esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection Network Based on Feature Fusion and Attention Mechanism
## 3 Methods
### Feature Fusion  
Faster R-CNN extracts features through a convolutional neural network to generate feature maps for RPN and object classification and border regression. The quality of the feature map greatly affects the performance of the entire object detection algorithm. The Faster R-CNN uses the VGG-16 network as the basic network and produces a final feature map after the fifth convolution layer. Although the feature map obtains high-level semantic information through deep convolution layers, the high-level feature map loses a lot of detailed texture information due to high abstraction, resulting in inaccurate positioning of objects and difficulties in the detection of small target objects.  
To solve the above problems, the AF R-CNN model we proposed in this paper fuses features from different convolutional layers. Given a picture, AF R-CNN uses the pre-trained model, VGG-16, to compute feature maps. Shallow and Deep CNN features are complementary for object detection. Figure 5 shows the detailed process of feature fusion. We fused feature maps from the third, fourth, and fifth convolution layers (con_3, con_4, con_5). These feature maps have different resolutions because of subsampling and pooling operations. To get the feature maps of the same resolution, we adopted different sampling strategies. For the shallow layer, we added the maximum pooling to conduct subsampling. Additionally, we added the deconvolutional operation to carry out upsampling in the deep layer. The fourth convolutional layer remained at the original size. Then, we normalized multiple feature maps using local response normalization (LRN) [32]. For each feature map, an additional convolution layer was required. The feature maps were connected by the connection operation to form a new feature map. The new feature map contained shallow and deep layer information that was effective for object detection. The feature map resolution was more suitable for detection.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
###### Abstract
The principle of the least squares method for two-parameter Weibull distribution parameter estimation is to convert the Weibull distribution function into a linear regression model and obtain parameter estimates by linearly fitting the empirical distribution function values with fault data. It can be seen that different fault ranks and the selection of empirical distribution functions result in different parameter estimation results. Therefore, accurate fault ranks and finding the optimal empirical distribution function are crucial for the parameter estimation results of the least squares method [15,16]. This article focuses on the reliability modeling and evaluating of long-life high-voltage relays with small-sample fault data characteristics and proposes a reliability analysis method integrating average rank, the minimum mean square distance empirical distribution function, and total least squares estimation. In the random truncation experiment, considering the influence of random truncation data, the average rank method is used to correct the rank of small-sample fault data; then, the optimal empirical distribution function for small-sample fault data is obtained through the minimum average square distance. Under the assumption of the Weibull distribution model, the total least squares estimation method is used for reliability model parameter estimations, and the linear correlation coefficient and d-test method are used for model hypothesis testing. If two or more distribution models pass the linear correlation coefficient test and the d-test simultaneously, the root mean square error and relative root mean square error are applied to deter-mine the optimal reliability model. The effectiveness of this method is verified by comparing it with the maximum likelihood estimation method.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results
### Performance Evaluation of Latency  
The latency performance of the QESIF was evaluated across three datasets (TON_IoT, Edge-IIoTset, and Bot-IoT) and three channel scenarios (clean, noisy, and eavesdropped), as shown in Figure 4. The results demonstrate that the QESIF consistently maintained the lowest average latency across all the scenarios, highlighting its lightweight communication design and adaptive protocol switching. Under clean conditions, the QESIF achieved an average latency of 20.3 ms, 28.6 ms under noisy conditions, and 34.8 ms in the eavesdropped scenario. In comparison, the next-best performer, QuIDS, recorded 22.7 ms, 31.4 ms, and 38.1 ms, respectively, while QRHE and HyQuSec ranged from 24.1 to 29.2 ms and 26.3 to 31.8 ms under clean conditions, with the latencies rising to 44.2 ms and 47.3 ms, respectively, under attack conditions. QLSN consistently exhibited the highest latency due to its heavyweight lattice-based operations, peaking at 49.6 ms in the eavesdropped case.  
Figure 2: QBER across TON-IoT, Edge-IIoTset, and Bot-IoT for QESIF and baselines.
## 3 Results and Discussion
### Washing and Wearing Resistance Performance  
To assess washing resistance of coated fabrics all tested samples after washing procedures were measured not only for surface resistivity (Table 4), but also for shielding effectiveness. SE measurement results before and after washing (Figure 10) showed that, for all 4 groups of tested samples, the resistance to washing increased for samples with plasma treatment in comparison with untreated samples.  
The samples after washing also were assessed visually. As an example, the photos of wool samples (I group) are presented in Figure 11.  
Figure 11: An appearance of coated side of fabric after washing procedures.  
Figure 10: Influence of plasma treatment of shielding effectiveness before and after washing procedure.  
Figure 9: Absorption ability of tested samples: (a)—at 12 GHz frequency and (b)—at 18 GHz frequency.  
Assessing the influence of washing on the durability of conductive coating, it could be stated that the plasma treatment slightly improved the durability of PEDOT:PSS coating.  
The influence of plasma treatment on durability of coating was also observed after abrasion impact. As it can be seen from visual observations (Figure 12), the conductive coating survived after a longer period of abrasion cycles when samples were treated by plasma before coating procedure in comparison with untreated. These results clearly show that the plasma treatment was quite effective at improving the adhesion strength between textile substrate and the coating of a conductive composition containing PEDOT:PSS.  
Figure 12: Coated fabric samples after being subjected to abrasion in the Martindale abrasion tester.  
This change can be due to incorporation of additional functional groups formed by plasma reactions which changes the surface chemistry of textile substrate by decomposition of polymer chains and oxidation [50].
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0
### Latency  
One of the critical problems with smart industry application is latency/delay in the time it takes to reach the destination. Latency can make a huge difference when it comes to devices involving near-real-time communication (e.g., physical or virtual reality). The latency that one would predict from 4G networks today is around 50 ms-70 ms (0.05 s). It is reduced to 2 ms-5 ms with 5G and a potential target latency of 1 ms with enhancements made in the radio access network (RAN). Moreover, with too many applications and business data stored in the cloud, data rates and delay have become more significant than ever. The NR-U commercial networks open up new doors for such delay-sensitive applications such as robots' sensors, the systems requiring a high data rate like AI/AR, and those integrating both areas such as telemedicine and public health.  
Figure 2: 5G communication opportunity for Industry 4.0.
## 3 Underwater Routing Protocols
### Beec  
The balanced energy-efficient circular (BEEC) routing protocol [34] considers a circular network divided into ten circular regions called sectors. Two mobile sinks collect data from these sectors; one sink for five different sectors. The sinks move in a circular pattern to gather data from source nodes. This reduces and balances the energy consumption of the nodes and enhances the number of successful packet receptions by the sinks. However, the sinks do not prioritize the locations where nodes have data to send. They follow a fixed pattern of movements independent of the network conditions, which leads to packet loss and delay. Furthermore, the protocol performs poorly in sparse conditions when nodes are far apart from one another.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.1 ClusTile  
Research as [PERSON], [PERSON] [34] proposed ClusTile, a tiling approach that schemes each tile represents a DASH segment covering a portion of the 360-degree view with typically fixed time intervals, formulated by solving the set of integer linear programs (ILPs). Although this work mentions a decrease of such a high percentage in bandwidth reduction (76%), it does not allow varying the solution of representations but only their bitrate. The increasing number of tiles in the process is not sufficient for the segments downloaded and uploaded.
## 3 Materials and Methods
### Ground Surveys  
Simultaneous to the aerial survey, some ground measurements of surface temperature and emissivity were collected, to be used as ground truth data for the assessment of accuracy in radiometric correction (Table 3). The locations chosen are mainly squares or open areas, easily recognizable on the thermal mosaic, with homogeneous pavement materials. The geographic positions of targets were computed by post processing techniques using geodetic GNSS receiver recordings, in combination with data from a permanent GNSS stations, while the surface temperature and the emissivity values were derived using a thermal camera FLIR P620, characterized by an infrared sensor with a spatial resolution of \(640\times 480\) pixels operating approximately in the same spectral range of the infrared sensor used for the aerial infrared survey.  
\begin{table}
\begin{tabular}{c c} \hline
**Roofing Material** & **Emissivity Value** \\ \hline Bituminous & 0.91 \\ Clay tiles & 0.88 \\ Concrete & 0.90 \\ Gravel & 0.96 \\ Metal & 0.70 \\ Other & 1.00 \\ \hline \end{tabular}
\end{table}
Table 2: Emissivity values for the roofing types detected in the classification. These values were derived partly from literature data and partly from special surveys (see the discussion in the text).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Site** & **Material** & **Apparent T** & **Emissivity** & **Measured T** \\ \hline BL01 & cobblestones & \(-\)0.2 & 0.91 & 5.3 \\ BL02 & concrete & 0.8 & 0.91 & 5.4 \\ BL08 & asphalt & 0.7 & 0.94 & 8.1 \\ BL10 & asphalt & \(-\)0.5 & 0.93 & 3.7 \\ BL11 & stoneware tiles & 0.8 & 0.81 & 8.3 \\ \hline \end{tabular}
\end{table}
Table 3: Apparent temperature derived from the uncalibrated aerial thermal images and emissivity and temperature measured on the ground at the five locations selected for the field surveys. T, transmittance.
## 4 Discussion and Results
The suggested neural boosted model overcomes these constraints by providing a prediction framework that is more dynamic and adaptive. * The study conducted by [30] investigated the use of multi-task learning to forecast the RUL in different working settings. The results showed that advanced learning approaches have the potential to enhance the accuracy of RUL predictions. The findings of our study demonstrate that the combination of bootstrap forests and neural networks can produce comparable or better performance, especially when dealing with diverse operational scenarios. * The significance of feature fusion and similarity-based prediction models for precise estimation of RUL was highlighted by [1]. Our suggested model improves upon current approaches by using hyperbolic tangent functions and bootstrap aggregation, resulting in increased prediction accuracy. Table 7 demonstrates the enhanced performance of the suggested methodology compared to established models through statistical comparisons. In summary, the NtanH(3)NBoost(20) model shows outstanding effectiveness in predicting aircraft systems, as supported by strong performance indicators. This indicates that the model possesses the capacity to significantly enhance predictive maintenance protocols. Further research might focus on comprehending the -LogLikelihood values within a broader context, exploring the implications of Sum Freq, and comparing the effectiveness of the model with existing prognostic methods to strengthen its practical importance in the field.
## 2 Materials and Methods
### Design of Experiments  
Experimental investigation necessitates the execution of a number of costly and time-consuming experiments. Design of experiment (DOE) methods, such as Taguchi's method, response surface method (RSM), etc., are statistical techniques used to simultaneously examine the effects of multiple input parameters on performance measures [34; 35]. Recently, the Taguchi and RSM methods for estimating machining performance have been developed [34]. The Taguchi method has been extensively utilized in engineering analysis and is an effective tool for the design of high-quality systems. Additionally, the Taguchi method employs a unique design of an orthogonal array to investigate the effects of all machining parameters through multiple experiments [36].  
The response surface method (RSM) is a statistical and mathematical technique that aids in the design of experiments, modeling, the estimation of the proportional importance of various independent parameters and the determination of optimal conditions for achieving optimal responses [24]. The primary objective of this method is multiresponse optimization that is influenced by multiple input process parameters [35].  
Taguchi analysis is an effective technique for modeling the SLA process, and the success and implementation of additive manufacturing technology may be dependent on the understanding of process parameters [27]. In this study, the Taguchi L9 orthogonal array (OA) was used to consider three parameters: (1) normal exposure time, (2) bottom exposure time and (3) bottom layers. The Taguchi design permits the development of an L9 OA capable of simulating the most accurate configurations of experiments to predict outcomes. From preliminary experiments and dental lab technicians' experience, the ranges of parameters were as follows: normal exposure time: 1.2-2.5 s, bottom layers: 1-7 and bottom exposure time: 15-55 s. It is recommended that the bottom exposure time be eight to twelve times longer than the typical layer cure time [27]. The abbreviations of printing parameters and output responses are shown in Table 2. The printing parameters and their levels are shown in Table 3. The nine experiments with the Taguchi L9 orthogonal array are shown in Table 4. The experimental measurements were then analyzed using the response surface method in order to determine the influencing factors and optimal levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Unit** & **Value** \\ \hline Density & g/cm3 & 1.05–1.15 \\ Viscosity & Cps & 150–250 \\ Elongation at break & & 10–14\% \\ Flexural strength & Mpa & 45–75 \\ Tensile strength & Mpa & 30–46 \\ Hardness & D & 78–79 \\ Shelf life & & 6 months (3 months after opening) \\ Wavelength (UV light) & nm & 405 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specifications of water-washable resin.  
Based on response data, MINITAB (v18) was used to create the RSM model's empirical connection, which best matches experimental findings for predicting output. Analysis of variance (ANOVA) was utilized to conduct statistical analyses.
## 7 Limitations and Future Research  
The research has several limitations, which are as follows:  
1. The study lacks modeling and theoretical analysis, focusing only on field in-situ tests.
2. The test findings only consider the surface vibration response without investigating the vibration attenuation pattern at different depths.  
The present study, namely the law of vibration effect, is a fundamental piece of research. Based on this premise, the tasks to be undertaken encompass the following:  
1. Micro-vibration control is crucial for safeguarding cultural artifacts and structures. The effects of vibration on these entities can compromise both their safety and integrity. Therefore, it is imperative to develop a method for assessing the susceptibility of cultural artifacts or sites to micro-vibrations.
2. Maintenance has a beneficial impact on enhancing the ability of structures to withstand vibrations, particularly for cultural relics made of earth blocks classified as \"cultural sites or relics.\" It is necessary to implement suitable reinforcing measures for these relics.
3. Development of vibration standards for \"earth-block cultural artifacts\".  
Conceptualization, L.Z., J.M. and L.C.; methodology, L.C.; software, L.C.; validation, L.C.; formal analysis, X.H.; resources, L.C.; data curation, X.H.; writing--original draft preparation, L.Z. and J.M.; writing--review and editing, L.Z., J.M., L.C. and X.H.; visualization, X.H.; supervision, L.C.; project administration, L.Z., J.M. and L.C.; funding acquisition, L.C.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is part of the research work of the National Key R&D Project of China Grant Nos. 2021 YFB2600602 and 2021 YFB2600600. The authors would like to gratefully acknowledge the support for this research by the Yangzhou Key R&D Program (Social Development) Grant No. YZ2023077.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** The corresponding author thanks the 2018 Jiangsu Provincial Government Scholarship Program (No. 228) for supporting his visit to the University of California, Los Angeles. Special thanks to [PERSON], [PERSON], [PERSON] and [PERSON] for their valuable advice to this work.  
**Conflicts of Interest:** Author [PERSON] was employed by the company NJTech Bridge & Tunnel and Rail Transit Institute; the remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author [PERSON] was employed by the company Transportation Science Institute of Nanjing University of Technology (Chuzhou) Co., Ltd.; the remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
###### Abstract  
With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.  
Keywords:wireless sensor network deployment; health and environment applications; IoT; Bees Algorithm; PSO algorithm; coverage; lifetime; Minimum Spanning Tree +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Softening in Solvent  
Five adhesive samples from each group (\(n\) = 5) were prepared with 5 mm diameter and 1 mm thickness after light-curing for 20 s on each side. After the polymerization, the samples were stored in distilled water for 24 h at 37 \({}^{\circ}\)C. Then, the adhesive samples were bonded with double-face tape on a glass table and soaked in self-cure acrylic resin (Classico Produtos Odontologicos, Sao Paulo, Brazil). Each acrylic resin set contained five samples per group, which were polished using sandpapers (600, 1200, 2000-grit, 1 min each paper) and distilled water, followed by felt disks with 0.5 \(\upmu\)m alumina suspension (3 min) (Fortel, Sao Paulo, SP, Brazil). After 24 h, the samples were subjected to a microhardness test in which five indentations (10 g for 5 s) were performed per sample at 40\(\times\) magnification (HMV 2, Shimadzu, Tokyo, Japan) to obtain the initial Knoop hardness number (KHN1). The samples were immersed in a solvent solution (70% ethanol and 30% water) for two hours, washed with distilled water, and tested again to obtain the final Knoop hardness number (KHN2). The Knoop hardness values were recorded as the five indentations per sample average. Softening in solvent (\(\Delta\)KHN) was calculated by the percentage difference between KHN1 and KHN2 for each group [16].
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Deep Learning-Based Models for Real-Time Super-Resolution
#### 3.4.3 Generative Adversarial Networks (GANs) Based Models for Real-Time Super-Resolution  
[PERSON] et al. [81] present a new GAN-based model for SR image reconstructions. Their method is based on Residual-in-Residual Dense Blocks (RRDBs) as the basic components, and they employed high-order degradation to simulate real-world image degradation. Furthermore, they introduced an innovative attention mechanism known as the All-Attention Mechanism (AAM), which effectively recognizes and uses crucial visual elements, expanding the number of pixels exploited. This strategy significantly increased the quality of the re-built images. In addition, [PERSON] et al. [82] proposed an approach utilizing an existing deep learning approach to accomplish real-time SR microscopy using a conventional GPU. They suggest a tiling approach, which utilizes GPU parallelism to speed training networks. They also offer simple changes to the design of the SRGAN's generator and discriminator. The researchers then assessed the model's result quality and runtime, proving that it is suitable for implementation across a variety of sectors, including low-end benchtop and mobile microscopes.