esa-sceva/satcom-chunk-collection huggingface dataset:
# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 6 Simulation Results and Discussion
### Qualitative Assessments
#### 6.1.8 Computational Complexity  
The computational efficiency of the DL algorithms and high-performance computers is usually measured in giga floating point operations per second (GFLOPs) for real-time utilization. The algorithm, which has high GFLOPs, requires GPUs for the efficient detection of concealed weapons. For instance, the tiny YOLOv3 model has a computational complexity of 19.0 GFLOPs to detect the concealed weapons, as shown in Figure 19. Hence, the tiny YOLOv3 model requires fewer computational resources than the other models. However, the tiny YOLOv3 model fails to detect concealed guns in many cases, as the tiny YOLOv3 model achieves a precision of up to 87.3%. Therefore, the unconvincing performance of the tiny YOLOv3 model again poses a limitation to its use in a real-time concealed weapon detection system. The complete quantitative analysis of the tiny YOLOv3 model is presented in Tables 3 and 4 of the manuscript.  
Moreover, lightweight models are usually incapable of extracting sufficient contextual information from grayscale noise-perturbed images. Whereas, the images captured by the  
Figure 17: Precision-recall curves of tiny YOLOv3 and proposed FER-YOLOv8 models. (**a**) Tiny YOLOv3 PR curves. (**b**) Proposed FER-YOLOv8 PR curves.  
Figure 18: Comparison of precision–recall curves of various YOLO models. (**a**) YOLOv8n PR curves. (**b**) YOLOv8n-WF PR curves. (**c**) YOLOv8m PR curves. (**d**) YOLOv8m-WF PR curves.  
PMMW imaging systems suffer intensely from high noise and low resolution, which makes lightweight models impractical for the task of the detection of small concealed objects.  
On the other hand, the YOLOv8m-WF model requires 79.1 GFLOPs to achieve a precision of up to 92.9% while detecting the hidden guns. Hence, the YOLOv8m-WF model surpasses the tiny YOLOv3 model on account of increased model complexity. This paper successfully proposes a new BWFER-YOLOv8 model, which surpasses the YOLOv8m-WF model by achieving a precision of 98.1% while reducing the GFLOPs to 78.0. The proposed BWFER-YOLOv8 model also improves the other evaluation metrics, as discussed above, without increasing the GFLOPs compared to YOLOv8-WF. The extensive experiments demonstrate that the accuracy of concealed weapon detection systems can be compromised by using low-complexity models. The proposed work recommends that a trade-off between the complexity of the model and the efficiency of the concealed weapon detection system is the best solution to address life-threatening scenarios in real-time surveillance systems.
## 3 Results and Discussion
### Lifetime Prediction Methods
The modulus can be used to assess the strain-hardening behavior of material after being subjected to deformation, as well as its ability to deform under complex loading. The strain-hardening modulus is expressed by the individual strain-hardening coefficient \(Gp\)[28], as shown in the Equation (2). \[Gp=\frac{1}{N}\sum_{i=1}^{N}\frac{\sigma_{i+1}-\sigma_{i}}{\lambda_{i+1}- \lambda_{i}} \tag{2}\]  
where \(N\) is the number of measurement points in the strain-hardening interval (usually stretching ratio 8 to 12); \(\lambda\) is the stretching ratio of the specimen, i.e., the ratio of the stretching length between the specimen's standard moments recorded at a certain moment in the test to the specimen's initial pitch; \(\sigma\) is the true stress that the specimen undergoes during the stretching process. The tensile ratio \(\lambda\) is calculated from the specimen breaking length \(l\) (mm) and the specification length \(l_{0}\) (mm), as shown in Equation (3). \[\lambda=\frac{l}{l_{0}}=1+\frac{\Delta l}{l_{0}} \tag{3}\]  
where \(\Delta l\) is the increase of the specimen length between the gauge marks (mm). The true stress \(\sigma_{true}\) (MPa) is calculated according to Equation (4), assuming volume conservation between the gauge marks. \[\sigma_{ture}=\lambda\cdot\frac{F}{A} \tag{4}\]  
where \(F\) is the measured force (\(N\)) and \(A\) is the product of the initial width and thickness (mm\({}^{2}\)). The relationship between failure time and strain-hardening modulus obtained by [PERSON] et al. is shown in Equation (5). \[t_{f}=1.7\times 10^{0.046G_{p}} \tag{5}\]  
Before calculating the specimen stretching ratio, the length \(l\) of the specimen at break needs to be known. The elongation at break of the specimen is known during the tensile test, and then the length \(l\) of the specimen at break can be obtained according to the calculation of elongation at break, as in Equation (6). \[e=(l-l_{0})/l_{0} \tag{6}\]where \(e\) is the elongation at break, \(l_{0}\) (mm) is the original length of the specimen, and \(l\) (mm) is the length of the specimen when it is pulled off.
## 2 Materials and Methods
### Diclofenac Sodium Adsorption Studies  
To examine the diclofenac sodium adsorption abilities of synthesized MIL-88A samples, a sequence of adsorption experiments was performed. Portions of 25 mg of each material were introduced into the drug solutions with a concentration range of 100-3000 mg/L. Next, the samples were continuously agitated for 2 h in the orbital shaker (KS 4000i control, IKA, Staufen, Germany) at a constant temperature (25 \(\pm\) 1 \({}^{\circ}\)C) and shaking rate of 250 rpm. The residual concentration of the drug solution was determined by an Agilent Cary 60 UV-Vis spectrophotometer (Santa Clara, CA, USA) at a wavelength of 276 nm. The sorption capacities (\(Q_{e}\)) of the MIL-88A were calculated using the following formula:  
\[Q_{e}=\frac{C_{0}-C_{e}}{m}. \tag{1}\]  
To describe the MIL-88A carrier-diclofenac sodium interactions, the adsorption data were fitted to non-linear Langmuir and Freundlich adsorption models. The determination of the adsorption mechanism was based on the correlation coefficients (R\({}^{2}\)) of the models.  
The non-linear Langmuir isotherm is described by Equation (2):  
\[Q_{e}=Q_{m}K_{L}\frac{C_{e}}{1+K_{L}C_{e}} \tag{2}\]  
where \(Q_{m}\) is the maximum adsorption capacity (mg/g), and the \(K_{L}\) constant is associated with the free energy of adsorption (L/mg).  
The non-linear form of Freundlich isotherm can be expressed by Equation (3):  
\[Q_{e}=K_{F}\cdot C_{e}^{1/n} \tag{3}\]  
where \(n\) is the constant representing the degree of favorability of the adsorption process, and \(K_{F}\) is related to the adsorption affinity of the adsorbent.
# Prediction of Surface Deformation Induced by Ultra-Shallow-Buried Pilot Tunnel Construction
###### Abstract  
The prediction of ground deformation during ultra-shallow-buried pilot tunnel construction is critical for urban rail transit projects in complex geological settings, yet existing cross-section models often lack accuracy. This study proposes an enhanced non-uniform convergence model based on stochastic medium theory, which decomposes surface settlement into uniform soil shrinkage and non-uniform initial support deformation. A computational formula for horseshoe-shaped sections is derived and validated through field data from Kunming Rail Transit Phase I, demonstrating a 59% improvement in maximum settlement prediction accuracy (reducing error from 7.5 mm to 3.1 mm) compared to traditional methods. Its application to Beijing Metro Line 13 reveals two distinct deformation patterns: significant ground heave occurs at 2.5 times the tunnel width from the centerline, while maximum settlement concentrates above the excavation center and diminishes radially. To mitigate heave, early strengthening of the secondary lining is recommended to control initial horizontal deformation. These findings enhance prediction reliability and provide actionable insights for deformation control in similar urban tunneling projects, particularly under ultra-shallow burial conditions.  
non-uniform convergence model; stochastic medium theory; ultra-shallow-buried tunnel; surface settlement prediction 22 May 2025  
27 June 2025  
1 July 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction of Surface Deformation Induced by Ultra-Shallow-Buried Pilot Tunnel Construction. _Appl. Sci._**2025**, _15_, 7546. [[https://doi.org/10.3390/app15137546](https://doi.org/10.3390/app15137546)]([https://doi.org/10.3390/app15137546](https://doi.org/10.3390/app15137546))  
**Copyright:** 62025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 2 Background Knowledge
### Routing Generation Methods and Ant Colony Optimization
In Equation (1), \(\mathrm{P}_{ij}^{(k)}\) denotes the probability that the \(k^{th}\) ant moves from node \(i\) to node \(j\), \(N_{i}^{(k)}\) denotes the set of all movable nodes directly connected to node \(i\), and \(\tau_{ij}\) is the pheromone deposited in the route from node \(i\) to node \(j\). \[f_{k}=\sum_{i\in S}\sum_{j\in S}\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i} -y_{j}\right)^{2}},\ \ \ k=1,2,\ldots,N \tag{2}\]  
Equation (2) indicates the cost function (\(f_{k}\)) when the \(k^{th}\) ant goes from node \(i\) to node \(j\). \(S\) is the set of all nodes passed by the \(k^{th}\) ant. When the \(k^{th}\) ant is at node \(i\), the coordinates are presented as \((x_{i},y_{i})\); and when it is at node \(j\), the coordinates are presented as \(\left(x_{j},\ y_{j}\right)\). \[f_{best}=\min_{k=1,2,\ldots,N}f_{k} \tag{3}\]  
\[f_{worst}=\max_{k=1,2,\ldots,N}f_{k} \tag{4}\]  
The optimal route (Equation (3)) and the worst route (Equation (4)) between routes selected by the \(k^{th}\) ant are determined. When \(f_{best}\) is determined at one iteration count, the algorithm updates the deposited pheromone for the next iteration. The updated pheromone is used as a guide to search for the future route of other ants. As shown in Equations (5) and (6), the ants deposit more pheromone on the selected route, using Equations (2) and (3). This enhanced route is used as a key indicator to the preferred route in future route selection by other ants. Longer routes that do not attract attention are reduced relative to pheromone evaporation as shown in Equation (7), and the pheromone in each route is updated as learning processing. \[\tau_{ij}\leftarrow\tau_{ij}+\sum_{k=1}^{N}\Delta\tau_{ij} \tag{5}\]  
\[\Delta\tau_{ij}=\left\{ \begin{array}{ll}\frac{\mathbb{C}_{best}}{f_{worst}} \,,&\ \ \ if\ (i,j)\in\text{{global best tour}}\\ 0&\ \ \ otherwise\\ \end{array} \right. \tag{6}\]  
\[\tau_{ij}\leftarrow(1-\rho)\tau_{ij} \tag{7}\]  
In Equation (6), \(\mathbb{C}\) is a parameter to control the scale of the pheromone updated as an ant moves, while in Equation (7), \(\rho\) is a pheromone decay parameter. The pheromones of all the route evaporate, as shown in Equation (7). The pheromone of the route with the minimum objective function value is updated, as shown in Equation (5). The renewal amount of pheromone is determined using Equation (6) with \(f_{best}\) and \(f_{worst}\). Closest nodes with fewer objective function values are selected using these pheromone updating processes. Finally, the algorithm generates the efficient solution route. While a number of research studies have applied the ACO method to relevant application fields, it is limited in a complex network with dynamically changing topology. This research proposes the MPACO and CMPACO methods. The detailed algorithms are provided in Sections 3 and 4. The following subsection explains the Gaussian Process Regression (GPR) method, which the proposed method uses for estimating and updating pheromones.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 3 Methods  
Cambodian electricity consumption data were used in this work in conjunction with VIIRS/DNB night-light remote sensing data. The data were first pre-processed by cropping the downloaded monthly VIIRS/DNB images from 2012 to 2019 to obtain images of the Cambodian region. Afterwards, they were denoised and projected as Albers equivalent cone projections and resampled to a 500 m spatial resolution. Then, three different methods were used--the annual average method, annual cloud-free average method, and month-specific substitution method--to obtain average night-light remote sensing images of Cambodia for each year from 2012 to 2019. Finally, electricity consumption data for Cambodia from 2012 to 2019 were fitted by functional models of total night-light data obtained from each of the three methods to determine the best model. This provided estimates of Cambodia's electricity consumption and its spatial distribution. A flowchart of the research process is presented in Figure 1.
## 2 Research Approach
### Mesoscopic Evaluation Indices for the Main Skeleton  
In the composition of asphalt mixture materials, aggregates with a mass ratio of more than 90% constitute the skeleton. The main skeleton is composed of coarse aggregates that are in contact, and such contact provides a path for the transmission of load stress. A contact point is a part of the main skeleton and is a connecting medium of load stress transfer. Because a contact point is a weak link in the skeleton, it is an important parameter that reflects the performance of the main skeleton [20; 21]. At present, there are no unified mesoscopic indices to evaluate the meso-structure of asphalt mixtures. Previous researches have demonstrated that the number of aggregate-to-aggregate proximity zones, total proximity zone length and proximity zone plane orientation [22], the number of contact points and the orientation of coarse aggregates [14; 16], as well as the contact force at the contact points [23; 24], as the indies to evaluate the stress characteristics of the main skeleton. In this study, the DIP is used to evaluate the interlocked characteristics of the main skeleton; moreover, appropriate mesoscopic evaluation indices, including the coordination number, average coordination number, and C value are proposed.  
(1) Coordination number and average coordination number  
The coordination number--as one of the main factors that affects stress transfer, strength, and deformation properties of granular materials--is defined as the quantity of contact points between a particle in the aggregate and its adjacent particles. It is an important meso-structure parameter that reflects the accumulation and mesoscopic mechanical behaviour of granular materials.  
The average coordination number is defined as the average contact quantity of the particles in the particle system. The larger the average coordination number, the closer the main skeleton structure and the better the stability of the mixture when subjected to load. The calculation expression is shown in Equation (1) [25]:  
\[\overline{n}_{c}=\frac{1}{N}{\sum_{i=1}^{N}n_{c}^{i}} \tag{1}\]  
where \(\overline{n}_{c}\) is the average coordination number; N is the quantity of coarse aggregates in the asphalt mixture; \(n_{c}^{i}\) is the coordination number of coarse aggregates, i.  
(2) C value of the'suspended' coarse aggregate content  
Asphalt mixture is composed of coarse aggregate and asphalt mortar; non-uniformity is one of its characteristics. Some coarse aggregates in asphalt mixtures do not come into contact with other aggregates and are'suspended' in the asphalt mortar. The C value of the'suspended' coarse aggregate content is defined as the ratio of the quantity of coarse aggregates without contact points to the total quantity of coarse aggregates. The smaller the C value, the closer the main skeleton structure. The calculation method is expressed in Equation (2):  
\[C=\frac{n}{N} \tag{2}\]  
where N and n are the total quantity of coarse aggregates in the asphalt mixture and the quantity of coarse aggregates without contact points, respectively.
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 4 Optimization Design
### Analysis of Optimization Results  
The average magnetic flux density of the optimal MR damper was obtained by integrating the magnetic flux density of the gap, as shown in Figure 12. The average magnetic flux density gradually increases with the increase in the applied current. Then, the average magnetic flux density of the damping gaps increases from the outer gaps to the inner gaps due to the decreasing magnetic flux area from outer gaps to inner gaps with a specific current. This also explains that the magnetic flux densities of the two outer damping gaps are approximately the same. The average magnetic flux densities with a current of 1.5 A are 0.5 T for the inner damping gaps, 0.41 T for the middle damping gaps, and 0.32 T for the outer damping gaps. The magnetic field simulation results show that the magnetic flux density at each flow channel of the initial MR damper is better than that of the initial MR damper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable/mm** & **Initial Value** & **Optimal Value** \\ \hline First shear effective length \(L_{1}\) & 8 & 7 \\ Second shear effective length \(L_{2}\) & 7 & 9 \\ Nonmagnetic length \(t_{a}\) & 13 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detailed parameters of the optimal result.  
Figure 11: Optimal Pareto front.  
Figure 13 shows the comparison curves of the damping performance indicators of the initial and optimal MR damper. As shown in Figure 13a, the discrepancy of the damping force between the optimal and initial MR damper linearly increases with the increasing current. Specifically, the damping force increased significantly from 4585 N to 6917 N at an applied current of 1.5 A of the optimal damper, which is improved by 50.8% compared to the initial damper. As shown in Figure 13b, the discrepancy of the dynamic range is not as significant as the damping force. This is determined by the characteristics of the dynamic range and the slightly larger field-off damping force of the optimal damper. The dynamic range of the optimal damper reaches 13.2 at a current of 1.5 A, which was increased by 6.4% compared to that of the initial damper (12.4). These indicate that the damping performance of the optimal MR damper was effectively and remarkably improved. Consequently, by using Opt LHD and EBFNN as the prediction models, the computational workload can be reduced, and the computer analysis time can be shortened, which can greatly improve the efficiency of global optimization.
## 3 Saline-Based One Lung Filling (OLF)
### Clinical Relevance  
So far, OLF in-vivo was been performed only using large animal models. The promising imaging results have not brought OLF into clinical practice. Therefore, consecutive work investigated minimal invasive procedures, which justified OLF. Herein, sonographic guided tissue sampling was successfully evaluated, resulting in a high (98%) puncture rate [32]. Complications such as pneumothoraxes, which occurs frequently during lung puncture in a ventilated condition, was not found during OLF. The liquid's gravity causes expansion of the lung and avoids pneumothoraxes. These results indicate the favorability of applicator-based procedures during OLF in comparison to ventilated lung. Therefore, the future use of such interstitial based ablation (e.g., MW, RFA, LITT, Electrophoresis) using ultrasound guidance should be conducted in future research.  
Ideally, thermal ablation should be performed non-invasively. Substantial work investigating the acoustic conditions in saline flooded lung revealed ideal conditions for non-invasive ablation using therapeutic ultrasound (HIFU). Saline flooded lung provides a low attenuation, minimizing intensity loss and increasing HIFU temperature induction in targeted central cancer tissue. It was found that therapeutic ultrasound intensities interact with malignant tissue inducing lethal thermal damage. The same acoustic dose targeted to the healthy parenchyma causes a non-lethal temperature rise,  
Figure 6: Sonographic imaging of hypoechoic Non-Small-Cell Lung Carcinoma (NSCLC) in saline flooded lung using ex-vivo human lung models (With permission from [PERSON] et al. [18]).  
Figure 7: Endobronchial sonographic imaging of intrapulmonary nodal metastasis in saline flooded lung using ex-vivo human lung models.  
therefore leaving the healthy lung parenchyma unaffected [22]. Even acoustic cavitation not causes erosive damage to lung parenchyma at intensities below than 10.000 W/cm\({}^{2}\)[33].  
In addition to HIFU ablation of pulmonary malignancies, [PERSON] et al. [34] proposed the use for OLF and HIFU for the treatment of tuberculosis. Using the lung as a trans-pulmonary path, targets in adjacent organs such as in the upper liver dome could be targeted by therapeutic ultrasound [35]. Recent work investigated optimal HIFU settings and providing treatment planning modalities [36]. Challenging, are optimal design settings, avoiding beam defocusing and rib heating, which requires modern HIFU modalities such as non-thermal histotripsy [37] or endobronchial insonation [38].
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 2 Free-Space Optical Communications
### FSO Links for LEO Scenarios  
In a common LEO scenario, the data are downloaded from a satellite that moves in orbit, as seen by a terrestrial observer, to a ground station, which is usually static. As a result of the low orbit and the fast motion of the satellite, contacts are short, in the range of 5 to 10 min. To maximize the use of the short contact time, the connection is exploited at the lowest elevation angle possible, which is between 5 and 10 degrees, thus increasing the possible throughput during each contact. The use of adaptive coding and modulation techniques, which adapt the data-rates to the actual conditions of the link, can provide three times higher throughput than system with a static data-rate, at the expense of an increase in system complexity [12].  
Optimizing the throughput at lower angles requires characterizing the power scintillation of the channel, which can be evaluated in a LEO-to-ground scenario. The characterization of the channel phenomena is carried out by means of spatial and temporal statistics over link elevation. During a satellite pass, there is a variation in link distance, elevation angle, slew rate, and the path section of the atmospheric turbulence. The link suffers from atmospheric turbulence, scintillation, and fading, as shown in Reference [13]. The conclusions and specific details of characterization from this paper can be used for practical FSO applications.