esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposal
### Virtualization of Existing Event Sources  
The basic functionality of the model can be described in three stages. In the first one the gateway proceeds to discover the nodes in the WSN, and detects if they provide events. The way the discovery is accomplished is not covered in this paper, and it can be done in several ways, usually using either the passive or the active methods that are both covered by the nSOM middleware.  
Once an event source has been detected, the event manager component in the gateway automatically subscribes itself to it as an event consumer. Afterwards, it--the event manager--exposes the event as a REST service. This REST services follow the design pattern proposed by [PERSON] [15], and corresponds to the messages labeled as 1.X in the sequence diagram shown in Figure 2.  
The second stage is applied when any kind of user, either a human or another application or service, requires to be notified whenever an event source generates an event. This is done using a traditional subscriber methodology. The interested entity, once it becomes aware about the existence of a specific event source, sends a POST message to the virtualized event source, so it can register a new subscriber, or by means of REST, create a new resource representing the subscriber. All the resources managed by the proposed system are described in Table 1.  
In order to be notified of an event, the subscriber must provide some callback function. In this proposal, this callback is also a REST resource managed directly by the subscriber, and linked to the resource created by the event manager to represent it. All this second stage corresponds to the messages labeled as 2.X in Figure 2.  
Finally, when an event is generated in the event source, it is notified firstly to the event manager by using a lightweight message from the nSOM middleware. The event manager then processes the event and notifies every event consumer using the subscriber's callback URI to the REST resource that represents the handler for that specific event. To accomplish this purpose, a POST method is used, which means that a new event has been detected and can be interpreted as a request for the creation of the resource that handles the event. This stage corresponds to the messages labeled as 3.X in Figure 2.  
Figure 2: Event source virtualization and event notification and processing.  
All the messages transferring the information through the REST requests are JSON based. The messages interchanged in the WSN are shown in uppercase, and use a lightweight protocol specifically developed for the nSOM middleware. In Figure 2 the _Event Source_ is any device in the WSN capable of generating an event. The _discovery Service_, the _Event Manager_ and the _REST Services Repository_ are nSOM elements running in the gateway.
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach  
### Assumptions and Framework of the Proposed Method  
**Assumption 1**.: _The understanding of software architecture in this paper is based on [7]. Software architecture is composed of components and interactions between components. In modelling software architecture, we borrow the concepts from service-oriented architecture (SOA), which is widely used in both software engineering and systems engineering. In SOA, each service is an independent function unit composed of a single component or several components [54]. So, the software architecture in this paper is viewed as a composition of services and interactions between services._  
**Assumption 2**.: _As mentioned in the Introduction section, designers tend to focus on the design and implementation of functional requirements, while non-functional requirements tend to be neglected [1]. Hence, the second assumption is that the architectures that this paper focuses on are designed without considering non-functional requirements, especially security requirements. So, these architectures only reflect functional-level information flow and propagation._  
**Assumption 3**.: _The types of service invocation operation are limited to reading and writing. While in software engineering, there are three types of operations--Reading, writing, and execution. The execution operation is not considered in this paper, because execution operations occur in source code level or executable program level [55]. Execution operation is not suitable for architecture level._  
In this paper, we first propose a definition and formal description of information flow vulnerability in software architecture. Then, security policies of information flow in architecture are given based on Bell-LaPadula model and Biba model. After that, the construction method for service invocation diagram of software architecture is proposed based on graph theory. Finally, we designed and implemented a corresponding algorithm for locating vulnerabilities. The framework of the research method is shown in Figure 1, where green blocks represent background and domain knowledge needed in this research, rounded rectangles represent research that needed to be conducted, blue rectangles represent the results and outputs of each part of the research, gray rectangles represent the analysis results.
## 6 Lamb Waves and Shear Horizontal Waves
### Shear Horizontal Guided Wave Mode  
Since the shear horizontal (SH) guided wave modes are similar to each other, a single discussion of how each behaves is possible. The dispersion relationship found in Rose [4] is:  
\[(M\pi)^{2}=\left(\frac{\omega h}{c_{S}}\right)^{2}-(kh)^{2} \tag{44}\]  
\(M\) is the guided wave mode number, and \(h=H/2\). The phase velocity is equal to the shear wave velocity for \(M=0\), and for large \(\omega\) values, it approaches the shear wave velocity for all \(M>0\). The shear wave velocity represents the boundary between regions 1 and 2 (Figure 1a,b). That is, \(k_{z}=0\) and \(k_{x}=\frac{\omega}{c_{S}}\). The SH partial wave exists as a bulk wave in the plate that is propagating parallel to the traction-free boundaries. When \(M>0\) and \(\omega\) is not large, then the SH guided wave modes increase in phase velocity and are highly dispersive. With the increasing phase velocity, the incidence angles of the SH bulk waves decrease as the solution of the Christoffel equation traces the shear slowness curve (the slowness curve representation of this dispersive SH guided wave mode is shown in Auld [2]).
## 4 Results and Discussions
### Acoustic Performance Using Cores with Cells of Different Size  
Figure 10 shows STL curves obtained using two Nomex honeycomb sandwich structures with cores made of cells of different sizes (the length of the sides of the cells (\(l\)) was 1.83 mm and 2.75 mm respectively). As can clearly be seen in Figure 10, the two curves are very similar, which indicates that cell size had an insignificant effect on the STL values of the materials. We note that the equivalent densities of the two honeycomb structures involved were both 48 kg/m\({}^{3}\).  
Interestingly, [PERSON] et al. [32] also carried out similar research. They found that the increase of the length of the sides of the cells reduced the stiffness of the panel. If the length of the sides of the cells was increased, the number of honeycomb core per unit area was reduced. In this situation, since the equivalent area density of the honeycomb core stays the same, the honeycomb core wall will be thicker, and its rigidity will increase. They found that changing the cell size did not significantly affect the natural frequency of the panel, due to the variation in the counterbalance between the stiffness and weight of the panel. As a result, the effect of cell size on the sound radiation characteristics was not significant.  
Figure 10: STL results obtained using cores with differently sized cells.  
Figure 9: Test reproducibility results for an A2-type panel.  
As shown in Figure 10, the resonance frequency of the small sample was larger than the large sample [27], and until 5000 Hz, STL was mainly in the stiffness region. Figure 10 also clearly shows that the STL curve decreased with increasing frequency in the frequency band 1.6-5 kHz, and that the STL curve displays noticeable and characteristic discrete valleys throughout the frequency band 2.5-3.8 kHz. Moreover, a valley and a peak can be seen in the STL curves around 5 kHz and 5.8 kHz, respectively.  
Figure 11 shows the shapes of the modes occurring in the A\({}_{2}\)-type panel according to simulations. Considering the frequencies corresponding to the first, it can be seen that the significant valley in the STL curve at 5.1 kHz can be attributed to the vibration of the aluminium face sheet of the panel. The STL curve decreased due to the influence of the structural mode of the panel, and it can justify the statement that the modal region starts at 5.1 kHz.
## 4 Statistical Analysis  
Textural features extracted from transformed NCCT images were compared to conventional GLCM features to demonstrate the performance of local patterns in differentiating stroke occurrence. The distinguishing ability of 14 GLCM features and 42 Ranklet features (14 for each of the horizontal, vertical, and diagonal orientations) were first tested. The Kolmogorov-Smirnov test [26; 27; 28] was used to evaluate whether the value distribution of a feature was normal. Student's \(t\)-test [26; 27; 28] was then used to test features with normal distributions, while the Mann-Whitney U-test [26; 27; 28] tested non-normal features. A resulting \(p\) value of \(<\)0.05 indicated whether a feature was statistically significant. A prediction model was then constructed using a logistic regression classifier. By stepwise backward elimination, features with the most relevance were selected with the lowest error rate. After validation by the leave-one-out method [26], the generalizability was considered by picking each case to test the trained model from the remaining cases in the iterations. As a result, each case obtained a probability of being a stroke according to its features. Probabilities of \(>\)0.5 were regarded as a stroke while others were normal tissues. The accuracy, sensitivity, and specificity of Ranklet and GLCM features were then calculated and compared using a Chi-squared test in SPSS (vers. 16 for Windows; SPSS, Chicago, IL, USA). Trade-offs between sensitivity and specificity were illustrated by a receiver operating characteristic (ROC) curve, and Az, the area under the curve, was also compared.
# CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements
###### Abstract  
This article describes Computational Fluid Dynamics (CFD) simulations of flows in a wind farm in complex terrain in Shaanxi, China and the comparisons of the computational results with utility scale field measurements. The CFD simulations performed in the study are using either a Reynolds-Averaged Navier-Stokes (RANS) or Large-Eddy Simulation (LES) solver. The RANS method together with an Actuator Disc (AD) approach is employed to predict the performance of the 25 wind turbines in the farm, while the LES and Actuator Line (AL) technique is used to obtain a detailed description of the flow field around a specific wind turbine #14 near two met masts. The AD-RANS simulation results are compared with the mean values of power obtained from field measurements. Furthermore, the AL-LES results are compared with the mean values of power, rotor speed, and wind speed measured from the wind turbine and its nearby two masts. Results from the simulations indicate that both AD-RANS and AL-LES methods can reasonably predict the performance of the wind farm and wind turbine #14, respectively, in complex terrain in Shaanxi. The mean percent difference obtained for power in the AD-RANS simulations was approximately 20%. Percent differences obtained for power and rotor RPM in the AL-LES varied between 0.08% and 11.6%. The mean percent differences in the AL-LES for power and rotor RPM are approximately 7% and 1%, respectively.  
computational fluid dynamics; wind farm; complex terrain; SCADA; met mast measurements 2018 8788 doi:10.3390/app8050788
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Study
#### 4.3.2 The Effect of Four-Stream Fusion  
As with many state-of-the-art methods [12; 13; 15; 28], we also adopted a multi-stream fusion strategy in our work for fair comparison. Four streams were applied as input skeleton data. The joint stream was the original 3D skeleton coordinates, and the second stream was the bone stream, which was the differential of the spatial coordinates. Both the joint and bone streams were calculated through the differential in the temporal dimension, called the joint motion stream and the bone motion stream. As shown in Table 3, the results of the four-stream fusion strategy brought a great gain for the final accuracy. The four kinds of skeleton data provided additional prior knowledge for skeleton-based action recognition. At the same time, our PGT was able to learn robust representation for these data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transformer Block** & **Acc. (\%)** & **FLOPs (G)** & **Param. (M)** \\ \hline
4 Joint & 88.5 & 3.48 & 11.36 \\ \hline
1 ST + 3 Joint & 89.3 & 3.75 & 11.87 \\ \hline
2 ST + 2 Joint (PGT) & 90.9 & 4.01 & 12.36 \\ \hline
3 ST + 1 Joint & 90.6 & 4.37 & 12.87 \\ \hline
4 ST & 90.7 & 4.64 & 13.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ablation study on the NTU-RGBD 60 CS benchmark, denoting the effect of ST and joint transformer blocks.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.11. PhreeQC  
PHREEQC [476; 689] is a computer program for the simulation of reactions and transport processes in pristine and polluted waters. The calculations include reactions with minerals, gases, solid solutions, and exchangers. Advanced users can use specialised modules to simulate more complex processes defined in the BASIC language. 1D transport,which includes diffusion, advection, and dispersion, also allows for the diffusion into stagnant zones, known as double porosity. Recently, new tools have been released that add Python programming and plotting capabilities [690, 691]. Due to its availability and flexibility, PHREEQC is widely used. It is not only popular, but also reliable. Its modelling results have been compared with those of other codes several times [692, 693, 694, 695]. PHREEQC has also been used to model the migration and biodegradation of hydrocarbons [696, 478, 697, 698]. For example, Bailey et al. modelled the migration of hydrocarbons through sandstone formations at Rainbow Rocks in the USA. The presence of bitumen changed the colours of the sandstone and led to changes in trace elements and mineral assemblage.
## 3 Methodology
### Distance Estimation Using Position Information
#### 3.1.2 Arbitrary Pitch Angle  
The proposed FOV model can also be extended such that the camera is held at an arbitrary pitch angle. Assuming the pitch angle is \(\delta\), \(w\) becomes one-half the width of the captured centerline surface in real coordinates. The top and side views of the model at an arbitrary pitch angle are illustrated in Figures 3-6. In terms of the arbitrary pitch angle, Equations (1)-(3) can be expressed as Equations (7)-(9), respectively.  
\[\alpha+\beta+\delta=90^{\circ} \tag{7}\]  
\[\tan\beta=\frac{b}{h} \tag{8}\]  
\[\tan(\alpha+\beta)=\frac{b+d}{h} \tag{9}\]  
\[\tan\gamma=\frac{w}{d+b} \tag{10}\]  
For a point in pixel coordinate \(P^{{}^{\prime}}(u,v)\), the relationship of the distance along the \(x\)- and \(y\)-axis in real-world coordinate remains as Equations (4) and (5). In such a case, \(\beta\) becomes a parameter depending on the pitch angle \(\delta\).  
Figure 4: Image view of POI and bounding box. (**a**) an object is perpendicular to the camera axis and located in the left half of the image; (**b**) an object is oblique to the camera axis and located in the left half of the image; (**c**) an object is perpendicular to the camera axis and locates across the centerline of the image. It is difficult to determine the closest point of the object from the information of the bounding box.  
There are various methods to acquire the pitch angle \(\delta\). For example, (1) measures the blind area \(b\) to derive \(\delta\) from \(\alpha\) and \(\beta\). It is infeasible as the user needs to measure the blind area from time to time. (2) Detect the vanishing point of the image to derive the pitch angle. It is difficult in the city area as lots of buildings and crowds may block the discovery of vanishing points. (3) Using an inertial measurement unit or gyroscope for continuously measuring the pitch angle for updating the rotation matrix of the camera in order to eliminate the estimated error caused by the vibration from movement [38]. It can also be used to deal with the roll angle changes when moving. However, the acquisition of the pitch angle is beyond the scope of this work so the latter sections will remain on the scenario of the horizontal camera axis.  
Figure 5: Experiments for acquiring the camera’s vertical and horizontal FOV.  
Figure 6: Experiments for acquiring the camera’s focal length.
## 7 Conclusion
### Directions for Future Research  
Building on the findings from the exploration of DFDLOF, several directions for future research have been identified:  
Longitudinal studies: There is a need for longitudinal studies to assess the long-term impact of DFDLOF on learning outcomes and retention. This will provide a deeper understanding of the sustained effects of personalized learning over time.  
Broader demographic application: further research should explore the application of DFDLOF in diverse demographic and cultural settings, ensuring its effectiveness and accessibility across different learner populations.  
Integration with traditional learning: future studies should investigate how DFDLOF can be integrated with traditional classroom settings, balancing the benefits of technology with the essential human elements of teaching.  
Educator's role in ML-driven environments: further research is required to understand the evolving role of educators in machine learning-driven educational environments and how they can best leverage these tools to enhance teaching and learning.  
Ethical considerations and data privacy: As reliance on learner data increases, research into the ethical considerations and data privacy concerns associated with machine learning in education becomes paramount. This includes developing robust protocols and guidelines to ensure the ethical use and security of learner data.  
In conclusion, the Dynamic Feedback-Driven Learning Optimization Framework represents a significant advancement in education, harnessing machine learning to create highly personalized and adaptive learning experiences. The findings from this study provide a foundation for future research, aiming to optimize and refine the framework for broader application in the diverse landscape of global education.
# Antifungal Activity of Quaternary Pyridinium Salts Against _Fusarium culmorum_ in Wheat Seedlings
###### Abstract  
Wheat (_Triticum aestivum_ L.) is a major cereal crop globally, but its production is increasingly threatened by fungal pathogens, particularly _Fusarium culmorum_ (Wm. G. Sm.) Sacc., which causes seedling blight and root rot, leading to yield losses and mycotoxin contamination. Conventional control strategies, such as crop rotation and the use of fungicides, are often inadequate and contribute to the development of resistance, particularly with the overuse of similar modes of action. This study investigated quaternary pyridinium salts--nicotinamide and isonicotinamide derivatives--as potential sustainable antifungal agents. In vivo tests involved treating sterilized wheat seeds grown in sterile sand that had been inoculated with _F. culmorum_, using compounds previously confirmed to be active in vitro. Disease index, shoot and root length, and fresh and dry biomass were measured. Among the tested compounds, nicotinamide derivatives **(2)** and **(3)** showed the lowest disease index (0.9) at a concentration of 10 \(\upmu\)g/mL. Most compounds promoted plant and root growth. Isonicotinamide derivatives **(6)** and **(7)** at 100 \(\upmu\)g/mL increased root dry weight, while compound **(6)** at 10 \(\upmu\)g/mL resulted in the most significant increase in plant length. These findings highlight the dual antifungal and growth-promoting potential of certain eco-friendly derivatives for managing _F. culmorum_ and supporting wheat seedling development.  
**Keywords:** bioactive compounds; _Fusarium_; isonicotinamide derivatives; nicotinamide derivatives; green synthesis; crop protection; sustainable agriculture; seedling blight
## 2 Theoretical Background
### Sound Transmission Loss  
The sound transmission loss of a single leaf panel depends on the bending stiffness of the structure and on several other parameters [33]. It is convenient to define the critical frequency, \(f_{c}\), for which the propagation speed of bending waves equates the propagation speed of acoustic wave in the surrounding medium:  
\[f_{c}=\frac{c^{2}}{2\pi}\sqrt{\frac{\mu}{D}} \tag{5}\]  
where \(c\) is the speed of sound in air, \(\mu\) is the mass per unit area and \(D\) the bending stiffness per unit width of the homogeneous structure. The transmission coefficient \(\tau(\varphi)\) is, for a given angle of incidence \(\varphi\) and for the entire frequency range of interest:  
\[\tau(\varphi)=\left\{\left[1+\frac{\mu\omega}{2Z}\cos\varphi\left(\frac{f}{f_ {c}}\right)^{2}(\sin\varphi)^{4}\eta\right]^{2}+\left[\frac{\mu\omega}{2Z}\cos \varphi\left[\left(\frac{f}{f_{c}}\right)^{2}(\sin\varphi)^{4}-1\right]\right] ^{2}\right\}^{-1} \tag{6}\]  
\(Z=\rho_{0}\)\(c\) is the impedance of the medium and \(\eta\) is the loss factor of the structure. The sound transmission loss, \(STL\), in decibels is:  
\[STL=\ -10\log\tau_{d} \tag{7}\]  
where \(\tau_{d}\) is the sound transmission coefficient for diffuse incidence, defined, for a perfectly diffuse field, as:  
\[\tau_{d}=2\int\limits_{0}^{\varphi_{lim}}\tau(\varphi)\cos\varphi\sin\varphi d\varphi \tag{8}\]  
Assuming that all the angles of incidence are equally probable, the integration range is 0 to \(\pi/2\). However, it is known [34] that the \(STL\) obtained in this way overestimates the values found in reverberation rooms, thus, an arbitrary limitation of the integration range is often introduced, with \(\varphi_{lim}\) usually restricted between \(78^{\circ}\) and \(85^{\circ}\). It has been shown [25] that the sound transmission loss of a sandwich panel can be predicted fairly well by using the same theory, once the apparent bending stiffness per unit width \(D_{a}=D^{\prime}{}_{a}/\psi\) of the composite structure is considered in Equation (5) in place of the constant \(D\) value used for the homogeneous panel. As a result, a frequency-dependent parameter \(f_{c}=(c^{2}/2\pi)\) (\(\mu/D_{a}\))\({}^{1/2}\) is defined and used in Equation (6) to calculate the transmission coefficient.