esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 4 Performance Evaluation and Lessons Learnt
### Lessons Learnt and Research Gap  
Given that, the performance of ML algorithms tend to improve with more data, the scale and quality of the dataset is very relevant. Although much of the data from the shop floor origin is structured, there is the need for task-specific feature engineering or regularization to improve the performance of ML [58]. This is due to the multimodality and time-series alignment of the various input feature data, the intermittent scenario of unstructured data in the case of datasets derived from the sensors. Moreover, real-world time-series data are quite skewed in their distribution which presents another challenge in the form of class imbalance. Given the multitude of ML algorithms, the selection of the most appropriate one for a complex manufacturing process remains a challenge, as there is no universal rule that determines such selections, and multiple algorithms are often trained and tested before the one with best accuracy and generalization ability is selected [58; 59]. This limitation can be addressed through a lifelong learning approach where the selected algorithm continuously trains and learns from historical data and evolves over a period of time [60]. In addition, data fusion is still a challenge as sensors had different data types making integration a research concern.
## 4 Numerical Study
### Formation in Sun-Synchronous Orbits  
The stability of a satellite formation designed for SAR applications is evaluated. The orbit parameters of the chief satellite and the formation configuration, reported in Tables 1 and 2, are selected according to the requirements for the implementation of SAR technology for the Micro-Satellite Clusters (MIRACLE)-II, a project by the European Defence Agency in which the author was involved for the mission analysis and platform design [26].  
The relative orbit obtained from the integration of the initial conditions in Table 2 is represented in Figure 1, where the red line indicates the relative orbit for the first period and the black line is the complete trajectory over 30 days. The maximum and the minimum values of the three position coordinates for the first and the last orbit are reported in Table 3, showing that the amplitude of the in-plane oscillations increases by less than 0.05% and that of the out-of-plane oscillations decreases by 4%.  
The results above are compared to the ones obtained from a trajectory designed based on the HCW Equation (23) having the same geometric properties. The effects of the perturbation on such a trajectory are clearly shown in Figure 2; neither the shape nor the center of rotation is preserved, as it is when using \(J_{n}\)-invariant initial states. The drift along the \(\hat{y}\) direction is shown in Figure 3 and evolves at an almost constant rate of 11 km/day. Figure 4 shows the time evolution of the \(z\) coordinate, representing the out-of-plane oscillations, which increase by 100% over 30 days, an effect 25 times higher than the one registered for the \(J_{n}\)-invariant case.  
\begin{table}
\begin{tabular}{c c c} \hline semimajor axis & \(\bar{a}\) & 6919 km \\ eccentricity & \(e\) & 0.002 \\ inclination & \(i\) & 97.79 deg \\ local time ascending node & - & 11:00 am \\ epoch (UTC) & - & 24 January 2023 12:00:00 \\ \hline \end{tabular}
\end{table}
Table 1: SSO formation: orbit parameters chief satellite.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Hamiltonian Variables**} & \multicolumn{2}{c}{**Position/Velocity Components**} \\ \hline \(w_{1}\) & \(0\) & \(x\) & 9.7806 km \\ \(w_{2}\) & \((1+\mathbb{j})\cdot 10^{-3}\) & \(y\) & \(-19.5611\) km \\ \(w_{3}\) & \((1+\mathbb{j})\cdot 10^{-4}\) & \(z\) & \(-0.9781\) km \\ \(z_{1}\) & \(0\) & \(v_{x}\) & \(-14.2517\) m/s \\ \(z_{2}\) & \((-1-\mathbb{j})\cdot 10^{-3}\) & \(v_{y}\) & \(-21.4989\) m/s \\ \(z_{3}\) & \((-1-\mathbb{j})\cdot 10^{-4}\) & \(v_{z}\) & \(0\) m/s \\ \hline \end{tabular}
\end{table}
Table 2: SSO formation: initial state of deputy satellite.
## 8 Conclusions  
To achieve thrust regulation through GG, the effect of secondary GOX injection in the RP1-rich jet was investigated. A total of four cases of GOX injection were studied, involving normal and tangential injection techniques and injection location as a variable. The computational data presented show that, in practice, the normal injection technique supersedes the tangential technique. The comparison of CFD and experimental data revealed a strong correlation between simulations and experiments. Chemical equilibrium calculations were also in good agreement with the CFD results of the combustion species.  
Figure 28: (**a**) Specific fuel consumption. (**b**) Thermal efficiency. (**c**) Propulsive efficiency. (**d**) Overall efficiency.  
It was observed that the GG performance was greatest when GOX is injected normally at 90\({}^{\circ}\), from a position located 20 mm at of the throat, with an injection velocity of 300 m/s. These injection parameters allowed the GOX to penetrate and mix with unburnt RP1 more efficiently.  
Normal injection technique operating at sea level resulted in a 42% increase in thrust and a 46% increase in specific impulse. The increment in thrust is dependent on the injection position. Additionally, a stronger combustion flame inside the secondary combustion zone results in greater exhaust velocity. Stronger combustion displaces the exhaust jet towards the centerline and reduces the expansion ratio, instigating an increase in the exit Mach number. This gives increased turbine shaft power, hence increasing the mass flow in the main chamber, resulting in the increased thrust of the engine. Thus, thrust regulation of the main engine is achieved through secondary combustion in GG.  
Conceptualization, I.Q. and S.K.; methodology, I.Q. and M.U.S.; software, S.K.; validation, S.K., M.T. and M.U.S.; formal analysis, S.K. and M.U.S.; investigation, M.U.S. and M.T.; resources, I.Q. and M.U.S.; data curation, S.K.; writing--original draft preparation, S.K. and M.T.; writing--review and editing, R.F.S., M.T. and I.Q.; visualization, I.Q., M.U.S. and M.T.; supervision, I.Q. and M.T.; project administration, I.Q., M.T. and M.U.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Uni-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications
## 3 Conclusions  
A uni-planar MIMO antenna design is presented for sub-6 GHz 5G-enabled mobile phone applications. Four loop-shaped radiators are designed at each corner of a smartphone board, and they all follow the design configuration of pattern diversity. Several MIMO antenna features are evaluated, such as 5-parameters, gain patterns, radiation and total efficiency, ECC, TARC, and DG. From the reported results, it may be concluded that the presented MIMO antenna system successfully meets the criteria of 5G-enabled mobile phones.  
Conceptualization, U.R. and S.K.; methodology, U.R., S.K. and S.M.A.; software, U.R., S.K. and S.H.K.; validation, U.R., S.H.K., S.M.A. and S.I.S.; formal analysis, U.R., M.M.A., S.M.A., S.I.S. and M.D.; investigation, U.R., M.M.A., S.H.K., S.M.A. and M.A.; resources, S.H.K., S.I.S., M.A. and M.D.; writing--original draft preparation, U.R. and S.K.; writing--review and editing, M.M.A., S.H.K., S.M.A. and S.I.S.; visualization, U.R.; supervision, M.M.A. and S.M.A.; project administration, M.M.A., M.A. and M.D.; funding acquisition, M.A. and M.D. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538.  
Not applicable.  
The authors sincerely appreciate the support from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538.  
The authors declare no conflict of interest.
# Special Issue on Trends and Challenges in Robotic Applications
## 2 Fields of Application
### Service Robotics  
Service robots [1] are gaining popularity and significantly impacting how businesses operate and humans interact with technology. In the hospitality industry, robots are now employed to carry out room service, reception duties and concierege services. In healthcare, robots assist in patient care, including as nanoelectromechanical devices for medical applications [8], in rehabilitation support [9, 10] and in surgical robotics [11]. Moreover, servicerobots are also making inroads in agriculture [12], retail and public spaces, presenting new opportunities for automation in areas that previously relied solely on human labor.  
Robotic rehabilitation is a challenging field that involves a transversal flow of knowledge with other robotic technologies. For example, the similarity between lower limb rehabilitation robotics and humanoid auto-balanced walking robotics [13] lies in their focus on stability and balance. Both lower limb rehabilitation robotics and humanoid auto-balanced walking robotics prioritize maintaining stability and balance during movement. In lower limb rehabilitation robotics, ensuring the patient's safety and preventing falls are crucial aspects of the rehabilitation process. The robot's control algorithms and mechanical design are geared towards providing stable and controlled movements during therapy sessions. On the other hand, humanoid auto-balanced walking robotics aims to replicate human-like walking patterns while autonomously maintaining balance. These robots often have advanced sensors, such as inertial measurement units (IMUs), cameras and force/torque sensors, to continually assess their orientation and stability during walking. Advanced control algorithms are employed to adjust the robot's posture and foot placement in real-time, ensuring balance and stability, even on uneven or challenging terrain. Technologies and insights developed in one field can potentially inform and improve the other, fostering cross-disciplinary advancements in robotics research [14].
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 3 Communication Architecture of FANETs  
The communication architecture determines the mode of information exchanging between the base stations and the UAVs. As for our FANETs architecture, UAVs cooperatively achieve the communication in a special way, eliminating the dependence on infrastructure and reducing the limitations of the communication range [30]. In situations where real-time communication and range limitations are the main issues and infrastructure is difficult to provide, FANET's architecture plays an important role [31]. In [32], the author has proposed several outstanding communication architectures for multi-UAVs system, among which three common communication architectures for FANET are introduced.
# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### Finance Domain Classification  
ANNs have demonstrated effectiveness in accurately detecting credit card fraud transactions with high precision and minimal cost. To enhance the accuracy of the detection process, an MLP was introduced. The results demonstrated that the proposed model is efficient and achieves an accuracy of 85.13% [19]. [PERSON] et al. (2022) [20] compared various ML algorithms to efficiently predict the legitimacy of financial transactions. The RF was the best classifier for unbalanced datasets. However, for balanced datasets, the bagging classifier outperformed the others with an accuracy of 99.96%.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### RBFNN-CNN Model
This is considered as the input to CNN, where the \"Softmax\" activation layer was used to normalize it into a probability distribution containing K probabilities, ending up with the identified speaker. RBFNN is an unconventional machine learning method that is also incredibly fast, effective, and straightforward. RBFNN models the data using smooth transitioning circular shapes rather than harsh cut-off circles, creating a pattern that best approximates the position of the clusters (features of speaker x). It also provides information on the prediction confidence rate, which is the core use of RBFNN as a deep feature match/robust for stressed conditions. The color intensity gradually lowers as you move away from the cluster centroids. An exponential function with a negative power of distance can be utilized to achieve such a smooth transition. We can regulate how fast the function decays by multiplying the distance with a scalar coefficient \"Beta.\" As a result, a larger Beta indicates a more rapid drop. RBFNN is, in general, one of the most powerful models for classification and regression applications. Using a large number of RBF curves, RBF nets can learn to approximate the underlying patterns. In comparison to MLP structured networks, the use of a statistical equation for the optimization process makes the method more conducive and faster.
## 8 Application FVIM to Solve Structural Engineering Design Problems
### Experimental Setup
#### 8.1.1 Tension/Compression Spring Design Problem
These parameter values specify the dimensions that yield the minimum objective value, which in the context of engineering design, translates to an optimal spring configuration. However, the number of active coils \(P=x1\), the diameter of the winding \(D=x2\), and the diameter of the wire \(d=x3\). FVIMDE has converged to a set of dimensions given by \(x1=5.1686\)E-02, \(x2=3.5663\)E-01, and \(x3=1.1294\)E+01, which lie within the physical ranges of [0.05,2], [0.25,1.3], and [2,15], respectively. Comparatively, FVIMDE outperforms the other algorithms, which exhibit higher means and standard deviations, suggesting a less optimal mean performance and a higher variability in their search outcomes. \begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Optimizer** & **Mean** & **Std** & **Max** & **Min Value** & **X1** & **X2** & **X3** \\ \hline FVIMDE & 1.2665E-02 & 1.4363E-09 & 1.2665E-02 & 1.2665E-02 & 5.1686E-02 & 3.5663E-01 & 1.1294E+01 \\ GWO & 1.3401E-02 & 3.4570E-04 & 1.4014E-02 & 1.3009E-02 & 5.4997E-02 & 4.3879E-01 & 7.8022E+00 \\ HHO & 1.3186E-02 & 6.4970E-05 & 1.3258E-02 & 1.3051E-02 & 5.00E-02 & 3.1378E-01 & 1.4637E+01 \\ MVO & 1.8210E-01 & 1.5741E-01 & 5.2245E-01 & 2.1680E-02 & 6.0214E-02 & 5.2439E-01 & 9.4024E+00 \\ CPO & 1.4828E-02 & 1.8026E-03 & 1.8760E-02 & 1.3051E-02 & 5.5954E-02 & 4.5532E-01 & 7.2742E+00 \\ FOX & 5.4527E-02 & 4.7292E-02 & 1.4619E-01 & 1.0625E-02 & 5.000E-02 & 2.5000E-01 & 1.5000E+01 \\ ZOA & 2.9691E-02 & 0.9047E-03 & 4.4030E-02 & 1.8975E-02 & 5.7850E-02 & 4.0690E-01 & 1.1395E+01 \\ FOX & 1.3122E-02 & 1.5122E-04 & 1.3220E-02 & 1.2747E-02 & 5.00E-02 & 3.1725E-01 & 1.4072E+01 \\ AVOA & 1.3466E-02 & 6.6278E-04 & 1.5278E-02 & 1.2804E-02 & 5.0140E-02 & 3.2034E-01 & 1.3899E+01 \\ SCA & 1.3106E-02 & 1.6643E-04 & 1.3248E-02 & 1.2798E-02 & 5.00E-02 & 3.1728E-01 & 1.4134E+01 \\ MFO & 1.2709E-02 & 1.9473E-05 & 1.2720E-02 & 1.2666E-02 & 5.1757E-02 & 3.5907E-01 & 1.1153E+01 \\ WOA & 1.3323E-02 & 1.2549E-04 & 1.3498E-02 & 1.3077E-02 & 5.00E-02 & 3.1683E-01 & 1.4510E+01 \\ \hline \end{tabular}
\end{table} 
Table 18: Results of tension/compression spring design problem, run = 30, FES = 1000, agents No. = 50.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.3 Adaptive Least Temperature Rise (ALTR)  
The adaptive least temperature routing protocol (ALTR), proposed in [55], is designed to improve the LTR protocol. ALTR works in the same manner as LTR with a minor modification of replacing MAX_HOP by MAX_HOP_ADAPTIVE. In LTR if the hop-count of any packet exceeds the threshold value of MAX_HOP then it is discarded, however in ALTR it is not the same. If the hop-count of any packet exceeds the threshold value of MAX_HOP_ADAPTIVE, then a Shortest Hop Algorithm (SHA) is used to forward the packet towards its destination instead of dropping the packet. A sample of how ALTR works is shown in Figure 10b, redrawn from [15].  
Figure 10: Example of (**a**) LTR, (**b**) ALTR [15].
# Experimental Investigation into Permeable Asphalt Pavement Based on Small-Scale Accelerated Testing
## 3 Results and Discussion
### Dry Condition
Since the wheel track of MMLS3 was not long enough to load three types of structures at the same time, 7 PA-CTB was loaded separately at wheel track 1 under dry conditions. As shown in Figure 7c, the range in the transverse resilient tensile strain of 7 PA-CTB-D was between 30 \(\mu\epsilon\) and 50 \(\mu\epsilon\), which was significantly less than that of 7 PA-CTPB-D. That was because the base layer of 7 PA-CTB-D was a dense cement-treated base with a larger modulus. Figure 7b,d, respectively, demonstrate the variation in the resilient strains of the three types of structures on the first day. The longitudinal resilient tensile strain of 4 PA-CTPB-D had a more significant increase than the transverse resilient strain. On the contrary, the transverse tensile resilient strain of 7 PA-CTPB-D and 7 PA-CTB-D increased significantly, while the longitudinal tensile strain of 7 PA-CTPB-D did not change significantly. In general, the longitudinal strain of the bottom of the surface layer of asphalt pavement is chosen to evaluate its fatigue performance, and it can be used to design the thickness of pavement structures. The specified thickness can be calculated by use of a mechanistic-empirical method based on accelerated pavement testing. In this study, the results of strain responses indicated the transverse strain of 7 PA-CTPB-D or 7 PA-CTB-D was higher than its longitudinal strain, whereas the transverse strain of 4 PA-CTPB-D was lower than its longitudinal strain. Therefore, a higher strain response was chosen to compare the performance of different structures. From Figure 7, it can be seen that 4 PA-CTPB-D produced a minimum resilient tensile strain, which indicated that the structure with a thinner surface layer could obtain better anti-fatigue performance, whereas a thinner surface was more  
Figure 6: Dry condition: (**a**) longitudinal and (**b**) transverse strains versus time (t). likely to produce shear failure (depending on the nature of the supporting layer). This was consistent with the results of some studies; the asphalt layer was subjected to the alternating effect of tensile and compressive strain, and the thicker asphalt layer had a lower tensile stress and better fatigue resistance [40; 41]. Moreover, the strain of 7 PA-CTB-D was far below the strain of 7 PA-CTPB-D, which indicated the higher modulus of the base layer induced a much lower strain response. When using the same thickness of surface and base layer, a porous base layer with a lower modulus induced a little higher strain at the bottom of the surface layer. Materials with a relatively high modulus and a thicker surface could be beneficial to fully permeable pavement. However, the construction costs and economic benefits should be considered.
## 3 Bibliometric Analysis: Findings and Research Insights
### Ranking of Publication Outlets by Bibliographic Indices  
The most prominent publication outlets contributing to research on green shipping corridors are presented in Table 2. Key bibliometric indicators, such as the number of articles, local citations, local citation to number of articles ratio, h-index, and starting publication year underscore each publication outlet's impact and thematic relevance. This information offers valuable insights into how scholarly interest and publishing trends surrounding green shipping corridors have evolved across different journals over time.  
Sustainability is the leading journal for the total number of publications (20), followed by Transportation Research Part D (16) and Journal of Marine Science and Engineering  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Publication Outlet** & **NA*** & **LC*** & **LC/NA*** & **h-Index** & **PY Start*** \\ \hline
1 & Sustainability & **20** & 200 & 10.0 & 8 & 2018 \\ \hline
2 & Transportation Research Part D & 16 & **617** & 38.6 & **12** & 2014 \\ \hline
3 & Journal of Marine Science and & & & & & \\  & Engineering & 13 & 154 & 11.8 & 6 & 2019 \\ \hline
4 & Maritime Policy \& Management & 11 & 307 & 27.9 & 8 & 2014 \\ \hline
5 & Journal of Cleaner Production & 10 & 431 & **43.1** & 8 & 2015 \\ \hline
6 & Transportation Research Part E & 10 & 310 & 31.0 & 7 & 2015 \\ \hline
7 & Ocean \& Coastal Management & 9 & 116 & 12.9 & 5 & **2002** \\ \hline
8 & Marine Policy & 8 & 183 & 22.9 & 4 & 2017 \\ \hline
9 & International Journal on & & & & & \\  & Hydrogen Energy & 7 & 227 & 32.4 & 4 & 2021 \\ \hline
10 & Ocean Engineering & 7 & 242 & 34.6 & 6 & 2015 \\ \hline \end{tabular}  
* NA (Number of Articles); LC (Local Citations); LC/NA (Local Citation-to-Number of Articles Ratio); PY Start (Publication Year Start).  
\end{table}
Table 2: Publication outlet rankings by bibliographic indices.  
Figure 2: Annual scholarly output in green shipping corridors domain.  
(13). Transportation Research Part D also tops local citations with 617, reflecting its prominence in the field. Journal of Cleaner Production has the highest local-citation-to-article ratio (43.1), underscoring its strong impact within the dataset. The highest h-index (12) belongs to Transportation Research Part D, indicating both a high citation volume and consistent influence across multiple papers. Ocean & Coastal Management is the earliest journal in the dataset (2002), highlighting an enduring interest in maritime environmental issues. Overall, these outlets serve as critical platforms for disseminating scholarship on green shipping corridors, with strong thematic emphases on sustainability, transport, and maritime engineering.