esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### Cross-Validation Evaluation and Ablation Study

Table 6: Comprehensive quantitative analysis of firearm detection results obtained with different deep architectures as feature extractor and four state-of-the-art OCCs implemented on visible _handgun_ extracted from YouTube dataset. of Table 8), respectively. Compared to the DeepGun-CNN, DeepGun performs better in terms of F1, ACC, P, and AP, while yielding the same performance outcome in terms of R.  
To assess the effectiveness of the encoder module in the VAE, we compare DeepGun trained with four NN encoder layers to DeepGun trained with two NN encoder layers, as shown in the second column of Table 8. Both models are trained using the same parameter settings. The model trained with two encoder layers is referred to as DeepGun-E2. A comparison between DeepGun and DeepGun-E2 reveals that their performance is nearly identical, with only a slight reduction in performance for DeepGun-E2. This suggests that the VAE with NN implementation is effective in extracting deep features that align well with those obtained from the HPE module, facilitating firearm detection in frames extracted from videos. Additionally, by comparing the performance of DeepGun with variable patch sizes extracted from video frames, as explained in Section 3.2, we evaluate the impact of patch size on firearm detection. We refer to the model trained with fixed patch sizes (40 \(\times\) 40) as DeepGun-FPS. From Table 8, we observe that features extracted from patches based on the subject's distance from the camera outperform those from fixed-size patches, leading to improved firearm detection performance by DeepGun. We suggest that this difference occurs because fixed-size patches may be too small or too large as the subject moves closer to or farther from the imaging device, respectively. This can lead to capturing irrelevant background details or missing important features, such as the hand, due to improper patch sizing. In contrast, adaptively selecting patches is more effective, as it focuses on optimizing feature-level similarity, leading to improved firearm detection.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Experiment and Analysis  
#### 4.4.1 Backbone Comparison Experiment  
In this study, we conducted experiments on different backbone networks for the encoder, aiming to compare their performance in lawn environment segmentation. Taking DeepLabV3+ as the baseline, we substituted the backbone network and carried out comparative experiments involving major backbones such as MobileNetV2, ResNet18 [37], ResNet50, and ResNet101.  
By observing Table 3, we can see that among various backbone networks, MobileNetV2 achieves satisfactory results owing to its depthwise separable convolutional structure. Not only does this model reduce to a comparable level with ResNet18 in terms of GFLOPs and its parameter count but also in the recognition metric of FPS; the recognition speed of MobileNetV2 is almost twice that of ResNet50. It is noteworthy that although ResNet18 has a relatively small parameter count, its recognition accuracy is lower than that of MobileNetV2. Although the MIoU of MobileNetV2 is only 0.33% higher than that of ResNet18, the FPS value of ResNet18 is 16 higher than that of MobileNetV2, which might lead to the misconception that ResNet18 performs better. However, in practical applications, we need to input semantic segmentation results into ORB-SLAM3 systems to generate 3D semantic maps, with the system being most suitable for processing speeds of around 30 FPS. Exceeding this speed would result in an excessive burden on the system, possibly leading to delays in processing. Therefore, when FPS requirements are met, MobileNetV2, which has a higher MIoU, is the more appropriate choice.  
Hence, in scenarios in which simplifying backbone networks, improving recognition speed, and adapting to ORB-SLAM3 systems are objectives, selecting MobileNetV2 as the backbone network is an extremely attractive option. Overall, the results of this experiment indicate that in the task of lawn environment segmentation, MobileNetV2 demonstrates excellent performance and efficiency as the backbone network, providing strong support for achieving model lightweighting and accelerating recognition speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **MIoU/\%** & **MPA/\%** & **GFLOPs** & **Params/M** & **FPS** \\ \hline ResNet18 & 90.24 & 93.74 & 54.27 & 11.75 & 44.85 \\ ResNet50 & 92.28 & 95.52 & 176.52 & 39.31 & 14.86 \\ ResNet101 & 91.91 & 95.30 & 254.31 & 57.42 & 10.40 \\ MobileNetV2 & 90.55 & 94.66 & 68.24 & 14.32 & 28.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Backbone comparative results: MobileNetV2 achieves the best balance between accuracy and model parameters.
# Characterisation of Holographic Recording in Environmentally Stable Photopolymerisable Glass
## 3 Results and Discussion
### Fixation of Holographic Grating by UV-Curing  
In many instances, it may be preferable to prevent the effect of the dark process on the refractive index modulation and to fix the diffraction efficiency at the level obtained after exposure. The suitability of UV-curing for this purpose was studied. For this experiment, transmission gratings were recorded using exposure time of 15, 30, 45, and 60 s by means of the set-up presented in Figure 1. The diffraction efficiency was monitored immediately after exposure and it was found to reach up 85% (60 s exposure time) in layers with thickness of about 120 \(\upmu\)m. Then, the samples were exposed to UVA light by means of a Dymax UV-curing System (ECE Series) for 297 s with 29 mW cm\({}^{-2}\) intensity and the diffraction efficiency was measured again.  
The effect of UV-curing was analysed by calculating the normalised diffraction efficiency as the ratio of the diffraction efficiency obtained after UV-curing and after exposure (Figure 5a). As seen from Figure 5a, the decrease of diffraction efficiency of 7 and 16% was obtained for gratings recorded with the exposure time of 30 and 15 s, respectively. Longer exposures (45 and 60 s) provided an increase of the diffraction efficiency for few percent and allowed fixing the diffraction efficiency at the level that was close to that achieved during holographic recording. For example, the grating with 85% diffraction efficiency measured after recoding had 87% after UV-curing.  
The stability of the diffraction efficiency after UV-curing was verified by further monitoring the diffraction efficiency for 6 days. Bragg selectivity curves for the typical grating recorded using 30 s exposure time are presented in Figure 5b. As seen from Figure 5b, the diffraction efficiency decreases from 59% (obtained after recording) to 52% after UV-curing. Within 24 h, the diffraction efficiency enhanced just for few percent (up to 56%) and then remained unchanged. Thus, UV-curing was confirmed as an effective approach to prevent enhancement of the refractive index modulation observed during the subsequent dark process as described in Section 3.3. Here, it should be noted that after UV-curing cracking of some layers with thickness above 100 \(\upmu\)m was noticed. To avoid layer cracking, further optimisation of the layer preparation method is required which might include dilution of the sol with solvents. This will be the scope of future research.
## 4 Materials and Methods  
The reflection type of active metasurface is fabricated on silicon substrate. Firstly, 90 nm thick aluminum thin film and 100 nm thick silicon dioxide layer are deposited by electron beam evaporation (Korea Vacuum Tech, Gimpo-si, Korea, KVE-ENS4004). Then, the polymethyl methacrylate (PMMA) layer is spin-coated onto the substrate. Similarly, a conductive polymer layer (Showa Denko, Tokyo, Japan, E-spacer 300Z) is also spin-coated to prevent the charging effect from the silica layer. Electron beam lithography (ELIONIX, Tokyo, Japan, ELS-7800, 80 kV, 50 pA) is used to expose nanorod patterns on photoresist. After exposing, the conductive layer is firstly removed by DI water and exposed nanorod patterns are developed by the MIBKIPA 1:3 solutions. Finally, an adhesion layer of 3 nm thick chromium and a structure layer of 40 nm thick aluminum are deposited by electron beam evaporation and nanorod patterns are transferred onto the substrate. Then white-balanced images are collected using a \(\times\)4, 0.07 numerical aperture objective on an optical microscope (Hyperion 1000) with LED light source (Opto Semiconductors, Regensburg, Germany, LE UW S2W) and Infinity 1-2 CCD camera.  
Conceptualization & Supervision, J.R.; Numerical simulation & Writing manuscript, M.K.; Fabrication, I.K.; Measurement, J.J. and D.L.  
This research was funded by the LGD-SNU incubation program from LG Display and the National Research Foundation of Korea (NRF-2017R1E1A1A03070501, NRF-2015R1A5A1037668, CAMM-2014M3A6B3063708) funded by the Ministry of Science and ICT (MSIT), and Global PhD Fellowships from Korean government (NRF-2017H1A2A1043204, NRF-2016H1A2A1906519).  
Figure 4: (**a**) Schematic of transmission type metasurface and (**b**) its unit cell. Aluminum film of thickness 40 nm on a silica substrate has three slits numbered by 1, 2 and 3, and the unit cell has two of each slit. \(i\)-th slit is tilted by \(\phi_{i}\) where \(a_{i}\) and \(b_{i}\) denote horizontal and vertical dimension. Geometric parameters indicated in the figure are: \(p_{x}\) = 600 nm, \(p_{y}\) = 700 nm, \(a_{1}\) = 100 nm, \(a_{2}\) = 70 nm, \(a_{3}\) = 180 nm, \(b_{1}\) = 150 nm, \(b_{2}\) = 60 nm, \(b_{3}\) = 110 nm, \(\phi_{1}\) = 0\({}^{\circ}\), \(\phi_{2}\) = 70\({}^{\circ}\), \(\phi_{3}\) = 130\({}^{\circ}\). (**c**) Polarization dependent transmission spectrum. (**d**) CIE 1931 chromaticity diagram and (**e**) color palette of transmission type metasurface.  
**Acknowledgments:** [PERSON] acknowledges [PERSON] at the University of Birmingham for the useful discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Experimental Details  
Validation was performed using the PyTorch (version 2.6) framework with the Adam optimizer. All experiments were conducted on an Intel(r) Xeon(r) Platinum 8255C CPU and an NVIDIA 3080 Ti GPU. The training parameters were configured as follows based on insights from previous studies and data analysis: the initial learning rate was set to 0.001 to promote faster convergence, the number of training iterations was set to 500, the hyperparameter \(\omega\) was set to 0.015, and the number of point cloud sampling points (\(n\)) was set to 512. The optimizer and learning rate schedule were configured in line with the settings used in DenseFusion. Additionally, the batch size was set to 8 to accommodate GPU memory limitations, and several data augmentation strategies were applied to enhance generalization. With these settings, the model demonstrated rapid convergence without significant oscillations during training. Table 1 outlines the model parameters for the training process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Parameters** \\ \hline CPU & Intel (R) Xeon (R) Platinum 8255C CPU @ \\  & 2.40 GHz \\ GPU & NVIDIA GeForce RTX 3080 Ti \\ Operating System & Ubuntu 18.04.4 \\ Initial learning rate & 0.001 \\ Epochs & 500 \\ Hyperparameter (\(\omega\)) & 0.015 \\ Sampling point (\(n\)) & 512 \\ Batch size & 8 \\ Learning rate schedule & CosineAnnealingLR \\ Augmentation & ColorJitter and Pose Random Translation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training environment and parameters.
# Embedded One-Dimensional Orifice Elements for Slosh Load Calculations in Volume-Of-Fluid CFD
## 4 Discussion  
Figures 13 and 14 show the pressure difference over the tank walls calculated using the one-dimensional orifice element model with each computational mesh. It can be seen that the results are in good agreement with both the experimental and full-resolution numerical results. Another metric to consider when evaluating the one-dimensional orifice element model is the flow rates through the orifices.  
The flow rates through the orifices (Figures 15-17) using the one-dimensional orifice element model for both Mesh A and Mesh B are in reasonable agreement with the full-resolution model for the first 25% of the simulation (Mesh C results are slightly lower). This also correlates with the similarity in the position of liquid-gas interface between the meshes during this period, as shown on the left of Figure 20. From this point onward, the slosh becomes particularly chaotic and the differences in interface position between the different mesh coarseness levels grow more pronounced, as shown on the right of Figure 20. This is reflected in the larger differences in flow rate predicted through the orifices in the latter part of the simulation. Despite this, the overall amount of liquid contained in the two compartments is still predicted with reasonable accuracy. This is evident from the total mass in the left tank (Figures 18 and 19) being similar in the one-dimensional element model simulations and the full-resolution simulation.  
The computational cost of the full-resolution CFD simulation and the one-dimensional element model simulations are presented in Table 3. Note that serial calculations were performed in all cases due to the small mesh sizes. As per the table, the one-dimensional element model requires less mesh nodes. This also requires less time steps to complete the same simulation due to the larger allowable time-step sizes (larger elements).  
Figure 19: Relative mass difference in left-hand side of the tank of one-dimensional orifice element model simulation to full-resolution simulation of the violent slosh test case.  
Figure 20: Free surface at time \(\frac{t}{t_{max}}=0.2\) (left) and \(\frac{t}{t_{max}}=0.4\) (right) on the full-resolution mesh (**top**) and Mesh C (**bottom**) of violent slosh test case.  
It is interesting to note that Meshes B and C require slightly more time steps than Mesh A, even though the majority of the mesh element sizes are much larger. This can be attributed to the high flow velocities through the top orifice. The average velocity over time through the top orifice is six times the velocity through the middle and bottom orifices. All three meshes have similarly sized elements around the top orifice; therefore, the time step sizes are limited by those elements. Importantly, the one-dimensional element model simulations all require less CPU time, without any significant loss in the accuracy of the pressure differential over the tank walls or the relative mass difference in the tanks. Mesh C achieves the largest speed-up factor of 2 compared with the full-resolution model.
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 4 Conclusions  
In the present paper, large eddy simulation (LES) and dynamic mode decomposition (DMD) were used to study turbulent mixing layers. The results allow for the following conclusions to be drawn:  
(1) The LES method used in this paper can accurately calculate the vorticity thickness growth rate of the turbulent mixing layer, and the peak values of each component of Reynolds stress at several streamwise locations are close to the results of DNS in literature, indicating that the present LES provides accurate big data for DMD.  
(2) For the velocity in \(x\)-direction, the first-order mode is the dominant mode of the flow field, which represents the average flow field; the low-frequency mode presents a  
Figure 18: 150 order reconstruction and prediction of periodical forcing case. (**a**) Comparison of reconstruction (right) and instantaneous contours (left); (**b**) relative error.  
strip-shaped large-scale structure, and the high-frequency mode presents a small-scale structure, which indicates that the small-scale vortex formation in the flow field is mainly related to the high-frequency mode. For the velocity in y-direction, the first-order mode is unstable, which cannot represent the average flow field; there is no mode in which frequency and growth rate are both zero, which indicates that the flow field of velocity in y-direction is more complex than that of velocity in \(x\)-direction.  
(3) The dominant-mode selection criterion based on mode amplitude is more suitable for turbulent mixing layer flow compared with the other three criteria based on singular value, modal energy and integral modal amplitude.  
(4) The standard DMD method reconstructs and predicts the periodically-perturbed mixing layer well. In contrast, for the mixing layer with random disturbance, the standard DMD method could only accurately reconstruct and predict the region before instability happens but is not qualified in the regions after that as nonlinearity is stronger, which implies that improved dynamic mode decomposition methods need to be utilized or developed for the future dynamic mode decomposition of turbulent mixing layers.  
Investigation, computation, visualization, formal analysis, and writing--original draft, Y.C.; conceptualization, methodology, investigation, formal analysis, writing--original draft, writing--review, and supervision, Q.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 91741102), the Shenzhen Fundamental Research Program (Grant No. JCYJ20190807160413162) and the Fundamental Research Funds for the Central Universities (Grant No. 19 lgzd15).  
Not applicable.  
The authors would like to express their thanks for the support of the National Natural Science Foundation of China (Grant No. 91741102), the Shenzhen Fundamental Research Program (Grant No. JCYJ20190807160413162) and the Fundamental Research Funds for the Central Universities (Grant No. 19 lgzd15).  
The authors declare no conflict of interest.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
#### 4.1.3 Bayesian Method  
Bayesian methods employ Bayes' theorem for classification and regression tasks. Common algorithms encompass Naive Bayes, Gaussian Naive Bayes, Bayesian belief networks, and Bayesian networks. They are utilized in building automation systems, including indoor localization and detection of HVAC system problems [54]. Naive Bayes classifier methodologies have been utilized for human activity recognition, determining actions with the highest likelihood based on sensor information. These models are constructed upon specialized knowledge and virtual sensors [55].
## 5 Results and Discussion  
### Initial Characteristics of Wastewater  
Wastewater was collected from the paper mill at the effluent channel, which is the final channel before being discharged into the waterbody. The in-situ parameters were recorded by using a YSI multiprobe, whereas other physico-chemical analyses were determined in the laboratory. Then, 1 L of the wastewater sample was collected and analyzed by inductively coupled plasma mass spectrometry for the determination of the heavy metal content. Results indicated that the heavy metal content is in compliance with the industrial effluent discharge standards as stated in the Environmental Quality (Sewage and Industrial Effluents) Regulation. Table 1 summarizes the results obtained from the analysis of the physico-chemical and heavy metal contents. According to the results obtained herein, Al and Fe were selected for the simulation of wastewater remediation to examine the ability of the selected aquatic plants to extract heavy metals. Then, wastewater was treated by using 30 g of I. aquatica and C. asiatica in a 6-L reservoir in the open air for 10 days.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 2 Review and Related Work
### Related Work  
The surveys by [PERSON] et al. [19; 29] present background information on MEC and examine the security and privacy vulnerabilities of MEC deployment scenarios in 5G use cases. The security solutions and countermeasures for these use cases are also provided. [PERSON] et al. [30] describe the privacy issues in MEC and introduce a case study considering machine learning privacy-preserving for MEC. [PERSON] et al. [31] discuss the security of cloud computing and MEC in 5G. Privacy and security threats for different use cases are defined, and decent solutions are proposed for these threats. [PERSON] et al. and [PERSON] et al. [32; 33] emphasize that the MEC system hosts heterogeneous third-party applications that are based on cloud and virtualization technologies, which can cause security threats. [PERSON] et al. [32] present the structure and the features of MEC frameworks and security threats in the 5G system. [PERSON] et al. [33] analyze the MEC-related threats in 5G and propose how to mitigate them.  
[PERSON] et al. [34] introduce the privacy issues due to the location of MEC-enabled IoT devices and present a learning algorithm to mitigate this privacy issue. [PERSON] et al. [35] also raise the problems due to location privacy. They propose a protocol to improve user anonymity and allow traceability for the trusted parties for providing services.  
[PERSON] et al. [36] raise privacy concerns related to cellular networks and introduce a solution using MEC that provides better privacy protection compared to typical VPN and cellular networks.  
[PERSON] et al. [37] introduce a new access control architecture for the 5G MEC network to prevent non-authorized access to edge services and to protect network and computation resources from unnecessary allocations.  
[PERSON] et al., [PERSON] et al., and [PERSON] et al. [38; 39; 40] deal with innovative technologies of 5G, including software-defined networking (SDN), network function virtualization (NFV), and MEC, along with security and privacy concerns when using these technologies. The survey by [PERSON] et al. [38] explores the 5G security model and threat analysis in 5G networks. [PERSON] et al. [39] describe the security challenges, possible security solutions, and security standardization activities for 5G systems. The paper by [PERSON] et al. [40] proposes and analyzes a risk-aware edge server orchestrator mechanism.  
Our work differs from the above prior work in the way that we consider the situation where several parties work together to provide a service to customers while at the same time trying to gather more data about these customers than what is needed to provide their part of the service. For example, these additional data could be the identities and usage patterns relevant only for other parties.