esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 2 Materials and Methods  
### Data Collection  
Data applied in this bibliometric analysis was extracted from Web of Science and Scopus because they are valid and consistent databases of journal articles, conference proceedings, reports and technical reports [17; 18]. Web of Science and Scopus databases were selected because of their compatibility with the Gephi bibliometric analysis tool used in this study. Web of Science is considered one of the most relevant scientific citation index databases in the academic field [19; 20; 21; 22]. Scopus remains the largest abstract and citation database of peer-reviewed literature, indexing content from 24,600 active titles and 5000 publishers, and is rigorously vetted and selected by an independent review board [23; 24]. The results generated from the databases were profiled to eliminate duplicate articles and relevance. Google Scholar was not included in the database because the generality of research publications can be found in Web of Science and Scopus [24].  
The first step in the bibliometric analysis followed in the steps adopted by previous bibliometric studies in smart campus studies like [PERSON] et al. [19] and [PERSON] et al. [20] by developing a list of keyword combinations to ensure the collection of comprehensive and relevant data. Table 1 below presents the list of keywords combinations applied in the search for smart campus technologies.  
The keyword combination in Table 1 was limited to 2011 to 2021 because their publications within this time range will effectively articulate the developments in smart campuses research.  
The second stage of this analysis produced 538 outputs in conference proceedings, journal articles, survey data, books, book chapters on smart campus technologies, and construction. An estimated 538 articles were profiled for suitability. Upon reviewing the outputs, 285 relevant articles were selected and transferred to produce the matrix for the data analysis. The reduction of the articles from Web of Science and Scopus used the input variables in Table 1 to evaluate the content of the abstracts to filter out publications outside the scope of the input variables. Duplicate publications were also eliminated from the list of publications.  
In the third stage of the analysis, the matrix produced nodes and edges for application in the Gephi software. In all, 578 nodes and 3217 edges were developed in the matrix. The nodes are the keywords in a network, and the edges represent connecting lines for each node within a network [25; 26; 27]. The nodes and edges were derived from the abstracts and keywords.  
The fourth step made use of the nodes and edges tables in the matrix file to visualise the smart campus technologies in Gephi. This process led to identifying the trends in smart campus research, the geographical distribution of the publications and emerging themes from the analysis. The themes led to developing the framework containing the infrastructure elements for a smart campus, which will be essential for procurement and construction purposes.
## 2 Related Work  
Wireless channel estimation in Low-Power Internet of Things (LP-IoT) networks constitutes a significant research domain. Its application is essential for energy efficiency, precise localisation, and reliable link quality evaluation [19]. The implementation of ML methodologies, such as Random Forest (RF), Support Vector Machine (SVM), and Deep Learning (DL)-based models, has garnered considerable interest and provided improved accuracy and flexibility in the field of channel estimation [20]. Recent studies based on RSSI-based channel estimation using ML models have demonstrated enhanced accuracy for indoor localisation, as demonstrated in [14; 21].  
Similarly, predictive ML-based models such as Decision Trees (DT) and SVM have shown prominent enhancement for IoT networks by estimating RSSI for changing weather and environmental factors, as mentioned in [22; 23]. However, these developed models can be computationally intensive and may require recalibration, which creates a challenge when utilised in real-time applications for LP-IoT networks. RSSI estimation in outdoor settings, such as forested or agricultural areas, has also benefitted from ensemble learning and regression models incorporating environmental data [5; 24]. These methods account for natural obstructions but struggle with fluctuating environmental factors and demand higher computational resources. Recent studies on power optimisation integrate RSSI estimation with environmental factors to balance energy efficiency with signal accuracy [7; 25]. However, these models also often require complex calculations and periodic updates, which may be infeasible for LP-IoT devices. Moreover, ML has enhanced Signal-to-Interference-plus-Noise Ratio (SINR) and SNR predictions in 5G/6G and Wi-Fi networks [26; 27]. Although these approaches provide accurate estimations, they are also resource-heavy and challenging, even when adapted to simpler LP-IoT networks, specifically indoor networks. Time series ML models like LSTM and CNN have also shown promising outcomes in real-time RSSI prediction, especially for dynamic environments [28; 29]. However, these techniques also face a similar challenge of meeting computational demand, which exceeds typical LP-IoT capacities.  
Overall, the significant limitations of wireless channels in indoor LP-IoT networks, from signal fluctuations to constrained computational resources, limit the scalability of the developed models across varied settings. ML techniques, particularly RF and ensemble methods, have improved RSSI estimation for diverse LP-IoT applications to resolve these issues. However, challenges remain in balancing the model's complexity with IoT constraints, such as limited power and computational capacity. Therefore, there is a need to develop lightweight ML models that combine the adaptability of ML with the simplicity required for efficient deployment in constrained environments.
## 6 Choosing the Optimal Model
### Analysis of Simulation Results  
As a result of the conducted simulation, confusion matrices were obtained for each of the models and for the indicators RMA1, RMA2, RMA3, and ADL. Below are the simulation results for the best of the analyzed models.
## 4 Numerical Results and Discussion
### Evolution of Transient Dynamic Response of Blade off  
The transient response induced by blade off is complicated, as the sudden unbalance not only can produce a large impact force but also a constant unbalance force. In order to effectively reveal the time-frequency characteristics of the transient response for the gas generator rotor suffering blade off, on the basis of considering the actual operating conditions and the fact that the first stage gas turbine is the earliest possible position where the blade is most likely to be lost during operation, the transient response in 1.5 s is calculated at the highest speed of the engine with the blade off occurring at the first stage gas turbine at the moment of 0.05 s.
# Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas
## 5 Analysis and Discussion of the Models  
The summary of the significant variables and the respective signals identified in all valid models is presented in Table 5. It is important to point out that, though the effect of all the variables was consistent, the models did not identify the same variables. The best fitting statistics also cannot be compared between models generated with different databases.  
A general analysis of the models developed reveals that broader exposure considerations (through variables that aggregate vehicular and pedestrian volumes) were significant only for the GEE models. A possible explanation could be the difference between the database sizes, formed by 40 sets of observations for the ZTNB model and by 280 for the GEE models. However, it is important to highlight that for all models, the LnVped\({}_{\text{mt}}\) (the offset variable) is included, and it is also an exposure variable.  
Model 0 is the one that identified the higher number of contributory factors for pedestrian crashes in the streets considered (Table 5). However, according to this model, the vehicular traffic volume has no impact on the number of pedestrian crashes.  
Taking into account that the objective of this study is to identify factors that contribute to the occurrence of crashes involving pedestrians and not to obtain estimates of the predicted numbers of such occurrences, both models prove to be favorable for the study under the point of view of traffic engineering. However, it is essential to verify the quality of the models to sustain the previous analysis. For that purpose, the plot of the Pearson residuals for the two models are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**ZTNB Model**} & \multicolumn{2}{c}{**GEE Models**} \\ \cline{3-5}  & & **Model 0** & **Model 1/2** & **Model 3** \\ \hline Built & ROAD\_L & + & + & + \\ environment & PARKS & – & – & \\ NET\_C & – & & + \\ Pedestrian & & & & \\ infrastructure & DIST & + & + & + \\  & SLOPE & + & + & + \\ Road & WIDTH\_W & + & + & + \\ infrastructure & DIRECTIONS & – & & – \\  & R\_MED & – & & \\ Exposure & LnPVQ & – & & – \\ Intercept (\(\beta_{0}\)) & –\(6.8345\)*** & –\(9.0026\)*** & –\(7.7187\)*** \\ AIC & \(238.3586\) & & & \\ QIC & & & \(455.5955\) & \(461.728\) \\ MAD & \(2.8962\) & \(0.97362\) & \(0.97769\) \\ RMSE & \(3.8673\) & \(1.2525\) & \(1.24312\) \\ \hline \hline \end{tabular}  
* **\(p\)-Value < 0.0001.  
\end{table}
Table 5: Summary of the impact of significant variables.  
These plots suggest that the residuals appear to be randomly scattered around zero, mainly between \(-2\) and \(2\), indicating good fit of these models.  
When plotting the Pearson residuals versus the fitted values for two models (see Figure 6), it is noticeable that the residuals are equally distributed around zero.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 6 Conclusions  
In this paper, a Bi-LSTM model-based multiuser uplink CE and signal detection for the NOMA-OFDM system are proposed. Compared with the traditional SIC schemes, the proposed model provides better CE and signal detection performance. The conventional CE methods such as MMSE, LS, and ML are less robust than the proposed Bi-LSTM network in terms of signal recovery. Moreover, to observe the SER performance, the proposed model is compared with the CNN model with different SNR values. It is seen from the simulation results that the SER detection performance rate of the proposed system is also extensively high compared with traditional methods and the CNN model. The proposed model receiver is combined with CE, equalization, and demodulation in an end-to-end model. The proposed model is a good solution for 5G wireless communication and beyond. In the future, this system can be applied in a more complex system such as a MIMO-based NOMA system. It can also be applied to a promising physical layer, such as reflecting intelligent surfaces.  
Conceptualization, M.H.R. and M.A.S.S.; methodology, M.H.R.; software, M.H.R., M.A.S.S., S.-G.Y. and M.-A.K.; validation, M.H.R. and M.A.S.S.; formal analysis, M.H.R. and M.A.S.S.; investigation, M.H.R. and M.A.S.S.; resources, H.-K.S.; data curation, M.H.R. and M.A.S.S.; writing--original draft preparation, M.H.R. and M.A.S.S.; writing--review and editing, M.H.R. and M.A.S.S., S.-G.Y., M.-A.K. and H.-K.S.; visualization, M.H.R. and M.A.S.S.; supervision, Y.-H.Y. and H.-K.S.; project administration, Y.-H.Y. and H.-K.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the ICT R&D Program of MSIT/IITP [IITP-2022-2021-0-0-01816, Research on Core technology of Autonomous Twins for Metaverse] and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A0308540).  
Not applicable.  
The authors declare no conflict of interest.
# A Bayesian In-Play Prediction Model for Association Football Outcomes
## 5 Application to In-Play Betting  
### Betting Strategy  
The three aforementioned common markets are renowned for providing opportunities to bet on football matches. In the point-spread market, if a bettor bets on the favourite, he or she wins the bet if the favourite wins by more than the point spread. If a bettor bets on the underdog, he or she wins the bet if the underdog either wins, or loses by less than the point spread. If the difference in points is equal to the point spread, the bettor receives his wager back and the bet is effectively cancelled (known as a 'push'). Betting on the over-under market is analogous to the point-spread market, where the bettor can place money on more or fewer points being scored in the game than the figure identified by the bookmaker (the details are shown in the Appendix A).  
In order to demonstrate the obtained return mainly originates from the model, we test the models using a simple one unit betting strategy: if the expected value of the bet was in excess of some value, then we bet one unit. To be more specific, for event type \(A\), we only bet if  
\[P(A)\times Odds(A)-1>\tau, \tag{25}\]  
Figure 3: Calibration curve for predicting outcomes in home-draw-away market. The black filled circles represent our model’s calibration curve and the red filled circles represent the pure birth process model’s calibration curve. The size of the circles is proportional to the number of observations in each bin. The blue dot line represents the \(y=x\) line. The horizontal axis shows model prediction probability, and the vertical axis represents the empirical frequency. The first row shows calibration curve for home team win; the middle row describes calibration curve for away team win; the bottom row exhibits calibration for draw. Subgraph (**a,d,g**) show the model prediction probability on condition of score information at 10 min versus empirical frequency; the middle column (subgraph (**b,e,h**)) presents the model prediction probability conditional on score information at 30 min versus empirical frequency. The right column (subgraph (**c,f,i**)) illustrates the model prediction probability conditional on score information at 50 min versus empirical frequency.  
where \(P(A)\) and \(Odds(A)\) are the prediction probability and betting price of event \(A\), \(\tau\) is the threshold parameter. Increasing \(\tau\) leads to a stricter betting regime, but consequently produces fewer bets. One unit will be staked when the aforementioned condition is satisfied. On the over-under and point-spread markets, no more than a single event will satisfy the betting condition; however, there may be more than one events meeting the betting condition on outcome betting market. When more than one events meet the condition, we only bet on the event with the highest expectation return. The strategy has also been applied in other papers contributed by [PERSON] et al. [12], [PERSON] et al. [7] and [PERSON] [9].
## 3 Results
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Make** & **Model** & **Type** & **MSRP Base** & **Fuel 5** & **Tax Credits** & **Maintenance** & **Residual** & **Ownership Costs** \\ \hline Hyundai & Ioniq Electric & Electric & 529,500.00 & 53172.50 & 57500.00 & 53781.40 & 55605.00 & 523,348.90 \\ Ford & Focus Electric & Electric & 529,120.00 & 54072.65 & 57500.00 & 53781.40 & 55532.80 & 523,341.25 \\ Nissan & Leaf & Electric & 529,990.00 & 53968.52 & 57500.00 & 53781.40 & 55698.10 & 524,541.82 \\ VW & e-Golf & Electric & 530,495.00 & 54061.08 & 57500.00 & 53781.40 & 55794.05 & 525,043.43 \\ Kia & Soul EV & Electric & 532,250.00 & 53818.11 & 57500.00 & 53781.40 & 56127.50 & 526,222.01 \\ Fiat & 500e & Electric & 532,995.00 & 53899.10 & 57500.00 & 53781.40 & 56209.05 & 526,906.45 \\ Hundai & Ioniq & Hybrid & 522,000.00 & 54866.37 & - & 56482.40 & 56380.00 & 526,968.77 \\ Toyota & Prius c & Hybrid & 520,630.00 & 55858.79 & - & 56482.40 & 5982.70 & 526,988.49 \\ Hyundai & Ioniq Blue & Hybrid & 522,000.00 & 54818.50 & - & 56482.40 & 56438.00 & 52707.20 \\ Toyota & Prius & Hybrid & 523,475.00 & 54914.23 & - & 56482.40 & 56807.75 & 528,063.88 \\ Tesla & Model 3 & Electric & 535,000.00 & 53505.72 & 57500.00 & 53781.40 & 56605.00 & 528,137.12 \\ Kia & Nino FE & Hybrid & 523,340.00 & 55743.91 & 56482.40 & 56766.00 & 528,797.71 \\ Kia & Nino & Hybrid & 523,340.00 & 55772.63 & - & 56482.40 & 56768.60 & 528,826.43 \\ Toyoto & Prus Eco & Hybrid & 525,165.00 & 54850.41 & - & 56482.40 & 57297.85 & 529,199.96 \\ Chevrolet & Bolt EV & Electric & 536,020.00 & 53505.72 & 57500.00 & 53781.40 & 56957.80 & 529,493.22 \\ Honda & Accord Hybrid & Hybrid & 525,100.00 & 55830.07 & - & 56482.40 & 57279.00 & 530,133.47 \\ Toyota & Camry Hybrid LE & Hybrid & 527,950.00 & 54914.23 & - & 56482.40 & 58105.50 & 531,241.13 \\ Chevrolet & Malib Hybrid & Hybrid & 527,920.00 & 55858.79 & - & 56482.40 & 58908.80 & 532,164.39 \\ Honda & clarity Electric & Electric & 533,400.00 & 54061.08 & Lease Only & 53781.40 & 56366.00 & 534,966.48 \\ Toyota & Camry Hybrid LLE & Hybrid & 532,400.00 & 5585.79 & - & 56482.40 & 59396.00 & 535,451.91 \\ BMW & 3 & Electric & 544,450.00 & 54170.36 & 57500.00 & 53781.40 & 58445.05 & 536,932.62 \\ Tesla & Model X 75 & Electric & 570,532.00 & 54442.89 & 57500.00 & 53781.40 & 513,401.08 & 557,855.21 \\ Tesla & Model S 75D & Electric & 574,500.00 & 53852.82 & 57500.00 & 53781.40 & 514,155.00 & 560,479.22 \\ Tesla & Model S 100D & Electric & 594,000.00 & 54037.94 & 57500.00 & 53781.40 & 517,860.00 & 576,459.34 \\ Tesla & Model X 100D & Electric & 596,000.00 & 54859.41 & 57500.00 & 53781.40 & 518,240.00 & 578,900.81 \\ Tesla & Model S P100D & Electric & 5135,000.00 & 54199.92 & 57500.00 & 53781.40 & 525,650.00 & 5109,831.32 \\ Tesla & Model X P100D & Electric & 5140,000.00 & 55137.09 & 57500.00 & 53781.40 & 526,600.00 & 5114,818.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ownership costs excluding insurance based on vehicle value.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 1 Introduction  
The rapid expansion of the Internet of Things (IoT) has significantly changed the way technology interacts with the world, connecting billions of devices across a wide range of industries. IoT technology is at the core of modern automation, enabling smart cities, self-regulating industrial systems, interconnected medical devices, and advanced transportation networks. These devices are designed to improve efficiency and streamline operations by facilitating real-time data transfer and intelligent decision making. However, the increasing reliance on IoT introduces severe security concerns that cannot be overlooked. IoT devices, by their nature, are highly susceptible to a broad spectrum of vulnerabilities across different layers, including hardware, software, cloud infrastructure, and network communications. These vulnerabilities range from unpatched firmware and weak authentication mechanisms to unsecured wireless transmissions and exploitable network protocols. The presence of these weaknesses poses a direct risk to data confidentiality, system integrity, and operational availability, leaving IoT environments vulnerable to malicious actors. Unlike traditional computing systems, IoT devices often operate with limitedsecurity capabilities, making them attractive targets for cyber attacks. This study aims to provide a thorough investigation of IoT vulnerabilities by systematically categorizing them based on their origin and impact. Additionally, we examine potential attack vectors that adversaries may use to exploit these vulnerabilities and compromise IoT ecosystems. Understanding these weaknesses is essential in developing effective countermeasures that mitigate security risks and enhance the resilience of IoT deployments. As IoT adoption continues to grow, the need for robust security frameworks becomes increasingly critical. Through this research, we seek to highlight the importance of securing IoT environments by analyzing current security gaps, exploring potential threats, and proposing mitigation strategies that will contribute to the overall safety and reliability of IoT technology.
# A Review of Femtosecond Laser-Induced Emission Techniques for Combustion and Flow Field Diagnostics
## 4 Femtosecond Laser-Induced Filament Emission  
Different from the strong focusing in fs-LIBS, when an fs laser is focused with a longer focal length lens, the focused femtosecond laser can form a stable plasma channel in the optical medium. This phenomenon is also known as filamentation, and the plasma channel is called a filament. Femtosecond laser-induced filamentation is a unique phenomenon that appears during the propagation of a high-intensity ultrashort laser in a transparent medium. This phenomenon was first observed by [PERSON] et al. [96] in 1995. The appearance of fs filament is due to the dynamic balance between the optical Kerr  
Figure 3: Intensity variation of the CN (388.3 nm) band as a function of the equivalence ratio at different delay times in a CH\({}_{4}\)/air flame [93]. (Reproduced from [PERSON], and [PERSON], Femtosecond laser induced breakdown for combustion diagnostics. _Appl. Phys. Lett._ 2012, _100_, 264104, with the permission of AIP Publishing).  
effect-induced self-focusing and the defocusing effect of the self-generated plasma. [PERSON] et al. [13] suggested that filament could be defined as the propagation zone where there is intensity clamping, and its length is at least several times longer than Rayleigh range.  
The clamping intensity inside the plasma channel induced by fs laser is up to 10\({}^{13}\)\(\sim\)10\({}^{14}\) W/cm\({}^{2}\)[13], which is intense enough to excite atoms/molecules into higher excited states through photolysis, ionization, dissociation, and collision. Then, the excited atoms/molecules release characteristic fluorescence. Therefore, the emission spectra of the plasma channel are rich in information, and the component information of the measured flow field can be obtained qualitatively or quantitatively through spectral analysis. Such non-resonant spectroscopy techniques include filament-induced nonlinear spectroscopy (FINS) and femtosecond laser-induced plasma spectroscopy (FLIPS). In addition, the plasma channel can also excite molecules into higher excited states through laser-induced photochemical reactions, which can extend the fluorescence \"lifetime\". These long-life signals can be used to measure the velocity of the flow field. Such tagging velocimetry includes femtosecond laser electronic excitation tagging (FLEET) and femtosecond laser-induced cyano chemiluminescence (FLICC).