esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 5 Simulation Results
### Simulation Setup  
In the simulation, we construct an STIN simulation scenario based on the real-world parameters from the STK (Systems Tool Kit) [41], which models the LEO constellation and satellite gateways. We use the Iridium constellation and place five satellite gateways in the countries of the Belt and Road. The distribution of satellite gateways is listed in Table 2. The Iridium constellation consists of 66 satellites. They are evenly distributed in six orbit planes with a height of 780 km and an orbital inclination of 86.4\({}^{\circ}\). Considering the dynamic changes in the satellite network, we captured the network topology on 1 January 2020 12 PM GMT for simulation, which is shown in Figure 4.  
Furthermore, MATLAB is used to implement the MSAP algorithm, and the energy consumption and average latency are used as evaluation indices. The values of key simulation parameters are listed in Table 3[39,42]. The port rate of the switch \(R\) is set to different values according to the traffic demands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite Gateway** & **Country, City** & **Longitude** & **Latitude** \\ \hline SG1 & China, Beijing & 116.4 & 39.9 \\ SG2 & Indonesia, Jakarta & 106.8 & \(-\)6.3 \\ SG3 & Russia, Moskva & 37.7 & 55.8 \\ SG4 & Kazakhstan, Almaty & 76.9 & 43.3 \\ SG5 & Saudi Arabia, Al Madinah & 39.7 & 24.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of satellite gateways.
## Case Studies
### Case Study 2: Duolingo
#### 5.3.2 Implementation of DDPLM  
Several strategic steps were undertaken to embed the Data-Driven Personalized Learning Model (DDPLM) within Duolingo. The aim was to leverage the platform's strong data analytics features and make language learning more personalized. The aim was to easily fit DDPLM with Duolingo's current foundation while enhancing the adaptive learning features to greater accuracy and efficiency.  
In the beginning, the integration process started by mapping Duolingo's data architecture in detail to deduce the key data points that the DDPLM algorithms could use. This required the identification of user interaction data, such as the error rates in different language tasks, the response times, and the types of module engagement. These data were significant in understanding single learning habits and preferences.  
As a result, the DDPLM was set up to use these data to create comprehensive learner profiles. Dynamic, real-time updating of such profiles forms a basis for personalized learning adaptation. Using advanced machine learning algorithms, DDPLM scrutinizes these profiles to determine learning patterns and predict future performance to impact the adaptive content delivery system.  
The implementation focused on adaptive content delivery, where DDPLM's algorithms adapt educational materials, the pace of lessons, and the challenge level according to a learner's achievements and proficiency. For challenges associated with particular vocabulary and grammatical structures, the system generates related exercises and returns the learner to the basics to reinforce their understanding. On the other hand, the system presents advanced challenges for fast-progressing learners that encourage more profound learning and retention.  
The implementation also incorporated a feedback system in Duolingo's interface that provides tailor-made feedback and tips to users. This functionality promotes user participation in the learning process by offering instant, data-driven feedback relevant to the user's current learning environment.  
During integration, testing and optimization were ongoing and aimed at verifying that the deployment of DDPLM indeed improved learning outcomes while not disrupting the user. The partnership between Duolingo's technical team and the educational researchers behind DDPLM helped to overcome the platform's challenges and satisfy the model's complex requirements.  
The accomplishment of DDPLM in Duolingo is a big step in using big data for educational purposes, showing promise in transforming language learning through customized, data-driven teaching methodologies.
## 3 Results and Discussions
### Wall Effect on Packing Structures
Figure 10a,b,d show a decaying oscillation in the axial packing factor in the direction perpendicular to the wall (\(x\)- or \(y\)-axis) caused by the presence of a fixed wall in both initial beds and processed beds. The results in this study are consistent with the [PERSON] empirical model [39]. Figure 10e,g,h show that the packing factor is more uniformly distributed without violent oscillations due to the use of periodic boundaries in the \(y\)-axis without the presence of fixed walls. This demonstrates that the pebble bed model attained through DEM simulation in this work is in agreement with the actual pebble bed, which can reflect the actual random packing structure of the pebble bed. Additionally, Figure 10c,f,i illustrate the axial packing factor distribution along the vertical (\(z\)-axis) direction, revealing that the packing factor oscillations occur in the top and bottom regions. The observed phenomenon stem from the packing of particles in the bottom wall area and the very low load compression in the pebble bed. Moreover, axial packing factor oscillations persist in the middle pebble bed region, attributable to the significant wall effect at a distance of 5d  
Figure 9: Porosity and pebble center distribution in pebble beds with (**a**,**a**\({}^{\prime}\)) 4 fixed walls, (**b**,**b**\({}^{\prime}\)) 2 fixed walls and 2 periodic boundaries, and (**c**,**c**\({}^{\prime}\)) 4 periodic boundaries. from the wall [40]. In this paper, the pebble arrangements within the central region of the pebble bed remains influenced by the side wall effect.
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
## 4 Discussion  
The results obtained in this work have shown that the use of amplitude information alone from SAR images is enough to estimate the m-m generated by bridges. As a matter of fact, the exploitation of high-resolution SAR data has allowed to appreciate the m-m. The vibrations have been estimated in  
Figure 34: Longitudinal vibration trend versus space of the “Morandi” bridge: (**a**) temporal event \(1\), (**b**) temporal event \(2\).  
Figure 32: “Morandi” bridge. Satellite image.  
Figure 33: Optical representation of the Morandi bridge, the images were taken from a car crossing the bridge a few days before the collapse. (**a–f**): The connection joints of the “Morandi” bridge.  
both time and frequency domains considering different bridges infrastructures. In the first domain, an analysis from the energy point of view can be performed, while in the second domain the main oscillation modes can be identified at frequencies ranging from a few Hz to a several tens of Hz. The case studies we have dealt with have taught us that bridges are highly dynamic structures and are subject to numerous stresses, natural and not. All atmospheric agents such as wind, sea as well as other natural phenomena such as earthquakes are included in natural stresses. In man-made vibration phenomena are mainly those due to car traffic. However, in all cases the oscillating energies and resonance frequencies vary not only according to the shape and materials of the bridge but also according to the season and time of day (day or night) or the particular seasonal period.  
We have analyzed various types of bridges, metal and reinforced concrete, all of various shapes and with different construction methods. What we found is that all bridges have different resonance frequencies and various harmonics located in discrete pints of the oscillating mechanical spectrum. It clearly turn out that the bridges have a frequency response to low-pass oscillations and, in most cases, the main resonance harmonic is observed at frequencies below 10 Hz. We have considered the anomalous case of the \"Morandi\" bridge in Genoa collapsed due to natural causes. In particular, the frequency analysis related to point P2 and presented in Figure 31,b underline the presence of anomalies that are confirmed through visual inspection (see Figure 35, where longitudinal cracks can be clearly observed). The time-frequency analysis made on the stall has returned the presence of the most resonant harmonic localized at about 210 Hz. In addition, given the high quality of the vibration profiles, we believe that this work opens the way to a new paradigm for large infrastructure monitoring, design of high-performance damage detection, classification, and recognition methods.
## 4 Discussion
### The Use of AR Models and the Limitations of Time Series Analysis  
The ARIMA model for time series analysis, which is a generalization of the ARMA model, is widely used in statistics and econometrics. Both models are well suited for time series data to better identify the data or to forecast future points in the series (prediction). In an ARIMA model, the initial difference step (the term \"d\") can be fitted one or more times to eliminate non-stationary mean functions.  
The AR model used in this study is used in other fields. [PERSON] et al. used a spatiotemporal change series with ARIMA modeling for drought forecasting. The AR model predicted the time series of vegetation temperature condition index images (time change of pixel values and spatial information) compared with general predictions for series that change with time [27]. [PERSON] et al. used the AR model and an estimator for the weakly dependent process corresponding to the deterministic component together with the functional AR model. Relatively better results were obtained by reducing the prediction error compared with the prediction methods using only nonfunctional prediction techniques, such as the AR model [28]. [PERSON] et al. applied modeling to the parametric AR, nonparametric AR, ARMA, and vector AR models to predict the spot price of electricity. Filtering techniques were important processes for reducing errors in the time series data. The results of their study showed that filtering techniques, such as the recursive filter on prices and the moving window filter on prices (MFP), combined with attenuation schemes and threshold substitution produced lower error values [29]. In this study, the seasonal derivative was applied to remove the seasonal component from the time series. Moreover, preprocessing including differencing was performed to create a stationary dataset. A fundamental part of this study was how well the MA was reflected using autocorrelation: that is, whether it predicts a sharp increase in downtime from the regression error after 2022 or whether it converges gently to a specific time. Therefore, we focused on the model that obtained the performance shown in Table 1 by appropriately finding \(p\), d, and \(q\) using the ARIMA (\(p\), d, and \(q\)) model.  
Nevertheless, time series analysis has the following limitations considered in this study [18; 30; 31]. First, time series analysis does not support missing values like other models. Therefore, missing values are treated with preprocessing. Second, the relationship between the data points must be linear. Here, the dataset had 2 years of abnormal peaks in 2009 and 2016. It was not judged to have a specific periodic correlation. Thus, we smoothened the observation curve to treat the preprocessing procedure. Third, time series analysis requires data transformation to continuous time values. However, our model works mainly on univariate data.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### How Do FANETs Differ from Other Kinds of Ad Hoc Networks?
#### 2.1.2 How Do FANETs Differ from MANETs and VANETs in Terms of Characteristics?  
Table 2 compares the parameters and characteristics of FANETs with those of MANETs and VANETs as follows:  
* Mobility degree (or the dynamicity of network topology) of FANETs is high (i.e., 30-460 km/h [20]) compared to MANETs (i.e., 5-50 km/h [50]) and VANETs (i.e., 10-120 km/h [8,9]). Hence, in FANET, the highly dynamic network topology causes frequent link disconnections and network partitions, resulting in low link quality [51,52].
* Mobility models of FANETs is different in terms of the capabilities of devices (e.g., UAVs move in the three-dimensional space, while nodes and vehicles in MANETs and VANETs move in a two-dimensional space) and the operating environment (e.g., FANETs operate in the sky, while MANETs operate in the terrain most of the time using random waypoint as it also considered harmful [53] but other model in random trip model [54] and VANETs operate in the highways using prediction based model). Examples of mobility models for FANETs are semi-random circular movement (SRCM) model [55,56], realistic model, and random waypoint model [57]. More details about the mobility models are presented in Section 2.4.
* Energy constraint in FANETs is moderate [7] compared to MANETs (i.e., has the highest energy constraint) and VANETs (i.e., has the lowest energy constraint). The availability of energy affects route lifetime, and so, small UAVs must conserve energy to support long flight time. In VANETs, vehicles are generally powered by vehicular battery with less energy constraint.
* Availability of line of sight (LOS) between UAVs is commonplace in FANETs contributing to higher robustness of a link, while there may be non-LOS (NLOS) in MANETs and VANETs as a result of obstacles in a link.
* Localization method in FANETs, such as inertia measurement unit, provides accurate coordinates of a UAV and neighboring UAVs in a real-time manner [58]. In contrast, MANETs use GPS to find the coordinates of nodes with an accuracy of 10-15 m [59], while VANETs use assisted-GPS or differential-GPS with an accuracy of 10 cm to reduce collisions and find routes [60].  
Figure 3: Four types of links in FANETs.  
* Node density (or the number of nodes in a unit area) of FANETs is low compared to MANETs and VANETs. This means that UAVs are spaced far apart in the sky and that the distance between the UAVs is comparatively long. Lower node density can increase the effects of the dynamicity of network topology. Nevertheless, node density can be high for multi-UAV swarm whereby a large swarm of UAVs is deployed.
## 2 System Description and Its Mathematical Modeling
### Control-Oriented Model of the PV Generator  
The nonlinear \(v-i\) characteristic equation of a PV module can be found in many references in the literature. The PV generators have a nonlinear characteristic changing with the temperature \(\Theta\)  
Figure 3: Block diagram of the control system for the single-stage grid-connected PV-fed boost inverter.  
and irradiation \(S\). For constant or slowly varying temperature and irradiance levels, the \(v-i\) equation of the PV model can be approximated by the following linear Norton equivalent model [31]:  
\[i_{pv}\approx I_{\text{mpp}}-\frac{I_{\text{mpp}}}{V_{\text{mpp}}}(v_{\text{pv} }-V_{\text{mpp}})=2I_{\text{mpp}}+\frac{I_{\text{mpp}}}{V_{\text{mpp}}}v_{\text{mpp}}. \tag{1}\]  
Under nominal conditions, the used PV generator is composed by four series connected TRINA PV modules each one with open circuit voltage around 46.5 V according to the data provided in [32] with a maximum power of 350 W [32]. The resulting PV generator has a nominal power of about 1.4 kW with a nominal MPP value of about 154 V and a nominal MPP current of 9.53 A at nominal conditions of irradiance and temperature. Figure 4 shows its \(i-v\) curve for different values of irradiance \(S\in\)(200,1000) W/m\({}^{2}\) and temperature \(\Theta\in(20,60)\)\({}^{\circ}\)C. More details about the parameter values used will be provided in Section 5.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Impact of the number of markers  
Because many previous studies indicated that the registration error decreases as the number of markers increases, an additional analysis was performed to confirm whether it also holds true for \(w\) = 0.75. For this analysis, five groups based on the number of markers were used, each consisting of four MRE: 2 (number of models) \(\times\) 2 (number of iterations). Owing to the small number of data, the non-parametric Kruskal-Wallis test was performed. As a result of this test, there was no statistically significant difference between the groups (H = 9.300, \(p\) = 0.054 \(>\) 0.05), but this is likely due to the small sample size of the MRE comprising each group.
# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 5 Discussion
### Interest of Researchers in Near Misses in Construction (Question 1)  
In examining the first research question, the quantitative analysis revealed a notable increase in interest in this subject area, particularly over the past decade.  
A second observation is the concentration of the research analysed in a few specific countries. The subject matter is especially prevalent in the United States, China, the United Kingdom, Australia, and Hong Kong. In other countries, articles on NMs are scarce. The results of the research are published in a number of journals specialising in the fields of occupational safety and construction. These include _Safety Science, Journal of Construction Engineering and Management, Automation in Construction, Advanced Engineering Informatics, International Journal of Construction Management, Accident Analysis and Prevention, Engineering Construction and Architectural Management, Heliyon_, and others.  
This topic could be the subject of further research in international bodies set up by different countries. In order to foster greater interest among scientists from different countries in the subject of near misses, it would be prudent to undertake joint research in international projects with the objective of establishing a uniform approach to these incidents.  
While quantitative research offers insights into the trajectory of knowledge advancement in the domain of near misses, the qualitative research conducted, in addition to its scientific value, is of significant relevance for engineering practice.
## 2 The Wear Data Analysis for PCCS and Problem Statement
### The Life Mileage Analysis of PCCS  
In this section, life mileage (LM) of PCCS at Guangzhou Metro is analyzed. Three types of PCCS are used in Guangzhou Metro, including H-type, M-type and P-type, which are installed on the odd number terminal and even number terminal. H type is a metal impregnated material. M- and P-types are the all-carbon carbon strips. A total of 720 sets of wear data in the even number terminal are used for the LM analysis. Among the 720 data sets, 240 data sets are for the LM analysis of H-, M- and P-type PCCS, respectively.  
The PCCS of each type have the ended life cycle of specific kilometers, which is called LM. The LM of each type PCCS is divided into twelve LM intervals, which take ten thousand kilometers as unit and begin from zero to twelve. The number of each type of PCCS in the intervals are shown in Table 1.  
It can be seen from Table 1, the LM of the three types for the PCCS are quite different. H type PCCS has a much longer LM than the other two types. M-type and P-type have similar LM. The histogram of these three types of PCCS is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{10}{c}{**LM Interval (\(\times\)10,000 km)**} \\ \cline{2-13}  & **[0,1)** & **[1,2)** & **[2,3)** & **[3,4)** & **[4,5)** & **[5,6)** & **[6,7)** & **[7,8)** & **[8,9)** & **[9,10)** & **[10,11)** & **[11,12)** \\ \hline H type number & 0 & 0 & 4 & 24 & 100 & 82 & 19 & 4 & 3 & 2 & 1 & 1 \\ M type number & 1 & 72 & 140 & 24 & 3 & 0 & – & – & – & – & – & – \\ P type number & 1 & 120 & 108 & 8 & 2 & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of three types for PCCS in the LM interval.  
Figure 3: The LM histogram of the three types of PCCS.  
Figure 2: The carbon contact strip.  
Figure 3 demonstrates the LM for the three types of PCCS intuitively. The LM of these three types of PCCS from large to small is H-, M-, and P-type. The LM statistical distributions of the three types are assumed as Gaussian distribution according to Figure 3. The fitting results are described in Figure 4. The goodness of fit for the Gaussian distribution of the three types of PCCS are statistically analyzed. The statistical results are shown in Table 2. It should be noted that the closer the sum of squares due to error (SSE) is to zero, the better model fitting, and the more successful data prediction. Root mean squared error (RMSE) and SSE have the same effect. The typical value range of degree-of-freedom adjusted coefficient of determination (called Adjusted R-square) is [0, 1], and the closer it is to 1, the better the model fits the data.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle
### Characteristics of Flow through Ramjet Nozzles
#### 3.2.2 Under- and Over-Expansion  
As discussed previously, in ideal conditions the nozzle produces adequate thrust under correct expansions conditions. Under non-ideal conditions, the reservoir pressure, i.e., the pressure leaving the combustion chamber according to the geometry of the nozzle, determines the expansion criteria of the gases being exhausted. In cases where the ambient pressure is not equal to the pressure of the supersonic gas stream at the nozzle exit, the gas tries to adjust the conditions prior to or beyond the nozzle exit, thus affecting the thrust levels available.  
Under conditions where the ambient pressure is significantly lower than the exit pressure, the expansion continues after leaving the nozzle and deviates from the nozzle axis after suffering what is known as \"Prandtl-Meyer Expansion\" [6], as depicted in Figure 5a. This leads to a vacuum forming at the axis of the jet, which sucks back the momentum of the gases towards it, creating recompression as the expansion waves are reflected from the constant pressure jet boundary as compression waves. The expansion is produced by wavelets, as depicted in Figure 5a, at the opposite jet boundary which, along with the radial expansion, cause a series of small returning shocks which coalesce near the axis to form diamond patterns [6]. The gases therefore return to the conditions at the nozzle exit and the process is repeated, resulting in a phenomenon known as \"under-expansion\". Since the flow is supersonic, the expansion followed by the compression of the exhaust jet beyond the nozzle is achieved by Mach and shock waves [9].  
On the other hand, if the outside pressure is higher than that of the nozzle exit, an oblique shock is generated, which raises its pressure and is compressed inwards. However, the compressed flow  
Figure 5: Shock diamond patterns of (**a**) under-expansion; (**b**) over-expansion.  
pressure may exceed the atmospheric pressure, in which case the flow expands back outward to reduce the pressure again. This process may be repeated several times until the exhaust pressure is matched with the ambient pressure. In other words, the flow will repeatedly compress and expand while gradually dissipating towards the equalisation of the pressure difference between the atmosphere and the exhaust. A shock diamond pattern is created, which is in this case known as over-expansion (Figure 5b).