esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 4 Experimental Results Analysis
### Detection Effect and Analysis  
The PSP-YOLO model, enhanced by the newly added P2 small-object detection head and the introduction of the SPD-Conv module, demonstrates superior detection performance in low-quality and small-object scenarios. The PConv module further contributes to balancing performance improvement with reduced computational resources, making the model more efficient, accurate, and fast in handling scene detection tasks. As shown in  
Figure 8: Comparison of PR curves between PSP-YOLO and YOLOv8n.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]-0.95\%** & **Params/M** & **FLOPs/G** & **FPS/s** \\ \hline Faster-RCNN & 77.6 & 47.2 & 137.1 & 402.2 & 16.0 \\ RT-DETR-l & 85.4 & 63.0 & 32.0 & 103.4 & 28.6 \\ YOLOv5n & 87.7 & 61.4 & 2.5 & 7.1 & 89.4 \\ YOLOv5s & 90.8 & 68.2 & 9.1 & 23.8 & 70.0 \\ YOLOv6n & 85.6 & 61.7 & 4.2 & 11.8 & 108.2 \\ YOLOv6s & 87.6 & 65.0 & 16.3 & 44.0 & 72.6 \\ YOLOv7-tiny & 83.0 & 51.2 & 6.0 & 13.1 & 57.5 \\ YOLOv8n(baseline) & 88.7 & 64.7 & 3.0 & 8.1 & 113.7 \\ YOLOv8s & 92.3 & 69.3 & 11.1 & 28.4 & 83.7 \\ PSP-YOLO & 92.6 & 68.3 & 2.5 & 13.3 & 104.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Different algorithm comparison experiment.  
In Figure 9, under occluded conditions, the PSP-YOLO model detects more targets compared to the original model, indicating better small-object detection capabilities in complex environments. In Figure 9, where the scene is both occluded and poorly lit, the PSP-YOLO model still accurately identifies the target objects, demonstrating its effectiveness in recognizing features even under challenging conditions.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 8 Conclusions  
A DL LSTM handover decision algorithm utilizing LUTs and MEC was proposed, and its impact on UEs and BSs against the benchmark 3 GPP scheme was investigated. The results showed that the QoE targets are achieved with improvement in the UE satisfaction rate by 40% over the 3 GPP scheme. By replacing the TTT with a dynamic triggering function, the proposed algorithm provided a very fast response to UE mobilities when the LUT requirements were met. This allowed the QoS targets to be met with lower HOF and ping-pong rates than the benchmark by 30% and 86%, respectively. These performance gains are achieved despite a higher occurrence of handovers. This is due to the algorithm attempting to accommodate user data rate requirements and/or user CQI expectations. Furthermore, the proposed modification to the admission control process resulted in handovers with lower latencies that approach the user plane eMBB latency target. As future work, we plan to extend our approach to enhancing inter-RAT handovers as coexistence between legacy and successor systems has always been a requirement in different generations of cellular networks. We also plan to use reinforcement learning to  
Figure 11: UE satisfaction for (**a**) downlink and (**b**) uplink.  
make our LSTM models autonomously adaptive to changing future environments and user requirements.  
Conceptualization, P.K. and B.-C.S.; methodology, B.-C.S.; software, P.K.; validation, P.K. and B.-C.S.; formal analysis, P.K.; investigation, P.K.; resources, B.-C.S.; data curation, P.K.; writing--original draft preparation, P.K.; writing--review and editing, B.-C.S.; visualization, P.K.; supervision, B.-C.S.; project administration, B.-C.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Risk-Sensitive Policy with Distributional Reinforcement Learning
## 3 Materials and Methods
### Methodology
#### 3.2.3 Risk-Based Utility Function  
In order to pursue the objective criterion defined in Equation (11) for risk-sensitive RL, this research work introduces a new concept: the _state-action risk-based utility function_. Denoted \(U^{\pi}(s,a)\), the utility function assesses the quality of an action \(a\in\mathcal{A}\) in a certain state \(s\in\mathcal{S}\) in terms of expected performance and risk, assuming that the policy \(\pi\) is followed afterwards. In fact, the intent is to extend the popular \(Q\) function so that the risk is taken into consideration by taking advantage of the risk function defined in Section 3.2.2. More precisely, the utility function \(U^{\pi}\) is built as a linear combination of the \(Q^{\pi}\) and \(R^{\pi}\) functions. Formally, the risk-based utility function \(U^{\pi}:\mathcal{S}\times\mathcal{A}\to\mathbb{R}\) of a policy \(\pi\) is defined as the following:  
\[U^{\pi}(s,a) =\alpha\ Q^{\pi}(s,a)\ +\ (1-\alpha)\ R^{\pi}(s,a) \tag{15}\] \[=\alpha\ \mathbb{E}[Z^{\pi}(s,a)]\ +\ (1-\alpha)\ \mathcal{R}_{ \rho}[Z^{\pi}(s,a)], \tag{16}\]  
where \(\alpha\in[0,1]\) is a parameter controlling the trade-off between expected performance and risk. If \(\alpha=0\), the utility function will be maximised with a fully risk-averse decision-making policy. On the contrary, if \(\alpha=1\), the utility function degenerates into the \(Q\) function quantifying the performance on expectation. Figure 3 graphically describes the utility function \(U^{\pi}\), which moves along the x-axis between the quantities \(R^{\pi}\) and \(Q^{\pi}\) when modifying the value of the parameter \(\alpha\).  
Figure 3: Illustration of the utility function \(U^{\pi}\) for a typical random return probability distribution, with \(\alpha=0.75\) in this case.  
Figure 2: Illustration of the risk modelling adopted in this research work, based on the probability distribution of the random return \(Z^{\pi}\) learnt by a distributional RL algorithm.
## 3 Results and Discussion
### Thermodynamic Evaluation of the Process  
Thermodynamic parameters such as standard free energy change (\(\Delta G^{\circ}\)), standard enthalpy change (\(\Delta H^{\circ}\)), and standard entropy change (\(\Delta S^{\circ}\)) are very important for analyzing the nature of an ion-exchange process [50]. To estimate these parameters, the influence of temperature on the adsorption process was studied.  
The thermodynamic parameters, including \(\Delta G^{\circ}\), \(\Delta H^{\circ}\), and \(\Delta S^{\circ}\), were determined using the following equations:  
\[\Delta G^{\circ}=-RTlnK\,\ K\ =\frac{q_{e}}{C_{e}} \tag{8}\]  
\[\Delta G^{\circ}=\Delta H^{\circ}-T\ \Delta S^{\circ} \tag{9}\]  
Figure 11: Pseudo-first-order modelling for adsorption of Pb(II) onto G-26 and MTS9570 resins.  
Figure 12: Pseudo-second-order modelling for adsorption of Pb(II) onto G-26 and MTS9570 resins.  
or  
\[lnK\ =-\frac{\Delta H^{\circ}}{RT}+\frac{\Delta S^{\circ}}{R} \tag{10}\]  
where \(K\) is the the distribution coefficient calculated from the Langmuir equation, \(R\) is the universal gas constant (8.314 J/mol/K), and \(T\) is absolute temperature (K). The values of \(\Delta H^{\circ}\) and \(\Delta S^{\circ}\) could be obtained from the slope and intercept of the linear plot between ln\(K\) versus \(1/T\). The calculated values of thermodynamic parameters are listed in Table 5.  
The negative values for free energy change \(\Delta G^{\circ}\) confirm the feasibility and spontaneous process for Pb (II) adsorption onto both resins, and the positive values of \(\Delta H^{\circ}\) (42.3 and 32.07 kJ/mol for G-26 and MTS9570, respectively) indicate the endothermic nature of the adsorption process. The higher value of \(T\Delta S\) than \(\Delta H\) suggests that the adsorption process is enthalpy-driven [51].
## Materials and Methods
### Methods of calculating FRP  
FRP was calculated by two methods: differential method and simple method, with decomposed fine roots being accounted for based on [PERSON] et al. [9]. FRP\({}_{ij}\) between sampling time \(i\) and \(j\) was the sum of differences in live fine root biomass at (\(\Delta B_{ij}\), g m\({}^{-2}\)), the difference in fine root necromass (\(\Delta N_{ij}\), g m\({}^{-2}\)) and decomposed dead fine roots (\(d_{ij}\), g m\({}^{-2}\)). The following equations were used:  
\[\mathrm{FRP}_{ij}=\Delta B_{ij}+\Delta N_{ij}+d_{ij} \tag{1}\]  
\[\Delta B_{ij}=B_{j}-B_{i} \tag{2}\]  
\[\Delta N_{ij}=N_{j}-N_{i} \tag{3}\]  
where \(B_{i}\) and \(B_{j}\) are live fine root biomass at sampling time \(i\) and \(j\) and \(N_{i}\) and \(N_{j}\) are fine root necromass at sampling time \(i\) and \(j\). \(d_{ij}\) was estimated as:  
\[d_{ij}=-\Delta N_{ij}-(\Delta N_{ij}/\gamma_{ij}+N_{i})\times\mathrm{In}(1- \gamma_{ij}) \tag{4}\]  
where, \(\gamma_{ij}\) is the decomposition ratio of fine root necromass between times \(i\) and \(j\) and \(N_{i}\) (g m\({}^{-2}\)) is fine root necromass at time \(i\). \(\gamma_{ij}\) was estimated based on the litter bags experiments as:  
\[\gamma_{ij}=1-\mathrm{N_{i}^{C}}/\mathrm{N_{i}^{C}} \tag{5}\]  
where \(\mathrm{N_{i}^{C}}\) and \(\mathrm{N_{i}^{C}}\) are the fine root necromass in litter bags at time \(i\) and \(j\). Equation (2) was generated by [PERSON] and [PERSON] [9], based on the assumption of constant mortality and decomposition rate between times \(i\) and \(j\). When the values of fine root production were negative, they were assigned as zero, because negative values are not possible in ecology [9]. FRP, \(\Delta B\), \(\Delta N\) and \(d\) were calculated to annual values using the g m\({}^{-2}\) year\({}^{-1}\) unit to allow comparison among sampling intervals.  
In the differential method, FRP was derived from the difference in the amount of root that was sampled at two different sampling times, similar to Osawa and Aizawa [9]. In the differential method,  
Figure 1: Air temperature and 30-day cumulative rainfall with the timing of core sampling during the study period.  
all combinations of \(i\) and \(j\) using T1, T2, T3 and T4 were calculated at different sampling intervals. For example, the sampling interval combining T1 and T2 was 3 months, while that between T1 and T3 was 9 months. There were six combinations in total (T1-T2, T1-T3, T1-T4, T2-T3, T2-T4 and T3-T4). FRP between T3 and T4 with a sampling interval of 12 months was used as a reference to compare all sampling intervals because one-year interval was often used in previous studies [7; 13; 26] and the period between core installation and root sampling should be extended to allow for maximum root colonization [27].  
In the simple method, the quantity of roots that grew into the mesh bags was regarded as total production for the incubation period. Thus, \(i\) was always set as T0, with just four time combinations (T0-T1, T0-T2, T0-T3 and T0-T4).
# Photodynamic Properties of CdSe/CdS Quantum Dots in Intracellular Media
## 3 Results and Discussion
### Fluorescence Spectra  
Figure 3a-c show the fluorescence spectra of the QDs seeded in the Jurkat cells after 3 h, 6 h, and 9 h of incubation, respectively. The fluorescence spectra of the Jurkat cells without CdSe/CdS QDs are also presented in Figure 3a for comparison. The 618 nm fluorescence peak is characteristic of the CdSe/CdS QDs. This fluorescence peak appeared in samples incubated for 3 h with low cell concentration and treated with PEG (C\({}_{\mathrm{L}}\)-P), low cell concentration without PEG (C\({}_{\mathrm{L}}\)), high cell concentration treated with PEG (C\({}_{\mathrm{H}}\)-P), and high cell concentration without PEG (C\({}_{\mathrm{H}}\)). For the sample incubated for 6 h, the fluorescence peak was only observed after treatment with PEG. This fluorescence peak was not observed in the sample incubated for 9 h and 12 h, even when treated with PEG. This could be because when the incubation time increased, a large number of QDs are inserted into the cells, causing cell death. Subsequently, the QDs were eliminated in the process of washing the sample after incubation. This causes the fluorescence signal of the sample for the 6 h uptake to be lesser than the sample uptake in 3 h. Only one experiment was done for each of the exposure times. The same experimental parameters were used for the samples with 3 h and 6 h exposure times, giving some confidence to the experimental results that indicate the uptake for the 6 h exposure time is less compared to that of the 3 h exposure time. We surmise that more CdSe/CdS QDs have been seeded into the cells when the cell concentration was low.  
Therefore, the peak fluorescence intensity is higher in the sample with a low cell concentration compared to the one with a higher cell concentration. Further enhancement in the fluorescence peak intensity is observed when the cells are treated with PEG. When treated with PEG, small holes in the cell membrane would have been enlarged, thereby allowing the QDs to be seeded easily. Several works have reported that polymers such as PEG can improve biocompatibility and facilitate intracellular delivery by driving initial interactions with the plasma membrane and ultimately internalization [34]. Then, the effective seeding resulted in enhanced fluorescence in the PEG-treated cells. As shown in Figure 3a, no fluorescence is observed for the QDs-free Jurkat cells. These results also confirm that the QDs were indeed successfully seeded inside the cells and the fluorescence observed in the microscope images originated from within the cells.  
Figure 2: Fluorescence microscope images of the CdSe/CdS QDs in Jurkat cells under 532 nm laser excitation: (**a**) without and (**b**) with white light irradiation from a xenon lamp in reflection geometry.
###### Abstract  
With the continuous construction of large-scale Low Earth Orbit (LEO) constellations, their potential for Global Navigation Satellite System (GNSS) applications has been emphasized. This study aims to derive an optimal positioning configuration formula based on the ratio of high-elevation and low-elevation satellites, which can improve the positioning accuracy and overcome the accuracy loss due to signal occlusion. A genetic algorithm is used to solve the optimal positioning configuration problem for large-scale satellite selection. Through a simulation using Starlink satellites currently in orbit, it is verified that the traditional recursive algorithm cannot be applied to satellite selection for large-scale constellations. The proposed formula has a similar accuracy to the Quasi-Optimal algorithm when there is no signal occlusion and the satellites are uniformly selected. However, the accuracy of the latter deteriorates significantly under signal occlusion. Our algorithm can effectively overcome this problem. Moreover, we discuss the effect of different types of obstructions on the accuracy loss. We find that the Quasi-Optimal algorithm is more sensitive to a single large-angle obstruction than multiple small-angle obstructions. Our proposed formula can reduce the localization accuracy degradation caused by signal occlusions in both scenarios.  
satellite positioning; low earth orbit; satellite selection Article
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
###### Abstract  
As an important part of the transportation network, the reliability of bridge structures is of great significance to people's personal safety, as well as to the national economy. In order to evaluate the performance of complex bridge structures, their mechanical behavior and fundamental characteristics need to be studied. Structural health monitoring (SHM) has been introduced into bridge engineering, and the structural response assessment, load effects monitoring, and reliability evaluation have been developed based on the collected SHM information. In this study, a performance evaluation method for complex bridge structures based on non-destructive field loading tests is proposed. The cable-stayed bridge in Guangxi with the largest span (Pingnan Xiangsizhou Bridge) was selected as the research object, and loading on the main girder was transferred to the piers and tower through the stay cables, whose structural responses are critical in the process of bridge operation. Therefore, the field loading tests--including deflection and strain testing of the main girder, as well as cable force tests--were also conducted for Pingnan Xiangsizhou Bridge by using non-destructive measurement techniques (multifunctional static strain test system, radar interferometric deformation measurement technology, etc.). Based on the numerically simulated results of a finite element model for Pingnan Xiangsizhou Bridge, reasonable field loading test conditions and loading arrangement were determined. Non-destructive field loading test results showed that the quality of the bridge's construction is up to standard, due to a good agreement between the calculated and measured frequencies of the bridge. In addition, the calibration coefficients of displacement and strain were less than \(1\), indicating that Pingnan Xiangsizhou Bridge has satisfactory stiffness and strength.  
cable-stayed bridge; non-destructive field loading test; performance evaluation; structural health monitoring; finite element method +
Footnote †: journal: Applied Sciences
## 4 Example
### Extracting Feature Data
#### 4.2.2 Feature Selection  
After removing redundancy, excluding outliers, and imputing missing values, we use the Kendall correlation coefficient to measure the correlation between various parameters and plot the correlation coefficient measurement results among various parameters. Figure 15 displays a heatmap of the correlation among various parameters in the CMG system.  
Figure 14: Missing value imputation visualization example (regression imputation).  
Figure 15: CMG system parameter correlation heatmap.  
To improve the performance of feature extraction, we excluded parameters with lower correlations with other variables and selected seven parameters from the system parameters for feature extraction. Referring to the heatmap above, the seven selected parameter codes are CMG003, CMG005, CMG011, CMG012, CMG0013, CMG015, and CMG017.
## 2 Numerical Methodology
### Lattice-Boltzmann Method
As a result of this collision, the distribution function transitions from \(f_{i}(\overset{\rightarrow}{x},\,t)\) to \(f_{i}^{*}(\overset{\rightarrow}{x},\,t)=\frac{1}{\tau}(f_{i}^{eq}-f_{i}( \overset{\rightarrow}{x},\,t))\), where \(\tau\) is a characteristic time representing the relaxation rate of the distribution function towards its equilibrium state \(f_{i}^{eq}\). In the three-dimensional lattice Boltzmann method (LBM), the physical space is discretized into uniform Cartesian cells known as lattices. Each lattice unit consists of nodes interacting with their neighbouring nodes through a set of lattice velocities determined as part of the solution. LBM schemes are categorized based on the spatial dimension (d) and the number of distribution functions (b), denoted as DdQb. The commonly used schemes in two dimensions are D2Q7 and D2Q9, while in three dimensions, schemes such as D3Q13, D3Q15, D3Q19, and D3Q27 are widely employed. The XFlow solver, for instance, utilizes the D3Q27 lattice model, which consists of twenty-seven velocities, as depicted in Figure 1.  
By employing these lattice models, the LBM can accurately simulate fluid flow and capture complex flow phenomena in three-dimensional space. Each lattice node carries information about the fluid's local characteristics, such as velocity and density, allowing for the simulation of various fluid dynamics problems. Usually, the discretized equilibrium distribution function for D3Q27 adopts the following expression [2]:  
\[f_{i}^{eq}=\rho\omega_{i}(1+\frac{3}{c_{s}^{2}}(e_{ia}u_{a})+\frac{9}{2c_{s}^{4}} (e_{ia}u_{a})^{2}-\frac{3}{2c_{s}^{2}}(u_{a})^{2}) \tag{5}\]  
where \(e_{ia}\) discrete velocity, \(c_{s}\) is the sound speed, \(u\) the macroscopic velocity, \(\omega_{i}\) are built to preserve the isotropy. For this purpose, the D3Q27 scheme has been implemented for the analysis of VAWT and Wall-Adapting Local Eddy-viscosity (WALE), Spalart-Allmaras and Smagorinsky turbulence models have been selected to analyse static pressure, velocity, and turbulence models. The WALE has good properties both near to and far from the wall turbulent flows [17; 18].
## 1 Introduction
We utilized natural gamma ray logging (GR), spontaneous potential logging (SP), acoustic logging (AC), lateral deep resistivity logging (LLD), and lateral shallow resistivity logging (LLS), obtaining a total of 2390 valid logging records. Additionally, we designed a multimodal convolutional neural network that jointly inputs dynamic scale imaging slices with the sequence stratigraphic domain conditions represented by one-hot encoding. Through multiple layers of Conv2D and MaxPooling2D operations, the network extracts key features such as sand body boundaries, bedding structures, and sedimentary architectures from the well logging images. These features are then concatenated with the geological significance encoding of the stratigraphic sequence to enhance the model's understanding of the sedimentary facies control relationships under different stratigraphic sequences, thereby achieving precise classification of sedimentary facies. The application of this multimodal convolutional neural network not only enables accurate classification of sedimentary facies but also provides crucial geological information support for reservoir engineering. This aids in optimizing oil and gas reservoir development strategies and enhancing recovery rates, thereby promoting the efficient utilization and sustainable development of oil and gas resources.
## 2 Specimens and Experiment Setup
### Laser Doppler Vibrometer (LDV)  
We employed a laser Doppler vibrometer (LDV) (PDV-100, Polytec GmbH, Waldbronn, Germany) to obtain point-wise displacement measurements to be compared with the results of ESPI. This device is designed for non-contact measurement at vibration frequencies of up to 22 kHz, based on the principles underlying the Michelson interferometer and Doppler effect. This type of interferometer generates a high-frequency carrier signal within the photo detector using an acousto-optical modulator (Bragg cell). A helium neon laser beam applied to the specimen is scattered back, allowing the measurement of vibration speeds based on the difference in frequency between the two light beams. The working distance of this device ranges from 0.1 m to 30 m, and the laser spot is 30 \(\upmu\)m in diameter (based on a focal length of 0.234 m). In this study, the focal length was varied between 0.15-0.5 m, resulting in a maximum laser spot of 124 \(\upmu\)m. Velocity is the original data measured using an LDV; however, displacement and acceleration can both be derived via computation. In this study, a swept-sine signal  
Figure 4: Schematic diagrams and implementations showing set-up of the amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) experiment for out-of-plane measurements of upper and lower bimorphs: (**a**) the schematic diagram for the upper bimorph, (**b**) the implementation for the upper bimorph, (**c**) the schematic diagram for the lower bimorph, and (**d**) the implementation for the lower bimorph.  
generated by a function generator (controlled by MATLAB) was input to excite the piezoelectric bimorphs. A computer was used to record the velocity values measured using the LDV system when the bimorph is stimulated by a converse piezoelectric effect. The peaks that appeared as local maxima in the response curve indicate the resonant frequencies of the specimens under transverse vibration. This LDV system enables accurate vibration analysis; however, it is not conducive to deriving full-field resonance characteristics.