esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Driving Behavior of Micromobility Vehicle Users at Mini-Roundabouts
###### Abstract  
The rapid spread of micromobility vehicles such as bicycles and electric scooters poses new challenges to urban transportation systems, particularly in terms of road safety and infrastructure integration. This study investigates the driving behavior of micromobility users at a mini-roundabout, focusing on their speed profiles and their position within the lane during the entry, circulation, and exit phases. A structured recruitment process was used to select 20 participants with previous micromobility experience. Participants performed crossing maneuvers at a mini-roundabout in Gravina di Catania, Italy, which were monitored using drone footage and analyzed with tracking software to extract trajectories and speed data. The results show significant differences between e-scooter and bicycle users, with bicycles showing less speed variability, especially during the crossing and exit phases, while e-scooters showed greater variability, especially during the entry and exit phases. The results highlight the influence of vehicle stability and user posture on riding behavior and emphasize the need for infrastructure adaptations to increase safety. Mini-roundabouts designed for moderate speed are identified as a promising solution to improve the coexistence of micromobility and motor vehicles. This research identifies key differences in speed profiles and behavioral patterns between e-scooter and bicycle users, offering actionable insights and recommendations for safer and more efficient urban infrastructure. These contributions provide valuable guidance for urban planners and policymakers in promoting safer and more sustainable urban mobility.  
micromobility; electric scooters; bicycles; roundabouts; urban transportation; driving behavior; road safety; sustainable mobility; trajectories; speed profiles +
Footnote †: journal: applied sciences
## 3 Results
### Performance Analysis
In the three groups of comparative experiments on loss functions, the impact of the proposed weight optimization strategy on the model's loss functions for generating different types of PTCs curves is validated, as shown in Figures 15-17. It can be observed that for the AC, ATI, and NDT curves, when the weight optimization strategy is not used, the generator's loss function fails to decrease steadily. On the contrary, when the weight optimization strategy is employed, the generator's loss function decreases consistently, with increasing iterations, and eventually reaches a stable state. Additionally, the weight optimization strategy also has an effect on the discriminator's loss function, showing a smoother descent process. Figure 14: The comparison of recognition accuracy for each curve among the various network models. Figure 13: Confusion matrices for different data augmentation ratios: (**a**) 600 dataset size; (**b**) 900 dataset size; (**c**) 1050 dataset size; (**d**) 1200 dataset size.
## 3 Results and Discussion
### NIRS Calibration and Validation of PLS Models
#### 3.1.2 Sweet Potato
The \(\Delta^{13}\)C also showed low discrimination in the prediction of the validation set for both organs, showing \(r^{2}_{\text{pred}}\) of 0.629 and 0.580 for tubers and shoots, respectively. Low discrimination was expected, since NIRS is hardly efficient for constituents representing less than 1% of the dry matter weight. In contrast, the M-tuber and M-shoot equations proved to be excellent predictive models. They show high \(r^{2}_{\text{cv}}\) and \(r^{2}_{\text{pred}}\) values, similar SEC and SECV values, and RPD values of 5.40 and 8.16 for M-tuber and M-shoot, respectively. The nitrogen and protein models were also demonstrated to be excellent predictive models in both organs, with very high \(r^{2}_{\text{cv}}\), \(r^{2}_{\text{pred}}\), and RPD values, and low average difference between SEC, SECV, and SEP values, which indicates a robust fitting. Previous studies [10; 16] also observed good predictive performance for nitrogen and protein constituents in sweet potato tubers. The acidity prediction by CaOX constituent was shown to be a good model for CaOX-shoot, and an approximate model for CaOX-tuber, with RPD values of 2.74 and 2.41, respectively. It also showed SEC, SECV, and SEP similarity within the models, and quantitatively approximate \(r^{2}_{\text{cv}}\) and \(r^{2}_{\text{pred}}\) values. Lastly, the starch models showed a better prediction performance in St-shoot than in St-tuber, with RPD values of 5.03 and 2.56, respectively. This difference in prediction performance among the tissues could be due to the starch content being almost ten times lower in shoots than in tubers, allowing improved discrimination [18]. The models generated allow the reading of one unique sweet potato sample to predict the St, Pt, and M major chemical constituents and thus provide a quick assessment of the variety, chemotype, and quality with good accuracy [16]. Figure 3: NIR spectra of the eight sweet potato accessions, with 129 root (**A**) and 135 shoot (**B**) samples, and 24 external set validation for both root (**C**) and shoot (**D**) samples. Wavelengths from 1100–2498.2 nm of the NIR range.
# A Systematic Review of Computational Fluid Dynamics Models in the Stomach and Small Intestine
## 2 Materials and Methods
### Literature Selection, Exclusion, and Data Extraction  
Two of the investigators (NP and SH) conducted an extensive search of the available literature up to February 2023. The search results were filtered to only include peer-reviewed journal articles and those published in English. Two separate lists of articles were generated for the stomach and small intestine, and the article titles, abstracts and keywords were screened to identify relevant articles that conducted computational modeling of flow within the organ of interest. The list of relevant articles was screened to exclude studies that: (i) used semi-analytical approaches (such as [28; 29]), (ii) used CFD models of the stomach from other animals, besides humans, (iii) used 2D or non-anatomical geometries (such as the conical one used by [30]) for the stomach, and (iv) used fixed walls with no boundary deformations.  
Finally, two more stomach CFD articles [31; 32] and one small intestine CFD article [33] were added from a bibliographic search cross-referenced with the other included articles. These articles did not appear in the search due to the article title, abstract and/or keywords missing the search terms outlined previously.  
The investigators were not blinded by the authors, institutions, and titles of the included studies, and two investigators (NP and SH) extracted information such as the numerical technique, geometry, boundary deformation patterns, and key findings from the included articles.
## 5 Conclusions  
In the current work, we investigated whether adding a graphene layer to the flow field may prevent the formation of new atherosclerotic plaque or aneurysmal sac. It was supposed that the post-graphene zone encompassed the stenosis lump or aneurysmal sac. Through free and porous media flow topologies, the chemical characteristics of graphene were integrated with the conventional fluid flow conditions. The existence and uniqueness of the solution were examined in the governing equations. A FEM solver was used to resolve the system of partial differential equations. The following conclusions were drawn from the investigation.  
* On comparing the velocity vector profiles of the cases without graphene and with graphene, it can be noted that the inclusion of the graphene layer significantly changes the flow profile.
* Inclusion of the graphene layer induced a zone of higher velocity neighboring the foot of the graphene layer.
* Velocity in the PG region dropped as porosity increased.
* A high-pressure gradient was generated due to introduction of the graphene layer to the flow field.
* The PG region has the least potential for development of new aneurys sacs.
* At the foot of the graphene sheet, a low WSS development occurred.
* The graphene layer introduced in the flow field harmed the flow pattern in terms of the velocity field vector. The negative impact on the flow can be due to the position of the graphene layer placed.  
The present investigation, although a start to a new research domain, contributes to the existing methodologies to handle arteriole pathologies by insertion of a chemical compound layer that could bear a chance of developing the flow positively. The article contributes to the field of biomedicine through innovative key concepts of exploration such as critical porosity, the distal stand of the graphene layer across the arteriole length, and the flow parameter discussions.
## 7 Challenges and Future Prospects in Design for Additive Manufacturing of Lattice Structures
### Future Prospects in Design for Additive Manufacturing of Lattice Structures
The setting up of websites for sharing knowledge will allow experts to share their experiences, successes, and challenges with AM lattice designs. This collective exchange of information will assist in identifying best practices, efficient design strategies, and insights gained, thus allowing a community-driven strategy for advancement. In addition, mimicking the design concepts observed in biological structures, such as trabecular bones or honeycombs, should provide new alternative designs for lattice parts [17]. This can be achieved through the use of optimisation algorithms inspired by natural phenomena, such as genetic algorithms or swarm intelligence, to improve lattice design. Nature-inspired optimisation algorithms are designed to replicate natural problem-solving processes. Applying these methods to lattice design could result in novel geometries that are optimised according to particular performance criteria. For instance, evolutionary algorithms can iteratively evolve lattice structures to attain desired characteristics, resulting in alternative designs that are challenging to envision using traditional methods. The use of DfAM for lattice structures offers a promising future, with the capacity to change industries such as aerospace, automotive, and medical engineering. Addressing the existing challenges and exploring prospects in this respect should pave the path for new and efficient designs that take full advantage of the capacities of AM.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### OCR Deep-Learning Works
#### 7.1.2 Transformer-Based Works [55; 56]  
[PERSON] et al. [55] were limited by their shortage of resources and computational power, as their experiments were performed using a subset of 15,000 images of single-line text instead of the whole dataset of 30.5 million images of single-line text. Moreover, if such resources had been available, they may have achieved better results. In addition, alternating the numbers of parameters and encoders and decoders can enhance the model. Lastly, the model suffers from high temporal complexity, as it operates in quadratic run time due to the use of self-attention in the multi-head attention layer in the encoder and the decoder.  
[PERSON] et al. [56] proposed a model that suffers from high time complexity and requires a long time for training. The Transformer Transducer has 144.7 million parameters, while the Transformer with cross-attention has 153.1 million. Moreover, a high number of K in the beam search can increase the CER. On the other hand, high latency is produced, as 223.7 ms of latency is produced when K = 5 in Transformer with cross-attention. In addition, since the model is initialized with DeiT-base and Arabic BERT, the rest of the weights that are not in the pre-trained models are initialized randomly. For example, firstly, DeiT takes the input as a 384 \(\times\) 384 RGB image, whereas their encoder expects variable-length grayscale images. Secondly, Arabic BERT does not have the encoder-decoder attention layers of the attention-based decoder in [PERSON] et al.'s model. Thirdly, the Transducer decoder has an extra joint network. These differences can be seen as problems or as opportunities to use other techniques for such parameters rather than random initialization to obtain a better CER.  
From the works discussed above, the following conclusions can be drawn:  
* Transformer-based works are limited by computational resources and take significantly longer to train. Moreover, they require more annotated training data than other architectures;
* Transformer architecture has the potential to provide robust results. Nevertheless, latency is produced during the search;
* Transformer-based works have a higher number of parameters than other architectures;
* Transformer-based works are preferred if more models are developed that have compatible weights, avoiding the random initialization of weights, which, it is suggested, is responsible for lowering the recognition rate.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 3 Proposed Architecture  
As mentioned above, our NoC targeted at heterogeneous multi-core real-time SOC. The requirement of the NoC is to support relatively fixed task flows which usually have a large amount of data to transfer, require excellent real-time performance, support parallel periodic jobs, and call for short setup time.  
For example, the baseband processor for wireless communication is one of the target applications, whose main tasks are the symbol domain task and bit domain task. In a certain period of time, there are multiple parallel task flows to deal with which have large data volume. Task flow changes over time, and task switching needs to be completed quickly. However, the traditional CS NoC has difficulty meeting these requirements due to the long establishment time, and the PS NoC cannot guarantee data throughput and communication quality.  
We redesigned the router and network interface of traditional PS NoC to combine the merits of both circuit switching and packet switching. The router design is described in Section 3.2. The proposed packet connected circuit (PCC) as network on chip offers short setup time, flexible routing, certain transmission time, and zero overhead of data transmission latency with low area and low power overhead, which is suitable for heterogeneous multi-core real-time SoCs.
## 5 Conclusions  
To track multiple targets in the presence of unknown clutter density, unknown survival probability and unknown initial state and error covariance, we propose an adaptive marginal multi-target Bayes filter without the need for clutter density. This filter delivers the track label, existence probability and PDF of the object. It uses the least squares technique to deal with two consecutive scans of the unused measurements to establish the potential birth target, uses the gating technique to remove the clutter-originated measurements, and uses the 2-dimensional assignment to associate the measurements with the existing targets. In terms of the assignment result, the AMTB filter selects either one of multiple updated PDFs of an existing object or its predicted PDF as its PDF. It establishes newborn targets by using  
Figure 8: Cardinality estimations of filters in Example 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filter** & **ACMeMber** & **AMB** & **APHD** & **AGLMB** & **AMTB** \\ \hline OSPA error (m) & 29.3956 & 21.5289 & 24.7589 & 12.2358 & 8.4621 \\ OSPA\({}^{(2)}\) error (m) & NA & NA & NA & 18.1280 & 12.6685 \\ Cardinality error & 0.2029 & 0.2976 & 0.4577 & 0.1287 & 0.0908 \\ Performing time (s) & 1.0739 & 0.4489 & 0.2995 & 5.0927 & 0.4370 \\ \hline \hline \end{tabular}
\end{table}
Table 5: OSPA and OSPA\({}^{(2)}\) errors, cardinality error and performing time in Example 2.  
Figure 7: OSPA\({}^{(2)}\) errors of AMTB and AGLMB filters in Example 2.  
the 2-dimensional assignment to associate the unused measurements with the potential birth targets. A newborn target is established if an unused measurement is assigned to a potential birth target. The simulation results demonstrate that higher tracking accuracy can be acquired by the AMTB filter than by the adaptive RFS-based filters. Its OSPA and OSPA(2) errors, and cardinality error were lower than those of the adaptive RFS-based filters. The AMTB filter achieved higher tracking accuracy than the adaptive RFS-based filters, and therefore it has potential applications in real-world multi-target tracking systems, especially in situations where obtaining the clutter density and survival probability is difficult. This article involves the application of the AMTB filter in radar MTT. Further research and real-world testing may help solidify its practical utility in various applications.  
Z.L.: Conceptualization, Methodology, Supervision, Writing--original draft preparation. C.Z.: Software, Resources. J.L.: Visualization, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant no. 62171287 and Science & Technology Program of Shenzhen under grant no. JCYJ20220818100004008.  
Not applicable.  
The data presented in this study are partially available on request from the corresponding author. The data are not publicly available due to their current restricted access.  
The authors declare no conflict of interest.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 3.1.4 Experimental Design  
Both feedback modes employed a periodic stimulation pattern (Figure 2), with haptic and visual feedback using the same frequency pattern. Each frequency cycle was evenly distributed between stimulation (1) and pause (0) phases, continuing until the participant made a decision. Each mode featured 12 parameter combinations (4 frequencies \(\times\) 3 sizes), totaling 24 experimental conditions. The experiment proceeded in two stages. In the pre-experiment phase, participants familiarized themselves with each feedback mode's characteristics (24 trials). In the formal experiment phase (24 trials), we randomized the order of haptic and visual modes to minimize sequence effects. Within each mode, parameter combinations were presented with an increasing size, followed by an increasing frequency to aid participants' recall in subsequent questionnaires and interviews. This design facilitated more accurate evaluations of perceived urgency and decision confidence under each condition. Each participant completed a total of 48 trials (24 haptic feedback; 24 visual feedback), with 24 trials in the pre-experiment phase and 24 in the formal experiment phase.
## 4 The Proposed Multi-Population Global-Best Modified Brain Storm Optimization with Migration
### Update Equations of The Proposed MP-GMBSO with Migration  
This paper proposes MP-GMBSO with migration. Equations for Fitness-based grouping, updating \(x_{ij}^{old}\) in order to apply information of \"gbest\" to \(x_{ij}^{old}\), and equations for new individual generation are shown below.  
Following Equation (12) is utilized for fitness-based grouping of MP-GMBSO.  
\[g_{s}(i)=(R_{s}(i)-1)\%K_{s}+1\qquad(i=1,..,NumInd,\;s=1,\ldots,NumSubPop)) \tag{12}\]  
where \(g_{s}(i)\) is a selected group number of individual \(i\) in a sub-population \(s\), \(R_{s}(i)\) is the rank of individual \(i\) in a sub-population \(s\), \(K_{s}\) is the number of cluster in a sub-population \(s\), and \(NumSubPop\) is the number of sub-populations.  
Figure 4: A concept of a multi-population model using only abest (© 2018 IEEE [27]).  
Figure 5: A concept of a multi-population model using both migration and abest (© 2018 IEEE [27]).  
Figure 6: Examples of ring topologies using various numbers of sub-populations (© 2018 IEEE [27]).  
Figure 7: Topologies with three and more connections using various numbers of sub-populations (© 2018 IEEE [27]).  
An equation for updating \(x_{ij}^{old}\) in order to apply information of \"gbest\" to \(x_{ij}^{old}\) is expanded for multi-population. There are two equations. One is for the only migration model. Another one is for the only abest model, and the combined migration and abest model.  
- the proposed MP-GMBSO with only migration model (see Figure 3):  
\[x_{ij}^{old}=x_{ij}^{old}+rand(1,0)\times C\times\Big{(}x_{j}^{abest}-x_{ij}^{ old}\Big{)}(i=1,\dots,NumInd,\ j=1,\dots,NumDim,\ s=1,\dots,NumSubPop) \tag{13}\]  
where \(x_{ij}^{old}\) is decision variable \(j\) of the \(i\)th individual in sub-population \(s\), and \(x_{js}^{best}\) is decision variable \(j\) of the best individual in sub-population \(s\).  
- only abest model (see Figure 4), and a model using both migration and abest (see Figure 5):  
\[x_{ij}^{old}=x_{ij}^{old}+rand(1,0)\times C\times\Big{(}x_{j}^{abest}-x_{ij}^{ old}\Big{)}(i=1,\dots,NumI,\ j=1,\dots,NumDim,\ s=1,\dots,NumSubPop) \tag{14}\]  
where \(x_{j}^{abest}\) is decision variable \(j\) of the best individual among all individuals. (14) is utilized for comparison in Simulations.  
An equation for new individual generation is expanded for multi-population as shown below.  
\[x_{ij}^{new}=\left\{\begin{array}{c}random\Big{(}H_{j},L_{1}\Big{)}\ \ (random(1,0)<pr)\\ x_{ij}^{old}+rand(1,0)\times\Big{(}x_{js}^{1}-x_{ij}^{2}\Big{)}\end{array} \right.(i=1,\dots,NumInd,\ j=1,\dots,NumDim,\ s=1,\dots,NumSubPop) \tag{15}\]  
where \(x_{ij}^{new}\) is decision variable \(j\) of the \(i\)th newly generated individual in sub-population \(s\), and \(x_{ij}^{s1}\) and \(x_{ij}^{s2}\) are decision variable \(j\) of the randomly selected two individuals of individual \(i\) in sub-population s.  
Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration