esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussion
### Discussion on the Critical Stability Function  
The critical stability function of the crowd stability criterion proposed in this study is inversely proportional to the crowd density; that is, the larger the crowd density, the smaller the critical stability value of the crowd. In other words, to maintain the stability of the crowd, the denser the crowd, the stricter the required control of pedestrian acceleration,  
Figure 4: Crowd density distribution based on [PERSON] crowd flow model (information table as a block in a waiting hall).  
Figure 3: Internal disturbance pressure propagation of crowd around I-table (information table as a block in a waiting hall). Note: pressure is a dimensionless parameter defined in Equation (5).  
which is consistent with reality. It is worth noting that the value of the critical stability function in the criterion should vary with the duration of the disturbance and distance from the disturbance center [35; 36].  
First, the influence of the disturbance duration on the stability of the crowd flow is discussed. According to the reference [37], assuming that the pedestrian walking acceleration range is 0 m/s\({}^{2}\)-1.5 m/s\({}^{2}\), then Equation (39) takes \(x=a\), \(\tau_{p}^{x}=75\), \(\rho_{0}=3\) p/m\({}^{2}\).  
The relationship between the critical stability function and duration of the disturbance is shown by the blue line in Figure 5. Assuming that the maximum allowable acceleration of the studied scenario is 1.5m/s\({}^{2}\), the relationship between the probability of crowd flow instability and the disturbance duration is obtained, as shown by the red line in Figure 5. It can be seen from Figure 5 that the critical stability function value increases with time, and the instability probability decreases. When the simulation time is 6.87 min, the instability probability is 80%. This shows that the disturbance energy decays and the risk probability of the unstable crowd flow decreases with time.  
Similarly, taking Equation (39) as an example, the influence of the distance from the disturbance center on the critical stability function value and instability probability of the crowd flow is discussed. If \(\tau_{p}^{x}=75\), \(\rho_{0}=3\)p/m\({}^{2}\), it can be seen from Figure 5 that the attenuation time of the disturbance is approximately 20 min. Therefore, in Equation (39), t = 20 min, and the abscissa of the disturbance center \(a=0\)m. The relationship between the distance from the disturbance center and the critical stability function is shown by the blue line in Figure 6. Similarly, assuming that the maximum allowable acceleration of the studied scenario is 1.5m/s\({}^{2}\), the relationship between the distance from the disturbance center and the probability of crowd flow instability is obtained, as shown by the red line in Figure 6. As shown in Figure 6, with the expansion of displacement, the disturbance energy at a certain point fluctuates and spreads, affecting the stability of the surrounding crowd flow. Without considering the influence of the crowd flow direction, the instability probability of the crowd with the disturbance
# A Two-Step High-Order Compact Corrected WENO Scheme
## 5 Conclusions  
This study introduces an innovative high-order correction methodology for CFD, built upon a two-step compact scheme framework. Unlike conventional single-formula approaches, the proposed method enhances flux function calculations by incorporating high-order compact schemes over identical stencils. This strategy ensures improved accuracy in regions with significant flow variations. The application of this new method leads to the development of a more stable and efficient compact corrected WENO schemes. The proposed CCWENO scheme demonstrates substantial performance improvements, particularly in sharply resolving shock waves and maintaining numerical stability in complex flow scenarios, and it also shows the excellent efficiency compared to other methods. These advancements have been rigorously validated using one-dimensional benchmark tests. Moreover, this study included a thorough error analysis for the two proposed schemes. This analysis further solidifies the practical utility of the proposed method in high-fidelity CFD simulations. Finally, in contrast to purely compact-based schemes, this method or scheme has the flexibility to be applied selectively rather than across the entire computational domain. By focusing on specific regions where higher accuracy is essential, it can further optimize computational efficiency while ensuring precision in critical areas.  
Conceptualization and methodology, Y.Y. ([PERSON]) and Y.Y. ([PERSON]); formal analysis, C.C., S.Y., Y.Y. ([PERSON]), and Y.Y. ([PERSON]); writing--original draft preparation, C.C. and Y.Y. ([PERSON]); writing--review and editing, Y.Y. ([PERSON]), Y.Y. ([PERSON]), and C.C.; visualization, C.C., S.Y., and Y.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research is supported by the Mississippi NASA EPSCoR program. Data Availability Statement: Data is contained within the article. This work relied on computations performed on the WTAMU HPC cluster, which was funded by the National Science Foundation (NSF CC\({}^{*}\) GROWTH 2018841). The authors declare no conflicts of interest.  
Figure 8: Runtime comparison for the problem of two interacting blast waves.
# Transfer Learning for Multi-Premise Entailment with Relationship Processing Module
###### Abstract
We propose using SNLI, a large-scale SPE data set, to learn the weight parameters of the pre-trained model, which can realize the feature extraction and classification between single premise and hypothesis. In addition, we then use a small-scale MPE corpus to learn a module that can infer the relationship  
Figure 2. Multi-premise entailment task. from multiple premises. We call this module the relationship processing module (R module), which combines with the pre-trained model to construct a complete MPE classification model;
* To make the R module focus on the purpose of capturing the relationship from multiple premises, a three-step training method is used in our model. First, the model parameters are learned based on the source corpus. Second, the pre-trained parameters are frozen, the R module parameters are learned based on the target corpus. Finally, the pre-trained parameters are unfrozen to fine-tune the whole model based on the target corpus. We believe that this approach limits the R module to deal with multi-premise relationships, thus keeping the pre-trained model intact. * We propose a specific R module structure, the Attention-backtracking mechanism, which integrates all premises' sentence representation to obtain the preliminary contextual representation. The preliminary contextual representation and the context-related word vector of each premise sentence are calculated to obtain the final premise representation of multiple premises. Experiments show that this mechanism can fully consider the relationship of multiple premises. Through the experiments of different transferring methods, it is proved that the proposed method can inherit the prior knowledge of the source corpus most effectively and improve the accuracy of MPE problem judgment the most. This article proceeds as follows: in the Introduction section, we introduce the definitions of SPE and MPE tasks and their respective problems. This section also describes the contributions made by this article. In the related work section, some technical frameworks in related fields are introduced. In the methods section, a three-step training algorithm and attention-backtracking mechanism are proposed. In the experimental part, we compare the results of each model. The last part is the summary, which summarizes the advantages and disadvantages of the model.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 3 Case Study Materials and Methods
### Accuracy Assessment and Position Correction  
Horizontal accuracy was calculated from the root mean square error statistic on easting (\(RMSE_{x}\), Equation (4)) and northing (\(RMSE_{y}\), Equation (5)) measurements for a number of checkpoints (\(n\)) on a given farm. Overall accuracy (\(RMSE_{r}\)) was calculated following the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy  
Figure 2: The location of the mango orchards overlaid with GE web imagery with the farm-block boundaries in green and checkpoints in red circles with a black center spot. (**A**) Location of farms in Far North and Central Queensland. (**B–E**) Image farms CQ-1, FNQ-1, FNQ-2, and FNQ-3, respectively. The red arrows are the mean misfit vectors observed for the checkpoints on each farm. Arrow length represents 2.075 and 3.271 m in B for 2016 and 2022, respectively, and 1.976, 2.111, and 5.919 m for (**B**), (**C**), (**D**), and (**E**), respectively, for 2021 imagery.  
standard for digital geospatial data [62] (Equation (6)), with a 95% confidence interval on horizontal accuracy calculated as \(RMSE_{r}\) x 1.7308 [63].  
\[RMSE_{x}=\sqrt{\frac{\sum(E_{GEI}-E_{GNSS})^{2}}{n}} \tag{4}\]  
\[RMSE_{y}=\sqrt{\frac{\sum(N_{GEI}-N_{GNSS})^{2}}{n}} \tag{5}\]  
\[RMSE_{r}=\sqrt{RMSE_{x}{}^{2}+RMSE_{y}{}^{2}} \tag{6}\]
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
## 2 Methodology  
### Site Description  
The Pokhara AERONET site is located at 28.19\({}^{\circ}\) N, 83.97\({}^{\circ}\) E in the Pokhara Sub-Metropolitan city of Nepal. Pokhara is Nepal's second-largest city after Kathmandu, with a population of about two hundred sixty thousand [31]. The Cimel Sun Photometer is located on the roof of Shangrila Village Resort, in the south-western suburbs of Pokhara.  
The site is approximately at an altitude of 805 m above sea level, and about 140 km west of Kathmandu. It is surrounded by hills, between 1000 to 2000 m above sea level. The IGP is about 90 km to the south, while to the north, the elevation rises quickly to over 7000 m in the span of 35 km. The presence of high mountains near Pokhara assists in lifting the humid air-masses, mainly in the summertime, resulting in a large amount of precipitation, which can significantly affect the aerosol concentration in the atmosphere [9; 25; 27; 32]. Figure 1 shows the location of Pokhara, along with the potential source regions of air masses over its atmospheric column from Nepal and outside the region, such as from IGP, along with other parts of Asia, including the Punjab region of Pakistan and the Thar Desert [33].
# Reliable Application Layer Routing Using Decentralized Identifiers
## 5 Implementation and Evaluation
### Computational Overhead  
The only operation that each IoT device has to perform is signing an advertisement message. In a RaspberryPi 2 Model B Rev 1.1 with a 900 MHz quad-core ARM Cortex-A7 CPU and 1 GB RAM, the generation of a signature using EdDSA requires 10.1 ms. Similarly, signature generation in an Espressif ESP32 WROOM-32 IoT device (240 MHz dual-core Xtensa LX7 CPU) requires 160 ms. EdDSA signatures are used in our system for providing authentication of the advertisement messages. In any case, our solution is not tightly bound to a particular authentication mechanism: other authentication mechanisms that may be more suitable for constrained devices may be considered and integrated in our approach. For example, many systems rely on MACs and pre-shared keys for providing authentication: such an approach could also be used in our system, e.g., by including a 'hint' of the used pre-shared key in the DID document. However, such approaches are left as future work.  
Similarly, an edge node in our system has only to verify a digital signature. Using an Ubuntu 22.04 machine equipped with an intel i7-3770 CPU, 3.40 GHz and 8 GB of RAM, digital signature verification requires less than 1 ms.  
The solution of [36] uses the same signing procedure; hence, its computational overhead is the same as that in our system.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 2 Related Work
### The Cloud Computing Layer  
The cloud layer contains processors and storage capacity. In this layer, data from edges through the fog layer are processed and stored in a database. This layer employs an algorithm that monitors the speed of edges and identifies those that exceed the defined limits. Moreover, this layer sends updates to the monitoring stations concerning the status of all edges on the roads.  
The cloud-layer software consists of services that receive the estimation data from vehicles. The data initially are stored on the cloud. Since the cloud subsystem considers multiple edges (the number of vehicles), a map-reduce method is utilized to distribute the load on the servers in such a way that allows the efficient handling of data coming from all edges (vehicles). A map-reduce function reads the data from the cloud storage and aggregates the data based on the vehicle ID. Then, any fine requested by the edge-layer algorithms is verified and transferred to the monitoring station's notification service. The cloud also re-estimates the vehicle speeds and compares them with the permitted speeds  
Figure 4: The flow chart of the seatbelt checking algorithm in the fog layer. The algorithm checks whether the seatbelt is fastened or not when the vehicle is moving and the seat is occupied. The algorithm warns the driver three times before sending the fine request to the cloud if the seatbelt is unfastened.  
on the roads to allow a double check of speed. If the speed limit is violated, the cloud notifies the driver and the traffic station using a speed notification service.  
Other services include sending data to the fog layer to update vehicles with data related to their locations, such as the permitted speed on the current road. We assume that the digital map has the speed limit of each road--for example, the open-source map [52]. Each vehicle downloads the map when it registers to use the system. The time required to download the map on the 3G network is approximately three minutes. The cloud updating service sends updates about speed changes if, for some reason, e.g., maintenance, the speed at a road segment has been altered by the authorities. The update message is a JSON file of size 1.5 KB. This design is based on the main objective of fog computing, which is to bring the computation closer to the edge devices so as to reduce the number of transmissions and the amount of transmitted data.
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 2 Theory and Methods
### The Non-Uniform Conformal FDTD Algorithm
#### 2.1.2 Boundary Conditions
In this scheme, however, the electromagnetic fields, as well as the associated grid and material information, at the interfaces between subdomains, are all included within the current subdomain. This ensures that the field update information in each subdomain is readily available within its own domain. Figure 3 depicts a three-dimensional schematic of the exchange of electromagnetic fields between adjacent subdomains. The orange markings denote the interface between the subdomains. The virtual mesh indices in processes \(\mathrm{N}\) and \(\mathrm{N}+1\) are \((nx+1,j,k)\) and \((0,j,k)\), respectively, which are used to store the magnetic field and conformal grid information from adjacent subdomains. It is only necessary to exchange the magnetic fields \(H_{y}\) and \(H_{z}\) at the interface between the two neighboring subdomains. The subdomains are interrupted only once in each iterative step. Figure 3: Three-dimensional schematic of electromagnetic field exchange between adjacent subdomains. Figure 2: Schematic of field update at subdomain interfaces. To minimize the impact of transmission time on the overall computation time, we adopted the scheme of separating the outermost layer of the subregion from the inner region [15], so the update of the electric field in each subdomain is divided into boundary updates and internal updates, as shown in Figure 4. The gray area represents the virtual grid, and the orange lines denote the interface. Since the transmission of the magnetic field mainly occurs at the boundaries of the subdomains, we can improve the parallel performance by overlapping internal computation with boundary magnetic field transmission. While performing a non-blocking transmission of boundary magnetic fields, the electric field within the subdomain is updated. After the transmission is completed, the boundary electric field is updated. This method allows the computation time for field updates to overlap with the transmission time, especially when there is a large amount of data to be transmitted for the boundary magnetic fields, resulting in a higher efficiency.
## 3 Results and Discussion
### Characterization of Carbohydrate Materials and Prepared Emulsions with Pumpkin Oil
#### 3.1.2 Emulsion Properties  
Figure 2 shows the droplet size distributions of prepared emulsions. The emulsion with wheat starch showed the widest distribution of droplets, in which the size varied from 5 to 45 \(\upmu\)m, with a peak at 20 \(\upmu\)m. The emulsions with maize and rice starches contained droplets with a diameter ranging from 5 to about 30 \(\upmu\)m, with a peak at 15 \(\upmu\)m. In turn, the emulsions with maltodextrin, lactose and trehalose had similar size distributions, and particle diameter was in the range of 0.2-10 \(\upmu\)m. [PERSON] et al. [37] used maize, rice and wheat starches to stabilize oil-in water emulsion and also observed the highest mean droplet size for wheat starch. Similarly, the low particle size of emulsions with maltodextrin and trehalose was confirmed by [PERSON] et al. [32]. The authors emphasized that the small droplet size provides good stability of emulsion during spray drying.  
To verify droplet size distribution data and to assess droplet flocculation, emulsion microstructure was visualised using a confocal microscope (Figure 3). It was observed the presence of starch granules in the emulsions with native cereal starches, which is probably a result of relative low temperature of emulsion preparation (40 \({}^{\circ}\)C). This is consistent with the study of [PERSON] and [PERSON] [38], who found that at room temperature potato starch granules remained unmodified and light microscopy micrographs showed them as black spots. As reported [PERSON] et al. [39] all native starches show increasing solubility as the temperature and time of heating are increased. Furthermore, the solubility of wheat starch is lower than the other starches, which is result of the presence amylose-lipid complex, which inhibits swelling, cracking and dispersion of the granules.  
[PERSON] et al. [27] reported that the surface roughness of native rice starch had a negative impact on emulsifying power possibility because the reduced surface contact considerably lessens the interfacial potential well holding smooth particles at the interface.  
Figure 2: Particle size distribution of emulsions prepared with pumpkin oil and carbohydrate materials: maltodextrin (M), wheat starch (WS), rice starch (RS), maize starch (MS), lactose (L) and trehalose (T).
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
###### Abstract  
Positioning systems are integral to Unmanned Underwater Vehicle (UUV) operation, enabling precise navigation and control in complex underwater environments. This paper comprehensively reviews the key technologies employed for UUV positioning, including acoustic systems, inertial navigation, Doppler velocity logs, and GPS when near the surface. These systems are essential for seabed mapping, marine infrastructure inspection, and search and rescue operations. The review highlights recent technological advancements and examines the integration of these systems to enhance accuracy and operational efficiency. It also addresses ongoing challenges, such as communication constraints, environmental variability, and discrepancies between theoretical models and field applications. Future trends in positioning system development are discussed, with a focus on improving reliability and performance in diverse underwater conditions to support the expanding capabilities of UUVs across scientific, commercial, and rescue missions.  
unmanned underwater vehicles; positioning systems; modeling; autonomous underwater vehicles; remotely operated underwater vehicles +
Footnote †: journal: Applied  
_Applied_  
_sciences_
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 4 Results
### Stability Results with Aerodynamic Interference
This effect can be attributed to the higher induced velocity generated by the propeller in thrusting conditions, leading to higher local dynamic pressures on the wing and, in turn, higher aerodynamic loads, reducing the flutter onset. In summary, one can discern trends in damping and frequencies across the four combinations of time-domain approaches and trim conditions depicted in classical flutter curves. These trends are illustrated in Figure 13. The predicted flutter mode 3 by SDBox is also included for both trim conditions. Yellow, red-crossed, and blue-circled lines represent the SDBox, hybrid, and fully coupled approaches, respectively. Solid lines were obtained for the windmilling condition and dashed lines for the thrusting condition. The frequency of the critical mode aligns well with the prediction made by SDBox for mode 3, regardless of the trim condition. Additionally, the occurrence of flutter within the considered velocity range is accurately captured. Across all time-domain approaches, it is observed that the flutter speed is slightly overpredicted by SDBox. In contrast, the fully coupled approach (blue lines) always predicts the lowest flutter speed, irrespective of the trim condition. The largest difference between SDBox and the fully coupled approach in the flutter speed is 4.5% and 3.3% between the hybrid and the fully coupled approach for the thrusting case. Interestingly, it is noted that with the hybrid and SDBox approaches, the windmilling condition is predicted to be more critical than the thrusting condition. With the fully coupled approach, however, the situation is reversed. Last but not least, it must be remarked that, although the thrusting condition provides a stabilizing effect on whirl motion due to the propeller's aerodynamic torque [9], the aerodynamic interactions between the wing and propeller return a stronger destabilizing effect, leading to a reduced flutter speed.