esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 4 Seismic Capability Index System of Highway Tunnels  
The earthquake disaster resistance of highway tunnels refers to the ability of highway tunnels to absorb and resist disasters and to control damage and accidents quickly and effectively when they are subjected to earthquake disasters. It is related to the geological conditions, tunnel structures, protective measures, maintenance and management of highway tunnels [29]. Based on the influencing factors identified by highway tunnel seismic damage accidents, this paper determines the important influencing factors of the earthquake disaster resistance capability of highway tunnels in combination with the relevant norms and literature [30; 31; 32] and then constructs a seismic capability index system for highway tunnels. The established index system should follow the scientific principle, relative completeness principle, hierarchy principle, independence principle and quantitative and qualitative combination principle.
## 2 Signal Model
The local reference signal for stretching operation can be written as:  
\[s_{LO}(t)=\text{rect}\left(\frac{t}{\hat{T}}\right)e^{-j\left(2\pi f_{c}t+\pi \gamma t^{2}\right)}, \tag{4}\]  
where \(\hat{T}\) is the duration of the reference signal, which is usually slightly larger than the width of the transmitted pulse, so that it covers the received signal. The stretched signal is obtained by multiplication:  
\[ \begin{split} s_{st}(t,t_{m})&=s_{ur}(t,t_{m}) \cdot s_{LO}(t)\\ &=\sum_{p=0}^{P-1}\text{rect}\left(\frac{t-\tau_{p}(t_{m})}{T} \right)A_{p}\text{e}^{-j2\pi\gamma\tau_{p}(t_{m})t}\\ &\quad\times e^{j\pi\gamma\tau_{p}^{2}(t_{m})}e^{-j2\pi f_{c}\tau _{p}(t_{m})}+\omega_{1}(t,t_{m})\\ &=s_{st,s}(t,t_{m})+\omega_{1}(t,t_{m}),\end{split}  \tag{5}\]  
where \(s_{st,s}(t,t_{m})\) is the signal term of the \(s_{st}(t,t_{m})\) and \(\omega_{1}(t,t_{m})=\omega_{0}(t,t_{m})\cdot s_{LO}(t)\) is the noise. In practical wideband radar systems, the sampling frequency of the stretched signal is much lower than that of the UR signal. Thus, the data size of the stretched signal is much smaller than that of the UR signal. When parameter estimation is performed on the stretched signal, the computational complexity decreases significantly. The proposed motion parameters estimation method can be applied to both UR and stretched signals. In the following section, the idea of \"stretching\" processing is first illustrated to show the availability of the CCAE method to the UR signal, then the algorithm of the proposed CCAE estimation method is derived using the stretched signal model.
## 3 Experiments and Analysis
### Comparison with Different Models
Table 9 shows the \(P_{macro}\), \(R_{macro}\), \(F1_{macro}\), parameters, modeling time, and training time for the four network models. As can be seen in Table 9, after 50 iterations the proposed model with GAN yielded the highest \(P_{macro}\), \(R_{macro}\), and \(F1_{macro}\) indicating that the detection results of the proposed model were accurate and comprehensive. Compared to Vgg11, the parameters of the proposed model with GAN were reduced from 128.80 M to 5.76 M, the modeling time was reduced from 1.72 h to 0.34 h, the training time was reduced from 0.73 h to 0.32 h, and the \(F1_{macro}\) increased from 97.06% to 98.40%. It can be seen that the proposed model is superior to Vgg11 network in the above indicators. Compared with the proposed model without GAN, the proposed model with GAN was 0.14 h and 0.15 h slower in modeling time and training time, respectively. However, as the GAN network was fully trained with data, the \(F1_{macro}\) for the proposed model with GAN increased from 95.36% to 98.40%. Therefore, this model discarded some time costs in exchange for greater accuracy, which is often considered desirable. To sum up, the proposed model with GAN can effectively compress the network and remove some redundant information while improving the network performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Macro-Precision (\%)** & **Macro-Recall (\%)** & **Macro-F1 Score (\%)** & **Parameters (M)** & **Modeling Time (h)** & **Training TIME (h)** \\ \hline Proposed model & 95.65 & 95.47 & 95.36 & **5.76** & **0.20** & **0.17** \\ Without GAN & **98.46** & **98.40** & **98.40** & **5.76** & 0.34 & 0.32 \\ With GAN & 97.64 & 97.07 & 97.06 & 128.80 & 1.72 & 0.73 \\ Vgg16 & 96.55 & 96.27 & 96.26 & 134.30 & 2.24 & 1.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of model performance.
## 5 Experiment and Result Analysis
### Contributions  
In our endeavor to predict the content of rare earth elements, a meticulous comparison was carried out relative to extant literature. To illustrate, in [9; 10] the authors solely utilized the color features of rare earth solution images, focusing on the first-order moments of the H, S, and I components. Conversely, in [11] the authors incorporated a slightly broader scope, extracting ten color features from the images, which included RGB, HIS, RR, RG, RB, and CVA, among others. Nevertheless, these singular feature extraction methodologies exhibit pronounced limitations, often inadequately representing the complete information spectrum present in the images. This is evident in their Root Mean Square Error (RMSE) values of 1.7062, 0.6358, and 1.1480 respectively. In stark contrast, our proposed method yielded a significantly reduced RMSE of 0.4441, underscoring its superiority.  
To overcome this limitation, our study proposes a multi-feature extraction method that includes color features, spatial texture features, and key features. This approach enables us to capture more diverse information, significantly improving the predictive performance of the model. Additionally, we introduced a model combining one-dimensional convolutional neural networks with residual attention blocks (MRAB-CNN) to better utilize the extracted multi-feature information. Our experimental results demonstrate that the MRAB-CNN model exhibits outstanding performance in predicting rare earth element content, and the size of model is just 3.3 MB. This research provides robust support for real-time and rapid content analysis in rare earth extraction processes, offering a more accurate and efficient solution built upon the foundation of the existing literature.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 8 Performance Evaluation and Simulation
### Throughput  
It is fruitful to transmit data from SN to the SN or sink/coordinator node in a given time. More throughputs per unit time are considered a good feature for any kind of WSN, especially WBANs. It is the successful delivery of packets in data transmission. Throughput is one of the most important parameters while judging any scheme or protocol for data transmission. High throughput means better performance. The results in Table 7 and Figure 10 show the success of EHCRP in this aspect. At the initial stage of the throughput simulation, the fluctuations among the compared protocols were minimum but after the 7500 th round, it increased with a distinct margin. The reason is that at round 7500 the throughput of EHCRP becomes more than double (6.7) compared to the 5000 th round, which was 3. Overall the proposed protocol achieved \(10.7\times 10^{4}\) in 20,000 s, whereas its counterparts, did not exceeded \(4.7\times 10^{4}\), except for E-HARP which was the closest and achieved \(9.6\times 10^{4}\) throughput. The main reason behind the better performance of EHCRP in terms of throughput is that it considers different parameters of the communication link before the transmission, such as SNR, link quality, etc. This ultimately avoids the loss of data packets during transmission.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Protocol** \\ **Name** \\ \end{tabular} } & \multicolumn{8}{c}{**Residual Energy (J)**} \\ \cline{2-10}  & **0** & **2500** & **5000** & **7500** & **10,000** & **12,500** & **15,000** & **17,500** & **20,000** \\ \hline
\begin{tabular}{c} EHCRP \\ (Proposed) \\ \end{tabular} & 7 & 6.5 & 6 & 5 & 4.2 & 3 & 2.3 & 1.5 & 0.5 \\ E-HARP & 7 & 6 & 4.8 & 4.2 & 3.2 & 2.6 & 1.4 & 0.3 & 0 \\ PCRP & 8 & 6.5 & 4.8 & 3 & 0.8 & 0 & - & - & - \\ EERP & 4 & 1.8 & 0.8 & 0 & - & - & - & - & - \\ EB-MADM & 4 & 2.5 & 1.2 & 0.5 & 0.2 & 0 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Residual Energy vs. Time (s).  
Figure 9: Breakdown of Energy Consumption.
## 4 Discussion
### Computational Limitations and Future Directions  
Although the proposed LKF-DCANet demonstrates strong performance, fast inference speed, and good generalization ability in small-scale, high-resolution UAV remote sensing scenarios, its effectiveness declines in certain complex real-world conditions. For example, in ultra-high-resolution satellite imagery such as Sentinel-2 (10 m per pixel), detecting narrow water bodies--such as irrigation canals and drainage ditches spanning only 1-3 pixels--remains challenging due to insufficient spatial detail. In addition, on the GLH-water dataset, the model achieves an IoU of 85.95%, which is not extremely high but reflects a reasonable trade-off under the lightweight design constraints. Architectural simplifications--such as reduced channel dimensions and a shallower encoder--were intentionally introduced to minimize computational complexity, which inevitably leads to some performance gap compared to heavier models. Moreover, physical environments such as tree-shaded rivers, sediment-laden ponds, or temporarily dried-up ditches can cause severe spectral ambiguity, often resulting in missed detections or blurred boundaries.  
To evaluate the computational feasibility of LKF-DCANet in real-world satellite applications, we tested its performance on a full Sentinel-2 scene (12,800 \(\times\) 12,800 pixels) using an NVIDIA RTX 4090 GPU. The image was divided into 1024 \(\times\) 1024 patches with a 128-pixel overlap to ensure seamless reconstruction and reduce edge artifacts. Each patch requires 71.38 GFLOPs, and the inference time per patch is approximately 30-40 ms. Processing the entire scene involves about 200 overlapping patches, followed by probabilistic stitching and boundary refinement during post-processing. As a result, the end-to-end processing time for a full Sentinel-2 scene is approximately 10-12 min, including GPU computation, memory I/O, and reconstruction. These findings highlight the practical limitations of deploying the model on ultra-high-resolution imagery, where data volume and memory overhead become significant despite its lightweight design (only 0.22 M parameters).
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication
### Underwater Acoustic Communication
#### 3.2.1 Scheduling Modes  
Since aPads are on the surface and thus easily accessible via wireless communication, they act as masters in acoustic communication by initiating transitions between the FSM states-communication modes. There are four modes of communication: Setup, Default, Priority, and Localisation.  
When working with underwater acoustic networks in a real-world experimental environment, the deployment of the system is equally as important as the monitoring phase. None of the sensors should start broadcasting data over the acoustic channel while the system is still in the deployment state, nor should they broadcast if changes are made to the configuration of the sensor network. The first mode of communication addresses this issue by silencing all scheduled communication. In Setup mode, only surface vehicles are allowed to broadcast data or commands. No time scheduling is implemented, and it is the operator's task not to send commands over the acoustic channel from multiple surface vehicles at the same time. None of the underwater vehicles may initiate communication, but they can respond to requests issued by surface vehicles. This mode achieves the fastest response time from underwater vehicles, which can be useful for issuing urgent commands or reconfiguring the system parameters of the swarm.  
In the Default mode, time scheduling is configured using the widely-used round-robin principle [18]. Time slots are assigned equally among all agents in a circular order, handling communication without priority as can be seen from Figure 13a. This is the main operating communication mode. Each of the agents in the swarm has an equal amount of time for data sharing, which is well suited to periodically reporting a desired measured environmental variable, for example, monitoring the value of water temperature in the area of deployment.  
Figure 12: State machine representation of modes of communication.  
During mission execution in a real-world environment, it is sometimes desirable for surface agents to issue commands to the underwater agents. A Priority mode was added, in which time slots alternate between each distinctive group of agents by using the round-robin approach, meaning time slots of all aPads and time slots of all aMussels alternate. Within the time slots of each respective group, each individual agent is again scheduled via the round-robin approach. An example of time slot order in this communication mode can be seen in Figure 12(b).  
Finally, in the Localisation mode all underwater vehicles are silent and are listening for localisation messages sent from the surface vehicles, i.e., anchors. Time scheduling is implemented using the round-robin principle and is shown in Figure 12(c).
## 4 Discussion
In this study, the different pipeline designs were presented for the sake of completeness and a direct comparison of the present results with studies already published. Pipelines 1, 2, and 3 were applied to a synthetic dataset, obtaining ROC AUC scores of 0.89, 0.89, and 0.78, respectively. Notably better results were observed using Pipeline 1 and Pipeline 2. This enforced the results obtained on the real radiomic dataset, confirming the potential cross-contamination problem in general radiomic studies. The same behavior observed on a synthetic dataset additionally validated the authors' hypothesis, confirming that cross-contamination is a methodology problem. Pipelines 1, 2, and 3 identified the same set of features as the most relevant. It was observed that the features extracted from the VOIs (11 out of 15) were prominent, with a balancing between the portal (6) and the arterial (5) phases, depicting the importance of multi-phase CECT acquisition, which allowed considering both the arterial and the portal veins for the evaluation of the MVI lesions. This result confirmed the hypothesis proposed regarding the stronger predictive value of multi-phase analysis. The pipelines selected 4 out of 15 features from the ZOT volumes; however, their coefficient relevance suggested that the tumor penumbra could play a valuable role in MVI classification. This result was in agreement with the biomedical evidence of possible MVI presence in ZOT volumes.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
###### Abstract  
Nanofibrous membranes have gained interest for their small pore size, light weight, and excellent filtration. When produced from high-performance polymers, nanofibrous membranes also benefit from excellent mechanical properties, thermal resistance, and chemical resistance. Electrospinning is a common method of producing high-performance nanofibres. However, there are still major challenges with the dissolution and electrospinningning of these polymers, as well as in the performance of the resulting nanofibres, which is often less than what would be expected from a conventional high-performance fibre. This review assesses the state of progress in the electrospinning of five high-performance fibres: meta-aramid (m-aramid), para-aramid (p-aramid), polyamide-imide (PAI), polybenzoxazole (PBO), and polybenzimidazole (PBI). Polymers that can be readily dissolved in organic solvents, such as m-aramid, PAI, and PBI, have been more widely researched for electrospinning compared to those that can only be spun from precursors or dissolved in non-volatile solvents. Major focuses within the literature include optimizing the electrospinning process and improving the mechanical performance of the nanofibres. This review demonstrates a clear need for more standardized characterization methods and consideration for the longevity of the nanofibrous membranes. Future research should also focus on scale-up methods of electrospinningning so that the benefits of nanofibres made from high-performance polymers can be leveraged by the industry.  
electrospinning; high-performance fibres; nanofibre
# Magnetooptics in Cylindrical Structures
## Appendix A Relations among Amplitudes  
Relations among the amplitudes in isotropic cylindrical media [30] defined in Equation (13) for the fields proportional to the factor \(\mathrm{e}^{\mathrm{j}(\
u\varphi-\beta_{0}z)}\) follow from the Maxwell Equation (4) reduced to the scalar \(\varepsilon_{0}^{(i)}\) and \(\mu_{0}^{(i)}\)  
\[\left(\begin{array}{cccccc}-\omega\varepsilon_{0}^{(i)}&0&0&\mathrm{j} \beta_{0}&0&2^{-1/2}\kappa_{0}^{(i)}\\ 0&\omega\varepsilon_{0}^{(i)}&0&0&\mathrm{j}\beta_{0}&-2^{-1/2}\kappa_{0}^{(i) }\\ 0&0&\omega\varepsilon_{0}^{(i)}&2^{-1/2}\kappa_{0}^{(i)}&2^{-1/2}\kappa_{0}^{(i )}&0\\ \mathrm{j}\beta_{0}&0&2^{-1/2}\kappa_{0}^{(i)}&\omega\mu_{0}^{(i)}&0&0\\ 0&-\mathrm{j}\beta_{0}&2^{-1/2}\kappa_{0}^{(i)}&0&\omega\mu_{0}^{(i)}&0\\ 2^{-1/2}\kappa_{0}^{(i)}&2^{-1/2}\kappa_{0}^{(i)}&0&0&-\omega\mu_{0}^{(i)}\\ \end{array}\right)\left(\begin{array}{c}A_{\
u+}^{(i)}\\ A_{\
u-}^{(i)}\\ A_{\
u-}^{(i)}\\ B_{\
u+}^{(i)}\\ B_{\
u-}^{(i)}\\ B_{\
u-}^{(i)}\\ \end{array}\right)=0.\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(x\) & **0.2** & **0.4** & **0.6** & **0.8** & **1.0** & **1.2** & **1.4** \\ \hline \(\Delta\lambda\kappa_{0}\times 10^{3}\) & 1.98134 & 1.98262 & 1.98182 & 1.98028 & 1.97902 & 1.97756 & 1.97642 \\ \(\Delta N\times 10^{3}\) & 2.76591 & 2.58059 & 2.41696 & 2.25978 & 2.14032 & 2.05772 & 1.95713 \\ \(d\)\(\left[\upmu\right]\) & 140.099 & 150.159 & 160.325 & 171.477 & 181.048 & 188.315 & 197.994 \\ \hline \end{tabular}
\end{table}
Table 3: Effect of diamagnetic substitution, \(x\), in yttrium iron garnet \(\mathrm{Y_{3}Fe_{5-x}Ga_{x}O_{12}}\) on the difference in effective guide indices in a waveguide with nonmagnetic cladding, \(\Delta N_{co}\), on the difference in effective guide indices in the waveguide with magnetic cladding, \(\Delta N\), and on the distance, \(d\), required for the phase shift \(\pi/4\).
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Critical Latency Estimation and Error Analysis Results
#### 4.2.2 Error Analysis of Critical Latency Estimates  
Figure 10 shows the estimation errors of critical latency for four scenarios. In Figure 10a, the errors with vessel speed changes are shown to increase as the vessel speed increases, while in Figure 10b, the errors with rudder angle changes decrease or become similar values as the rudder angle increases.  
In four scenarios, the error appears to occur within 8 m. This value is small enough to correspond to one sample when a measurement error value of 7.72 m (0.5 \(\times\) 15.43) is considered, which can result from the sampling time (0.5 s) applied to the OS trajectory measurement and the maximum vessel speed (15.43 m/s) given. As a result, the critical latency estimating method proposed in this study was verified to be partially effective.
## 2 Experimental Section
### Experimental Setup  
#### 2.2.1 Fractionated Condensation with CO\({}_{2}\) Stripping Gas Experiments  
The experimental setup, presented in Figure 1, was built to recover aromas by CO\({}_{2}\) stripping without the assistance of a membrane, followed by fractionated condensation. This setup consists of a feed vessel with CO\({}_{2}\) supersaturation by the injection of gas inside the model solution, which aims to mimic the wine fermentation vessel. The vapour stream was produced in the same way as in the integrated vapour permeation-fractionated condensation. All the tubing was stainless steel, and the fractionated condensation approach included two U-shaped glass trap condensers in series. The first condenser was refrigerated using an FP 500-MC (Julabo, Seelbach, Germany), and liquid nitrogen was used to submerge the second condenser.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**(A)** & **Aroma Compound** & **Family of Chemicals** & **Reason to Include the Aroma in the Model Solution** & **Concentration of the Model Solution** \\ \hline Ethyl acetate & & & & 50 ppm \\ \hline Ethyl hexanoate & Esters & Esters are the most abundant aromas produced by wine yeasts. Ethyl acetate, isoamyl acetate and ethyl hexanoate are considered the main component of a fruit flavour [17] & 8 ppm \\ \hline
**Isoamyl acetate** & & & & 6 ppm \\ \hline
**4-ethylphenol** & Phenols & When volatile phenols reach limiting concentrations \({}^{\star}\), they affect the quality of the wine leading to aroma defects normally described as “horse sweat”, “animal” and “medicinal” [6] & 0.6 ppm \\ \hline \hline \end{tabular}  
* The odour threshold is 440 ppb for 4-EP and 33 ppb for 4-EG [18].  
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**(B)** & **Compound** & **MW (g/mol)** & **BP (\({}^{\circ}\)C)** & **p\({}^{\text{v}}\)\({}_{25\ ^{\circ}C}\) (Pa)** & **\({}^{\text{v}}\)\({}^{\text{o}}\)\({}_{25\ ^{\circ}C}\), water** & **Y\({}^{\text{o}}\)\({}_{25\ ^{\circ}C}\), 10\% Etoth** \\ \hline Ethyl acetate & 88.11 & 77.10 & 12,622.12 & 50 & 37 \\ Ethyl hexanoate & 144.21 & 167.00 & 49,898.73 & 12,615 & 9014 \\ Isoamyl acetate & 130.18 & 142.50 & 1,470,959.90 & 3865 & 2280 \\
4-ethyl guaicaol & 152.19 & 236.50 & 7.56 & 8383 & * \\
4-ethyl phenol & 122.16 & 217.90 & 33.19 & 23,742 & * \\ \hline \hline \end{tabular}  
* (MW = molecular weight, BP = boiling point, pvi = saturation vapour pressure, \(\gamma\infty\) = activity coefficient at infinite dilution).
* For ethyl phenols, the activity coefficient of the aromas in a mixture with 10% ethanol was not calculated due to the lack of information about the activity coefficients at 70 \({}^{\circ}\)C in water  
\end{table}
Table 1: Composition of the model solution of red wine: (**A**) characterisation of the selected red wine aroma compounds and (**B**) the physicochemical properties of the aroma compounds.
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Algorithm
#### 3.2.1 Case 1  
If the accumulating requests are more than the initially available slots and less than the maximum limit of 255, then slots are allocated to all data-requesting nodes in that session by increasing the extra slots in that session in such a way that the slots allocation to more sensitive patients' data is prioritized over less sensitive data.