esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Design
### Cross-Service Communications  
The system is composed of multiple components. Naturally, those components need to communicate with one another to accomplish the purpose of the system. In the distributed system world, microservices can establish two types of communication channels.  
Synchronous communication between services refers to a communication pattern when a service sends a request and waits for the response before continuing its execution.  
Asynchronous communication between services is a pattern where a service sends a message or request without waiting for an immediate response. In contrast to synchronous communication, which involves a request and an immediate waiting period for a response, asynchronous communication allows services to operate independently and asynchronously process messages over time.  
Event-driven architecture (EDA) (Figure 3) is a design paradigm where systems respond to and communicate through events, allowing for real-time responsiveness and flexibility [40]. Events representing meaningful occurrences trigger actions in a decoupled manner, enhancing scalability and modularity. Synchronous communication in EDA involves immediate and direct interactions between components, where a sender expects an immediate response from a receiver.  
There are multiple software implementations available in the field for implementing an EDA. Most of them gravitate around the idea of message queues.  
The EDA has also a major drawback: it requires a change in paradigm in terms of how the actual application code is designed to run. The synergy of event-driven architecture and synchronous communication addresses diverse requirements, offering a versatile solution for dynamic and responsive systems in technological landscapes.  
Figure 2: Initial application diagram.  
The microservices of the system will use both communication means to achieve the desired business logic. The synchronous communication pattern is used for operations that will not imply a bottleneck when performed and are not runtime-sensitive,, e.g., deleting a document and its related content. The microservice responsible for deleting the document record (containing the title, author, timestamps of creation/update, shared users, etc.) can synchronously wait for the deletion of the actual content. Since this step is performed once per resource and by a single user, the synchronous pattern will simplify the implementation.  
On the opposite end, features such as broadcasting editing events between users in a collaboration session cannot be performed synchronously due to the possible high volume of events and the time-sensitive nature of the application. In this case, the asynchronous pattern offers more flexibility in processing high volumes of events at the cost of higher complexity.
## 4 Experiment Results and Analyses
For this reason, the computation load of the time-frequency representation according to the resolution in time series and frequency bins is studied. In this paper, the GPS receiver with a sampling frequency of 16.368 MHz is used to collect the raw GPS signal. The resolution of time-frequency plane in this paper was chosen as 83.33 Hz (_i.e._, \(\pm 5\) kHz divided into 120 bins) in frequency and 61.09 ns (1 ms divided by 16,368 samples) in time. Accordingly, the SEV process requires 122.4 seconds to complete the time-frequency representation. That is, each frequency bin with a number of 16,368 samples in time consumes 1.02 seconds, as shown in Table 5. For a practical issue, a trade-off could be made between the time-frequency resolution and the computation load. It is possible to implement a down-sampling process to reduce the samples of the data stream prior to the SEV process. In addition, the reduction in the Doppler window and the number of frequency bins according to the acquired Doppler information will be able to lower the computation load as well. Consequently, this paper also examines the computation loads for possible combinations of samples in time, as depicted in Table 5.
## 5 Implementation and Results
### Implementation  
The experiments have been performed on the dataset obtained in Section 3. The images have been apportioned into 90%, 5%, and 5% segments for training, validation, and testing datasets, respectively. We used three CNNs with the proposed AdaBoost-based optimisation technique as base classifiers for implementation and testing. GoogleNet [58], MobileNet-v2 [59], and a 15-layer custom CNN based on the concept presented in [60] is used. A detailed description of the layers of the custom CNN is presented in Table 4. A decision was made to use a shallower network as compared to GoogleNet and MobileNet to optimise for the proposed technique. The Google and MobileNets were considered based on the assumption of a deeper and lighter weight CNN respectively to test our concept, where MobileNet is also intended to be used for mobile platforms with less computational power in our future extensions. Considering the small input image size of GoogleNet and MobileNet, we considered a larger input image size in the custom CNN. The custom CNN consists of a sequential combination of convolutional (Conv), pooling and Fully Connected (FC) layers followed by a softmax classification layer to estimate the class-level probability distribution. Considering the capabilities of sparse representation and equivariant parameters sharing we used a sequence of convolutional and pooling layers for RL. The architecture used for the custom CNN has been reported as state-of-the-art in comparison to logistic regression, Extreme Learning (EL), and SVM in [60]. The local features of a fruit or vegetable image are extracted by the application of a convolution operation with particular kernel size and number of nodes as described in Table 4. A ReLU function is applied as a threshold on the features obtained from the convolutional nodes where filtered features are represented as the output of the layer. The neighbouring statistical summary of the features is extracted and converted to an invariant representation with the help of a pooling operation applied to the output of the convolutional layers. The depth of the custom CNN is considered carefully in comparison to the Google and MobileNets where the custom CNN is considered as a weak classifier for optimisation with the proposed AdaBoost technique. The ReLU was used as an activation function for all hidden layers where the weighted training sample based cross entropy loss defined in (8) was used for training. Experiments have been performed on a 12 GB Tesla K80 with 32 GB of installed memory.
## 2 Computation Method and Case Validation  
### Computation Method  
This study employs numerical computation as the primary research method, extensively utilizing commercial computational fluid dynamics (CFD) software Ansys Fluent 2021 for numerical simulation. The software utilizes the finite volume method (FVM) to discretize the Navier-Stokes (N-S) equations [15], providing a robust foundation for subsequent viscous flow analysis. The FVM is employed to discretize the Navier-Stokes equations, transforming the continuous equations into discrete algebraic equations suitable for numerical solution. The density-based solver in Ansys Fluent [16] is used for the simulations, which is particularly well suited for high-speed compressible flows such as those encountered in hypersonic flight. The Roe-FDS flux difference splitting method is used for flux calculation. This method provides robust and accurate flux evaluation for shock capturing and high-speed flow simulations. A second-order upwind scheme is used for spatial discretization, which improves the accuracy of the solution by considering the gradients of the variables. The least squares cell-based method is employed for gradient calculation, enhancing the accuracy of gradient reconstruction in the computational domain. In terms of turbulence models, the present study selects the shear stress transport (SST) model, coupled with the Sutherland formula, for precise calculation of fluid viscosity. To address the high-temperature effects generated during hypersonic flight, the research employs a thermally complete gas model with variable specific heat ratio to more accurately reflect gas properties in real flight environments, thereby enhancing the simulation's realism and reliability. This computation method has been validated by [PERSON] [17].  
In terms of boundary condition settings, the wall is defined as adiabatic with no-slip conditions to ensure no relative motion between the fluid and the wall while prohibiting heat exchange between the wall and the environment. Pressure far-field boundary conditions are applied at the domain's inlet or far-field, with values precisely determined based on simulated flight altitude and conditions. Pressure outlet boundary conditionsare implemented at the computational domain's exit to simulate a realistic exit pressure environment. For models with symmetry planes, in the absence of considering side slip angles, a half-mode mesh is utilized, and symmetry plane boundary conditions are set. This measure effectively reduces computational volume and enhances research efficiency.
## 1 Introduction  
The tooth impaction is diagnosed when two-thirds of the root's length is developed, and the eruption time has passed [1]. The second mandibular premolars are one of the most frequently affected teeth after mandibular third molars, maxillary canines, with a prevalence of 0.6-2.6% [2, 3]. The etiology of this phenomenon is multifactorial [4]. A dental arch shortage due to early loss of the deciduous second molar is a common underlying cause of second premolar impaction in permanent dentition [5, 6]. Furthermore, the ankylosis of primary teeth, the ectopic position of the tooth buds, supernumerary teeth, cysts, trauma, and sometimes, the ankylosis of the impacted tooth might be determining factors in the etiology of this anomaly [4, 5].  
The ankylosis of the impacted tooth has been described only in 0.79% of all impaction cases and is rarely detected in young subjects prior to orthodontic treatment [4, 7]. The etiology of a priori ankylosis needs further investigation and might be associated with dental follicle stem cells, which are able to differentiate into cell types that create ankylosed lacunas in the impacted teeth [7].  
The consequences of impacted teeth are malocclusion, poor smile aesthetics, and rarely severe complications such as root resorption of the adjacent teeth or cyst formation [8; 9]. The treatment of an impacted tooth is complex, requiring an interdisciplinary team [10]. The treatment method depends on the patient's age, tooth's position, and other individual characteristics [11]. Interceptive treatment, including primary tooth extraction, has been discussed in several studies [12; 13; 14]. However, in 42% of such cases, the tooth does not always erupt spontaneously after removing the obstruction [15]. Therefore, surgical exposure and orthodontic traction are often indicated to facilitate the eruption [11]. Outcomes are not always predictable and successful, and sometimes impacted teeth may need to be extracted to avoid complications [11]. The ankylosis of impacted permanent teeth and its significance for the orthodontic treatment outcome is scarcely described in the literature.  
The present case report aimed to describe an unusual external cervical resorption (ECR) of an impacted second premolar associated with the ankylosis.
## 3 Results and Discussion
### Analysis of Phase Two  
Figure 7 depicts the introduction of the reflector material added at the top of the window compared to LS2H2. There is a remarkable increase of about 10% in WPI level distribution in the back daylit area. However, the WPI value pertaining to the front and back daylit area during the winter solstice is much higher than the recommended value, especially at midday. The uniformity index also witnessed improvement for all design days compared to the LS2H2 configuration (without a reflector), except for during the winter solstice. Thus, the use of a reflector at the top of the window, along with external and internal light shelves, is a useful daylight configuration for improving the WPI in the back space of the model. This result aligned with [PERSON] et al.'s research [6]. Furthermore, uniformity is also enhanced in the said configuration; however, remarkable reflection is a concern when the Sun's path is low during the winter season.  
Figure 6: The effect of the position of internal and external light shelves at a distance of 1 m and 7 m from the window.
# Insights into Bending Stiffness Modeling of Elementary Flexure Hinges
## 6 Comparison of Analytical and Numerical Solutions  
In this section, a comparison is presented between the solutions obtained applying the linear analytical model, solving numerically the second differential equation in Equation (3) and by means of Finite Element (FE) simulations using the software ANSYS, estimating the error due to the approximations made. Each simplifying assumption, namely the negligibility of the axial deformation, the effect of linearization due to small rotations, and the negligibility of the axial load \(F\), has been considered. In the absence of experimental data, the values obtained from FE simulations are taken as a reference to evaluate the errors made from the nonlinear model through the series of approximations. FE analyses were performed on a three-dimensional elliptical flexure with depth derived from Equation (54) when the equal sign is taken. They are provided in more detail in a previous work of the same authors [42]. Still, some information can be recalled here: the model of the flexure hinge was divided into three parts in order to refine the mesh in the thinnest central area. The whole number of nodes was about \(5\cdot 10^{5}\), and a quadratic element with a \"Hex Dominant\" mesh method was used in the central area of the flexure hinge. In all the analyses, an elliptical flexure hinge made of steel with \(\alpha_{x}\ =\ 1\) has been taken into account (\(E=210\) GPa and \(\sigma_{sn}=600\) MPa). Finally, the force \(F\) acting at the free end of the beam can be defined by using the expression in Equation (25):  
\[F=\alpha_{F}\frac{2}{3}Ela_{x}\left(\frac{\beta_{y}}{\rho}\right)^{3} \tag{55}\]  
where \(\alpha_{F}\in[-1;1]\). The smaller \(\alpha_{F}\) is, in modulus, the more the condition in Equation (25) is verified. That is, the analytical solution well describes the behavior of the flexure joint.  
Some cases of interest are presented in the following varying the dimensionless parameters \(\rho\), \(\beta_{y}\), \(\alpha_{F}\), \(\alpha_{Q}\) and \(\alpha_{M}\) in their range of existence: \(\beta_{y}\in\left]0;\frac{1}{2}\right]\), \(\left(\alpha_{F},\alpha_{Q}\right)\in[-1;1]\) and \(\alpha_{M}\in[0;1]\). The proposed studies are not intended to be exhaustive but only to give an idea of the behavior of an elliptical flexure joint as the most significant parameters change.
## 2 Uncertainty Analysis and Wind Farm Modeling  
### Wind Speed  
Wind speed is a random variable, and its fluctuations over a period are expressed by probability distribution functions. In general, wind speed can be described using either a two-parameter Rayleigh or a Weibull distribution [22]. The Rayleigh distribution is the simplest distribution used to describe average wind speed because it has only a single model parameter: \(b\). Its probability distribution and cumulative distribution functions are, respectively,  
\[f_{S_{w}}(S_{w};b)=\frac{S_{w}}{b^{2}}\exp\left(-\frac{1}{2}\frac{{S_{w}}^{2} }{b^{2}}\right) \tag{1}\]  
and  
\[F_{S_{w}}(S_{w};b)=1-exp\left(-\frac{1}{2}\frac{{S_{w}}^{2}}{b^{2}}\right) \tag{2}\]  
As a distribution recommended in literature, the Weibull distribution is also widely utilized to both represent wind speed distribution and compute wind energy potential [23]. It is thought to fit the probability distribution for wind speed better than the Rayleigh distribution does because of its more flexible shape granted by its additional parameter. The probability density function of the Weibull distribution is formulated as  
\[f_{Sw}(S_{w};k,a)=\frac{kS_{w}^{k-1}}{a^{k}}\exp\left[-\left(\frac{S_{w}}{a} \right)^{k}\right],\ S_{w}>0,\quad k,a>0. \tag{3}\]  
The Weibull distribution has a two-parameter function characterized by both \(a\) and \(k\). These parameters determine the wind speed necessary for the optimum performance of a wind-energy conversion system. A survey of the literature indicates that the shape parameter of the Weibull distribution for estimating the global wind energy conditions ranges from 1.2 to 2.75. The cumulative distribution function of the Weibull distribution, which gives the probability of the wind speed, is expressed as  
\[F_{S_{w}}(S_{w};k,a)=1-exp\left[-\left(\frac{S_{w}}{a}\right)^{k}\right] \tag{4}\]
# Numerical Analysis for Critical Structures Protection against Blast Loading Using Metallic Panels
## 3 Mathematical Model and Model Hypothesis
### CPS 4 and Masonry Wall with No Distance between Them  
The masonry wall used in the model consisted of standard small bricks placed in the wide direction. Cement mortar joined the brick with each other. The wall dimensions were concurrent with the panel dimensions. The panel was separated from the wall by zero distance; it was totally in contact with the front plate. Boundary condition selected for the wall represented a hinged wall, as shown in Figure 15. Reasons behind the use of a masonry wall include two main points. Firstly for security reasons; in most cases, the panel would be placed on the inner side of the premises' fence wall to work as the main protective component without being visible from the outside. Secondly, a masonry wall would fail once the charge was detonated, which would reduce the intensity of the blast wave before hitting the front panel plates. Consequently, the deformation after adding the masonry wall was reduced significantly, recording a maximum deformation of 13 mm. Figure 16 shows the maximum deformation, which occurred at 11 milliseconds. Furthermore, the deformation first occurred in the mortar of the wall; after that, bricks start failing. Figure 17 shows that the maximum deformation occurred in CPS4 at the end of analysis and failure of the masonry wall. Moreover, the plastic deformation regions of the rear, front, vertical, and horizontal plates were lower than CPS4 because the masonry wall consumed part of blast wave, as shown in Figures 18-20.  
Figure 14: Plastic deformation region in connector plates for CPS 4.  
Figure 13: Plastic deformation region in rear plate for CPS 4.  
Figure 16: Time displacement curve of CPS 4 and masonry wall with no distance.  
Figure 17: The maximum deformation for CPS 4 and wall failure.  
Figure 15: Model setup for CPS 4 and masonry wall with no distance.
## 3 Results
### Findings from the Stakeholder Workshop  
The results presented in this section are a response to the question which stakeholders are involved in the Emergency DES concept? It includes the mapping of forum participants, domestic examples of existing and planned DES projects, and the analysis of stakeholder interviews using the PESTEL framework.  
\begin{table}
\begin{tabular}{c c c c} \hline
**State/Teritory** & **Climate Impacts** & **Existing DES Projects** & **Potential for Stand-Alone Power Systems (SAPS)** \\ \hline Tasmania & 2018/19 wildfires [51] & King Island (2010-2021) and Flanders Islands & TasNetworks has identified 5 possible SAPS sites over [52,3]. & TasNetworks has identified 5 possible SAPS sites over [53]. \\ \hline Western Australia (WA) & Over 90\% of WA is wildfire-prone [55] & A microgrid is also trialled in Kalkarri, a major tourist site [56] & Western Power, a state-owned energy company, has identified 15,000 potential DES sites until 2030 [54] \\ \hline South Australia (SA) & 2019/20 Black Summer fires, Kangaroo Island & 2017; The SA government installed a utility-scale Tesla battery storage system, the largest installation & Two potential SAPS sites have been identified by the 100% RE proposal for Kangaroo Island was unsuccessful due to lack of support to remove the undersea network cable [59,60] \\ \hline Queensland & 2019/20 Black Summer fires, and is at risk of extreme weather events & 2019; University of Queensland installed a Tesla battery system and showcases its economic and environmental benefits [61] & 1000-200 potential sites have been identified for SAPS over the next 10 to 20 years [54] \\ \hline Regional New South Wales & 2019/20 Black Summer fires & & 2000 SAPS over the next decade [54]. The network covers 95\% of NSW customers and parts of Southern Queensland [62] \\ \hline Victoria & 2019/20 Black Summer fires & Yacandandandand (TRY) [63] and Licola Wildermes Village (located 50 km off the grid and 254 km east of Melbourne) has installed 600 solar panels and 2 large batteries [64] & The regulator identified 300–400 SAPS sites operated by AusNet Services [54], focused on Regional Victoria including East Gipplaand \\ \hline \end{tabular}
\end{table}
Table 2: Climate impacts, and existing and potential DES in Australia states and territories.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Security, Monitoring, and Surveillance  
UAVs are playing an integral role in military surveillance missions. Several countries have added UAVs in their defense strategic plans. Countries are using these flying robotics machines for enemy detection, anti-poaching, border control and maritime monitoring of critical sea lanes. Low cost, reliable and versatile UAVs are currently playing a significant contribution in aerial surveillance, monitoring and survey of any specific area to prevent any illegal activity. For instance, surveillance for any threat can be detected through drones and they can be used to monitor any movement activity in any restricted area. UAV can provide these services by sensing an automatic alert with minimal manual efforts.