esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of the Results
### Comparison and Analysis of the Accuracy of the Different Models  
The different models are trained with their own optimal parameter settings, and the complexity of each model is consistent. Figure 8 shows the average classification accuracy of the different models under the same complexity. The horizontal axis represents the number of parameters of the model, and each model has the same number of parameters and the same complexity. The results show that in the blast furnace image dataset collected in this paper, based on the ResNet50 model, the accuracy of the proposed LSERNet is 98.59%, the accuracy of the SE-ResNet50 model is 97.52%, and the accuracy of the ResNet50 model is 97.42%. In comparison, the LSERNet model constructed by ResNet50 combined with our proposed LSEB module has the highest accuracy. At the same time, based on the ResNeXt model, the accuracy of LSE-ResNeXt model is 97.94%, the accuracy of the SE-ResNeXt model is 97.63%, and the accuracy of the ResNeXt model is 97.49%. For comparison, the LSE-ResNeXt model constructed by combining ResNeXt with our proposed LSEB module, also achieved the highest accuracy. In general, the combination of our proposed LSEB module with ResNet50 and ResNeXt achieves the highest accuracy. This is because the LSEB module focuses on the local information of the image, so that the regional information can be fully utilized and the accuracy of the model is improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Batch Size** & **Learning Rate** & **Average Accuracy** \\ \hline LSERNet & 16 & 0.1 & 98.59\% \\ SE-ResNet50 & 32 & 0.1 & 97.52\% \\ ResNet50 & 32 & 0.001 & 97.42\% \\ LSE-ResNeXt & 16 & 0.1 & 97.94\% \\ SE-ResNeXt & 32 & 0.1 & 97.63\% \\ ResNeXt & 16 & 0.1 & 97.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimal parameter combinations of the different models.  
Figure 8: Average classification accuracy of the different models.
## 3 The Study  
### System Level Simulator  
The simulator builds upon INET/SimuLTE over the OMNeT\(++\) Discrete Event Network Simulator. OMNeT\(++\) is an extensible, modular, component-based C\(++\) simulation library and framework primarily developed for building network simulators.  
SimuLTE is an innovative simulation tool enabling complex performance evaluation at the system level for LTE and LTE Advanced networks in the OMNeT\(++\) framework. It simulates the data plane of the LTE/LTE-A Radio Access Network and Evolved Packet Core. SimuLTE implements eNBs and User Equipment (UE) as OMNET\(++\) compound modules. These can be connected with other nodes (e.g., routers and applications) to compose networks, as shown, e.g., in Figure 1.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 2 Materials and Methods
### Literature Review
#### 2.1.3 Structural Fire Engineering
These authors use the governing PDEs to construct a loss function that guides the neural network's training, effectively guiding the surrogate model towards complying with the physical constraints of the system. Similarly, [PERSON] et al. introduced an adaptive approach to tackle dynamic coupled problems in functionally graded materials with large-size ratios [100]. They adopted the standard LCT method of embedding PDEs into the loss function and extended this with an adaptive loss balancing algorithm, which dynamically adjusts loss weights during training, enhancing performance for complex geometries. Furthermore, their model employs separate neural networks to represent distinct physical fields (displacement and temperature), effectively incorporating ACT aligned with the system's structure. Another application of ACT was presented by [PERSON] et al., who employed causal discovery and inference to evaluate the fire resistance of reinforced concrete columns, moving beyond traditional predictive modelling by focusing on uncovering causal relationships and interventional effects [101]. Notably, the authors incorporated domain knowledge to refine the causal structure generated by the algorithm. By manually adjusting the links between variables based on an established understanding of the fire resistance phenomenon, they effectively introduce an architecture constraint. [PERSON] et al. [102] also used ACT and LCT as part of their PIML approach. To improve how physics is included in neural networks for complex problems, they used a \"mixed formulation\" technique, which predicts not only the absolute value of variables of interest (like temperature or movement)but also their rates of change (like heat flow or stress). To achieve this, they designed their network in such a way that separate parts of the network result in each prediction. They also created a special loss function, combining regular and more advanced forms of the physics equations, i.e., adopting LCT. By carefully choosing how the network is built and what physics it learns, they made a system that can solve tough problems without needing a lot of real-world data.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Model of AOA Measurement
From the global perspective of the UAV system, the azimuth AOA measurement error of the \(i\)-th UAV in the \(t\)-th time slot in the swarm could be defined as \(n_{\theta,i}^{t}\). If \(n_{\theta}^{t}\) represented the state vector composed of the azimuth AOA measurement errors of all UAVs in the \(t\)-th time slot, there was:  
\[n_{\theta}^{t}=\left[n_{\theta,1}^{t},n_{\theta,2}^{t},\cdots,n_{\theta,M}^{t }\right]^{T} \tag{14}\]  
The covariance matrix of \(n_{\theta}^{t}\) was:  
\[\mathbf{C}_{\theta}^{t}=diag\Big{\{}\left(\sigma_{\theta,1}^{t}\right)^{2}, \left(\sigma_{\theta,2}^{t}\right)^{2},\cdots,\left(\sigma_{\theta,M}^{t} \right)^{2}\Big{\}} \tag{15}\]  
The measurement model of elevation AOA was similar to the above content, and in order to avoid repetition, the definitions of \(\mathbf{\varphi}_{i}^{t}\), \(\mathbf{\overline{\varphi}}_{i}^{t}\), \(\tilde{\mathbf{n}}_{\varphi,i}^{t}\), \(\tilde{\mathbf{C}}_{\varphi,i}^{t}\), \(n_{\varphi}^{t}\), \(\mathbf{C}_{\varphi}^{t}\) were not repeated. Meanwhile, in order to simplify the problem appropriately and reasonably, it was believed that \(\sigma_{\theta,i}^{t}=\sigma_{\varphi,i}^{t}\) held for \(i=1,2,\cdots,N\). Each UAV was of the same measurement error for the elevation and azimuth AOA in a time slot.
## 5 Experimental Results  
We evaluated DICTION in terms of its (1) impact on model performance (fidelity); (2) robustness against three watermark removal techniques (overwriting, fine-tuning, and weight pruning); and (3) resilience to watermark detection attacks (security). For all attacks, we considered the most challenging threat model, assuming that the attacker has access to or knowledge of the training data and the watermarked layer.
# A 6 DOF Virtual Environment Space Docking Operation with Human Supervision
## 4 Results and Discussion  
The controller parameter solution for the isodamping property is achieved fora gain crossover frequency \(\omega_{gc}\) of 150 rad/s and phase margin (PM) of 50\({}^{\circ}\). The assumed linear model is (5) and the resulting controller is  
\[C_{FOPI}(s)=0.065\left(1+\frac{1.15\cdot 10^{4}}{s^{1.656}}\right) \tag{15}\]  
The parameters of the FOPI controller in (15) were obtained by solving the set of nonlinear equations in (14). First of all, the \(K_{i}\) parameter was estimated as a function of the fractional order \(\mu\), using the phase margin and the iso-damping, according to the second and third equations in (7). The two functions were then plotted as indicated in Figure 7a. The intersection of the two functions gives the two control parameters that ensure both a certain phase margin and the iso-damping property. According to Figure 7a, the intersection point yields \(K_{i}=1.15*10^{4}\) and \(\mu=1.656\). Once these two parameters are computed, the proportional gain is estimated from the magnitude from the (first) equation in (14) as \(K_{p}=0.065\). Figure 7b presents the Bode diagram for the controller and the two systems denoting two extreme cases (\(P_{1}\) and \(P_{2}\)) of LPV values (i.e., a variation of 250% in the reflected mass value in the 6 DOF platform simulator).  
The discretization scheme proposed in [21] has been employed to obtain a discretized version of pole-zero pairs. This scheme is particularly efficient at delivering stable, low-order approximations of the non-rational function of the FOPI into a proper filter form, and for our study case, it delivers a fifth order rational transfer function.  
An example of a co-simulation interface and real-time tracking between the 6 DOF platform and virtual model is illustrated in Figure 8. In this figure, the real world platform can be observed, including the active part of the BDM system (label 1), the front panel of the LabVIEW application (label 2) and the 3D model of the simulated platform (label 3).  
The adaptive controller maintains the same specifications in closed loop performance for the two variations in the reflected mass parameters, with the step response for three platform legs (the other are similar) given in Figure 9. It can be observed that the controller maintains performance despite the great variability in system parameters mimicking the reflected mass for one leg with or without contact at docking.  
Figure 8: Example of co-simulation interface and real-time tracking between the 6 DOF platform and virtual model.  
Figure 7: (**a**) The cross-section point of the phase margin and iso-damping criteria; (**b**) a Bode diagram of the open loop system in the two extreme cases.
## 3 Sustainability of Additive Manufacturing
### Sustainable Design through AM  
The principal application of AM compared to conventional design is in the process of design. The prototyping process is traditionally too expensive due to the essential tools, molds, and specialized workers, whereas the application of AM has a significant influence on the manufacturing of prototypes in terms of time and cost. AM is also able to produce products that are geometrically more complex. The optimal design of AM leads to a reduction in the consumption of around 40% [21].  
The definition of eco-design in AM is the consideration of the environment in every design process and fabrication process so that every step has the minimum environmental effect through the product's life [124]. Accordingly, eco-design can be applied as a tool for environmental assessment and environmental design. In this case, life cycle analysis (LCA) is the most appropriate tool to evaluate the environmental effect of a product, which is described in Section 3.3.2 with more details. The LCA analyzing method is employed by numerous users with focuses on the technicalities of the design, for example, the optimization of design and process. Moreover, the other assessment tools such as the Eco-Indicator 99 allow an earlier assessment, nonetheless, at the cost of an accurate evaluation [125]. In general, the applied tools for environmental design principally involve manuals, guidelines, or checklists (such as [126]), or using the green products for inspiration, e.g., the _Eco-Design Handbook_[127]. It leads to evaluate the environment in the design and product development process [125].  
Sustainable product design is categorized as an extension of eco-design. Sustainable design is defined as \"design which strives to fabricate products that diminish their impact on the environment\" while it achieves acceptable economic profits and a positive effect on society at the same time [118; 128]. Much research in sustainable product design focuses on features such as reducing the environmental effects of the resource, energy, and material usage while it regularly disregards understanding the quality of the design as a technique which maximizes product advantages [129; 130]. [PERSON] et al. [128] showed that AM has a great capability to address both cost efficiency and design quality while it remains as an operative tool to empower sustainable product design. [PERSON] et al. [119] presented that  
Figure 8: Product and material life cycle stages [123].  
sustainable product design can be performed through the optimizing of internal lightweight structures. Table 3 demonstrates a summary of the benefits and challenges of sustainable AM process.
# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 3 Improved White Shark Optimisation Algorithm
### Algorithm Flowchart and Steps  
The search flow of the FMWSO algorithm is shown below (Figure 4):  
1. Set the number of great white shark population N and the total number of iterations \(K\), and set the initial temperature \(T_{0}\) and temperature attenuation coefficient \(\alpha\) of simulated annealing, set the number of tempering as 2, initialise the initial position \(w\) and the initial velocity \(v\) of the white shark population by random numbers within the set range, and calculate the fitness value \(\textit{fitness}(x_{0})\) of the white shark population, and set the optimal position \(\textit{best}_{x}\) and the optimal value \(G_{best}\) settings are kept.
2. The great white shark updates the individual velocity, updates its own position \(x_{1}\) by individual behaviour, and computes the fitness function value \(\textit{fitness}(x_{1})\), which is preserved using a greedy strategy.  
3. The great white shark updates position \(x_{1}\) based on the movement of sharks towards the optimal position and the behaviour of the school, performing the calculation of the fitness function value \(\mathit{fitness}(x_{1})\). If \(\mathit{fitness}(x_{1})\) is less than \(\mathit{fitness}(x_{0})\), position \(x_{1}\) is updated, and if \(\mathit{fitness}(x_{1})\) is greater than \(\mathit{fitness}(x_{0})\), it is not immediately discarded and the difference between \(\mathit{fitness}(x_{1})\) and \(\mathit{fitness}(x_{0})\), \(\mathit{\Delta fitness}\), is computed, and by the Metropolis criterion the probability of accepting the new position \(x_{1}\) is compared with a random number uniformly distributed in \([0,1]\), and if the probability is greater than the random number, the position \(x_{1}\) is updated, and vice versa, it is discarded.
4. Determine whether the algorithm reaches the maximum number of iterations, if not, then fast annealing, and determine whether the need for tempering as well as return to search, the algorithm returns (2) to continue the cycle until the algorithm reaches the termination conditions, the output of the optimal value \(G_{\mathit{best}}\).
5. Optimising paths. The output paths are optimised with third-order Bessel curves to make the planned paths smoother and safer.
6. The flow of the improved White Shark algorithm is shown schematically below to search for the minimum value:  
Figure 4: Algorithm flow chart of FMWSO.
## 3 Results and Discussion
### Influence of the Sensor Position  
The results presented herein are based on the AE signals located along the gauge length by the two couples of sensors denoted u80 P1-P2 and u80 P3-P4 on CMC composites. For the whole number of signals, the median values of the descriptors are calculated. Table 2 shows the median value for the sensor located at P1 and for the sensor at P3. For the configuration comprising the sensors placed directly on the specimen on the 25 calculated descriptors, 17 descriptors display a relative difference greater than 25% between the two acquisition configurations. Overall, the values of descriptors related to time (rise time, duration) are higher for the sensor located in the grips while  
Figure 5: Stress-strain curve and the cumulated recorded energy during a tensile test on glass fibres/vinylester matrix monitored with two types of sensors (u80 and pico HF) located at the same place on the gauge length on each face. those descriptors such as energy are lower. A decreasing trend is seen again both for the frequency (Figure 9a). The mean value of the central frequency decreases about 25% and the mean value of the peak frequency decreases about 50%. Peak frequency, which seems to be more relevant with the AU measure, is not suitable to describe the source and could affect the ability to identify damage modes. It is understandable that if a separation distance of 40 mm is responsible for a change in the frequency content more than 25%, one should be very careful in application of any laboratory characterization scheme in a real structure. The effect of the location of the sensor is clearly highlighted here even in the laboratory scale. It can be explained by the nature of the considered acoustic waves, mainly surface waves, probably modified during propagation in the part of the specimen that is affixed to the clamps. This observation emphasizes a little more the importance of understanding the link between the signal received and the source. Figure 6: Frequency centroid versus amplitude for the signals recorded during a tensile test with two types of sensors \(\upmu\)80 and pico HF located on the gauge length at the same place (**a**) Glass fibres/polyamide 6.6 matrix and (**b**) glass fibres/vinylester matrix. Figure 8: Radar chart for the four classes obtained with the two kinds of sensors for the data recorded during tensile tests of glass fibre/Vinylester composites (**a**) \(\upmu\)80 sensor and (**b**) picoHF sensor (class blue: highest rise time, Black class: Highest energy, green class: second class in energy term, red class: the last one class) (E energy, RT rise time, D duration, A amplitude, RA rise angle, AF average frequency and FC frequency centroid). Figure 7: Results of the classification in the plane Frequency Centroid/Amplitude for the data recorded during tensile tests of glass fibre/Vinylester composites (**a**) data recorded with the pico HF sensors and (**b**) with the \(\upmu\)80 sensors. (For the classification, the selected descriptors are Rise time, Duration, amplitude, energy, FP (frequency peak) and FC (frequency centroid)).
## 2 Methodology
### Reviewing state-of-the-art smart city applications  
Researchers and urban planners can identify successful implementations and best practices by studying existing smart city applications. Reviewing existing smart city applications provides a benchmark for assessing the effectiveness and performance of new approaches.  
Studying modern applications allows researchers and developers to evolve their systems and solutions based on the applications' demands and features. Moreover, every city is unique regarding its infrastructure, demographics, and challenges. By studying state-of-the-art applications, city planners can adapt successful models to suit their specific local context, ensuring that smart city solutions are tailored and relevant.
## 4 Measurement Results  
The proposed PA has been implemented in 55-nm CMOS, occupying an area of 1.37 mm \(\times\) 0.66 mm, as shown in Figure 19. Multiple grounding pads are placed to reduce the inductance of the bonding wires. For the measurements, an off-chip LC balun with a 50-50 ohm turn ratio is used at the PA's input to implement the single-ended to differential conversion. All the measurements are conducted at the output of the PA, and the balun loss has been included in the following measured results. Figure 20 shows the large signal CW measurements' setup and the measurements' environments.  
Figure 18: (**a**) Simulated bias impedance with voltage buffer at different bias voltage. (**b**) Simulated bias impedance without voltage buffer at different bias voltage.  
Figure 21 shows the measured small signal gain characteristics in pure HP and LP mode by the static control signal.  
Figure 22 presents the measured single tone (continuous wave, CW) performance of the PA at pure LP and HP modes, demonstrating a significant improvement in PAE and DC current in the pure LP mode. The PA achieves an output P1 dB of 27.6 dBm in pure HP mode with 32.7% max PAE, and 17.7 dBm in pure LP mode with 10% PAE. Notably, in the output power range of 10-15 dBm, the PAE in pure LP mode is 1.5 times that of the pure HP mode. Moreover, the total AM-AM and AM-PM distortions are very low in both modes.  
Figure 19: Die photo and PCB photo.  
Figure 21: Measured S21 small signal gain.  
Figure 20: (**a**) Large-signal CW measurements’ setup. (**b**) Measurements’ environment.  
Figure 23 presents the measured single tone (continuous wave, CW) performance of the PA at the pure LP and HP modes with the same gain to ensure that the power control mode works properly. The results show that in this case, there is still a large improvement in PAE, and the gain difference is within 1 dB.  
Figure 22: Measured CW performance in pure HP and LP mode.  
Figure 23: Measured CW performance with same gain.  
Figure 24 presents the measured CW performance of the PA in power control mode. It can be seen that the DC current is significantly reduced by about 100 mA at low power levels, and the PAE is also improved while maintaining acceptable gain and phase discontinuities.  
Finally, to verify whether the linearity characteristics of the proposed PA are suitable for WLAN system applications, modulation signals are applied. Figure 25 shows the measured performance for an 802.11 n MCS7 signal. The results indicate that even without digital pre-distortion (DPD), the proposed PA meets the linearity requirements of the WLAN 802.11n protocol.  
Figure 24: Measured CW Performance in power control mode.  
Table 4 shows a comprehensive summary of the performance of the presented CMOS PA and compares it with other similar works. In comparison to the state-of-the-art designs, the proposed PA achieves a similar or better performance with a much smaller chip area.