of both the particles and the matrix exceed the interface strength, cracks propagate intergranularly along phase interfaces. This results in increased fracture energy and a deflection toughening effect, as shown in Figure 14. \begin{table}
\begin{tabular}{c c c} \hline
**Element** & **Weight Percent (\%)** & **Atomic Percent (\%)** \\ \hline Carbon & 52.95 & 67.01 \\ Oxygen & 28.92 & 27.47 \\ Calcium & 4.27 & 1.62 \\ Iron & 13.49 & 3.67 \\ Magnesium & 0.29 & 0.18 \\ Silicon & 0.08 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 4: Quantitative SEM-EDS analysis of mineral compositions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Original**} & \multicolumn{4}{c}{**Cracked**} & \multirow{2}{*}{**Variation**} \\ \hline
**Volume** & **Number** & **Percentage** & **Volume** & **Number** & **Percentage** & \\ \hline
10\({}^{-4}\)\(\sim\)10\({}^{-3}\) mm\({}^{3}\) & 9995 & 33.11\% & 10\({}^{-4}\)\(\sim\)10\({}^{-3}\) mm\({}^{3}\) & 10,383 & 33.79\% & 388 \\
10\({}^{-3}\)\(\sim\)10\({}^{-2}\) mm\({}^{3}\) & 15,746 & 52.17\% & 10\({}^{-3}\)\(\sim\)10\({}^{-2}\) mm\({}^{3}\) & 15,891 & 51.73\% & 145 \\
10\({}^{-2}\)\(\sim\)10\({}^{-1}\) mm\({}^{3}\) & 3384 & 11.21\% & 10\({}^{-2}\)\(\sim\)10\({}^{-1}\) mm\({}^{3}\) & 3389 & 11.03\% & 5 \\
10\({}^{-1}\)\(\sim\)1 mm\({}^{3}\) & 975 & 3.23\% & 10\({}^{-1}\)\(\sim\)1 mm\({}^{3}\) & 973 & 3.17\% & \(-2\) \\
1–10 mm\({}^{3}\) & 85 & 0.28\% & 1–10 mm\({}^{3}\) & 85 & 0.28\% & 0 \\ \hline \end{tabular}
\end{table} 
Table 5: Statistical analysis of mineral particles. 2. Blunting Toughening: When a large crack approaches the interface of a perpendicular mineral particle, it enters the particle if the stress intensity factor at the crack tip exceeds the particle strength. Due to the high internal strength and fracture energy of the particle, the crack tip is \"blunted,\" causing the crack to arrest temporarily, as shown in Figure 14. Afterward, the crack either penetrates the particle or circumvents it,  
Figure 14: Influence of mineral particles on crack propagation. Figure 13: Particle volume distribution statistics. (**a**) Original sample mineral particle volume distribution; (**b**) Cracked sample mineral particle volume distribution; (**c**) Volume comparison between original and cracked samples. re-entering the matrix and continuing propagation. During this process, propagation resistance decreases, the crack tip sharpens, and the propagation speed recovers. However, the path momentarily deviates before returning to its original direction. The presence of more mineral particles increases the cumulative fracture energy along the crack path, enhancing the overall toughening effect.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 2 Literature Review
### Constrained Optimization  
The solution of the OD matrix can be formalized as a constrained optimization problem. These methods include: Entropy Maximization (EM), Maximum Likelihood Estimation (MLE), and Generalized Least Squares (GLS). In thermodynamics, states with higher entropy are considered to have a higher probability of occurring. [PERSON] and [PERSON] first introduced the concept of entropy into the OD estimation problem, and expressed the convergence to the most likely demand distribution as entropy maximization [31]. EM establishes clear and intuitive formulas with strong interpretability and can be applied in the absence of a prior matrix. It reaches high accuracy in static OD estimation, but is hard to extend to dynamic OD estimation, and the insufficient modeling of dynamics makes the accuracy decrease when the demand structure changes drastically. Different from EM, which considers all states to be equally likely to occur, MLE hypothesizes the prior distribution of OD flows through experience and calculates the most probable states through a Bayesian model [32; 33]. The performance of MLE would benefit from an accurate prior probability, but would also be affected by human-induced errors in the prior experience. GLS directly correlates the observations (i.e., traffic counts) and target variables (i.e., OD flows) through the statistical covariance matrix, and minimizes the errors of observations and target variables simultaneously [34,35]. The original model of GLS lacks consideration of the evolution of traffic demand and traffic flow over time and is susceptible to localized noise. To improve the accuracy and stability, dynamic time windows are introduced into GLS with quasi-dynamic assumptions [15]. Since the principles of the groups of optimization methods are similar, they might also be applied in combination. For instance, [PERSON] et al. presented a combined form of EM and least squares, namely EM-LS [36]. A common limitation of this category of models is the homogeneity of traffic demand structure and traffic assignment pattern, both of which tend to be diverse in reality.