esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Grid Independency Test and Model Validation  
In computational fluid dynamics, the mesh structure is an important prerequisite for numerical computation; too few meshes may not necessarily converge and affect the accuracy of the simulation, while too many meshes may lead to a geometrically increasing computation volume and prolonged computation cycle time, which are not conducive to the normal development of numerical simulation computation in both cases [49]. Therefore, mesh-independent verification is the core verification process to ensure that the simulation results are independent of the mesh resolution. To systematically quantify the influence of the number of grids on the performance of the aerated jet-cleaning model, seven sequentially encrypted grid systems (with cell counts of 339,702, 422,542, 791,713, 1,100,756, 1,637,541, 2,378,512, respectively) were constructed based on the ANSYS Meshing module (ANSYS 2024 R1, Commonwealth of Pennsylvania, USA) with the boundary conditions and geometrical structure parameters unchanged, to evaluate their impact on jet-cleaning-performance prediction. As shown in Figure 6, when the number of grids increases, the maximum impact pressure on the wall surface also increases; when the number of grids reaches 1,100,756, the maximum impact pressure on the wall surface is 1.89 \(\times\) 104 Pa, but with the number of grids continuing to increase, the change is very small. Therefore, in order to save time and costs and ensure the accuracy of the model, the number of grids around 1,100,756 is selected for the mesh division of the jet-cleaning model.  
[PERSON] et al. [47] carried out relevant derivation calculations for the theoretical flow rate of the fan nozzle, and the calculated results were highly consistent with the experimental values. To validate the mathematical model, the CFD-predicted water consumption results were rigorously benchmarked against experimental data from [PERSON] et al. [47]. The basic parameters and operating conditions of the numerical simulation were the same as the experimental settings. As illustrated in Figure 7, the maximum discrepancy between simulated and experimental datasets remains below 5% across all measurement points. It is noteworthy that the simulated water consumption agrees very well with the empirical measurements and the calculation errors are within the permissible limits [29]. This close correlation validates the appropriateness of the selected numerical scheme, mesh topology, and grid resolution density. Consequently, the validated computational modeling provides a reliable basis for parametric optimization studies targeting jet-cleaning efficacy enhancement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of variables & 6 \\ Population size & 200 \\ Maximum number of genetic iterations & 150 \\ The fitness function deviation & \(1.0\times 10^{-4}\) \\ Crossover fraction & 0.85 \\ Mutation rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimized design parameters in the GA.
## 1 Introduction  
The development of nanorobots is a technological breakthrough that can enable real time _in vivo_ prognosis for application in a variety of biomedical problems [1]. Particularly interesting is the fact that medical nanorobots should also provide an effective tool for defense against biohazard contaminants [2-4]. This paper presents the use of nanorobots with embedded protein based nanobiosensors [5], providing a practical molecular machine for medical defense technology.  
Normally, for areas in public calamity or conflict zones, the absence of drinking water, any sort of fuel, electricity, and the lack of towers for network communication, including cable and wireless telephony, is a constant [6]. In such a situation, the available infrastructure is far from ideal to enable a large scale medical laboratory with precise and fast analysis. For such aspect, nanorobots integrated with nanobiosensors can help to transmit real time information, using international mobile phones for wireless data transmission through satellite communication [5,7,8]. In fact, nanorobots should mean an efficient and powerful clinical device to provide precious biomedical monitoring [9], both for soldiers as for civilian population. Therefore, the architecture presented in this work can help to address the development of just in time accurate information, protecting lives in urban areas against biohazard materials.  
The proposed hardware architecture aims the use of medical nanorobots as an integrated platform to control contagious epidemic diseases [1,10]. Details on communication required for surveillance assistance, and the integration platform to interface long distance monitoring with nanorobots are also given through the paper. Thus, the present model serves to help monitoring contagious diseases [11], which in practical ways should protect personnel on patrol across conflict areas or during humanitarian missions. Furthermore, an important and interesting aspect in the proposed architecture is the fact that the same technique can be useful for other situations, like natural catastrophes or possible biohazard contamination [12], helping against pandemic outbreaks [13], when time and fast information is a key factor for public management [14,15].  
To visualize how stages of the actual and in development technologies can be used to biohazard defense, the nanorobots are applied to detect influenza inside body based on blood flow patterns and protein signals [16,17]. The nanorobot architecture and integrated system are described [1,5,18], and the nanobiosensor is simulated based on electrochemical properties for digital-analog sensor activation. Therefore, the work developed is also useful as a practical methodology for control and equipment design analyses.
## 4 SSO, Crowding Distance, and Elite Selection
### Simplified Swarm Optimization  
SSO, developed by [PERSON] in 2009 [18], is a simple but powerful machine leaning algorithm that is a hybrid of leader-solution swarm intelligence and population-based evolutionary computation.  
In traditional SSO, all variables need to be updated (called the all-variable update in SSO) such that the \(j\)th variable of the \(i\)th solution (i.e., \(x_{i,j}\)) is obtained from either the \(j\)th variable of the \(P_{gBest}\) (i.e., \(p_{gBest}\)) with probability \(c_{g}\) of the \(P_{i}\) with probability \(c_{p}\) of its current value (i.e., \(x_{i,j}\)) with probability \(c_{w}\) of a randomly generated feasible new value (say \(x\)) with probability \(c_{r}\), where the \(P_{gBest}\) is the best solution among all existing solutions; \(P_{i}\) is the best \(i\)th solution in its evolutionary history, and \(c_{g}+c_{p}+c_{w}+c_{r}=1\).  
The above update mechanism of SSO is very simple, efficient, and flexible [17; 18; 35; 36; 37; 38; 39; 40], and can be presented as a stepwise-function update:  
\[x_{i,j}=\begin{cases}p_{gBest,ij}&\text{if }\rho_{[0,1]}\in[0,C_{g})\\ p_{i,j}&\text{if }\rho_{[0,1]}\in[C_{g},C_{p})\\ x_{i,j}&\text{if }\rho_{[0,1]}\in[C_{p},C_{w})\\ x&\text{if }\rho_{[0,1]}\in[C_{w},\,1]\end{cases}, \tag{5}\]  
where \(C_{g}=c_{g}\), \(C_{p}=C_{g}+c_{p}\), and \(C_{w}=C_{p}+c_{w}\).  
The SSO pseudo code is provided below [17; 18; 35; 36; 37; 38; 39]:  
**STEP S0.** Generate \(X_{i}\) randomly, find \(gBest\), and let \(t=1\), \(k=1\), and \(P_{i}=X_{i}\) for \(i=1\), \(2\),..., \(\mathbf{N}_{\text{sol}}\).  
**STEP S1.** Update \(X_{k}\).  
**STEP S2.** If \(F(X_{k})\) is better than \(F(P_{k})\), let \(P_{k}=X_{k}\). Otherwise, go to STEP S5.  
**STEP S3.** If \(F(P_{k})\) is better than \(F(P_{gBest})\), let \(gBest=k\).  
**STEP S4.** If \(k<\mathbf{N}_{\text{sol}}\), let \(k=k+1\) and go to STEP S1.  
**STEP S5.** If \(t<\mathbf{N}_{\text{gen}}\), let \(t=t+1\), \(k=1\), and go to STEP S1. Otherwise, halt.  
The stepwise-function update mechanism is very simple and efficient, but it is also very powerful and has various successful applications, e.g., the redundancy allocation problem [35; 36], flexible grid trading [37], the disassembly sequencing problem [38], artificial neural networks [39], power systems [40], energy problems [41; 42], and various other problems [17; 18; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. Moreover, the stepwise-function update mechanism is easier to customize by replacing any item of its stepwise function either with other algorithms [36; 39] or hybrid algorithms [41], in sequence or in parallel [42].
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### Autonomous Ship  
Remotely piloted and even more so autonomous marine vessels are going to revolutionize maritime operations and will be undoubtedly more vulnerable to cyberattacks [66; 67]. Navigation issues and cyber risks should be taken into serious consideration by IMO when creating rules for autonomous shipping. It is expected that the maritime attack surface will continue to expand and autonomous ships will form a prominent piece of the future maritime landscape, underscoring the growing reliance on interconnected information systems [74; 113].  
Figure 1: Automatic Identification System (AIS) spoofing analysis near Elba Island.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 5 Challenges and Future Directions  
This study faced challenges such as the computational complexity of jointly optimizing IRS phase shifts and time allocation, the difficulty of acquiring accurate CSI in dynamic environments, and hardware limitations like finite phase resolution and energy losses in IRS elements. Future research can explore uneven user distributions and dynamic environments to better represent real-world IoT deployments. Advanced optimization techniques, including machine learning-based approaches, could enhance adaptability and efficiency in such scenarios. Furthermore, integrating IRS with emerging technologies, such as massive MIMO, terahertz communication, and AI-driven resource allocation, presents promising opportunities. Exploring hybrid security strategies and optimizing energy harvesting in multi-cluster networks are additional avenues for advancing IRS-assisted networks.  
Figure 8: Sum secrecy throughput versus transmit power at the H-AP.  
Figure 7: Sum secrecy throughput versus the number of IRS elements \(N\).
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 5 Results and Discussion
### Fairness Index  
[PERSON]'s formula [25] as given in Equation (6) was used to determine the fairness index of the transport protocols being studied. The protocols that are understudy are associated at flow-1 on their turn whereas flow-2 to flow-5 are additional flows introduced in the network.  
\[\text{\emph{Fairness Index}}\ =\ \frac{(\sum x_{i})^{2}}{n\ \sum x_{i}{}^{2}} \tag{6}\]  
As shown in Table 4, the fairness indexes of SCTP, Fast TCP, TCP New Reno, and EMFast TCP are 0.9221, 0.9338, 0.9296, and 0.9328, respectively. Native Fast TCP has the best fairness index value among the four transport protocols. Since, EMFast TCP improves overall throughput of its flow, its fairness index is slightly lesser than that of Fast TCP. The behavior of EMFast TCP only deviates from the native Fast TCP behavior after the fast-recovery phase. Therefore, there is a slight variation in its fairness index compare to that of native Fast TCP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{3}{c}{**Throughput (Kbps)**} \\ \cline{2-3}  & **Without Any Competing Flow** & **With Four Competing Flows** \\ \hline SCTP & 1096 & 407 \\ TCP New Reno & 1236 & 397 \\ Fast TCP & 1446 & 430 \\ EMFast TCP & 1454 & 433 \\ \hline \hline \end{tabular}
\end{table}
Table 2: End-to-end throughput of the protocols (average for five tests) [Table S2].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{3}{c}{**Goodput (Kbps)**} \\ \cline{2-3}  & **Without Any Competing Flow** & **With Four Competing Flows** \\ \hline SCTP & 1043 & 387 \\ TCP New Reno & 1183 & 379 \\ MFast TCP & 1389 & 413 \\ EMFast TCP & 1396 & 415 \\ \hline \hline \end{tabular}
\end{table}
Table 3: End-to-end goodput of the protocols (average for five tests) [Table S3].
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### Soundfield Reproduction
#### 3.3.1 Reproduction Methods
Wave-Field Synthesis (WFS) is another well-known sound reproduction technique initially conceived by [PERSON] [110; 111]. The fundamental principle is based on the Kirchhoff-Helmholtz integral to represent a soundfield in the interior of a bounded region of the space by a continuous distribution of monopole and normally oriented dipole secondary sources, arranged on the boundary of that region [112]. An array of equally spaced loudspeakers is used to approximate the continuous distribution of secondary sources. Reproduction artifacts due to the finite size of the array and the spatial discretization of the ideally continuous distribution of secondary sources were investigated [113]. The WFS technique has been mainly implemented in 2D sound reproduction using linear and planar arrays [114; 115], for which a 2.5D operator was proposed to replace the secondary line sources by point sources (known as 2.5D WFS) [112; 116; 117]. In WFS, a large number of closely spaced loudspeakers is necessary. For arbitrarily placed loudspeakers, a simple approach in sound reproduction known as inverse filtering is to use multiple microphones as matching points based on the least-square match to derive the loudspeaker weights [118], with the knowledge of the acoustic channel between the loudspeakers and matching points, i.e., the RTF. Tikhonov regularization is the common method for obtaining loudspeaker weights with limited energy and also for improving the system robustness. In addition, the fluctuation of the RTF requires an accurate online estimation of the inverse filter coefficients in this method [119]. Table 1 summarises the above mentioned soundfield reproduction techniques. A comparison of different soundfield reproduction techniques especially from the perception point of view was presented in the review paper [120]. One of the current research interests is to further improve these techniques with a thorough perceptual assessment [121].
## 4 Experiments
### Comparative Results
#### 4.4.2 Results on the Hongqi Canal Dataset  
As presented in Table 2, the proposed MSGATN achieve the overall superiority over the Hongqi Canal dataset compared to other selected CD methods. That is, our method outperforms the other methods in all evaluation indicators, i.e., precision, recall, \(F1\), and \(OA\), with a great gap. More exactly, the best precision (80.96%) and recall (57.17%) are achieved by the proposed MSGATN, which leads to the best F1 (67.02%) for our method. It indicates that the proposed MSGATN can capture finer complete land cover and acquire better mapping for changed multi-scale objects with the help of input graphs with varied scales, and similar conclusions can be discovered in the visualized CD results depicted in Figure 5. Given the fact that the annotation of the Hongqi Canal dataset mainly focuses on the river change, massive false alarms can be found in the CMs generated by other methods. These false alarms are basically caused by the unchanged farmland around the canal. However, they are well filtered out in the proposed MSGATN, which can be attributed to the finer feature representation of our method. As a result, the river course change in the Hongqi Canal dataset is well denoted by the proposed MSGATN, which suggests the advantage of our method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **\(F1\)** & \(OA\) \\ \hline PCA\_K-means & 97.33 & 78.22 & 86.74 & 96.43 \\ ASEA & 95.45 & 79.73 & 86.88 & 96.40 \\ GaborPCANet & 51.61 & **94.98** & 66.88 & 85.94 \\ DBN & **98.05** & 78.51 & 87.20 & 96.55 \\ CWNN & 42.19 & 87.17 & 56.86 & 80.23 \\ Proposed MSGATN & 91.04 & 90.04 & **90.54** & **97.19** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy comparison (in %) of different methods on the Guangzhou City dataset. The best evaluation value are presented in bold for different metrics.  
Figure 5: The results of different methods on the Hongqi Canal dataset: (**a**) PCA\_K-means, (**b**) ASEA, (**c**) GaborPCANet, (**d**) DBN, (**e**) CWNN, and (**f**) proposed MSGATN.
## 4 Unit Cell Simulations and Experimental Results
This strengthened our decision to define two different resistance components, namely, \(R_{S}\) and \(R_{P}\), separately. The \(R_{P}\) resistance component depends on the production, frequency, unit cell structure, and MS panel. These two resistance values were extracted as a sum of \(R_{S}+R_{P}\) in the measurements, making it difficult to determine their values separately. The reflection simulation results compared to the measurement results for \(f_{res}\) around the operating frequencies of 11 GHz, 12 GHz, and 13.5 GHz are shown in Figure 11.  
The unit cell reflection measurement results for the varactor's reverse bias voltages of 2.5 \(V\), 4 \(V\), and 6 \(V\) were compared to the simulation results in solid lines for Cd capacitance values of 0.09 pF, 0.069 pF, and 0.052 pF, respectively. The simulation values of \(R_{S}+R_{P}\) were 17 \(\Omega\), 14.8 \(\Omega\), and 13.6 \(\Omega\) for \(f_{res}\) around 11 GHz, 12 GHz, and 13.5 GHz, respectively. The measurement results showed good agreement with the simulation results. As \(C_{d}\) decreased, the absorption became smaller and wider, and the reflection phase changed around the \(f_{res}\) become moderated, as shown in Figures 10 and 11. These changes resulted  
Figure 11: Unit cell reflection measurement and simulation results in continuous lines and dashed lines, respectively: (**a**) reflection magnitude and (**b**) reflection phase. Figure 10: Unit cell reflection measurement and simulation results in continuous lines and dashed lines, respectively: (**a**) reflection magnitude and (**b**) reflection phase. from increasing the bandwidth and decreasing \(Q\) according to Equations (4) and (5). The absorption intensity of the resonance at the working frequency represented the maximum absorption for the working frequency. Therefore, an increase in the working frequencies reduced the losses, as shown in Figure 11a.
## 1 Introduction
### Aircraft Structures and Defect Locations  
Aircraft structures are engineered to withstand high aerodynamic loads, temperature variations, and operational stress. The primary structural components, as illustrated in Figure 1, include:  
* Fuselage: The main body of the aircraft, housing passengers, crew, and cargo.
* Wings: Provide lift and house fuel tanks and control surfaces.
* Empennage (Tail Section): Ensures stability and control through the horizontal and vertical stabilisers.
* Landing Gear: Supports the aircraft during takeoff, landing, and taxiing.
* Jet Engines: Generate thrust for propulsion.  
Over time, these structures are exposed to various mechanical, environmental, and operational stressors, leading to material degradation and defects.  
Aircraft materials are primarily metallic (e.g., aluminium, titanium) and composite (e.g., carbon fibre-reinforced polymers, honeycomb structures). As shown in Figure 1, each material type is prone to specific defects based on its composition and operational function.  
* Metallic Defects
* Fuselage: Fatigue cracks, corrosion, pores, and material inclusions.
* Doors and Structural Joints: Welding issues, foreign inclusions, and adhesive failures.
* Landing Gear: Overload, wear, and creep due to repeated impact forces.
* Composite Defects
* Wings: Delamination, fibre/matrix cracking, and impact damage.
* Tail (Vertical/Horizontal Stabilisers): Honeycomb cell wall damage, core crushing, and skin-to-core debonding.
* Jet Engines: Thermal stress cracking due to high-temperature exposure.  
The early detection of these defects is critical to maintaining aircraft safety, performance, and lifespan.  
Effective maintenance strategies are essential, given the financial impact of aircraft downtime and structural repairs. Non-Destructive Testing (NDT) and AI-based inspection techniques are increasingly being adopted to reduce operational costs, optimise maintenance schedules, and enhance defect detection accuracy.
## 4 Experimental Analysis
### Reconstruction Ability Index  
It should be noted that the classification model does not belong to the main work of this study. Different models have different effects. Therefore, in order to reduce the impact of the classifier on the reconstruction accuracy, we take the classification results of the original network traffic as a baseline and calculate the ratio of the classification results of the reconstructed data with the original data. This ratio is called \"Reconstruction Ability Index\" (RAI), which forms an evaluation index to represent the reconstruction ability, as shown in Formula (4):  
\[\left\{\begin{array}{l}FT_{i}=2\times\frac{PT\times RT}{PT+RT}\\ FO_{i}=2\times\frac{PO\times RO}{PO+RO}\\ RAI=\frac{1}{n}\sum_{i=1}^{n}\frac{FT_{i}}{FO_{i}}\end{array}\right. \tag{4}\]  
\(FT\), \(PT\), and \(RT\) represent the \(F1\)-score, _Precision_, and _Recall_ of classification on the reconstructed data, respectively; \(FO\), \(PO\), and \(RO\) represent the \(F1\)-score, _Precision_, and _Recall_ of classification on the original data, respectively; \(i\) represents the number of network traffic categories, and \(n\) represents the total number of traffic categories; \(RAI\) is the evaluation index of reconstruction ability.
## 1 Introduction
### Goal and Arrangement of This Paper  
In this study, the size of heartbeats divided from the ECG is not equal and thus unsuitable for SPP-net training. To avoid the network switching, a new SPP-net based CNNs model has been constructed in this paper for the heartbeats classification. This model retains the advantages of the SPP and allows different-sized heartbeats to be sent to the same network for training, thus reducing the complexity of the network. This approach also avoids the complexity of data reconstruction during feature extraction and classification. The SPP structure is employed for the classification of heartbeats, and such a structure can guarantee heartbeat signals with different heartbeat durations, and it also enables the adaption of the CNN structure without cropping or warping the original heartbeat signal. In addition, the input of the SPP structure is simplified into one-dimension (1-D), which is suitable for the heartbeat classification with less of a computational burden.  
Additionally, due to the non-stationary nature of the ECG signal, frequency domain filters may distort a transient interval of the signal and important biomedical information may get lost [21; 22; 23]. However, a wavelet is simply a small wave, which enables the analyzing of the transient, non-stationary or time-varying signals easily [24]. Moreover, due to the sparsity, locality, and multi-resolution nature [25] of the WT, WT is therefore employed as the pre-processing method for the ECG signal. The rest of the paper is arranged as follows: Section 2 introduces the methods and procedure adopted in this study, including the SPP, ECG-SPP-net, pre-processing of input data to the ECG-SPP-net, feature extraction, and classification. To validate the performance of the proposed method, the accuracies of different network structures are analyzed in Section 3, and a conclusion is finally provided in Section 4.