esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Evaluation and Comparison
The F1 score was 0.8370, 0.8488, and 0.8550 for AdaBoost, GBDT, and XGBoost, respectively. With regard to AUC, GBDT achieved 0.8689, followed by XGBoost with 0.8659 and AdaBoost with 0.8426. Based on the comprehensive analysis of the above five evaluation indicators, the RF model stands out as the most suitable choice for constructing prediction models. Figure 2 illustrates the ROC curves of models based on different feature selection methods. The ROC curve plots show the performance of models across various threshold settings. Models incorporating all features exhibited the best performance. Learning curves were plotted for the models incorporating all features, as shown in Figure S2. The curves for both training and validation scores are closely aligned, and stabilize as the training size increases. Finally, RF outperformed all other models, with an accuracy of 0.9467. A comparative analysis was conducted between our study and existing research utilizing ML for in-hospital mortality prediction in NSTEMI patients with established optimal predictive models. The comparison was structured across three critical dimensions: database characteristics, optimal model architectures, and predictive performance metrics, with detailed comparative outcomes presented in Table 3. The data were derived from datasets across diverse regions. The findings of this study are consistent with [PERSON] et al. [23]. Among all models, RF achieved the optimal performance. Although the accuracy obtained in this study (0.8513) is slightly lower than that of the XGBoost model (0.874) constructed by [PERSON] et al. [22], our study obtained the best recall (0.8513) and AUC (0.8903). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline RF (all) & 0.8513 & 0.9016 & 0.8513 & 0.8670 & 0.8903 \\ AdaBoost (all) & 0.8201 & 0.8657 & 0.8201 & 0.8370 & 0.8426 \\ GBDT (all) & 0.8345 & 0.8730 & 0.8345 & 0.8488 & 0.8689 \\ XGBoost (all) & 0.8417 & 0.8780 & 0.8417 & 0.8550 & 0.8659 \\ RF (Lasso) & 0.7746 & 0.8590 & 0.7746 & 0.8032 & 0.8388 \\ AdaBoost (Lasso) & 0.7338 & 0.8502 & 0.7338 & 0.7718 & 0.8396 \\ GBDT (Lasso) & 0.7674 & 0.8574 & 0.7674 & 0.7977 & 0.8402 \\ XGBoost (Lasso) & 0.7530 & 0.8572 & 0.7530 & 0.7870 & 0.8355 \\ RF (RF) & 0.7650 & 0.8781 & 0.7650 & 0.7987 & 0.8501 \\ AdaBoost (RF) & 0.7986 & 0.8792 & 0.7986 & 0.8241 & 0.8320 \\ GBDT (RF) & 0.7794 & 0.8779 & 0.7794 & 0.8096 & 0.8634 \\ XGBoost (RF) & 0.7650 & 0.8750 & 0.7650 & 0.7983 & 0.8556 \\ \hline \hline \end{tabular} Note: Random Forest (RF); Adaptive Boosting (AdaBoost); Gradient Boosting Decision Trees (GBDT); eXtreme Gradient Boosting (XGBoost); Least absolute shrinkage and selection operator (Lasso); Area under the curve (AUC).  
\end{table} 
Table 2: Performance of prediction models with different feature selection methods.
## 5 Discussion
### Technology Portfolio  
With Option 4 being the optimal technology mix based on the results of this analysis, the differences between the technologies that were considered could be due to a number of different factors, such as the development timeframe, capacities and production efficiencies and maturity of each technology. The difference in percentage share was not that big between these two technologies, since energy-efficient technology usually complements other RE technologies to maximise the reduction in energy demand from the grid [68]. Energy efficiency technology allows buildings to save electricity. The results of the case study showed that efforts that are applied to saving electricity through energy-efficient technology lessen the demand for grid-sourced energy. On the other hand, solar energy serves as a resource that provides clean and sustainable energy to augment the demand for energy from the grid. The installation of more rooftop PVs in the FB project would not be a challenge to implement due to the increasing awareness and uptake within the community. For instance, the growth in the installation of rooftop solar panels in Australia in 2020 was massive and added up to 3 GW of new capacity [69].  
Ground source heat pumps ranked third in the optimal technology options that were identified from the case study. The results of the analysis suggested that 25% of the electricity demand from heating and/or cooling could be satisfied by heat pumps. The resulting share of heat pumps coincides with the findings of [PERSON] and [PERSON] [70], wherein they found that a typical household in Melbourne consumes 25% of the total electricity demand for heating and cooling purposes. However, compared to energy efficiency and rooftop PVs, there could be a challenge in widely utilising ground source heat pumps. Data on performance and the system's associated costs are not publicly available; thus, the suitability of application can be difficult to assess [71].  
On the other hand, unlike the other technologies, the implementation of wind technology (which finished last in the results) needs to consider land requirement, which may limit the number of wind technology devices that can be installed, as well as the wind speed in the area to determine the size or capacity of the technology. The Wiraway precinct (one of the residential precincts) near Port Phillip Bay has a low average wind speed [72]. Thus, for the FB project, a typical 5 MW precinct-scale wind turbine would not be viable [73]. Given the limitations for onsite wind farms, it was reasonable to allocate only a small energy share from this RE source.
## 3 Results
### Minimum Graph Requirements  
Figure 7 shows the runtime of the DisCOptER algorithm for various graph parameters \((h,l)\). Some key observations could be made for all four test problems. Towards the top right of each figure the graph density increased, which came with an increased runtime dictated by the graph searching part of the algorithm. The black dashed line represents the theoretically optimal graph structure \(h=l^{2}\), cf. (14); it is shown for orientation as the results in the following sections were computed with this setting.  
From left to right the wind fields became increasingly more non-convex. As these plots reveal, this came with generally higher runtimes, but more importantly with a region of low graph densities where the algorithm converged either to a local minimum or not at all (gray areas). As discussed before, this was not an issue in case (a) since this problem was convex and [PERSON]'s method converged even from a trivial initialization. Even in case (b) we found that a graph with \(h_{x}<1\) was good enough such that we found the optimal flight path (only one additional column of nodes between start and destination was required). This outcome is not visible here due to the limited resolution of the plot. The convergence problem became all the more apparent with instances (c) and (d). In both cases a good number of local minima existed, each of which had a rather small radius of convergence such that [PERSON]'s method failed if not initialized with sufficient accuracy. Especially in case (d) we saw a patchwork of runs that converged presumably by chance in an unpredictable way. Some of this might be compensated by globalization techniques and an explicit treatment of non-convexity, which, however, would affect the efficiency of [PERSON]'s method. In order to provide reliable results, we need to use a graph density of at least \(l<0.15\) and \(0.11\) for case (c) and (d), respectively.  
Interestingly, the node distance \(h\) appeared to have a much stronger effect on the convergence than the connectedness width \(l\). This might be explained to some extent in terms of the distance and angular error (cf. Section 2.3.2). Low \(l\) decreased the angular resolution and thus induced an increased angular error. The discrete flight path then tended to zig-zag along the optimum. This could easily be smoothed by [PERSON]'s method. However, if the discrete flight path was parallelly offset from the optimum (distance error due to large node distance \(h\)), it might quickly leave the convergence radius of [PERSON]'s method.  
Figure 6: Test case (a). Orange: Discrete-only approach, polynomial trend line of order 6. Purple: Newton-Karush–Kuhn–Tucker (KKT) initialized with straight line, \(l\trianglelefteq l_{C}=\delta\tau L\), linear trend line.
## 2 State of Knowledge
### AI Applications in Port Management  
Artificial intelligence (AI) is increasingly being adopted to enhance port operations, particularly in planning and optimization [14]. Machine learning algorithms can predict traffic demand, improve infrastructure utilization, and minimize operational delays. AI has proven particularly useful in identifying congestion patterns and enhancing security through automated anomaly detection [15].  
In traffic management and space allocation, AI-based systems analyze vast amounts of data in real time, enabling faster and more accurate decision-making [16]. For example, optimization algorithms can automatically assign parking areas for waiting vehicles, improving Ro-Ro terminal efficiency. Similarly, neural networks and predictive models help anticipate operational disruptions and adjust logistics dynamically.  
Despite its advantages, implementing AI in ports faces some challenges. The reliability of AI models depends on data quality and availability, and integrating AI with existing port management systems can be technically complex [17]. Moreover, automated decision-making requires a balance between system autonomy and human oversight, particularly in critical situations that demand immediate intervention [18].
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Post Hoc Interpretation Techniques
#### 3.4.1 Deep Learning Important Features  
DeepLIFT [22] is a recursive prediction explanation method for deep learning models. For a given output, it assigns importance scores to the inputs in a backpropagation-like way, where an importance signal is propagated from an output neuron backwards to the input in one pass. It features framing importance in terms of differences from a reference state and allowing separate consideration of the effects of positive and negative contributions, which enables it to work even when gradients are discontinuous and discover dependencies possibly missed by other methods.
###### Abstract  
The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.  
multi-objective routing; electric vehicles; flying vehicles; genetic algorithm; optimization
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 5 Simulation Results
(2) The lower dashed red curve (\(I_{A,C}\) vs. \(I_{E,C}\)) characterizes the check-node processors (CNPs), depicting output extrinsic mutual information (\(I_{E,C}\)) versus input a priori information (\(I_{A,C}\)). (Note that this curve is conventionally plotted with reversed axes for EXIT chart representation.) The iterative decoding process manifests as a trajectory bouncing between these curves, where extrinsic information from VNPs becomes a priori information for CNPs and vice-versa. Beginning at the (0,0) point (complete uncertainty),  
Figure 3: The error performance of the constructed (925,851) BIBD-LDPC code and the comparable (925,851) 5G-LDPC code in [36]. Figure 2: The decoding convergence rate of the constructed (925,851) BIBD-LDPC code. successful decoding is achieved when the trajectory converges to (1,1) (perfect information transfer and error-free decoding). This graphical representation vividly demonstrates the information exchange dynamics between VNPs and CNPs. The widening \"tunnel\" between the curves at higher SNR values accelerates decoder convergence. The shown SNR (slightly above 3.9437 dB) represents the decoding threshold for the (3,37) code ensemble. Below this critical threshold, the tunnel closes, preventing the trajectory from reaching the (1,1) point and resulting in nonzero error rates.
## 3 Results  
### Experimental Calibration  
In this section, the analytical model is calibrated according to the experimental data from [12]. In their study, a layer of quartz sand with a fixed thickness of 2 mm but varied water content of 0%, 2%, 5%, 10%, and 15% (saturated) was sandwiched between two norite bars to simulate filled joints with different water contents. The stress wave was induced by a split Hopkinson rock bar (SHRB) system. The quartz sand had a porosity of around 0.4 and material density of 2620 kg\(\cdot\)m\({}^{-3}\) (solid density). The density of fillings with different water contents can be calculated and listed in Table 1. The norite had a P-wave velocity of 6000 m\(\cdot\)s\({}^{-1}\) and density of 2900 kg\(\cdot\)m\({}^{-3}\)[12,42]. The S-wave velocity of the norite was taken as 2/3 of its P-wave velocity (4000 m\(\cdot\)s\({}^{-1}\)). The experimental incident wave was adopted as the incident wave to predict transmitted waves after propagating through filled joints with different water contents. The start time of the wave signal to oscillate has a delay of around 0.0012 s compared with that in [12]. Table 1 shows the calibrated parameters in cases of different water contents. Except the density of the filled medium, other parameters all decrease with increasing water content. According to Section 2, the decrease in the quality factor indicates the increase in the viscosity of the filled joint.  
Figure 2 shows the experimental incident wave, transmitted waves through a single filled joint with different water contents and predicted transmitted waves. The amplitude of the experimental transmitted wave obviously decreases with increasing water content. Predicted transmitted waves are basically consistent with experimental results except that their durations are a bit larger than those in the experiment. Figure 3 shows a comparison of the transmission coefficient between the experimental and predicted results. We can see that the experimental transmission coefficient decreases with increasing water content, which agrees well with the experimental result. From  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters of the Filled Medium**} & \multicolumn{5}{c}{**Water Content**} \\ \cline{2-6}  & **0\%** & **2\%** & **5\%** & **10\%** & **15\%** \\ \hline \(\rho_{t}\) (kg\(\cdot\)m\({}^{-3}\)) & 1572 & 1603 & 1650 & 1729 & 1807 \\ \(V_{\text{P}}^{\text{P}}\) (m\(\cdot\)s\({}^{-1}\)) & 2  
in Section 3.1. Parameters of the filled medium for different water contents are taken according to Table 1.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 3 Link Budget Analysis  
A link budget analysis is performed to evaluate the feasibility and reliability of the communication system. This analysis involves calculating all the gains and losses encountered along the transmission path to determine whether the signal strength is sufficient for effective communication. This assessment takes into account various factors such as distance, frequencies, antenna gains, weather, and signal losses. [33]  
This research explores the following:  
* An evaluation of path losses in different weather conditions.
* The effect of altitude on the QoS parameters of the UAV communication system.  
The system parameters considered for the study are tabulated below in Table 1.  
The values considered for the analysis were assumed based on a literature review, as no standards have been established for 6G yet [34; 35].
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Voltage Measurement  
In battery packs assembled from lithium-ion cells, typically each cell voltage as well as the overall pack voltage is measured. While the cell voltages are just a few volts, the pack voltage can reach voltages of more than 800 V [39; 40]. Therefore, it has to be distinguished between cell voltage measurement and pack voltage measurement. The fact that the sum of the individual cell voltages must be identical to the total pack voltage can be used as a plausibility criterion [41].
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 4 Design of Dual-Band Microstrip Antenna
### Antenna Structure Design  
The antenna structure is shown in Figure 14. The antenna is printed on a square FR4 substrate, the thickness and length of which are 1.6 mm and Ls, respectively. The radiating element of the antenna is a square patch, whose size length is L0. The mode of feeding adopts the coaxial feeding of characteristic impedance 50 \(\Omega\), and the distance from the feed point to the center of the patch is xp. In addition, the trap cut slot is opened on the opposite sides of the antenna, the length and width of each step are all 1 mm, the total length of the ladder shaped groove is L1, and the total width is W1. We simulate the design of the antenna with the simulation software HFSS.  
Figure 12: The gain curve of the antenna.  
Figure 13: Antenna pattern (**a**) 2.45 GHz; (**b**) 5.2 GHz; (**c**) 5.8 GHz.
# Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results
## 3 Mechanical Design of the Prototype
### Ankle Joint Prototype
#### 3.1.1 Stress Analysis  
Stress analysis of the structure was performed to determine the materials and dimensions to be used for each part; we wanted the prototype to be lightweight and meet the previously established safety requirements. This analysis was performed in the SolidWorks program using its finite element tool. For material selection, aspects such as material availability, mechanical strength and weight were taken into account. After many tests, aluminium alloy was selected as the main material.  
When designing a structure, it is desirable that the factor of safety be a number greater than one. This value indicates the ability of the system to exceed the requirements. The factor of safety is the ratio between the maximum power value of a system and the value to which it will be subject. The minimum factor of safety achieved for the prototype is 1.5.
## 4 Discussion
### Comparison with Other Structures of Sensors
#### 4.2.2 Surface Plasmon Resonance Structural Biochemical Sensing  
Surface plasmons are a type of surface electromagnetic wave caused by free electron oscillations on a metal surface, which realizes local enhancement of light and tuning of the electromagnetic field. It is an important research technology in the fields of micro/nano processing and integrated optoelectronics. SPR sensing technology has the advantages of real-time monitoring, high response sensitivity, and elimination of labeling [162]. In the film structure waveguide SPR sensor, when the analyte refractive index is in the range of 1.43-1.479 at a wavelength of 850 nm, the measured sensitivity is 1489 dB/RIU, and the maximum power loss is 15.5 dB [163]. A tapered fiber-optic tungsten disulfide (WS2)-SPR sensor with enhanced sensitivity was proposed in a previous study [164]. Experiments were conducted by sequentially coating WS2 and sputtering gold films onto the tapered optical fiber, as shown in Figure 24. The experimental results show that the refractive index sensitivity can reach 4158.171 nm/RIU, which is 125.8% higher than that of the multimode optical fiber SPR sensor, and higher than that of the multimode fiber SPR sensor. The tapered fiber-optic SPR sensor was improved by 50.1%. SPR sensors can achieve high sensitivity; however, they exhibit have poor selectivity and low anti-interference ability.
## 2 Materials and Methods
### Bioreactor Characteristics, Operation, and Substrate Supply  
The study utilized a pilot-scale semi-continuously stirred tank reactor (s-CSTR) with a total and working volume of 60 L and 43 L, respectively (Figure 1). Stirring was intermittent, operating for 1.15 min every 4.15 min. This was achieved using a 24 V DC motor (56B14, Transtechno, Bologna, Italy) with an RPM reducer, set to maintain a stirring speed of 750 RPM. The reactor's effluent overflowed into a 10 L collection bottle via a silicone tube, positioned at a height that ensured the working volume was maintained. The effluent was released by a manual valve positioned at the bottom of the effluent bottle (Supplementary Materials (SM), Figure S1). An inlet was created at the top of the bottle to prevent air ingress during the process of emptying the effluent bottle. This inlet was connected to a bag filled with nitrogen N\({}_{2}\) gas (N\({}_{2}\) \(>\) 99%), which remained closed and opened only when emptying the effluent bottle.  
A stainless-steel, helix-shaped heat exchanger (6 m length, 12 mm internal diameter) was placed inside the reactor to maintain the substrate temperature at 37 \(\pm\) 0.5 \({}^{\circ}\)C. This was controlled by circulating hot water heated by an element located in a separate stainless-steel tank. The heating element was managed by a digital thermostat (TZ4 ST, Autonics, Busan, Republic of Korea) to maintain a constant temperature.  
Figure 1: Schematic of the experimental IoT pilot bioreactor system. 1. Main body of the reactor. 2. Effluent bottle. 3. Connection tube for free effluent and biogas flow between bioreactor and effluent bottle. 4. Stainless-steel, helix-shaped heat exchanger. 5. Stirrer. 6. Inlets for pH and REDOX electrodes. 7. Sample outlet for the measurement cell. 8. Inlet for temperature sensor. 9. Analog thermometer. 10. Biogas outlet. 11. Biogas outlet for methane content measurement. 12. CO\({}_{2}\) scrubber. 13. Methane water volume measurement device. 14. Biogas volume measurement device. 15. Triode valve, 16. N\({}_{2}\) gas inlet.  
Substrate feeding was automated using four peristaltic pumps, each delivering glucose, gelatin, oleic acid, and BA medium. The pumps were activated twice daily via timer switches to supply the bioreactor with fresh substrates. The target Organic Loading Rate (OLR) was approximately 2 g VS per \(\mathrm{L_{R}}^{-1}\) (L of bioreactor) d\({}^{-1}\), with a Hydraulic Retention Time (HRT) of 25 days. Daily substrate input OLRs are provided in Table 2.  
To investigate the CH\({}_{4}\) potential of the used substrate mixture, four BioMethane Potential (BMP) tests were performed in triplicate under mesophilic conditions (37 \(\pm\) 1 \({}^{\circ}\)C). Detailed information about the batch reactors used in the BMP experiment and the followed procedure can be found in the study of [PERSON] et al. (2014).